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mode 100644 sllm_store/sllm_store/transformers.py create mode 100644 sllm_store/sllm_store/utils.py create mode 100644 sllm_store/tests/.DS_Store create mode 100644 sllm_store/tests/cpp/CMakeLists.txt create mode 100644 sllm_store/tests/cpp/test_load_to_host.cpp create mode 100644 sllm_store/tests/cpp/test_pinned_memory.cpp create mode 100644 sllm_store/tests/python/test_cli.py create mode 100644 sllm_store/tests/python/test_save_lora.py create mode 100644 sllm_store/tests/python/test_save_model.py create mode 100644 sllm_store/tests/python/test_save_vllm_model.py create mode 100644 sllm_store/tests/python/test_utils.py create mode 100644 sllm_store/using_existing_torch.py create mode 100755 sllm_store/vllm_patch/check_patch.sh create mode 100755 sllm_store/vllm_patch/patch.sh create mode 100755 sllm_store/vllm_patch/remove_patch.sh create mode 100644 sllm_store/vllm_patch/sllm_load.patch create mode 100644 tests/.DS_Store create mode 100644 tests/__init__.py create mode 100644 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100644 tests/replay_test/README.md create mode 100644 tests/replay_test/convert_gsm8k.py create mode 100644 tests/replay_test/deploy_dummy_models.py create mode 100644 tests/replay_test/generate_random_workload.py create mode 100644 tests/utils.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..7b13b55343ac6d916719422b3a964fe447137315 GIT binary patch literal 8196 zcmeHMJ#Q015S_i870WbKC>7jKND2@Q(h-bAL6;u@_#+A6EptCiwvV?z?YKUKZy+Ty4jWei=L6mHFqGk~-iyp`3PT7O=bc>2v6noLLOFEWrn@x`6kV$$B#$q>XC;rNc_f#1RKUk=k}-|IXSk$Bd~gCL zl}~bbf4Sly{0k+T{6XH%98+Vb9@8T{=gDY_^%yVZr`bvqM)J7w>fE9KoYdx$YWxTn z@G|SnE5=i&30_(7=~y|vAbpa@*XVh<58f}g1|Lg6~|1C%dRX`Q^Hx)2(f2+TV_vUNs+PmahJ3+sV q&W&+}vo-_+L-@We$6>2K3~`-!U+Qh)EIerXL%_: + +(optional) + +``` +- **Type**: Choose from `build`, `ci`, `docs`, `feat`, `fix`, `perf`, `refactor`, `test`, `chore`. +- **Summary**: A brief description of the change. +- **Body**: Mandatory for all commits except those of type "docs". Must be at least 20 characters long. + + +Examples: + +``` +feat: add logging in sllm worker +``` + +``` +docs: add new example for serving vision model + +Vision mode: xxx +Implemented xxx in `xxx.py` +``` + +For more details, read the [Angular Commit Format](https://github.com/angular/angular/blob/main/CONTRIBUTING.md#-commit-message-format). diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..82a3bf3 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,121 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +# Adapted from https://github.com/vllm-project/vllm/blob/23c1b10a4c8cd77c5b13afa9242d67ffd055296b/Dockerfile +ARG CUDA_VERSION=12.1.1 +#################### BASE BUILD IMAGE #################### +# prepare basic build environment +FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS builder +ARG CUDA_VERSION=12.1.1 +ARG PYTHON_VERSION=3.10 +ARG TARGETPLATFORM +ENV DEBIAN_FRONTEND=noninteractive + +# Install Python and other dependencies +RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \ + && echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \ + && apt-get update -y \ + && apt-get install -y ccache software-properties-common git curl sudo \ + && add-apt-repository ppa:deadsnakes/ppa \ + && apt-get update -y \ + && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \ + && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \ + && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \ + && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \ + && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \ + && python3 --version && python3 -m pip --version + +# Set the working directory +WORKDIR /app + +# Build checkpoint store +ENV TORCH_CUDA_ARCH_LIST="8.0 8.6 8.9 9.0" +COPY sllm_store/requirements-build.txt /app/sllm_store/requirements-build.txt +RUN cd sllm_store && \ + pip install -r requirements-build.txt && \ + pip install setuptools wheel + +COPY sllm_store/cmake /app/sllm_store/cmake +COPY sllm_store/CMakeLists.txt /app/sllm_store/CMakeLists.txt +COPY sllm_store/csrc /app/sllm_store/csrc +COPY sllm_store/sllm_store /app/sllm_store/sllm_store +COPY sllm_store/setup.py /app/sllm_store/setup.py +COPY sllm_store/pyproject.toml /app/sllm_store/pyproject.toml +COPY sllm_store/MANIFEST.in /app/sllm_store/MANIFEST.in +COPY sllm_store/requirements.txt /app/sllm_store/requirements.txt +COPY sllm_store/README.md /app/sllm_store/README.md +COPY sllm_store/proto/storage.proto /app/sllm_store/proto/storage.proto +RUN cd sllm_store && python3 setup.py bdist_wheel + +COPY requirements.txt requirements-worker.txt /app/ +COPY pyproject.toml setup.py py.typed /app/ +COPY sllm/serve /app/sllm/serve +COPY sllm/cli /app/sllm/cli +COPY README.md /app/ +RUN python3 setup.py bdist_wheel + +# Stage 2: Runner with conda environments +FROM pytorch/pytorch:2.3.0-cuda12.1-cudnn8-devel + +# Set non-interactive installation +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 + +# Set the working directory +WORKDIR /app + +# Create conda environments for head and worker +RUN conda create -n head python=3.10 -y && \ + conda create -n worker python=3.10 -y + +RUN conda run -n head pip install -U pip +RUN conda run -n worker pip install -U pip + +# Copy requirements files +COPY requirements.txt /app/ + +RUN conda run -n head pip install -r /app/requirements.txt +COPY requirements-worker.txt /app/ + +RUN conda run -n worker pip install -r /app/requirements-worker.txt + +# Copy vllm patch for worker +COPY sllm_store/vllm_patch /app/vllm_patch + +# Copy the built wheels from the builder +COPY --from=builder /app/sllm_store/dist /app/sllm_store/dist +COPY --from=builder /app/dist /app/dist + +# Install packages in head environment +RUN conda run -n head pip install /app/sllm_store/dist/*.whl && \ + conda run -n head pip install /app/dist/*.whl + +# Install packages in worker environment +RUN conda run -n worker pip install /app/sllm_store/dist/*.whl && \ + conda run -n worker pip install /app/dist/*.whl + +# Apply vLLM patch in worker environment +RUN conda run -n worker bash -c "cd /app && ./vllm_patch/patch.sh" + +# Copy the entrypoint +COPY entrypoint.sh . +RUN chmod +x entrypoint.sh + +# Set the entrypoint directly to the entrypoint script +ENTRYPOINT ["/app/entrypoint.sh"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..29c75df --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2024] [ServerlessLLM Team] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..15bfe5d --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,3 @@ +include LICENSE +include requirements.txt +include requirements-worker.txt \ No newline at end of file diff --git a/README.en.md b/README.en.md index 7c33233..cc01193 100644 --- a/README.en.md +++ b/README.en.md @@ -1,36 +1,87 @@ +

+ + ServerlessLLM + +

+ +

+| Documentation | Paper | Discord | WeChat | + +

+ # ServerlessLLM -#### Description -ServerlessLLM is a distributed model deployment system that supports efficient multi-checkpoint saving, offering fast cold starts and serverless deployment capabilities +ServerlessLLM (`sllm`, pronounced "slim") is an open-source serverless framework designed to make custom and elastic LLM deployment easy, fast, and affordable. As LLMs grow in size and complexity, deploying them on AI hardware has become increasingly costly and technically challenging, limiting custom LLM deployment to only a select few. ServerlessLLM solves these challenges with a full-stack, LLM-centric serverless system design, optimizing everything from checkpoint formats and inference runtimes to the storage layer and cluster scheduler. + +Curious about how it works under the hood? Check out our [System Walkthrough](https://github.com/ServerlessLLM/ServerlessLLM/tree/main/blogs/serverless-llm-architecture) for a deep dive into the technical design—perfect if you're exploring your own research or building with ServerlessLLM. + +## News + +- **[03/25]** We're excited to share that we'll be giving a ServerlessLLM tutorial at the SESAME workshop, co-located with ASPLOS/EuroSys 2025 in Rotterdam, Netherlands, on March 31. [Slides](https://docs.google.com/presentation/d/1ioGCVpsg0x3oCxX19EiE820aMiY22X5MG6jgImZ1W18/edit?usp=sharing) | [More info](https://sesame25.github.io/) +- **[11/24]** We have added experimental support of fast checkpoint loading for AMD GPUs (ROCm) when using with vLLM, PyTorch and HuggingFace Accelerate. Please refer to the [documentation](https://serverlessllm.github.io/docs/stable/store/rocm_quickstart) for more details. +- **[10/24]** ServerlessLLM was invited to present at a global AI tech vision forum in Singapore. +- **[10/24]** We hosted the first ServerlessLLM developer meetup in Edinburgh, attracting over 50 attendees both offline and online. Together, we brainstormed many exciting new features to develop. If you have great ideas, we’d love for you to join us! +- **[10/24]** We made the first public release of ServerlessLLM. Check out the details of the release [here](https://github.com/ServerlessLLM/ServerlessLLM/releases/tag/v0.5.0). +- **[09/24]** ServerlessLLM now supports embedding-based RAG + LLM deployment. We’re preparing a blog and demo—stay tuned! +- **[08/24]** ServerlessLLM added support for vLLM. +- **[07/24]** We presented ServerlessLLM at Nvidia's headquarters. +- **[06/24]** ServerlessLLM officially went public. + +## Goals + +ServerlessLLM is designed to support multiple LLMs in efficiently sharing limited AI hardware and dynamically switching between them on demand, which can increase hardware utilization and reduce the cost of LLM services. This multi-LLM scenario, commonly referred to as Serverless, is highly sought after by AI practitioners, as seen in solutions like [Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html), [Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated), and [Model Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2). However, these existing offerings often face performance overhead and scalability challenges, which ServerlessLLM effectively addresses through three key capabilities: + +**ServerlessLLM is Fast**: +- Supports leading LLM inference libraries like [vLLM](https://github.com/vllm-project/vllm) and [HuggingFace Transformers](https://huggingface.co/docs/transformers/en/index). Through vLLM, ServerlessLLM can support various types of AI hardware (summarized by vLLM at [here](https://docs.vllm.ai/en/stable/getting_started/installation.html)) +- Achieves 5-10X faster loading speeds compared to [Safetensors](https://github.com/huggingface/safetensors) and the PyTorch Checkpoint Loader. +- Features an optimized model loading scheduler, offering 5-100X lower start-up latency than [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) and [KServe](https://github.com/kserve/kserve). + +**ServerlessLLM is Cost-Efficient**: +- Allows multiple LLM models to share GPUs with minimal model switching overhead and supports seamless inference live migration. +- Maximizes the use of local storage on multi-GPU servers, reducing the need for expensive storage servers and excessive network bandwidth. + +**ServerlessLLM is Easy-to-Use**: +- Simplifies deployment through [Ray Cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and [Kubernetes](https://kubernetes.io/) via [KubeRay](https://github.com/ray-project/kuberay). +- Supports seamless deployment of [HuggingFace Transformers](https://huggingface.co/docs/transformers/en/index) and custom LLM models. +- Supports NVIDIA and AMD GPUs +- Easily integrates with the [OpenAI Query API](https://platform.openai.com/docs/overview). + +## Getting Started + +1. Install ServerlessLLM with pip or [from source](https://serverlessllm.github.io/docs/stable/deployment/single_machine#installation). + +```bash +conda create -n sllm python=3.10 -y +conda activate sllm +pip install serverless-llm +``` + +2. Start a local ServerlessLLM cluster using the [Quick Start Guide](https://serverlessllm.github.io/docs/stable/getting_started). -#### Software Architecture -Software architecture description +3. Want to try fast checkpoint loading in your own code? Check out the [ServerlessLLM Store Guide](https://serverlessllm.github.io/docs/stable/store/quickstart). -#### Installation +## Documentation -1. xxxx -2. xxxx -3. xxxx +To install ServerlessLLM, please follow the steps outlined in our [documentation](https://serverlessllm.github.io). ServerlessLLM also offers Python APIs for loading and unloading checkpoints, as well as CLI tools to launch an LLM cluster. Both the CLI tools and APIs are demonstrated in the documentation. -#### Instructions +## Benchmark -1. xxxx -2. xxxx -3. xxxx +Benchmark results for ServerlessLLM can be found [here](./benchmarks/README.md). -#### Contribution +## Community -1. Fork the repository -2. Create Feat_xxx branch -3. Commit your code -4. Create Pull Request +ServerlessLLM is maintained by a global team of over 10 developers, and this number is growing. If you're interested in learning more or getting involved, we invite you to join our community on [Discord](https://discord.gg/AEF8Gduvm8) and [WeChat](./docs/images/wechat.png). Share your ideas, ask questions, and contribute to the development of ServerlessLLM. For becoming a contributor, please refer to our [Contributor Guide](./CONTRIBUTING.md). +## Citation -#### Gitee Feature +If you use ServerlessLLM for your research, please cite our [paper](https://arxiv.org/abs/2401.14351): -1. You can use Readme\_XXX.md to support different languages, such as Readme\_en.md, Readme\_zh.md -2. Gitee blog [blog.gitee.com](https://blog.gitee.com) -3. Explore open source project [https://gitee.com/explore](https://gitee.com/explore) -4. The most valuable open source project [GVP](https://gitee.com/gvp) -5. The manual of Gitee [https://gitee.com/help](https://gitee.com/help) -6. The most popular members [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) +```bibtex +@inproceedings{fu2024serverlessllm, + title={ServerlessLLM: Low-Latency Serverless Inference for Large Language Models}, + author={Fu, Yao and Xue, Leyang and Huang, Yeqi and Brabete, Andrei-Octavian and Ustiugov, Dmitrii and Patel, Yuvraj and Mai, Luo}, + booktitle={18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)}, + pages={135--153}, + year={2024} +} +``` diff --git a/README.md b/README.md index 07adb89..cc01193 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,87 @@ +

+ + ServerlessLLM + +

+ +

+| Documentation | Paper | Discord | WeChat | + +

+ # ServerlessLLM -#### 介绍 -ServerlessLLM is a distributed model deployment system that supports efficient multi-checkpoint saving, offering fast cold starts and serverless deployment capabilities +ServerlessLLM (`sllm`, pronounced "slim") is an open-source serverless framework designed to make custom and elastic LLM deployment easy, fast, and affordable. As LLMs grow in size and complexity, deploying them on AI hardware has become increasingly costly and technically challenging, limiting custom LLM deployment to only a select few. ServerlessLLM solves these challenges with a full-stack, LLM-centric serverless system design, optimizing everything from checkpoint formats and inference runtimes to the storage layer and cluster scheduler. + +Curious about how it works under the hood? Check out our [System Walkthrough](https://github.com/ServerlessLLM/ServerlessLLM/tree/main/blogs/serverless-llm-architecture) for a deep dive into the technical design—perfect if you're exploring your own research or building with ServerlessLLM. + +## News + +- **[03/25]** We're excited to share that we'll be giving a ServerlessLLM tutorial at the SESAME workshop, co-located with ASPLOS/EuroSys 2025 in Rotterdam, Netherlands, on March 31. [Slides](https://docs.google.com/presentation/d/1ioGCVpsg0x3oCxX19EiE820aMiY22X5MG6jgImZ1W18/edit?usp=sharing) | [More info](https://sesame25.github.io/) +- **[11/24]** We have added experimental support of fast checkpoint loading for AMD GPUs (ROCm) when using with vLLM, PyTorch and HuggingFace Accelerate. Please refer to the [documentation](https://serverlessllm.github.io/docs/stable/store/rocm_quickstart) for more details. +- **[10/24]** ServerlessLLM was invited to present at a global AI tech vision forum in Singapore. +- **[10/24]** We hosted the first ServerlessLLM developer meetup in Edinburgh, attracting over 50 attendees both offline and online. Together, we brainstormed many exciting new features to develop. If you have great ideas, we’d love for you to join us! +- **[10/24]** We made the first public release of ServerlessLLM. Check out the details of the release [here](https://github.com/ServerlessLLM/ServerlessLLM/releases/tag/v0.5.0). +- **[09/24]** ServerlessLLM now supports embedding-based RAG + LLM deployment. We’re preparing a blog and demo—stay tuned! +- **[08/24]** ServerlessLLM added support for vLLM. +- **[07/24]** We presented ServerlessLLM at Nvidia's headquarters. +- **[06/24]** ServerlessLLM officially went public. + +## Goals + +ServerlessLLM is designed to support multiple LLMs in efficiently sharing limited AI hardware and dynamically switching between them on demand, which can increase hardware utilization and reduce the cost of LLM services. This multi-LLM scenario, commonly referred to as Serverless, is highly sought after by AI practitioners, as seen in solutions like [Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html), [Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated), and [Model Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2). However, these existing offerings often face performance overhead and scalability challenges, which ServerlessLLM effectively addresses through three key capabilities: + +**ServerlessLLM is Fast**: +- Supports leading LLM inference libraries like [vLLM](https://github.com/vllm-project/vllm) and [HuggingFace Transformers](https://huggingface.co/docs/transformers/en/index). Through vLLM, ServerlessLLM can support various types of AI hardware (summarized by vLLM at [here](https://docs.vllm.ai/en/stable/getting_started/installation.html)) +- Achieves 5-10X faster loading speeds compared to [Safetensors](https://github.com/huggingface/safetensors) and the PyTorch Checkpoint Loader. +- Features an optimized model loading scheduler, offering 5-100X lower start-up latency than [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) and [KServe](https://github.com/kserve/kserve). + +**ServerlessLLM is Cost-Efficient**: +- Allows multiple LLM models to share GPUs with minimal model switching overhead and supports seamless inference live migration. +- Maximizes the use of local storage on multi-GPU servers, reducing the need for expensive storage servers and excessive network bandwidth. + +**ServerlessLLM is Easy-to-Use**: +- Simplifies deployment through [Ray Cluster](https://docs.ray.io/en/latest/cluster/getting-started.html) and [Kubernetes](https://kubernetes.io/) via [KubeRay](https://github.com/ray-project/kuberay). +- Supports seamless deployment of [HuggingFace Transformers](https://huggingface.co/docs/transformers/en/index) and custom LLM models. +- Supports NVIDIA and AMD GPUs +- Easily integrates with the [OpenAI Query API](https://platform.openai.com/docs/overview). + +## Getting Started + +1. Install ServerlessLLM with pip or [from source](https://serverlessllm.github.io/docs/stable/deployment/single_machine#installation). + +```bash +conda create -n sllm python=3.10 -y +conda activate sllm +pip install serverless-llm +``` -#### 软件架构 -软件架构说明 +2. Start a local ServerlessLLM cluster using the [Quick Start Guide](https://serverlessllm.github.io/docs/stable/getting_started). +3. Want to try fast checkpoint loading in your own code? Check out the [ServerlessLLM Store Guide](https://serverlessllm.github.io/docs/stable/store/quickstart). -#### 安装教程 +## Documentation -1. xxxx -2. xxxx -3. xxxx +To install ServerlessLLM, please follow the steps outlined in our [documentation](https://serverlessllm.github.io). ServerlessLLM also offers Python APIs for loading and unloading checkpoints, as well as CLI tools to launch an LLM cluster. Both the CLI tools and APIs are demonstrated in the documentation. -#### 使用说明 +## Benchmark -1. xxxx -2. xxxx -3. xxxx +Benchmark results for ServerlessLLM can be found [here](./benchmarks/README.md). -#### 参与贡献 +## Community -1. Fork 本仓库 -2. 新建 Feat_xxx 分支 -3. 提交代码 -4. 新建 Pull Request +ServerlessLLM is maintained by a global team of over 10 developers, and this number is growing. If you're interested in learning more or getting involved, we invite you to join our community on [Discord](https://discord.gg/AEF8Gduvm8) and [WeChat](./docs/images/wechat.png). Share your ideas, ask questions, and contribute to the development of ServerlessLLM. For becoming a contributor, please refer to our [Contributor Guide](./CONTRIBUTING.md). +## Citation -#### 特技 +If you use ServerlessLLM for your research, please cite our [paper](https://arxiv.org/abs/2401.14351): -1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md -2. Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com) -3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目 -4. [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目 -5. Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) -6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) +```bibtex +@inproceedings{fu2024serverlessllm, + title={ServerlessLLM: Low-Latency Serverless Inference for Large Language Models}, + author={Fu, Yao and Xue, Leyang and Huang, Yeqi and Brabete, Andrei-Octavian and Ustiugov, Dmitrii and Patel, Yuvraj and Mai, Luo}, + booktitle={18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)}, + pages={135--153}, + year={2024} +} +``` diff --git a/benchmarks/.DS_Store b/benchmarks/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..3ca77a83b7a955cad35c25dae697b1e570f8ca07 GIT binary patch literal 6148 zcmeHKOHRW;47E!IL2SBYg@z;a2B8Wk=mj7Y6{JL|fbLk#7B+ALj>Ac~1S_7$R>_Bo z9YU2YdEUfhPn?%%91{`G){808gorYz;OG#;4)_#} t)&}SWR7Cuu;wA)>REps%rT7?{1oj{kU~U){VS)IMK%&71XW&m6_yS{jPpAL@ literal 0 HcmV?d00001 diff --git a/benchmarks/README.md b/benchmarks/README.md new file mode 100644 index 0000000..eb92562 --- /dev/null +++ b/benchmarks/README.md @@ -0,0 +1,120 @@ +# Benchmarking for ServerlessLLM Store + +Welcome to the benchmarking suite for ServerlessLLM Store. This suite is designed to measure the model loading performance of ServerlessLLM Store and compare it against several baselines on a variety of State-of-the-Art Large Language Models (LLMs). + +## Table of Contents + +- [Hardware Requirements](#hardware-requirements) +- [Prerequisites](#prerequisites) +- [Setting Up the Environment](#setting-up-the-environment) +- [Running the Benchmarks](#running-the-benchmarks) + - [1. Random Multi-Model Load Test](#1-random-multi-model-load-test) + - [1.1 Small Models](#11-small-models) + - [1.2 Large Models](#12-large-models) +- [Contact](#contact) + +## Hardware Requirements + +To ensure optimal performance and reliability of the ServerlessLLM Store benchmarking tests, we recommend the following hardware setup: + +- **RAM:** A minimum of 32GB is required for small model tests. For large model tests, at least 150GB of RAM is recommended to effectively manage the pinned memory pool utilized by the ServerlessLLM Store server. + +- **GPU:** An NVIDIA GPU with at least 24GB of VRAM is necessary for small model tests. For large model tests, a setup with at least 160GB of available VRAM is essential to handle the extensive model parameters. + +- **Storage:** A minimum of 500GB of disk space is required for small model tests, while at least 1.5TB is necessary for large model tests. Utilizing an NVMe SSD is highly recommended for its superior bandwidth, which is critical for optimizing ServerlessLLM Store's performance. + +The specifications used to obtain the following benchmark results are detailed in [this document](./server-specs.md). + + +## Prerequisites + +Before running the benchmarks, ensure the following dependencies are installed: + +```bash +pip install seaborn matplotlib pandas sentencepiece +``` + +Additionally, you will need to log in to the Hugging Face CLI to enable loading models such as meta-llama/Meta-Llama-3-8B: + +```bash +huggingface-cli login +``` + +## Setting Up the Environment + +Create a directory to store the models and establish a symbolic link to it. We highly recommend using a fast storage device such as an NVMe SSD to store the models: +```bash +mkdir /mnt/raid0nvme1/models +ln -s /mnt/raid0nvme1/models models +``` + +Optionally, you can install the Flexible I/O Tester (FIO) to benchmark the storage device's performance: +```bash +apt install fio +fio fio-config.ini +``` + +## Running the Benchmarks + +### 1. Random Multi-Model Load Test + +The random load test emulates **model serving scenarios** (such as Serverless LLM Inference), where a variety of different models are requested in random order, simulating a real-world use case. + +#### 1.1 Small Models + +Start the ServerlessLLM Store server with a 18GB memory pool: + +```bash +sllm-store start --chunk-size=16MB --mem-pool-size=18GB --num-thread=4 --storage-path=./models +``` + +In a separate terminal, run the following commands to benchmark the loading performance of several small models. We repeat each test 30 times to ensure statistical stability: +```bash +CUDA_VISIBLE_DEVICES=0 bash benchmark_random_load.sh facebook/opt-6.7b ./models 30 +CUDA_VISIBLE_DEVICES=0 bash benchmark_random_load.sh meta-llama/Meta-Llama-3-8B ./models 30 +CUDA_VISIBLE_DEVICES=0 bash benchmark_random_load.sh mistralai/Mistral-7B-v0.3 ./models 30 +CUDA_VISIBLE_DEVICES=0 bash benchmark_random_load.sh google/gemma-7b ./models 30 +``` + +Plot the results with the following command, saving the output for review and documentation: + +```bash +python plot.py --models facebook/opt-6.7b meta-llama/Meta-Llama-3-8B mistralai/Mistral-7B-v0.3 google/gemma-7b \ +--test-name random --num-repeats 30 --results-dir results --output-file images/random_small_loading_latency.png +``` + +Here is an example of the output visualization: + +![Random Small Loading Latency](images/random_small_loading_latency.png) + +#### 1.2 Large Models + +Restart the ServerlessLLM Store server with an increased memory pool size to accommodate large models. Please note that the server startup can be lengthy due to the initialization of a 140GB memory pool: + +```bash +sllm-store start --chunk-size=16MB --mem-pool-size=140GB --num-thread=4 --storage-path=./models +``` + +To benchmark the loading performance of several large models, run the following commands. We limit the number of repeats to 10 to manage disk capacity efficiently +```bash +bash benchmark_random_load.sh facebook/opt-66b ./models 10 +bash benchmark_random_load.sh meta-llama/Meta-Llama-3-70B ./models 10 +bash benchmark_random_load.sh mistralai/Mixtral-8x7B-v0.1 ./models 10 +bash benchmark_random_load.sh tiiuae/falcon-40b ./models 10 +``` + +Plot the results using the following command: + +```bash +python plot.py --models facebook/opt-66b meta-llama/Meta-Llama-3-70B mistralai/Mixtral-8x7B-v0.1 tiiuae/falcon-40b \ +--test-name random --num-repeats 10 --results-dir results --output-file images/random_large_loading_latency.png +``` + + + + +## Contact + +If you have any questions or need help with the benchmarks, please feel free to reach out to [Y.Fu@ed.ac.uk](mailto:y.fu@ed.ac.uk) or submit an issue on GitHub. \ No newline at end of file diff --git a/benchmarks/benchmark_random_load.sh b/benchmarks/benchmark_random_load.sh new file mode 100644 index 0000000..85f9907 --- /dev/null +++ b/benchmarks/benchmark_random_load.sh @@ -0,0 +1,102 @@ +#!/bin/bash + +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +set -e + +# Check if the correct number of arguments is provided +if [ "$#" -ne 3 ]; then + echo "Usage: $0 model_name models_directory num_replicas" + echo "Example: $0 facebook/opt-6.7b ./models 30" + exit 1 +fi + +# Assign arguments to variables +MODEL_NAME=$1 +MODELS_DIR=$2 +NUM_REPLICAS=$3 + +# Validate number of replicas +if [[ ! "$NUM_REPLICAS" =~ ^[0-9]+$ ]]; then + echo "Error: Number of replicas must be a positive integer." + exit 1 +fi + +# Check if models directory exists +if [ ! -d "$MODELS_DIR" ]; then + echo "Error: Models directory $MODELS_DIR does not exist. Please create it before running this script." + exit 1 +fi + +# Function to download models +download_models() { + local model_name="$1" + local save_format="$2" + local save_dir="$3" + local num_replicas="$4" + + echo "Downloading $model_name model..." + python3 download_models.py \ + --model-name "$model_name" \ + --save-format "$save_format" \ + --save-dir "$save_dir" \ + --num-replicas "$num_replicas" +} + +# Function to run benchmark +run_benchmark() { + local model_name="$1" + local model_format="$2" + local model_dir="$3" + local num_replicas="$4" + local benchmark_type="$5" + + echo "Running benchmark for $model_name..." + python3 test_loading.py \ + --model-name "$model_name" \ + --model-format "$model_format" \ + --model-dir "$model_dir" \ + --num-replicas "$num_replicas" \ + --benchmark-type "$benchmark_type" +} + +# Function to clean models directory +clean_models_dir() { + echo "Cleaning models directory..." + rm -rf "$MODELS_DIR"/* +} + +# Check if models directory exists +if [ ! -d "$MODELS_DIR" ]; then + echo "Error: Models directory does not exist. Please create it before running this script." + exit 1 +fi + +# Clean models directory before starting benchmarks +clean_models_dir + +# Start measuring performance for each format +for MODEL_FORMAT in safetensors sllm; do + echo "Measuring $MODEL_FORMAT performance for $MODEL_NAME..." + download_models "$MODEL_NAME" "$MODEL_FORMAT" "$MODELS_DIR" "$NUM_REPLICAS" + run_benchmark "$MODEL_NAME" "$MODEL_FORMAT" "$MODELS_DIR" "$NUM_REPLICAS" "random" + clean_models_dir +done + +echo "Benchmark completed successfully." diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py new file mode 100644 index 0000000..b1befae --- /dev/null +++ b/benchmarks/benchmark_utils.py @@ -0,0 +1,129 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import gc +import os +import time + +import torch +from torch import nn +from tqdm import tqdm +from transformers import AutoModelForCausalLM, AutoTokenizer + +from sllm_store.transformers import load_model + + +def _warmup_cuda(): + num_gpus = torch.cuda.device_count() + print(f"Warming up {num_gpus} GPUs") + for i in tqdm(range(num_gpus)): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + + +def _warmup_inference(): + print("Warming up inference") + model_name = "facebook/opt-6.7b" + model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.float16, device_map="auto" + ) + prompts = [ + "The quick brown fox jumps over the lazy dog.", + ] + tokenizer = AutoTokenizer.from_pretrained(model_name) + inputs = tokenizer(prompts, return_tensors="pt").to("cuda") + with torch.no_grad(): + outputs = model.generate(**inputs, max_new_tokens=50) + del outputs, tokenizer, inputs, model + gc.collect() + torch.cuda.empty_cache() + + +def benchmark_inference(model: nn.Module, model_path: str): + # Inference + prompts = [ + "The quick brown fox jumps over the lazy dog.", + ] + tokenizer = AutoTokenizer.from_pretrained(model_path) + inputs = tokenizer(prompts, return_tensors="pt").to("cuda") + with torch.no_grad(): + start_time = time.time() + outputs = model.generate(**inputs, max_new_tokens=50) + end_time = time.time() + output_text = tokenizer.decode(outputs[0], skip_special_tokens=True) + end_to_end_time = end_time - start_time + throughput = outputs.shape[1] / end_to_end_time + + del outputs, tokenizer, inputs + gc.collect() + torch.cuda.empty_cache() + + return end_to_end_time, throughput, output_text + + +def measure( + model_name: str, model_format: str, model_dir: str, loading_order: list +): + results = [] + print( + f"Measuring loading time for {model_format} model={model_name}, repeating {len(loading_order)} times" + ) + # loading_order = torch.randperm(num_replicas) + for model_idx in loading_order: + print(f"Loading {model_name}_{model_idx}") + model_record = {"model_name": f"{model_name}_{model_idx}"} + + # Model Loading + if model_format == "sllm": + model_path = os.path.join(model_dir, f"{model_name}_{model_idx}") + start_time = time.time() + model = load_model( + f"{model_name}_{model_idx}", + storage_path=model_dir, + device_map="auto", + torch_dtype=torch.float16, + ) + end_time = time.time() + elif model_format == "safetensors": + model_path = os.path.join( + model_dir, f"{model_name}_safetensors_{model_idx}" + ) + start_time = time.time() + model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch.float16, + device_map="auto", + ) + end_time = time.time() + model_record["loading_time"] = end_time - start_time + + # Inference + end_to_end_time, throughput, output_text = benchmark_inference( + model, model_path + ) + + model_record["end_to_end_time"] = end_to_end_time + model_record["throughput"] = throughput + model_record["output_text"] = output_text + + results.append(model_record) + + del model + gc.collect() + torch.cuda.empty_cache() + + return results diff --git a/benchmarks/download_models.py b/benchmarks/download_models.py new file mode 100644 index 0000000..ff25919 --- /dev/null +++ b/benchmarks/download_models.py @@ -0,0 +1,99 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import argparse +import os + +import torch +from tqdm import tqdm +from transformers import AutoModelForCausalLM, AutoTokenizer + +from sllm_store.transformers import save_model + + +def get_args(): + parser = argparse.ArgumentParser( + description="Save a model with ServerlessLLM" + ) + parser.add_argument( + "--model-name", + type=str, + required=True, + help="Name of the model to save", + ) + parser.add_argument( + "--save-format", + type=str, + required=True, + choices=["sllm", "safetensors"], + help="Format to save the model in", + ) + parser.add_argument( + "--save-dir", + type=str, + required=True, + help="Directory to save models", + ) + parser.add_argument( + "--num-replicas", + type=int, + default=1, + help="Number of replicas to save", + ) + return parser.parse_args() + + +def main(): + args = get_args() + + save_dir = args.save_dir + save_format = args.save_format + model_name = args.model_name + replicas = args.num_replicas + + # Check if save_dir exists + if not os.path.exists(save_dir): + raise FileNotFoundError(f"Directory {save_dir} does not exist") + + # Load model into memory + print(f"Loading model {model_name} into memory") + model = AutoModelForCausalLM.from_pretrained( + args.model_name, torch_dtype=torch.float16, trust_remote_code=True + ) + tokenizer = AutoTokenizer.from_pretrained(args.model_name) + + # # Save model + if save_format == "sllm": + print(f"Saving {replicas} sllm models to {save_dir}") + for i in tqdm(range(replicas)): + model_dir = os.path.join(save_dir, f"{model_name}_{i}") + save_model(model, model_dir) + tokenizer.save_pretrained(model_dir) + elif save_format == "safetensors": + print(f"Saving {replicas} safetensors models to {save_dir}") + for i in tqdm(range(replicas)): + model_dir = os.path.join(save_dir, f"{model_name}_safetensors_{i}") + model.save_pretrained(model_dir) + tokenizer.save_pretrained(model_dir) + else: + raise ValueError(f"Invalid save format {save_format}") + + print("Done!") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/fio-config.ini b/benchmarks/fio-config.ini new file mode 100644 index 0000000..7e9f35f --- /dev/null +++ b/benchmarks/fio-config.ini @@ -0,0 +1,14 @@ +[global] +ioengine=libaio +iodepth=16 +rw=read +bs=1M +direct=1 +size=13GB +numjobs=4 +runtime=60 +group_reporting + +[job1] +name=Sequential Read Test +filename=./models/fio_testfile diff --git a/benchmarks/images/random_small_loading_latency.png b/benchmarks/images/random_small_loading_latency.png new file mode 100644 index 0000000000000000000000000000000000000000..5c5c13f4302e6fac69d7680992c0c229633cb98a GIT binary patch literal 100751 zcmeFaby$_@7d4DJ>Zl+yqoRO_j)8?3lt`$EiYVQwBArS%>WHI=flW$xNjHj!igX-l zP^3fR(DAMNAo_Ez_uub&|9QEtnNi@J^E~%+-+QmU*4le+NlTt$Sjn=Ij*gB&{PYP~ zI=WS9bacNP{=N*~VP7SihyNV56jQd8Gt;%Sxo|~??%V}Sb7M10V}pyEt#z(g7?_zJ z+IM*0{=J*^EiKJ01h}|Ne*D5dvnzUB+`pYp!JDiwKdoXxN4IY)`Dc-Bh=?`aB04(p z6UXFjeR>=9&dXO%rVJj*Rub6u@1s8!bycjrzvEg($myLcZ~yyZ6RUE`qO}EXOJ$Dz zdCbf5k3a609lO0VTj|&t{jThfQ?J&~eh&WV=SgoqBm6v>Vqf#{e(xRg0iJz39=_j( zkN)-R_A?_HzF)tEzqy{sWBcznTekh4w&cIx^jW;f=D*)_mD}91`oG^aU-?@D?(o+y z$*ZnMe*F#|UEZ!g%$NW7oBT@`_y6~MbYVyT4;Q)i|6Sz5LjB)e$$qiM?-%0w`oP6E zzjLZ4z56_2R1+u_C;Q|2VKM{jUC zK=h{Jr}zu2BZRDbm96^fsy|OGyrJ4Ej{nnE{PE2^PTkhmHw0|@eF}z)9`7lHAe z7T(iUjc(zHH0;-YOP{&CVf|j^^KxnAfZZtSTrr`MVcB}&#Qe2HH(ed^VCC!n`23J{ z)X$UDGJ101xbQs}amlHutn8CDnjGn}j`(@1TS|VMDZX1C9@V8HbH*0Fm0?mwMNc@b zJAeQF891fB*A|ZM$B#aTEBEo7-SLnuRZLCQ%b#Esmpo^*!jntKu(+vK_t>99}>FyL5E?D59BgGhB9-}9|ng4SI9RG6#J_ipT* zidkZQ{Qdj)w{P8&!Dqy3W=CQ-g@lFe;p9Ab`0(MOk&#DXVPVIQAD7Rvy?o>5%_z&R z3bDqN%KgvJ3Rtw?De)JHb6?N38=n>t5pm`6{g6^wp$@G7-u$0Axbf$CpL3sGM@w<=?R?m^3ZeMmYro=LPA5C(%$Chi&jS| zQLZxzhhsw=>FZcl|K`mb{i)GD0rM8uyX+#dxSsA0kN1!TwYSd@6cYN{*B4V(CjEGq z+(|5|Jhi*ps@Z95*WSG!FE~9vyTez|P8Kh(OwcYQm&JC(`t16B>e7;ucLT_+M=B=- zcGtv2=@xrMs3hv%V6v4ubEX2jC0VD4iA~s1nf&sVD=!5F1=s%Z$BUKgc8A+jyA7MN z(pk9m&v*K{a2&=$)x>H9gCmEaEY$IosOWx_W>E z%h5xLqqG-$Tj7R%g&Y*|#IIOcDI7iO`Z_;fA=yxY$D~f4QSh5=Y1CW#u!i%5j={^yra%3kx=`*ohP2cEjz4lvv+N=VD1e!H8rd zO7~c^3+2`t4ohmcLP$u+uKoL^3vREIi;xYeO1rGhW7edu5GlW_s_Dv=D@iVM&VBW% zQp*`PRn;cyVXIqwu`50nAJ4&{w-O3?4Wv1)x)q{1E@J-zzmn^?q8oNw&g z-u*c<{^j1}*QZ*RUq7siGPJd~j}(8nqq`|PQ#DDywQPSU?sMclhkqwU%OzPq$KBn1 z6P|(4-1KmyN@Dvt%E>z$0>s>zRI*Zqa{6~X6yKv5U6y1}ndC5L>TQTi495@c`_nt* zgHAGd8;V!FzOjlr*%PNGICqoXSvud1!K8_@X>znL3L8mTF!$K?WnRKg8B2~F+_z=R zF?S};3eW8lmMwXUqIynXk@0k}xx9~yiuP61yD_kGJB%9l)g?<@zI@p_V!~|EqDAD& z{TzqHAMaJE*y6Bl+qO<5yc(aw7LR;f8>M@;4nE(>s0o-@w4Ykt#W<_l1QWZ^yM>A^y;{THs0y@g$c<5w2yMV2$es>isW zQ{>jITjc4re|#RaUdypudVj@VrHMSEr^d$>F{38@aZ7gwfT{<86}vK$2x zI^X>JCzHte5viVvsj+6PC@k zr}oyYS;=cuvv>QB9Rim3f==Bt#QS*hns_xi3Vtn zD6oz+o)?c{)y_XMwqB6aYIYp

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zTB8v=Pzh}XVoZ`{1418BjX*T67%is0)FfcUG#N?0T!3@KYOKSN_zgagN9caNM4CrC zml`-&Pb0_qd+!wl2-qp6ai-9y#G+zKBzWziu@0bSE{Z33~$8+Gn|1}we#&(mrV}-+u!M$+D^d literal 0 HcmV?d00001 diff --git a/benchmarks/plot.py b/benchmarks/plot.py new file mode 100644 index 0000000..7a3f3d7 --- /dev/null +++ b/benchmarks/plot.py @@ -0,0 +1,152 @@ +import argparse +import json +import os + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns + +pattern = "{model_name}_{model_format}_{num_repeats}_{test_name}.json" + + +def get_args(): + parser = argparse.ArgumentParser(description="Plot benchmark results.") + parser.add_argument( + "--models", + type=str, + nargs="+", + required=True, + help="Model names to show in the plot.", + ) + parser.add_argument( + "--test-name", + type=str, + required=True, + choices=["random", "single"], + help="Name of the test.", + ) + parser.add_argument( + "--num-repeats", + type=int, + required=True, + help="Number of repeats for the benchmark.", + ) + parser.add_argument( + "--results-dir", + type=str, + default="./results", + help="Directory to load results from.", + ) + parser.add_argument( + "--output-filename", + type=str, + default="loading_latency.png", + help="Output filename for the plot.", + ) + return parser.parse_args() + + +def load_results(models, model_format, num_repeats, test_name, results_dir): + """Load results from files and check for the expected number of repeats.""" + all_results = {} + for model in models: + model = model.replace("/", "_") + filename = pattern.format( + model_name=model, + model_format=model_format, + num_repeats=num_repeats, + test_name=test_name, + ) + filename = os.path.join(results_dir, filename) + with open(filename) as f: + results = json.load(f) + if len(results) != num_repeats: + print( + f"Error: Expected {num_repeats} repeats, but found {len(results)} in {filename}." + ) + exit(1) + all_results[model] = results + return all_results + + +def create_dataframe(sllm_results, safetensors_results): + """Convert results list to pandas DataFrame.""" + sllm_loading_latency = [] + safetensors_loading_latency = [] + sllm_model_list = [] + safetensors_model_list = [] + for model_name, results in sllm_results.items(): + for result in results: + sllm_loading_latency.append(result["loading_time"]) + sllm_model_list.append(model_name) + for result in safetensors_results[model_name]: + safetensors_loading_latency.append(result["loading_time"]) + safetensors_model_list.append(model_name) + sllm_model_list = [model.split("_")[1] for model in sllm_model_list] + safetensors_model_list = [ + model.split("_")[1] for model in safetensors_model_list + ] + df = pd.DataFrame( + { + "Model": sllm_model_list + safetensors_model_list, + "System": ["Sllm"] * len(sllm_model_list) + + ["SafeTensors"] * len(safetensors_model_list), + "Loading Time": list(sllm_loading_latency) + + list(safetensors_loading_latency), + } + ) + return df + + +def plot_results(df, output_filename): + """Plot the results using seaborn's boxplot for loading latency.""" + # Set the style for the plot + sns.set_theme(style="whitegrid", palette="pastel", font_scale=1.2) + + # Create a box plot using seaborn + plt.figure(figsize=(12, 8)) + # box_plot = sns.boxplot(x='Model', y='Loading Time', hue='System', data=df, showmeans=True, meanprops={"marker":"o", "markerfacecolor":"black", "markeredgecolor":"black"}) + # use bar plot instead of box plot + sns.barplot(x="Model", y="Loading Time", hue="System", data=df) + + # Customize the plot + plt.title("Model Loading Latency", fontsize=16, weight="bold") + plt.xlabel("Model") + plt.ylabel("Loading Time (s)") + plt.legend(title="System") + + # Remove top and right spines for better aesthetics + sns.despine(trim=True) + + # Save the plot as an image file + plt.savefig(output_filename, bbox_inches="tight", dpi=300) + + +def main(): + args = get_args() + + models = args.models + test_name = args.test_name + num_repeats = args.num_repeats + results_dir = args.results_dir + output_filename = args.output_filename + + if not os.path.exists(results_dir): + raise FileNotFoundError(f"Directory {results_dir} does not exist.") + + output_dir = os.path.dirname(output_filename) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + sllm_results = load_results( + models, "sllm", num_repeats, test_name, results_dir + ) + safetensors_results = load_results( + models, "safetensors", num_repeats, test_name, results_dir + ) + df = create_dataframe(sllm_results, safetensors_results) + plot_results(df, output_filename) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/results/facebook_opt-6.7b_phantom_30_random.json b/benchmarks/results/facebook_opt-6.7b_phantom_30_random.json new file mode 100644 index 0000000..6249a47 --- /dev/null +++ b/benchmarks/results/facebook_opt-6.7b_phantom_30_random.json @@ -0,0 +1,212 @@ +[ + { + "model_name": "facebook/opt-6.7b_16", + "loading_time": 1.0603504180908203, + "end_to_end_time": 1.898390769958496, + "throughput": 32.13247818379017, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_10", + "loading_time": 1.0053458213806152, + "end_to_end_time": 2.425994873046875, + "throughput": 25.144323542361153, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_18", + "loading_time": 1.068990707397461, + "end_to_end_time": 2.372910261154175, + "throughput": 25.7068297097463, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_3", + "loading_time": 1.0486304759979248, + "end_to_end_time": 2.239630699157715, + "throughput": 27.236633264109575, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_4", + "loading_time": 1.8052313327789307, + "end_to_end_time": 2.1210079193115234, + "throughput": 28.75991147633269, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_22", + "loading_time": 1.9855210781097412, + "end_to_end_time": 2.2109146118164062, + "throughput": 27.590391629772007, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_25", + "loading_time": 1.0989010334014893, + "end_to_end_time": 2.0908117294311523, + "throughput": 29.175271566223845, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_13", + "loading_time": 1.0800120830535889, + "end_to_end_time": 2.2011797428131104, + "throughput": 27.71241203684799, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_5", + "loading_time": 1.0510895252227783, + "end_to_end_time": 2.2066802978515625, + "throughput": 27.643333771271703, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_17", + "loading_time": 1.0642368793487549, + "end_to_end_time": 2.160844564437866, + "throughput": 28.22970286891916, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_29", + "loading_time": 1.0636045932769775, + "end_to_end_time": 2.189013719558716, + "throughput": 27.866431103180574, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_12", + "loading_time": 1.0601158142089844, + "end_to_end_time": 2.238553762435913, + "throughput": 27.249736425191777, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_27", + "loading_time": 1.0342729091644287, + "end_to_end_time": 2.213266134262085, + "throughput": 27.561077746458057, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_1", + "loading_time": 1.0532996654510498, + "end_to_end_time": 2.3837199211120605, + "throughput": 25.59025473577537, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_11", + "loading_time": 1.0353169441223145, + "end_to_end_time": 2.4344098567962646, + "throughput": 25.057407580610647, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_7", + "loading_time": 1.0830974578857422, + "end_to_end_time": 2.432058334350586, + "throughput": 25.081635229891955, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_28", + "loading_time": 1.00742769241333, + "end_to_end_time": 2.2210607528686523, + "throughput": 27.46435455275787, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_20", + "loading_time": 1.013396978378296, + "end_to_end_time": 2.121671199798584, + "throughput": 28.750920503511992, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_19", + "loading_time": 1.0360965728759766, + "end_to_end_time": 2.096001148223877, + "throughput": 29.10303749198352, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_2", + "loading_time": 2.090054750442505, + "end_to_end_time": 2.1884405612945557, + "throughput": 27.873729393827315, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_15", + "loading_time": 1.947800874710083, + "end_to_end_time": 2.1587047576904297, + "throughput": 28.257685439700012, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_23", + "loading_time": 1.2132229804992676, + "end_to_end_time": 1.3594067096710205, + "throughput": 44.872516492700065, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_9", + "loading_time": 1.0096378326416016, + "end_to_end_time": 2.3395631313323975, + "throughput": 26.073243839015397, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_24", + "loading_time": 1.0526819229125977, + "end_to_end_time": 2.381564140319824, + "throughput": 25.613418915439418, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_8", + "loading_time": 1.0283787250518799, + "end_to_end_time": 2.4220774173736572, + "throughput": 25.18499184313622, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_14", + "loading_time": 1.0534887313842773, + "end_to_end_time": 2.424642562866211, + "throughput": 25.158347434060907, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_0", + "loading_time": 1.0570142269134521, + "end_to_end_time": 2.218724489212036, + "throughput": 27.49327385919092, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_26", + "loading_time": 1.061762809753418, + "end_to_end_time": 2.2024800777435303, + "throughput": 27.696050745891558, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_6", + "loading_time": 1.0514404773712158, + "end_to_end_time": 2.1386401653289795, + "throughput": 28.522797331180108, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_21", + "loading_time": 1.0297183990478516, + "end_to_end_time": 2.2441351413726807, + "throughput": 27.181963721974356, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + } +] \ No newline at end of file diff --git a/benchmarks/results/facebook_opt-6.7b_safetensors_30_random.json b/benchmarks/results/facebook_opt-6.7b_safetensors_30_random.json new file mode 100644 index 0000000..48b9e5c --- /dev/null +++ b/benchmarks/results/facebook_opt-6.7b_safetensors_30_random.json @@ -0,0 +1,212 @@ +[ + { + "model_name": "facebook/opt-6.7b_26", + "loading_time": 5.315510511398315, + "end_to_end_time": 2.43576717376709, + "throughput": 25.04344448720815, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_11", + "loading_time": 4.825915098190308, + "end_to_end_time": 2.2741358280181885, + "throughput": 26.823375828504876, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_12", + "loading_time": 4.599301338195801, + "end_to_end_time": 2.1387481689453125, + "throughput": 28.521356972139976, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_25", + "loading_time": 5.044696092605591, + "end_to_end_time": 2.2212560176849365, + "throughput": 27.461940233065135, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_27", + "loading_time": 4.907486915588379, + "end_to_end_time": 2.1005144119262695, + "throughput": 29.040505341765382, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_24", + "loading_time": 5.455355644226074, + "end_to_end_time": 2.221276044845581, + "throughput": 27.46169263453278, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_10", + "loading_time": 4.681467533111572, + "end_to_end_time": 2.2695939540863037, + "throughput": 26.877054325145778, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_2", + "loading_time": 5.696061134338379, + "end_to_end_time": 2.241537094116211, + "throughput": 27.213468900478297, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_13", + "loading_time": 3.9381392002105713, + "end_to_end_time": 1.9413731098175049, + "throughput": 31.421059502433405, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_3", + "loading_time": 5.191221714019775, + "end_to_end_time": 2.068458318710327, + "throughput": 29.4905628255701, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_22", + "loading_time": 6.379522085189819, + "end_to_end_time": 2.100472927093506, + "throughput": 29.041078898554396, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_0", + "loading_time": 6.706932067871094, + "end_to_end_time": 2.339123010635376, + "throughput": 26.078149683727222, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_16", + "loading_time": 4.864023208618164, + "end_to_end_time": 2.1991493701934814, + "throughput": 27.737997621613676, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_18", + "loading_time": 3.9082493782043457, + "end_to_end_time": 2.186828136444092, + "throughput": 27.894281669152797, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_1", + "loading_time": 5.857372045516968, + "end_to_end_time": 2.213028907775879, + "throughput": 27.564032166803344, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_4", + "loading_time": 5.022972106933594, + "end_to_end_time": 2.510160446166992, + "throughput": 24.301235442199253, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_21", + "loading_time": 6.8377602100372314, + "end_to_end_time": 2.33209490776062, + "throughput": 26.156739932413334, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_20", + "loading_time": 4.595490455627441, + "end_to_end_time": 2.221282720565796, + "throughput": 27.461610102680822, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_29", + "loading_time": 5.144659042358398, + "end_to_end_time": 2.1676270961761475, + "throughput": 28.141371782816545, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_5", + "loading_time": 4.985952377319336, + "end_to_end_time": 2.1304242610931396, + "throughput": 28.632794469163787, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_6", + "loading_time": 5.69625186920166, + "end_to_end_time": 1.8594741821289062, + "throughput": 32.804972817724895, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_23", + "loading_time": 5.118912220001221, + "end_to_end_time": 2.108344554901123, + "throughput": 28.932652330567844, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_15", + "loading_time": 5.045670509338379, + "end_to_end_time": 2.1164777278900146, + "throughput": 28.82147031181513, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_9", + "loading_time": 4.685817241668701, + "end_to_end_time": 2.2286877632141113, + "throughput": 27.370366099210145, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_28", + "loading_time": 4.862567663192749, + "end_to_end_time": 2.0547993183135986, + "throughput": 29.686597351055926, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_19", + "loading_time": 4.807375907897949, + "end_to_end_time": 2.1207022666931152, + "throughput": 28.76405658542508, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_14", + "loading_time": 4.883404731750488, + "end_to_end_time": 2.1128876209259033, + "throughput": 28.870442230746168, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_8", + "loading_time": 4.704470634460449, + "end_to_end_time": 2.209514856338501, + "throughput": 27.6078704902153, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_17", + "loading_time": 4.310231924057007, + "end_to_end_time": 1.579456090927124, + "throughput": 38.620890033222544, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + }, + { + "model_name": "facebook/opt-6.7b_7", + "loading_time": 4.768707990646362, + "end_to_end_time": 2.080918073654175, + "throughput": 29.313984424615803, + "output_text": "The quick brown fox jumps over the lazy dog.\nI'm not sure if this is a joke or not, but I'm going to assume it is.\nIt's a joke. The joke is that the dog is lazy. The fox is quick. The dog is brown." + } +] \ No newline at end of file diff --git a/benchmarks/server-specs.md b/benchmarks/server-specs.md new file mode 100644 index 0000000..f276d6d --- /dev/null +++ b/benchmarks/server-specs.md @@ -0,0 +1,10 @@ +# Server Specifications + +This document outlines the detailed hardware specifications utilized for the ServerlessLLM Store benchmarking tests. + +## Hardware Overview + +- **CPU:** 2 x AMD EPYC 7453 +- **GPU:** 8 x NVIDIA A5000 24GB +- **Memory:** 1TB DDR4 3200MHz +- **Storage:** 2 x 3.84TB NVMe SSD, Intel P5510, PCIe 4.0 interface, configured in RAID 0 \ No newline at end of file diff --git a/benchmarks/test_loading.py b/benchmarks/test_loading.py new file mode 100644 index 0000000..8445e68 --- /dev/null +++ b/benchmarks/test_loading.py @@ -0,0 +1,106 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import argparse +import json +import os + +import torch +from benchmark_utils import _warmup_cuda, _warmup_inference, measure + + +def get_args(): + parser = argparse.ArgumentParser(description="Load test") + parser.add_argument( + "--model-name", + type=str, + required=True, + help="Name of the model to serve", + ) + parser.add_argument( + "--model-format", + type=str, + required=True, + choices=["sllm", "safetensors"], + help="Format to save the model in", + ) + parser.add_argument( + "--model-dir", + type=str, + required=True, + help="Directory to load models", + ) + parser.add_argument( + "--num-replicas", + type=int, + default=1, + help="Number of replicas to load", + ) + parser.add_argument( + "--output-dir", + type=str, + default="./results", + help="Directory to save results", + ) + parser.add_argument( + "--benchmark-type", + type=str, + required=True, + choices=["random", "single"], + help="Name of the test.", + ) + return parser.parse_args() + + +def main(): + args = get_args() + _warmup_cuda() + _warmup_inference() + + model_format = args.model_format + model_name = args.model_name + model_dir = args.model_dir + num_replicas = args.num_replicas + output_dir = args.output_dir + benchmark_type = args.benchmark_type + + # Check if model_dir exists + if not os.path.exists(model_dir): + raise FileNotFoundError(f"Directory {model_dir} does not exist") + + if benchmark_type == "random": + loading_order = torch.randperm(num_replicas) + elif benchmark_type == "single": + loading_order = [0] * num_replicas + else: + raise ValueError(f"Unknown benchmark type {benchmark_type}") + + results = measure(model_name, model_format, model_dir, loading_order) + + output_filename = ( + f"{model_name}_{model_format}_{num_replicas}_{benchmark_type}.json" + ) + output_filename = output_filename.replace("/", "_") + output_filename = os.path.join(output_dir, output_filename) + + with open(output_filename, "w") as f: + json.dump(results, f, indent=4) + print(f"Results saved to {output_filename}") + + +if __name__ == "__main__": + main() diff --git a/blogs/README.md b/blogs/README.md new file mode 100644 index 0000000..22820fb --- /dev/null +++ b/blogs/README.md @@ -0,0 +1 @@ +All ServerlessLLM blogs are available in this directory. \ No newline at end of file diff --git a/blogs/serverless-llm-architecture/README.md b/blogs/serverless-llm-architecture/README.md new file mode 100644 index 0000000..2d41053 --- /dev/null +++ b/blogs/serverless-llm-architecture/README.md @@ -0,0 +1,113 @@ +# ServerlessLLM Architecture Overview + +## Table of Contents + +- [ServerlessLLM Architecture Overview](#serverlessllm-architecture-overview) + - [Table of Contents](#table-of-contents) + - [Introduction](#introduction) + - [ServerlessLLM Serve](#serverlessllm-serve) + - [ServerlessLLM Store](#serverlessllm-store) + - [Conclusion and Future Work](#conclusion-and-future-work) + + +## Introduction + +ServerlessLLM (SLLM, pronounced “slim”) enables low-latency, serverless LLM inference via two core components: **sllm-serve** and **sllm-store**. **sllm-serve** is a serving platform that manages auto-scaling, load-balancing, and resource allocation for deployed LLMs across a distributed GPU cluster. *sllm-store*, built in C++, serves as a high-performance checkpoint store optimized for cold-start with efficient model loading and caching. + +This article will walk you through the system architecture of *sllm* and aims to answer the following questions: + +- What happens when we **deploy a model** in our cluster? +- How is an **inference** request served? +- How to handle **cold start**? +- How do we **save and load model** checkpoints? + +The goal is to offer a clear picture of how ServerlessLLM functions under the hood, providing insights that will help readers both understand the architecture and potentially contribute to its development. + +## ServerlessLLM Serve + +

+ arch_overview.jpg +

+ +The above figure outlines ServerlessLLM Serve, divided into three planes: user interface, control, and data. + +- **User Interface**: Includes a CLI for model and cluster management and an API gateway that routes control messages to the controller and inference requests to the appropriate router. +- **Control Plane**: Contains the controller, storage-aware scheduler, and store manager, managing cluster status and model lifecycles. +- **Data Plane**: Comprises the request router, inference backend for processing requests, and checkpoint store for efficient model loading and caching. + +We’ll next walk through these planes from a user’s perspective: first deploying an LLM on the control plane, then processing an inference request on the data plane without cold-start, and finally examining the cold-start process. + +![image.png](./images/outlines1.png) + +**Step 1: Deploy an LLM** + +![arch_step1.jpg](./images/arch_step1.jpg) + +Starting with the control plane, model developers use *sllm* to deploy a model, specifying its name on the HuggingFace hub. Upon receiving the model registration request, the controller processes configurations such as backend choice (e.g., [Transformers](https://github.com/huggingface/transformers) or [vLLM](https://github.com/vllm-project/vllm)), auto-scaling settings (e.g., concurrency limits, minimum/maximum instances), and resource requirements (e.g., CPU and GPU allocation). + +Once configured, the controller creates a router for the model and registers its checkpoint with the store manager. The store manager determines an initial server for downloading model checkpoints, then instructs *sllm-store* to download and convert the model into the optimized format. This approach ensures the model is ready for rapid loading and minimal latency during cold starts. + +**Step 2: Query the LLM (Simple Case, No Cold Start)** + +![arch_step2.jpg](./images/arch_step2.jpg) + +In the data plane, when a model inference request is received, the API gateway routes it to the corresponding model router. The router selects one of the available backend instances and forwards the request. Each backend instance operates on one or more dedicated GPUs within a shared server, processing the inference request with an inference engine (such as *Transformers* or *vLLM*) and returning the result to the client. + +**Step 3: Query the Model with Cold Start** + +![arch_step3.jpg](./images/arch_step3.jpg) + +In a cold-start scenario, when traffic exceeds capacity, the router scales up by creating additional backend instances. It issues a resource allocation request to the storage-aware scheduler, which selects an optimal server based on model details (e.g., size), hardware specifications (e.g., PCIe and disk bandwidth), and storage status (e.g., whether the model is stored on the server’s local disks or host memory). The request router then initiates a new inference backend, which loads the model from the checkpoint store before becoming available. + +In summary, these steps illustrate how ServerlessLLM efficiently deploys, serves, and scales LLMs, achieving low-latency serverless inference even with frequent cold starts. The figure below illustrates the interplay between these components. + +![arch_full.jpg](./images/arch_full.jpg) + +## ServerlessLLM Store + +

+ sllm-store.jpg +

+ +ServerlessLLM Store enables fast checkpoint loading with two core modules: + +- A checkpoint parser that saves and restores model checkpoints in a cold-start optimized format (detailed in Step 1 below). +- A dedicated checkpoint manager on each GPU server that loads checkpoints into GPUs efficiently and caches frequently used ones in host memory. + +Built on these core modules, ServerlessLLM Store offers a two-level Python API: + +- A lower-level tensor API that saves and restore tensors for each specific deep learning library. For examples, PyTorch API for saving and loading a PyTorch `state_dict` . +- A higher-level model API, built on the tensor API, that saves and loads models for inference libraries like *Transformers* and *vLLM*. + +To illustrate, let’s walk through two steps: 1) saving a *Transformers* pre-trained model into the *sllm* cold-start optimized format, and 2) loading the *sllm* checkpoint to restore a *Transformers* pre-trained model. + +

+ outlines2.png +

+ +**Step 1: Save a Model** + +

+ save_model.jpg +

+ +The`save_model`function takes a *Transformers* pre-trained model and an output path as inputs. It first saves model configurations using *Transformers*’ built-in API, then calls the PyTorch API (`sllm_store.torch.save_dict`) to save the model’s `state_dict` in a cold-start optimized format. + +The `save_dict` function uses the checkpoint parser via `save_tensors`, which saves each tensor’s data in a binary file and returns its offset within that file. After saving tensors, the `save_dict` function records tensor metadata and offsets to an index file. This setup enables efficient retrieval during model loading. + +**Step 2: Load a Model** + +

+ load_model.jpg +

+ + +The `load_model`function takes a model path as input and returns a *Transformers* pre-trained model. It initializes the model with saved configurations and concurrently calls the PyTorch API (`sllm_store.torch.load_dict`) to load the tensors. + +The PyTorch API allocates GPU memory for each tensor, calling the standalone checkpoint manager (via gRPC) to load tensor data into designated addresses. Simultaneously, `load_model` uses the checkpoint parser to restore tensors based on the saved tensor metadata, inferring actual GPU memory addresses using the base GPU memory address and saved tensor offsets. + +Before returning the model, a final sync call is sent to the checkpoint manager to ensure all data has loaded correctly. + +## Conclusion and Future Work + +In the next blog post, we’ll demonstrate a deployment example of Serverless RAG. 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--git a/configs/.linkcheck.json b/configs/.linkcheck.json new file mode 100644 index 0000000..7d2740c --- /dev/null +++ b/configs/.linkcheck.json @@ -0,0 +1,28 @@ +{ + "ignorePatterns": [ + { + "pattern": "^https://opensource.org*" + }, + { + "pattern": "^https://twitter.com*" + }, + { + "pattern": "^https://platform.openai.com*" + } + + ], + "replacementPatterns": [ + { + "pattern": "^/", + "replacement": "/github/workspace/" + } + ], + "httpHeaders": [ + { + "urls": ["https://docs.github.com/"], + "headers": { + "Accept-Encoding": "zstd, br, gzip, deflate" + } + } + ] +} \ No newline at end of file diff --git a/docs/.DS_Store b/docs/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..25135e78fda58412d6eed8d5a60df4d42ddeb6fc GIT binary patch literal 6148 zcmeHKT}s115S~p-spv!TLHZ=QLh%A=i6`g(LEuXkC@<`_DJ|&MW-6M~JDq2N(IA4Z5TK0AxPs@uJ_Z&{ zYwjgDCRNN7y&xbnKr7cXFAa&BlN+ z5Hrw|#}U_m^j(Mg|2W8=i~(cdUoqhN`8=QDm9(~YUXE*Rim||8VLvap3&A3kV)$|? c9%BT7KIH{4Q}lw+K` + Model name to deploy (must be a HuggingFace pretrained model name). +- `--config ` + Path to the JSON configuration file. +- `--backend ` + Overwrite the backend in the configuration. +- `--num-gpus ` + Number of GPUs to allocate. +- `--target ` + Target concurrency. +- `--min-instances ` + Minimum number of instances. +- `--max-instances ` + Maximum number of instances. + +**Examples:** +```bash +sllm deploy --model facebook/opt-1.3b +sllm deploy --config /path/to/config.json +sllm deploy --model facebook/opt-1.3b --backend transformers +sllm deploy --model facebook/opt-1.3b --num-gpus 2 --target 5 --min-instances 1 --max-instances 5 +``` + +#### Example Configuration File (`config.json`) + +```json +{ + "model": "facebook/opt-1.3b", + "backend": "transformers", + "num_gpus": 1, + "auto_scaling_config": { + "metric": "concurrency", + "target": 1, + "min_instances": 0, + "max_instances": 10, + "keep_alive": 0 + }, + "backend_config": { + "pretrained_model_name_or_path": "facebook/opt-1.3b", + "device_map": "auto", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM", + "enable_lora": true, + "lora_adapters": { + "demo_lora1": "crumb/FLAN-OPT-1.3b-LoRA", + "demo_lora2": "GrantC/alpaca-opt-1.3b-lora" + } + } +} +``` + +##### Example Quantization Configuration (`config.json`) +`quantization_config` can be obtained from any configuration used in `transformers` via the `.to_json_file(filename)` function: + +```python +quantization_config = BitsAndBytesConfig(load_in_8bit=True) +quantization_config.to_json_file("quantization_config.json") + +``` +Then copy it into `config.json`: + +```json +{ + "model": "", + "backend": "transformers", + "num_gpus": 1, + "auto_scaling_config": { + "metric": "concurrency", + "target": 1, + "min_instances": 0, + "max_instances": 10, + "keep_alive": 0 + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM", + "quantization_config": { + "_load_in_4bit": false, + "_load_in_8bit": true, + "bnb_4bit_compute_dtype": "float32", + "bnb_4bit_quant_storage": "uint8", + "bnb_4bit_quant_type": "fp4", + "bnb_4bit_use_double_quant": false, + "llm_int8_enable_fp32_cpu_offload": false, + "llm_int8_has_fp16_weight": false, + "llm_int8_skip_modules": null, + "llm_int8_threshold": 6.0, + "load_in_4bit": false, + "load_in_8bit": true, + "quant_method": "bitsandbytes" + } + } +} +``` + +Below is a description of all the fields in config.json. + +| Field | Description | +| ----- | ----------- | +| model | HuggingFace model name, used to identify model instance. | +| backend | Inference engine, supports `transformers` and `vllm`. | +| num_gpus | Number of GPUs used to deploy a model instance. | +| auto_scaling_config | Config about auto scaling. | +| auto_scaling_config.metric | Metric used to decide whether to scale up or down. | +| auto_scaling_config.target | Target value of the metric. | +| auto_scaling_config.min_instances | Minimum number of model instances. | +| auto_scaling_config.max_instances | Maximum number of model instances. | +| auto_scaling_config.keep_alive | How long a model instance stays alive after inference ends. | +| backend_config | Config about inference backend. | +| backend_config.pretrained_model_name_or_path | HuggingFace model name or local path. | +| backend_config.device_map | Device map config used to load the model. | +| backend_config.torch_dtype | Torch dtype of the model. | +| backend_config.hf_model_class | HuggingFace model class. | +| backend_config.enable_lora | Set to true to enable loading LoRA adapters during inference. | +| backend_config.lora_adapters| A dictionary of LoRA adapters in the format `{name: path}`, where each path is a local or Hugging Face-hosted LoRA adapter directory. | +| backend_config.quantization_config| A dictionary specifying the desired `BitsAndBytesConfig`. Can be obtained by saving a `BitsAndBytesConfig` to JSON via `BitsAndBytesConfig.to_json_file(filename). Defaults to None.| + +--- + +### sllm delete + +Delete deployed models by name. + +**Usage:** +```bash +sllm delete [MODELS...] +``` +**Arguments:** +- `MODELS...` + One or more space-separated model names to delete. + +**Example:** +```bash +sllm delete facebook/opt-1.3b facebook/opt-2.7b meta/llama2 +``` +**Example output:** +```text +[✓] Deleted model: facebook/opt-1.3b +[✓] Deleted model: facebook/opt-2.7b +[✓] Deleted model: meta/llama2 +``` + +--- + +### sllm status + +Check the current status of all deployed models. This command displays the list of models currently running in the ServerlessLLM cluster, including their state, GPU usage, and endpoint. + +**Usage:** +```bash +sllm status +``` +**Example output:** +```text +[✓] Deployed Models: +- facebook/opt-1.3b Running +- meta/llama2 Running + +--- + +## Notes + +- All commands should be run as `sllm ...` after installation. +- For advanced configuration, refer to the [Example Configuration File](#example-configuration-file-configjson) section. +- For more details, see the official documentation and guides linked above. + + + +#### Example +```bash +sllm status +``` + +#### Example +```bash +sllm-cli status +``` diff --git a/docs/api/intro.md b/docs/api/intro.md new file mode 100644 index 0000000..f3a7d29 --- /dev/null +++ b/docs/api/intro.md @@ -0,0 +1,9 @@ +--- +sidebar_position: 1 +--- + +# API Introduction + +Welcome to the ServerlessLLM API documentation. This section contains detailed information about the various APIs provided by ServerlessLLM: + +- [CLI API](./cli.md) - Documentation for the `sllm` command-line interface diff --git a/docs/api/sllm-store-cli.md b/docs/api/sllm-store-cli.md new file mode 100644 index 0000000..5ab0900 --- /dev/null +++ b/docs/api/sllm-store-cli.md @@ -0,0 +1,242 @@ +--- +sidebar_position: 2 +--- + +# ServerlessLLM Store CLI + +ServerlessLLM Store's CLI allows the use `sllm-store`'s functionalities within a terminal window. It has the functions: +- `start`: Starts the gRPC server with the specified configuration. +- `save`: Convert a HuggingFace model into a loading-optimized format and save it to a local path. +- `load`: Load a model into given GPUs. + +## Requirements +- OS: Ubuntu 22.04 +- Python: 3.10 +- GPU: compute capability 7.0 or higher + +## Installations + +### Create a virtual environment +``` bash +conda create -n sllm-store python=3.10 -y +conda activate sllm-store +``` + +### Install C++ Runtime Library (required for compiling and running CUDA/C++ extensions) +``` bash +conda install -c conda-forge libstdcxx-ng=12 -y +``` + +### Install with pip +```bash +pip install serverless-llm-store +``` + +## Example Workflow +1. Firstly, start the ServerlessLLM Store server. By default, it uses ./models as the storage path. +Launch the checkpoint store server in a separate process: +``` bash +# 'mem_pool_size' is the maximum size of the memory pool in GB. It should be larger than the model size. +sllm-store start --storage-path $PWD/models --mem-pool-size 4GB +``` + +2. Convert a model to ServerlessLLM format and save it to a local path: +``` bash +sllm-store save --model facebook/opt-1.3b --backend vllm +``` + +3. Load a previously saved model into memory, ready for inference: +```bash +sllm-store load --model facebook/opt-1.3b --backend vllm +``` + +## sllm-store start + +Start a gRPC server to serve models stored using ServerlessLLM. This enables fast, low-latency access to models registered via sllm-store save, allowing external clients to load model weights, retrieve metadata, and perform inference-related operations efficiently. + +The server supports in-memory caching with customizable memory pooling and chunking, optimized for parallel read access and minimal I/O latency. + +#### Usage +```bash +sllm-store start [OPTIONS] +``` + +#### Options + +- `--host ` + - Host address to bind the gRPC server to. + +- `--port ` + - Port number on which the gRPC server will listen for incoming requests. + +- `--storage-path ` + - Path to the directory containing models previously saved with sllm-store save. + +- `--num-thread ` + - Number of threads to use for I/O operations and chunk handling. + +- `--chunk-size ` + - Size of individual memory chunks used for caching model data (e.g., 64MiB, 512KB). Must include unit suffix. + +- `--mem-pool-size ` + - Total memory pool size to allocate for the in-memory cache (e.g., 4GiB, 2GB). Must include unit suffix. + +- `--disk-size ` + - (Currently unused) Would set the maximum size sllm-store can occupy in disk cache. + +- `--registration-required` + - If specified, models must be registered with the server before loading. + +#### Examples + +Start the server using all default values: +``` bash +sllm-store start +``` + +Start the server with a custom storage path: +``` bash +sllm-store start --storage-path /your/folder +``` + +Specify a custom port and host: +``` bash +sllm-store start --host 127.0.0.1 --port 9090 +``` + +Use larger chunk size and memory pool for large models in a multi-threaded environment: +``` bash +sllm-store start --num-thread 16 --chunk-size 128MB --mem-pool-size 8GB +``` + +Run with access control enabled: +``` bash +sllm-store start --registration-required True +``` + +Full example for production-style setup: +``` bash +sllm-store start \ + --host 0.0.0.0 \ + --port 8000 \ + --storage-path /data/models \ + --num-thread 8 \ + --chunk-size 64MB \ + --mem-pool-size 16GB \ + --registration-required True +``` + +## sllm-store save + +Saves a model to a local directory through a backend of choice, making it available for future inference requests. Only model name and backend are required, with the rest having default values. + +It supports download of [PEFT LoRA (Low-Rank Adaptation)](https://huggingface.co/docs/peft/main/en/index) for transformer models, and varying tensor sizes for parallel download of vLLM models. + + +#### Usage +```bash +sllm-store save [OPTIONS] +``` + +#### Options + +- `--model ` + - Model name to deploy with default configuration. The model name must be a Hugging Face pretrained model name. You can find the list of available models [here](https://huggingface.co/models). + +- `--backend ` + - Select a backend for the model to be converted to `ServerlessLLM format` from. Supported backends are `vllm` and `transformers`. + +- `--adapter` + - Enable LoRA adapter support. Overwrite `adapter`, which is by default set to False. Only `transformers` backend is supported. + +- `--adapter-name ` + - Adapter name to save. Must be a Hugging Face pretrained LoRA adapter name. + +- `--tensor-parallel-size ` + - Number of GPUs you want to use. Only `vllm` backend is supported. + +- `--local-model-path ` + - Saves the model from a local path if it contains a Hugging Face snapshot of the model. + +- `--storage-path ` + - Location where the model will be saved. + +#### Examples +Save a vLLM model name with default configuration: +```bash +sllm-store save --model facebook/opt-1.3b --backend vllm +``` + +Save a transformers model to a set location: +```bash +sllm-store save --model facebook/opt-1.3b --backend vllm --storage-path ./your/folder +``` + +Save a vLLM model from a locally stored snapshot and overwrite the tensor parallel size: +```bash +sllm-store save --model facebook/opt-1.3b --backend vllm --tensor-parallel-size 4 --local-model-path ./path/to/snapshot +``` + +Save a transformers model with a LoRA adapter: +```bash +sllm-store save --model facebook/opt-1.3b --backend transformers --adapter --adapter-name crumb/FLAN-OPT-1.3b-LoRA +``` + +## sllm-store load + +Load a model from local storage and run example inference to verify deployment. This command supports both the transformers and vllm backends, with optional support for PEFT LoRA adapters and quantized precision formats including int8, fp4, and nf4 (LoRA and quantization supported on transformers backend only). + +When using the transformers backend, the function warms up GPU devices, loads the base model from disk, and optionally merges a LoRA adapter if specified. With vllm, it loads the model in the ServerlessLLM format. + +#### Usage +```bash +sllm-store load [OPTIONS] +``` + +#### Options + +- `--model ` + - Model name to deploy with default configuration. The model name must be a Hugging Face pretrained model name. You can find the list of available models [here](https://huggingface.co/models). + +- `--backend ` + - Select a backend for the model to be converted to `ServerlessLLM format` from. Supported backends are `vllm` and `transformers`. + +- `--adapter` + - Enable LoRA adapter support for the transformers backend. Overwrite `adapter` in the default configuration (`transformers` backend only). + +- `--adapter-name ` + - Adapter name to save. Must be a Hugging Face pretrained LoRA adapter name. + +- `--precision ` + - Precision to use when loading the model (`transformers` backend only). For more info on quantization in ServerlessLLM, visit [here](https://serverlessllm.github.io/docs/stable/store/quickstart#quantization). + +- `--storage-path ` + - Location where the model will be loaded from. + +#### Examples +Load a vllm model from storage: +``` bash +sllm-store load --model facebook/opt-1.3b --backend vllm +``` + +Load a transformers model from storage with int8 quantization: +``` bash +sllm-store load --model facebook/opt-1.3b --backend transformers --precision int8 --storage-path ./your/models +``` + +Load a transformers model with a LoRA adapter: +``` bash +sllm-store load --model facebook/opt-1.3b --backend transformers --adapter --adapter-name crumb/FLAN-OPT-1.3b-LoRA +``` + +#### Note: loading vLLM models + +To load models with vLLM, you need to apply a compatibility patch to your vLLM installation. This patch has been tested with vLLM version `0.9.0.1`. + +```bash + ./sllm_store/vllm_patch/patch.sh +``` + +:::note +The patch file is located at `sllm_store/vllm_patch/sllm_load.patch` in the ServerlessLLM repository. +::: \ No newline at end of file diff --git a/docs/images/favicon.ico b/docs/images/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..4ef51a7c4d8cb55f59100813fb8ffa9c88795d74 GIT binary patch literal 46169 zcmc$Gg;SQz_cz@g(j_I`_0S+CjocvJ4bt5u(j_S&As{J@G)Q+N-QC^rTfX!D53i2n zJ!7!fUc2Xf>Hq}=4Sv48!qY$HjkLYr;@6}%a^z|1K0{BhW zsmL4(Dga9Ey|}u^{9&eR2I>n*Txc763L#Hs`ZFEUStE_||2sIw98$7B62 z^C+HnWkmn~f9yRAcBw)fi>+W)_<4a7CE98&(wSKxla`2*R5(+(yJ5${8lHWHrHb0%y)EK$4p>Up9s=F7pZCe+g| z@kVPn?RXdtFL7K5ykh(0IJ_s6(r#_@Q+^p z#uYAx$F&!IK;RCHM)BZ{!|Z^O;=rQ{m#D!PVqEQy-#{lDWe5@b+V_GT5He0_5@&`7 zi6);x@j=5{ydCP#`to9R$rMn*J~VkEO-O_EPP!c?KNr~pBQER*mN?aze!HC|Q$r{} z48~dLBBeY{sv?aJZlA3DTQ;uf+(onx=Ufc^qnpFZeCKnGdW5D#hPb~sOh=VP@A2C; 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b/docs/stable/community/meetups.md new file mode 100644 index 0000000..8e3811a --- /dev/null +++ b/docs/stable/community/meetups.md @@ -0,0 +1,11 @@ +# ServerlessLLM Meetups + +We host regular biweekly developer meetings online. We will share project updates from the ServerlessLLM developer team presented during these meetings. Please find the materials of our previous meetups below: + +Date |Topic |Slides +---------------|-------------|--------- +February 21st 2025 | Fine Tuning | [Slides](https://docs.google.com/presentation/d/1rnw3mieAAbMabDIoIGS-ciMGc3hJ7AICYSaNJp-Fk4s/edit?usp=sharing) +March 7th 2025 |Quantization |[Slides](https://docs.google.com/presentation/d/1uSbP-LzGbbvPsemIAE6jCFsggYm_ATxQguCHDmdwoXE/edit?usp=sharing) + +We are always looking for contributors to join us on the developer team. If you are interested in contributing, consult our [job board](https://github.com/orgs/ServerlessLLM/projects/2) and claim a feature. For any other questions, please contact us on [this email](mailto:Y.Fu@ed.ac.uk) or on [our Discord server](https://discord.gg/AEF8Gduvm8). + diff --git a/docs/stable/community/talks.md b/docs/stable/community/talks.md new file mode 100644 index 0000000..66a2531 --- /dev/null +++ b/docs/stable/community/talks.md @@ -0,0 +1,8 @@ +# ServerlessLLM Talks + +Materials for ServerlessLLM talks will be listed here. + +Topic |Location |Date |Links +-------------|----------------|---------------|------------------------------------ +Efficient Sharing of AI Infrastructures with Specialized Serverless Computing | University of Pennsylvania |January 29th 2025 |[Slides](https://drive.google.com/file/d/17GwXsqaDDS7Xw8nX_-RaKiwpaPQgu9WD/view) \| [Event](https://asset.seas.upenn.edu/event/yao-fu-university-of-edinburgh/) +ServerlessLLM Tutorial | SESAME'25 | March 31st 2025 |[Slides](https://docs.google.com/presentation/d/1ioGCVpsg0x3oCxX19EiE820aMiY22X5MG6jgImZ1W18/edit?usp=sharing) \| [Event](https://sesame25.github.io/) diff --git a/docs/stable/deployment/_category_.json b/docs/stable/deployment/_category_.json new file mode 100644 index 0000000..534be1d --- /dev/null +++ b/docs/stable/deployment/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Deployment", + "position": 3 +} diff --git a/docs/stable/deployment/multi_machine.md b/docs/stable/deployment/multi_machine.md new file mode 100644 index 0000000..21583c3 --- /dev/null +++ b/docs/stable/deployment/multi_machine.md @@ -0,0 +1,296 @@ +--- +sidebar_position: 2 +--- + +# Multi-machine + +This guide will help you get started with running ServerlessLLM on multiple machines using Docker containers. You'll learn how to set up a head node on one machine and connect worker nodes from different machines using Docker, ensuring proper network communication between the containers. You can extend this setup to use as many nodes as you need. + +## Prerequisites + +This guide requires **two machines**: +- One machine for the head node (no GPU required) +- One machine with an NVIDIA GPU to serve as the worker node + +You can add more worker machines with GPUs as needed to scale out your deployment. + +### For All Machines + +Ensure you have the following installed and configured on all machines (both head node and worker machines): + +1. **Docker**: Installed on your system. You can download it from [here](https://docs.docker.com/get-docker/). +2. **Network connectivity**: Ensure all machines can communicate with each other on the required ports (6379 for Ray, 8343 for ServerlessLLM API, and 8073 for storage service). + +:::tip +The **ServerlessLLM CLI** (`pip install serverless-llm`) can be installed on any machine that needs to manage model deployments. This could be your local computer or any machine within the cluster that can connect to the head node. +::: + +### For Worker Machines Only + +These requirements are only necessary for the worker machines that will run the models: + +1. **GPUs**: At least one NVIDIA GPU is required on each worker machine. If you have multiple GPUs, you can adjust the Docker configuration accordingly. +2. **NVIDIA Docker Toolkit**: This enables Docker to utilize NVIDIA GPUs. Follow the installation guide [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). + +## Multi-Machine Setup + +We'll start a head node on one machine using Docker, then add a worker node from another machine using Docker containers with host networking. + +### Step 1: Start the Head Node + +1. **Start the head node using Docker:** + +```bash +# Get the machine's IP address that will be accessible to other machines +export HEAD_IP=$(hostname -I | awk '{print $1}') +echo "Head node IP address: $HEAD_IP" + +docker run -d \ + --name sllm_head \ + --network host \ + -e MODE=HEAD \ + -e RAY_NODE_IP=$HEAD_IP \ + serverlessllm/sllm:latest +``` + +:::important +For multi-machine setups, setting the `RAY_NODE_IP` is critical. It should be set to an IP address that is accessible from all worker machines. The command above attempts to automatically determine your machine's primary IP, but in complex network environments, you may need to specify it manually. + +If your machine has multiple network interfaces, ensure you use the IP that other machines in your network can access. +::: + +:::tip +If you don't have the ServerlessLLM Docker image locally, Docker will automatically pull it from the registry. You can also adjust the CPU and resource allocations by setting additional environment variables like `RAY_NUM_CPUS` and `RAY_RESOURCES`. +::: + +2. **Verify the head node is running and note the external IP:** + +```bash +docker logs sllm_head +``` + +Expected output should include: + +```bash +> docker logs sllm_head +... +2025-05-29 14:29:46,211 INFO scripts.py:744 -- Local node IP: 129.215.164.107 +... +(SllmController pid=380) INFO 05-29 14:29:53 controller.py:59] Starting store manager +(SllmController pid=380) INFO 05-29 14:29:56 controller.py:68] Starting scheduler +(StoreManager pid=417) INFO 05-29 14:29:56 store_manager.py:226] Initializing store manager +(StoreManager pid=417) INFO 05-29 14:29:56 store_manager.py:237] Initializing cluster and collecting hardware info +(StoreManager pid=417) ERROR 05-29 14:29:56 store_manager.py:242] No worker nodes found +INFO: Started server process [1] +INFO: Waiting for application startup. +INFO: Application startup complete. +INFO: Uvicorn running on http://0.0.0.0:8343 (Press CTRL+C to quit) +(FcfsScheduler pid=456) INFO 05-29 14:29:56 fcfs_scheduler.py:54] Starting FCFS scheduler +(FcfsScheduler pid=456) INFO 05-29 14:29:56 fcfs_scheduler.py:111] Starting control loop +``` + +Make note of the IP address shown in the logs. This is the address that worker nodes will use to connect to the head node. + +### Step 2: Start Worker Node on a Different Machine + +:::tip +You can adjust the memory pool size and other parameters based on the resources available on your worker machine. +::: + +1. **On the worker machine, create a directory for model storage:** + +```bash +mkdir -p /path/to/your/models +export MODEL_FOLDER=/path/to/your/models +``` + +Replace `/path/to/your/models` with the actual path where you want to store the models. + +2. **Start the worker node:** + +```bash +# Replace with the actual IP address of the head node from the previous step +# DO NOT copy-paste this line directly - update with your actual head node IP +export HEAD_IP= +``` + +```bash +# Get the worker machine's IP address that will be accessible to the head node +export WORKER_IP=$(hostname -I | awk '{print $1}') +echo "Worker node IP address: $WORKER_IP" + +docker run -d \ + --name sllm_worker_0 \ + --network host \ + --gpus '"device=0"' \ + -e WORKER_ID=0 \ + -e STORAGE_PATH=/models \ + -e MODE=WORKER \ + -e RAY_HEAD_ADDRESS=${HEAD_IP}:6379 \ + -e RAY_NODE_IP=$WORKER_IP \ + -v ${MODEL_FOLDER}:/models \ + serverlessllm/sllm:latest \ + --mem-pool-size 4GB --registration-required true +``` + +:::important +For multi-machine setups, setting the `RAY_NODE_IP` on worker nodes is just as critical as on the head node. It should be set to an IP address that is accessible from the head node. Without this, workers might report internal Docker IPs that aren't accessible across machines. + +Make sure to replace `192.168.1.100` with the actual IP address of your head node that you noted earlier. +::: + +3. **Verify worker node is connected:** + +On the worker machine, check if the worker has properly connected to the Ray cluster: + +```bash +docker exec -it sllm_worker_0 bash -c "source /opt/conda/etc/profile.d/conda.sh && conda activate worker && ray status" +``` + +Expected output should include both the head node and worker node resources: + +```bash +> docker exec -it sllm_worker_0 bash -c "source /opt/conda/etc/profile.d/conda.sh && conda activate worker && ray status" +======== Autoscaler status: 2025-05-29 14:42:30.434645 ======== +Node status +--------------------------------------------------------------- +Active: + 1 node_f0a8e97ca64c64cebd551f469a38d0d66ce304f7cc1cc9696fe33cf3 + 1 node_3b7db178afb8bdb16460d0cb6463dc7b9b3afbcc00753c3be110c9b3 +Pending: + (no pending nodes) +Recent failures: + (no failures) + +Resources +--------------------------------------------------------------- +Usage: + 3.0/52.0 CPU + 0.0/1.0 GPU + 0.30000000000000004/1.0 control_node + 0B/526.36GiB memory + 0B/18.63GiB object_store_memory + 0.0/1.0 worker_id_0 + 0.0/1.0 worker_node + +Demands: + (no resource demands) +``` + +This output confirms that both the head node and worker node are properly connected and their resources are recognized by the Ray cluster. + +:::tip +**Adding more worker nodes:** You can add more worker nodes by repeating Step 2 on additional machines with GPUs. Just make sure to: +1. Use a unique `WORKER_ID` for each worker (1, 2, 3, etc.) +2. Point each worker to the same head node IP address +3. Ensure each worker has its own `RAY_NODE_IP` set correctly +::: + +### Step 3: Use `sllm` to manage models + +#### Configure the Environment + +**On any machine with `sllm` installed, set the `LLM_SERVER_URL` environment variable:** + +> Replace `` with the actual IP address of the head node. + +```bash +export LLM_SERVER_URL=http://:8343 +``` + +#### Deploy a Model Using `sllm` + +```bash +sllm deploy --model facebook/opt-1.3b +``` + +> Note: This command will spend some time downloading the model from the Hugging Face Model Hub. You can use any model from the [Hugging Face Model Hub](https://huggingface.co/models) by specifying the model name in the `--model` argument. + +Expected output: + +```bash +INFO 07-24 06:51:32 deploy.py:83] Model registered successfully. +``` + +### Step 4: Query the Model Using OpenAI API Client + +**You can query the model using any OpenAI API client. For example, use the following command:** + +**Make sure the model is successfully deployed before querying.** + +> Replace `` with the actual IP address of the head node. + +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` + +Expected output: + +```json +{"id":"chatcmpl-23d3c0e5-70a0-4771-acaf-bcb2851c6ea6","object":"chat.completion","created":1721706121,"model":"facebook/opt-1.3b","choices":[{"index":0,"message":{"role":"assistant","content":"system: You are a helpful assistant.\nuser: What is your name?\nsystem: I am a helpful assistant.\n"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":16,"completion_tokens":26,"total_tokens":42}} +``` + +#### Delete a Deployed Model Using `sllm` + +When you're done using a model, you can delete it: + +```bash +sllm delete facebook/opt-1.3b +``` + +This will remove the specified model from the ServerlessLLM server. + +## Clean Up + +To stop and remove all ServerlessLLM containers: + +1. **Stop all containers:** + +```bash +# On head node machine +docker stop sllm_head +docker rm sllm_head + +# On each worker machine +docker stop sllm_worker_0 # Use appropriate container name (sllm_worker_1, sllm_worker_2, etc.) +docker rm sllm_worker_0 +``` + +2. **Optional: Remove the Docker image:** + +```bash +docker rmi serverlessllm/sllm:latest +``` + +:::tip +If you don't have the ServerlessLLM Docker image locally, Docker will automatically pull it from the registry. You can also adjust the CPU and resource allocations by setting additional environment variables like `RAY_NUM_CPUS` and `RAY_RESOURCES`. +::: + +## Troubleshooting + +### Network Issues + +1. **Connection refused errors**: Ensure that firewalls on all machines allow traffic on ports 6379, 8343, and 8073. + +2. **Ray cluster connection issues**: + - Verify that the head node IP address is correct and that the Ray port (6379) is accessible from worker machines + - Ensure both head and worker nodes have their `RAY_NODE_IP` set to an IP address that is accessible from other machines + - Check that you're not using private Docker network IPs (typically 172.x.x.x) which aren't accessible across machines + +3. **Workers can't connect to head node**: + - Make sure the `RAY_HEAD_ADDRESS` points to the external IP of the head node, not localhost or an internal Docker IP + - Verify network connectivity with `ping` or `telnet` from worker machines to the head node IP on port 6379 + +4. **GPU access issues**: Make sure the NVIDIA Docker toolkit is properly installed and that the `--gpus` flag is used for worker containers. + +### Container Management + +- **View running containers**: `docker ps` \ No newline at end of file diff --git a/docs/stable/deployment/single_machine.md b/docs/stable/deployment/single_machine.md new file mode 100644 index 0000000..758980a --- /dev/null +++ b/docs/stable/deployment/single_machine.md @@ -0,0 +1,217 @@ +--- +sidebar_position: 1 +--- + +# Single machine (from scratch) + +This guide provides instructions for setting up ServerlessLLM from scratch on a single machine. This 'from scratch' approach means you will manually initialize and manage the Ray cluster components. It involves using multiple terminal sessions, each configured with a distinct Conda environment, to run the head and worker processes on the same physical machine, effectively simulating a multi-node deployment locally. + +:::note +We strongly recommend using Docker (Compose) as detailed in the [Docker Compose guide](../getting_started.md). Docker provides a smoother and generally easier setup process. Follow this guide only if Docker is not a suitable option for your environment. +::: + +## Installation + +### Requirements + +Ensure your system meets the following prerequisites: + +- **OS**: Ubuntu 20.04 +- **Python**: 3.10 +- **GPU**: NVIDIA GPU with compute capability 7.0 or higher + +### Installing with pip + +Follow these steps to install ServerlessLLM using pip: + +**Create the head environment:** + +```bash +# Create and activate a conda environment +conda create -n sllm python=3.10 -y +conda activate sllm + +# Install ServerlessLLM and its store component +pip install serverless-llm serverless-llm-store +``` + +**Create the worker environment:** + +```bash +# Create and activate a conda environment +conda create -n sllm-worker python=3.10 -y +conda activate sllm-worker + +# Install ServerlessLLM (worker version) and its store component +pip install "serverless-llm[worker]" serverless-llm-store +``` + +:::note +If you plan to integrate vLLM with ServerlessLLM, a patch needs to be applied to the vLLM repository. For detailed instructions, please refer to the [vLLM Patch](#vllm-patch) section. +::: + +### Installing from Source + +To install ServerlessLLM from source, follow these steps: + +1. Clone the repository: + ```bash + git clone https://github.com/ServerlessLLM/ServerlessLLM.git + cd ServerlessLLM + ``` + +2. Create the head environment: + ```bash + # Create and activate a conda environment + conda create -n sllm python=3.10 -y + conda activate sllm + + # Install sllm_store (pip install is recommended for speed) + cd sllm_store && rm -rf build + pip install . + cd .. + + # Install ServerlessLLM + pip install . + ``` + +3. Create the worker environment: + ```bash + # Create and activate a conda environment + conda create -n sllm-worker python=3.10 -y + conda activate sllm-worker + + # Install sllm_store (pip install is recommended for speed) + cd sllm_store && rm -rf build + pip install . + cd .. + + # Install ServerlessLLM (worker version) + pip install ".[worker]" + ``` + +### vLLM Patch + +To use vLLM with ServerlessLLM, you must apply a patch. The patch file is located at `sllm_store/vllm_patch/sllm_load.patch` within the ServerlessLLM repository. This patch has been tested with vLLM version `0.9.0.1`. + +Apply the patch using the following script: + +```bash +conda activate sllm-worker +./sllm_store/vllm_patch/patch.sh +``` + +## Running ServerlessLLM Locally + +These steps describe how to run ServerlessLLM on your local machine. + +### 1. Start a Local Ray Cluster + +First, initiate a local Ray cluster. This cluster will consist of one head node and one worker node (on the same machine). + +**Start the head node:** + +Open a new terminal and run: + +```bash +conda activate sllm +ray start --head --port=6379 --num-cpus=4 --num-gpus=0 \ + --resources='{"control_node": 1}' --block +``` + +**Start the worker node:** + +Open another new terminal and run: + +```bash +conda activate sllm-worker +export CUDA_VISIBLE_DEVICES=0 # Or your desired GPU ID +ray start --address=0.0.0.0:6379 --num-cpus=4 --num-gpus=1 \ + --resources='{"worker_node": 1, "worker_id_0": 1}' --block +``` + +### 2. Start the ServerlessLLM Store Server + +Next, start the ServerlessLLM Store server. By default, it uses `./models` as the storage path. + +Open a new terminal and run: + +```bash +conda activate sllm-worker +export CUDA_VISIBLE_DEVICES=0 # Or your desired GPU ID +sllm-store start +``` + +Expected output: + +```log +$ sllm-store start +INFO 12-31 17:13:23 cli.py:58] Starting gRPC server +INFO 12-31 17:13:23 server.py:34] StorageServicer: storage_path=./models, mem_pool_size=4294967296, num_thread=4, chunk_size=33554432, registration_required=False +WARNING: Logging before InitGoogleLogging() is written to STDERR +I20241231 17:13:23.947276 2165054 checkpoint_store.cpp:41] Number of GPUs: 1 +I20241231 17:13:23.947299 2165054 checkpoint_store.cpp:43] I/O threads: 4, chunk size: 32MB +I20241231 17:13:23.947309 2165054 checkpoint_store.cpp:45] Storage path: "./models" +I20241231 17:13:24.038651 2165054 checkpoint_store.cpp:71] GPU 0 UUID: c9938b31-33b0-e02f-24c5-88bd6fbe19ad +I20241231 17:13:24.038700 2165054 pinned_memory_pool.cpp:29] Creating PinnedMemoryPool with 128 buffers of 33554432 bytes +I20241231 17:13:25.557906 2165054 checkpoint_store.cpp:83] Memory pool created with 4GB +INFO 12-31 17:13:25 server.py:243] Starting gRPC server on 0.0.0.0:8073 +``` + +### 3. Start ServerlessLLM + +Now, start the ServerlessLLM service process using `sllm start`. + + +Open a new terminal and run: + +```bash +sllm start +``` + +At this point, you should have four terminals open: one for the Ray head node, one for the Ray worker node, one for the ServerlessLLM Store server, and one for the ServerlessLLM service (started via `sllm start`). + +### 4. Deploy a Model + +With all services running, you can deploy a model. + +Open a new terminal and run: + +```bash +conda activate sllm +sllm deploy --model facebook/opt-1.3b +``` + +This command downloads the specified model from Hugging Face Hub. To load a model from a local path, you can use a `config.json` file. Refer to the [CLI API documentation](../../api/cli.md#example-configuration-file-configjson) for details. + +### 5. Query the Model + +Once the model is deployed, you can query it using any OpenAI API-compatible client. For example, use the following `curl` command: + +```bash +curl http://127.0.0.1:8343/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` + +Expected output: + +```json +{"id":"chatcmpl-9f812a40-6b96-4ef9-8584-0b8149892cb9","object":"chat.completion","created":1720021153,"model":"facebook/opt-1.3b","choices":[{"index":0,"message":{"role":"assistant","content":"system: You are a helpful assistant.\nuser: What is your name?\nsystem: I am a helpful assistant.\n"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":16,"completion_tokens":26,"total_tokens":42}} +``` + +## Clean Up + +To delete a deployed model, use the following command: + +```bash +sllm delete facebook/opt-1.3b +``` + +This command removes the specified model from the ServerlessLLM server. \ No newline at end of file diff --git a/docs/stable/deployment/slurm_cluster.md b/docs/stable/deployment/slurm_cluster.md new file mode 100644 index 0000000..5d3a476 --- /dev/null +++ b/docs/stable/deployment/slurm_cluster.md @@ -0,0 +1,411 @@ +--- +sidebar_position: 3 +--- + +# SLURM cluster + +This guide will help you get started with running ServerlessLLM on SLURM cluster. It provides two deployment methods, based on `sbatch` and `srun`. If you are in development, we recommend using `srun`, as it is easier to debug than `sbatch`, and if you are in production mode, `sbatch` is recommended. Please make sure you have installed the ServerlessLLM following the [installation guide](./single_machine.md#installation) on all machines. + +## Pre-requisites +Before you begin, make sure you have checked the following: +### Some Tips about Installation +- If 'not enough disk space' is reported when `pip install` on the login node, you can submit it to a job node for execution + ```shell + #!/bin/bash + #SBATCH --partition=Teach-Standard + #SBATCH --job-name=ray-head + #SBATCH --output=sllm_pip.out + #SBATCH --error=sllm_pip.err + #SBATCH --nodes=1 + #SBATCH --ntasks=1 + #SBATCH --cpus-per-task=4 + #SBATCH --gpus-per-task=0 + + # Identify which conda you are using, here is an example that conda is in /opt/conda + source /opt/conda/bin/activate + + conda create -n sllm python=3.10 -y + conda activate sllm + pip install serverless-llm + pip install serverless-llm-store + + conda deactivate sllm + + conda create -n sllm-worker python=3.10 -y + conda activate sllm-worker + pip install serverless-llm[worker] + pip install serverless-llm-store + ``` + +### Command for Querying GPU Resource Information +Run the following commands in the cluster to check GPU resource information. +```shell +sinfo -O partition,nodelist,gres +``` +**Expected Output** +```shell +PARTITION NODELIST GRES +Partition1 JobNode[01,03] gpu:gtx_1060:8 +Partition2 JobNode[04-17] gpu:a6000:2,gpu:gtx_ +``` + +### Identify an idle node +Use `sinfo -p ` to identify some idle nodes + +**Expected Output** +```shell +$ sinfo -p compute +PARTITION AVAIL NODES STATE TIMELIMIT NODELIST +compute up 10 idle infinite JobNode[01-10] +compute up 5 alloc infinite JobNode[11-15] +compute up 2 down infinite JobNode[16-17] +``` + +### Job Nodes Setup +**`srun` Node Selection** + +Only one JobNode is enough. + +**`sbatch` Node Selection** +Let's start a head on the main job node (`JobNode01`) and add the worker on other job node (`JobNode02`). The head and the worker should be on different job nodes to avoid resource contention. The `sllm-store` should be started on the job node that runs worker (`JobNode02`), for passing the model weights, and the `sllm start` should be started on the main job node (`JobNode01`), finally you can use `sllm` to manage the models on the login node. + + +Note: `JobNode02` requires GPU, but `JobNode01` does not. +- **Head**: JobNode01 +- **Worker**: JobNode02 +- **sllm-store**: JobNode02 +- **sllm-serve**: JobNode01 +- **sllm**: Login Node + +--- +## SRUN +If you are in development, we recommend using `srun` to start ServerlessLLM, as it is easier to debug than `sbatch` +### Step 1: Use `srun` enter the JobNode +To start an interactive session on the specified compute node (JobNode), use: +``` +srun --partition --nodelist --gres :1 --pty bash +``` +This command requests a session on the specified node and provides an interactive shell. `--gres :1` specifies the GPU device you will use, for example: `--gres gpu:gtx_1060:1` + +### Step 2: Install ServerlessLLM +Firstly, please make sure CUDA driver available on the node. Here are some commands to check it. +```shell +nvidia-smi + +which nvcc +``` +If `nvidia-smi` has listed GPU information, but `which nvcc` has no output. Then use the following commands to load `nvcc`. Here is an example that cuda is located at `/opt/cuda-12.2.0` +```shell +export PATH=/opt/cuda-12.2.0/bin:$PATH +export LD_LIBRARY_PATH=/opt/cuda-12.2.0/lib64:$LD_LIBRARY_PATH +``` +Then, following the [installation guide](./single_machine.md#installation) to install ServerlessLLM. +### Step 3: Prepare multiple windows with `tmux` +Since srun provides a single interactive shell, you can use tmux to create multiple windows. Start a tmux session: +```shell +tmux +``` +This creates a new tmux session + +**Create multiple windows** +- Use `Ctrl+B` → `C` to start a new window +- Repeat the shortcut 4 more times to create a total of 5 windows. + +**What if `Ctrl+B` does not work?** + +If `Ctrl + B` is unresponsive, reset tmux key bindings: +```shell +tmux unbind C-b +tmux set-option -g prefix C-b +tmux bind C-b send-prefix +``` + +**Command to switch windows** + +Once multiple windows are created, you can switch between them using: + +`Ctrl + B` → `N` (Next window) +`Ctrl + B` → `P` (Previous window) +`Ctrl + B` → `W` (List all windows and select) +`Ctrl + B` → [Number] (Switch to a specific window, e.g., Ctrl + B → 1) + +### Step 4: Run ServerlessLLM on the JobNode +First find ports that are already occupied. Then pick your favourite number from the remaining ports to replace the following placeholder ``. For example: `6379` + +It should also be said that certain slurm system is a bit slow, **so please be patient and wait for the system to output**. + +In the first window, start a local ray cluster with 1 head node and 1 worker node: +```shell +source /opt/conda/bin/activate +conda activate sllm +ray start --head --port= --num-cpus=4 --num-gpus=0 --resources='{"control_node": 1}' --block +``` +In the second window, start the worker node: +```shell +source /opt/conda/bin/activate +conda activate sllm-worker +export CUDA_VISIBLE_DEVICES=0 +ray start --address=0.0.0.0: --num-cpus=4 --num-gpus=1 --resources='{"worker_node": 1, "worker_id_0": 1}' --block +``` +In the third window, start ServerlessLLM Store server: +```shell +source /opt/conda/bin/activate +conda activate sllm-worker +export CUDA_VISIBLE_DEVICES=0 +sllm-store start +``` +In the 4th window, start ServerlessLLM Serve: +```shell +source /opt/conda/bin/activate +conda activate sllm +sllm-serve start +``` +Everything is set! + + +In the 5th window, let's deploy a model to the ServerlessLLM server. You can deploy a model by running the following command: +```shell +source /opt/conda/bin/activate +conda activate sllm +sllm deploy --model facebook/opt-1.3b --backend transformers +``` +This will download the model from HuggingFace transformers. After deploying, you can query the model by any OpenAI API client. For example, you can use the following Python code to query the model: +```shell +curl http://127.0.0.1:8343/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` +Expected output: +```shell +{"id":"chatcmpl-9f812a40-6b96-4ef9-8584-0b8149892cb9","object":"chat.completion","created":1720021153,"model":"facebook/opt-1.3b","choices":[{"index":0,"message":{"role":"assistant","content":"system: You are a helpful assistant.\nuser: What is your name?\nsystem: I am a helpful assistant.\n"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":16,"completion_tokens":26,"total_tokens":42}} +``` + +### Step 5: Clean up +To delete a deployed model, use the following command: +```shell +sllm delete facebook/opt-1.3b +``` +This will remove the specified model from the ServerlessLLM server. + +In each window, use `Ctrl + c` to stop server and `exit` to exit current `tmux` session. + +--- +## SBATCH +### Step 1: Start the Head Node +Since the head node does not require a gpu, you can find a low-computing capacity node to deploy the head node. +1. **Activate the `sllm` environment and start the head node:** + + Here is the example script, named `start_head_node.sh`. + ```shell + #!/bin/bash + #SBATCH --partition=your-partition # Specify the partition + #SBATCH --nodelist=JobNode01 # Specify an idle node + #SBATCH --job-name=ray-head + #SBATCH --output=sllm_head.out + #SBATCH --error=sllm_head.err + #SBATCH --nodes=1 + #SBATCH --ntasks=1 + #SBATCH --cpus-per-task=12 + #SBATCH --gpus-per-task=0 + + cd /path/to/ServerlessLLM + + source /opt/conda/bin/activate # make sure conda will be loaded correctly + conda activate sllm + + ray start --head --port=6379 --num-cpus=12 --num-gpus=0 --resources='{"control_node": 1}' --block + ``` + - Replace `your-partition`, `JobNode01` and `/path/to/ServerlessLLM` + +2. **Submit the script** + + Use ```sbatch start_head_node.sh``` to submit the script to certain idle node. + +3. **Expected output** + + In `sllm_head.out`, you will see the following output: + + ```shell + Local node IP: + -------------------- + Ray runtime started. + -------------------- + ``` + **Remember the IP address**, denoted ``````, you will need it in following steps. + +4. **Find an available port for serve** + - Some HPCs have a firewall that blocks port 8343. You can use `nc -zv 8343` to check if the port is accessible. + - If it is not accessible, find an available port and replace `available_port` in the following script. + - Here is an example script, named `find_port.sh` + + ```shell + #!/bin/bash + #SBATCH --partition=your-partition + #SBATCH --nodelist=JobNode01 + #SBATCH --job-name=find_port + #SBATCH --output=find_port.log + #SBATCH --time=00:05:00 + #SBATCH --mem=1G + + echo "Finding available port on $(hostname)" + + python -c " + import socket + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + print(f'Available port: {s.getsockname()[1]}') + " + ``` + Use `sbatch find_port.sh` to submit the script to JobNode01, and in `find_port.log`, you will see the following output: + ``` + Finding available port on JobNode01 + Available port: + ``` + Remember this ``, you will use it in Step 4 + +### Step 2: Start the Worker Node & Store +We will start the worker node and store in the same script. Because the server loads the model weights onto the GPU and uses shared GPU memory to pass the pointer to the client. If you submit another script with ```#SBATCH --gpres=gpu:1```, it will be possibly set to use a different GPU, as specified by different ```CUDA_VISIBLE_DEVICES``` settings. Thus, they cannot pass the model weights. +1. **Activate the ```sllm-worker``` environment and start the worker node.** + + Here is the example script, named```start_worker_node.sh```. + ```shell + #!/bin/sh + #SBATCH --partition=your_partition + #SBATCH --nodelist=JobNode02 + #SBATCH --gres=gpu:a6000:1 # Specify device on JobNode02 + #SBATCH --job-name=sllm-worker-store + #SBATCH --output=sllm_worker.out + #SBATCH --error=sllm_worker.err + #SBATCH --gres=gpu:1 # Request 1 GPU + #SBATCH --cpus-per-task=4 # Request 4 CPU cores + #SBATCH --mem=16G # Request 16GB of RAM + + cd /path/to/ServerlessLLM + + conda activate sllm-worker + + HEAD_NODE_IP= + + export CUDA_HOME=/opt/cuda-12.5.0 # replace with your CUDA path + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH + + ray start --address=$HEAD_NODE_IP:6379 --num-cpus=4 --num-gpus=1 \ + --resources='{"worker_node": 1, "worker_id_0": 1}' --block & + + sllm-store start & + + wait + ``` + - Read the HPC's documentation to find out which partition you can use. Replace ```your_partition``` in the script with that partition name. + - Replace ```/path/to/ServerlessLLM``` with the path to the ServerlessLLM installation directory. + - Replace `````` with the IP address of the head node. + - Replace ```/opt/cuda-12.5.0``` with the path to your CUDA path. + +2. **Find the CUDA path** + - Some slurm-based HPCs have a module system, you can use ```module avail cuda``` to find the CUDA module. + - If it does not work, read the HPC's documentation carefully to find the CUDA path. For example, my doc said CUDA is in ```\opt```. Then you can use ```srun``` command to start an interactive session on the node, such as ```srun --pty -t 00:30:00 -p your_partition --gres=gpu:1 /bin/bash```. A pseudo-terminal will be started for you to find the path. + - Find it and replace ```/opt/cuda-12.5.0``` with the path to your CUDA path. +3. **Submit the script on the other node** + + Use ```sbatch start_worker_node.sh``` to submit the script to certain idle node (here we assume it is ```JobNode02```). In addition, We recommend that you place the head and worker on different nodes so that the Serve can start smoothly later, rather than queuing up for resource allocation. +4. **Expected output** + + In ```sllm_worker.out```, you will see the following output: + + - The worker node expected output: + ```shell + Local node IP: xxx.xxx.xx.xx + -------------------- + Ray runtime started. + -------------------- + ``` + - The store expected output: + ```shell + I20241030 11:52:54.719007 1321560 checkpoint_store.cpp:41] Number of GPUs: 1 + I20241030 11:52:54.773468 1321560 checkpoint_store.cpp:43] I/O threads: 4, chunk size: 32MB + I20241030 11:52:54.773548 1321560 checkpoint_store.cpp:45] Storage path: "./models/" + I20241030 11:52:55.060559 1321560 checkpoint_store.cpp:71] GPU 0 UUID: 52b01995-4fa9-c8c3-a2f2-a1fda7e46cb2 + I20241030 11:52:55.060798 1321560 pinned_memory_pool.cpp:29] Creating PinnedMemoryPool with 128 buffers of 33554432 bytes + I20241030 11:52:57.258795 1321560 checkpoint_store.cpp:83] Memory pool created with 4GB + I20241030 11:52:57.262835 1321560 server.cpp:306] Server listening on 0.0.0.0:8073 + ``` +### Step 3: Start the Serve on the Head Node +1. **Activate the ```sllm``` environment and start the serve.** + + Here is the example script, named```start_serve.sh```. + ```shell + #!/bin/sh + #SBATCH --partition=your_partition + #SBATCH --nodelist=JobNode01 # This node should be the same as head + #SBATCH --output=serve.log + + cd /path/to/ServerlessLLM + + conda activate sllm + + sllm start --host + # sllm start --host --port # if you have changed the port + ``` + - Replace `your_partition` in the script as before. + - Replace `/path/to/ServerlessLLM` as before. + - Replace `` you have found in Step 1 (if port 8343 is not available). +2. **Submit the script on the head node** + + Use ```sbatch start_serve.sh``` to submit the script to the head node (```JobNode01```). + +3. **Expected output** + ```shell + -- Connecting to existing Ray cluster at address: xxx.xxx.xx.xx:6379... + -- Connected to Ray cluster. + INFO: Started server process [1339357] + INFO: Waiting for application startup. + INFO: Application startup complete. + INFO: Uvicorn running on http://xxx.xxx.xx.xx:8343 (Press CTRL+C to quit) + ``` +### Step 4: Use sllm to manage models +1. **You can do this step on login node, and set the ```LLM_SERVER_URL``` environment variable:** + ```shell + $ conda activate sllm + (sllm)$ export LLM_SERVER_URL=http://:8343 + ``` + - Replace `` with the actual IP address of the head node. + - Replace ```8343``` with the actual port number (`` in Step1) if you have changed it. +2. **Deploy a Model Using ```sllm```** + ```shell + (sllm)$ sllm deploy --model facebook/opt-1.3b + ``` +### Step 5: Query the Model Using OpenAI API Client + **You can use the following command to query the model:** + ```shell + curl $LLM_SERVER_URL/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' + ``` + - Replace `````` with the actual IP address of the head node. + - Replace ```8343``` with the actual port number (`` in Step 1) if you have changed it. +### Step 6: Stop Jobs +On the SLURM cluster, we usually use the ```scancel``` command to stop the job. Firstly, list all jobs you have submitted (replace ```your_username``` with your username): +```shell +$ squeue -u your_username +JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) + 1234 compute sllm-head your_username R 0:01 1 JobNode01 + 1235 compute sllm-worker-store your_username R 0:01 1 JobNode02 + 1236 compute sllm-serve your_username R 0:01 1 JobNode01 +``` +Then, use ```scancel``` to stop the job (```1234```, ```1235``` and ```1236``` are JOBIDs): +```shell +$ scancel 1234 1235 1236 +``` diff --git a/docs/stable/developer/_category_.json b/docs/stable/developer/_category_.json new file mode 100644 index 0000000..89a7abc --- /dev/null +++ b/docs/stable/developer/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Developer Guide", + "position": 6 +} diff --git a/docs/stable/developer/supporting_a_new_hardware.md b/docs/stable/developer/supporting_a_new_hardware.md new file mode 100644 index 0000000..312cb6c --- /dev/null +++ b/docs/stable/developer/supporting_a_new_hardware.md @@ -0,0 +1,25 @@ +--- +sidebar_position: 0 +--- + +# Supporting a New Hardware + +ServerlessLLM actively expands support for new hardware configurations to meet diverse deployment needs. + +## Support Standards +Hardware is considered supported by ServerlessLLM if: +1. Any of the inference backends used (e.g., Transformers, vLLM) can run model inference on the hardware. +2. ServerlessLLM Store can successfully load model checkpoints on the hardware. + +## Steps to Support a New Hardware +1. **Check Inference Backend Compatibility**: Refer to the specific inference backend documentation (e.g., for vLLM, Transformers) for hardware support. +2. **ServerlessLLM Store Configuration**: + - If the hardware provides CUDA-compatible APIs (e.g., ROCm), adjust the build script (`CMakeLists.txt`) by adding necessary compiler flags. + - For non-CUDA-compatible APIs, implementing a custom checkpoint loading function might be required. + +## Verifying Hardware Support in ServerlessLLM Store +The hardware support is verified if it successfully completes the [Quick Start Guide](https://serverlessllm.github.io/docs/stable/getting_started/) examples, showcasing checkpoint loading and inference functionality without errors. + +If the hardware is not publicly available (i.e., can't be tested by the ServerlessLLM team), a screenshot or output log of the successful execution of the Quick Start Guide examples is required to verify hardware support. + +If you encounter any issues or have questions, please reach out to the ServerlessLLM team by raising an issue on the [GitHub repository](https://github.com/ServerlessLLM/ServerlessLLM/issues). \ No newline at end of file diff --git a/docs/stable/features/_category_.json b/docs/stable/features/_category_.json new file mode 100644 index 0000000..56e0bf0 --- /dev/null +++ b/docs/stable/features/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Features", + "position": 2 +} \ No newline at end of file diff --git a/docs/stable/features/live_migration.md b/docs/stable/features/live_migration.md new file mode 100644 index 0000000..2db4b1d --- /dev/null +++ b/docs/stable/features/live_migration.md @@ -0,0 +1,211 @@ +--- +sidebar_position: 1 +--- + +# Live Migration of Inference Instances + +This example illustrates the live migration of inference instances in a ServerlessLLM cluster by constructing a scenario where two models are deployed to the cluster. Model `Qwen2.5-3B` is stored on both nodes, while model `Qwen2.5-1.5B` is only stored on node 0 (e.g., due to being less popular). This example will show a locality-contention scenario where `Qwen2.5-3B` is being served on node 0 but `Qwen2.5-1.5B` is requested to be served on the same node for optimal locality. We will find that: + +- **Without migration**, `Qwen2.5-1.5B` would have to wait for the completion of the ongoing inference instance of `Qwen2.5-3B` on node 0. +- **With live migration**, the ongoing inference instance of `Qwen2.5-3B` is migrated to node 1, and `Qwen2.5-1.5B` is allocated to node 0, thus can be served immediately. + +## Prerequisites + +To run this example, we will use Docker Compose to set up a ServerlessLLM cluster. Before proceeding, please ensure you have read the [Quickstart Guide](../getting_started.md). + +**Requirements:** + +- **Two GPUs** are required to illustrate the live migration of inference instances. +- **At least 20 GB of host memory** (this can be adjusted by using smaller models). +- **ServerlessLLM version 0.6**: Ensure you have `sllm==0.6` and `sllm-store==0.6` installed. + +## Usage + +Start a local Docker-based ray cluster using Docker Compose. + +### Clone the ServerlessLLM Repository + +If you haven't already, clone the ServerlessLLM repository: + +```bash +git clone https://github.com/ServerlessLLM/ServerlessLLM.git +cd ServerlessLLM/examples/live_migration +``` + +### Configure the Model Directory + +Create a directory on your host machine where models will be stored, and set the MODEL_FOLDER environment variable to point to this directory: + +```bash +export MODEL_FOLDER=/path/to/your/models +``` + +Replace `/path/to/your/models` with the actual path where you want to store the models. + +The Docker Compose configuration is already located in the `examples/live_migration` directory. + +## Test ServerlessLLM Without Live Migration + +1. **Start the ServerlessLLM Services Using Docker Compose** + +```bash +docker compose up -d +``` + +This command will start the Ray head node and two worker nodes defined in the `docker-compose.yml` file. + +:::tip +Use the following command to monitor the logs of the head node: + +```bash +docker logs -f sllm_head +``` +::: + +2. **Deploy Models with the Placement Spec Files** + +Activate the ServerlessLLM environment and set the server URL: +```bash +conda activate sllm +export LLM_SERVER_URL=http://127.0.0.1:8343 +``` + +Deploy the models: +```bash +sllm deploy --config config-qwen-1.5b.json +sllm deploy --config config-qwen-3b.json +``` + +3. **Verify the Deployment** + +Start two inference requests in parallel. The first request is for `Qwen2.5-3B`, and the second request, sent shortly after, is for `Qwen2.5-1.5B`. The `sleep` command is used to introduce a short interval between the two requests: + +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "Qwen/Qwen2.5-3B-Instruct", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Could you share a story of the history of Computer Science?"} + ], + "max_tokens": 1024 + }' & + +sleep 3 + +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "Qwen/Qwen2.5-1.5B-Instruct", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ], + "max_tokens": 64 + }' +``` + +Since `Qwen2.5-3B` is requested first, `Qwen2.5-1.5B` must wait for the ongoing inference instance of `Qwen2.5-3B` to complete on node 0 before it can start processing. + + +4. Clean up. + +```bash +docker compose down +``` + +## Test ServerlessLLM With Live Migration + +1. **Start the ServerlessLLM Services with Live Migration Enabled** + +Use the following command to start the ServerlessLLM services with live migration enabled. This configuration includes the `enable-migration.yml` file: + +```bash +docker compose -f docker-compose.yml -f enable-migration.yml up -d +``` + +This command will start the Ray head node and two worker nodes, enabling the live migration feature. + +2. **Deploy Models with the Placement Spec Files** + +Activate the ServerlessLLM environment and set the server URL: + +```bash +conda activate sllm +export LLM_SERVER_URL=http://127.0.0.1:8343 +``` + +Deploy the models: + +```bash +sllm deploy --config config-qwen-1.5b.json +sllm deploy --config config-qwen-3b.json +``` + +3. **Verify the Deployment** + +Start two inference requests in parallel. The first request is for `Qwen2.5-3B`, and the second request, sent shortly after, is for `Qwen2.5-1.5B`. The `sleep` command is used to introduce a short interval between the two requests: + +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "Qwen/Qwen2.5-3B-Instruct", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Could you share a story of the history of Computer Science?"} + ], + "max_tokens": 1024 + }' & + +sleep 3 + +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "Qwen/Qwen2.5-1.5B-Instruct", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ], + "max_tokens": 64 + }' +``` + +According to the response, you should observe that `Qwen2.5-1.5B` completes ahead of `Qwen2.5-3B`. This is because the ongoing inference instance of `Qwen2.5-3B` is live-migrated from node 0 to node 1, allowing `Qwen2.5-1.5B` to be served immediately on node 0. + +As shown in the log message, the ongoing inference instance of the model `Qwen/Qwen2.5-3B-Instruct` is live-migrated from node 0 to node 1. And model `Qwen/Qwen2.5-1.5B-Instruct` is allocated to node 0. + +```bash +(MigrationRouter pid=1724) INFO 12-10 22:05:02 migration_router.py:106] Executing migration plan: MigrationPlan(target_node_id='1', source_instance=InstanceStatus(instance_id='Qwen/Qwen2.5-3B-Instruct_dedb945f-74e5-403f-8677-35965453abab', node_id='0', num_gpu=1, concurrency=0, model_name='Qwen/Qwen2.5-3B-Instruct', num_current_tokens=0)) +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:164] Initialized backend for instance Qwen/Qwen2.5-3B-Instruct_2c9ef57f-c432-45d6-a4a9-1bae9c792853 for model Qwen/Qwen2.5-3B-Instruct +# Start multi-round live migration +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:178] Migration iteration 0 +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:183] Number of tokens: 353, delta: 353 +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:198] Migration iteration 0 completed +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:178] Migration iteration 1 +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:183] Number of tokens: 14, delta: 14 +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:188] Migration completed: remained 14 tokens +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:201] Migrated instance Qwen/Qwen2.5-3B-Instruct_dedb945f-74e5-403f-8677-35965453abab to Qwen/Qwen2.5-3B-Instruct_2c9ef57f-c432-45d6-a4a9-1bae9c792853 +# Finish multi-round live migration +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:215] Instance Qwen/Qwen2.5-3B-Instruct_dedb945f-74e5-403f-8677-35965453abab removed +(MigrationRouter pid=1724) DEBUG 12-10 22:05:13 migration_router.py:77] Preempted request: ... +# Resume the instance on target node +(MigrationRouter pid=1724) INFO 12-10 22:05:13 migration_router.py:83] Resuming request on target instance: Qwen/Qwen2.5-3B-Instruct_2c9ef57f-c432-45d6-a4a9-1bae9c792853 +# Qwen/Qwen2.5-1.5B is allocated to node 0 +(StoreManager pid=1459) INFO 12-10 22:05:14 store_manager.py:344] Loading Qwen/Qwen2.5-1.5B-Instruct to node 0 +(StorageAwareScheduler pid=1574) INFO 12-10 22:05:14 fcfs_scheduler.py:92] Deallocating model Qwen/Qwen2.5-3B-Instruct instance Qwen/Qwen2.5-3B-Instruct_dedb945f-74e5-403f-8677-35965453abab +(StorageAwareScheduler pid=1574) INFO 12-10 22:05:14 fcfs_scheduler.py:103] Node 0 deallocated 1 GPUs +(StorageAwareScheduler pid=1574) INFO 12-10 22:05:14 fcfs_scheduler.py:108] Model Qwen/Qwen2.5-3B-Instruct instance Qwen/Qwen2.5-3B-Instruct_dedb945f-74e5-403f-8677-35965453abab deallocated +(StorageAwareScheduler pid=1574) INFO 12-10 22:05:14 storage_aware_scheduler.py:188] Migrated instance Qwen/Qwen2.5-3B-Instruct to node 1 instance Qwen/Qwen2.5-3B-Instruct_2c9ef57f-c432-45d6-a4a9-1bae9c792853 +(StorageAwareScheduler pid=1574) INFO 12-10 22:05:14 storage_aware_scheduler.py:195] Allocated node 0 for model Qwen/Qwen2.5-1.5B-Instruct +``` + +4. Clean up. + +```bash +docker compose down +``` + + diff --git a/docs/stable/features/peft_lora_serving.md b/docs/stable/features/peft_lora_serving.md new file mode 100644 index 0000000..4c7d3a6 --- /dev/null +++ b/docs/stable/features/peft_lora_serving.md @@ -0,0 +1,116 @@ +--- +sidebar_position: 2 +--- +# PEFT LoRA Serving + +This example illustrates the process of deploying and serving a base large language model enhanced with LoRA (Low-Rank Adaptation) adapters in a ServerlessLLM cluster. It demonstrates how to start the cluster, deploy a base model with multiple LoRA adapters, perform inference using different adapters, and update or remove the adapters dynamically. + +## Pre-requisites + +To run this example, we will use Docker Compose to set up a ServerlessLLM cluster. Before proceeding, please ensure you have read the [Quickstart Guide](../getting_started.md). + +We will use the following example base model & LoRA adapters +- Base model: `facebook/opt-125m` +- LoRA adapters: + - `peft-internal-testing/opt-125m-dummy-lora` + - `monsterapi/opt125M_alpaca` + - `edbeeching/opt-125m-lora` + - `Hagatiana/opt-125m-lora` + +## Usage + +Start a local Docker-based ray cluster using Docker Compose. + +### Step 1: Download the Docker Compose File + +Download the `docker-compose.yml` file from the ServerlessLLM repository: +```bash +# Create a directory for the ServerlessLLM Docker setup +mkdir serverless-llm-docker && cd serverless-llm-docker + +# Download the docker-compose.yml file +curl -O https://raw.githubusercontent.com/ServerlessLLM/ServerlessLLM/main/examples/docker/docker-compose.yml + +# Alternatively, you can use wget: +# wget https://raw.githubusercontent.com/ServerlessLLM/ServerlessLLM/main/examples/docker/docker-compose.yml +``` + +### Step 2: Configuration + +Set the Model Directory. Create a directory on your host machine where models will be stored and set the `MODEL_FOLDER` environment variable to point to this directory: + +```bash +export MODEL_FOLDER=/path/to/your/models +``` + +Replace `/path/to/your/models` with the actual path where you want to store the models. + +### Step 3: Start the Services + +Start the ServerlessLLM services using Docker Compose: + +```bash +docker compose up -d +``` + +This command will start the Ray head node and two worker nodes defined in the `docker-compose.yml` file. + +:::tip +Use the following command to monitor the logs of the head node: + +```bash +docker logs -f sllm_head +``` +::: + +### Step 4: Deploy Models with LoRA Adapters +1. Configuration +```bash +conda activate sllm +export LLM_SERVER_URL=http://127.0.0.1:8343 +``` +2. Deploy models with specified lora adapters. +```bash +sllm deploy --model facebook/opt-125m --backend transformers --enable-lora --lora-adapters "demo_lora1=peft-internal-testing/opt-125m-dummy-lora demo_lora2=monsterapi/opt125M_alpaca" +``` +3. Verify the deployment. +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-125m", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ], + "lora_adapter_name": "demo_lora1" + }' +``` +If no lora adapters specified, the system will use the base model to do inference +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-125m", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` +### Step 5: Update LoRA Adapters +If you wish to switch to a different set of LoRA adapters, you can still use `sllm deploy` command with updated adapter configurations. ServerlessLLM will automatically reload the new adapters without restarting the backend. +```bash +sllm deploy --model facebook/opt-125m --backend transformers --enable-lora --lora-adapters "demo-lora1=edbeeching/opt-125m-lora demo-lora2=Hagatiana/opt-125m-lora" +``` + +### Step 6: Clean Up + +Delete the lora adapters by running the following command (this command will only delete lora adapters, the base model won't be deleted): +```bash +sllm delete facebook/opt-125m --lora-adapters "demo-lora1 demo-lora2" +``` +If you need to stop and remove the containers, you can use the following commands: +```bash +docker compose down +``` \ No newline at end of file diff --git a/docs/stable/features/quantized_models.md b/docs/stable/features/quantized_models.md new file mode 100644 index 0000000..df09751 --- /dev/null +++ b/docs/stable/features/quantized_models.md @@ -0,0 +1,175 @@ +--- +sidebar_position: 3 +--- + +# Quantization + +This example demonstrates the use of quantization within the ServerlessLLM framework to optimize model serving. Quantization is a technique used to reduce the memory footprint and computational requirements of a large language model by representing its weights with lower-precision data types, such as 8-bit integers (int8). This example will showcase how to deploy and serve a quantized model in a ServerlessLLM cluster. + +## Pre-requisites + +We will use Docker Compose to run a ServerlessLLM cluster in this example. Therefore, please make sure you have read the Quickstart Guide before proceeding. + +## Usage +Start a local Docker-based ray cluster using Docker Compose. + +## Step 1: Set up the Environment + +Create a directory for this example and download the `docker-compose.yml` file. + +```bash +mkdir sllm-quantization-example && cd sllm-quantization-example +curl -O https://raw.githubusercontent.com/ServerlessLLM/ServerlessLLM/main/examples/docker/docker-compose.yml + +## Step 2: Configuration + +Set the Model Directory. Create a directory on your host machine where models will be stored and set the `MODEL_FOLDER` environment variable to point to this directory: + +```bash +export MODEL_FOLDER=/path/to/your/models +``` + +Replace `/path/to/your/models` with the actual path where you want to store the models. + +## Step 3: Start the Services + +Start the ServerlessLLM services using Docker Compose: + +```bash +docker compose up -d +``` + +This command will start the Ray head node and two worker nodes defined in the `docker-compose.yml` file. + +:::tip +Use the following command to monitor the logs of the head node: + +```bash +docker logs -f sllm_head +``` +::: + +## Step 4: Create Quantization and Deployment Configurations + +First, we'll generate a standard Hugging Face BitsAndBytesConfig and save it to a JSON file. Then, we'll create a deployment configuration file with these quantization settings embedded in it. + +1. Generate the Quantization Config + +Create a Python script named `get_config.py` in the current directory with the following content: +```python +# get_config.py +from transformers import BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig(load_in_4bit=True) +quantization_config.to_json_file("quantization_config.json") + +``` + +Run the script to generate `quantization_config.json`: +```bash +python get_config.py +``` + + +2. Create the Deployment Config + +Now, create a file named `quantized_deploy_config.json`. This file tells ServerlessLLM which model to deploy and instructs the backend to use the quantization settings. You should copy the contents of `quantization_config.json` into the `quantization_config` field below. A template can be found in `sllm/cli/default_config.json`. + +```json +{ + "model": "facebook/opt-1.3b", + "backend": "transformers", + "num_gpus": 1, + "auto_scaling_config": { + "metric": "concurrency", + "target": 1, + "min_instances": 0, + "max_instances": 10, + "keep_alive": 0 + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM", + "quantization_config": { + "_load_in_4bit": true, + "_load_in_8bit": false, + "bnb_4bit_compute_dtype": "float32", + "bnb_4bit_quant_storage": "uint8", + "bnb_4bit_quant_type": "fp4", + "bnb_4bit_use_double_quant": false, + "llm_int8_enable_fp32_cpu_offload": false, + "llm_int8_has_fp16_weight": false, + "llm_int8_skip_modules": null, + "llm_int8_threshold": 6.0, + "load_in_4bit": true, + "load_in_8bit": false, + "quant_method": "bitsandbytes" + } + } +} + +``` + +> Note: Quantization currently only supports the "transformers" backend. Support for other backends will come soon. + +## Step 5: Deploy the Quantized Model +With the configuration files in place, deploy the model using the `sllm-cli`. + +```bash +conda activate sllm +export LLM_SERVER_URL=http://127.0.0.1:8343 + +sllm-cli deploy --config quantized_deploy_config.json +``` + +## Step 6: Verify the deployment. +Send an inference to the server to query the model: + +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` + +To verify the model is being loaded in the desired precision, check the logs (`docker logs sllm_head`). You should see that the model is indeed being loaded in `fp4`. + + +```log +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 transformers.py:321] load config takes 0.0030286312103271484 seconds +(RoundRobinRouter pid=481) INFO 07-02 20:01:49 roundrobin_router.py:272] [] +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 transformers.py:331] load model takes 0.2806234359741211 seconds +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 transformers.py:338] device_map: OrderedDict([('', 0)]) +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 transformers.py:345] compute_device_placement takes 0.18753838539123535 seconds +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 transformers.py:376] allocate_cuda_memory takes 0.0020012855529785156 seconds +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 client.py:72] load_into_gpu: transformers/facebook/opt-1.3b, 70b42a05-4faa-4eaf-bb73-512c6453e7fa +(TransformersBackend pid=352, ip=172.18.0.2) INFO 07-02 20:01:49 client.py:113] Model loaded: transformers/facebook/opt-1.3b, 70b42a05-4faa-4eaf-bb73-512c6453e7fa +(TransformersBackend pid=352, ip=172.18.0.2) INFO 07-02 20:01:49 transformers.py:398] restore state_dict takes 0.0007319450378417969 seconds +(TransformersBackend pid=352, ip=172.18.0.2) DEBUG 07-02 20:01:49 transformers.py:411] using precision: fp4 +(TransformersBackend pid=352, ip=172.18.0.2) INFO 07-02 20:01:50 client.py:117] confirm_model_loaded: transformers/facebook/opt-1.3b, 70b42a05-4faa-4eaf-bb73-512c6453e7fa +``` + +You should receive a successful JSON response from the model. + +## Step 7: Clean Up + +Delete the model deployment by running the following command: + +```bash +sllm-cli delete facebook/opt-1.3b +``` + +If you need to stop and remove the containers, you can use the following commands: + +```bash +docker compose down +``` + + diff --git a/docs/stable/features/storage_aware_scheduling.md b/docs/stable/features/storage_aware_scheduling.md new file mode 100644 index 0000000..81723f9 --- /dev/null +++ b/docs/stable/features/storage_aware_scheduling.md @@ -0,0 +1,123 @@ +--- +sidebar_position: 0 +--- + +# Storage Aware Scheduling with Docker Compose + +## Pre-requisites + +We will use Docker Compose to run a ServerlessLLM cluster in this example. Therefore, please make sure you have read the [Quickstart Guide](../getting_started.md) before proceeding. + +## Usage + +Start a local Docker-based ray cluster using Docker Compose. + +### Step 1: Clone the ServerlessLLM Repository + +If you haven't already, clone the ServerlessLLM repository: + +```bash +git clone https://github.com/ServerlessLLM/ServerlessLLM.git +cd ServerlessLLM/examples/storage_aware_scheduling +``` + +### Step 2: Configuration + +Set the Model Directory. Create a directory on your host machine where models will be stored and set the `MODEL_FOLDER` environment variable to point to this directory: + +```bash +export MODEL_FOLDER=/path/to/your/models +``` + +Replace `/path/to/your/models` with the actual path where you want to store the models. + +### Step 3: Enable Storage Aware Scheduling in Docker Compose + +The Docker Compose configuration is already located in the `examples/storage_aware_scheduling` directory. To activate storage-aware scheduling, ensure the `docker-compose.yml` file includes the necessary configurations(`sllm_head` service should include the `--enable-storage-aware` command). + +:::tip +Recommend to adjust the number of GPUs and `mem_pool_size` based on the resources available on your machine. +::: + + +### Step 4: Start the Services + +Start the ServerlessLLM services using Docker Compose: + +```bash +docker compose up -d +``` + +This command will start the Ray head node and two worker nodes defined in the `docker-compose.yml` file. + +:::tip +Use the following command to monitor the logs of the head node: + +```bash +docker logs -f sllm_head +``` +::: + +### Step 5: Deploy Models with Placement Spec + +In the `examples/storage_aware_scheduling` directory, the example configuration files (`config-opt-2.7b.json` and `config-opt-1.3b.json`) are already given. + +> Note: Storage aware scheduling currently only supports the "transformers" backend. Support for other backends will come soon. + +2. Deploy models with the placement spec files. + +```bash +conda activate sllm +export LLM_SERVER_URL=http://127.0.0.1:8343 + +sllm deploy --config config-opt-2.7b.json +sllm deploy --config config-opt-1.3b.json +``` + +3. Verify the deployment. + +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-2.7b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' + +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` + +As shown in the log message, the model "facebook/opt-2.7b" is scheduled on server 0, while the model "facebook/opt-1.3b" is scheduled on server 1. + +```log +(StorageAwareScheduler pid=1543) INFO 11-12 23:48:27 storage_aware_scheduler.py:137] Sorted scheduling options: [('0', 4.583079601378258)] +(StorageAwareScheduler pid=1543) INFO 11-12 23:48:27 storage_aware_scheduler.py:144] Allocated node 0 for model facebook/opt-2.7b +(StorageAwareScheduler pid=1543) INFO 11-12 23:48:38 storage_aware_scheduler.py:137] Sorted scheduling options: [('1', 2.266678696047572)] +(StorageAwareScheduler pid=1543) INFO 11-12 23:48:38 storage_aware_scheduler.py:144] Allocated node 1 for model facebook/opt-1.3b +``` + +### Step 6: Clean Up + +Delete the model deployment by running the following command: + +```bash +sllm delete facebook/opt-1.3b facebook/opt-2.7b +``` + +If you need to stop and remove the containers, you can use the following commands: + +```bash +docker compose down +``` + diff --git a/docs/stable/getting_started.md b/docs/stable/getting_started.md new file mode 100644 index 0000000..f98364d --- /dev/null +++ b/docs/stable/getting_started.md @@ -0,0 +1,136 @@ +--- +sidebar_position: 1 +--- + +# Getting Started + +This guide demonstrates how to quickly set up a local ServerlessLLM cluster using Docker Compose on a single machine. We will initialize a minimal cluster, consisting of a head node and a single worker node. Then, we'll deploy a model using the `sllm` and query the deployment through an OpenAI-compatible API. + +:::note +We strongly recommend using Docker (Compose) to manage your ServerlessLLM cluster, whether you are using ServerlessLLM for testing or development. However, if Docker is not a viable option for you, please refer to the [deploy from scratch guide](./deployment/single_machine.md). +::: + +## Prerequisites + +Before you begin, ensure you have the following installed and configured: + +1. **Docker**: Installed on your system. You can download it from [here](https://docs.docker.com/get-docker/). +2. **ServerlessLLM CLI**: Installed on your system. Install it using `pip install serverless-llm`. +3. **GPUs**: At least one NVIDIA GPU is required. If you have multiple GPUs, you can adjust the `docker-compose.yml` file accordingly. +4. **NVIDIA Docker Toolkit**: This enables Docker to utilize NVIDIA GPUs. Follow the installation guide [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). + +## Start the ServerlessLLM Cluster + +We will use Docker Compose to simplify the ServerlessLLM setup process. + +### Step 1: Download the Docker Compose File + +Download the `docker-compose.yml` file from the ServerlessLLM repository: + +```bash +# Create a directory for the ServerlessLLM Docker setup +mkdir serverless-llm-docker && cd serverless-llm-docker + +# Download the docker-compose.yml file +curl -O https://raw.githubusercontent.com/ServerlessLLM/ServerlessLLM/main/examples/docker/docker-compose.yml + +# Alternatively, you can use wget: +# wget https://raw.githubusercontent.com/ServerlessLLM/ServerlessLLM/main/examples/docker/docker-compose.yml +``` + +### Step 2: Configuration + +Create a directory on your host machine to store models. Then, set the `MODEL_FOLDER` environment variable to point to this directory: + +```bash +export MODEL_FOLDER=/path/to/your/models +``` + +Replace `/path/to/your/models` with the actual path where you intend to store the models. This directory will be mounted into the Docker containers. + +### Step 3: Start the Services + +Start the ServerlessLLM services using Docker Compose: + +```bash +docker compose up -d +``` + +This command will start the Ray head node and a worker node as defined in the `docker-compose.yml` file. + +Verify that the services are ready: + +```bash +docker logs sllm_head +``` + +Ensure the services are ready before proceeding. You should see output similar to the following: + +```bash +... +(SllmController pid=1435) INFO 05-26 15:40:49 controller.py:68] Starting scheduler +INFO: Started server process [1] +INFO: Waiting for application startup. +INFO: Application startup complete. +INFO: Uvicorn running on http://0.0.0.0:8343 (Press CTRL+C to quit) +(FcfsScheduler pid=1604) INFO 05-26 15:40:49 fcfs_scheduler.py:54] Starting FCFS scheduler +(FcfsScheduler pid=1604) INFO 05-26 15:40:49 fcfs_scheduler.py:111] Starting control loop +``` + +## Deploy a Model Using sllm + +Set the `LLM_SERVER_URL` environment variable: + +```bash +export LLM_SERVER_URL=http://127.0.0.1:8343 +``` + +Deploy a model to the ServerlessLLM cluster using the `sllm`: + +```bash +sllm deploy --model facebook/opt-1.3b +``` +> Note: This command will take some time to download the model from the Hugging Face Model Hub. +> You can use any model from the [Hugging Face Model Hub](https://huggingface.co/models) by specifying its name in the `--model` argument. + +Expected output: + +```plaintext +INFO 08-01 07:38:12 deploy.py:36] Deploying model facebook/opt-1.3b with default configuration. +INFO 08-01 07:39:00 deploy.py:49] Model registered successfully. +``` + +## Query the Model + +You can now query the model using any OpenAI API client. For example, use the following `curl` command: +```bash +curl $LLM_SERVER_URL/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "facebook/opt-1.3b", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is your name?"} + ] + }' +``` + +Expected output: + +```plaintext +{"id":"chatcmpl-8b4773e9-a98b-41db-8163-018ed3dc65e2","object":"chat.completion","created":1720183759,"model":"facebook/opt-1.3b","choices":[{"index":0,"message":{"role":"assistant","content":"system: You are a helpful assistant.\nuser: What is your name?\nsystem: I am a helpful assistant.\n"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":16,"completion_tokens":26,"total_tokens":42}}% +``` + +## Clean Up +To delete a deployed model, execute the following command: + +```bash +sllm delete facebook/opt-1.3b +``` + +This command removes the specified model from the ServerlessLLM server. + +To stop the ServerlessLLM services, use the following command: +```bash +docker compose down +``` \ No newline at end of file diff --git a/docs/stable/intro.md b/docs/stable/intro.md new file mode 100644 index 0000000..8786a40 --- /dev/null +++ b/docs/stable/intro.md @@ -0,0 +1,38 @@ +--- +sidebar_position: 0 +--- + +# Serverless LLM + + +ServerlessLLM + +ServerlessLLM is a **fast** and **easy-to-use** serving system designed for **affordable** multi-LLM serving, also known as LLM-as-a-Service. ServerlessLLM is ideal for environments with multiple LLMs that need to be served on limited GPU resources, as it enables efficient dynamic loading of LLMs onto GPUs. By elastically scaling model instances and multiplexing GPUs, ServerlessLLM can significantly reduce costs compared to traditional GPU-dedicated serving systems while still providing low-latency (Time-to-First-Token, TTFT) LLM completions. + +ServerlessLLM now supports NVIDIA and AMD GPUs, including following hardware: +* NVIDIA GPUs: Compute Capability 7.0+ (e.g, V100, A100, RTX A6000, GeForce RTX 3060) +* AMD GPUs: ROCm 6.2.0+ (tested on MI100s and MI200s) + +## Documentation + +### Getting Started + +- [Quickstart](./getting_started.md) +- [Single Machine Deployment (From Scratch)](./deployment/single_machine.md) +- [Multi-machine Deployment](./deployment/multi_machine.md) +- [SLURM Cluster Deployment](./deployment/slurm_cluster.md) + +### Advanced Features + +- [Storage-Aware Scheduler](./features/storage_aware_scheduling.md) +- [Live Migration](./features/live_migration.md) +- [PEFT LoRA Serving](./features/peft_lora_serving.md) + +### ServerlessLLM Store + +- [Quickstart](./store/quickstart.md) +- [ROCm Quickstart](./store/rocm_quickstart.md) + +### ServerlessLLM CLI + +- [ServerlessLLM CLI API](../api/cli.md) diff --git a/docs/stable/models/_category_.json b/docs/stable/models/_category_.json new file mode 100644 index 0000000..67c0cfe --- /dev/null +++ b/docs/stable/models/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Models", + "position": 7 +} diff --git a/docs/stable/models/supported_models.md b/docs/stable/models/supported_models.md new file mode 100644 index 0000000..6077615 --- /dev/null +++ b/docs/stable/models/supported_models.md @@ -0,0 +1,13 @@ +# Supported Models + +ServerlessLLM supports a plethora of language models from [Huggingface (HF) Transformers](https://huggingface.co/models). This page lists the models and model architectures currently supported by ServerlessLLM. + +To test a model, simply add it to the `supported_models.json` inside `/ServerlessLLM/tests/inference_tests` and the Github Actions will automatically test whether not it is supported. + +## Text-only Language Models + +Architecture |Models |Example HF Models |vLLM |Transformers +------------------|--------------|--------------------|-----|------------- +`OPTForCausalLM` |OPT, OPT-IML |`facebook/opt-6.7b` |✅ |✅ + + diff --git a/docs/stable/store/_category_.json b/docs/stable/store/_category_.json new file mode 100644 index 0000000..78b547f --- /dev/null +++ b/docs/stable/store/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "ServerlessLLM Store", + "position": 5 +} diff --git a/docs/stable/store/quantization.md b/docs/stable/store/quantization.md new file mode 100644 index 0000000..3450410 --- /dev/null +++ b/docs/stable/store/quantization.md @@ -0,0 +1,102 @@ +--- +sidebar_position: 2 +--- + +# Quantization + +> Note: Quantization is currently experimental, especially on multi-GPU machines. You may encounter issues when using this feature in multi-GPU environments. + +ServerlessLLM currently supports `bitsandbytes` quantization, which reduces model memory usage by converting weights to lower-precision data types. You can configure this by passing a `BitsAndBytesConfig` object when loading a model. + +Available precisions include: +- `int8` +- `fp4` +- `nf4` + +> Note: CPU offloading and dequantization is not currently supported. + +## 8-bit Quantization (`int8`) + +8-bit quantization halves the memory usage compared to 16-bit precision with minimal impact on model accuracy. It is a robust and recommended starting point for quantization. + +```python +from transformers import AutoModelForCausalLM, BitsAndBytesConfig + +# Configure 8-bit quantization +quantization_config = BitsAndBytesConfig( + load_in_8bit=True +) + +# Load the model with the config +model_8bit = AutoModelForCausalLM.from_pretrained( + "facebook/opt-1.3b", + quantization_config=quantization_config, + device_map="auto", +) +``` + +## 4-bit Quantization (`fp4`) +FP4 (4-bit Floating Point) quantization offers more aggressive memory savings than 8-bit. It is a good option for running very large models on consumer-grade hardware. + +```python +from transformers import AutoModelForCausalLM, BitsAndBytesConfig + +# Configure 4-bit FP4 quantization +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="fp4" +) + +# Load the model with the config +model_fp4 = AutoModelForCausalLM.from_pretrained( + "facebook/opt-1.3b", + quantization_config=quantization_config, + device_map="auto", +) +``` + +## 4-bit Quantization (`nf4`) +NF4 (4-bit NormalFloat) is an advanced data type optimized for models whose weights follow a normal distribution. NF4 is generally the recommended 4-bit option as it often yields better model accuracy compared to FP4. + +```python +import torch +from transformers import AutoModelForCausalLM, BitsAndBytesConfig + +# Configure 4-bit NF4 quantization +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4" +) + +# Load the model with the config +model_nf4 = AutoModelForCausalLM.from_pretrained( + "facebook/opt-1.3b", + quantization_config=quantization_config, + device_map="auto", +) +``` + +## `torch_dtype` (Data Type for Unquantized Layers) +The `torch_dtype` parameter sets the data type for model layers that are not quantized (e.g. `LayerNorm`). Setting this to `torch.float16` or `torch.bfloat16` can further reduce memory usage. If unspecified, these layers default to `torch.float16`. + +```python +import torch +from transformers import AutoModelForCausalLM, BitsAndBytesConfig + +# Configure 4-bit NF4 quantization +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4" +) + +# Load model, casting non-quantized layers to float16 +model_mixed_precision = AutoModelForCausalLM.from_pretrained( + "facebook/opt-1.3b", + quantization_config=quantization_config, + torch_dtype=torch.float16, + device_map="auto", +) +``` + +For further information, consult the [HuggingFace Documentation for BitsAndBytes](https://huggingface.co/docs/transformers/main/en/quantization/bitsandbytes). + diff --git a/docs/stable/store/quickstart.md b/docs/stable/store/quickstart.md new file mode 100644 index 0000000..83492ea --- /dev/null +++ b/docs/stable/store/quickstart.md @@ -0,0 +1,245 @@ +--- +sidebar_position: 0 +--- + +# Quickstart Guide + +ServerlessLLM Store (`sllm-store`) is a Python library that supports fast model checkpoint loading from multi-tier storage (i.e., DRAM, SSD, HDD) into GPUs. + +ServerlessLLM Store provides a model manager and two key functions: +- `save_model`: Convert a HuggingFace model into a loading-optimized format and save it to a local path. +- `load_model`: Load a model into given GPUs. + + +## Requirements +- OS: Ubuntu 20.04 +- Python: 3.10 +- GPU: compute capability 7.0 or higher + +## Installations + +### Create a virtual environment +```bash +conda create -n sllm-store python=3.10 -y +conda activate sllm-store +``` + +### Install with pip +```bash +pip install serverless-llm-store +``` + +### Install from source +1. Clone the repository and enter the `store` directory + +``` bash +git clone https://github.com/ServerlessLLM/ServerlessLLM.git +cd ServerlessLLM/sllm_store +``` + +2. Install the package from source + +```bash +rm -rf build +pip install . +``` + +## Usage Examples +:::tip +We highly recommend using a fast storage device (e.g., NVMe SSD) to store the model files for the best experience. +For example, create a directory `models` on the NVMe SSD and link it to the local path. +```bash +mkdir -p /mnt/nvme/models # Replace '/mnt/nvme' with your NVMe SSD path. +ln -s /mnt/nvme/models ./models +``` +::: + +1. Convert a model to ServerlessLLM format and save it to a local path: +```python +from sllm_store.transformers import save_model + +# Load a model from HuggingFace model hub. +import torch +from transformers import AutoModelForCausalLM +model = AutoModelForCausalLM.from_pretrained('facebook/opt-1.3b', torch_dtype=torch.float16) + +# Replace './models' with your local path. +save_model(model, './models/facebook/opt-1.3b') +``` + +2. Launch the checkpoint store server in a separate process: +```bash +# 'mem_pool_size' is the maximum size of the memory pool in GB. It should be larger than the model size. +sllm-store start --storage-path $PWD/models --mem-pool-size 4GB +``` + + + +3. Load model in your project and make inference: +```python +import time +import torch +from sllm_store.transformers import load_model + +# warm up the GPU +num_gpus = torch.cuda.device_count() +for i in range(num_gpus): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + +start = time.time() +model = load_model("facebook/opt-1.3b", device_map="auto", torch_dtype=torch.float16, storage_path="./models/", fully_parallel=True) +# Please note the loading time depends on the model size and the hardware bandwidth. +print(f"Model loading time: {time.time() - start:.2f}s") + +from transformers import AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained('facebook/opt-1.3b') +inputs = tokenizer('Hello, my dog is cute', return_tensors='pt').to("cuda") +outputs = model.generate(**inputs) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +4. Clean up by "Ctrl+C" the server process. + +## Usage with vLLM + +ServerlessLLM integrates with vLLM to provide fast model loading capabilities. Follow these steps to set up and use ServerlessLLM with vLLM. + +### Prerequisites + +Before using ServerlessLLM with vLLM, you need to apply a compatibility patch to your vLLM installation. This patch has been tested with vLLM version `0.9.0.1`. + +### Apply the vLLM Patch + +1. **Check patch status** (optional): + ```bash + ./sllm_store/vllm_patch/check_patch.sh + ``` + +2. **Apply the patch**: + ```bash + ./sllm_store/vllm_patch/patch.sh + ``` + +3. **Remove the patch** (if needed): + ```bash + ./sllm_store/vllm_patch/remove_patch.sh + ``` + +:::note +The patch file is located at `sllm_store/vllm_patch/sllm_load.patch` in the ServerlessLLM repository. +::: + + +Our api aims to be compatible with the `sharded_state` load format in vLLM. Thus, due to the model modifications about the model architecture done by vLLM, the model format for vLLM is **not** the same as we used in transformers. Thus, the `ServerlessLLM format` mentioned in the subsequent sections means the format integrated with vLLM, which is different from the `ServerlessLLM format` used in the previous sections. + +Thus, for fist-time users, you have to load the model from other backends and then converted it to the ServerlessLLM format. + +1. Download the model from HuggingFace and save it in the ServerlessLLM format: +``` bash +python3 examples/sllm_store/save_vllm_model.py --model-name facebook/opt-1.3b --storage-path $PWD/models --tensor-parallel-size 1 + +``` + +You can also transfer the model from the local path compared to download it from network by passing the `--local-model-path` argument. + +After downloading the model, you can launch the checkpoint store server and load the model in vLLM through `sllm` load format. + +2. Launch the checkpoint store server in a separate process: +```bash +# 'mem_pool_size' is the maximum size of the memory pool in GB. It should be larger than the model size. +sllm-store start --storage-path $PWD/models --mem-pool-size 4GB +``` + +3. Load the model in vLLM: +```python +from vllm import LLM, SamplingParams + +import os + +storage_path = os.getenv("STORAGE_PATH", "./models") +model_name = "facebook/opt-1.3b" +model_path = os.path.join(storage_path, model_name) + +llm = LLM( + model=model_path, + load_format="serverless_llm", + dtype="float16" +) + +prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", +] + +sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +outputs = llm.generate(prompts, sampling_params) + +# Print the outputs. +for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") +``` + +# Fine-tuning +ServerlessLLM currently supports LoRA fine-tuning using peft through the Hugging Face Transformers PEFT. + +ServerlessLLM Store provides a model manager and two key functions: +- save_lora: Convert an LoRA adapter into a loading-optimized format and save it to a local path. +- load_lora: Load an adapter into loaded model. + +> Note: Fine-tuning is currently experimental, especially on multi-GPU machines. You may encounter issues when using this feature in multi-GPU environments. + +## Usage Examples + +1. Convert an adapter to ServerlessLLM format and save it to a local path: +``` +from sllm_store.transformers import save_lora + +# TODO: Load an adapter from HuggingFace model hub. + + +# Replace './models' with your local path. +save_lora(adapter, './models/facebook/opt-1.3b') +``` + +2. Launch the checkpoint store server in a separate process: +``` +# 'mem_pool_size' is the maximum size of the memory pool in GB. It should be larger than the model size. +sllm-store start --storage-path $PWD/models --mem-pool-size 4GB +``` + +3. Load the adapter on your model and make inference: +``` +import time +import torch +from sllm_store.transformers import load_model, load_lora + +model = load_model("facebook/opt-1.3b", device_map="auto", torch_dtype=torch.float16, storage_path="./models/", fully_parallel=True) + +model = load_lora("facebook/opt-1.3b", adapter_name="demo_lora", adapter_path="ft_facebook/opt-1.3b_adapter1", device_map="auto", torch_dtype=torch.float16, storage_path="./models/") + +# Please note the loading time depends on the base model size and the hardware bandwidth. +print(f"Model loading time: {time.time() - start:.2f}s") + +from transformers import AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained('facebook/opt-1.3b') +inputs = tokenizer('Hello, my dog is cute', return_tensors='pt').to("cuda") +outputs = model.generate(**inputs) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +4. Clean up by `Ctrl+C` the server process. diff --git a/docs/stable/store/rocm_quickstart.md b/docs/stable/store/rocm_quickstart.md new file mode 100644 index 0000000..7807301 --- /dev/null +++ b/docs/stable/store/rocm_quickstart.md @@ -0,0 +1,164 @@ +--- +sidebar_position: 1 +--- + +# ROCm Quick Start + +ServerlessLLM Store (`sllm-store`) currently supports ROCm platform. However, there are no pre-built wheels for ROCm. + +Due to an internal bug in ROCm, serverless-llm-store may face a GPU memory leak in ROCm before version 6.2.0, as noted in [issue](https://github.com/ROCm/HIP/issues/3580). + +1. Clone the repository and enter the `store` directory: + +```bash +git clone https://github.com/ServerlessLLM/ServerlessLLM.git +cd ServerlessLLM/sllm_store +``` +After that, you may either use the Docker image or build the `sllm-store` wheel from source and install it in your environment. + +## Use the Docker image + +We provide a Dockerfile with ROCm support. Currently, it's built on base image `rocm/vllm-dev:base_ROCm-6.3.1_20250528_tuned_20250530` + +2. Build the Docker image: + +``` bash +docker build -t sllm_store_rocm -f Dockerfile.rocm . +``` + +3. Start the Docker container: + +:::tip +If you want to run inference outside the Docker container, you need to pass the port to the host machine. For example, `-p 8073:8073`. You can also get the wheel from the Docker container after starting it via `docker cp sllm_store_server:/app/dist .`. +::: + +``` bash +docker run --name sllm_store_server --rm -it \ + --device /dev/kfd --device /dev/dri \ + --security-opt seccomp=unconfined \ + -v $(pwd)/models:/models \ + sllm_store_rocm +``` + +Expected output: + +``` bash +INFO 06-05 12:59:07 cli.py:76] Starting gRPC server +INFO 06-05 12:59:07 server.py:40] StorageServicer: storage_path=/models, mem_pool_size=4294967296, num_thread=4, chunk_size=33554432, registration_required=False +WARNING: Logging before InitGoogleLogging() is written to STDERR +I20250605 12:59:11.141070 1 checkpoint_store_hip.cpp:42] Number of GPUs: 1 +I20250605 12:59:11.141098 1 checkpoint_store_hip.cpp:44] I/O threads: 4, chunk size: 32MB +I20250605 12:59:11.141103 1 checkpoint_store_hip.cpp:46] Storage path: "/models" +I20250605 12:59:11.141119 1 checkpoint_store_hip.cpp:72] GPU 0 UUID: 61363865-3865-3038-3831-366132376261 +I20250605 12:59:11.519277 1 pinned_memory_pool_hip.cpp:30] Creating PinnedMemoryPool with 128 buffers of 33554432 bytes +I20250605 12:59:12.487957 1 checkpoint_store_hip.cpp:84] Memory pool created with 4GB +INFO 06-05 12:59:12 server.py:231] Starting gRPC server on 0.0.0.0:8073 + +``` + +After starting the Docker container, you can enter the container and run the following command to test the installation. + +``` bash +docker exec -it sllm_store_server /bin/bash +``` + +Try to save and load a transformer model: + +``` bash +python3 examples/save_transformers_model.py --model-name "facebook/opt-1.3b" --storage-path "/models" +python3 examples/load_transformers_model.py --model-name "facebook/opt-1.3b" --storage-path "/models" +``` +Expected output: + +``` bash +DEBUG 06-05 13:01:01 transformers.py:203] load_dict_non_blocking takes 0.0071375370025634766 seconds +DEBUG 06-05 13:01:01 transformers.py:213] load config takes 0.003943443298339844 seconds +DEBUG 06-05 13:01:01 torch.py:137] allocate_cuda_memory takes 0.0012660026550292969 seconds +DEBUG 06-05 13:01:01 client.py:72] load_into_gpu: facebook/opt-1.3b, 93b1932e-4b43-42cb-b82d-7228ef21810b +INFO 06-05 13:01:01 client.py:113] Model loaded: facebook/opt-1.3b, 93b1932e-4b43-42cb-b82d-7228ef21810b +INFO 06-05 13:01:01 torch.py:160] restore state_dict takes 0.0004298686981201172 seconds +DEBUG 06-05 13:01:02 transformers.py:224] load model takes 0.9706132411956787 seconds +INFO 06-05 13:01:02 client.py:117] confirm_model_loaded: facebook/opt-1.3b, 93b1932e-4b43-42cb-b82d-7228ef21810b +INFO 06-05 13:01:06 client.py:125] Model loaded +Model loading time: 5.28s +tokenizer_config.json: 100%|██████████████████████████████| 685/685 [00:00<00:00, 6.68MB/s] +vocab.json: 100%|███████████████████████████████████████| 899k/899k [00:00<00:00, 4.05MB/s] +merges.txt: 100%|███████████████████████████████████████| 456k/456k [00:00<00:00, 3.05MB/s] +special_tokens_map.json: 100%|████████████████████████████| 441/441 [00:00<00:00, 4.10MB/s] +/usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py:125: UserWarning: Failed validator: GCN_ARCH_NAME (Triggered internally at /app/pytorch/aten/src/ATen/hip/tunable/Tunable.cpp:366.) + return F.linear(input, self.weight, self.bias) +Hello, my dog is cute and I want to give him a good home. I have a lot of experience with dogs and I +``` + +Try to save and load a model in vLLM: + +``` bash +python3 examples/save_vllm_model.py --model-name "facebook/opt-125m" --storage-path "/models" +python3 examples/load_vllm_model.py --model-name "facebook/opt-125m" --storage-path "/models" +``` +Expected output: + +``` bash +INFO 06-05 13:02:51 [__init__.py:243] Automatically detected platform rocm. +INFO 06-05 13:02:52 [__init__.py:31] Available plugins for group vllm.general_plugins: +INFO 06-05 13:02:52 [__init__.py:33] - lora_filesystem_resolver -> vllm.plugins.lora_resolvers.filesystem_resolver:register_filesystem_resolver +INFO 06-05 13:02:52 [__init__.py:36] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load. +INFO 06-05 13:03:00 [config.py:793] This model supports multiple tasks: {'reward', 'embed', 'generate', 'classify', 'score'}. Defaulting to 'generate'. +INFO 06-05 13:03:00 [arg_utils.py:1594] rocm is experimental on VLLM_USE_V1=1. Falling back to V0 Engine. +INFO 06-05 13:03:04 [config.py:1910] Disabled the custom all-reduce kernel because it is not supported on current platform. +INFO 06-05 13:03:04 [llm_engine.py:230] Initializing a V0 LLM engine (v0.9.0.1) with config: model='/models/facebook/opt-125m', speculative_config=None, tokenizer='/models/facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.SERVERLESS_LLM, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=True, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=/models/facebook/opt-125m, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=True, pooler_config=None, compilation_config={"compile_sizes": [], "inductor_compile_config": {"enable_auto_functionalized_v2": false}, "cudagraph_capture_sizes": [256, 248, 240, 232, 224, 216, 208, 200, 192, 184, 176, 168, 160, 152, 144, 136, 128, 120, 112, 104, 96, 88, 80, 72, 64, 56, 48, 40, 32, 24, 16, 8, 4, 2, 1], "max_capture_size": 256}, use_cached_outputs=False, +INFO 06-05 13:03:04 [rocm.py:208] None is not supported in AMD GPUs. +INFO 06-05 13:03:04 [rocm.py:209] Using ROCmFlashAttention backend. +INFO 06-05 13:03:05 [parallel_state.py:1064] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0 +INFO 06-05 13:03:05 [model_runner.py:1170] Starting to load model /models/facebook/opt-125m... +DEBUG 06-05 13:03:05 torch.py:137] allocate_cuda_memory takes 0.0004763603210449219 seconds +DEBUG 06-05 13:03:05 client.py:72] load_into_gpu: facebook/opt-125m/rank_0, e8e7d900-652d-4822-8992-ad22f734b9c8 +INFO 06-05 13:03:05 client.py:113] Model loaded: facebook/opt-125m/rank_0, e8e7d900-652d-4822-8992-ad22f734b9c8 +INFO 06-05 13:03:05 torch.py:160] restore state_dict takes 0.00021338462829589844 seconds +INFO 06-05 13:03:05 client.py:117] confirm_model_loaded: facebook/opt-125m/rank_0, e8e7d900-652d-4822-8992-ad22f734b9c8 +INFO 06-05 13:03:05 client.py:125] Model loaded +INFO 06-05 13:03:05 [model_runner.py:1202] Model loading took 0.2363 GiB and 0.711783 seconds +/app/third_party/vllm/vllm/model_executor/layers/utils.py:80: UserWarning: Failed validator: GCN_ARCH_NAME (Triggered internally at /app/pytorch/aten/src/ATen/hip/tunable/Tunable.cpp:366.) + return torch.nn.functional.linear(x, weight, bias) +INFO 06-05 13:03:17 [worker.py:303] Memory profiling takes 11.68 seconds +INFO 06-05 13:03:17 [worker.py:303] the current vLLM instance can use total_gpu_memory (23.98GiB) x gpu_memory_utilization (0.90) = 21.59GiB +INFO 06-05 13:03:17 [worker.py:303] model weights take 0.24GiB; non_torch_memory takes 0.53GiB; PyTorch activation peak memory takes 0.49GiB; the rest of the memory reserved for KV Cache is 20.33GiB. +INFO 06-05 13:03:17 [executor_base.py:112] # rocm blocks: 37011, # CPU blocks: 7281 +INFO 06-05 13:03:17 [executor_base.py:117] Maximum concurrency for 2048 tokens per request: 289.15x +INFO 06-05 13:03:18 [model_runner.py:1526] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage. +Capturing CUDA graph shapes: 100%|█████████████████████████| 35/35 [00:09<00:00, 3.55it/s] +INFO 06-05 13:03:28 [model_runner.py:1684] Graph capturing finished in 10 secs, took 0.13 GiB +INFO 06-05 13:03:28 [llm_engine.py:428] init engine (profile, create kv cache, warmup model) took 22.81 seconds +Adding requests: 100%|█████████████████████████████████████| 4/4 [00:00<00:00, 2079.22it/s] +Processed prompts: 100%|█| 4/4 [00:00<00:00, 6.71it/s, est. speed input: 43.59 toks/s, out +Prompt: 'Hello, my name is', Generated text: ' Joel, my dad is my friend and we are in a relationship. I am' +Prompt: 'The president of the United States is', Generated text: ' speaking out against the release of some State Department documents which show the Russians were involved' +Prompt: 'The capital of France is', Generated text: ' a worldwide knowledge center. What better place to learn about the history and culture of' +Prompt: 'The future of AI is', Generated text: " here: it's the future of everything\nIf you want to test your minds" +[rank0]:[W605 13:03:30.532018298 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +``` + +## Build the wheel from source and install + +Currently, `pip install .` does not work with ROCm. We suggest you build `sllm-store` wheel and manually install it in your environment. + + + +If there's a customized PyTorch version installed, you may need to run the following command to modify the `torch` version in `requirements.txt`: + +```bash +python3 using_existing_torch.py +``` + +2. Build the wheel: + +```bash +python setup.py sdist bdist_wheel +``` + +## Known issues + +1. GPU memory leak in ROCm before version 6.2.0. + +This issue is due to an internal bug in ROCm. After the inference instance is completed, the GPU memory is still occupied and not released. For more information, please refer to [issue](https://github.com/ROCm/HIP/issues/3580). + diff --git a/entrypoint.sh b/entrypoint.sh new file mode 100755 index 0000000..dc4b798 --- /dev/null +++ b/entrypoint.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +set -e + +# Default values +DEFAULT_RAY_PORT=6379 +DEFAULT_RAY_RESOURCES_HEAD='{"control_node": 1}' +DEFAULT_RAY_NUM_CPUS=20 +DEFAULT_RAY_HEAD_ADDRESS="sllm_head:6379" +DEFAULT_STORAGE_PATH="/models" + +# Source conda +source /opt/conda/etc/profile.d/conda.sh + +# Function to initialize the head node +initialize_head_node() { + echo "Initializing head node..." + + # Activate head environment + echo "Activating head conda environment..." + conda activate head + + RAY_PORT="${RAY_PORT:-$DEFAULT_RAY_PORT}" + RAY_RESOURCES="${RAY_RESOURCES:-$DEFAULT_RAY_RESOURCES_HEAD}" + RAY_NUM_CPUS="${RAY_NUM_CPUS:-$DEFAULT_RAY_NUM_CPUS}" + + # Construct the command + CMD="ray start --head --port=$RAY_PORT --resources='$RAY_RESOURCES' --num-cpus=$RAY_NUM_CPUS" + + # Add node IP if specified + if [ ! -z "$RAY_NODE_IP" ]; then + echo "Using specified node IP: $RAY_NODE_IP" + CMD="$CMD --node-ip-address=$RAY_NODE_IP" + else + echo "No node IP specified. Ray will attempt to determine the best IP automatically." + fi + + # Display and execute the command + echo "Executing: $CMD" + eval "$CMD" + + # Start sllm-serve with any additional arguments passed to the script + echo "Starting sllm-serve with arguments: $@" + exec sllm-serve start "$@" +} + +# Function to initialize the worker node +initialize_worker_node() { + echo "Initializing worker node..." + + # Activate worker environment + echo "Activating worker conda environment..." + conda activate worker + + # Start the worker + RAY_HEAD_ADDRESS="${RAY_HEAD_ADDRESS:-$DEFAULT_RAY_HEAD_ADDRESS}" + + if [ -z "$WORKER_ID" ]; then + echo "WORKER_ID must be set" + exit 1 + fi + + RAY_RESOURCES='{"worker_node": 1, "worker_id_'$WORKER_ID'": 1}' + + # Construct the command + CMD="ray start --address=$RAY_HEAD_ADDRESS --resources='$RAY_RESOURCES'" + + # Add node IP if specified + if [ ! -z "$RAY_NODE_IP" ]; then + echo "Using specified node IP: $RAY_NODE_IP" + CMD="$CMD --node-ip-address=$RAY_NODE_IP" + else + echo "No node IP specified. Ray will attempt to determine the best IP automatically." + fi + + # Display and execute the command + echo "Executing: $CMD" + eval "$CMD" + + # Start sllm-store with any additional arguments passed to the script + STORAGE_PATH="${STORAGE_PATH:-$DEFAULT_STORAGE_PATH}" + echo "Starting sllm-store with arguments: --storage-path=$STORAGE_PATH $@" + exec sllm-store start --storage-path=$STORAGE_PATH "$@" +} + +# Determine the node type and call the appropriate initialization function +if [ "$MODE" == "HEAD" ]; then + initialize_head_node "$@" +elif [ "$MODE" == "WORKER" ]; then + initialize_worker_node "$@" +else + echo "MODE must be set to either HEAD or WORKER" + exit 1 +fi diff --git a/examples/.DS_Store b/examples/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..a555fcac7c8db962ee1136c6357bddaf544f49d3 GIT binary patch literal 6148 zcmeHKOHRWu5FJB>`jMdGBXxzm-~jXnp$dDX9st?~r4?yYwUq84OT-qjX2AtmvtY{! zxDRh^D@{n+0+kR#p2~iXJ+|LGCw5IlqA~I6L{%axA~6=q$a0L^Ic03k)a(L<8snTs zfnRTS!u~MhZ7Zw-R)N2!0Drq12~&2;O1nSYY=&U4Ip;T%c!U+t3WcOz5sa-Bp+?hS1$nFO8pLFw&^IlQ5SLVP+QQh9cDL7+)%P5{^cj zTLr8FX$98xW0lYUzO~7>qQc24?>VC>d;K75G&JJ^= 8.3.2 +pytest-asyncio >= 0.23.8 diff --git a/requirements-worker.txt b/requirements-worker.txt new file mode 100644 index 0000000..7e3cbfc --- /dev/null +++ b/requirements-worker.txt @@ -0,0 +1,12 @@ +accelerate==1.7.0 +datasets==3.6.0 +GPUtil==1.4.0 +# ServerlessLLM does not directly depend on outlines. +# This is to fix issue: https://github.com/dottxt-ai/outlines/issues/1198 +outlines==0.1.11 +peft==0.15.2 +ray==2.46.0 +speedtest-cli==2.1.3 +torch==2.7.0 +transformers==4.52.4 +vllm==0.9.0.1 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..199356c --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +fastapi==0.115.12 +GPUtil==1.4.0 +openai==1.52.0 +pydantic==2.11.5 +pydantic_core==2.33.2 +ray==2.46.0 +requests==2.32.2 +serverless-llm-store +speedtest-cli==2.1.3 +types-requests==2.31.0.20240125 +uvicorn[standard]==0.23.1 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..d79e0dd --- /dev/null +++ b/setup.py @@ -0,0 +1,87 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import io +import os +import sys +from pathlib import Path + +from setuptools import setup + +ROOT_DIR = os.path.dirname(__file__) + + +def fetch_requirements(path): + with open(path, "r") as fd: + return [r.strip() for r in fd.readlines()] + + +def remove_prefix(text, prefix): + if text.startswith(prefix): + return text[len(prefix) :] + return text + + +def get_path(*filepath) -> str: + return os.path.join(ROOT_DIR, *filepath) + + +def read_readme() -> str: + """Read the README file if present.""" + p = get_path("README.md") + if os.path.isfile(p): + return io.open(get_path("README.md"), "r", encoding="utf-8").read() + else: + return "" + + +install_requires = fetch_requirements("requirements.txt") +install_requires_worker = fetch_requirements("requirements-worker.txt") + +extras = {} +extras["test"] = install_requires_worker + ["pytest", "pytest-asyncio"] +extras["worker"] = install_requires_worker + +sys.path.append(Path.cwd().as_posix()) + +setup( + name="serverless-llm", + version="0.7.0", + install_requires=install_requires, + long_description=read_readme(), + long_description_content_type="text/markdown", + extras_require=extras, + entry_points={ + "console_scripts": [ + "sllm = sllm.clic:cli", + "sllm-serve=sllm.serve.commands.serve.sllm_serve:main", + ], + }, + include_package_data=True, + package_data={ + "sllm.serve": ["py.typed", "sllm.sllm_serve"], + "sllm": ["default_config.json"], + }, + packages=[ + "sllm", + "sllm.serve", + "sllm.serve.commands.serve", + "sllm.serve.backends", + "sllm.serve.routers", + "sllm.serve.schedulers", + ], +) diff --git a/sllm/.DS_Store b/sllm/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e1478dc5c7fa8a55e291cc01200ff8af0b617c63 GIT binary patch literal 6148 zcmeHKJ4ysW5Utu-VZmf0F_KO^z}{e7#uMxXWIw1d!z_x1(y@0iG7!9osYfvK44dhz zuF{>x!B9l2p!;>#cjm!#PZ1GMua*;{F%gY01X&h6BI;h#z?{X%l4CMkw9UFF#qU+g zvlq0Wn$GLf?(^T>Z!Jw*Rr9)?Lo<5Z+}y^4r=zZQ`Hgjb*3J5YFxsI@TG0hv(<-Yu z*u3V}Zr!)Ty$7q?iEr9%?S7A}e(@~zU7P`Dz!`7`{x<`-vqeU`iat97&VV!U$$*>> z0Yk7fjEd>#z?515;1KF0SW7Pi(_2jM% v7$+Da;umEB!8-d1z(&rI{bW>okTv{D!>A}(M4!Te{v(hG@yQwZ0S4XyPZ~!y literal 0 HcmV?d00001 diff --git a/sllm/_cli_utils.py b/sllm/_cli_utils.py new file mode 100644 index 0000000..056257c --- /dev/null +++ b/sllm/_cli_utils.py @@ -0,0 +1,308 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import json +import os +import subprocess +import sys + +import click +import requests + + +# ----------------------------- START COMMAND ----------------------------- # +def start_server(): + """Start the SLLM server using docker-compose.""" + compose_file = os.getenv( + "SLLM_COMPOSE_FILE", "examples/docker/docker-compose.yml" + ) + compose_file = os.path.abspath(compose_file) + + if not os.path.exists(compose_file): + click.echo(f"[❌] Cannot find docker-compose.yml at {compose_file}") + return + + try: + click.echo(f"[ℹ] Starting services using {compose_file}...") + subprocess.run( + ["docker", "compose", "-f", compose_file, "up", "-d"], check=True + ) + click.echo(f"[🚀] SLLM server started successfully.") + except Exception as e: + click.echo(f"[❌] Failed to start services: {e}") + + +# ----------------------------- DEPLOY COMMAND ----------------------------- # +def read_config(config_path: str) -> dict: + try: + with open(config_path, "r") as file: + return json.load(file) + except FileNotFoundError: + print(f"[ERROR] Config file {config_path} not found.") + sys.exit(1) + except json.JSONDecodeError: + print(f"[ERROR] JSON decode error in config file {config_path}.") + sys.exit(1) + + +def deep_update(original: dict, updates: dict) -> dict: + for key, value in updates.items(): + if isinstance(value, dict) and isinstance(original.get(key), dict): + original[key] = deep_update(original.get(key, {}), value) + else: + original[key] = value + return original + + +def deploy_model( + model, + config=None, + backend=None, + num_gpus=None, + target=None, + min_instances=None, + max_instances=None, + enable_lora=None, + lora_adapters=None, + precision=None, +): + default_config_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), "default_config.json") + ) + + if not os.path.exists(default_config_path): + print(f"[ERROR] Default config not found at {default_config_path}") + return + + config_data = read_config(default_config_path) + + if config: + # Try to find the config file in multiple locations + config_path = None + search_paths = [ + os.path.abspath( + config + ), # Absolute or relative to current directory + os.path.join( + os.path.dirname(__file__), config + ), # Relative to sllm package + os.path.expanduser(f"~/.sllm/{config}"), # User config directory + os.path.join("/etc/sllm", config), # System config directory + ] + + for path in search_paths: + if os.path.exists(path): + config_path = path + break + + if not config_path: + print( + f"[ERROR] Config file '{config}' not found in any of these locations:" + ) + for path in search_paths: + print(f" - {path}") + print("") + print("Available config files:") + # Show available config files in current directory + current_dir_configs = [ + f for f in os.listdir(".") if f.endswith(".json") + ] + if current_dir_configs: + for cf in current_dir_configs: + print(f" - {cf}") + else: + print(" - No JSON config files found in current directory") + + # Show the default config location + default_config_rel = os.path.join( + os.path.dirname(__file__), "default_config.json" + ) + if os.path.exists(default_config_rel): + print(f" - {default_config_rel} (default config)") + sys.exit(1) # Exit with error code + + user_config = read_config(config_path) + config_data = deep_update(config_data, user_config) + + if model: + config_data["model"] = model + config_data.setdefault("backend_config", {})[ + "pretrained_model_name_or_path" + ] = model + + if not config_data.get("model"): + print("[❌ ERROR] Model name is required!") + print("Please specify the model in one of these ways:") + print(" 1. Command line: --model MODEL_NAME") + print(" 2. Config file: include 'model' field in your JSON config") + print(" 3. Both: --config CONFIG_FILE --model MODEL_NAME") + print("") + print("Examples:") + print(" sllm deploy --model microsoft/DialoGPT-small") + print(" sllm deploy --config my_config.json") + print(" sllm deploy --config my_config.json --model facebook/opt-1.3b") + sys.exit(1) + + if backend: + config_data["backend"] = backend + if num_gpus is not None: + config_data["num_gpus"] = num_gpus + if target is not None: + config_data.setdefault("auto_scaling_config", {})["target"] = target + if min_instances is not None: + config_data["auto_scaling_config"]["min_instances"] = min_instances + if max_instances is not None: + config_data["auto_scaling_config"]["max_instances"] = max_instances + if lora_adapters: + # Only parse if not already a dict + if isinstance(lora_adapters, dict): + adapters_dict = lora_adapters + else: + adapters_dict = {} + if isinstance(lora_adapters, str): + items = lora_adapters.replace(",", " ").split() + elif isinstance(lora_adapters, (list, tuple)): + items = [] + for item in lora_adapters: + items.extend(item.replace(",", " ").split()) + else: + items = [str(lora_adapters)] + for module in items: + module = module.strip() + if not module: + continue + if "=" not in module: + print( + f"[ERROR] Invalid LoRA module format: {module}. Expected =." + ) + sys.exit(1) + name, path = module.split("=", 1) + adapters_dict[name] = path + config_data.setdefault("backend_config", {})["lora_adapters"] = ( + adapters_dict + ) + if enable_lora is not None: + config_data.setdefault("backend_config", {})["enable_lora"] = ( + enable_lora + ) + if precision: + config_data.setdefault("backend_config", {})["precision"] = precision + + base_url = os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + url = f"{base_url.rstrip('/')}/register" + headers = {"Content-Type": "application/json"} + + try: + response = requests.post(url, headers=headers, json=config_data) + if response.status_code == 200: + print( + f"[✅ SUCCESS] Model '{config_data['model']}' deployed successfully." + ) + else: + print( + f"[❌ ERROR] Deploy failed with status {response.status_code}: {response.text}" + ) + sys.exit(1) # Exit with error code to indicate failure + except Exception as e: + print(f"[EXCEPTION] Failed to deploy: {str(e)}") + sys.exit(1) # Exit with error code to indicate failure + + +# ----------------------------- DELETE COMMAND ----------------------------- # +def delete_model(models, lora_adapters=None): + if not models: + print("[⚠️ WARNING] No model names provided for deletion.") + return + + base_url = os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + headers = {"Content-Type": "application/json"} + + if lora_adapters is not None and len(models) > 1: + print( + "[❌ ERROR] You can only delete one model when using --lora-adapters." + ) + return + + for model in models: + url = f"{base_url.rstrip('/')}/delete" + data = {"model": model} + # Robust lora_adapters parsing (same as deploy) + if lora_adapters is not None: + # Accept: demo-lora1 demo-lora2 OR demo-lora1=path ... + if isinstance(lora_adapters, dict): + adapters = lora_adapters + else: + # flatten and split + if isinstance(lora_adapters, str): + items = lora_adapters.replace(",", " ").split() + elif isinstance(lora_adapters, (list, tuple)): + items = [] + for item in lora_adapters: + items.extend(item.replace(",", " ").split()) + else: + items = [str(lora_adapters)] + # If all items have '=', parse as dict; else, treat as list + if all("=" in module for module in items if module.strip()): + adapters = {} + for module in items: + module = module.strip() + if not module: + continue + name, path = module.split("=", 1) + adapters[name] = path + else: + # Only adapter names + adapters = [ + module.strip() for module in items if module.strip() + ] + data["lora_adapters"] = adapters + try: + response = requests.post(url, headers=headers, json=data) + if response.status_code == 200: + print( + f"[✅ SUCCESS] Delete request for '{model}' sent successfully." + ) + else: + print( + f"[❌ ERROR] Failed to delete '{model}'. Status: {response.status_code}, Response: {response.text}" + ) + except Exception as e: + print(f"[EXCEPTION] Failed to delete '{model}': {str(e)}") + + +# ----------------------------- STATUS COMMAND ----------------------------- # +def show_status(): + """Query the information of registered models.""" + endpoint = "/v1/models" + base_url = os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + url = base_url.rstrip("/") + endpoint + headers = {"Content-Type": "application/json"} + + try: + response = requests.get(url, headers=headers) + if response.status_code == 200: + try: + data = response.json() + print(f"Model status: {data}") + except ValueError: + print("[❌ ERROR] Invalid JSON received from server.") + else: + print( + f"[❌ ERROR] Failed with status {response.status_code}: {response.text}" + ) + except requests.exceptions.RequestException as e: + print(f"[EXCEPTION] Failed to query status: {str(e)}") diff --git a/sllm/cli/default_ft_config.json b/sllm/cli/default_ft_config.json new file mode 100644 index 0000000..2ac2537 --- /dev/null +++ b/sllm/cli/default_ft_config.json @@ -0,0 +1,27 @@ +{ + "model": "", + "ft_backend": "peft", + "output_dir": "ft_facebook/opt-125m_adapter1", + "dataset_config": { + "dataset_source": "hf_hub", + "hf_dataset_name": "fka/awesome-chatgpt-prompts", + "tokenization_field": "prompt", + "split": "train", + "data_files": "", + "extension_type": "" + }, + "lora_config": { + "r": 4, + "lora_alpha": 32, + "lora_dropout": 0.05, + "bias": "none", + "task_type": "CAUSAL_LM" + }, + "training_config": { + "auto_find_batch_size": true, + "save_strategy": "no", + "num_train_epochs": 2, + "learning_rate": 0.0001, + "use_cpu": false + } +} \ No newline at end of file diff --git a/sllm/cli/delete.py b/sllm/cli/delete.py new file mode 100644 index 0000000..1aac320 --- /dev/null +++ b/sllm/cli/delete.py @@ -0,0 +1,73 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class DeleteCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + delete_parser = parser.add_parser( + "delete", help="Delete deployed models by name." + ) + delete_parser.add_argument( + "models", nargs="+", type=str, help="Model names to delete." + ) + delete_parser.add_argument( + "--lora-adapters", + nargs="+", + type=str, + help="LoRA adapters to delete.", + ) + delete_parser.set_defaults(func=DeleteCommand) + + def __init__(self, args: Namespace) -> None: + self.models = args.models + self.lora_adapters = getattr(args, "lora_adapters", None) + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + "/delete" + ) + + def run(self) -> None: + headers = {"Content-Type": "application/json"} + if self.lora_adapters is not None and len(self.models) > 1: + logger.error( + "You can only delete one model when using --lora-adapters." + ) + exit(1) + + for model in self.models: + data = {"model": model} + if self.lora_adapters is not None: + data["lora_adapters"] = self.lora_adapters + response = requests.post(self.url, headers=headers, json=data) + + if response.status_code == 200: + logger.info( + f"Successfully sent the request to delete model {model}" + ) + else: + logger.error( + f"Failed to delete model: {model}. Status code: {response.status_code}. Response: {response.text}" + ) diff --git a/sllm/cli/deploy.py b/sllm/cli/deploy.py new file mode 100644 index 0000000..7a5869e --- /dev/null +++ b/sllm/cli/deploy.py @@ -0,0 +1,212 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.cli._cli_utils import read_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class DeployCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + deploy_parser = parser.add_parser( + "deploy", help="Deploy a model using a config file or model name." + ) + deploy_parser.add_argument( + "--model", + type=str, + help="Model name to deploy with default configuration.", + ) + deploy_parser.add_argument( + "--config", type=str, help="Path to the JSON config file." + ) + deploy_parser.add_argument( + "--backend", + type=str, + help="Overwrite the backend in the default configuration.", + ) + deploy_parser.add_argument( + "--num-gpus", + type=int, + help="Overwrite the number of GPUs in the default configuration.", + ) + deploy_parser.add_argument( + "--target", + type=int, + help="Overwrite the target concurrency in the default configuration.", + ) + deploy_parser.add_argument( + "--min-instances", + type=int, + help="Overwrite the minimum instances in the default configuration.", + ) + deploy_parser.add_argument( + "--max-instances", + type=int, + help="Overwrite the maximum instances in the default configuration.", + ) + deploy_parser.add_argument( + "--enable-lora", + action="store_true", + help="Enable LoRA adapter support.", + ) + deploy_parser.add_argument( + "--lora-adapters", + type=str, + nargs="+", + help="Specify LoRA adapters in the format =.", + ) + deploy_parser.set_defaults(func=DeployCommand) + + def __init__(self, args: Namespace) -> None: + self.model = args.model + self.config_path = args.config + self.backend = args.backend + self.num_gpus = args.num_gpus + self.target = args.target + self.min_instances = args.min_instances + self.max_instances = args.max_instances + self.enable_lora = args.enable_lora + self.lora_adapters = ( + self.parse_lora_adapters(args.lora_adapters) + if args.lora_adapters + else None + ) + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + "/register" + ) + self.default_config_path = os.path.join( + os.path.dirname(__file__), "default_config.json" + ) + + def parse_lora_adapters(self, lora_adapters): + parsed_modules = {} + if lora_adapters: + for module in lora_adapters: + if "=" not in module: + raise ValueError( + f"Invalid LoRA module format: {module}. Expected =." + ) + name, path = module.split("=", 1) + parsed_modules[name] = path + return parsed_modules + + def validate_config(self, config_data: dict) -> None: + """Validate the provided configuration data to ensure correctness.""" + try: + num_gpus = config_data["num_gpus"] + target = config_data["auto_scaling_config"]["target"] + min_instances = config_data["auto_scaling_config"]["min_instances"] + max_instances = config_data["auto_scaling_config"]["max_instances"] + except KeyError as e: + raise ValueError(f"Missing key in config_data: {e}") + + if num_gpus < 1: + raise ValueError("Number of GPUs cannot be less than 1.") + if target < 1: + raise ValueError("Target concurrency cannot be less than 1.") + if min_instances < 0: + raise ValueError("Minimum instances cannot be negative.") + if max_instances < 0: + raise ValueError("Maximum instances cannot be negative.") + if min_instances > max_instances: + raise ValueError( + "Minimum instances cannot be greater than maximum instances." + ) + + def update_config( + self, default_config: dict, provided_config: dict + ) -> dict: + """Update the default configuration with values from the provided configuration.""" + for key, value in provided_config.items(): + if isinstance(value, dict) and key in default_config: + default_config[key] = self.update_config( + default_config[key], value + ) + else: + default_config[key] = value + return default_config + + def run(self) -> None: + default_config = read_config(self.default_config_path) + + if self.config_path: + provided_config = read_config(self.config_path) + config_data = self.update_config(default_config, provided_config) + # If pretrained_model_name_or_path is not provided, use the model name + if ( + config_data["backend_config"]["pretrained_model_name_or_path"] + == "" + ): + config_data["backend_config"][ + "pretrained_model_name_or_path" + ] = config_data["model"] + elif self.model: + config_data = default_config + config_data["model"] = self.model + config_data["backend_config"]["pretrained_model_name_or_path"] = ( + self.model + ) + if self.backend: + config_data["backend"] = self.backend + if self.num_gpus is not None: + config_data["num_gpus"] = self.num_gpus + if self.target is not None: + config_data["auto_scaling_config"]["target"] = self.target + if self.min_instances is not None: + config_data["auto_scaling_config"]["min_instances"] = ( + self.min_instances + ) + if self.max_instances is not None: + config_data["auto_scaling_config"]["max_instances"] = ( + self.max_instances + ) + else: + logger.error("You must specify either --model or --config.") + exit(1) + + if self.enable_lora: + config_data["backend_config"]["enable_lora"] = True + config_data["backend_config"]["lora_adapters"] = self.lora_adapters + + self.validate_config(config_data) + logger.info(f"Deploying model {config_data['model']}.") + self.deploy_model(config_data) + + def deploy_model(self, config_data: dict) -> None: + headers = {"Content-Type": "application/json"} + + if "lora_adapters" in config_data["backend_config"]: + logger.info( + f"Using LoRA modules: {config_data['backend_config']['lora_adapters']}" + ) + + # Send POST request to the /register endpoint + response = requests.post(self.url, headers=headers, json=config_data) + + if response.status_code == 200: + logger.info("Model registered successfully.") + else: + logger.error( + f"Failed to register model. Status code: {response.status_code}. Response: {response.text}" + ) diff --git a/sllm/cli/encode.py b/sllm/cli/encode.py new file mode 100644 index 0000000..e05acf8 --- /dev/null +++ b/sllm/cli/encode.py @@ -0,0 +1,97 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import concurrent.futures +import json +import logging +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.cli._cli_utils import read_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class EncodeCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + encode_parser = parser.add_parser( + "encode", help="Encode using the deployed model." + ) + encode_parser.add_argument( + "input_path", type=str, help="Path to the JSON input file." + ) + encode_parser.add_argument( + "-t", + "--threads", + type=int, + default=1, + help="Number of parallel encoding processes.", + ) + encode_parser.set_defaults(func=EncodeCommand) + + def __init__(self, args: Namespace) -> None: + self.input_path = args.input_path + self.threads = args.threads + self.endpoint = "/v1/embeddings" # TODO: as a argument + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + self.endpoint + ) + + def run(self) -> None: + input_data = read_config(self.input_path) + if self.threads > 1: + with concurrent.futures.ThreadPoolExecutor( + max_workers=self.threads + ) as executor: + futures = [ + executor.submit(self.encode, input_data) + for _ in range(self.threads) + ] + results = [ + future.result() + for future in concurrent.futures.as_completed(futures) + ] + # Validate results + if not all(results): + logger.error("Some encoding processes failed.") + else: + logger.info( + f"All encoding processes done. Results: {results}" + ) + else: + result = self.encode(input_data) + logger.info(f"Embedding result: {result}") + + def encode(self, input_data: dict) -> dict: + headers = {"Content-Type": "application/json"} + + # Send POST request to the /v1/chat/completions endpoint + response = requests.post(self.url, headers=headers, json=input_data) + + if response.status_code == 200: + logger.info("Encoding successful.") + return response.json() + else: + logger.error( + f"Failed to encode. Status code: {response.status_code}" + ) + logger.error(f"Response: {response.text}") + return None diff --git a/sllm/cli/fine_tuning.py b/sllm/cli/fine_tuning.py new file mode 100644 index 0000000..8d4ea15 --- /dev/null +++ b/sllm/cli/fine_tuning.py @@ -0,0 +1,94 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import concurrent.futures +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.cli._cli_utils import read_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class FineTuningCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + fine_tuning_parser = parser.add_parser( + "fine-tuning", help="fine-tuning base model." + ) + fine_tuning_parser.add_argument( + "--base-model", type=str, help="base model name" + ) + fine_tuning_parser.add_argument( + "--config", + type=str, + help="path to fine-tuning configuration JSON file", + default=os.path.join( + os.path.dirname(__file__), "default_ft_config.json" + ), + ) + fine_tuning_parser.set_defaults(func=FineTuningCommand) + + def __init__(self, args: Namespace) -> None: + self.base_model = args.base_model + self.config_path = args.config + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + + "/fine-tuning" + ) + + def validate_config(self, config_data: dict) -> None: + """Validate the provided configuration data to ensure correctness.""" + try: + model = config_data["model"] + ft_backend = config_data["ft_backend"] + dataset_source = config_data["dataset_config"]["dataset_source"] + tokenization_field = config_data["dataset_config"][ + "tokenization_field" + ] + except KeyError as e: + raise ValueError(f"Missing key in ft_config_data: {e}") + + if dataset_source not in ["hf_hub", "local"]: + raise ValueError("dataset_source only supports hf_hub or local") + + def run(self) -> None: + config_data = read_config(self.config_path) + config_data["model"] = self.base_model + self.validate_config(config_data) + logger.info(f"Start fine-tuning base model {config_data['model']}") + result = self.fine_tuning(config_data) + logger.info(f"{result}") + + def fine_tuning(self, config: dict) -> dict: + headers = {"Content-Type": "application/json"} + + # Send POST request to the /fine-tuning endpoint + response = requests.post(self.url, headers=headers, json=config) + + if response.status_code == 200: + logger.info(f"{config['model']} fine-tuned successful.") + return response.json() + else: + logger.error( + f"Failed to do fine-tuning. Status code: {response.status_code}" + ) + logger.error(f"Response: {response.text}") + return None diff --git a/sllm/cli/generate.py b/sllm/cli/generate.py new file mode 100644 index 0000000..7fac1f8 --- /dev/null +++ b/sllm/cli/generate.py @@ -0,0 +1,93 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import concurrent.futures +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.cli._cli_utils import read_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class GenerateCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + generate_parser = parser.add_parser( + "generate", help="Generate using the deployed model." + ) + generate_parser.add_argument( + "input_path", type=str, help="Path to the JSON input file." + ) + generate_parser.add_argument( + "-t", + "--threads", + type=int, + default=1, + help="Number of parallel generation processes.", + ) + generate_parser.set_defaults(func=GenerateCommand) + + def __init__(self, args: Namespace) -> None: + self.input_path = args.input_path + self.threads = args.threads + self.endpoint = "/v1/chat/completions" # TODO: as a argument + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + self.endpoint + ) + + def run(self) -> None: + input_data = read_config(self.input_path) + if self.threads > 1: + with concurrent.futures.ThreadPoolExecutor( + max_workers=self.threads + ) as executor: + futures = [ + executor.submit(self.generate, input_data) + for _ in range(self.threads) + ] + results = [ + future.result() + for future in concurrent.futures.as_completed(futures) + ] + # Validate results + if not all(results): + logger.error("Some generations failed.") + else: + logger.info(f"All generations done. Results: {results}") + else: + result = self.generate(input_data) + logger.info(f"Generation result: {result}") + + def generate(self, input_data: dict) -> dict: + headers = {"Content-Type": "application/json"} + + # Send POST request to the /v1/chat/completions endpoint + response = requests.post(self.url, headers=headers, json=input_data) + + if response.status_code == 200: + logger.info("Generation successful.") + return response.json() + else: + logger.error( + f"Failed to generate. Status code: {response.status_code}" + ) + logger.error(f"Response: {response.text}") + return None diff --git a/sllm/cli/replay.py b/sllm/cli/replay.py new file mode 100644 index 0000000..25a0cdf --- /dev/null +++ b/sllm/cli/replay.py @@ -0,0 +1,149 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +import json +import os +import time +from argparse import Namespace, _SubParsersAction + +from openai import AsyncOpenAI + +from sllm.cli._cli_utils import read_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class ReplayCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + replay_parser = parser.add_parser( + "replay", help="Replay requests based on workload and dataset." + ) + replay_parser.add_argument( + "--workload", + type=str, + required=True, + help="Path to the JSON workload file.", + ) + replay_parser.add_argument( + "--dataset", + type=str, + required=True, + help="Path to the JSON dataset file.", + ) + replay_parser.add_argument( + "--output", + type=str, + default="latency_results.json", + help="Path to the output JSON file for latency results.", + ) + replay_parser.set_defaults(func=ReplayCommand) + + def __init__(self, args: Namespace) -> None: + self.workload_path = args.workload + self.dataset_path = args.dataset + self.output_path = args.output + self.url = os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + self.base_url = self.url + "/v1" + + self.client = AsyncOpenAI( + base_url=self.base_url, + api_key="API_KEY_PLACEHOLDER", # Placeholder for API key + ) + self.latency_results = [] + + async def run(self) -> None: + workload = read_config(self.workload_path) + dataset = read_config(self.dataset_path) + + input_texts = dataset.get("input_text", []) + output_lengths = dataset.get("output_length", []) + + if not input_texts or not output_lengths: + logger.error( + "Dataset is missing required fields: input_text and output_length" + ) + return + + tasks = [] + for model_name, times in workload.items(): + for i, time_offset in enumerate(times): + if i >= len(input_texts) or i >= len(output_lengths): + logger.error(f"Index {i} is out of bounds for the dataset") + break + input_text = input_texts[i] + output_length = output_lengths[i] + tasks.append( + self.schedule_request( + model_name, input_text, output_length, time_offset + ) + ) + + await asyncio.gather(*tasks) + self.write_latency_results() + + async def schedule_request( + self, + model_name: str, + input_text: str, + output_length: int, + time_offset: float, + ) -> None: + await asyncio.sleep(time_offset) + request_data = { + "model": model_name, + "messages": [{"role": "user", "content": input_text}], + "max_tokens": output_length, + } + await self.send_request(request_data) + + async def send_request(self, request_data: dict) -> None: + start_time = time.time() + try: + response = await self.client.chat.completions.create(**request_data) + end_time = time.time() + latency = end_time - start_time + logger.info( + f"Generation successful: {response.choices[0].message.content}" + ) + self.latency_results.append( + { + "model": request_data["model"], + "input_text": request_data["messages"][0]["content"], + "latency": latency, + } + ) + return response + except Exception as e: + end_time = time.time() + latency = end_time - start_time + logger.error(f"Failed to generate. Error: {str(e)}") + self.latency_results.append( + { + "model": request_data["model"], + "input_text": request_data["messages"][0]["content"], + "latency": latency, + "error": str(e), + } + ) + + def write_latency_results(self) -> None: + with open(self.output_path, "w") as f: + json.dump(self.latency_results, f, indent=4) + logger.info(f"Latency results written to {self.output_path}") diff --git a/sllm/cli/sllm_cli.py b/sllm/cli/sllm_cli.py new file mode 100644 index 0000000..d2f5800 --- /dev/null +++ b/sllm/cli/sllm_cli.py @@ -0,0 +1,66 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import argparse +import asyncio + +from sllm.cli.delete import DeleteCommand +from sllm.cli.deploy import DeployCommand +from sllm.cli.encode import EncodeCommand +from sllm.cli.fine_tuning import FineTuningCommand +from sllm.cli.generate import GenerateCommand +from sllm.cli.replay import ReplayCommand +from sllm.cli.status import StatusCommand +from sllm.cli.update import UpdateCommand +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +def main(): + parser = argparse.ArgumentParser( + "sllm-cli", usage="sllm-cli []" + ) + commands_parser = parser.add_subparsers(help="sllm-cli command helpers") + + # Register commands + DeployCommand.register_subcommand(commands_parser) + GenerateCommand.register_subcommand(commands_parser) + EncodeCommand.register_subcommand(commands_parser) + ReplayCommand.register_subcommand(commands_parser) + DeleteCommand.register_subcommand(commands_parser) + UpdateCommand.register_subcommand(commands_parser) + FineTuningCommand.register_subcommand(commands_parser) + StatusCommand.register_subcommand(commands_parser) + + # Let's go + args = parser.parse_args() + + if not hasattr(args, "func"): + parser.print_help() + exit(1) + + # Run + service = args.func(args) + if asyncio.iscoroutinefunction(service.run): + asyncio.run(service.run()) + else: + service.run() + + +if __name__ == "__main__": + main() diff --git a/sllm/cli/status.py b/sllm/cli/status.py new file mode 100644 index 0000000..3f3167f --- /dev/null +++ b/sllm/cli/status.py @@ -0,0 +1,72 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.cli._cli_utils import read_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class StatusCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + status_parser = parser.add_parser( + "status", + help="Query the information of registered models.", + ) + status_parser.set_defaults(func=StatusCommand) + + def __init__(self, args: Namespace) -> None: + self.endpoint = "/v1/models" # TODO: as an argument + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + self.endpoint + ) + + def run(self) -> None: + status = self.query_status() + if status: + print(f"Model status: {status}") + else: + logger.error("Failed to fetch model status.") + + def query_status(self) -> dict: + headers = {"Content-Type": "application/json"} + try: + # Send GET request to the status endpoint + response = requests.get(self.url, headers=headers) + + if response.status_code == 200: + logger.info("Status query successful.") + try: + return response.json() + except ValueError: + logger.error("Invalid JSON response received.") + return None + else: + logger.error( + f"Failed to query status. Status code: {response.status_code}" + ) + logger.error(f"Response: {response.text}") + return None + except requests.exceptions.RequestException as e: + logger.error(f"Request failed:{str(e)}") + return None diff --git a/sllm/cli/update.py b/sllm/cli/update.py new file mode 100644 index 0000000..c6d64be --- /dev/null +++ b/sllm/cli/update.py @@ -0,0 +1,85 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +from argparse import Namespace, _SubParsersAction + +import requests + +from sllm.cli._cli_utils import read_config, validate_config +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class UpdateCommand: + @staticmethod + def register_subcommand(parser: _SubParsersAction): + update_parser = parser.add_parser( + "update", help="Update a model using a config file or model name." + ) + update_parser.add_argument( + "--model", + type=str, + help="Model name to update with new configuration.", + ) + update_parser.add_argument( + "--config", type=str, help="Path to the JSON config file." + ) + update_parser.set_defaults(func=UpdateCommand) + + def __init__(self, args: Namespace) -> None: + self.model = args.model + self.config_path = args.config + self.url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + "/update" + ) + self.default_config_path = os.path.join( + os.path.dirname(__file__), "default_config.json" + ) + + def run(self) -> None: + if self.config_path: + config_data = read_config(self.config_path) + validate_config(config_data) + self.update_model(config_data) + elif self.model: + config_data = read_config(self.default_config_path) + config_data["model"] = self.model + config_data["backend_config"]["pretrained_model_name_or_path"] = ( + self.model + ) + logger.info( + f"Updating model {self.model} with default configuration." + ) + self.update_model(config_data) + else: + logger.error("You must specify either --model or --config.") + exit(1) + + def update_model(self, config_data: dict) -> None: + headers = {"Content-Type": "application/json"} + + # Send POST request to the /update endpoint + response = requests.post(self.url, headers=headers, json=config_data) + + if response.status_code == 200: + logger.info("Model updated successfully.") + else: + logger.error( + f"Failed to update model. Status code: {response.status_code}. Response: {response.text}" + ) diff --git a/sllm/clic.py b/sllm/clic.py new file mode 100644 index 0000000..3ebe95a --- /dev/null +++ b/sllm/clic.py @@ -0,0 +1,142 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import click + +from sllm._cli_utils import ( + delete_model, + deploy_model, + show_status, + start_server, +) + + +@click.group() +def cli(): + """Unified CLI for ServerlessLLM.""" + pass + + +@cli.command() +@click.option( + "--model", + help="Model name from HuggingFace model hub (required if not specified in config file)", +) +@click.option("--config", help="Path to configuration file") +@click.option("--backend", help="Backend framework (e.g., vllm, transformers)") +@click.option( + "--num-gpus", type=int, help="Number of GPUs to use for the model" +) +@click.option("--target", type=int, help="Target number of requests per second") +@click.option( + "--min-instances", type=int, help="Minimum number of model instances" +) +@click.option( + "--max-instances", type=int, help="Maximum number of model instances" +) +@click.option( + "--lora-adapters", + help=( + 'List of LoRA adapters, e.g. "demo_lora1=... demo_lora2=..." or "demo_lora1=...,demo_lora2=...". ' + "Must be wrapped in quotes if using spaces or commas. Each adapter must be in = format." + ), +) +@click.option( + "--enable-lora", + is_flag=True, + default=False, + help="Enable LoRA support for the model", +) +@click.option( + "--precision", + help="Model precision for quantization (e.g., int8, fp4, nf4)", +) +def deploy( + model, + config, + backend, + num_gpus, + target, + min_instances, + max_instances, + lora_adapters, + enable_lora, + precision, +): + """Deploy a model using a config file or model name. + + Either --model or a config file with a model specified is required. + Command line options override values from the config file. + """ + # ...existing code... + adapters_dict = None + if lora_adapters: + adapters_dict = {} + # If it's a string, split by comma or space + if isinstance(lora_adapters, str): + items = lora_adapters.replace(",", " ").split() + elif isinstance(lora_adapters, (list, tuple)): + items = [] + for item in lora_adapters: + items.extend(item.replace(",", " ").split()) + else: + items = [str(lora_adapters)] + for module in items: + if "=" not in module: + click.echo( + f"[ERROR] Invalid LoRA module format: {module}. Expected =." + ) + continue + name, path = module.split("=", 1) + adapters_dict[name] = path + + deploy_model( + model=model, + config=config, + backend=backend, + num_gpus=num_gpus, + target=target, + min_instances=min_instances, + max_instances=max_instances, + lora_adapters=adapters_dict, + enable_lora=enable_lora, + precision=precision, + ) + + +@cli.command() +@click.argument("models", nargs=-1) +@click.option("--lora-adapters", help="LoRA adapters to delete.") +def delete(models, lora_adapters): + """Delete deployed models, or remove only the LoRA adapters.""" + delete_model(models, lora_adapters=lora_adapters if lora_adapters else None) + + +@cli.command() +def start(): + """Start the head node of the SLLM cluster.""" + start_server() + + +@cli.command() +def status(): + """Show all deployed models.""" + show_status() + + +if __name__ == "__main__": + cli() diff --git a/sllm/default_config.json b/sllm/default_config.json new file mode 100644 index 0000000..b2895aa --- /dev/null +++ b/sllm/default_config.json @@ -0,0 +1,18 @@ +{ + "model": "", + "backend": "vllm", + "num_gpus": 1, + "auto_scaling_config": { + "metric": "concurrency", + "target": 1, + "min_instances": 0, + "max_instances": 10, + "keep_alive": 0 + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM" + } +} \ No newline at end of file diff --git a/sllm/serve/.DS_Store b/sllm/serve/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..1c1be8bc1f14b73205ffc6158783cbb3dad2a026 GIT binary patch literal 6148 zcmeHKF;2rU6#a%uRAuP~145D5SfC?YC_zZ9mbNUK@ z;+A#3vQCZlasX#GM{`?HYjr>!PzM$c@cs~>Foq6uLA!OJvR43L1Kl>z=1&UF2^@wF zb3u$CjHCi3)wnB$k#zJ2ju$%21tpz~yL=e8vT-*QV^_!gfu)lP1+`WO)Pb@CoATJ^ z^Z$JH{lA=~PwId=@UI*&ji?uO@k;J&t-KtcwE;>Wg@bvypiM#Lj$_;4qj(p^2F3zz W07HklAbJq`Az*FLN*(x92fhIOXO*!4 literal 0 HcmV?d00001 diff --git a/sllm/serve/__init__.py b/sllm/serve/__init__.py new file mode 100644 index 0000000..d925a19 --- /dev/null +++ b/sllm/serve/__init__.py @@ -0,0 +1,17 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # diff --git a/sllm/serve/app_lib.py b/sllm/serve/app_lib.py new file mode 100644 index 0000000..baaa4cb --- /dev/null +++ b/sllm/serve/app_lib.py @@ -0,0 +1,178 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from contextlib import asynccontextmanager + +import ray +import ray.exceptions +from fastapi import FastAPI, HTTPException, Request + +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +def create_app() -> FastAPI: + @asynccontextmanager + async def lifespan(app: FastAPI): + # Connect to the Ray cluster + # ray.init() + yield + # Shutdown the Ray cluster + ray.shutdown() + + app = FastAPI(lifespan=lifespan) + + @app.get("/health") + async def health_check(): + return {"status": "ok"} + + @app.post("/register") + async def register_handler(request: Request): + body = await request.json() + + controller = ray.get_actor("controller") + if not controller: + raise HTTPException( + status_code=500, detail="Controller not initialized" + ) + try: + await controller.register.remote(body) + except Exception as e: + raise HTTPException( + status_code=500, + detail="Cannot register model, please contact the administrator", + ) + + return {"status": "ok"} + + @app.post("/update") + async def update_handler(request: Request): + body = await request.json() + model_name = body.get("model") + if not model_name: + raise HTTPException( + status_code=400, detail="Missing model_name in request body" + ) + + controller = ray.get_actor("controller") + if not controller: + raise HTTPException( + status_code=500, detail="Controller not initialized" + ) + + logger.info(f"Received request to update model {model_name}") + try: + await controller.update.remote(model_name, body) + except ray.exceptions.RayTaskError as e: + raise HTTPException(status_code=400, detail=str(e.cause)) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + return {"status": f"updated model {model_name}"} + + @app.post("/delete") + async def delete_model(request: Request): + body = await request.json() + + model_name = body.get("model") + if not model_name: + raise HTTPException( + status_code=400, detail="Missing model_name in request body" + ) + lora_adapters = body.get("lora_adapters", None) + + controller = ray.get_actor("controller") + if not controller: + raise HTTPException( + status_code=500, detail="Controller not initialized" + ) + + if lora_adapters is not None: + logger.info( + f"Received request to delete LoRA adapters {lora_adapters} on model {model_name}" + ) + await controller.delete.remote(model_name, lora_adapters) + else: + logger.info(f"Received request to delete model {model_name}") + await controller.delete.remote(model_name) + + return {"status": f"deleted model {model_name}"} + + async def inference_handler(request: Request, action: str): + body = await request.json() + model_name = body.get("model") + logger.info(f"Received request for model {model_name}") + if not model_name: + raise HTTPException( + status_code=400, detail="Missing model_name in request body" + ) + + request_router = ray.get_actor(model_name, namespace="models") + logger.info(f"Got request router for {model_name}") + + result = request_router.inference.remote(body, action) + return await result + + async def fine_tuning_handler(request: Request): + body = await request.json() + model_name = body.get("model") + logger.info(f"Received request for model {model_name}") + if not model_name: + raise HTTPException( + status_code=400, detail="Missing model_name in request body" + ) + + request_router = ray.get_actor(model_name, namespace="models") + logger.info(f"Got request router for {model_name}") + + result = request_router.fine_tuning.remote(body) + return await result + + @app.post("/v1/chat/completions") + async def generate_handler(request: Request): + return await inference_handler(request, "generate") + + @app.post("/v1/embeddings") + async def embeddings_handler(request: Request): + return await inference_handler(request, "encode") + + @app.post("/fine-tuning") + async def fine_tuning(request: Request): + return await fine_tuning_handler(request) + + @app.get("/v1/models") + async def get_models(): + logger.info("Attempting to retrieve the controller actor") + try: + controller = ray.get_actor("controller") + if not controller: + logger.error("Controller not initialized") + raise HTTPException( + status_code=500, detail="Controller not initialized" + ) + logger.info("Controller actor found") + result = await controller.status.remote() + logger.info("Controller status retrieved successfully") + return result + except Exception as e: + logger.error(f"Error retrieving models: {str(e)}") + raise HTTPException( + status_code=500, detail="Failed to retrieve models" + ) + + return app diff --git a/sllm/serve/backends/__init__.py b/sllm/serve/backends/__init__.py new file mode 100644 index 0000000..91c243c --- /dev/null +++ b/sllm/serve/backends/__init__.py @@ -0,0 +1,22 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from .dummy_backend import DummyBackend +from .transformers_backend import TransformersBackend +from .vllm_backend import VllmBackend + +__all__ = ["DummyBackend", "VllmBackend", "TransformersBackend"] diff --git a/sllm/serve/backends/backend_utils.py b/sllm/serve/backends/backend_utils.py new file mode 100644 index 0000000..1ce46c4 --- /dev/null +++ b/sllm/serve/backends/backend_utils.py @@ -0,0 +1,67 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from abc import ABC, abstractmethod +from enum import Enum, auto +from typing import Any, Dict, List, Optional + + +class BackendStatus(Enum): + UNINITIALIZED = auto() + RUNNING = auto() + STOPPING = auto() + DELETING = auto() + + +class SllmBackend(ABC): + @abstractmethod + def __init__( + self, model_name: str, backend_config: Optional[Dict[str, Any]] = None + ) -> None: + pass + + @abstractmethod + async def init_backend(self) -> None: + pass + + @abstractmethod + async def encode(self, request_data: Dict[str, Any]): + pass + + @abstractmethod + async def generate(self, request_data: Dict[str, Any]): + pass + + @abstractmethod + async def shutdown(self): + pass + + @abstractmethod + async def stop(self): + pass + + @abstractmethod + async def get_current_tokens(self) -> List[List[int]]: + pass + + @abstractmethod + async def resume_kv_cache(self, request_datas: List[List[int]]) -> None: + pass + + @abstractmethod + async def fine_tuning(self, request_data: Dict[str, Any]): + pass diff --git a/sllm/serve/backends/dummy_backend.py b/sllm/serve/backends/dummy_backend.py new file mode 100644 index 0000000..4b9b3ff --- /dev/null +++ b/sllm/serve/backends/dummy_backend.py @@ -0,0 +1,116 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +import time +import uuid +from typing import Any, Dict, Optional + +from sllm.serve.backends.backend_utils import SllmBackend +from sllm.serve.logger import init_logger + + +class DummyBackend(SllmBackend): + def __init__(self, backend_config: Optional[Dict[str, Any]] = None) -> None: + self.backend_config = backend_config + + def init_backend(self) -> None: + # sleep to simulate model latency + sleep_time = 5 + self.log( + f"Sleeping for {sleep_time} seconds to simulate model init time." + ) + time.sleep(sleep_time) + + def log(self, msg): + logger = init_logger(__name__) + logger.info(msg) + + async def generate(self, request_data): + model_name = request_data.get("model", "dummy-model") + messages = request_data.get("messages", []) + # temperature = request_data.get("temperature", 0.7) + max_tokens = request_data.get("max_tokens", 10) + token_latency = request_data.get("token_latency", 0.1) + + # Combine messages to form the prompt + prompt = "\n".join( + [ + f"{message['role']}: {message['content']}" + for message in messages + if "content" in message + ] + ) + + # Simulate model latency + self.log( + f"Sleeping for {max_tokens * token_latency} seconds to simulate model response time." + ) + for i in range(max_tokens): + await asyncio.sleep(token_latency) + + # Dummy response content + response_content = f"Debug model received prompt: {prompt}" + + # Simulate token counts for the response + prompt_tokens = len(prompt.split()) + completion_tokens = max_tokens + total_tokens = prompt_tokens + completion_tokens + + # Generate response compatible with OpenAI's API + response = { + "id": f"chatcmpl-{uuid.uuid4()}", + "object": "chat.completion", + "created": int(time.time()), + "model": model_name, + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": response_content, + }, + "logprobs": None, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, + }, + } + + return response + + async def shutdown(self): + pass + + async def stop(self): + pass + + async def get_current_tokens(self): + self.log("Not implemented") + raise NotImplementedError + + async def resume_kv_cache(self, request_datas): + self.log("Not implemented") + raise NotImplementedError + + async def fine_tuning(self, request_data: Dict[str, Any]): + self.log("Not implemented") + raise NotImplementedError diff --git a/sllm/serve/backends/transformers_backend.py b/sllm/serve/backends/transformers_backend.py new file mode 100644 index 0000000..4e8d6c0 --- /dev/null +++ b/sllm/serve/backends/transformers_backend.py @@ -0,0 +1,633 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import json +import os +import threading +import time +import uuid +from copy import deepcopy +from typing import Any, Dict, List, Optional + +import peft +import torch +import torch.nn.functional as F +import transformers +from datasets import load_dataset +from peft import LoraConfig, PeftModel, get_peft_model +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + PreTrainedTokenizerBase, + Trainer, + TrainingArguments, +) +from transformers.generation.streamers import BaseStreamer + +from sllm.serve.backends.backend_utils import BackendStatus, SllmBackend +from sllm.serve.logger import init_logger +from sllm_store.transformers import load_lora, load_model, save_lora + +logger = init_logger(__name__) + + +class DeletingException(Exception): + pass + + +class InferenceStatus(BaseStreamer): + def __init__(self, status: BackendStatus): + super().__init__() + self.status = status + self.intermediate = [] + + def put(self, value): + value = value.tolist() + if not self.intermediate: + self.intermediate = value + else: + # NOTE: This does not support in-flight batching + # or dynamic batch size + for i, v in enumerate(value): + self.intermediate[i].append(v) + logger.warning(f"Intermediate output: {self.intermediate}") + if self.status == BackendStatus.DELETING: + raise DeletingException("Backend is deleting") + + def end(self): + logger.error("Inference completed") + + def get(self): + return deepcopy(self.intermediate) + + def delete(self): + logger.info("Deleting intermediate output") + self.intermediate = [] + + +class TransformersBackend(SllmBackend): + def __init__( + self, model_name: str, backend_config: Optional[Dict[str, Any]] = None + ) -> None: + self.backend_config = backend_config + logger.info( + f"Initializing TransformersBackend for {model_name} with config: {backend_config}" + ) + self.model_name = model_name + self.pretrained_model_name_or_path = backend_config.get( + "pretrained_model_name_or_path" + ) + self.status: BackendStatus = BackendStatus.UNINITIALIZED + self.inf_status = InferenceStatus(self.status) + self.status_lock = threading.Lock() + self.model = None + self.tokenizer = None + self.past_key_values = None + + def convert_str_to_json(self, json_str): + try: + # Parse the JSON string and return the corresponding Python object + json_obj = json.loads(json_str) + return json_obj + except json.JSONDecodeError as e: + logger.error(f"Failed to decode JSON string: {e}") + return None + + def init_backend(self) -> None: + with self.status_lock: + if self.status != BackendStatus.UNINITIALIZED: + return + device_map = self.backend_config.get("device_map", "auto") + torch_dtype = self.backend_config.get("torch_dtype", torch.float16) + torch_dtype = getattr(torch, torch_dtype) + hf_model_class = self.backend_config.get("hf_model_class", None) + if torch_dtype is None: + logger.warning( + f"Invalid torch_dtype: {torch_dtype}. Using torch.float16" + ) + torch_dtype = torch.float16 + if hf_model_class is None: + logger.error( + f"hf_model_class cannot be None. Please provide a valid model class" + ) + raise ValueError( + "hf_model_class cannot be None. Please provide a valid model class" + ) + quantization_config = self.backend_config.get( + "quantization_config", None + ) + + storage_path = os.getenv("STORAGE_PATH", "./models") + model_path = os.path.join("transformers", self.model_name) + self.model = load_model( + model_path, + device_map=device_map, + torch_dtype=torch_dtype, + storage_path=storage_path, + hf_model_class=hf_model_class, + quantization_config=quantization_config, + ) + tokenizer_path = os.path.join( + storage_path, "transformers", self.model_name, "tokenizer" + ) + if os.path.exists(tokenizer_path): + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + else: + # Fall back to load from system's cache + self.tokenizer = AutoTokenizer.from_pretrained( + self.pretrained_model_name_or_path + ) + self.status = BackendStatus.RUNNING + + def _tokenize(self, prompt: str): + return self.tokenizer(prompt, return_tensors="pt").to("cuda:0") + + def _encoder_tokenize(self, query: str, max_length: int): + return self.tokenizer( + query, + max_length=max_length, + padding=True, + truncation=True, + return_tensors="pt", + ).to("cuda:0") + + def encode(self, request_data: Optional[Dict[str, Any]]): + with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Model not initialized"} + + def last_token_pool( + last_hidden_states: torch.Tensor, attention_mask: torch.Tensor + ) -> torch.Tensor: + left_padding = ( + attention_mask[:, -1].sum() == attention_mask.shape[0] + ) + if left_padding: + return last_hidden_states[:, -1] + else: + sequence_lengths = attention_mask.sum(dim=1) - 1 + batch_size = last_hidden_states.shape[0] + return last_hidden_states[ + torch.arange(batch_size, device=last_hidden_states.device), + sequence_lengths, + ] + + def get_detailed_instruct(task_description: str, query: str) -> str: + return f"Instruct: {task_description}\nQuery: {query}" + + model_name = request_data.get("model", "dummy-model") + task_instruct = request_data.get("task_instruct", "") + max_length = request_data.get("max_length", 4096) + query = request_data.get("input", []) + + if not query: + return {"error": "Missing query in request data"} + + query = [get_detailed_instruct(task_instruct, q) for q in query] + + batch_dict = self._encoder_tokenize(query, max_length) + with torch.no_grad(): + output = self.model(**batch_dict, output_hidden_states=True) + embeddings = last_token_pool( + output.hidden_states[-1], batch_dict["attention_mask"] + ) + + embeddings = F.normalize(embeddings, p=2, dim=1) + + query_tokens = sum([len(self.tokenizer.tokenize(q)) for q in query]) + response = { + "object": "list", + "data": [ + { + "object": "embedding", + "index": i, + "embedding": embeddings[i].tolist(), + } + for i in range(len(embeddings)) + ], + "model": model_name, + "usage": { + "query_tokens": query_tokens, + "total_tokens": query_tokens, + }, + } + + return response + + def generate(self, request_data: Optional[Dict[str, Any]]): + with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Model not initialized"} + + assert self.model is not None + + model_name = request_data.get("model", "dummy-model") + messages = request_data.get("messages", []) + temperature = request_data.get("temperature", 0.7) + max_tokens = request_data.get("max_tokens", 10) + lora_adapter_name = request_data.get("lora_adapter_name", None) + + # Combine messages to form the prompt + try: + prompt = self.tokenizer.apply_chat_template( + messages, add_generation_prompt=True, tokenize=False + ) + except Exception as e: + prompt = "\n".join( + f"{message['role'].capitalize()}: {message['content']}" + for message in messages + ) + + if not prompt: + return {"error": "Missing prompt in request data"} + + generate_kwargs = { + "max_new_tokens": max_tokens, + "temperature": temperature, + "streamer": self.inf_status, + } + + if lora_adapter_name: + if ( + not hasattr(self.model, "peft_config") + or lora_adapter_name not in self.model.peft_config + ): + return {"error": f"LoRA adapter {lora_adapter_name} not found"} + generate_kwargs["adapter_names"] = [lora_adapter_name] + + inputs = self._tokenize(prompt) + prompt_tokens = inputs.input_ids.shape[1] + + # Generate response + try: + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + **generate_kwargs, + ) + except DeletingException: + logger.info("Backend is shutting down. Aborting request") + output_tokens = self.inf_status.get() + self.inf_status.delete() + return { + "preempted": "True", + "current_output": output_tokens, + "completed_tokens": len(output_tokens[0]) - prompt_tokens, + } + except Exception as e: + logger.error(f"Failed to generate response: {e}") + raise e + else: + output_text = self.tokenizer.decode( + outputs[0][prompt_tokens:], skip_special_tokens=True + ) + total_tokens = len(outputs[0]) + completion_tokens = total_tokens - prompt_tokens + # FIXME: consider corner case when max_tokens is reached + finish_reason = ( + "stop" if completion_tokens < max_tokens else "length" + ) + + # Generate response compatible with OpenAI's API + response = { + "id": f"chatcmpl-{uuid.uuid4()}", + "object": "chat.completion", + "created": int(time.time()), + "model": model_name, + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": output_text, + }, + "logprobs": None, + "finish_reason": finish_reason, + } + ], + "usage": { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, + }, + } + + self.inf_status.delete() + + return response + + def _load_dataset( + self, + dataset_config: Optional[Dict[str, Any]], + tokenizer: PreTrainedTokenizerBase, + ): + dataset_source = dataset_config.get("dataset_source") + hf_dataset_name = dataset_config.get("hf_dataset_name") + tokenization_field = dataset_config.get("tokenization_field") + split = dataset_config.get("split", None) + data_files = dataset_config.get("data_files", None) + extension_type = dataset_config.get("extension_type") + + if dataset_source not in {"hf_hub", "local"}: + logger.error( + "Invalid 'dataset_source'. Must be 'hf_hub' or 'local'." + ) + raise ValueError( + "Invalid 'dataset_source'. Must be 'hf_hub' or 'local'." + ) + + if dataset_source == "hf_hub": + if not hf_dataset_name: + logger.error( + "hf_dataset_name must be provided in the dataset configuration." + ) + raise ValueError( + "hf_dataset_name must be provided in the dataset configuration." + ) + data = load_dataset(hf_dataset_name, split=split) + data = data.map( + lambda samples: tokenizer(samples[tokenization_field]), + batched=True, + ) + return data + elif dataset_source == "local": + if not extension_type: + logger.error( + "extension_type must be provided in the dataset configuration." + ) + raise ValueError( + "extension_type must be provided in the dataset configuration." + ) + if not data_files: + logger.error( + "data_files must be provided in the dataset configuration." + ) + raise ValueError( + "data_files must be provided in the dataset configuration." + ) + data = load_dataset( + extension_type, data_files=data_files, split=split + ) + data = data.map( + lambda samples: tokenizer(samples[tokenization_field]), + batched=True, + ) + return data + + def fine_tuning(self, request_data: Optional[Dict[str, Any]]): + with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Model not initialized"} + + tokenizer = AutoTokenizer.from_pretrained(self.model_name) + if tokenizer.pad_token is None: + if tokenizer.eos_token: + tokenizer.add_special_tokens({"pad_token": tokenizer.eos_token}) + else: + logger.warning( + "pad_token is not set and eos_token is not available; training may fail." + ) + dataset_config = request_data.get("dataset_config") + try: + dataset = self._load_dataset(dataset_config, tokenizer) + except ValueError as e: + logger.error(f"Failed to load dataset: {e}") + return {"error": str(e)} + + lora_config = request_data.get("lora_config") + try: + lora_config = LoraConfig(**lora_config) + except TypeError as e: + logger.error(f"Failed to load lora_config: {e}") + raise e + peft_model = get_peft_model(self.model, lora_config) + + training_config = request_data.get("training_config") + storage_path = os.getenv("STORAGE_PATH", "./models") + output_dir = request_data.get( + "output_dir", f"ft_{self.model_name}_adapter" + ) + lora_save_path = os.path.join( + storage_path, + "transformers", + output_dir, + ) + try: + training_args = TrainingArguments( + output_dir=lora_save_path, **training_config + ) + except TypeError as e: + logger.error(f"Failed to load training_config: {e}") + raise e + trainer = Trainer( + model=peft_model, + args=training_args, + train_dataset=dataset, + data_collator=transformers.DataCollatorForLanguageModeling( + tokenizer, mlm=False + ), + ) + trainer.train() + + save_lora(peft_model, lora_save_path) + logger.info( + f"Fine-tuning completed. LoRA adapter and config saved to {lora_save_path}" + ) + + response = { + "model": self.model_name, + "lora_save_path": lora_save_path, + } + + return response + + def load_lora_adapter(self, lora_name: str, lora_path: str): + with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Model not initialized"} + + if ( + hasattr(self.model, "peft_config") + and lora_name in self.model.peft_config + ): + logger.info(f"LoRA adapter {lora_name} already loaded") + return + + lora_path = os.path.join("transformers", lora_path) + storage_path = os.getenv("STORAGE_PATH", "./models") + device_map = self.backend_config.get("device_map", "auto") + torch_dtype = self.backend_config.get("torch_dtype", torch.float16) + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype is None: + logger.warning( + f"Invalid torch_dtype: {torch_dtype}. Using torch.float16" + ) + torch_dtype = torch.float16 + self.model = load_lora( + self.model, + lora_name, + lora_path, + device_map=device_map, + storage_path=storage_path, + torch_dtype=torch_dtype, + ) + logger.info(f"Loaded LoRA adapter {lora_name} from {lora_path}") + + def shutdown(self): + """Abort all requests and shutdown the backend.""" + with self.status_lock: + if self.status == BackendStatus.DELETING: + return + self.status = BackendStatus.DELETING + if self.inf_status: + self.inf_status.status = BackendStatus.DELETING + + while self.inf_status and len(self.inf_status.get()) > 0: + logger.info("Waiting for all requests to finish") + time.sleep(1) + + if self.model is not None: + del self.model + + def stop(self) -> None: + """Wait for all requests to finish and shutdown the backend.""" + with self.status_lock: + if self.status.value >= BackendStatus.STOPPING.value: + return + self.status = BackendStatus.STOPPING + while self.inf_status and len(self.inf_status.get()) > 0: + logger.info("Waiting for all requests to finish") + time.sleep(1) + logger.info("All requests finished. Shutting down the backend.") + self.shutdown() + + def get_current_tokens(self) -> List[List[int]]: + """Return a list of all ongoing request tokens.""" + with self.status_lock: + if self.status != BackendStatus.RUNNING: + return [] + + status = self.inf_status.get() + logger.info(f"Current tokens: {status}") + return status + + def resume_kv_cache(self, request_datas): + logger.info(f"Resuming cache for {request_datas}") + with torch.no_grad(): + device = self.model.device + input_ids = torch.tensor(request_datas).to(device) + logger.info(input_ids) + output = self.model.generate( + input_ids, + past_key_values=self.past_key_values, + max_new_tokens=1, + return_dict_in_generate=True, + return_legacy_cache=True, + ) + self.past_key_values = output.past_key_values + self.current_tokens = output.sequences + logger.info(f"Resumed {len(self.past_key_values[0][0][0][0])} tokens") + + def resume_generate( + self, request_data: Optional[Dict[str, Any]], current_output + ): + with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Model not initialized"} + + assert self.model is not None + + model_name = request_data.get("model", "dummy-model") + messages = request_data.get("messages", []) + temperature = request_data.get("temperature", 0.7) + max_tokens = request_data.get("max_tokens", 10) + + # Combine messages to form the prompt + try: + prompt = self.tokenizer.apply_chat_template( + messages, add_generation_prompt=True, tokenize=False + ) + except Exception as e: + prompt = "\n".join( + f"{message['role'].capitalize()}: {message['content']}" + for message in messages + ) + + if not prompt: + return {"error": "Missing prompt in request data"} + + inputs = self._tokenize(prompt) + prompt_tokens = inputs.input_ids.shape[1] + + # Generate response + try: + with torch.no_grad(): + device = self.model.device + current_output = torch.tensor(current_output).to(device) + if len(current_output[0]) < len(self.current_tokens[0]): + current_output = self.current_tokens + outputs = self.model.generate( + current_output, + past_key_values=self.past_key_values, + max_new_tokens=max_tokens, + temperature=temperature, + streamer=self.inf_status, + ) + except DeletingException: + logger.error("Backend is shutting down. Aborting request") + raise DeletingException("Backend is shutting down") + except Exception as e: + logger.error(f"Failed to generate response: {e}") + raise e + else: + output_text = self.tokenizer.decode( + outputs[0][prompt_tokens:], skip_special_tokens=True + ) + total_tokens = len(outputs[0]) + completion_tokens = total_tokens - prompt_tokens + # FIXME: consider corner case when max_tokens is reached + finish_reason = ( + "stop" if completion_tokens < max_tokens else "length" + ) + + # Generate response compatible with OpenAI's API + response = { + "id": f"chatcmpl-{uuid.uuid4()}", + "object": "chat.completion", + "created": int(time.time()), + "model": model_name, + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": output_text, + }, + "logprobs": None, + "finish_reason": finish_reason, + } + ], + "usage": { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, + }, + } + + self.inf_status.delete() + + return response diff --git a/sllm/serve/backends/vllm_backend.py b/sllm/serve/backends/vllm_backend.py new file mode 100644 index 0000000..df304d8 --- /dev/null +++ b/sllm/serve/backends/vllm_backend.py @@ -0,0 +1,367 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +import gc +import inspect +import logging +import os +import time +import uuid +from dataclasses import fields +from typing import Any, Dict, List, Optional, Sequence, Union, cast + +import torch +from vllm import ( + AsyncEngineArgs, + AsyncLLMEngine, + EmbeddingRequestOutput, + PoolingParams, + PromptType, + RequestOutput, + SamplingParams, +) +from vllm.inputs import TokensPrompt +from vllm.utils import Counter + +from sllm.serve.backends.backend_utils import ( + BackendStatus, + SllmBackend, +) + +logger = logging.getLogger("ray") + + +def process_output(output: RequestOutput, model_name: str) -> Dict[str, Any]: + choices: List[Dict[str, Any]] = [ + { + "index": idx, + "message": { + "role": "assistant", + "content": result.text, + }, + "logprobs": result.logprobs, + "finish_reason": result.finish_reason, + } + for idx, result in enumerate(output.outputs) + ] + + api_response = { + "id": output.request_id, + "object": "chat.completion", + "created": ( + int(time.time()) + if output.metrics is None + else output.metrics.arrival_time + ), + "model": model_name, + "choices": choices, + "usage": { + "prompt_tokens": len(output.prompt_token_ids), + "completion_tokens": sum( + len(result.token_ids) for result in output.outputs + ), + "total_tokens": len(output.prompt_token_ids) + + sum(len(result.token_ids) for result in output.outputs), + }, + } + return api_response + + +def process_embedding_output( + outputs: List[EmbeddingRequestOutput], model_name: str +) -> Dict[str, Any]: + valid_outputs = [output for output in outputs if output is not None] + query_tokens = sum(len(output.prompt_token_ids) for output in valid_outputs) + api_response = { + "object": "list", + "data": [ + { + "object": "embedding", + "index": i, + "embedding": output.outputs.embedding, + } + for i, output in enumerate(outputs) + ], + "model": model_name, + "usage": { + "query_tokens": query_tokens, + "total_tokens": query_tokens, + }, + } + return api_response + + +class LLMEngineStatusDict: + def __init__(self): + self.status_dict: Dict[str, Union[RequestOutput, str]] = {} + self.lock = asyncio.Lock() + + async def update_status( + self, request_id: str, request_output: Union[RequestOutput, str] + ): + async with self.lock: + self.status_dict[request_id] = request_output + + async def delete_request(self, request_id: str): + async with self.lock: + del self.status_dict[request_id] + + async def return_all_results(self) -> List[Union[RequestOutput, str]]: + async with self.lock: + return list(self.status_dict.values()) + + async def return_all_request_ids(self) -> List[str]: + async with self.lock: + return list(self.status_dict.keys()) + + async def request_count(self) -> int: + async with self.lock: + return len(self.status_dict) + + +# Note the GPU resource will be decided when the backend is created +class VllmBackend(SllmBackend): + # This class implements every method in vllm.entrypoints.openai.api_server + # https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/api_server.py + # except that we use ray.remote instead of @app and we also add a few new methods: + # - stop: stops every ongoing request and then stops the backend + # - get_current_tokens: returns a list of all ongoing request tokens + # - resume_kv_cache: resumes the key-value cache for the given requests + def __init__( + self, model: str, backend_config: Optional[Dict[str, Any]] = None + ) -> None: + if backend_config is None: + raise ValueError("Backend config is missing") + + self.status: BackendStatus = BackendStatus.UNINITIALIZED + self.status_lock = asyncio.Lock() + self.backend_config = backend_config + self.request_trace = LLMEngineStatusDict() + # if trace_debug is True, request trace will not be deleted after completion + self.trace_debug = backend_config.get("trace_debug", False) + self.enforce_eager = backend_config.get("enforce_eager", False) + self.enable_prefix_caching = backend_config.get( + "enable_prefix_caching", True + ) + self.task = backend_config.get("task", "auto") + + async_engine_fields = {f.name for f in fields(AsyncEngineArgs)} + filtered_engine_config = { + k: v for k, v in backend_config.items() if k in async_engine_fields + } + + load_format = backend_config.get("load_format") + torch_dtype = backend_config.get("torch_dtype") + if torch_dtype is not None: + filtered_engine_config["dtype"] = torch_dtype + + if load_format is not None: + filtered_engine_config["load_format"] = load_format + filtered_engine_config["model"] = backend_config.get( + "pretrained_model_name_or_path" + ) + else: + storage_path = os.getenv("STORAGE_PATH", "./models") + model_path = os.path.join(storage_path, "vllm", model) + filtered_engine_config["model"] = model_path + filtered_engine_config["load_format"] = "serverless_llm" + + # NOTE: Automatic enable prefix cachinging + filtered_engine_config["enforce_eager"] = self.enforce_eager + filtered_engine_config["enable_prefix_caching"] = ( + self.enable_prefix_caching + ) + filtered_engine_config["task"] = self.task + + logger.info( + f"Creating new VLLM engine with config: {filtered_engine_config}" + ) + + self.engine_args = AsyncEngineArgs(**filtered_engine_config) + + self.engine = None + + async def init_backend(self) -> None: + async with self.status_lock: + if self.status != BackendStatus.UNINITIALIZED: + return + self.engine = AsyncLLMEngine.from_engine_args(self.engine_args) + self.status = BackendStatus.RUNNING + + async def generate(self, request_data: Dict[str, Any]): + async with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Engine is not running"} + + assert self.engine is not None + + if request_data is None: + return {"error": "Request data is missing"} + + model_name: str = request_data.pop("model", "vllm-model") + messages: Dict[Dict[str, str], str] = request_data.pop("messages", []) + construct_prompt: str = "\n".join( + [ + f"{message['role']}: {message['content']}" + for message in messages + if "content" in message + ] + ) + + # If prompt is not provided, construct it from messages + inputs: Union[str, TokensPrompt] = request_data.pop( + "prompt", construct_prompt + ) + if request_data.get("input_tokens") is not None: + inputs = TokensPrompt( + prompt_token_ids=request_data.pop("input_tokens"), + ) + + request_id: str = request_data.pop( + "request_id", f"chatcmpl-{uuid.uuid4()}" + ) + + try: + sampling_params = SamplingParams(**request_data) + except Exception as e: + return {"error": f"Invalid sampling parameters: {e}"} + + results_generator = self.engine.generate( + inputs, sampling_params, request_id + ) + + # TODO stream results + + # Non-stream case + final_output = None + async for response_output in results_generator: + final_output = response_output + await self.request_trace.update_status(request_id, response_output) + + assert final_output is not None + + if not self.trace_debug: + await self.request_trace.delete_request(request_id) + + return process_output(final_output, model_name) + + async def shutdown(self): + """Abort all requests and shutdown the backend.""" + async with self.status_lock: + if self.status == BackendStatus.DELETING: + return + self.status = BackendStatus.DELETING + + # Abort all requests + requests = await self.request_trace.return_all_request_ids() + tasks = [self.engine.abort(request_id) for request_id in requests] + await asyncio.gather(*tasks) + if hasattr(self, "engine"): + del self.engine + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + async def stop(self) -> None: + """Wait for all requests to finish and shutdown the backend.""" + async with self.status_lock: + if self.status.value >= BackendStatus.STOPPING.value: + return + self.status = BackendStatus.STOPPING + while await self.request_trace.request_count() > 0: + logger.info("Waiting for all requests to finish") + await asyncio.sleep(1) + logger.info("All requests finished. Shutting down the backend.") + await self.shutdown() + + async def get_current_tokens(self) -> List[List[int]]: + """Return a list of all ongoing request tokens.""" + async with self.status_lock: + if self.status != BackendStatus.RUNNING: + return [] + results = await self.request_trace.return_all_results() + ongoing_results: List[RequestOutput] = [ + result for result in results if isinstance(result, RequestOutput) + ] + tokens: List[List[int]] = [ + result.prompt_token_ids + result.outputs[0].token_ids + for result in ongoing_results + ] + return tokens + + async def resume_kv_cache(self, request_datas: List[List[int]]) -> None: + async with self.status_lock: + if self.status != BackendStatus.RUNNING: + return + constructed_inputs = [ + { + "input_tokens": request_data, + "max_tokens": 1, + } + for request_data in request_datas + ] + tasks = [self.generate(inputs) for inputs in constructed_inputs] + await asyncio.gather(*tasks) + + async def encode(self, request_data: Dict[str, Any]): + async with self.status_lock: + if self.status != BackendStatus.RUNNING: + return {"error": "Engine is not running"} + + assert self.engine is not None + + if not request_data: + return {"error": "Request data is missing"} + + request_counter: Counter = Counter() + pooling_params: PoolingParams = PoolingParams() + model_name = request_data.get("model", "vllm-model") + query = request_data.get("input", []) + + if not query: + return {"error": "No inputs provided"} + + inputs = cast(Union[PromptType, Sequence[PromptType]], query) + + async def process_input(input_data) -> List[EmbeddingRequestOutput]: + request_id = str(next(request_counter)) + res = self.engine.encode(input_data, pooling_params, request_id) + return [result async for result in res] + + raw_outputs = await asyncio.gather( + *[process_input(input_data) for input_data in inputs], + return_exceptions=True, + ) + + valid_outputs = [] + for output in raw_outputs: + if isinstance(output, Exception): + logger.error(f"Error encountered: {output}") + else: + valid_outputs.extend(output) + + if not valid_outputs: + return {"error": "All inputs failed"} + + return process_embedding_output(valid_outputs, model_name) + + async def fine_tuning(self, request_data: Dict[str, Any]): + raise NotImplementedError( + "Fine-tuning is not supported in this backend" + ) diff --git a/sllm/serve/commands/__init__.py b/sllm/serve/commands/__init__.py new file mode 100644 index 0000000..d925a19 --- /dev/null +++ b/sllm/serve/commands/__init__.py @@ -0,0 +1,17 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # diff --git a/sllm/serve/commands/serve/__init__.py b/sllm/serve/commands/serve/__init__.py new file mode 100644 index 0000000..d925a19 --- /dev/null +++ b/sllm/serve/commands/serve/__init__.py @@ -0,0 +1,17 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # diff --git a/sllm/serve/commands/serve/sllm_serve.py b/sllm/serve/commands/serve/sllm_serve.py new file mode 100644 index 0000000..306ea93 --- /dev/null +++ b/sllm/serve/commands/serve/sllm_serve.py @@ -0,0 +1,84 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import argparse +import json +import re +import sys + +import ray +import uvicorn + +from sllm.serve.app_lib import create_app +from sllm.serve.controller import SllmController +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +def main(): + parser = argparse.ArgumentParser( + description="ServerlessLLM CLI for model management." + ) + subparsers = parser.add_subparsers(dest="command") + + start_parser = subparsers.add_parser("start", help="Start the Sllm server.") + start_parser.add_argument( + "--host", + default="0.0.0.0", + type=str, + help="Host IP to run the server on.", + ) + start_parser.add_argument( + "--port", default=8343, type=int, help="Port to run the server on." + ) + start_parser.add_argument( + "--enable-storage-aware", + action="store_true", + help="Enable storage-aware scheduling.", + ) + start_parser.add_argument( + "--enable-migration", + action="store_true", + help="Enable live migration of model instances.", + ) + args = parser.parse_args() + + try: + if args.command == "start": + app = create_app() + controller_cls = ray.remote(SllmController) + controller = controller_cls.options( + name="controller", num_cpus=1, resources={"control_node": 0.1} + ).remote( + { + "enable_storage_aware": args.enable_storage_aware, + "enable_migration": args.enable_migration, + } + ) + ray.get(controller.start.remote()) + + uvicorn.run(app, host=args.host, port=args.port) + else: + parser.print_help() + sys.exit(1) + except KeyboardInterrupt: + ray.get(controller.shutdown.remote()) + + +if __name__ == "__main__": + main() diff --git a/sllm/serve/controller.py b/sllm/serve/controller.py new file mode 100644 index 0000000..b2fd3f0 --- /dev/null +++ b/sllm/serve/controller.py @@ -0,0 +1,326 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +import datetime +import os +from typing import List, Mapping, Optional + +import ray + +from sllm.serve.logger import init_logger +from sllm.serve.routers import MigrationRouter, RoundRobinRouter +from sllm.serve.schedulers import FcfsScheduler, StorageAwareScheduler +from sllm.serve.store_manager import StoreManager + + +class SllmControllerException(Exception): + def __init__(self, message, method): + real_message = f"[{method}]: {message}" + super().__init__(real_message) + + +logger = init_logger(__name__) + + +class SllmController: + def __init__(self, config: Optional[Mapping] = None): + self.config = config + + self.running_lock = asyncio.Lock() + self.running = False + + self.metadata_lock = asyncio.Lock() + self.request_routers = {} + # Register model info + self.registered_models = {} + + async def start(self): + async with self.running_lock: + if self.running: + logger.error("Controller already started") + raise RuntimeError("Controller already started") + self.running = True + + logger.info("Starting store manager") + enable_storage_aware = self.config.get("enable_storage_aware", False) + ray_manager_cls = ray.remote(StoreManager) + self.store_manager = ray_manager_cls.options( + name="store_manager", + resources={"control_node": 0.1}, + ).remote() + await self.store_manager.initialize_cluster.remote() + + logger.info("Starting scheduler") + if enable_storage_aware: + ray_scheduler_cls = ray.remote(StorageAwareScheduler) + else: + ray_scheduler_cls = ray.remote(FcfsScheduler) + + enable_migration = self.config.get("enable_migration", False) + if enable_migration: + self.router_cls = ray.remote(MigrationRouter) + else: + self.router_cls = ray.remote(RoundRobinRouter) + + self.scheduler = ray_scheduler_cls.options( + name="model_loading_scheduler", resources={"control_node": 0.1} + ).remote( + scheduler_config={ + "enable_migration": enable_migration, + } + ) + self.scheduler.start.remote() + + async def register(self, model_config): + if not self.running: + logger.error("Controller not running") + return + model_name = model_config.get("model") + backend = model_config.get("backend", None) + if backend is None: + logger.error(f"Backend not specified for model {model_name}") + return + backend_config = model_config.get("backend_config", {}) + router_config = model_config.get("router_config", {}) + auto_scaling_config = model_config.get("auto_scaling_config", None) + enable_lora = backend_config.get("enable_lora", False) + lora_adapters = backend_config.get("lora_adapters", {}) + async with self.metadata_lock: + if model_name in self.registered_models: + logger.info(f"Model {model_name} already registered") + if lora_adapters is not None: + for ( + lora_adapter_name, + lora_adapter_path, + ) in lora_adapters.items(): + await self.store_manager.register_lora_adapter.remote( + model_name, + lora_adapter_name, + lora_adapter_path, + backend_config, + ) + request_router = self.request_routers[model_name] + await request_router.update.remote( + lora_adapters=lora_adapters + ) + return + + logger.info(f"Registering new model {model_name}") + try: + await self.store_manager.register.remote(model_config) + for lora_adapter_name, lora_adapter_path in lora_adapters.items(): + await self.store_manager.register_lora_adapter.remote( + model_name, + lora_adapter_name, + lora_adapter_path, + backend_config, + ) + except RuntimeError as e: + error_message = e.args[0] + raise RuntimeError(f"{error_message}") + # TODO: put resource requirements in model_config + resource_requirements = { + "num_cpus": 1, + "num_gpus": model_config.get("num_gpus", 0), + } + request_router = self.router_cls.options( + name=model_name, + namespace="models", + num_cpus=1, + resources={"control_node": 0.1}, + ).remote( + model_name, + resource_requirements, + backend, + backend_config, + router_config, + enable_lora, + lora_adapters, + ) + + async with self.metadata_lock: + if model_name in self.request_routers: + logger.error(f"Model {model_name} already registered") + return + + request_router.start.remote(auto_scaling_config) + + logger.info(f"Model {model_name} registered") + + # Mark model as registered only after model registered successfully + async with self.metadata_lock: + self.registered_models[model_name] = model_config + self.request_routers[model_name] = request_router + + async def update(self, model_name: str, model_config: Mapping): + async with self.metadata_lock: + if ( + model_name not in self.registered_models + or model_name not in self.request_routers + ): + logger.error(f"Model {model_name} not found") + raise ValueError( + f"Model {model_name} not found, please register first" + ) + + # update auto-scaling config + auto_scaling_config = model_config.get("auto_scaling_config", None) + logger.info(f"Try to update the model {model_name} config") + if auto_scaling_config is not None: + async with self.metadata_lock: + self.registered_models[model_name]["auto_scaling_config"] = ( + auto_scaling_config + ) + request_router = self.request_routers[model_name] + await request_router.update.remote(auto_scaling_config) + # TODO: update other config (if possible) + + async def exists(self, model_name: str): + async with self.metadata_lock: + return model_name in self.registered_models + + async def delete( + self, model_name: str, lora_adapters: Optional[List[str]] = None + ): + router = None + async with self.metadata_lock: + if model_name not in self.request_routers: + logger.error(f"Model {model_name} not found") + return + if lora_adapters is not None: + await self.request_routers[model_name].delete_adapters.remote( + lora_adapters + ) + return + router = self.request_routers.pop(model_name) + self.registered_models.pop(model_name) + + # if router is not None: + # deleted_instance_id = await router.shutdown.remote() + del router + + async def get_models(self): + async with self.metadata_lock: + return self.registered_models + + async def status(self): + """ + Returns the status of all registered models in OpenAI-compliant format. + """ + async with self.metadata_lock: + models = [] + model_folder = os.getenv("MODEL_FOLDER") + for model_name, config in self.registered_models.items(): + # Extract or calculate relevant fields + model_path = config.get("_name_or_path", None) + created_time = ( + next( + ( + int(os.path.getctime(os.path.abspath(dirpath))) + for dirpath, _, _ in os.walk(model_folder) + if dirpath.endswith(model_path) + ), + None, + ) + if model_path + else None + ) + + created_time = config.get("created", None) + allow_create_engine = config.get("allow_create_engine", None) + allow_sampling = config.get("allow_sampling", None) + allow_logprobs = config.get("allow_logprobs", None) + allow_search_indices = config.get("allow_search_indices", None) + allow_view = config.get("allow_view", None) + allow_fine_tuning = config.get("allow_fine_tuning", None) + organization = config.get("organization", "*") + group = config.get("group", None) + is_blocking = config.get("is_blocking", None) + + max_model_len = config.get("max_position_embeddings", None) + + model_permission_id = f"modelperm-{model_name}" + permission = [ + { + "id": model_permission_id, + "object": "model_permission", + "created": created_time + if created_time is not None + else None, + "allow_create_engine": allow_create_engine + if allow_create_engine is not None + else None, + "allow_sampling": allow_sampling + if allow_sampling is not None + else None, + "allow_logprobs": allow_logprobs + if allow_logprobs is not None + else None, + "allow_search_indices": allow_search_indices + if allow_search_indices is not None + else None, + "allow_view": allow_view + if allow_view is not None + else None, + "allow_fine_tuning": allow_fine_tuning + if allow_fine_tuning is not None + else None, + "organization": organization + if organization is not None + else None, + "group": group if group is not None else None, + "is_blocking": is_blocking + if is_blocking is not None + else None, + } + ] + + # Build the model metadata entry + model_metadata = { + "id": model_name, + "object": "model", + "created": created_time + if created_time is not None + else None, + "owned_by": "sllm", + "root": model_name, + "parent": None, + "max_model_len": max_model_len + if max_model_len is not None + else None, + "permission": permission, + } + models.append(model_metadata) + + return {"object": "list", "models": models} + + async def shutdown(self): + # stop the control loop + async with self.running_lock: + if not self.running: + logger.error("Controller not running") + raise RuntimeError("Controller not running") + self.running = False + + # delete all models + async with self.metadata_lock: + delete_tasks = [ + self._delete(model_name) + for model_name in self.request_routers.keys() + ] + await asyncio.gather(*delete_tasks) diff --git a/sllm/serve/hardware_info_collector.py b/sllm/serve/hardware_info_collector.py new file mode 100644 index 0000000..67e67d5 --- /dev/null +++ b/sllm/serve/hardware_info_collector.py @@ -0,0 +1,169 @@ +import json +import os +import tempfile +import time +from typing import Dict, Set + +import GPUtil +import psutil +import ray +import speedtest +from tqdm import tqdm + +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +@ray.remote +def collect_all_info(): + """ + Collect all hardware information and return as a dictionary. + """ + hardware_info = {} + # TODO: collect pcie_bandwidth + hardware_info["pcie_bandwidth"] = 25000000000 # 25 GB/s, PCIe 4.0 x16 + hardware_info["disk_size"] = get_disk_info() + write_bw, read_bw = benchmark_disk_bandwidth() + hardware_info["disk_bandwidth"] = ( + write_bw + read_bw + ) / 2 # Average of write and read bandwidth + hardware_info["disk_write_bandwidth"] = write_bw + hardware_info["disk_read_bandwidth"] = read_bw + # Temporarily disabled due to network issues + # upload_bw, download_bw = get_network_bandwidth() + # hardware_info["network_upload_bandwidth"] = upload_bw + # hardware_info["network_download_bandwidth"] = download_bw + hardware_info["GPUs_info"] = get_gpu_info() + print(hardware_info) + return hardware_info + + +def get_disk_info(): + """ + Retrieves total size of the primary disk partition. + Returns: + str: Disk size in B + """ + try: + partitions = psutil.disk_partitions() + # Assume primary partition is mounted at '/' + for partition in partitions: + if partition.mountpoint == "/": + usage = psutil.disk_usage(partition.mountpoint) + return usage.total + # If '/' not found, return the first partition's size + if partitions: + usage = psutil.disk_usage(partitions[0].mountpoint) + return usage.total + return "N/A" + except Exception as e: + logger.error(f"Failed to retrieve disk info: {e}") + return "N/A" + + +def benchmark_disk_bandwidth(num_iterations=5): + """ + Measures disk read and write bandwidth by writing and reading a temporary file multiple times. + Args: + num_iterations (int): Number of iterations to run. + Returns: + tuple: Average write bandwidth (str) and average read bandwidth (str) in B/s + """ + write_results = [] + read_results = [] + try: + temp_dir = tempfile.gettempdir() + temp_file = os.path.join(temp_dir, "disk_bandwidth_test.tmp") + size = 100 * 1024 * 1024 # 100 MB + + for _ in range(num_iterations): + # Write Test + start_time = time.time() + with open(temp_file, "wb") as f: + f.write(os.urandom(size)) + end_time = time.time() + write_time = end_time - start_time + write_bandwidth_b_s = size / write_time + write_results.append(write_bandwidth_b_s) + + # Read Test + start_time = time.time() + with open(temp_file, "rb") as f: + _ = f.read() + end_time = time.time() + read_time = end_time - start_time + read_bandwidth_b_s = size / read_time + read_results.append(read_bandwidth_b_s) + + # Clean up + os.remove(temp_file) + + # Calculate averages + average_write = sum(write_results) / len(write_results) + average_read = sum(read_results) / len(read_results) + return average_write, average_read + except Exception as e: + logger.error(f"Disk bandwidth benchmark failed: {e}") + return "N/A", "N/A" + + +def get_network_bandwidth(num_iterations=1): + """ + Measures network upload and download bandwidth by performing speed tests multiple times. + Args: + num_iterations (int): Number of iterations to run. + Returns: + tuple: Average upload bandwidth (str) and average download bandwidth (str) in bps + """ + upload_results = [] + download_results = [] + try: + for _ in tqdm(range(num_iterations), desc="Testing network bandwidth"): + st = speedtest.Speedtest() + st.get_best_server() + download_speed_mbps = st.download() + upload_speed_mbps = st.upload(pre_allocate=False) + + # Convert to bps + download_results.append(download_speed_mbps * (1024**2) / 8) + upload_results.append(upload_speed_mbps * (1024**2) / 8) + + # Wait to avoid server overload + time.sleep(5) + # Calculate averages + average_upload = sum(upload_results) / len(upload_results) + average_download = sum(download_results) / len(download_results) + return average_upload, average_download + + except Exception as e: + logger.error(f"Network bandwidth test failed: {e}") + return "N/A", "N/A" + + +def get_gpu_info(): + """ + Retrieves information about each NVIDIA GPU. + Returns: + dict: Dictionary containing GPU information. + """ + gpus_info = {} + try: + gpus = GPUtil.getGPUs() + if not gpus: + gpus_info = "No GPUs found." + else: + for gpu in gpus: + gpu_id = gpu.id + 1 # To start GPU numbering from 1 + gpus_info[gpu_id] = { + # "info": gpu.__dict__, + "Name": gpu.name, + "Load": f"{gpu.load * 100:.1f}%", + "Free_Memory": f"{gpu.memoryFree}MB", + "Used_Memory": f"{gpu.memoryUsed}MB", + "Total_Memory": f"{gpu.memoryTotal}MB", + } + except Exception as e: + logger.error(f"Failed to retrieve GPU information: {e}") + gpus_info = "N/A" + return gpus_info diff --git a/sllm/serve/inference_instance.py b/sllm/serve/inference_instance.py new file mode 100644 index 0000000..f32fdfa --- /dev/null +++ b/sllm/serve/inference_instance.py @@ -0,0 +1,54 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # + +import ray + +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +@ray.remote +def start_instance( + instance_id, backend, model_name, backend_config, startup_config +): + logger.info(f"Starting instance {instance_id} with backend {backend}") + if backend == "vllm": + from sllm.serve.backends import VllmBackend + + model_backend_cls = VllmBackend + elif backend == "dummy": + from sllm.serve.backends import DummyBackend + + model_backend_cls = DummyBackend + elif backend == "transformers": + from sllm.serve.backends import TransformersBackend + + model_backend_cls = TransformersBackend + else: + logger.error(f"Unknown backend: {backend}") + raise ValueError(f"Unknown backend: {backend}") + + model_actor_cls = ray.remote(model_backend_cls) + + return model_actor_cls.options( + name=instance_id, + **startup_config, + max_concurrency=10, + lifetime="detached", + ).remote(model_name, backend_config) diff --git a/sllm/serve/logger.py b/sllm/serve/logger.py new file mode 100644 index 0000000..61d31aa --- /dev/null +++ b/sllm/serve/logger.py @@ -0,0 +1,81 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # + +# Adapted from https://github.com/vllm-project/vllm/blob/0ce0539d4750f9ebcd9b19d7085ca3b934b9ec67/vllm/logger.py +"""Logging configuration for sllm.""" + +import logging +import os +import sys + +_FORMAT = "%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s" +_DATE_FORMAT = "%m-%d %H:%M:%S" + + +class NewLineFormatter(logging.Formatter): + """Adds logging prefix to newlines to align multi-line messages.""" + + def __init__(self, fmt, datefmt=None): + logging.Formatter.__init__(self, fmt, datefmt) + + def format(self, record): + msg = logging.Formatter.format(self, record) + if record.message != "": + parts = msg.split(record.message) + msg = msg.replace("\n", "\r\n" + parts[0]) + return msg + + +_root_logger = logging.getLogger("sllm") +_default_handler = None + + +def _setup_logger(): + _root_logger.setLevel(logging.DEBUG) + global _default_handler + + _default_handler = logging.StreamHandler(sys.stdout) + _default_handler.flush = sys.stdout.flush # type: ignore + _default_handler.setLevel(logging.DEBUG) + _root_logger.addHandler(_default_handler) + + fmt = NewLineFormatter(_FORMAT, datefmt=_DATE_FORMAT) + _default_handler.setFormatter(fmt) + # Setting this will avoid the message + # being propagated to the parent logger. + _root_logger.propagate = False + + +_setup_logger() + + +def init_logger(name: str): + # Use the same settings as above for root logger + logger = logging.getLogger(name) + + log_level = os.getenv("LOG_LEVEL", "DEBUG").upper() + logger.setLevel(log_level) + + # Ensure the handler's level matches the logger's level + if _default_handler: + _default_handler.setLevel(log_level) + + logger.addHandler(_default_handler) + logger.propagate = False + + return logger diff --git a/sllm/serve/model_downloader.py b/sllm/serve/model_downloader.py new file mode 100644 index 0000000..c17c2d3 --- /dev/null +++ b/sllm/serve/model_downloader.py @@ -0,0 +1,246 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import importlib +import logging +import os +import shutil +from typing import Optional + +import ray +from torch import nn +from transformers import AutoTokenizer + +logger = logging.getLogger("ray") + + +# def get_directory_size(directory): +# total_size = 0 +# for dirpath, dirnames, filenames in os.walk(directory): +# for filename in filenames: +# file_path = os.path.join(dirpath, filename) +# if not os.path.islink(file_path): +# total_size += os.path.getsize(file_path) +# return total_size + + +@ray.remote(num_cpus=1) +def download_transformers_model( + model_name: str, + pretrained_model_name_or_path: str, + torch_dtype: str, + hf_model_class: str, +) -> bool: + storage_path = os.getenv("STORAGE_PATH", "./models") + model_path = os.path.join(storage_path, "transformers", model_name) + tokenizer_path = os.path.join( + storage_path, "transformers", model_name, "tokenizer" + ) + + if os.path.exists(model_path): + logger.info(f"{model_path} already exists") + return True + + import torch + + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype is None: + raise ValueError(f"Invalid torch_dtype: {torch_dtype}") + + logger.info(f"Downloading {model_path}") + + module = importlib.import_module("transformers") + hf_model_cls = getattr(module, hf_model_class) + model = hf_model_cls.from_pretrained( + pretrained_model_name_or_path, + torch_dtype=torch_dtype, + trust_remote_code=True, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name) + + from sllm_store.transformers import save_model + + logger.info(f"Saving {model_path}") + try: + save_model(model, model_path) + tokenizer.save_pretrained(tokenizer_path) + except Exception as e: + logger.error(f"Failed to save {model_path}: {e}") + # shutil.rmtree(model_path) # TODO: deal with error in save_model + raise RuntimeError( + f"Failed to save {model_name} for transformer backend: {e}" + ) + + # model_size = get_directory_size(model_path) + # logger.info(f"{model_name} (size: {model_size}) downloaded") + + return True + + +@ray.remote(num_cpus=1) +def download_lora_adapter( + base_model_name: str, + adapter_name: str, + adapter_name_or_path: str, + hf_model_class: str, + torch_dtype: str, +) -> bool: + storage_path = os.getenv("STORAGE_PATH", "./models") + adapter_path = os.path.join( + storage_path, "transformers", adapter_name_or_path + ) + + if os.path.exists(adapter_path): + logger.info(f"{adapter_path} already exists") + return True + + import torch + + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype is None: + raise ValueError(f"Invalid torch_dtype: {torch_dtype}") + + from transformers import AutoConfig + + config = AutoConfig.from_pretrained( + os.path.join(storage_path, "transformers", base_model_name), + trust_remote_code=True, + ) + config.torch_dtype = torch_dtype + module = importlib.import_module("transformers") + hf_model_cls = getattr(module, hf_model_class) + base_model = hf_model_cls.from_config( + config, + trust_remote_code=True, + ).to(config.torch_dtype) + + logger.info(f"Downloading {adapter_path}") + from peft import PeftModel + + try: + model = PeftModel.from_pretrained(base_model, adapter_name_or_path) + except Exception as e: + logger.error(f"Failed to load adapter: {e}") + raise RuntimeError(f"LoRA adapter load failed: {e}") + + from sllm_store.transformers import save_lora + + logger.info(f"Saving {adapter_path}") + try: + save_lora(model, adapter_path) + except Exception as e: + logger.error(f"Failed to save {adapter_path}: {e}") + # shutil.rmtree(model_path) # TODO: deal with error in save_model + raise RuntimeError( + f"Failed to save {adapter_name} for transformer backend: {e}" + ) + + return True + + +class VllmModelDownloader: + def __init__(self): + pass + + def download_vllm_model( + self, + model_name: str, + pretrained_model_name_or_path: str, + torch_dtype: str, + tensor_parallel_size: int = 1, + pattern: Optional[str] = None, + max_size: Optional[int] = None, + ): + import gc + from tempfile import TemporaryDirectory + + import torch + from huggingface_hub import snapshot_download + from vllm import LLM + + # set the storage path + storage_path = os.getenv("STORAGE_PATH", "./models") + model_path = os.path.join(storage_path, "vllm", model_name) + if os.path.exists(model_path): + logger.info(f"{model_path} already exists") + return + + cache_dir = TemporaryDirectory() + try: + if os.path.exists(pretrained_model_name_or_path): + input_dir = pretrained_model_name_or_path + else: + # download from huggingface + input_dir = snapshot_download( + model_name, + cache_dir=cache_dir.name, + allow_patterns=[ + "*.safetensors", + "*.bin", + "*.json", + "*.txt", + ], + ) + logger.info(f"Loading model from {input_dir}") + + # load models from the input directory + llm_writer = LLM( + model=input_dir, + download_dir=input_dir, + dtype=torch_dtype, + tensor_parallel_size=tensor_parallel_size, + num_gpu_blocks_override=1, + enforce_eager=True, + max_model_len=1, + ) + # model_executer = llm_writer.llm_engine.model_executor #V0 + model_executer = llm_writer.llm_engine.engine_core # For engine V1 + # save the models in the ServerlessLLM format + model_executer.save_serverless_llm_state( + path=model_path, pattern=pattern, max_size=max_size + ) + for file in os.listdir(input_dir): + # Copy the metadata files into the output directory + if os.path.splitext(file)[1] not in ( + ".bin", + ".pt", + ".safetensors", + ): + src_path = os.path.join(input_dir, file) + dest_path = os.path.join(model_path, file) + logger.info(src_path) + logger.info(dest_path) + if os.path.isdir(src_path): + shutil.copytree(src_path, dest_path) + else: + shutil.copy(src_path, dest_path) + del model_executer + del llm_writer + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + except Exception as e: + logger.info(f"An error occurred while saving the model: {e}") + # remove the output dir + shutil.rmtree(os.path.join(storage_path, "vllm", model_name)) + raise RuntimeError( + f"Failed to save {model_name} for vllm backend: {e}" + ) + finally: + cache_dir.cleanup() diff --git a/sllm/serve/routers/__init__.py b/sllm/serve/routers/__init__.py new file mode 100644 index 0000000..88cff91 --- /dev/null +++ b/sllm/serve/routers/__init__.py @@ -0,0 +1,22 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from .migration_router import MigrationRouter +from .roundrobin_router import RoundRobinRouter +from .router_utils import SllmRouter + +__all__ = ["SllmRouter", "RoundRobinRouter", "MigrationRouter"] diff --git a/sllm/serve/routers/migration_router.py b/sllm/serve/routers/migration_router.py new file mode 100644 index 0000000..e346a85 --- /dev/null +++ b/sllm/serve/routers/migration_router.py @@ -0,0 +1,252 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from typing import Dict + +import ray + +from sllm.serve.inference_instance import start_instance +from sllm.serve.logger import init_logger +from sllm.serve.routers.roundrobin_router import RoundRobinRouter +from sllm.serve.utils import InstanceHandle, InstanceStatus + +logger = init_logger(__name__) + + +class MigrationRouter(RoundRobinRouter): + def __init__( + self, + model_name: str, + resource_requirements: Dict[str, int], + backend: str, + backend_config: Dict, + router_config: Dict, + ) -> None: + super().__init__( + model_name, + resource_requirements, + backend, + backend_config, + router_config, + ) + self.migration_record = {} + self.migration_delta = self.router_config.get("migration_delta", 20) + + async def inference(self, request_data: dict, action: str): + async with self.running_lock: + if not self.running: + return {"error": "Instance stopped"} + + async with self.request_count_lock: + self.request_count += 1 + + instance_allocation = self.loop.create_future() + await self.request_queue.put(instance_allocation) + logger.info(f"Enqueued request for model {self.model_name}") + + instance_id = await instance_allocation + logger.info(f"{request_data}, type: {type(request_data)}") + async with self.instance_management_lock: + if instance_id not in self.ready_instances: + logger.error(f"Instance {instance_id} not found") + return {"error": "Instance not found"} + instance = self.ready_instances[instance_id] + # NOTE: `.remote(request_data)` does not work, don't know why. + # Looks like a known issue: + # https://github.com/ray-project/ray/issues/26283#issuecomment-1780691475 + if action == "generate": + result = await instance.backend_instance.generate.remote( + request_data=request_data + ) + if "preempted" in result: + logger.debug(f"Preempted request: {result}") + target_instance_id = self.migration_record.get(instance_id) + if not target_instance_id: + logger.error(f"No target instance found for {instance_id}") + return {"error": "No target instance found"} + target_instance = self.ready_instances[target_instance_id] + logger.info( + f"Resuming request on target instance: {target_instance_id}" + ) + if "max_tokens" in request_data: + request_data["max_tokens"] -= result["completed_tokens"] + result = await target_instance.backend_instance.resume_generate.remote( + request_data=request_data, + current_output=result["current_output"], + ) + + elif action == "encode": + result = await instance.backend_instance.encode.remote( + request_data=request_data + ) + else: + result = {"error": "Invalid action"} + logger.info(f"Finished processing request") + await instance.add_requests(-1) + async with self.request_count_lock: + self.request_count -= 1 + return result + + async def execute_migration_plan(self, migration_plan): + logger.info(f"Executing migration plan: {migration_plan}") + source_instance = migration_plan.source_instance + source_instance_id = source_instance.instance_id + target_node_id = migration_plan.target_node_id + # start the target_instance on the target node + startup_config = { + "num_cpus": self.resource_requirements["num_cpus"], + "num_gpus": self.resource_requirements["num_gpus"], + "resources": { + "worker_node": 0.1, + f"worker_id_{target_node_id}": 0.1, + }, + } + logger.info(f"Startup config: {startup_config}, {self.backend_config}") + + instance_id = self._new_instance_id() + logger.info( + f"Creating new instance {instance_id} for model {self.model_name}" + ) + # TODO: Add max_queue_length to target_instance + target_instance = InstanceHandle( + instance_id=instance_id, + max_queue_length=10, + num_gpu=self.resource_requirements["num_gpus"], + ) + + ret = await self.model_loading_scheduler.mark_resource.remote( + self.model_name, instance_id, target_node_id + ) + if not ret: + logger.error( + f"Failed to mark resource for instance {instance_id} for model {self.model_name}" + ) + return None + + await start_instance.options( + resources={ + "worker_node": 0.1, + f"worker_id_{target_node_id}": 0.1, + } + ).remote( + instance_id, + self.backend, + self.model_name, + self.backend_config, + startup_config, + ) + logger.info( + f"Started instance {instance_id} for model {self.model_name}" + ) + target_instance.backend_instance = ray.get_actor(instance_id) + async with target_instance.lock: + target_instance.ready = True + target_instance.node_id = target_node_id + logger.info( + f"Initialized instance {instance_id} for model {self.model_name}" + ) + await target_instance.backend_instance.init_backend.remote() + logger.info( + f"Initialized backend for instance {instance_id} for model {self.model_name}" + ) + # migrate the tokens from the source_instance (if still running) to the target_instance + if source_instance_id not in self.ready_instances: + logger.info(f"Instance {source_instance_id} not found") + target_instance.ready = False + await target_instance.backend_instance.shutdown.remote() + ray.kill(target_instance.backend_instance) + return None + source_instance = self.ready_instances[source_instance_id] + migration_iter = 0 + n_previous_tokens = 0 + while True: + logger.info(f"Migration iteration {migration_iter}") + current_tokens = ray.get( + source_instance.backend_instance.get_current_tokens.remote() + ) + if not current_tokens: + logger.info("No tokens found") + break + n_current_tokens = len(current_tokens[0]) + n_delta_tokens = n_current_tokens - n_previous_tokens + logger.info( + f"Number of tokens: {n_current_tokens}, delta: {n_delta_tokens}" + ) + n_previous_tokens = n_current_tokens + if n_delta_tokens <= self.migration_delta: + logger.info( + "Migration completed: remained " + f"{None if not current_tokens else n_delta_tokens} tokens" + ) + break + ray.get( + target_instance.backend_instance.resume_kv_cache.remote( + current_tokens + ) + ) + logger.info(f"Migration iteration {migration_iter} completed") + migration_iter += 1 + + logger.info(f"Migrated instance {source_instance_id} to {instance_id}") + async with self.instance_management_lock: + if source_instance_id not in self.ready_instances: + # source_instance has been removed + logger.error(f"Instance {instance_id} not found") + target_instance.ready = False + await target_instance.backend_instance.shutdown.remote() + ray.kill(target_instance.backend_instance) + return None + _ = self.ready_instances.pop(source_instance_id) + async with source_instance.lock: + source_instance.status = False + self.ready_instances[instance_id] = target_instance + self.migration_record[source_instance_id] = instance_id + logger.info(f"Instance {source_instance_id} removed") + await source_instance.backend_instance.shutdown.remote() + logger.info(f"Shutdown instance {source_instance_id}") + ray.kill(source_instance.backend_instance) + logger.info(f"Killed instance {source_instance_id}") + await self.model_loading_scheduler.deallocate_resource.remote( + self.model_name, source_instance_id, self.resource_requirements + ) + logger.info(f"Deallocated instance {source_instance_id}") + return instance_id + + async def get_instance_status(self, instance_id: str) -> InstanceStatus: + logger.info(f"Getting status for instance: {instance_id}") + async with self.instance_management_lock: + if instance_id not in self.ready_instances: + logger.info(f"Instance {instance_id} not found") + return None + instance = self.ready_instances[instance_id] + logger.info(f"Instance: {instance}") + instance_status = await instance.get_status() + instance_status.model_name = self.model_name + current_tokens = ( + await instance.backend_instance.get_current_tokens.remote() + ) + if current_tokens: + instance_status.num_current_tokens = len(current_tokens[0]) + else: + instance_status.num_current_tokens = 0 + logger.info(f"Instance status: {instance_status}") + return instance_status + + async def fine_tuning(self, request_data: dict): + raise NotImplementedError( + "Fine-tuning is only applicable on roundrobin router" + ) diff --git a/sllm/serve/routers/roundrobin_router.py b/sllm/serve/routers/roundrobin_router.py new file mode 100644 index 0000000..32b4908 --- /dev/null +++ b/sllm/serve/routers/roundrobin_router.py @@ -0,0 +1,459 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +import logging +import uuid +from typing import Dict, List, Optional + +import ray + +from sllm.serve.inference_instance import start_instance +from sllm.serve.logger import init_logger + +from ..utils import InstanceHandle +from .router_utils import SllmRouter + +logger = init_logger(__name__) + + +async def auto_scaler( + auto_scaling_metrics: Dict[str, int], auto_scaling_config: Dict[str, int] +) -> int: + """ + Returns desired number of instances for a model based on the auto-scaling policy + """ + + request_count = auto_scaling_metrics.get("request_count", 0) + + min_instances = auto_scaling_config.get("min_instances", 0) + max_instances = auto_scaling_config.get("max_instances", 10) + target_ongoing_requests = auto_scaling_config.get("target", 2) + + desired_instances = ( + request_count + target_ongoing_requests - 1 + ) // target_ongoing_requests + desired_instances = min( + max_instances, max(min_instances, desired_instances) + ) + + return desired_instances + + +class RoundRobinRouter(SllmRouter): + def __init__( + self, + model_name: str, + resource_requirements: Dict[str, int], + backend: str, + backend_config: Dict, + router_config: Dict, + enable_lora: bool = False, + lora_adapters: Optional[Dict[str, str]] = None, + ) -> None: + self.model_name = model_name + self.resource_requirements = resource_requirements + self.backend = backend + self.backend_config = backend_config + self.router_config = router_config + + self.loop_interval = 1 + self.loop = asyncio.get_running_loop() + self.request_queue = asyncio.Queue() # type:ignore + self.starting_instances: Dict[str, InstanceHandle] = {} # type:ignore + self.deleting_instances: Dict[str, InstanceHandle] = {} # type:ignore + self.ready_instances: Dict[str, InstanceHandle] = {} # type:ignore + self.instance_management_lock = asyncio.Lock() + + self.auto_scaling_config = {} + self.auto_scaling_lock = asyncio.Lock() + + self.request_count = 0 + self.request_count_lock = asyncio.Lock() + + self.fine_tuning_count = 0 + self.fine_tuning_count_lock = asyncio.Lock() + + self.running = False + self.running_lock = asyncio.Lock() + + self.idle_time = 0 + self.idle_time_lock = asyncio.Lock() + + self.enable_lora = enable_lora + self.loaded_lora_adapters = lora_adapters + self.lora_lock = asyncio.Lock() + + self.auto_scaler = None + logger.info(f"Created new handler for model {self.model_name}") + + async def start(self, auto_scaling_config: Dict[str, int]): + self.model_loading_scheduler = ray.get_actor("model_loading_scheduler") + async with self.auto_scaling_lock: + self.auto_scaling_config = auto_scaling_config + self.auto_scaler = asyncio.create_task(self._auto_scaler_loop()) + self.load_balancer = asyncio.create_task(self._load_balancer_loop()) + async with self.running_lock: + self.running = True + logger.info(f"Started handler for model {self.model_name}") + + async def update( + self, + auto_scaling_config: Optional[Dict[str, int]] = None, + lora_adapters: Optional[Dict[str, str]] = None, + ): + if auto_scaling_config is not None: + async with self.auto_scaling_lock: + self.auto_scaling_config = auto_scaling_config + + if lora_adapters is not None: + async with self.lora_lock: + self.loaded_lora_adapters = lora_adapters + + logger.info( + f"Model {self.model_name}'s auto scaling config updated to {auto_scaling_config}" + ) + + def _new_instance_id(self): + pattern = "{model_name}_{id}" + return pattern.format(model_name=self.model_name, id=uuid.uuid4()) + + async def inference(self, request_data: dict, action: str): + async with self.running_lock: + if not self.running: + return {"error": "Instance stopped"} + + async with self.fine_tuning_count_lock: + if self.fine_tuning_count > 0: + return { + "error": "Fine-tuning in progress, inference cannot start." + } + + async with self.request_count_lock: + self.request_count += 1 + + async with self.idle_time_lock: + self.idle_time = 0 + + instance_allocation = self.loop.create_future() + await self.request_queue.put(instance_allocation) + logger.info(f"Enqueued {action} request for model {self.model_name}") + + instance_id = await instance_allocation + logger.info(f"{request_data}, type: {type(request_data)}") + async with self.instance_management_lock: + if instance_id not in self.ready_instances: + logger.error(f"Instance {instance_id} not found") + return {"error": "Instance not found"} + instance = self.ready_instances[instance_id] + + # sanity check + if self.enable_lora and "lora_adapter_name" in request_data: + lora_adapter_name = request_data["lora_adapter_name"] + if lora_adapter_name not in self.loaded_lora_adapters: + logger.error(f"Lora adapter {lora_adapter_name} not found") + return {"error": f"Lora adapter {lora_adapter_name} not found"} + await instance.backend_instance.load_lora_adapter.remote( + lora_name=lora_adapter_name, + lora_path=self.loaded_lora_adapters[lora_adapter_name], + ) + # NOTE: `.remote(request_data)` does not work, don't know why. + # Looks like a known issue: + # https://github.com/ray-project/ray/issues/26283#issuecomment-1780691475 + if action == "generate": + result = await instance.backend_instance.generate.remote( + request_data=request_data + ) + elif action == "encode": + result = await instance.backend_instance.encode.remote( + request_data=request_data + ) + else: + result = {"error": "Invalid action"} + logger.info(f"Finished processing request") + await instance.add_requests(-1) + async with self.request_count_lock: + self.request_count -= 1 + return result + + async def fine_tuning(self, request_data: dict): + async with self.running_lock: + if not self.running: + return {"error": "Instance stopped"} + + async with self.request_count_lock: + if self.request_count > 0: + return { + "error": "Inference requests in progress, fine-tuning cannot start." + } + + async with self.fine_tuning_count_lock: + self.fine_tuning_count += 1 + + instance_allocation = self.loop.create_future() + await self.request_queue.put(instance_allocation) + logger.info(f"Enqueued fine-tuning request for model {self.model_name}") + + instance_id = await instance_allocation + logger.info(f"{request_data}, type: {type(request_data)}") + async with self.instance_management_lock: + if instance_id not in self.ready_instances: + logger.error(f"Instance {instance_id} not found") + return {"error": "Instance not found"} + instance = self.ready_instances[instance_id] + instance.concurrency = 1 + + result = await instance.backend_instance.fine_tuning.remote( + request_data=request_data + ) + + logger.info(f"Finished processing fine-tuning") + await instance.add_requests(-1) + async with self.fine_tuning_count_lock: + self.fine_tuning_count -= 1 + return result + + async def delete_adapters(self, lora_adapters: List[str]): + async with self.lora_lock: + for adapter_name in lora_adapters: + if adapter_name in self.loaded_lora_adapters: + del self.loaded_lora_adapters[adapter_name] + logger.info( + f"Deleted LoRA adapters {lora_adapters} on model {self.model_name}" + ) + + async def shutdown(self): + async with self.running_lock: + self.running = False + # stop all instances + # return all unfinished requests + while not self.request_queue.empty(): + request_data, done_event = await self.request_queue.get() + done_event.set_result({"error": "Instance cancelled"}) + + async with self.instance_management_lock: + deleted_instance_id = list(self.ready_instances.keys()) + delete_tasks = [ + self._shutdown_instance(instance_id) + for instance_id in deleted_instance_id + ] + await asyncio.gather(*delete_tasks) + + return deleted_instance_id + + async def _load_balancer_loop(self): + # this is a simple round-robin load balancer + round_robin_index = 0 + while True: + instance_allocation = await self.request_queue.get() + allocated = False + logger.info(f"A request is waiting for model {self.model_name}") + while not allocated: + # 1. get ready instances + instance_options = None + while not instance_options: + await asyncio.sleep(1) + async with self.instance_management_lock: + instance_options = list(self.ready_instances.keys()) + logger.info(f"{instance_options}") + logger.info(f"Got ready instances {instance_options}") + instance_id = instance_options[ + round_robin_index % len(instance_options) + ] + round_robin_index += 1 + async with self.instance_management_lock: + if instance_id not in self.ready_instances: + continue + instance = self.ready_instances[instance_id] + # check if the request queue reaches max length + if await instance.check_request_queue(): + allocated = await instance.add_requests(1) + if allocated: + instance_allocation.set_result(instance_id) + else: + logger.info( + f"Instance {instance_id} cannot add another request" + ) + if not allocated: + await asyncio.sleep(self.loop_interval) + + async def _auto_scaler_loop(self): + while True: + # logger.info(f"Auto-scaling for model {self.model_name}") + async with self.auto_scaling_lock: + auto_scaling_config = self.auto_scaling_config.copy() + auto_scaling_metrics = { + "request_count": self.request_count + self.fine_tuning_count + } + desired_instances = await auto_scaler( + auto_scaling_metrics, auto_scaling_config + ) + async with self.instance_management_lock: + num_running_instances = len(self.starting_instances) + len( + self.ready_instances + ) + logger.info( + f"{self.model_name}: {num_running_instances} instances," + f"need {desired_instances} instances", + ) + if desired_instances > num_running_instances: + logger.info("Creating new instance") + await self._create_instance() + elif desired_instances < num_running_instances: + keep_alive = auto_scaling_config.get("keep_alive", 0) + if self.idle_time >= keep_alive: + logger.info( + f"Stopping instance, idle_time: {self.idle_time}, keep_alive: {keep_alive}" + ) + await self._stop_instance() + async with self.idle_time_lock: + self.idle_time = 0 + else: + logger.info( + f"idle_time: {self.idle_time}, keep_alive: {keep_alive}" + ) + async with self.idle_time_lock: + self.idle_time += self.loop_interval + else: + # logger.info("No scaling needed") + pass + await asyncio.sleep(self.loop_interval) + + async def _create_instance(self): + instance_id = self._new_instance_id() + logger.info( + f"Creating new instance {instance_id} for model {self.model_name}" + ) + # get max_queue_length from auto_scaling_config + if self.auto_scaling_config.get("metric", "") == "concurrency": + max_request_length = self.auto_scaling_config.get("target", 1) + else: + max_request_length = 1 + logger.info( + f"Creating new instance {instance_id} for model {self.model_name}, max queue length is {max_request_length}" + ) + instance = InstanceHandle( + instance_id=instance_id, + max_queue_length=max_request_length, + num_gpu=self.resource_requirements["num_gpus"], + ) + async with self.instance_management_lock: + self.starting_instances[instance_id] = instance + self.loop.create_task(self._start_instance(instance_id)) + + return instance_id + + async def _start_instance(self, instance_id): + async with self.instance_management_lock: + if instance_id not in self.starting_instances: + logger.error(f"Instance {instance_id} not found") + return + instance = self.starting_instances[instance_id] + # Now ask model loading scheduler to load the model + logger.info( + f"Allocating resources for model {self.model_name} on instance {instance_id}" + ) + startup_node = ( + await self.model_loading_scheduler.allocate_resource.remote( + self.model_name, instance_id, self.resource_requirements + ) + ) + startup_config = { + "num_cpus": self.resource_requirements["num_cpus"], + "num_gpus": self.resource_requirements["num_gpus"], + "resources": { + "worker_node": 0.1, + f"worker_id_{startup_node}": 0.1, + }, + } + logger.info(f"Startup config: {startup_config}, {self.backend_config}") + + await start_instance.options( + resources={ + "worker_node": 0.1, + f"worker_id_{startup_node}": 0.1, + } + ).remote( + instance_id, + self.backend, + self.model_name, + self.backend_config, + startup_config, + ) + logger.info( + f"Started instance {instance_id} for model {self.model_name}" + ) + instance.backend_instance = ray.get_actor(instance_id) + async with instance.lock: + instance.ready = True + instance.node_id = startup_node + await instance.backend_instance.init_backend.remote() + + async with self.instance_management_lock: + self.ready_instances[instance_id] = instance + self.starting_instances.pop(instance_id) + return instance_id + + async def _stop_instance(self, instance_id: Optional[str] = None): + while len(self.ready_instances) <= 0: + await asyncio.sleep(1) + + async with self.instance_management_lock: + if instance_id is None: + instance_id, instance = self.ready_instances.popitem() + elif instance_id in self.ready_instances: + instance = self.ready_instances.pop(instance_id) + else: + logger.error(f"Instance {instance_id} not found") + return + self.deleting_instances[instance_id] = instance + logger.info( + f"Stopping instance {instance_id} for model {self.model_name}" + ) + self.loop.create_task(self._finish_instance(instance_id)) + + async def _finish_instance(self, instance_id: str): + async with self.instance_management_lock: + if instance_id not in self.deleting_instances: + logger.error(f"Instance {instance_id} not found") + return + instance = self.deleting_instances.pop(instance_id) + async with instance.lock: + instance.status = False + await instance.backend_instance.stop.remote() + ray.kill(instance.backend_instance) + await self.model_loading_scheduler.deallocate_resource.remote( + self.model_name, instance_id, self.resource_requirements + ) + + async def _shutdown_instance(self, instance_id: str): + logger.info( + f"Force deleting an instance (even if it is busy) for model {self.model_name}" + ) + async with self.instance_management_lock: + if instance_id not in self.ready_instances: + logger.error(f"Instance {instance_id} not found") + return + instance = self.ready_instances.pop(instance_id) + async with instance.lock: + instance.status = False + await instance.backend_instance.shutdown.remote() + ray.kill(instance.backend_instance) + await self.model_loading_scheduler.deallocate_resource.remote( + self.model_name, instance_id, self.resource_requirements + ) + return diff --git a/sllm/serve/routers/router_utils.py b/sllm/serve/routers/router_utils.py new file mode 100644 index 0000000..41e2072 --- /dev/null +++ b/sllm/serve/routers/router_utils.py @@ -0,0 +1,60 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Dict, Optional + +import ray + +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class SllmRouter(ABC): + @abstractmethod + def __init__( + self, + model_name: str, + resource_requirements: Dict[str, int], + backend: str, + backend_config: Dict, + router_config: Dict, + ) -> None: + pass + + @abstractmethod + async def start(self, auto_scaling_config: Dict[str, int]): + pass + + @abstractmethod + async def shutdown(self): + pass + + @abstractmethod + async def update(self, auto_scaling_config: Dict[str, int]): + pass + + @abstractmethod + async def inference(self, request_data: dict): + pass + + @abstractmethod + async def fine_tuning(self, request_data: dict): + pass diff --git a/sllm/serve/schedulers/__init__.py b/sllm/serve/schedulers/__init__.py new file mode 100644 index 0000000..d573c10 --- /dev/null +++ b/sllm/serve/schedulers/__init__.py @@ -0,0 +1,22 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from .fcfs_scheduler import FcfsScheduler +from .scheduler_utils import SllmScheduler +from .storage_aware_scheduler import StorageAwareScheduler + +__all__ = ["FcfsScheduler", "SllmScheduler", "StorageAwareScheduler"] diff --git a/sllm/serve/schedulers/fcfs_scheduler.py b/sllm/serve/schedulers/fcfs_scheduler.py new file mode 100644 index 0000000..d7ed529 --- /dev/null +++ b/sllm/serve/schedulers/fcfs_scheduler.py @@ -0,0 +1,203 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +import copy +import time +from typing import Mapping, Optional + +from sllm.serve.logger import init_logger +from sllm.serve.utils import get_worker_nodes + +from .scheduler_utils import SllmScheduler + +logger = init_logger(__name__) + + +class FcfsScheduler(SllmScheduler): + def __init__(self, scheduler_config: Optional[Mapping] = None): + super().__init__() + self.scheduler_config = scheduler_config + + self.queue_lock = asyncio.Lock() + self.model_loading_queues = {} + + self.metadata_lock = asyncio.Lock() + self.worker_nodes = {} + self.model_instance = {} + + self.loop = asyncio.get_running_loop() + + self.running_lock = asyncio.Lock() + self.running = False + + async def start(self) -> None: + async with self.running_lock: + if self.running: + logger.error("FCFS scheduler already started") + return + self.running = True + logger.info("Starting FCFS scheduler") + self.loop_task = self.loop.create_task(self._control_loop()) + + async def shutdown(self) -> None: + async with self.running_lock: + if not self.running: + logger.error("FCFS scheduler not running") + return + self.running = False + async with self.queue_lock: + self.model_loading_queues = {} + if self.loop_task is not None: + await self.loop_task + + async def allocate_resource( + self, model_name: str, instance_id: str, resources: Mapping + ) -> int: + logger.info(f"Model {model_name} requested") + # TODO: consider other resources + num_gpus = resources.get("num_gpus", 0) + async with self.queue_lock: + if model_name not in self.model_loading_queues: + self.model_loading_queues[model_name] = [] + allocation_result = self.loop.create_future() + self.model_loading_queues[model_name].append( + (time.time(), num_gpus, allocation_result) + ) + logger.info(f"Model {model_name} added to the loading queue") + node_id = await allocation_result + async with self.metadata_lock: + if model_name not in self.model_instance: + self.model_instance[model_name] = {} + self.model_instance[model_name][instance_id] = node_id + return node_id + + async def deallocate_resource( + self, model_name: str, instance_id: str, resources: Mapping + ): + logger.info(f"Deallocating model {model_name} instance {instance_id}") + # TODO: consider other resources + num_gpus = resources.get("num_gpus", 0) + async with self.metadata_lock: + if model_name not in self.model_instance: + logger.error(f"Model {model_name} not found") + return + if instance_id not in self.model_instance[model_name]: + logger.error(f"Instance {instance_id} not found") + return + node_id = self.model_instance[model_name].pop(instance_id) + logger.info(f"Node {node_id} deallocated {num_gpus} GPUs") + if node_id not in self.worker_nodes: + logger.error(f"Node {node_id} not found") + return + self.worker_nodes[node_id]["free_gpu"] += num_gpus + logger.info(f"Model {model_name} instance {instance_id} deallocated") + + async def _control_loop(self): + logger.info("Starting control loop") + while self.running: + loading_requests = [] + async with self.queue_lock: + for ( + model_name, + loading_queue, + ) in self.model_loading_queues.items(): + for idx, ( + request_time, + num_gpus, + allocation_result, + ) in enumerate(loading_queue): + loading_requests.append( + ( + model_name, + idx, + request_time, + num_gpus, + allocation_result, + ) + ) + # logger.info(f"Loading requests: {loading_requests}") + # first come first serve + if len(loading_requests) > 0: + worker_nodes = await self._get_worker_nodes() + logger.info(f"Worker nodes: {worker_nodes}") + loading_requests.sort(key=lambda x: x[1]) + for ( + model_name, + idx, + request_time, + num_gpus, + allocation_result, + ) in loading_requests: + allocated = False + for node_id, node_info in worker_nodes.items(): + if node_info["free_gpu"] >= num_gpus: + async with self.queue_lock: + # allocation_result was set + if allocation_result.done(): + allocated = True + # skip current instance + break + try: + self.model_loading_queues[ + model_name + ].remove( + ( + request_time, + num_gpus, + allocation_result, + ) + ) + allocation_result.set_result(node_id) + except ValueError: + break + allocated = True + logger.info( + f"Allocated node {node_id} for model {model_name}" + ) + node_info["free_gpu"] -= num_gpus + break + if not allocated: + logger.info(f"No available node for model {model_name}") + await self._update_worker_nodes(worker_nodes) + + await asyncio.sleep(1) + + async def _get_worker_nodes(self): + worker_nodes = get_worker_nodes() + async with self.metadata_lock: + updated_worker_nodes = copy.deepcopy(self.worker_nodes) + for node_id, node_info in worker_nodes.items(): + if node_id not in updated_worker_nodes: + updated_worker_nodes[node_id] = copy.deepcopy(node_info) + async with self.metadata_lock: + self.worker_nodes = updated_worker_nodes + + return updated_worker_nodes + + # TODO: implement a dedicated class to manage worker nodes + async def _update_worker_nodes(self, worker_nodes) -> None: + async with self.metadata_lock: + updated_worker_nodes = copy.deepcopy(self.worker_nodes) + for node_id, node_info in worker_nodes.items(): + if node_id not in updated_worker_nodes: + logger.error(f"Node {node_id} not found") + continue + updated_worker_nodes[node_id] = copy.deepcopy(node_info) + async with self.metadata_lock: + self.worker_nodes = updated_worker_nodes + logger.info(f"Worker nodes updated: {updated_worker_nodes}") diff --git a/sllm/serve/schedulers/scheduler_utils.py b/sllm/serve/schedulers/scheduler_utils.py new file mode 100644 index 0000000..402da20 --- /dev/null +++ b/sllm/serve/schedulers/scheduler_utils.py @@ -0,0 +1,49 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +from abc import ABC, abstractmethod +from typing import Mapping, Optional + +from sllm.serve.logger import init_logger + +logger = init_logger(__name__) + + +class SllmScheduler(ABC): + @abstractmethod + def __init__(self, scheduler_config: Optional[Mapping] = None): + super().__init__() + + @abstractmethod + async def start(self) -> None: + pass + + @abstractmethod + async def shutdown(self) -> None: + pass + + @abstractmethod + async def allocate_resource( + self, model_name: str, instance_id: str, resources: Mapping + ): + pass + + @abstractmethod + async def deallocate_resource( + self, model_name: str, instance_id: str, resources: Mapping + ): + pass diff --git a/sllm/serve/schedulers/storage_aware_scheduler.py b/sllm/serve/schedulers/storage_aware_scheduler.py new file mode 100644 index 0000000..ce620a0 --- /dev/null +++ b/sllm/serve/schedulers/storage_aware_scheduler.py @@ -0,0 +1,504 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +import asyncio +import copy +from dataclasses import dataclass +from typing import List, Mapping, Optional, Tuple + +import ray + +from sllm.serve.logger import init_logger + +from ..utils import InstanceStatus +from .fcfs_scheduler import FcfsScheduler + +logger = init_logger(__name__) + + +@dataclass +class MigrationPlan: + target_node_id: int + source_instance: InstanceStatus + + +class MigrationPlans: + def __init__(self): + # self.evictedInstances = [] # List to store the instances that are evicted + self.total_latency = float( + "inf" + ) # Initialize total_latency to infinity + self.evictedGPUs = 0 # Counter for the total num_gpu freed + self.plans = [] + self.store_info = None + + def append(self, plan: MigrationPlan, total_latency: float): + # Update the total_latency estimate for the current plan + self.total_latency = total_latency + # Update the total num_gpu freed + self.evictedGPUs += plan.source_instance.num_gpu + # Add a instance to the Migration plan + self.plans.append(plan) + + +@dataclass +class AllocationPlan: + node_id: int + latency: float + migration_plans: Optional[MigrationPlans] = None + + +class StorageAwareScheduler(FcfsScheduler): + def __init__(self, scheduler_config: Optional[Mapping] = None): + super().__init__(scheduler_config) + + self.enable_migration = scheduler_config.get("enable_migration", False) + + self.store_manager = None + + self.model_scheduler_config = {} + + async def _control_loop(self): + logger.info("Starting control loop") + while self.running: + loading_requests = [] + logger.info(f"Loading requests: {loading_requests}") + async with self.queue_lock: + for ( + model_name, + loading_queue, + ) in self.model_loading_queues.items(): + for idx, ( + request_time, + num_gpus, + allocation_result, + ) in enumerate(loading_queue): + loading_requests.append( + ( + model_name, + idx, + request_time, + num_gpus, + allocation_result, + ) + ) + if loading_requests: + logger.info(f"Loading requests are: {loading_requests}") + if self.store_manager is None: + try: + self.store_manager = ray.get_actor("store_manager") + except ValueError: + logger.error("Store manager not found") + await asyncio.sleep(1) + continue + # first come first serve + if len(loading_requests) > 0: + worker_nodes = await self._get_worker_nodes() + logger.info(f"Worker nodes: {worker_nodes}") + model_info = await self.store_manager.get_model_info.remote() + logger.info(f"Model info: {model_info}") + store_info = await self.store_manager.get_store_info.remote() + logger.info(f"Store info: {store_info}") + hardware_info = ( + await self.store_manager.get_hardware_info.remote() + ) + logger.info(f"Hardware info: {hardware_info}") + loading_requests.sort(key=lambda x: x[1]) + logger.info(f"Sorted loading requests: {loading_requests}") + for ( + model_name, + idx, + request_time, + num_gpus, + allocation_result, + ) in loading_requests: + logger.info(f"Processing request for model {model_name}") + scheduling_options = await self.schedule( + model_name, + num_gpus, + worker_nodes, + model_info, + store_info, + hardware_info, + ) + # sort by latency + if scheduling_options: + scheduling_options.sort( + key=lambda x: (x.latency, x.node_id) + ) + logger.info( + f"Sorted scheduling options: {scheduling_options}" + ) + allocation_plan = scheduling_options[0] + if allocation_plan.migration_plans is not None: + # execute migration plans + for ( + migration_plan + ) in allocation_plan.migration_plans.plans: + source_instance = migration_plan.source_instance + target_model = source_instance.model_name + target_request_router = ray.get_actor( + target_model, namespace="models" + ) + logger.info( + f"Executing migration plan: {migration_plan}" + ) + target_node_id = migration_plan.target_node_id + logger.info( + f"Target node {target_node_id} for model {target_model}" + ) + worker_nodes[target_node_id]["free_gpu"] -= ( + num_gpus + ) + logger.info( + f"Free GPU on node {target_node_id}: {worker_nodes[target_node_id]['free_gpu']}" + ) + try: + target_instance_id = await target_request_router.execute_migration_plan.remote( + migration_plan + ) + except Exception as e: + logger.error( + f"Failed to execute migration plan: {e}" + ) + target_instance_id = None + if target_instance_id is None: + logger.info( + f"Failed to execute migration plan: {migration_plan}" + ) + worker_nodes[target_node_id][ + "free_gpu" + ] += num_gpus + else: + logger.info( + f"Migrated instance {target_model} to node {target_node_id} instance {target_instance_id}" + ) + + node_id = allocation_plan.node_id + async with self.queue_lock: + self.model_loading_queues[model_name].pop(idx) + logger.info( + f"Allocated node {node_id} for model {model_name}" + ) + allocation_result.set_result(node_id) + + worker_nodes[node_id]["free_gpu"] -= num_gpus + await self.store_manager.load_to_host.remote( + node_id, model_name + ) + else: + logger.info(f"No available node for model {model_name}") + await self._update_worker_nodes(worker_nodes) + + await asyncio.sleep(1) + + async def schedule( + self, + model_name, + num_gpus, + worker_nodes, + model_info, + store_info, + hardware_info, + ) -> List[AllocationPlan]: + scheduling_options = [] + logger.info(f"Checking model {model_name}") + for node_id, node_info in worker_nodes.items(): + logger.info(f"Checking node {node_id}, node info: {node_info}") + if node_id not in store_info: + logger.error(f"Node {node_id} not found in store info") + continue + free_gpu = node_info["free_gpu"] + logger.info(f"Node {node_id} has {free_gpu} free GPUs") + ( + node_store_info, + pinned_memory_pool, + node_waiting_time, + ) = store_info[node_id] + if model_name not in node_store_info: + logger.info(f"Model {model_name} not found in node {node_id}") + # Note(Yao): Downloading from HuggingFace Hub is + # slower than network bandwidth and difficult to estimate. + # So we just consider local checkpoints for now. + continue + latency = self._get_model_loading_time( + model_name, + model_info[model_name], + hardware_info[node_id], + node_waiting_time, + pinned_memory_pool, + ) + if free_gpu >= num_gpus: + scheduling_options.append(AllocationPlan(node_id, latency)) + elif self.enable_migration: + gpu_shortage = num_gpus - free_gpu + logger.info( + f"Node {node_id} does not have enough GPU, trying migration" + ) + migration_plans = await self.get_migration_plans( + model_name, + gpu_shortage, + node_id, + copy.deepcopy(worker_nodes), + copy.deepcopy(model_info), + copy.deepcopy(store_info), + copy.deepcopy(hardware_info), + ) + if migration_plans is not None: + scheduling_options.append( + AllocationPlan( + node_id=node_id, + latency=latency + migration_plans.total_latency, + migration_plans=migration_plans, + ) + ) + logger.info( + f"Migration plans for model {model_name}: {migration_plans}" + ) + else: + logger.info( + f"Node {node_id} does not have enough GPU and migration is disabled" + ) + return scheduling_options + + async def get_migration_plans( + self, + model_name: str, + gpu_shortage: int, + source_node_id: int, + worker_nodes: Mapping, + model_info: Mapping, + store_info: Mapping, + hardware_info: Mapping, + ) -> Optional[MigrationPlans]: + migratable_instances = [] + request_routers = {} + async with self.metadata_lock: + logger.info(f"Checking migratable instances for model {model_name}") + for target_model_name in self.model_instance: + # Skip the instances that is already running the model + if target_model_name == model_name: + continue + for instance_id, node_id in self.model_instance[ + target_model_name + ].items(): + if node_id == source_node_id: + logger.info( + f"Checking instance {instance_id} of model {target_model_name}" + ) + if target_model_name not in request_routers: + request_routers[target_model_name] = ray.get_actor( + target_model_name, namespace="models" + ) + logger.info( + f"Getting status for instance {instance_id} of model {target_model_name}" + ) + instance_status = await request_routers[ + target_model_name + ].get_instance_status.remote(instance_id) + if instance_status: + logger.info( + f"Instance {instance_id} status: {instance_status}" + ) + alpha = self.model_scheduler_config.get( + target_model_name, {} + ).get("alpha", 0.01) + beta = self.model_scheduler_config.get( + target_model_name, {} + ).get("beta", 0.1) + num_current_tokens = ( + instance_status.num_current_tokens + ) + resuming_latency = alpha * num_current_tokens + beta + instance_status.resuming_latency = resuming_latency + migratable_instances.append(instance_status) + + logger.info(f"Migratable instances: {migratable_instances}") + + if not migratable_instances: + return None + + try: + numInstances = len( + migratable_instances + ) # Number of instances currently running + gpu_shortage = int(gpu_shortage) + logger.info( + f"Number of instances: {numInstances}, Required GPUs: {gpu_shortage}" + ) + # Initialize the DP table with MigrationPlans objects + dp = [ + [MigrationPlans() for _ in range(gpu_shortage + 1)] + for _ in range(numInstances + 1) + ] + dp[0][0].total_latency = 0 + dp[0][0].store_info = copy.deepcopy(store_info) + logger.info(f"Number of instances: {numInstances}") + + # Iterate over all instances and GPU capacities + for i in range(1, numInstances + 1): + logger.info(f"Checking instance {i}") + for j in range(gpu_shortage + 1): + logger.info(f"Checking GPU capacity {j}") + current_instance = migratable_instances[i - 1] + # GPU requirement for the current instance + n_gpus = current_instance.num_gpu + logger.info(f"GPU requirement for instance {i}: {n_gpus}") + # Check if the current instance can fit into the current GPU capacity + if j >= n_gpus: + # Calculate total_latency when including the current instance + logger.info( + f"Instance {i} can fit into GPU capacity {j}" + ) + logger.info( + f"Store info: {dp[i - 1][j - n_gpus].store_info}" + ) + target_node_id, loading_time = ( + self._get_migration_target( + current_instance.model_name, + n_gpus, + source_node_id, + worker_nodes, + model_info, + copy.deepcopy(dp[i - 1][j - n_gpus].store_info), + hardware_info, + ) + ) + if not current_instance.resuming_latency: + logger.warn( + f"Resuming latency not found for instance {i}" + ) + current_instance.resuming_latency = 0 + migration_latency = ( + current_instance.resuming_latency + loading_time + ) + plan = MigrationPlan( + target_node_id=target_node_id, + source_instance=current_instance, + ) + total_latency = ( + migration_latency + + dp[i - 1][j - n_gpus].total_latency + ) + logger.info( + f"Total latency for instance {i} with {j} GPUs: {total_latency}" + ) + if total_latency < dp[i - 1][j].total_latency: + # Copy the previous plan and include the current instance + dp[i][j] = copy.deepcopy(dp[i - 1][j - n_gpus]) + dp[i][j].append(plan, total_latency) + dp[i][j].store_info[target_node_id][2] += ( + loading_time + ) + logger.info(f"Store info: {dp[i][j].store_info}") + else: + # Copy the previous plan without including the current instance + dp[i][j] = copy.deepcopy(dp[i - 1][j]) + else: + logger.info( + f"Instance {i} cannot fit into GPU capacity {j}" + ) + # If the instance cannot fit, carry forward the previous plan + dp[i][j] = copy.deepcopy(dp[i - 1][j]) + + # Return the optimal Migration plans + minLatency = float("inf") + optimal_plans = None + for plans in dp[numInstances]: + logger.info(f"Total latency: {plans.total_latency}") + if ( + plans.total_latency < minLatency + and plans.evictedGPUs >= gpu_shortage + ): + minLatency = plans.total_latency + optimal_plans = plans + logger.info(f"Migration plans: {optimal_plans}") + return optimal_plans + except Exception as e: + logger.error(f"Failed to get migration plans: {e}") + return None + + async def mark_resource( + self, model_name: str, instance_id: str, node_id: int + ) -> bool: + logger.info(f"Model {model_name} instance {instance_id} marked") + async with self.metadata_lock: + if model_name not in self.model_instance: + self.model_instance[model_name] = {} + self.model_instance[model_name][instance_id] = node_id + return True + + async def set_model_scheduler_config( + self, model_name: str, scheduler_config: Mapping + ) -> bool: + logger.info(f"Setting scheduler config for model {model_name}") + async with self.metadata_lock: + self.model_scheduler_config[model_name] = scheduler_config + return True + + def _get_model_loading_time( + self, + model_name: str, + model_size: int, + hardware_info: Mapping, + node_waiting_time: float, + pinned_memory_pool: Mapping, + ) -> float: + latency = 0 + if model_name not in pinned_memory_pool: + latency += ( + node_waiting_time + model_size / hardware_info["disk_bandwidth"] + ) + logger.info( + f"Loading model {model_name} will take {latency} seconds" + ) + else: + latency += model_size / hardware_info["pcie_bandwidth"] + logger.info( + f"Loading model {model_name} will take {latency} seconds" + ) + return latency + + def _get_migration_target( + self, + model_name: str, + num_gpu: int, + source_node_id: int, + worker_nodes: Mapping, + model_info: Mapping, + store_info: Mapping, + hardware_info: Mapping, + ) -> Optional[Tuple[int, float]]: + candidate_plans = [] + for node_id, node_info in worker_nodes.items(): + if node_id == source_node_id: + continue + if node_info["free_gpu"] >= num_gpu: + loading_time = self._get_model_loading_time( + model_name, + model_info[model_name], + hardware_info[node_id], + store_info[node_id][2], + store_info[node_id][1], + ) + candidate_plans.append((node_id, loading_time)) + if not candidate_plans: + return None + candidate_plans.sort(key=lambda x: x[1]) + # return the node with the shortest loading time + return candidate_plans[0] diff --git a/sllm/serve/store_manager.py b/sllm/serve/store_manager.py new file mode 100644 index 0000000..275dfda --- /dev/null +++ b/sllm/serve/store_manager.py @@ -0,0 +1,546 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +import asyncio +import os +import time +from typing import List, Mapping, Optional + +import ray + +from sllm.serve.hardware_info_collector import collect_all_info +from sllm.serve.logger import init_logger +from sllm.serve.model_downloader import ( + VllmModelDownloader, + download_lora_adapter, + download_transformers_model, +) +from sllm.serve.utils import get_worker_nodes +from sllm_store.client import SllmStoreClient + +logger = init_logger(__name__) + + +class SllmLocalStore: + def __init__( + self, + node_id: str, + client: SllmStoreClient, + mem_pool_size: int, + chunk_size: int, + hardware_info: Mapping, + ): + self.node_id = node_id + self.client = client + self.hardware_info = hardware_info + self.disk_models = {} + self.queued_models = {} + + self.pinned_memory_pool = {} + self.chunk_size = chunk_size + self.pinned_memory_pool_chunks = mem_pool_size // chunk_size + self.pinned_memory_pool_usage = 0 + + self.io_queue = [] + self.lock = asyncio.Lock() + + # Start loading loop + self.loader = asyncio.create_task(self.loading_loop()) + + logger.info( + f"Initialized local store for node {self.node_id}" + f" with {self.pinned_memory_pool_chunks} chunks" + f" (chunk size: {chunk_size})" + ) + + async def register_model( + self, model_name: str, backend: str, backend_config + ): + async with self.lock: + if model_name in self.disk_models: + logger.error(f"{model_name} already registered") + return + model_path = self._get_model_path(model_name, backend) + if backend == "transformers": + model_size = self.client.register_model(model_path) + self.disk_models[model_name] = ([model_path], model_size) + elif backend == "vllm": + tensor_parallel_size = backend_config.get( + "tensor_parallel_size", 1 + ) + model_size = 0 + model_path_list = [] + for rank in range(tensor_parallel_size): + model_rank_path = os.path.join(model_path, f"rank_{rank}") + model_size += self.client.register_model(model_rank_path) + model_path_list.append(model_rank_path) + self.disk_models[model_name] = (model_path_list, model_size) + logger.info(f"{model_name} registered, {self.disk_models}") + + return model_size + + async def get_store_info(self): + async with self.lock: + delta_time = 0 + if len(self.io_queue) > 0: + delta_time = self.io_queue[-1]["estimated_time"] - time.time() + if delta_time < 0: + delta_time = 0 + return [self.disk_models, self.pinned_memory_pool, delta_time] + + async def load_to_host(self, model_name: str) -> bool: + async with self.lock: + if model_name not in self.disk_models: + logger.error(f"{model_name} not found on node {self.node_id}") + return False + if model_name in self.pinned_memory_pool: + logger.info( + f"{model_name} already loaded to node {self.node_id}" + ) + return True + elif model_name in self.queued_models: + logger.info( + f"{model_name} is being loaded to node {self.node_id}" + ) + return True + + _, model_size = self.disk_models[model_name] + start_time = time.time() + if len(self.io_queue) > 0: + start_time = max( + self.io_queue[-1]["estimated_time"], start_time + ) + disk_bandwidth = self.hardware_info.get("disk_bandwidth", 1) + estimated_completion_time = start_time + model_size / disk_bandwidth + self.io_queue.append( + { + "model_name": model_name, + "estimated_time": estimated_completion_time, + } + ) + self.queued_models[model_name] = True + logger.info( + f"{model_name} is being loaded to node {self.node_id}," + " estimated completion time: " + f"{self._format_time(estimated_completion_time)}" + ) + return True + + async def loading_loop(self): + while True: + async with self.lock: + if len(self.io_queue) == 0: + await asyncio.sleep(1) + continue + model_info = self.io_queue[0] + logger.info( + f"Loading {model_info['model_name']} to node {self.node_id}" + ) + + model_name = model_info["model_name"] + model_path_list, model_size = self.disk_models[model_name] + can_load = await self._lru_eviction(model_size) + if not can_load: + logger.warning( + f"{model_name} cannot be loaded to node {self.node_id}" + ) + await asyncio.sleep(1) + continue + logger.info(f"Loading {model_name} to node {self.node_id}") + ret = 1 + for model_path in model_path_list: + ret = ret and self.client.load_into_cpu(model_path) + self.io_queue.pop(0) + self.queued_models.pop(model_name) + if not ret: + logger.error(f"Failed to load {model_name}") + continue + self.pinned_memory_pool[model_name] = time.time() + self.pinned_memory_pool_usage += ( + model_size + self.chunk_size - 1 + ) // self.chunk_size + logger.info(f"{model_name} loaded to host") + + async def _lru_eviction(self, model_size): + # evict the least recently used models until the model can be loaded + async with self.lock: + sorted_models = sorted( + self.pinned_memory_pool.items(), key=lambda x: x[1] + ) + logger.info(f"Sorted models: {sorted_models}") + required_chunks = ( + model_size + self.chunk_size - 1 + ) // self.chunk_size + logger.info( + f"Pinned memory pool usage: {self.pinned_memory_pool_usage} / {self.pinned_memory_pool_chunks}" + f" requires {required_chunks} chunks" + ) + while ( + self.pinned_memory_pool_usage + required_chunks + > self.pinned_memory_pool_chunks + and len(sorted_models) > 0 + ): + model_name, _ = sorted_models.pop(0) + if model_name not in self.queued_models: + model_path_list, _ = self.disk_models[model_name] + for model_path in model_path_list: + self.client.unload_from_cpu(model_path) + self.pinned_memory_pool.pop(model_name) + unloaded_chunks = ( + self.disk_models[model_name][1] + self.chunk_size - 1 + ) // self.chunk_size + self.pinned_memory_pool_usage -= unloaded_chunks + logger.info( + f"{model_name} evicted {unloaded_chunks} chunks" + ) + return ( + self.pinned_memory_pool_usage + required_chunks + <= self.pinned_memory_pool_chunks + ) + + def _get_model_path(self, model_name, backend): + return os.path.join(backend, model_name) + + def _format_time(self, t): + return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(t)) + + +# @ray.remote(num_cpus=1, resources={"control_node": 0.1}) +class StoreManager: + def __init__(self): + logger.info("Initializing store manager") + self.hardware_info = {} + + self.metadata_lock = asyncio.Lock() + # Storage info + self.round_robin_index = 0 + self.local_servers = {} + self.model_info = {} + self.model_storage_info = {} + + async def initialize_cluster(self) -> bool: + logger.info("Initializing cluster and collecting hardware info") + + # Get worker nodes + worker_node_info = get_worker_nodes() + if not worker_node_info: + logger.error("No worker nodes found") + return False + + # Initialize hardware_info dictionary + self.hardware_info = {} + # Collect hardware info from each node + hardware_info_futures = { + node_id: collect_all_info.options( + resources={f"worker_id_{node_id}": 0.01} + ).remote() + for node_id in worker_node_info + } + + # Gather hardware info + for node_id, future in hardware_info_futures.items(): + try: + hardware_info = await future + self.hardware_info[node_id] = hardware_info + logger.info(f"Hardware info collected for node {node_id}") + except Exception as e: + logger.error( + f"Failed to collect hardware info from node {node_id}: {e}" + ) + continue + + uninitialized_nodes = list(self.hardware_info.keys()) + + while len(uninitialized_nodes) > 0: + for node_id in uninitialized_nodes: + if node_id in worker_node_info: + node_address = worker_node_info[node_id]["address"] + try: + sllm_store_client = SllmStoreClient( + f"{node_address}:8073" + ) + local_server_config = ( + sllm_store_client.get_server_config() + ) + except Exception as e: + logger.warning( + f"Failed to connect to node {node_id}: {e}" + ) + continue + else: + if not local_server_config: + logger.warning( + f"Failed to get server config for node {node_id}" # noqa: E501 + ) + continue + if "chunk_size" not in local_server_config: + logger.error( + f"Chunk size not found in server config for node {node_id}" # noqa: E501 + ) + chunk_size = local_server_config["chunk_size"] + if "mem_pool_size" not in local_server_config: + logger.error( + f"Memory pool size not found in server config for node {node_id}" + ) + mem_pool_size = local_server_config["mem_pool_size"] + self.local_servers[node_id] = SllmLocalStore( + node_id, + sllm_store_client, + mem_pool_size, + chunk_size, + self.hardware_info[node_id], + ) + uninitialized_nodes.remove(node_id) + logger.info( + f"Node {node_id} initialized, chunk size: {chunk_size}" # noqa: E501 + ) + break + logger.info( + f"Waiting for nodes {uninitialized_nodes} to be initialized" # noqa: E501 + ) + await asyncio.sleep(1) + + return True + + async def get_hardware_info(self): + return self.hardware_info + + async def get_model_info(self, model_name: Optional[str] = None): + logger.info(f"Getting info for {model_name}") + async with self.metadata_lock: + if model_name is not None: + return self.model_info.get(model_name, {}) + else: + return self.model_info + + async def get_store_info(self, node_id: Optional[str] = None): + async with self.metadata_lock: + if node_id is not None: + if node_id not in self.local_servers: + logger.error(f"Node {node_id} not found") + return {} + return await self.local_servers[node_id].get_store_info() + else: + node_info = {} + for node_id in self.local_servers: + node_info[node_id] = await self.local_servers[ + node_id + ].get_store_info() + return node_info + + async def load_to_host(self, node_id: str, model_name: str) -> bool: + async with self.metadata_lock: + if node_id not in self.local_servers: + logger.error(f"Node {node_id} not found") + return False + local_server = self.local_servers[node_id] + logger.info(f"Loading {model_name} to node {node_id}") + return await local_server.load_to_host(model_name) + + async def register(self, model_config): + model_name = model_config.get("model") + backend = model_config.get("backend", None) + if backend is None: + logger.error(f"Backend not specified for {model_name}") + return + backend_config = model_config.get("backend_config", {}) + placement_config = model_config.get("placement_config", {}) + if model_name not in self.model_info: + self.model_storage_info[model_name] = {} + logger.info(f"Registering new {model_name}") + + backend = model_config.get("backend", None) + pretrained_model_name_or_path = backend_config.get( + "pretrained_model_name_or_path", None + ) + # 1. download this model to one worker using round-robin + worker_node_info = get_worker_nodes() + + n_nodes = len(worker_node_info) + assert n_nodes > 0, "No worker nodes found" + + for node_id, node_info in worker_node_info.items(): + node_address = node_info["address"] + if node_id not in self.local_servers: + if self.local_servers: + first_node = next(iter(self.local_servers.values())) + self.local_servers[node_id] = SllmLocalStore( + node_id, + SllmStoreClient(f"{node_address}:8073"), + 1, + first_node.chunk_size, + first_node.hardware_info, + ) + else: + logger.error(f"Node {node_id} not found") + raise ValueError(f"Node {node_id} not found") + + local_disk = [] + if placement_config and "local_disk" in placement_config: + local_disk = placement_config["local_disk"] + if not all( + [node_id in worker_node_info for node_id in local_disk] + ): + logger.error( + f"Invalid target nodes {local_disk}, worker nodes: {worker_node_info}" # noqa: E501 + ) + return + else: + # round-robin + node_id = list(worker_node_info.keys())[ + self.round_robin_index % n_nodes + ] + self.round_robin_index += 1 + local_disk = [node_id] + + memory_pool = [] + if placement_config and "memory_pool" in placement_config: + memory_pool = placement_config["memory_pool"] + if not all( + [node_id in worker_node_info for node_id in memory_pool] + ): + logger.error( + f"Invalid target nodes {memory_pool}, worker nodes: {worker_node_info}" # noqa: E501 + ) + return + + logger.info( + f"Downloading model {pretrained_model_name_or_path} to nodes {local_disk}" # noqa: E501 + ) + for node_id in local_disk: + if backend == "transformers": + hf_model_class = backend_config.get("hf_model_class", None) + torch_dtype = backend_config.get("torch_dtype", "float16") + if hf_model_class is None: + logger.error( + "hf_model_type not specified in backend_config." + ) + break + await self.download_transformers_model( + model_name, + pretrained_model_name_or_path, + node_id, + hf_model_class, + torch_dtype, + ) + elif backend == "vllm": + await self.download_vllm_model( + model_name, + pretrained_model_name_or_path, + node_id, + model_config.get("num_gpus", 1), + backend_config.get("tensor_parallel_size", 1), + backend_config.get("torch_dtype", "float16"), + ) + else: + logger.error(f"Backend {backend} not supported") + break + local_server = self.local_servers[node_id] + model_size = await local_server.register_model( + model_name, backend, backend_config + ) + # record the storage info + self.model_storage_info[model_name][node_id] = True + logger.info(f"{model_name} downloaded to node {node_id}") + if node_id in memory_pool: + # preload to memory pool + await self.load_to_host( + node_id, pretrained_model_name_or_path + ) + logger.info(f"{model_name} loaded to memory pool") + self.model_info[model_name] = model_size + logger.info(f"{model_name} registered") + else: + # TODO: apply new placement config, if given + pass + + async def register_lora_adapter( + self, + base_model_name, + adapter_name, + adapter_path, + backend_config, + ) -> int: + if base_model_name not in self.model_storage_info: + logger.error( + f"Base model {base_model_name} not found in storage info" + ) + return -1 + + # Get the first node_id where the model is stored + node_id = next(iter(self.model_storage_info[base_model_name].keys())) + + hf_model_class = backend_config.get("hf_model_class", None) + torch_dtype = backend_config.get("torch_dtype", "float16") + logger.info(f"Downloading {adapter_path} to {node_id}") + return await download_lora_adapter.options( + resources={"worker_node": 0.1, f"worker_id_{node_id}": 0.1} + ).remote( + base_model_name, + adapter_name, + adapter_path, + hf_model_class, + torch_dtype, + ) + + async def download_transformers_model( + self, + model_name, + pretrained_model_name_or_path, + node_id, + hf_model_class, + torch_dtype, + ) -> int: + logger.info( + f"Downloading {pretrained_model_name_or_path} to node {node_id}" + ) + return await download_transformers_model.options( + resources={"worker_node": 0.1, f"worker_id_{node_id}": 0.1} + ).remote( + model_name, + pretrained_model_name_or_path, + torch_dtype, + hf_model_class, + ) + + async def download_vllm_model( + self, + model_name, + pretrained_model_name_or_path, + node_id, + num_gpus, + tensor_parallel_size, + torch_dtype, + ): + logger.info( + f"Downloading {pretrained_model_name_or_path} to node {node_id}" + ) + vllm_backend_downloader = ( + ray.remote(VllmModelDownloader) + .options( + num_gpus=num_gpus, + resources={"worker_node": 0.1, f"worker_id_{node_id}": 0.1}, + ) + .remote() + ) + return await vllm_backend_downloader.download_vllm_model.remote( + model_name, + pretrained_model_name_or_path, + torch_dtype, + tensor_parallel_size, + ) diff --git a/sllm/serve/utils.py b/sllm/serve/utils.py new file mode 100644 index 0000000..6d5e48d --- /dev/null +++ b/sllm/serve/utils.py @@ -0,0 +1,101 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import asyncio +from dataclasses import dataclass +from typing import List, Optional + +import ray + + +def get_worker_nodes(): + ray_nodes = ray.nodes() + worker_node_info = {} + for node in ray_nodes: + ray_node_id = node.get("NodeID", None) + assert ray_node_id is not None, "NodeID not found" + resources = node.get("Resources", {}) + assert resources != {}, "Resources not found" + node_address = node.get("NodeManagerAddress", None) + assert ( + node_address is not None and node_address != "" + ), "NodeManagerAddress not found" + if resources.get("control_node", 0) > 0: + continue # Skip the control node + + for key, value in resources.items(): + if key.startswith("worker_id_"): + node_id = key.split("_")[-1] + worker_node_info[node_id] = { + "ray_node_id": ray_node_id, + "address": node_address, + "free_gpu": resources.get("GPU", 0), + "total_gpu": resources.get("GPU", 0), + } + + return worker_node_info + + +@dataclass +class InstanceStatus: + instance_id: str + node_id: str + num_gpu: int + concurrency: int + + model_name: Optional[str] = None + num_current_tokens: Optional[int] = None + resuming_latency: Optional[float] = None + + +@dataclass +class InstanceHandle: + instance_id: str + max_queue_length: int + num_gpu: int + + node_id: Optional[str] = None + backend_instance: Optional[ray.actor.ActorHandle] = None + ready: bool = False + concurrency: int = 0 + + lock: asyncio.Lock = asyncio.Lock() + + async def add_requests(self, num_requests: int = 1): + async with self.lock: + if not self.ready: + return False + if ( + self.concurrency + num_requests > self.max_queue_length + or self.concurrency + num_requests < 0 + ): + return False + self.concurrency += num_requests + return True + + async def check_request_queue(self): + async with self.lock: + return self.concurrency + 1 <= self.max_queue_length + + async def get_status(self): + async with self.lock: + return InstanceStatus( + self.instance_id, + self.node_id, + self.num_gpu, + self.concurrency, + ) diff --git a/sllm_store/.DS_Store b/sllm_store/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..0bd77c9a95f79de96c7a0e569f785abcaea47c22 GIT binary patch literal 6148 zcmeHKJxc>Y5S`ToLqtdu5EQx2)_|>WhS&?Sv(S8Dz#JFyA$DG!%Aa5%_&aQj_$x%T zv#_=E&F*^HO*WL^@?qzfV1CSi6U~xi#m^+Aj~qJ6$`?jp9jV*SuAC>6+{2s705!?})n8 z!MJbLrybr8XJSkq#kW)gN8E(ZPjMD6N|;v`e0mhqfMT1`%9A~7a=Cv`xfuDTD((oo zKA!`W$J6NG6VVAqU;#Gv$`GTCd2uGE*X`tcjX1AqvRf2u@f6R@GYf8bYUrn)+~e4u zH94)-(zRpcn@rphc73hWMXFO5>u7*=+_sh-+KkT0$zbs0p1@1G{#V4qEK!f=;SK^Fax(VjOE@44A=n-H6{w-feDog zRH;J0VhEKEf1u++jfp~)PC{13b#!H+-%y0C4u2rSNrVc0?-lS0*a|GGuLVB;59YuB z+e!YLSHLUquM`lOO0`nPE$Op$=H~dU4bV=}*f=gxC{xhs<5&*(C|*aChPi+Tz))kN S5FVKSBOqn)omb#j75D-V)A9lU literal 0 HcmV?d00001 diff --git a/sllm_store/.gitignore b/sllm_store/.gitignore new file mode 100644 index 0000000..6ddd5e6 --- /dev/null +++ b/sllm_store/.gitignore @@ -0,0 +1,166 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# Ruff cache +.ruff_cache/ + +# CMake +build/ \ No newline at end of file diff --git a/sllm_store/CMakeLists.txt b/sllm_store/CMakeLists.txt new file mode 100644 index 0000000..65eb0f0 --- /dev/null +++ b/sllm_store/CMakeLists.txt @@ -0,0 +1,210 @@ +cmake_minimum_required(VERSION 3.20) + +# Set the project name +project(storage LANGUAGES CXX) + +# Set c++ 17 +set(CMAKE_CXX_STANDARD 17) + +option(BUILD_SLLM_TESTS "Build tests" OFF) +# Disable other tests +set(BUILD_TESTING OFF) + +include(FetchContent) +FetchContent_Declare( + glog + GIT_REPOSITORY https://github.com/google/glog.git + GIT_TAG v0.6.0 +) +FetchContent_Declare( + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + GIT_TAG v1.13.0 +) +set(FETCHCONTENT_QUIET OFF) + +FetchContent_MakeAvailable(glog) +FetchContent_MakeAvailable(googletest) + +find_package(CUDAToolkit QUIET) +find_package(HIP QUIET) + +# set CUDA or HIP +if (CUDAToolkit_FOUND) + message(STATUS "CUDA found") + set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.9;9.0") + set(SLLM_STORE_GPU_LANG "CUDA") + enable_language(CUDA) +elseif (HIP_FOUND) + message(STATUS "HIP found") + set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100") + set(SLLM_STORE_GPU_LANG "HIP") + enable_language(HIP) +else() + message(FATAL_ERROR "Neither CUDA nor HIP found") +endif() + + +# Adapted from https://github.com/vllm-project/vllm/blob/a1242324c99ff8b1e29981006dfb504da198c7c3/CMakeLists.txt +include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake) + +# +# Supported python versions. These versions will be searched in order, the +# first match will be selected. These should be kept in sync with setup.py. +# +set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12") + +# +# Try to find python package with an executable that exactly matches +# `SLLM_STORE_PYTHON_EXECUTABLE` and is one of the supported versions. +# +if (SLLM_STORE_PYTHON_EXECUTABLE) + find_python_from_executable(${SLLM_STORE_PYTHON_EXECUTABLE} "${PYTHON_SUPPORTED_VERSIONS}") +else() + message(FATAL_ERROR + "Please set SLLM_STORE_PYTHON_EXECUTABLE to the path of the desired python version" + " before running cmake configure.") +endif() + +# +# Update cmake's `CMAKE_PREFIX_PATH` with torch location. +# +append_cmake_prefix_path("torch" "torch.utils.cmake_prefix_path") + +# +# Import torch cmake configuration. +# Torch also imports CUDA (and partially HIP) languages with some customizations, +# so there is no need to do this explicitly with check_language/enable_language, +# etc. +# +find_package(Torch REQUIRED) + +# +# Normally `torch.utils.cpp_extension.CUDAExtension` would add +# `libtorch_python.so` for linking against an extension. Torch's cmake +# configuration does not include this library (presumably since the cmake +# config is used for standalone C++ binaries that link against torch). +# The `libtorch_python.so` library defines some of the glue code between +# torch/python via pybind and is required by SLLM_STORE extensions for this +# reason. So, add it by manually with `find_library` using torch's +# installed library path. +# +find_library(torch_python_LIBRARY torch_python PATHS + "${TORCH_INSTALL_PREFIX}/lib") + +# +# Override the GPU architectures detected by cmake/torch and filter them by +# the supported versions for the current language. +# The final set of arches is stored in `SLLM_STORE_GPU_ARCHES`. +# +override_gpu_arches(SLLM_STORE_GPU_ARCHES +${SLLM_STORE_GPU_LANG} +"${${SLLM_STORE_GPU_LANG}_SUPPORTED_ARCHS}") + +# +# Query torch for additional GPU compilation flags for the given +# `SLLM_STORE_GPU_LANG`. +# The final set of arches is stored in `SLLM_STORE_GPU_FLAGS`. +# +get_torch_gpu_compiler_flags(SLLM_STORE_GPU_FLAGS ${SLLM_STORE_GPU_LANG}) + +# +# Define extension targets +# + +# +# _C extension +# + +# +# Define source files used in SLLM_STORE_EXT +# + +set(SLLM_STORE_EXT_SRC + "csrc/checkpoint/aligned_buffer.cpp" + "csrc/checkpoint/checkpoint.cpp" + "csrc/checkpoint/checkpoint_py.cpp" + "csrc/checkpoint/tensor_writer.cpp" +) + +# +# NOTE: The following (pure CXX) source files will be excluded from the input +# of the hipify tool. This is a temporary solution to avoid the recursive +# inclusion of the hipify tool (#206). +# +# NOTE: If you are making changes to this section or adding new source files, +# please make sure to update the list of excluded files here. +# + +set(SLLM_STORE_CXX_EXT_SRC + "csrc/checkpoint/aligned_buffer.cpp" + "csrc/checkpoint/checkpoint_py.cpp" + "csrc/checkpoint/tensor_writer.cpp" +) + +define_gpu_extension_target( + _C + DESTINATION sllm_store + LANGUAGE ${SLLM_STORE_GPU_LANG} + SOURCES ${SLLM_STORE_EXT_SRC} + CXX_SRCS ${SLLM_STORE_CXX_EXT_SRC} + COMPILE_FLAGS ${SLLM_STORE_GPU_FLAGS} + ARCHITECTURES ${SLLM_STORE_GPU_ARCHES} + WITH_SOABI) + +# +# _checkpoint_store extension +# + +# pthread +find_package(Threads REQUIRED) + +# +# Define source files used in CHECKPOINT_STORE +# + +set(CHECKPOINT_STORE_SOURCES + "csrc/sllm_store/binary_utils.cpp" + "csrc/sllm_store/checkpoint_store.cpp" + "csrc/sllm_store/checkpoint_store_py.cpp" + "csrc/sllm_store/cuda_memory.cpp" + "csrc/sllm_store/cuda_memory_pool.cpp" + "csrc/sllm_store/gpu_replica.cpp" + "csrc/sllm_store/memory_state.cpp" + "csrc/sllm_store/model.cpp" + "csrc/sllm_store/pinned_memory.cpp" + "csrc/sllm_store/pinned_memory_pool.cpp" +) + +# +# Define pure CXX files used in CHECKPOINT_STORE (files without CUDA code) +# Used for hipify tool to only convert CUDA code +# + +set(CHECKPOINT_STORE_CXX_SOURCES + "csrc/sllm_store/binary_utils.cpp" + "csrc/sllm_store/memory_state.cpp" + "csrc/sllm_store/pinned_memory.cpp" +) + +set(CHECKPOINT_STORE_LIBRARIES + Threads::Threads + glog::glog) + +define_gpu_extension_target( + _checkpoint_store + DESTINATION sllm_store + LANGUAGE ${SLLM_STORE_GPU_LANG} + SOURCES ${CHECKPOINT_STORE_SOURCES} + CXX_SRCS ${CHECKPOINT_STORE_CXX_SOURCES} + COMPILE_FLAGS ${SLLM_STORE_GPU_FLAGS} + ARCHITECTURES ${SLLM_STORE_GPU_ARCHES} + LIBRARIES ${CHECKPOINT_STORE_LIBRARIES} + WITH_SOABI) + +# Enable testing if the option is ON +if(BUILD_SLLM_TESTS) + enable_testing() + + add_subdirectory(tests/cpp) +endif() \ No newline at end of file diff --git a/sllm_store/Dockerfile.builder b/sllm_store/Dockerfile.builder new file mode 100644 index 0000000..f5bdfcf --- /dev/null +++ b/sllm_store/Dockerfile.builder @@ -0,0 +1,60 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +# Adapted from https://github.com/vllm-project/vllm/blob/23c1b10a4c8cd77c5b13afa9242d67ffd055296b/Dockerfile +ARG CUDA_VERSION=12.1.1 +#################### BASE BUILD IMAGE #################### +# prepare basic build environment +FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS builder +ARG CUDA_VERSION=12.1.1 +ARG PYTHON_VERSION=3.10 +ARG TARGETPLATFORM +ENV DEBIAN_FRONTEND=noninteractive + +# Install Python and other dependencies +RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \ + && echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \ + && apt-get update -y \ + && apt-get install -y ccache software-properties-common git curl sudo \ + && add-apt-repository ppa:deadsnakes/ppa \ + && apt-get update -y \ + && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \ + && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \ + && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \ + && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \ + && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \ + && python3 --version && python3 -m pip --version + +WORKDIR /app + +COPY requirements-build.txt . +RUN python3 -m pip install --no-cache-dir -r requirements-build.txt + +# Add the rest of the source code +COPY cmake ./cmake +COPY CMakeLists.txt . +COPY csrc ./csrc +COPY sllm_store ./sllm_store +COPY setup.py . +COPY pyproject.toml . +COPY MANIFEST.in . +COPY requirements.txt . +COPY README.md . +COPY proto ./proto + +# ENTRYPOINT [ "/bin/bash", "-c" ] \ No newline at end of file diff --git a/sllm_store/Dockerfile.rocm b/sllm_store/Dockerfile.rocm new file mode 100644 index 0000000..094ef43 --- /dev/null +++ b/sllm_store/Dockerfile.rocm @@ -0,0 +1,108 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +ARG BASE_IMAGE="rocm/vllm-dev:base_ROCm-6.3.1_20250528_tuned_20250530" +ARG PYTORCH_ROCM_ARCH="gfx908;gfx90a;gfx942;gfx1100" + +# vLLM branch version +ARG VLLM_BRANCH="v0.9.0.1" + + +# Base image build stage +FROM ${BASE_IMAGE} AS base +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get purge -y sccache; python3 -m pip uninstall -y sccache; rm -f "$(which sccache)" + +# Set environment variables for LLVM and paths +ENV LLVM_SYMBOLIZER_PATH=/opt/rocm/llvm/bin/llvm-symbolizer +ENV PATH=$PATH:/opt/rocm/bin:/libtorch/bin +ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib/:/libtorch/lib +ENV CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/libtorch/include:/libtorch/include/torch/csrc/api/include/:/opt/rocm/include/ + +ENV PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} + +WORKDIR /app + +### sllm-store wheel build stage +FROM base AS builder +# Add project files +COPY cmake ./cmake +COPY CMakeLists.txt . +COPY csrc ./csrc +COPY sllm_store ./sllm_store +COPY setup.py . +COPY pyproject.toml . +COPY MANIFEST.in . +COPY requirements.txt . +COPY README.md . +COPY proto ./proto + +# Set up the environment using the existing torch version +COPY requirements-build.txt . +COPY using_existing_torch.py . +RUN python3 using_existing_torch.py +RUN python3 -m pip install -r requirements-build.txt + +RUN python3 setup.py sdist bdist_wheel + +### vLLM build stage +FROM base AS build_vllm +ARG VLLM_BRANCH + +WORKDIR /app/third_party + +RUN pip install /opt/rocm/share/amd_smi + +RUN if ls libs/*.whl; then \ + python3 -m pip install libs/*.whl; fi + +RUN git clone https://github.com/vllm-project/vllm.git \ + && cd vllm \ + && git checkout "${VLLM_BRANCH}" \ + && pip install --upgrade numba scipy \ + && pip install huggingface-hub[cli,hf_transfer] \ + && pip install --upgrade setuptools \ + && pip install setuptools_scm \ + && pip install "numpy<2" \ + && pip install -r requirements/rocm.txt + +RUN cd vllm && python3 setup.py clean --all && python3 setup.py develop + +COPY vllm_patch vllm_patch +RUN vllm_patch/patch.sh + +### Final image build stage +FROM build_vllm +WORKDIR /app + +# Copy the built wheels from the builder stage +COPY --from=builder /app/dist /app/dist +COPY --from=builder /app/requirements.txt /app/requirements.txt + +# Install the built wheels +RUN python3 -m pip install /app/dist/*.whl + +# Copy example files +COPY examples /app/examples + +# Set storage path env +ENV STORAGE_PATH=/models + +ENTRYPOINT [ "sllm-store", "start" ] +CMD [ "--storage-path", "/models", "--mem-pool-size", "4GB" ] diff --git a/sllm_store/MANIFEST.in b/sllm_store/MANIFEST.in new file mode 100644 index 0000000..af17fd4 --- /dev/null +++ b/sllm_store/MANIFEST.in @@ -0,0 +1,6 @@ +recursive-include csrc *.cpp *.h *.cc +include requirements.txt +include CMakeLists.txt +include cmake/utils.cmake +include proto/storage.proto +include python/proto/storage.proto \ No newline at end of file diff --git a/sllm_store/README.md b/sllm_store/README.md new file mode 100644 index 0000000..f268f94 --- /dev/null +++ b/sllm_store/README.md @@ -0,0 +1,31 @@ +# ServerlessLLM Store (`sllm-store`) + +## About + +`sllm-store` is an internal library of ServerlessLLM that provides high-performance model loading from local storage into GPU memory. It can also be installed and used independently in other projects. For more details, see our [quick start guide](https://serverlessllm.github.io/docs/stable/store/quickstart). For support for ROCm, see our [ROCm guide](https://serverlessllm.github.io/docs/stable/store/rocm_quickstart). + +## Installation + +### Install with pip + +```bash +pip install serverless-llm-store +``` + +### Install from source +1. Clone the repository and navigate to the `store` directory: + +``` bash +git clone https://github.com/ServerlessLLM/ServerlessLLM.git +cd ServerlessLLM/sllm_store +``` + +2. Install the package from source + +```bash +rm -rf build +pip install . +``` + +## Usage +Refer to our [quick start guide](https://serverlessllm.github.io/docs/stable/store/quickstart) for detailed usage instructions. diff --git a/sllm_store/cmake/hipify.py b/sllm_store/cmake/hipify.py new file mode 100755 index 0000000..cdd79d1 --- /dev/null +++ b/sllm_store/cmake/hipify.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 + +# +# A command line tool for running pytorch's hipify preprocessor on CUDA +# source files. +# +# See https://github.com/ROCm/hipify_torch +# and /utils/hipify/hipify_python.py +# + +import argparse +import os +import shutil + +from torch.utils.hipify.hipify_python import hipify + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # Project directory where all the source + include files live. + parser.add_argument( + "-p", + "--project_dir", + help="The project directory.", + ) + + # Directory where hipified files are written. + parser.add_argument( + "-o", + "--output_dir", + help="The output directory.", + ) + + # Source files to convert. + parser.add_argument( + "sources", help="Source files to hipify.", nargs="*", default=[] + ) + + args = parser.parse_args() + + # Limit include scope to project_dir only + includes = [os.path.join(args.project_dir, "*")] + + # Get absolute path for all source files. + extra_files = [os.path.abspath(s) for s in args.sources] + + # Copy sources from project directory to output directory. + # The directory might already exist to hold object files so we ignore that. + shutil.copytree(args.project_dir, args.output_dir, dirs_exist_ok=True) + + hipify_result = hipify( + project_directory=args.project_dir, + output_directory=args.output_dir, + header_include_dirs=[], + includes=includes, + extra_files=extra_files, + show_detailed=True, + is_pytorch_extension=True, + hipify_extra_files_only=True, + ) + + hipified_sources = [] + for source in args.sources: + s_abs = os.path.abspath(source) + hipified_s_abs = ( + hipify_result[s_abs].hipified_path + if ( + s_abs in hipify_result + and hipify_result[s_abs].hipified_path is not None + ) + else s_abs + ) + hipified_sources.append(hipified_s_abs) + + assert len(hipified_sources) == len(args.sources) + + # Print hipified source files. + print("\n".join(hipified_sources)) diff --git a/sllm_store/cmake/utils.cmake b/sllm_store/cmake/utils.cmake new file mode 100644 index 0000000..2fac3d2 --- /dev/null +++ b/sllm_store/cmake/utils.cmake @@ -0,0 +1,367 @@ +# Adapted from https://github.com/vllm-project/vllm/blob/a1242324c99ff8b1e29981006dfb504da198c7c3/cmake/utils.cmake +# +# Attempt to find the python package that uses the same python executable as +# `EXECUTABLE` and is one of the `SUPPORTED_VERSIONS`. +# +macro (find_python_from_executable EXECUTABLE SUPPORTED_VERSIONS) + file(REAL_PATH ${EXECUTABLE} EXECUTABLE) + set(Python_EXECUTABLE ${EXECUTABLE}) + find_package(Python COMPONENTS Interpreter Development.Module) + if (NOT Python_FOUND) + message(FATAL_ERROR "Unable to find python matching: ${EXECUTABLE}.") + endif() + set(_VER "${Python_VERSION_MAJOR}.${Python_VERSION_MINOR}") + set(_SUPPORTED_VERSIONS_LIST ${SUPPORTED_VERSIONS} ${ARGN}) + if (NOT _VER IN_LIST _SUPPORTED_VERSIONS_LIST) + message(FATAL_ERROR + "Python version (${_VER}) is not one of the supported versions: " + "${_SUPPORTED_VERSIONS_LIST}.") + endif() + message(STATUS "Found python matching: ${EXECUTABLE}.") +endmacro() + +# +# Run `EXPR` in python. The standard output of python is stored in `OUT` and +# has trailing whitespace stripped. If an error is encountered when running +# python, a fatal message `ERR_MSG` is issued. +# +function (run_python OUT EXPR ERR_MSG) + execute_process( + COMMAND + "${Python_EXECUTABLE}" "-c" "${EXPR}" + OUTPUT_VARIABLE PYTHON_OUT + RESULT_VARIABLE PYTHON_ERROR_CODE + ERROR_VARIABLE PYTHON_STDERR + OUTPUT_STRIP_TRAILING_WHITESPACE) + + if(NOT PYTHON_ERROR_CODE EQUAL 0) + message(FATAL_ERROR "${ERR_MSG}: ${PYTHON_STDERR}") + endif() + set(${OUT} ${PYTHON_OUT} PARENT_SCOPE) +endfunction() + +# Run `EXPR` in python after importing `PKG`. Use the result of this to extend +# `CMAKE_PREFIX_PATH` so the torch cmake configuration can be imported. +macro (append_cmake_prefix_path PKG EXPR) + run_python(_PREFIX_PATH + "import ${PKG}; print(${EXPR})" "Failed to locate ${PKG} path") + list(APPEND CMAKE_PREFIX_PATH ${_PREFIX_PATH}) +endmacro() + +# +# Add a target named `hipify${NAME}` that runs the hipify preprocessor on a set +# of CUDA source files. The names of the corresponding "hipified" sources are +# stored in `OUT_SRCS`. +# ORIG_SRCS - List of original source files. +# CXX_SRCS - List of C++ source files that should not be hipified. (e.g., no CUDA code) +# CXX_SRCS are removed from the list of ORIG_SRCS, in order to fix issue #206. +# +function (hipify_sources_target OUT_SRCS NAME ORIG_SRCS CXX_SRCS) + # + # Split into C++ and non-C++ (i.e. CUDA) sources. + # + set(SRCS ${ORIG_SRCS}) + + # remove CXX_SRCS from the list of SRCS + foreach(CXX_SRC ${CXX_SRCS}) + list(REMOVE_ITEM SRCS ${CXX_SRC}) + endforeach(CXX_SRC ${CXX_SRCS}) + + # + # Generate ROCm/HIP source file names from CUDA file names. + # Since HIP files are generated code, they will appear in the build area + # `CMAKE_CURRENT_BINARY_DIR` directory rather than the original csrc dir. + # + set(HIP_SRCS) + foreach (SRC ${SRCS}) + set(ORIGINAL_SRC ${SRC}) + string(REGEX REPLACE "\.cu$" "\.hip" SRC ${SRC}) + string(REGEX REPLACE "cuda" "hip" SRC ${SRC}) + if(${SRC} STREQUAL ${ORIGINAL_SRC}) + string(REGEX REPLACE "\.cpp$" "_hip\.cpp" SRC ${SRC}) + endif() + list(APPEND HIP_SRCS "${CMAKE_CURRENT_BINARY_DIR}/${SRC}") + endforeach() + + set(CSRC_BUILD_DIR ${CMAKE_CURRENT_BINARY_DIR}/csrc) + add_custom_target( + hipify${NAME} + COMMAND ${CMAKE_SOURCE_DIR}/cmake/hipify.py -p ${CMAKE_SOURCE_DIR}/csrc -o ${CSRC_BUILD_DIR} ${SRCS} + DEPENDS ${CMAKE_SOURCE_DIR}/cmake/hipify.py ${SRCS} + BYPRODUCTS ${HIP_SRCS} + COMMENT "Running hipify on ${NAME} extension source files.") + + # Swap out original extension sources with hipified sources. + list(APPEND HIP_SRCS ${CXX_SRCS}) + set(${OUT_SRCS} ${HIP_SRCS} PARENT_SCOPE) +endfunction() + +# +# Get additional GPU compiler flags from torch. +# +function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG) + if (${GPU_LANG} STREQUAL "CUDA") + # + # Get common NVCC flags from torch. + # + run_python(GPU_FLAGS + "from torch.utils.cpp_extension import COMMON_NVCC_FLAGS; print(';'.join(COMMON_NVCC_FLAGS))" + "Failed to determine torch nvcc compiler flags") + + if (CUDA_VERSION VERSION_GREATER_EQUAL 11.8) + list(APPEND GPU_FLAGS "-DENABLE_FP8") + endif() + if (CUDA_VERSION VERSION_GREATER_EQUAL 12.0) + list(REMOVE_ITEM GPU_FLAGS + "-D__CUDA_NO_HALF_OPERATORS__" + "-D__CUDA_NO_HALF_CONVERSIONS__" + "-D__CUDA_NO_BFLOAT16_CONVERSIONS__" + "-D__CUDA_NO_HALF2_OPERATORS__") + endif() + + elseif(${GPU_LANG} STREQUAL "HIP") + # + # Get common HIP/HIPCC flags from torch. + # + run_python(GPU_FLAGS + "import torch.utils.cpp_extension as t; print(';'.join(t.COMMON_HIP_FLAGS + t.COMMON_HIPCC_FLAGS))" + "Failed to determine torch nvcc compiler flags") + + list(APPEND GPU_FLAGS + "-DUSE_ROCM" + "-DENABLE_FP8" + "-U__HIP_NO_HALF_CONVERSIONS__" + "-U__HIP_NO_HALF_OPERATORS__" + "-fno-gpu-rdc") + + endif() + set(${OUT_GPU_FLAGS} ${GPU_FLAGS} PARENT_SCOPE) +endfunction() + +# Macro for converting a `gencode` version number to a cmake version number. +macro(string_to_ver OUT_VER IN_STR) + string(REGEX REPLACE "\([0-9]+\)\([0-9]\)" "\\1.\\2" ${OUT_VER} ${IN_STR}) +endmacro() + +# +# Override the GPU architectures detected by cmake/torch and filter them by +# `GPU_SUPPORTED_ARCHES`. Sets the final set of architectures in +# `GPU_ARCHES`. +# +# Note: this is defined as a macro since it updates `CMAKE_CUDA_FLAGS`. +# +macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) + set(_GPU_SUPPORTED_ARCHES_LIST ${GPU_SUPPORTED_ARCHES} ${ARGN}) + message(STATUS "${GPU_LANG} supported arches: ${_GPU_SUPPORTED_ARCHES_LIST}") + + if (${GPU_LANG} STREQUAL "HIP") + # + # `GPU_ARCHES` controls the `--offload-arch` flags. + # `CMAKE_HIP_ARCHITECTURES` is set up by torch and can be controlled + # via the `PYTORCH_ROCM_ARCH` env variable. + # + + # + # Find the intersection of the supported + detected architectures to + # set the module architecture flags. + # + set(${GPU_ARCHES}) + foreach (_ARCH ${CMAKE_HIP_ARCHITECTURES}) + if (_ARCH IN_LIST _GPU_SUPPORTED_ARCHES_LIST) + list(APPEND ${GPU_ARCHES} ${_ARCH}) + endif() + endforeach() + + if(NOT ${GPU_ARCHES}) + message(FATAL_ERROR + "None of the detected ROCm architectures: ${CMAKE_HIP_ARCHITECTURES} is" + " supported. Supported ROCm architectures are: ${_GPU_SUPPORTED_ARCHES_LIST}.") + endif() + + elseif(${GPU_LANG} STREQUAL "CUDA") + # + # Setup/process CUDA arch flags. + # + # The torch cmake setup hardcodes the detected architecture flags in + # `CMAKE_CUDA_FLAGS`. Since `CMAKE_CUDA_FLAGS` is a "global" variable, it + # can't modified on a per-target basis, e.g. for the `punica` extension. + # So, all the `-gencode` flags need to be extracted and removed from + # `CMAKE_CUDA_FLAGS` for processing so they can be passed by another method. + # Since it's not possible to use `target_compiler_options` for adding target + # specific `-gencode` arguments, the target's `CUDA_ARCHITECTURES` property + # must be used instead. This requires repackaging the architecture flags + # into a format that cmake expects for `CUDA_ARCHITECTURES`. + # + # This is a bit fragile in that it depends on torch using `-gencode` as opposed + # to one of the other nvcc options to specify architectures. + # + # Note: torch uses the `TORCH_CUDA_ARCH_LIST` environment variable to override + # detected architectures. + # + message(DEBUG "initial CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") + + # Extract all `-gencode` flags from `CMAKE_CUDA_FLAGS` + string(REGEX MATCHALL "-gencode arch=[^ ]+" _CUDA_ARCH_FLAGS + ${CMAKE_CUDA_FLAGS}) + + # Remove all `-gencode` flags from `CMAKE_CUDA_FLAGS` since they will be modified + # and passed back via the `CUDA_ARCHITECTURES` property. + string(REGEX REPLACE "-gencode arch=[^ ]+ *" "" CMAKE_CUDA_FLAGS + ${CMAKE_CUDA_FLAGS}) + + # If this error is triggered, it might mean that torch has changed how it sets + # up nvcc architecture code generation flags. + if (NOT _CUDA_ARCH_FLAGS) + message(FATAL_ERROR + "Could not find any architecture related code generation flags in " + "CMAKE_CUDA_FLAGS. (${CMAKE_CUDA_FLAGS})") + endif() + + message(DEBUG "final CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") + message(DEBUG "arch flags: ${_CUDA_ARCH_FLAGS}") + + # Initialize the architecture lists to empty. + set(${GPU_ARCHES}) + + # Process each `gencode` flag. + foreach(_ARCH ${_CUDA_ARCH_FLAGS}) + # For each flag, extract the version number and whether it refers to PTX + # or native code. + # Note: if a regex matches then `CMAKE_MATCH_1` holds the binding + # for that match. + + string(REGEX MATCH "arch=compute_\([0-9]+a?\)" _COMPUTE ${_ARCH}) + if (_COMPUTE) + set(_COMPUTE ${CMAKE_MATCH_1}) + endif() + + string(REGEX MATCH "code=sm_\([0-9]+a?\)" _SM ${_ARCH}) + if (_SM) + set(_SM ${CMAKE_MATCH_1}) + endif() + + string(REGEX MATCH "code=compute_\([0-9]+a?\)" _CODE ${_ARCH}) + if (_CODE) + set(_CODE ${CMAKE_MATCH_1}) + endif() + + # Make sure the virtual architecture can be matched. + if (NOT _COMPUTE) + message(FATAL_ERROR + "Could not determine virtual architecture from: ${_ARCH}.") + endif() + + # One of sm_ or compute_ must exist. + if ((NOT _SM) AND (NOT _CODE)) + message(FATAL_ERROR + "Could not determine a codegen architecture from: ${_ARCH}.") + endif() + + if (_SM) + # -real suffix let CMake to only generate elf code for the kernels. + # we want this, otherwise the added ptx (default) will increase binary size. + set(_VIRT "-real") + set(_CODE_ARCH ${_SM}) + else() + # -virtual suffix let CMake to generate ptx code for the kernels. + set(_VIRT "-virtual") + set(_CODE_ARCH ${_CODE}) + endif() + + # Check if the current version is in the supported arch list. + string_to_ver(_CODE_VER ${_CODE_ARCH}) + if (NOT _CODE_VER IN_LIST _GPU_SUPPORTED_ARCHES_LIST) + message(STATUS "discarding unsupported CUDA arch ${_VER}.") + continue() + endif() + + # Add it to the arch list. + list(APPEND ${GPU_ARCHES} "${_CODE_ARCH}${_VIRT}") + endforeach() + endif() + message(STATUS "${GPU_LANG} target arches: ${${GPU_ARCHES}}") +endmacro() + +# +# Define a target named `GPU_MOD_NAME` for a single extension. The +# arguments are: +# +# DESTINATION - Module destination directory. +# LANGUAGE - The GPU language for this module, e.g CUDA, HIP, +# etc. +# SOURCES - List of source files relative to CMakeLists.txt +# directory. +# +# Optional arguments: +# +# ARCHITECTURES - A list of target GPU architectures in cmake +# format. +# Refer `CMAKE_CUDA_ARCHITECTURES` documentation +# and `CMAKE_HIP_ARCHITECTURES` for more info. +# ARCHITECTURES will use cmake's defaults if +# not provided. +# COMPILE_FLAGS - Extra compiler flags passed to NVCC/hip. +# INCLUDE_DIRECTORIES - Extra include directories. +# LIBRARIES - Extra link libraries. +# WITH_SOABI - Generate library with python SOABI suffix name. +# CXX_SRCS - List of C++ sources files to not be hipified. +# Usually means they shouldn't contain CUDA code. +# +# Note: optimization level/debug info is set via cmake build type. +# +function (define_gpu_extension_target GPU_MOD_NAME) + cmake_parse_arguments(PARSE_ARGV 1 + GPU + "WITH_SOABI" + "DESTINATION;LANGUAGE" + "SOURCES;ARCHITECTURES;COMPILE_FLAGS;INCLUDE_DIRECTORIES;LIBRARIES;CXX_SRCS" + ) + + # Add hipify preprocessing step when building with HIP/ROCm. + if (GPU_LANGUAGE STREQUAL "HIP") + hipify_sources_target(GPU_SOURCES ${GPU_MOD_NAME} "${GPU_SOURCES}" "${GPU_CXX_SRCS}") + endif() + + if (GPU_WITH_SOABI) + set(GPU_WITH_SOABI WITH_SOABI) + else() + set(GPU_WITH_SOABI) + endif() + + Python_add_library(${GPU_MOD_NAME} MODULE "${GPU_SOURCES}" ${GPU_WITH_SOABI}) + + if (GPU_LANGUAGE STREQUAL "HIP") + # Make this target dependent on the hipify preprocessor step. + add_dependencies(${GPU_MOD_NAME} hipify${GPU_MOD_NAME}) + endif() + + if (GPU_ARCHITECTURES) + set_target_properties(${GPU_MOD_NAME} PROPERTIES + ${GPU_LANGUAGE}_ARCHITECTURES "${GPU_ARCHITECTURES}") + endif() + + set_property(TARGET ${GPU_MOD_NAME} PROPERTY CXX_STANDARD 17) + + target_compile_options(${GPU_MOD_NAME} PRIVATE + $<$:${GPU_COMPILE_FLAGS}>) + + target_compile_definitions(${GPU_MOD_NAME} PRIVATE + "-DTORCH_EXTENSION_NAME=${GPU_MOD_NAME}") + + target_include_directories(${GPU_MOD_NAME} PRIVATE csrc + ${GPU_INCLUDE_DIRECTORIES}) + + target_link_libraries(${GPU_MOD_NAME} PRIVATE torch ${torch_python_LIBRARY} + ${GPU_LIBRARIES}) + + # Don't use `TORCH_LIBRARIES` for CUDA since it pulls in a bunch of + # dependencies that are not necessary and may not be installed. + if (GPU_LANGUAGE STREQUAL "CUDA") + target_link_libraries(${GPU_MOD_NAME} PRIVATE ${CUDA_CUDA_LIB} + ${CUDA_LIBRARIES}) + else() + target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES}) + endif() + + install(TARGETS ${GPU_MOD_NAME} LIBRARY DESTINATION ${GPU_DESTINATION}) +endfunction() diff --git a/sllm_store/cpp_build.sh b/sllm_store/cpp_build.sh new file mode 100644 index 0000000..a77df33 --- /dev/null +++ b/sllm_store/cpp_build.sh @@ -0,0 +1,30 @@ +#!/bin/bash +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +set -e + +if [ ! -d "build" ]; then + mkdir build/ +fi +cd build/ + +export SLLM_STORE_PYTHON_EXECUTABLE=$(which python3) +cmake -DCMAKE_BUILD_TYPE=Release \ + -DSLLM_STORE_PYTHON_EXECUTABLE=$SLLM_STORE_PYTHON_EXECUTABLE \ + -G Ninja .. +cmake --build . --target all -j \ No newline at end of file diff --git a/sllm_store/csrc/.DS_Store b/sllm_store/csrc/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..839cbdaa1dd3e8886f3600ff29531b3528686f10 GIT binary patch literal 6148 zcmeHKy-veG47N*$082+E$jE|F-yl@s3Hkt3{SieZB!V(?tjs(BZ^MdsB0it3lBkVf zLKU*B^LO#tcg~k6u8D|8kMkkXfQS+d!QMX97Lj!s8`3k2EOOlAmTIc#hVE#!;q8W9 zWI&$X0nKPh3+Tr4E3Sv*Syk4W^yInAudMQXT2xc4CugruFX8z8qT6@=Yu|d=JpT<_ zBF_gZ@GI#GvlZU7ds?*e{(XddH&?v%9B4NPZwvv8E^)6odMKr zk>pU(wKL!hI0I`20+9w+&cL5C F@C8p;SYQAE literal 0 HcmV?d00001 diff --git a/sllm_store/csrc/checkpoint/aligned_buffer.cpp b/sllm_store/csrc/checkpoint/aligned_buffer.cpp new file mode 100644 index 0000000..4b93340 --- /dev/null +++ b/sllm_store/csrc/checkpoint/aligned_buffer.cpp @@ -0,0 +1,113 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "aligned_buffer.h" + +#include +#include + +#include +#include + +AlignedBuffer::AlignedBuffer(const std::string& filename) + : fd_(-1), buf_size_(kBufferSize), buf_pos_(0), file_offset_(0) { + fd_ = open(filename.c_str(), O_WRONLY | O_CREAT | O_DIRECT, 0644); + if (fd_ < 0) { + std::cerr << "Failed to open file " << filename << std::endl; + } + buffer_ = aligned_alloc(kAlignment, kBufferSize); +} + +AlignedBuffer::~AlignedBuffer() { + // if the buffer is not written to file, write it to file + if (buffer_ && buf_pos_) { + pwrite(fd_, buffer_, buf_pos_, file_offset_); + } + if (buffer_) { + free(buffer_); + } + if (fd_ >= 0) { + close(fd_); + } +} + +size_t AlignedBuffer::writeData(const void* data, size_t size) { + size_t written = 0; + while (written < size) { + // if data size is larger than buffer size and buffer is empty + // write data directly to file + if (size - written > buf_size_ && buf_pos_ == 0) { + size_t direct_write_size = (size - written) / kAlignment * kAlignment; + // allocate aligned memory + void* direct_write_buf = aligned_alloc(kAlignment, direct_write_size); + if (!direct_write_buf) { + char err_msg[256]; + strerror_r(errno, err_msg, sizeof(err_msg)); + std::cerr << "Failed to allocate aligned memory: " << err_msg + << std::endl; + std::cerr << "kAlignment: " << kAlignment + << " direct_write_size: " << direct_write_size; + exit(1); + } + memcpy(direct_write_buf, (char*)data + written, direct_write_size); + ssize_t ret = + pwrite(fd_, direct_write_buf, direct_write_size, file_offset_); + if (ret < 0 || ret != direct_write_size) { + std::cerr << "Failed to write to file, ret: " << ret + << " errno: " << errno << std::endl; + return written; + } + written += direct_write_size; + file_offset_ += direct_write_size; + free(direct_write_buf); + } + size_t to_write = std::min(size - written, buf_size_ - buf_pos_); + memcpy(static_cast(buffer_) + buf_pos_, (char*)data + written, + to_write); + buf_pos_ += to_write; + written += to_write; + if (buf_pos_ == buf_size_) { + ssize_t ret = pwrite(fd_, buffer_, buf_size_, file_offset_); + if (ret < 0 || ret != buf_size_) { + std::cerr << "Failed to write to file, ret: " << ret + << " errno: " << errno << std::endl; + return written; + } + buf_pos_ = 0; + file_offset_ += buf_size_; + } + } + return written; +} + +size_t AlignedBuffer::writePadding(size_t padding_size) { + if (padding_size >= 8) { + std::cerr << "Padding size should be less than 8 bytes" << std::endl; + return 0; + } + buf_pos_ += padding_size; + if (buf_pos_ > buf_size_) { + std::cerr << "Padding size is too large" << std::endl; + return 0; + } + if (buf_pos_ == buf_size_) { + pwrite(fd_, buffer_, buf_pos_, file_offset_); + buf_pos_ = 0; + file_offset_ += buf_size_; + } + return padding_size; +} \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/aligned_buffer.h b/sllm_store/csrc/checkpoint/aligned_buffer.h new file mode 100644 index 0000000..4d9ef0b --- /dev/null +++ b/sllm_store/csrc/checkpoint/aligned_buffer.h @@ -0,0 +1,40 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include + +const size_t kAlignment = 4096; // 4k +const size_t kBufferSize = 1L << 30; // 1GB + +// A write buffer that writes to a file (4k aligned). +class AlignedBuffer { + public: + explicit AlignedBuffer(const std::string& filename); + ~AlignedBuffer(); + + size_t writeData(const void* data, size_t size); + size_t writePadding(size_t padding_size); + + private: + int fd_; + size_t buf_size_; + size_t buf_pos_; + size_t file_offset_; + void* buffer_; +}; \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/checkpoint.cpp b/sllm_store/csrc/checkpoint/checkpoint.cpp new file mode 100644 index 0000000..58c3b6b --- /dev/null +++ b/sllm_store/csrc/checkpoint/checkpoint.cpp @@ -0,0 +1,250 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "checkpoint.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include // One-stop header. +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "progress_bar.h" +#include "tensor_writer.h" + +#define BUFFER_SIZE 1 << 30 + +std::unordered_map SaveTensors( + std::vector tensor_names, + std::unordered_map>& tensor_data, + const std::string& path) { + std::string tensor_filename = std::filesystem::path(path) / "tensor.data"; + // make a tensor writer + TensorWriter writer(tensor_filename); + // make a tensor index + std::unordered_map tensor_offsets; + // Some tensors may share the same data, so we need to record the data to + // avoid duplication + std::unordered_map data_record; + + int total = tensor_names.size(); + int count = 0; + + for (const auto& name : tensor_names) { + const auto& [base, size] = tensor_data[name]; + const char* data_ptr = reinterpret_cast(base); + if (data_record.find(data_ptr) != data_record.end()) { + tensor_offsets[name] = tensor_offsets[data_record[data_ptr]]; + continue; + } + data_record[data_ptr] = name; + + uint64_t offset = writer.writeRecord(data_ptr, size); + tensor_offsets[name] = offset; + + // Update progress bar + count++; + float progress = static_cast(count) / total; + showProgressBar(progress, "Saving tensors: "); + } + + return tensor_offsets; +} + +// Function to print the binary array in hexadecimal format +void printBinaryArrayInHex(const unsigned char* data, size_t size) { + std::cout << "Data in Hex: "; + for (size_t i = 0; i < size; ++i) { + std::cout << std::hex << std::setw(2) << std::setfill('0') + << static_cast(data[i]) << " "; + } + std::cout << std::dec + << std::endl; // Switch back to decimal for any future output +} + +// Mapping from string to at::ScalarType +at::ScalarType stringToScalarType(const std::string& dtype_str) { + static const std::unordered_map dtype_map = { + {"torch.float16", torch::kFloat16}, {"torch.float32", torch::kFloat32}, + {"torch.float64", torch::kFloat64}, {"torch.int16", torch::kInt16}, + {"torch.int32", torch::kInt32}, {"torch.int64", torch::kInt64}, + {"torch.uint8", torch::kUInt8}, {"torch.int8", torch::kInt8}, + {"torch.bfloat16", torch::kBFloat16}}; + + auto it = dtype_map.find(dtype_str); + if (it != dtype_map.end()) { + return it->second; + } else { + throw std::invalid_argument("Unknown dtype string: " + dtype_str); + } +} + +std::unordered_map RestoreTensors( + const std::unordered_map< + std::string, std::tuple, std::vector, + std::string>>& meta_state_dict, + const std::unordered_map& memory_base_address, + const std::unordered_map>& + tensor_device_offsets) { + std::unordered_map state_dict; + std::unordered_set handled_memory; + for (const auto& [device, tensor_offset] : tensor_device_offsets) { + for (const auto& p : tensor_offset) { + std::string name = p.first; + if (memory_base_address.find(device) != memory_base_address.end()) { + void* base_address = memory_base_address.at(device); + uint64_t offset = reinterpret_cast(base_address) + p.second; + + torch::Device tensor_device(torch::kCUDA, device); + auto [sizes, strides, type_str] = meta_state_dict.at(name); + at::ScalarType dtype = stringToScalarType(type_str); + // std::cerr << name << " " << sizes << " " << strides << " " << dtype + // << std::endl; + if (p.second == 0 && + handled_memory.find(base_address) == handled_memory.end()) { + torch::Tensor real_tensor = torch::from_blob( + reinterpret_cast(offset), c10::makeArrayRef(sizes), + c10::makeArrayRef(strides), [](void* ptr) { cudaFree(ptr); }, + torch::TensorOptions().device(tensor_device).dtype(dtype)); + state_dict[name] = real_tensor; + handled_memory.insert(base_address); + // std::cerr << "Tensor " << name << " is restored to device " << + // device << std::endl; + } else { + torch::Tensor real_tensor = torch::from_blob( + reinterpret_cast(offset), sizes, strides, [](void* ptr) {}, + torch::TensorOptions().device(tensor_device).dtype(dtype)); + state_dict[name] = real_tensor; + } + } else { + std::cerr << "Cannot find device " << device << std::endl; + exit(1); + } + } + } + return state_dict; +} + +std::unordered_map GetGpuUUID() { + int deviceCount = 0; + cudaGetDeviceCount(&deviceCount); // Get the number of CUDA devices + std::unordered_map uuidToDeviceIdMap; + + for (int devId = 0; devId < deviceCount; ++devId) { + cudaDeviceProp props; + cudaGetDeviceProperties(&props, devId); // Get properties for each device + + // Convert UUID bytes to string with unsigned char casting + char uuidStr[80]; + snprintf( + uuidStr, sizeof(uuidStr), + "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + (unsigned char)props.uuid.bytes[0], (unsigned char)props.uuid.bytes[1], + (unsigned char)props.uuid.bytes[2], (unsigned char)props.uuid.bytes[3], + (unsigned char)props.uuid.bytes[4], (unsigned char)props.uuid.bytes[5], + (unsigned char)props.uuid.bytes[6], (unsigned char)props.uuid.bytes[7], + (unsigned char)props.uuid.bytes[8], (unsigned char)props.uuid.bytes[9], + (unsigned char)props.uuid.bytes[10], + (unsigned char)props.uuid.bytes[11], + (unsigned char)props.uuid.bytes[12], + (unsigned char)props.uuid.bytes[13], + (unsigned char)props.uuid.bytes[14], + (unsigned char)props.uuid.bytes[15]); + + uuidToDeviceIdMap[std::string(uuidStr)] = devId; + } + + return uuidToDeviceIdMap; +} + +std::unordered_map AllocateCudaMemory( + const std::unordered_map& tensor_sizes) { + std::unordered_map memory_ptrs; + for (const auto& p : tensor_sizes) { + int device = p.first; + size_t size = p.second; + void* ptr = nullptr; + cudaSetDevice(device); + cudaMalloc(&ptr, size); + memory_ptrs[device] = ptr; + } + return memory_ptrs; +} + +std::unordered_map GetCudaMemoryHandles( + const std::unordered_map& memory_ptrs) { + std::unordered_map memory_handles; + for (const auto& p : memory_ptrs) { + int device = p.first; + void* ptr = p.second; + cudaIpcMemHandle_t handle; + cudaSetDevice(device); + cudaIpcGetMemHandle(&handle, ptr); + memory_handles[device] = std::string(reinterpret_cast(&handle), + sizeof(cudaIpcMemHandle_t)); + } + return memory_handles; +} + +std::unordered_map> GetCudaMemoryHandles( + const std::unordered_map>& memory_ptrs) { + std::unordered_map> memory_handles; + for (const auto& p : memory_ptrs) { + auto device = p.first; + const auto& ptrs = p.second; + cudaIpcMemHandle_t handle; + cudaSetDevice(device); + + std::vector handles; + for (const auto& ptr : ptrs) { + cudaIpcGetMemHandle(&handle, ptr); + handles.push_back(std::string(reinterpret_cast(&handle), + sizeof(cudaIpcMemHandle_t))); + } + memory_handles[device] = handles; + } + return memory_handles; +} + +std::unordered_map GetDeviceUuidMap() { + std::unordered_map gpu_uuid = GetGpuUUID(); + std::unordered_map device_uuid_map; + for (const auto& p : gpu_uuid) { + if (device_uuid_map.find(p.second) != device_uuid_map.end()) { + std::cerr << "Duplicate device id: " << p.second << std::endl; + exit(1); + } + device_uuid_map[p.second] = p.first; + } + return device_uuid_map; +} \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/checkpoint.h b/sllm_store/csrc/checkpoint/checkpoint.h new file mode 100644 index 0000000..692365e --- /dev/null +++ b/sllm_store/csrc/checkpoint/checkpoint.h @@ -0,0 +1,48 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include // One-stop header. + +#include +#include + +std::unordered_map SaveTensors( + std::vector tensor_names, + std::unordered_map>& tensor_data, + const std::string& path); + +std::unordered_map RestoreTensors( + const std::unordered_map< + std::string, std::tuple, std::vector, + std::string>>& meta_state_dict, + const std::unordered_map& memory_base_address, + const std::unordered_map>& + tensor_device_offsets); + +// {dev_id: ptr} +std::unordered_map AllocateCudaMemory( + const std::unordered_map& tensor_sizes); +std::unordered_map GetCudaMemoryHandles( + const std::unordered_map& memory_ptrs); +std::unordered_map> GetCudaMemoryHandles( + const std::unordered_map>& memory_ptrs); +std::unordered_map GetDeviceUuidMap(); + +std::unordered_map GetGpuUUID(); \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/checkpoint_py.cpp b/sllm_store/csrc/checkpoint/checkpoint_py.cpp new file mode 100644 index 0000000..5157289 --- /dev/null +++ b/sllm_store/csrc/checkpoint/checkpoint_py.cpp @@ -0,0 +1,84 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include + +#include "checkpoint.h" + +namespace py = pybind11; + +// define pybind11 module +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("save_tensors", &SaveTensors, "Save a state dict") + .def("restore_tensors", &RestoreTensors, "Restore a state dict") + .def("allocate_cuda_memory", &AllocateCudaMemory, "Allocate cuda memory") + .def( + "get_cuda_memory_handles", + [](const std::unordered_map& memory_ptrs) { + std::unordered_map memory_handles = + GetCudaMemoryHandles(memory_ptrs); + + std::unordered_map py_memory_handles; + for (const auto& kv : memory_handles) { + py_memory_handles[kv.first] = py::bytes(kv.second); + } + return py_memory_handles; + }, + py::arg("memory_ptrs"), "Get cuda memory handles") + .def( + "get_cuda_memory_handles", + [](const std::unordered_map>& memory_ptrs) { + auto memory_handles = GetCudaMemoryHandles(memory_ptrs); + + std::unordered_map> py_memory_handles; + for (const auto& kv : memory_handles) { + std::vector handles; + for (const auto& handle : kv.second) { + handles.push_back(py::bytes(handle)); + } + py_memory_handles[kv.first] = handles; + } + return py_memory_handles; + }, + py::arg("memory_ptrs"), "Get cuda memory handles") + .def( + "get_cuda_memory_handles", + [](const std::unordered_map>& + memory_ptrs) { + std::unordered_map> memory_ptrs_void; + for (const auto& kv : memory_ptrs) { + std::vector ptrs; + for (const auto& ptr : kv.second) { + ptrs.push_back(reinterpret_cast(ptr)); + } + memory_ptrs_void[kv.first] = ptrs; + } + auto memory_handles = GetCudaMemoryHandles(memory_ptrs_void); + + std::unordered_map> py_memory_handles; + for (const auto& kv : memory_handles) { + std::vector handles; + for (const auto& handle : kv.second) { + handles.push_back(py::bytes(handle)); + } + py_memory_handles[kv.first] = handles; + } + return py_memory_handles; + }, + py::arg("memory_ptrs"), "Get cuda memory handles") + .def("get_device_uuid_map", &GetDeviceUuidMap, "Get device uuid map"); +} \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/progress_bar.h b/sllm_store/csrc/checkpoint/progress_bar.h new file mode 100644 index 0000000..5644e39 --- /dev/null +++ b/sllm_store/csrc/checkpoint/progress_bar.h @@ -0,0 +1,41 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include + +void showProgressBar(float progress, std::string message = "") { + const int barWidth = 70; + int progress_percent = int(progress * 100.0); + std::cout << message << progress_percent << "% ["; + int pos = static_cast(barWidth * progress); + for (int i = 0; i < barWidth; ++i) { + if (i < pos) { + std::cout << "="; + } else if (i == pos) { + std::cout << ">"; + } else { + std::cout << " "; + } + } + std::cout << "] " << progress_percent << " %"; + if (progress_percent == 100) { + std::cout << std::endl; + } else { + std::cout << "\r"; + } + std::cout.flush(); +} \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/tensor_writer.cpp b/sllm_store/csrc/checkpoint/tensor_writer.cpp new file mode 100644 index 0000000..46eb304 --- /dev/null +++ b/sllm_store/csrc/checkpoint/tensor_writer.cpp @@ -0,0 +1,49 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "tensor_writer.h" + +#include + +TensorWriter::TensorWriter(const std::string& filename) : filename_(filename) {} + +TensorWriter::~TensorWriter() {} + +uint64_t TensorWriter::writeRecord(const char* data, size_t size) { + if (partition_idx_ == -1 || partition_size_ + size > kPartitionMaxSize) { + // create a new partition + partition_idx_++; + partition_size_ = 0; + std::string partition_filename = + filename_ + "_" + std::to_string(partition_idx_); + buffer_ = std::make_unique(partition_filename); + } + + uint64_t start_offset = offset_; + // make sure the data is 64-bit aligned + size_t padding = (size % 8) ? (8 - size % 8) : 0; + size_t written = buffer_->writeData(data, size); + if (padding) { + written += buffer_->writePadding(padding); + } + offset_ += written; + partition_size_ += written; + // std::cerr << "writeRecord: " << partition_idx_ << " " << partition_size_ << + // " " << kPartitionMaxSize << std::endl; + + return start_offset; +} diff --git a/sllm_store/csrc/checkpoint/tensor_writer.h b/sllm_store/csrc/checkpoint/tensor_writer.h new file mode 100644 index 0000000..0ad7d3f --- /dev/null +++ b/sllm_store/csrc/checkpoint/tensor_writer.h @@ -0,0 +1,42 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include + +#include "aligned_buffer.h" + +const size_t kPartitionMaxSize = 10L << 30; // 10GB + +// A tensor writer that writes the raw tensor data to a file in raw binary. +class TensorWriter final { + public: + explicit TensorWriter(const std::string& filename); + ~TensorWriter(); + + uint64_t writeRecord(const char* data, size_t size); + + private: + size_t offset_ = 0; + int partition_idx_ = -1; + size_t partition_size_ = 0; + std::string filename_; + std::unique_ptr buffer_; +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/binary_utils.cpp b/sllm_store/csrc/sllm_store/binary_utils.cpp new file mode 100644 index 0000000..e84449a --- /dev/null +++ b/sllm_store/csrc/sllm_store/binary_utils.cpp @@ -0,0 +1,32 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "binary_utils.h" + +#include +#include + +// Function to print the binary array in hexadecimal format +void PrintBinaryArrayInHex(const unsigned char* data, size_t size) { + std::cout << "Data in Hex: "; + for (size_t i = 0; i < size; ++i) { + std::cout << std::hex << std::setw(2) << std::setfill('0') + << static_cast(data[i]) << " "; + } + std::cout << std::dec + << std::endl; // Switch back to decimal for any future output +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/binary_utils.h b/sllm_store/csrc/sllm_store/binary_utils.h new file mode 100644 index 0000000..49a12a9 --- /dev/null +++ b/sllm_store/csrc/sllm_store/binary_utils.h @@ -0,0 +1,23 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include + +// Function to print the binary array in hexadecimal format +void PrintBinaryArrayInHex(const unsigned char* data, size_t size); \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/checkpoint_store.cpp b/sllm_store/csrc/sllm_store/checkpoint_store.cpp new file mode 100644 index 0000000..a32ae44 --- /dev/null +++ b/sllm_store/csrc/sllm_store/checkpoint_store.cpp @@ -0,0 +1,339 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "checkpoint_store.h" + +#include +#include +#include +#include +#include + +#include +#include +#include + +#include "error_handling.h" + +CheckpointStore::CheckpointStore(const std::string& storage_path, + size_t memory_pool_size, int num_thread, + size_t chunk_size) + : storage_path_(storage_path), + memory_pool_size_(memory_pool_size), + num_thread_(num_thread), + chunk_size_(chunk_size) { + // Get number of GPUs + cudaGetDeviceCount(&num_gpus_); + LOG(INFO) << "Number of GPUs: " << num_gpus_; + + LOG(INFO) << "I/O threads: " << num_thread + << ", chunk size: " << chunk_size / MB << "MB"; + LOG(INFO) << "Storage path: " << storage_path_; + + for (size_t i = 0; i < num_gpus_; ++i) { + cudaSetDevice(i); + + cudaDeviceProp props; + cudaGetDeviceProperties(&props, i); + + // Get GPU UUID + char uuidStr[80]; + snprintf( + uuidStr, sizeof(uuidStr), + "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + (unsigned char)props.uuid.bytes[0], (unsigned char)props.uuid.bytes[1], + (unsigned char)props.uuid.bytes[2], (unsigned char)props.uuid.bytes[3], + (unsigned char)props.uuid.bytes[4], (unsigned char)props.uuid.bytes[5], + (unsigned char)props.uuid.bytes[6], (unsigned char)props.uuid.bytes[7], + (unsigned char)props.uuid.bytes[8], (unsigned char)props.uuid.bytes[9], + (unsigned char)props.uuid.bytes[10], + (unsigned char)props.uuid.bytes[11], + (unsigned char)props.uuid.bytes[12], + (unsigned char)props.uuid.bytes[13], + (unsigned char)props.uuid.bytes[14], + (unsigned char)props.uuid.bytes[15]); + + gpu_info_map_[i].uuid_ = std::string(uuidStr); + LOG(INFO) << "GPU " << i << " UUID: " << gpu_info_map_[i].uuid_; + + // create stream + cudaError_t err = cudaStreamCreate(&gpu_info_map_[i].stream_); + if (err != cudaSuccess) { + LOG(FATAL) << "cudaStreamCreate error: " << cudaGetErrorString(err); + } + } + + // Create a memory pool + memory_pool_ = + std::make_shared(memory_pool_size_, chunk_size_); + LOG(INFO) << "Memory pool created with " << memory_pool_size_ / GB << "GB"; +} + +CheckpointStore::~CheckpointStore() { ClearMem(); } + +int64_t CheckpointStore::RegisterModelInfo(const std::string& model_path) { + std::unique_lock lock_info(model_info_mutex_); + if (model_map_.find(model_path) != model_map_.end()) { + // LOG(WARNING) << "Model " << model_path << " is already regfistered"; + auto model = model_map_.at(model_path); + return model->GetModelSize(); + } + + auto model = std::make_shared(model_path); + + int ret = model->Initialize(storage_path_); + if (ret != 0) { + LOG(ERROR) << "Failed to initialize model " << model_path; + return ret; + } + + model_map_[model_path] = model; + + LOG(INFO) << "Model " << model_path << " is registered"; + + return model->GetModelSize(); +} + +int CheckpointStore::LoadModelFromDisk(const std::string& model_path) { + std::unique_lock lock_info(model_info_mutex_); + auto model = GetModelPtr(model_path); + if (model == nullptr) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return -1; + } + lock_info.unlock(); + + // Allocate memory + lock_info.lock(); + int remaining_size = model->AllocatePinnedMemory(memory_pool_); + if (remaining_size < 0) { + LOG(ERROR) << "Failed to allocate memory for model " << model_path; + return -1; + } else if (remaining_size > 0) { + int mem_chunks_needed = remaining_size; + std::vector>> + model_last_access_time(model_last_access_time_.begin(), + model_last_access_time_.end()); + std::sort(model_last_access_time.begin(), model_last_access_time.end(), + [](const auto& a, const auto& b) { return a.second < b.second; }); + for (const auto& [model_path, last_access_time] : model_last_access_time) { + auto& model = model_map_[model_path]; + int freed_chunks = + model->TryFreeHost(); // try to free memory, non-blocking + LOG(INFO) << "Free " << freed_chunks << " chunks, remaining " + << mem_chunks_needed; + if (freed_chunks > 0) { + LOG(INFO) << "Model " << model_path << " is freed from memory"; + mem_chunks_needed -= freed_chunks; + if (mem_chunks_needed <= 0) { + break; + } + } + } + if (mem_chunks_needed > 0) { + LOG(ERROR) << "Failed to free enough memory for model " << model_path; + return -1; + } + ssize_t remaining_size = model->AllocatePinnedMemory(memory_pool_); + if (remaining_size < 0) { + LOG(ERROR) << "Failed to allocate memory for model " << model_path; + return -1; + } + } + model_last_access_time_[model_path] = std::chrono::system_clock::now(); + lock_info.unlock(); + + int ret = model->ToHost(num_thread_); + + if (ret != 0) { + LOG(ERROR) << "Failed to load model " << model_path << " to host"; + if (model->FreeHost() != 0) { + LOG(ERROR) << "Failed to free memory for model " << model_path; + } + return ret; + } + + return ret; +} + +int CheckpointStore::LoadModelFromDiskAsync(const std::string& model_path) { + std::unique_lock lock_info(model_info_mutex_); + async_tasks_.emplace(std::async(std::launch::async, [this, model_path]() { + return LoadModelFromDisk(model_path); + })); + + return 0; +} + +int CheckpointStore::LoadModelFromMem( + const std::string& model_path, const std::string& replica_uuid, + const MemCopyHandleListMap& gpu_memory_handles, + const MemCopyChunkListMap& mem_copy_chunks) { + // Sanity check + if (model_path.empty() || replica_uuid.empty()) { + LOG(ERROR) << "Model name or replica uuid is empty"; + return -1; + } else if (mem_copy_chunks.empty()) { + LOG(ERROR) << "No memory copy chunk provided"; + return -1; + } else if (gpu_memory_handles.size() != mem_copy_chunks.size()) { + LOG(ERROR) << "Mismatch between memory handles " + << gpu_memory_handles.size() << " and memory copy chunks " + << mem_copy_chunks.size(); + return -1; + } + std::unique_lock lock_info(model_info_mutex_); + auto model = GetModelPtr(model_path); + if (model == nullptr) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return -1; + } + model_last_access_time_[model_path] = std::chrono::system_clock::now(); + lock_info.unlock(); + + LOG(INFO) << "Loading model " << model_path; + + // Convert device uuid to device id + std::unordered_map converted_mem_copy_chunks; + for (auto& [device_id, gpu_info] : gpu_info_map_) { + if (mem_copy_chunks.find(gpu_info.uuid_) == mem_copy_chunks.end()) { + continue; + } + converted_mem_copy_chunks[device_id] = mem_copy_chunks.at(gpu_info.uuid_); + } + + std::unordered_map converted_mem_copy_handles; + for (auto& [device_id, gpu_info] : gpu_info_map_) { + if (gpu_memory_handles.find(gpu_info.uuid_) == gpu_memory_handles.end()) { + continue; + } + converted_mem_copy_handles[device_id] = + gpu_memory_handles.at(gpu_info.uuid_); + } + // Convert memory handles to device pointers + auto device_ptrs = GetDevicePtrsFromMemHandles(gpu_memory_handles); + + auto ret = model->ToGpu(replica_uuid, device_ptrs, converted_mem_copy_chunks, + converted_mem_copy_handles); + + // TODO: check if the model is loaded successfully + if (ret != 0) { + LOG(ERROR) << "Failed to load model " << model_path << " to GPU"; + if (model->FreeGpu(replica_uuid) != 0) { + LOG(ERROR) << "Failed to free memory for model " << model_path; + } + } + + return ret; +} + +int CheckpointStore::LoadModelFromMemAsync( + const std::string& model_path, const std::string& replica_uuid, + const std::unordered_map& + gpu_memory_handles, + const std::unordered_map& mem_copy_chunks) { + std::unique_lock lock_info(model_info_mutex_); + async_tasks_.emplace(std::async( + std::launch::async, + [this, model_path, replica_uuid, gpu_memory_handles, mem_copy_chunks]() { + return LoadModelFromMem(model_path, replica_uuid, gpu_memory_handles, + mem_copy_chunks); + })); + + return 0; +} + +int CheckpointStore::WaitModelInGpu(const std::string& model_path, + const std::string& replica_uuid) { + // check if the model is in memory + std::shared_ptr model; + std::unique_lock lock_info(model_info_mutex_); + if (model_map_.find(model_path) == model_map_.end()) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return 1; + } + model = model_map_[model_path]; + lock_info.unlock(); + + return model->WaitInGpu(replica_uuid); +} + +int CheckpointStore::ClearMem() { + std::unique_lock lock_info(model_info_mutex_); + for (auto& [model_path, model] : model_map_) { + LOG(INFO) << "Unloading model " << model_path; + int ret = model->FreeHost(); + if (ret != 0) { + LOG(ERROR) << "Failed to free memory for model " << model_path; + } + } + model_map_.clear(); + LOG(INFO) << "All models unloaded from memory\n"; + return 0; +} + +int CheckpointStore::UnloadModelFromHost(const std::string& model_path) { + std::unique_lock lock_info(model_info_mutex_); + if (model_map_.find(model_path) == model_map_.end()) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return 1; + } + auto model = model_map_.at(model_path); + lock_info.unlock(); + + return model->FreeHost(); +} + +ModelPtr CheckpointStore::GetModelPtr(const std::string& model_path) { + if (model_map_.find(model_path) == model_map_.end()) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return nullptr; + } + return model_map_.at(model_path); +} + +MemPtrListMap CheckpointStore::GetDevicePtrsFromMemHandles( + const MemCopyHandleListMap& memory_handles) { + MemPtrListMap gpu_ptrs; + for (const auto& [device_id, gpu_info] : gpu_info_map_) { + const std::string& uuid = gpu_info.uuid_; + if (memory_handles.find(uuid) == memory_handles.end()) { + continue; + } + auto& handle_list = memory_handles.at(uuid); + for (const auto& handle : handle_list) { + // Convert handle string to cuda handle + cudaIpcMemHandle_t* cuda_handle = + reinterpret_cast(const_cast( + reinterpret_cast(handle.cuda_ipc_handle_.data()))); + void* ptr = nullptr; + + cudaSetDevice(device_id); + cudaError_t err = cudaIpcOpenMemHandle(&ptr, *cuda_handle, + cudaIpcMemLazyEnablePeerAccess); + if (err != cudaSuccess || ptr == nullptr) { + LOG(ERROR) << "Failed to open cuda handle on device " << device_id + << " error: " << cudaGetErrorString(err); + exit(1); + } + + gpu_ptrs[device_id].push_back(ptr); + } + } + return gpu_ptrs; +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/checkpoint_store.h b/sllm_store/csrc/sllm_store/checkpoint_store.h new file mode 100644 index 0000000..14a004a --- /dev/null +++ b/sllm_store/csrc/sllm_store/checkpoint_store.h @@ -0,0 +1,107 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +// Third-party library headers +#include + +// Own Headers +#include "cuda_memory.h" +// #include "cuda_memory_pool.h" +#include "model.h" +#include "pinned_memory.h" +#include "pinned_memory_pool.h" +#include "types_and_defs.h" + +class CheckpointStore { + public: + CheckpointStore(const std::string& storage_path, size_t memory_pool_size, + int num_thread, size_t chunk_size); + ~CheckpointStore(); + + int64_t RegisterModelInfo(const std::string& model_path); + int LoadModelFromDisk(const std::string& model_path); + int LoadModelFromDiskAsync(const std::string& model_path); + int LoadModelFromMem(const std::string& model_path, + const std::string& replica_uuid, + const MemCopyHandleListMap& gpu_memory_handles, + const MemCopyChunkListMap& mem_copy_chunks); + int LoadModelFromMemAsync(const std::string& model_path, + const std::string& replica_uuid, + const MemCopyHandleListMap& gpu_memory_handles, + const MemCopyChunkListMap& mem_copy_chunks); + int WaitModelInGpu(const std::string& model_path, + const std::string& replica_uuid); + int UnloadModelFromHost(const std::string& model_path); + int ClearMem(); + + public: + // Get methods + size_t GetMemPoolSize() const { return memory_pool_size_; } + size_t GetChunkSize() const { return chunk_size_; } + + private: + // A GPU info struct + struct GpuInfo { + // uuid + std::string uuid_; + // int device_id_; + size_t total_memory_ = 0; + size_t free_memory_ = 0; + cudaStream_t stream_; + }; + + const std::filesystem::path storage_path_; + int num_gpus_; + std::unordered_map gpu_info_map_; + std::unordered_map> model_map_; + std::unordered_map> + model_last_access_time_; + std::mutex model_info_mutex_; + const size_t memory_pool_size_; + std::shared_ptr memory_pool_; + int num_thread_; + size_t chunk_size_; + + std::queue> async_tasks_; + + size_t GetNumChunkFromTensorSize(size_t tensor_size); + ModelPtr GetModelPtr(const std::string& model_path); + GpuReplicaPtr NewGpuReplica(const std::shared_ptr& model, + const std::string& replica_uuid); + int InitializeModel(const std::shared_ptr& model); + int AllocatePinnedMemory(const std::shared_ptr& model); + std::vector> CalculateChunks(size_t offset, + size_t size); + int AllocateCudaMemory( + const std::shared_ptr& gpu_replica, + std::vector> gpu_memory_sizes); + ModelPtr GetModelByName(const std::string& model_path); + MemPtrListMap GetDevicePtrsFromMemHandles( + const MemCopyHandleListMap& memory_handles); +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/checkpoint_store_py.cpp b/sllm_store/csrc/sllm_store/checkpoint_store_py.cpp new file mode 100644 index 0000000..9835e89 --- /dev/null +++ b/sllm_store/csrc/sllm_store/checkpoint_store_py.cpp @@ -0,0 +1,83 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include +#include +#include + +#include "checkpoint_store.h" +#include "types_and_defs.h" + +namespace py = pybind11; + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + // Binding for MemCopyChunk + py::class_(m, "MemCopyChunk") + .def(py::init<>()) + .def_readwrite("src_offset", &MemCopyChunk::src_offset_) + .def_readwrite("size", &MemCopyChunk::size_) + .def_readwrite("dst_offset", &MemCopyChunk::dst_offset_) + .def_readwrite("handle_idx", &MemCopyChunk::handle_idx_); + + py::class_(m, "CheckpointStore") + .def(py::init(), + py::arg("storage_path"), py::arg("memory_pool_size"), + py::arg("num_thread"), py::arg("chunk_size")) + .def("register_model_info", &CheckpointStore::RegisterModelInfo, + py::arg("model_path"), + "Register the model information and return its size.") + .def("load_model_from_disk_async", + &CheckpointStore::LoadModelFromDiskAsync, py::arg("model_path"), + "Load a model from disk asynchronously.") + .def( + "load_model_from_mem_async", + [](CheckpointStore& cs, const std::string& model_path, + const std::string& replica_uuid, + const std::unordered_map>& + gpu_memory_handles, + const MemCopyChunkListMap& mem_copy_chunks) { + // Convert memory handles to MemCopyHandleListMap + MemCopyHandleListMap gpu_memory_handles_map; + for (const auto& [device_id, handles] : gpu_memory_handles) { + MemCopyHandleList handle_list; + for (const auto& handle : handles) { + handle_list.push_back( + MemCopyHandle{handle.cast()}); + } + gpu_memory_handles_map[device_id] = handle_list; + } + return cs.LoadModelFromMemAsync(model_path, replica_uuid, + gpu_memory_handles_map, + mem_copy_chunks); + }, + py::arg("model_path"), py::arg("replica_uuid"), + py::arg("gpu_memory_handles"), py::arg("mem_copy_chunks"), + "Load a model from memory asynchronously.") + .def("wait_model_in_gpu", &CheckpointStore::WaitModelInGpu, + py::arg("model_path"), py::arg("replica_uuid"), + "Wait for a model to be available in GPU memory.") + .def("unload_model_from_host", &CheckpointStore::UnloadModelFromHost, + py::arg("model_path"), "Unload a model from the host memory.") + .def("clear_mem", &CheckpointStore::ClearMem, + "Clear all allocated memory.") + .def("get_mem_pool_size", &CheckpointStore::GetMemPoolSize, + "Get the memory pool size.") + .def("get_chunk_size", &CheckpointStore::GetChunkSize, + "Get the chunk size.") + .def("__repr__", + [](const CheckpointStore& cs) { return ""; }); +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/concurrent_queue.h b/sllm_store/csrc/sllm_store/concurrent_queue.h new file mode 100644 index 0000000..356ca15 --- /dev/null +++ b/sllm_store/csrc/sllm_store/concurrent_queue.h @@ -0,0 +1,65 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include + +template +class ConcurrentQueue { + public: + ConcurrentQueue() = default; + ConcurrentQueue(const ConcurrentQueue&) = delete; + ConcurrentQueue& operator=(const ConcurrentQueue&) = delete; + + void enqueue(T item); + T dequeue(); + bool isEmpty(); + + private: + std::queue queue_; + std::mutex mtx_; + std::condition_variable cond_; +}; + +template +void ConcurrentQueue::enqueue(T item) { + std::unique_lock lock(mtx_); + queue_.push(std::move(item)); + lock.unlock(); // explicitly unlock before notifying to minimize the waiting + // time of the notified thread + cond_.notify_one(); +} + +template +T ConcurrentQueue::dequeue() { + std::unique_lock lock(mtx_); + while (queue_.empty()) { + cond_.wait(lock); // release lock and wait to be notified + } + T item = std::move(queue_.front()); + queue_.pop(); + return item; +} + +template +bool ConcurrentQueue::isEmpty() { + std::unique_lock lock(mtx_); + return queue_.empty(); +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/concurrent_vector.h b/sllm_store/csrc/sllm_store/concurrent_vector.h new file mode 100644 index 0000000..df1fa6a --- /dev/null +++ b/sllm_store/csrc/sllm_store/concurrent_vector.h @@ -0,0 +1,89 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include +#include +#include + +template +class ConcurrentVector { + private: + std::vector data; + std::mutex mtx_; + std::condition_variable cond_; + size_t size_ = 0; + size_t capacity_ = 0; + std::string name_; + std::unordered_set keys_; + + public: + ConcurrentVector() = default; + ConcurrentVector(const ConcurrentVector&) = delete; + ConcurrentVector& operator=(const ConcurrentVector&) = delete; + void init(std::string name, size_t capacity); + size_t capacity(); + bool find(size_t key); + void enqueue(uint64_t key, T item); + T dequeue(size_t pivot); +}; + +template +void ConcurrentVector::init(std::string name, size_t capacity) { + std::lock_guard lock(mtx_); + capacity_ = capacity; + name_ = std::move(name); + data.resize(capacity_); + size_ = 0; +} + +template +size_t ConcurrentVector::capacity() { + std::lock_guard lock(mtx_); + return capacity_; +} + +template +bool ConcurrentVector::find(size_t key) { + std::lock_guard lock(mtx_); + return keys_.find(key) != keys_.end(); +} + +template +void ConcurrentVector::enqueue(uint64_t key, T item) { + std::lock_guard lock(mtx_); + + if (keys_.find(key) != keys_.end()) { + return; + } + + size_t idx = size_; + size_++; + data[idx] = std::move(item); + keys_.insert(key); + cond_.notify_all(); +} + +template +T ConcurrentVector::dequeue(size_t pivot) { + std::unique_lock lock(mtx_); + cond_.wait(lock, [this, pivot] { return pivot < size_; }); + return data[pivot]; +} diff --git a/sllm_store/csrc/sllm_store/cuda_memory.cpp b/sllm_store/csrc/sllm_store/cuda_memory.cpp new file mode 100644 index 0000000..06518fb --- /dev/null +++ b/sllm_store/csrc/sllm_store/cuda_memory.cpp @@ -0,0 +1,70 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "cuda_memory.h" + +#include + +CudaMemory::CudaMemory() : data_(nullptr), size_(0), device_(-1) {} + +CudaMemory::~CudaMemory() { + if (data_) { // Ensure we have data to free + cudaFree(data_); + } +} + +int CudaMemory::Allocate(size_t size, int device) { + if (data_) { + LOG(ERROR) << "Memory already allocated\n"; + return 1; // Indicate error + } + + // Check if device and size are valid + int deviceCount; + cudaGetDeviceCount(&deviceCount); + if (device >= deviceCount || size == 0) { + LOG(ERROR) << "Invalid device or size\n"; + return 1; // Indicate error + } + + // Set device and allocate memory on it + cudaSetDevice(device); + cudaError_t status = cudaMalloc(&data_, size); + if (status != cudaSuccess) { + LOG(ERROR) << "Failed to allocate memory on device " << device << ": " + << cudaGetErrorString(status) << "\n"; + return status; + } + device_ = device; + size_ = size; + + // Get IPC handle + status = cudaIpcGetMemHandle(&handle_, data_); + if (status != cudaSuccess) { + cudaFree(data_); + data_ = nullptr; + LOG(ERROR) << "Failed to get IPC handle: " << cudaGetErrorString(status) + << "\n"; + return status; + } + + return cudaSuccess; // Indicate success +} + +void* CudaMemory::get() const { return data_; } + +cudaIpcMemHandle_t CudaMemory::getHandle() const { return handle_; } diff --git a/sllm_store/csrc/sllm_store/cuda_memory.h b/sllm_store/csrc/sllm_store/cuda_memory.h new file mode 100644 index 0000000..9580aa8 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cuda_memory.h @@ -0,0 +1,44 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include + +// #include "cuda_memory_pool.h" + +class CudaMemory { + public: + CudaMemory(); + ~CudaMemory(); + + // Disable copying and moving + CudaMemory(const CudaMemory&) = delete; + CudaMemory& operator=(const CudaMemory&) = delete; + CudaMemory(CudaMemory&&) = delete; + CudaMemory& operator=(CudaMemory&&) = delete; + + int Allocate(size_t size, int device); + void* get() const; + cudaIpcMemHandle_t getHandle() const; + + private: + void* data_; + cudaIpcMemHandle_t handle_; + size_t size_; + int device_; +}; diff --git a/sllm_store/csrc/sllm_store/cuda_memory_pool.cpp b/sllm_store/csrc/sllm_store/cuda_memory_pool.cpp new file mode 100644 index 0000000..e689c52 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cuda_memory_pool.cpp @@ -0,0 +1,102 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "cuda_memory_pool.h" + +#include + +#include + +CudaMemoryPool::CudaMemoryPool(int device_count, size_t size_per_device) + : device_count_(device_count), size_per_device_(size_per_device) { + pool_.resize(device_count); + handles_.resize(device_count); + free_list_.resize(device_count); + LOG(INFO) << "Creating CudaMemoryPool with " << device_count + << " devices, each with " << size_per_device << " bytes"; + for (int i = 0; i < device_count; ++i) { + cudaError_t err = cudaSetDevice(i); + if (err != cudaSuccess) { + LOG(FATAL) << "Failed to set device: " << cudaGetErrorString(err); + } + void* ptr = nullptr; + err = cudaMalloc(&ptr, size_per_device); + if (err != cudaSuccess) { + LOG(FATAL) << "Failed to allocate memory on device " << i << ": " + << cudaGetErrorString(err); + } + err = cudaIpcGetMemHandle(&handles_[i], ptr); + if (err != cudaSuccess) { + LOG(FATAL) << "Error getting GPU memory handle " + << cudaGetErrorString(err); + } + pool_[i] = ptr; + free_list_[i] = true; + } +} + +CudaMemoryPool::~CudaMemoryPool() { + for (int i = 0; i < device_count_; ++i) { + cudaError_t err = cudaSetDevice(i); + if (err != cudaSuccess) { + LOG(FATAL) << "Failed to set device: " << cudaGetErrorString(err); + } + err = cudaFree(pool_[i]); + if (err != cudaSuccess) { + LOG(FATAL) << "Failed to free memory on device " << i << ": " + << cudaGetErrorString(err); + } + } +} + +int CudaMemoryPool::Allocate(size_t size, int device_id, void*& ptr, + cudaIpcMemHandle_t& handle) { + std::lock_guard lock(mutex_); + if (device_id < 0 || device_id >= device_count_) { + LOG(ERROR) << "Invalid device id " << device_id; + return -1; + } else if (size > size_per_device_) { + LOG(ERROR) << "Requested size " << size << " exceeds size per device " + << size_per_device_; + return -1; + } else if (!free_list_[device_id]) { + LOG(ERROR) << "Device " << device_id << " is not free"; + return -1; + } + + ptr = pool_[device_id]; + handle = handles_[device_id]; + free_list_[device_id] = false; + return 0; +} + +int CudaMemoryPool::Deallocate(int device_id, void* ptr) { + std::lock_guard lock(mutex_); + if (device_id < 0 || device_id >= device_count_) { + LOG(ERROR) << "Invalid device id " << device_id; + return -1; + } else if (free_list_[device_id]) { + LOG(ERROR) << "Device " << device_id << " is already free"; + return -1; + } else if (ptr != pool_[device_id]) { + LOG(ERROR) << "Invalid pointer " << ptr; + return -1; + } + + free_list_[device_id] = true; + return 0; +} diff --git a/sllm_store/csrc/sllm_store/cuda_memory_pool.h b/sllm_store/csrc/sllm_store/cuda_memory_pool.h new file mode 100644 index 0000000..b8bff67 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cuda_memory_pool.h @@ -0,0 +1,43 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include + +#include +#include + +class CudaMemoryPool { + public: + CudaMemoryPool(int device_count, size_t size_per_device); + CudaMemoryPool(const CudaMemoryPool&) = delete; + CudaMemoryPool& operator=(const CudaMemoryPool&) = delete; + ~CudaMemoryPool(); + + int Allocate(size_t size, int device_id, void*& ptr, + cudaIpcMemHandle_t& handle); + int Deallocate(int device_id, void* ptr); + + private: + std::mutex mutex_; + int device_count_; + size_t size_per_device_; + std::vector pool_; + std::vector handles_; + std::vector free_list_; +}; diff --git a/sllm_store/csrc/sllm_store/error_handling.h b/sllm_store/csrc/sllm_store/error_handling.h new file mode 100644 index 0000000..596e7fa --- /dev/null +++ b/sllm_store/csrc/sllm_store/error_handling.h @@ -0,0 +1,58 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include +#include + +#define CUDA_CHECK(x, msg) \ + { \ + if ((x) != cudaSuccess) { \ + LOG(ERROR) << msg << " " << cudaGetErrorString(cudaGetLastError()) \ + << std::endl; \ + return -1; \ + } \ + } + +#define CHECK_POSIX(x, msg) \ + { \ + if ((x) < 0) { \ + LOG(ERROR) << msg << " errno: " << errno << "msg: " << strerror(errno); \ + return -1; \ + } \ + } + +#define WAIT_FUTURES(futures, msg) \ + { \ + for (auto& future : futures) { \ + int ret = future.get() if (ret != 0) { \ + LOG(ERROR) << msg; \ + return ret; \ + } \ + } \ + } + +#define CHECK_RETURN(x, msg) \ + { \ + if ((x) != 0) { \ + LOG(ERROR) << msg; \ + return -1; \ + } \ + } diff --git a/sllm_store/csrc/sllm_store/gpu_replica.cpp b/sllm_store/csrc/sllm_store/gpu_replica.cpp new file mode 100644 index 0000000..694935f --- /dev/null +++ b/sllm_store/csrc/sllm_store/gpu_replica.cpp @@ -0,0 +1,36 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "gpu_replica.h" + +#include +#include + +void GpuReplica::Clear() { + for (auto& [device_id, device_ptr] : device_ptrs_) { + cudaSetDevice(device_id); + cudaError_t err = cudaIpcCloseMemHandle(device_ptr); + if (err != cudaSuccess) { + LOG(ERROR) << "Failed to close memory handle for device " << device_id + << " error: " << cudaGetErrorString(err); + } + } + gpu_loading_queue_.clear(); + tensor_offsets_.clear(); + state_ = MemoryState::INTERRUPTED; + cv_.notify_all(); +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/gpu_replica.h b/sllm_store/csrc/sllm_store/gpu_replica.h new file mode 100644 index 0000000..d9c5506 --- /dev/null +++ b/sllm_store/csrc/sllm_store/gpu_replica.h @@ -0,0 +1,36 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include + +#include "types_and_defs.h" + +class GpuReplica { + std::condition_variable cv_; + MemoryState state_ = MemoryState::UNINITIALIZED; + + std::unordered_map> gpu_loading_queue_; + std::unordered_map device_ptrs_; + + std::unordered_map tensor_offsets_; + + void Clear(); +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/memory_state.cpp b/sllm_store/csrc/sllm_store/memory_state.cpp new file mode 100644 index 0000000..d0eef9e --- /dev/null +++ b/sllm_store/csrc/sllm_store/memory_state.cpp @@ -0,0 +1,40 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "memory_state.h" + +#include + +std::ostream& operator<<(std::ostream& os, const MemoryState state) { + return os << [state]() -> const char* { +#define PROCESS_STATE(p) \ + case (p): \ + return #p; + switch (state) { + PROCESS_STATE(UNINITIALIZED); + PROCESS_STATE(UNALLOCATED); + PROCESS_STATE(ALLOCATED); + PROCESS_STATE(LOADING); + PROCESS_STATE(LOADED); + PROCESS_STATE(CANCELLED); + PROCESS_STATE(INTERRUPTED); + default: + return "UNKNOWN"; + } +#undef PROCESS_STATE + }(); +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/memory_state.h b/sllm_store/csrc/sllm_store/memory_state.h new file mode 100644 index 0000000..e911b43 --- /dev/null +++ b/sllm_store/csrc/sllm_store/memory_state.h @@ -0,0 +1,28 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +enum MemoryState : int { + UNINITIALIZED, + UNALLOCATED, + ALLOCATED, + LOADING, + LOADED, + CANCELLED = 100, + INTERRUPTED = 101 +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/model.cpp b/sllm_store/csrc/sllm_store/model.cpp new file mode 100644 index 0000000..2d8a62c --- /dev/null +++ b/sllm_store/csrc/sllm_store/model.cpp @@ -0,0 +1,610 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "model.h" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +// Third-party library headers +#include +#include + +#include "error_handling.h" + +int Model::Initialize(const std::filesystem::path storage_path) { + std::lock_guard lock(mutex_); + if (state_ != MemoryState::UNINITIALIZED) { + return 0; + } + model_size_ = 0; + partition_sizes_.clear(); + partition_paths_.clear(); + // Attempt to read from 0 until the file is not found + for (int partition_id = 0;; ++partition_id) { + auto tensor_path = storage_path / model_path_ / + ("tensor.data_" + std::to_string(partition_id)); + if (access(tensor_path.c_str(), F_OK) == -1) { + LOG(INFO) << "Tensor file " << tensor_path << " does not exist"; + break; + } + struct stat st; + if (stat(tensor_path.c_str(), &st) != 0) { + LOG(ERROR) << "Failed to get file size of " << tensor_path; + return -1; + } + model_size_ += st.st_size; + partition_sizes_.push_back(st.st_size); + partition_paths_.push_back(tensor_path); + } + if (model_size_ == 0) { + LOG(ERROR) << "Model " << model_path_ << " does not exist"; + return -1; + } + state_ = MemoryState::UNALLOCATED; + + return 0; +} + +int Model::ToHost(int num_threads) { + std::unique_lock lock(mutex_); + if (state_ != MemoryState::ALLOCATED) { + if (state_ == MemoryState::LOADING || state_ == MemoryState::LOADED) { + return 0; + } else { + LOG(ERROR) << "Model " << model_path_ << " is at state " << state_; + return -1; + } + } + + std::vector file_descriptors; + // Attempt to read from 0 until the file is not found + for (int partition_id = 0; partition_id < partition_sizes_.size(); + ++partition_id) { + auto tensor_path = partition_paths_[partition_id]; + if (access(tensor_path.c_str(), F_OK) == -1) { + LOG(ERROR) << "File " << tensor_path << " does not exist"; + return -1; + } + + // Open file + int fd = open(tensor_path.c_str(), O_DIRECT | O_RDONLY); + if (fd < 0) { + std::string err = "open() failed for file: " + tensor_path.string() + + ", error: " + strerror(errno); + LOG(ERROR) << err; + return -1; + } + + file_descriptors.push_back(fd); + } + + LOG(INFO) << "Loading model " << model_path_ << " size " << model_size_ + << " to host"; + if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { + LOG(ERROR) << "CPU memory not allocated"; + return 1; + } + + auto host_buffers = pinned_mem_->get(); + size_t num_chunks = pinned_mem_->num_chunks(); + size_t chunk_size = pinned_mem_->chunk_size(); + host_ptr_vector_ = std::make_shared(); + host_ptr_vector_->init("queue_name", num_chunks); + std::vector> futures; + size_t chunk_per_thread = (num_chunks + num_threads - 1) / num_threads; + LOG(INFO) << "Loading model " << model_path_ << " to host with " + << num_threads << " threads, " << num_chunks << " chunks, " + << chunk_size << " chunk size, " << chunk_per_thread + << " chunks per thread"; + + state_ = MemoryState::LOADING; + lock.unlock(); + + for (int thread_idx = 0; thread_idx < num_threads; ++thread_idx) { + futures.emplace_back(std::async(std::launch::async, [&, thread_idx]() { + size_t partition_id = 0; + size_t file_offset = thread_idx * chunk_per_thread * chunk_size; + while (partition_id < partition_sizes_.size() && + file_offset >= partition_sizes_.at(partition_id)) { + file_offset -= partition_sizes_.at(partition_id); + partition_id += 1; + } + if (partition_id >= partition_sizes_.size()) { + LOG(INFO) << "Thread " << thread_idx << " early exits"; + return 0; + } + LOG(INFO) << "Thread " << thread_idx << " starting from partition " + << partition_id << " offset " << file_offset; + for (size_t chunk_idx = thread_idx * chunk_per_thread; + chunk_idx < (thread_idx + 1) * chunk_per_thread && + chunk_idx < num_chunks; + ++chunk_idx) { + size_t size = + std::min(chunk_size, model_size_ - chunk_idx * chunk_size); + if (host_buffers[chunk_idx] == nullptr) { + LOG(ERROR) << "Host buffer not allocated"; + return -1; + } + + if (state_ == MemoryState::CANCELLED) { + LOG(INFO) << "Loading from disk for model " << model_path_ + << " is cancelled"; + return 0; + } + + int fd = file_descriptors[partition_id]; + ssize_t ret = + pread(fd, (void*)host_buffers[chunk_idx], size, file_offset); + if (ret < 0) { + auto tensor_path = partition_paths_[partition_id]; + LOG(ERROR) << "pread() failed for file: " << tensor_path + << ", error: " << strerror(errno); + return -1; + } else if (ret != size) { + if (ret < size && partition_id + 1 < file_descriptors.size()) { + partition_id += 1; + file_offset = 0; + size_t remaining_size = size - ret; + int fd = file_descriptors[partition_id]; + ret = pread(fd, (void*)(host_buffers[chunk_idx] + ret), + remaining_size, file_offset); + if (ret != remaining_size) { + auto tensor_path = partition_paths_[partition_id]; + LOG(ERROR) << "Failed to read file: " << tensor_path + << " read: " << ret << " expected: " << remaining_size; + return -1; + } + } else { + auto tensor_path = partition_paths_[partition_id]; + LOG(ERROR) << "Failed to read file: " << tensor_path + << " read: " << ret << " expected: " << size; + return -1; + } + } + file_offset += ret; + + host_ptr_vector_->enqueue(chunk_idx, Batch{chunk_idx, size}); + } + + return 0; + })); + } + + bool error = false; + for (auto& future : futures) { + int ret = future.get(); + if (ret != 0) { + LOG(ERROR) << "Error reading from disk, ret " << ret; + error = true; + } + } + + // close file + for (int fd : file_descriptors) { + close(fd); + } + + lock.lock(); + if (error) { + state_ = MemoryState::INTERRUPTED; + // Deal with gpu replicas + for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { + if (gpu_replica->state_ == MemoryState::LOADING) { + gpu_replica->state_ = MemoryState::CANCELLED; + gpu_replica->cv_.notify_all(); + } + // wait for gpu replicas to finish + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + // Note: gpu replicas will be handled by the caller + } + // release pinned memory + pinned_mem_.reset(); + state_ = MemoryState::UNALLOCATED; + + return -1; + } + + state_ = MemoryState::LOADED; + LOG(INFO) << "Finished loading model " << model_path_ << " from disk"; + + return 0; +} + +int Model::ToGpu( + const std::string& replica_uuid, const MemPtrListMap& device_ptrs, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles) { + std::unique_lock lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(ERROR) << "Model " << model_path_ << " is not initialized"; + return -1; + } + + if (gpu_replicas_.find(replica_uuid) != gpu_replicas_.end()) { + LOG(ERROR) << "Replica " << replica_uuid << " already exists"; + return -1; + } + LOG(INFO) << "Creating replica " << replica_uuid; + gpu_replicas_.emplace(replica_uuid, std::make_shared()); + GpuReplicaPtr gpu_replica = gpu_replicas_.at(replica_uuid); + for (const auto& [device_id, _] : device_ptrs) { + LOG(INFO) << "Creating queue for device " << device_id; + gpu_replica->gpu_loading_queue_.emplace(device_id, + std::make_shared()); + } + gpu_replica->device_ptrs_ = device_ptrs; + gpu_replica->state_ = MemoryState::LOADING; + LOG(INFO) << "Created replica " << replica_uuid; + cv_.notify_all(); + lock.unlock(); + + // Start a dispatcher first + auto dispatch_future = std::async( + std::launch::async, + [this, gpu_replica, mem_copy_chunks, mem_copy_handles]() { + return DispatchToGpu(gpu_replica, mem_copy_chunks, mem_copy_handles); + }); + + LOG(INFO) << "Dispatcher started for model " << model_path_; + + std::unordered_map> futures; + for (auto& [device_id, device_ptr_list] : device_ptrs) { + futures.emplace( + device_id, std::async(std::launch::async, [this, gpu_replica, device_id, + device_ptr_list]() { + auto gpu_loading_queue = + gpu_replica->gpu_loading_queue_.at(device_id); + if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { + LOG(ERROR) << "CPU memory not allocated"; + return 1; + } + + cudaError_t err = cudaSetDevice(device_id); + if (err != cudaSuccess) { + LOG(ERROR) << "Error setting device " << cudaGetErrorString(err); + return 1; + } + + auto& host_buffers = pinned_mem_->get(); + + size_t loaded_size = 0; + while (true) { + auto [chunk_id, chunk_offset, size, gpu_offset, handle_idx] = + gpu_loading_queue->dequeue(); + if (size == 0) { + break; + } + if (gpu_replica->state_ == MemoryState::CANCELLED) { + LOG(INFO) << "Loading from mem for model " << model_path_ + << " is cancelled," + << " chunk " << chunk_id << " offset " + << " size " << size; + return 0; + } + + CUDA_CHECK( + cudaMemcpy( + (void*)((char*)device_ptr_list[handle_idx] + gpu_offset), + (void*)(host_buffers[chunk_id] + chunk_offset), size, + cudaMemcpyHostToDevice), + "cudaMemcpy Error"); + loaded_size += size; + } + + LOG(INFO) << "Finished loading tensor from memory to device " + << device_id; + + return 0; + })); + } + + LOG(INFO) << "Waiting for model " << model_path_ << " num tasks " + << futures.size() << " state " << gpu_replica->state_; + dispatch_future.wait(); + bool error = false; + for (auto& [device_id, future] : futures) { + int ret = future.get(); + if (ret != 0) { + LOG(ERROR) << "Error copying to device " << device_id; + error = true; + } + } + + lock.lock(); + futures.clear(); + + if (error) { + LOG(ERROR) << "Failed to load model " << model_path_; + gpu_replica->state_ = MemoryState::INTERRUPTED; + } else { + gpu_replica->state_ = MemoryState::LOADED; + } + gpu_replica->cv_.notify_all(); + + // TODO: move to background thread + for (auto& [device_id, device_ptr_list] : gpu_replica->device_ptrs_) { + cudaSetDevice(device_id); + for (auto device_ptr : device_ptr_list) { + cudaError_t err = cudaIpcCloseMemHandle(device_ptr); + if (err != cudaSuccess) { + LOG(ERROR) << "Failed to close memory handle for device " << device_id + << " error: " << cudaGetErrorString(err); + } + } + } + + if (gpu_replica->state_ == MemoryState::INTERRUPTED) { + LOG(ERROR) << "Model " << model_path_ << " replica " << replica_uuid + << " is interrupted"; + return -1; + } + + return 0; +} + +int Model::WaitInHost() { + std::unique_lock lock(mutex_); + if (state_ < MemoryState::LOADED) { + cv_.wait(lock, [this] { + return state_ == MemoryState::LOADED || + state_ == MemoryState::INTERRUPTED; + }); + } + + if (state_ >= MemoryState::INTERRUPTED) { + LOG(INFO) << "Model " << model_path_ << " is interrupted"; + return 1; + } + + return 0; +} + +int Model::WaitInGpu(const std::string& replica_uuid) { + std::unique_lock lock(mutex_); + if (gpu_replicas_.find(replica_uuid) == gpu_replicas_.end()) { + cv_.wait(lock, [this, replica_uuid] { + return gpu_replicas_.find(replica_uuid) != gpu_replicas_.end(); + }); + } + + auto& gpu_replica = gpu_replicas_.at(replica_uuid); + + if (gpu_replica->state_ < MemoryState::LOADED) { + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + } + + if (gpu_replica->state_ >= MemoryState::INTERRUPTED) { + LOG(INFO) << "Model " << model_path_ << " is interrupted"; + return 1; + } + + return 0; +} + +int Model::FreeGpu(const std::string& replica_uuid) { + std::unique_lock lock(mutex_); + if (gpu_replicas_.find(replica_uuid) == gpu_replicas_.end()) { + LOG(ERROR) << "Model " << model_path_ << " replica " << replica_uuid + << " is not registered"; + return -1; + } + + auto& gpu_replica = gpu_replicas_.at(replica_uuid); + if (gpu_replica->state_ == MemoryState::UNINITIALIZED) { + LOG(WARNING) << "Model " << model_path_ << " replica " << replica_uuid + << " is not initialized"; + gpu_replicas_.erase(replica_uuid); + return 0; + } + + if (gpu_replica->state_ == MemoryState::LOADING) { + LOG(INFO) << "Waiting for model " << model_path_ << " replica " + << replica_uuid << " to be loaded"; + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + } + + gpu_replicas_.erase(replica_uuid); + return 0; +} + +int Model::FreeHost() { + std::unique_lock lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(WARNING) << "Model " << model_path_ << " is not initialized"; + return 1; + } + + if (state_ == MemoryState::UNALLOCATED) { + LOG(WARNING) << "Model " << model_path_ << " is not allocated"; + return 1; + } + + if (state_ == MemoryState::LOADING) { + LOG(INFO) << "Waiting for model " << model_path_ << " to be loaded"; + cv_.wait(lock, [this] { + return state_ == MemoryState::LOADED || + state_ == MemoryState::INTERRUPTED; + }); + } + + // make sure no gpu replicas are loading + for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { + if (gpu_replica->state_ == MemoryState::LOADING) { + LOG(INFO) << "Waiting for replica " << replica_uuid << " to be loaded"; + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + } + } + + // free pinned memory + int freed_chunks = pinned_mem_->num_chunks(); + pinned_mem_.reset(); + state_ = MemoryState::UNALLOCATED; + + return 0; +} + +int Model::TryFreeHost() { + std::unique_lock lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(WARNING) << "Model " << model_path_ << " is not initialized"; + return 0; + } + + if (state_ == MemoryState::UNALLOCATED) { + LOG(WARNING) << "Model " << model_path_ << " is not allocated"; + return 0; + } + + if (state_ == MemoryState::LOADING) { + return -1; + } + + // make sure no gpu replicas are loading + for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { + if (gpu_replica->state_ == MemoryState::LOADING) { + return -1; + } + } + + // free pinned memory + int freed_chunks = pinned_mem_->num_chunks(); + pinned_mem_.reset(); + state_ = MemoryState::UNALLOCATED; + + return freed_chunks; +} + +int Model::DispatchToGpu( + const std::shared_ptr& gpu_replica, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles) { + // device_id, chunk_offset, size, gpu_offset + + size_t num_chunks = pinned_mem_->num_chunks(); + std::vector> chunk_id_to_gpu_chunks(num_chunks); + for (const auto& [device_id, mem_copy_chunk_list] : mem_copy_chunks) { + const auto& device_handles = mem_copy_handles.at(device_id); + std::vector handle_offsets(device_handles.size(), 0); + + for (auto [host_offset, size, gpu_offset, handle_idx] : + mem_copy_chunk_list) { + handle_offsets[handle_idx] = gpu_offset; + + std::vector> chunks = + MapDataToChunks(host_offset, size, pinned_mem_->chunk_size()); + for (const auto& [chunk_id, chunk_offset, size] : chunks) { + chunk_id_to_gpu_chunks[chunk_id].push_back( + std::make_tuple(device_id, chunk_offset, size, + handle_offsets[handle_idx], handle_idx)); + handle_offsets[handle_idx] += size; + } + } + } + + for (int i = 0; i < host_ptr_vector_->capacity(); i++) { + auto data_chunk = host_ptr_vector_->dequeue(i); + auto chunk_id = data_chunk.chunk_id_; + auto& gpu_chunks = chunk_id_to_gpu_chunks[chunk_id]; + for (const auto& [device_id, chunk_offset, size, gpu_offset, handle_idx] : + gpu_chunks) { + auto& gpu_loading_queue = gpu_replica->gpu_loading_queue_.at(device_id); + // LOG(INFO) << "Enqueueing chunk " << chunk_id << " offset " << + // chunk_offset + // << " size " << size << " to device " << device_id; + gpu_loading_queue->enqueue( + GpuBatch{chunk_id, chunk_offset, size, gpu_offset, handle_idx}); + } + } + + // notify end of loading + for (auto& [device_id, gpu_loading_queue] : gpu_replica->gpu_loading_queue_) { + gpu_loading_queue->enqueue(GpuBatch{}); + } + + return 0; +} + +std::vector> Model::MapDataToChunks( + size_t offset, size_t size, size_t chunk_size) { + int start_chunk = offset / chunk_size; + size_t offset_in_start_chunk = offset % chunk_size; + size_t remaining_data = size; + std::vector> output; + + for (int chunk_id = start_chunk; remaining_data > 0; ++chunk_id) { + const size_t chunk_data_size = + (chunk_id == start_chunk) + ? std::min(chunk_size - offset_in_start_chunk, remaining_data) + : std::min(chunk_size, remaining_data); + output.emplace_back(chunk_id, + chunk_id == start_chunk ? offset_in_start_chunk : 0, + chunk_data_size); + remaining_data -= chunk_data_size; + } + + return output; +} + +int Model::AllocatePinnedMemory(std::shared_ptr pool) { + std::lock_guard lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(ERROR) << "Model " << model_path_ << " is not initialized"; + return -1; + } + if (state_ != MemoryState::UNALLOCATED) { + return 0; + } + pinned_mem_ = std::make_shared(); + int ret = pinned_mem_->Allocate(model_size_, pool); + if (ret < 0) { + LOG(ERROR) << "Error allocating CPU memory for model " << model_path_; + return ret; + } else if (ret > 0) { + LOG(WARNING) << "Not enough memory for model " << model_path_; + return ret; + } else if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { + LOG(ERROR) << "CPU memory not allocated"; + return -1; + } + + state_ = MemoryState::ALLOCATED; + return 0; +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/model.h b/sllm_store/csrc/sllm_store/model.h new file mode 100644 index 0000000..b60af26 --- /dev/null +++ b/sllm_store/csrc/sllm_store/model.h @@ -0,0 +1,92 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +// Third-party library headers +#include +#include + +#include "error_handling.h" +#include "pinned_memory.h" +#include "types_and_defs.h" + +struct GpuReplica { + std::condition_variable cv_; + MemoryState state_ = MemoryState::UNINITIALIZED; + + std::unordered_map> gpu_loading_queue_; + MemPtrListMap device_ptrs_; +}; +using GpuReplicaPtr = std::shared_ptr; + +class Model { + public: + Model(const std::filesystem::path& model_path) : model_path_(model_path) {} + int Initialize(const std::filesystem::path storage_path); + int AllocatePinnedMemory(std::shared_ptr pool); + int ToHost(int num_threads); + int ToGpu(const std::string& replica_uuid, const MemPtrListMap& device_ptrs, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles); + int WaitInHost(); + int WaitInGpu(const std::string& replica_uuid); + int FreeGpu(const std::string& replica_uuid); + int FreeHost(); + int TryFreeHost(); + uint64_t GetModelSize() const { return model_size_; } + + private: + std::mutex mutex_; + std::condition_variable cv_; + MemoryState state_ = MemoryState::UNINITIALIZED; + + // Model path + const std::string model_path_; + + // Model info needs to be initialized + size_t model_size_; + std::vector partition_sizes_; + std::vector partition_paths_; + std::shared_ptr pinned_mem_; + + std::unordered_map gpu_replicas_; + + std::shared_ptr host_ptr_vector_; + + std::vector> MapDataToChunks( + size_t offset, size_t size, size_t chunk_size); + int DispatchToGpu( + const std::shared_ptr& gpu_replica, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles); +}; +using ModelPtr = std::shared_ptr; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/pinned_memory.cpp b/sllm_store/csrc/sllm_store/pinned_memory.cpp new file mode 100644 index 0000000..2e93876 --- /dev/null +++ b/sllm_store/csrc/sllm_store/pinned_memory.cpp @@ -0,0 +1,41 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "pinned_memory.h" + +#include + +PinnedMemory::~PinnedMemory() { + LOG(INFO) << "Deallocating " << buffers_.size() << " memory chunks"; + int ret = mempool_->Deallocate(buffers_); + if (ret != 0) { + LOG(ERROR) << "Error deallocating CPU memory"; + } +} + +int PinnedMemory::Allocate(size_t size, + std::shared_ptr mempool) { + if (buffers_.size() > 0) { + LOG(ERROR) << "Memory already allocated"; + return 1; + } + + mempool_ = mempool; + return mempool_->Allocate(size, buffers_); +} + +std::vector& PinnedMemory::get() { return buffers_; } diff --git a/sllm_store/csrc/sllm_store/pinned_memory.h b/sllm_store/csrc/sllm_store/pinned_memory.h new file mode 100644 index 0000000..abf0b0d --- /dev/null +++ b/sllm_store/csrc/sllm_store/pinned_memory.h @@ -0,0 +1,44 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include + +#include "pinned_memory_pool.h" + +class PinnedMemory { + public: + PinnedMemory() = default; + ~PinnedMemory(); + + // Disable copying and moving + PinnedMemory(const PinnedMemory&) = delete; + PinnedMemory& operator=(const PinnedMemory&) = delete; + PinnedMemory(PinnedMemory&&) = delete; + PinnedMemory& operator=(PinnedMemory&&) = delete; + + int Allocate(size_t size, std::shared_ptr mempool); + std::vector& get(); + size_t num_chunks() const { return buffers_.size(); } + size_t chunk_size() const { return mempool_->chunk_size(); } + + private: + std::vector buffers_; + std::shared_ptr mempool_; +}; diff --git a/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp b/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp new file mode 100644 index 0000000..488c7c1 --- /dev/null +++ b/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp @@ -0,0 +1,100 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "pinned_memory_pool.h" + +#include +#include + +PinnedMemoryPool::PinnedMemoryPool(size_t total_size, size_t chunk_size) + : chunk_size_(chunk_size) { + size_t num_buffers = (total_size + chunk_size - 1) / chunk_size; + if (num_buffers * chunk_size != total_size) { + LOG(ERROR) << "PinnedMemoryPool size not multiple of chunk_size"; + } + LOG(INFO) << "Creating PinnedMemoryPool with " << num_buffers + << " buffers of " << chunk_size << " bytes"; + + for (size_t i = 0; i < num_buffers; ++i) { + char* buffer = static_cast(aligned_alloc(4096, chunk_size_)); + if (buffer == nullptr) { + LOG(FATAL) << "Malloc failed"; + } + + cudaError_t err = + cudaHostRegister(buffer, chunk_size_, cudaHostRegisterDefault); + if (err != cudaSuccess) { + LOG(FATAL) << "cudaHostRegister failed: " << cudaGetErrorString(err); + } + pool_.insert(buffer); + free_list_.insert(buffer); + } +} + +PinnedMemoryPool::~PinnedMemoryPool() { + for (char* buffer : pool_) { + cudaHostUnregister(buffer); + free(buffer); + } +} + +int PinnedMemoryPool::Allocate(size_t size, std::vector& buffers) { + std::lock_guard lock(mutex_); + if (size == 0) { + LOG(ERROR) << "PinnedMemoryPool Allocate size is zero"; + return -1; + } + + int num_buffers_needed = (size + chunk_size_ - 1) / chunk_size_; + if (num_buffers_needed > free_list_.size()) { + LOG(ERROR) << "PinnedMemoryPool out of memory (" << free_list_.size() + << " buffers available, " << num_buffers_needed + << " buffers needed)"; + return num_buffers_needed - free_list_.size(); + } + + buffers.clear(); + buffers.resize(num_buffers_needed); + auto it = free_list_.begin(); + for (size_t i = 0; i < num_buffers_needed; ++i) { + buffers[i] = *it; + it = free_list_.erase(it); + } + + LOG(INFO) << "PinnedMemoryPool Allocate " << buffers.size() << " buffers" + << " free buffers " << free_list_.size() << " total buffers " + << pool_.size(); + + return 0; // Success +} + +int PinnedMemoryPool::Deallocate(std::vector& buffers) { + std::lock_guard lock(mutex_); + for (char* buffer : buffers) { + if (pool_.find(buffer) == pool_.end()) { + LOG(ERROR) << "Buffer not found in pool"; + return -1; + } + if (free_list_.find(buffer) != free_list_.end()) { + LOG(ERROR) << "Buffer already in free list"; + return -1; + } + free_list_.insert(buffer); + } + LOG(INFO) << "Deallocated " << buffers.size() << " buffers"; + return 0; // Success +} diff --git a/sllm_store/csrc/sllm_store/pinned_memory_pool.h b/sllm_store/csrc/sllm_store/pinned_memory_pool.h new file mode 100644 index 0000000..ea3ce45 --- /dev/null +++ b/sllm_store/csrc/sllm_store/pinned_memory_pool.h @@ -0,0 +1,42 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include + +class PinnedMemoryPool { + public: + PinnedMemoryPool(size_t total_size, size_t chunk_size); + ~PinnedMemoryPool(); + + int Allocate(size_t size, std::vector& buffers); + int Deallocate(std::vector& buffers); + size_t chunk_size() const { return chunk_size_; } + + // Forbid copy and assignment + PinnedMemoryPool(const PinnedMemoryPool&) = delete; + PinnedMemoryPool& operator=(const PinnedMemoryPool&) = delete; + + private: + std::mutex mutex_; + std::unordered_set free_list_; + std::unordered_set pool_; + size_t chunk_size_; +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/types_and_defs.h b/sllm_store/csrc/sllm_store/types_and_defs.h new file mode 100644 index 0000000..2f8f797 --- /dev/null +++ b/sllm_store/csrc/sllm_store/types_and_defs.h @@ -0,0 +1,74 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include +#include + +#include "concurrent_queue.h" +#include "concurrent_vector.h" +#include "memory_state.h" + +struct Batch { + size_t chunk_id_ = 0; + size_t size_ = 0; +}; +typedef ConcurrentVector BatchVector; + +struct GpuBatch { + size_t chunk_id_ = 0; + size_t chunk_offset_ = 0; + size_t size_ = 0; + size_t gpu_offset_ = 0; + size_t handle_idx_ = 0; +}; +typedef ConcurrentQueue BatchQueue; + +struct FileChunk { + int fd_; + size_t file_offset_; + size_t size_; + size_t chunk_id_; + size_t chunk_offset_; +}; + +#define KB (1024LL) +#define MB (1024LL * KB) +#define GB (1024LL * MB) + +// using DeviceMap = std::unordered_map; +struct MemCopyChunk { + size_t src_offset_ = 0; + size_t size_ = 0; + size_t dst_offset_ = 0; + size_t handle_idx_ = 0; +}; +using MemCopyChunkList = std::vector; + +struct MemCopyHandle { + std::string cuda_ipc_handle_; +}; +using MemCopyHandleList = std::vector; + +typedef std::unordered_map MemCopyHandleListMap; +typedef std::unordered_map MemCopyChunkListMap; +typedef std::unordered_map> MemPtrListMap; + +// device_id, chunk_offset, size, gpu_offset. handle_idx +typedef std::tuple GpuChunk; diff --git a/sllm_store/examples/load_lora_adapter.py b/sllm_store/examples/load_lora_adapter.py new file mode 100644 index 0000000..9d56355 --- /dev/null +++ b/sllm_store/examples/load_lora_adapter.py @@ -0,0 +1,65 @@ +import argparse +import time + +import torch + +from transformers import AutoTokenizer + +from sllm_store.transformers import load_model, load_lora + +parser = argparse.ArgumentParser(description="Load a model from ServerlessLLM") +parser.add_argument( + "--model-name", type=str, required=True, help="Model name stored" +) +parser.add_argument( + "--adapter-name", type=str, required=True, help="Lora adapter name" +) +parser.add_argument( + "--adapter-path", type=str, required=True, help="Lora adapter path stored" +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path stored the model.", +) + +args = parser.parse_args() + +model_name = args.model_name +lora_adapter_name = args.adapter_name +lora_adapter_path = args.adapter_path +storage_path = args.storage_path + + +# warm up the GPU +num_gpus = torch.cuda.device_count() +for i in range(num_gpus): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + +start = time.time() +model = load_model( + model_name, + device_map="auto", + torch_dtype=torch.float16, + storage_path=storage_path, + fully_parallel=True, +) + +model = load_lora( + model, + lora_adapter_name, + lora_adapter_path, + device_map="auto", + storage_path=storage_path, + torch_dtype=torch.float16, +) +# Please note the loading time depends on model size and hardware bandwidth. +print(f"Model with lora adapter loading time: {time.time() - start:.2f}s") + +tokenizer = AutoTokenizer.from_pretrained(model_name) +inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to("cuda") +generate_kwargs = {"adapter_names": [lora_adapter_name]} +outputs = model.generate(**inputs, **generate_kwargs) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) diff --git a/sllm_store/examples/load_quantized_transformers_model.py b/sllm_store/examples/load_quantized_transformers_model.py new file mode 100644 index 0000000..3fb7d21 --- /dev/null +++ b/sllm_store/examples/load_quantized_transformers_model.py @@ -0,0 +1,66 @@ +import argparse +import time + +import torch + +from transformers import AutoTokenizer, BitsAndBytesConfig +from sllm_store.transformers import load_model + +parser = argparse.ArgumentParser(description="Load a model from ServerlessLLM") +parser.add_argument( + "--model-name", type=str, required=True, help="Model name stored" +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path stored the model.", +) + +parser.add_argument( + "--precision", + type=str, + default="int8", + help="Precision of quantized model. Supports int8, fp4, and nf4", +) + +args = parser.parse_args() + +model_name = args.model_name +storage_path = args.storage_path + +# Define quantization configuration with BitsAndBytesConfig +if args.precision == "int8": + quantization_config = BitsAndBytesConfig(load_in_8bit=True) +elif args.precision == "fp4": + quantization_config = BitsAndBytesConfig(load_in_4bit=True) +elif args.precision == "nf4": + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, bnb_4bit_quant_type="nf4" + ) +else: + quantization_config = None + +# warm up the GPU +num_gpus = torch.cuda.device_count() +for i in range(num_gpus): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + +start = time.time() +model = load_model( + model_name, + device_map="auto", + torch_dtype=torch.float16, + storage_path=storage_path, + fully_parallel=True, + quantization_config=quantization_config, +) +# Please note the loading time depends on model size and hardware bandwidth. +print(f"Model loading time: {time.time() - start:.2f}s") + + +tokenizer = AutoTokenizer.from_pretrained(model_name) +inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to("cuda") +outputs = model.generate(**inputs) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) diff --git a/sllm_store/examples/load_transformers_model.py b/sllm_store/examples/load_transformers_model.py new file mode 100644 index 0000000..fcbd63f --- /dev/null +++ b/sllm_store/examples/load_transformers_model.py @@ -0,0 +1,48 @@ +import argparse +import time + +import torch + +from transformers import AutoTokenizer + +from sllm_store.transformers import load_model + +parser = argparse.ArgumentParser(description="Load a model from ServerlessLLM") +parser.add_argument( + "--model-name", type=str, required=True, help="Model name stored" +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path stored the model.", +) + +args = parser.parse_args() + +model_name = args.model_name +storage_path = args.storage_path + + +# warm up the GPU +num_gpus = torch.cuda.device_count() +for i in range(num_gpus): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + +start = time.time() +model = load_model( + model_name, + device_map="auto", + torch_dtype=torch.float16, + storage_path=storage_path, + fully_parallel=True, +) +# Please note the loading time depends on model size and hardware bandwidth. +print(f"Model loading time: {time.time() - start:.2f}s") + + +tokenizer = AutoTokenizer.from_pretrained(model_name) +inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to("cuda") +outputs = model.generate(**inputs) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) diff --git a/sllm_store/examples/load_vllm_model.py b/sllm_store/examples/load_vllm_model.py new file mode 100644 index 0000000..dfc9a7f --- /dev/null +++ b/sllm_store/examples/load_vllm_model.py @@ -0,0 +1,44 @@ +import argparse +import os + +from vllm import LLM, SamplingParams + +parser = argparse.ArgumentParser( + description="Save a model from HuggingFace model hub." +) +parser.add_argument( + "--model-name", + type=str, + required=True, + help="Model name from HuggingFace model hub.", +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path to save the model.", +) + +args = parser.parse_args() + +model_name = args.model_name +storage_path = args.storage_path +model_path = os.path.join(storage_path, model_name) + +llm = LLM(model=model_path, load_format="serverless_llm", dtype="float16") + +prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", +] + +sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +outputs = llm.generate(prompts, sampling_params) + +# Print the outputs. +for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") diff --git a/sllm_store/examples/save_lora_adapter.py b/sllm_store/examples/save_lora_adapter.py new file mode 100644 index 0000000..0330dac --- /dev/null +++ b/sllm_store/examples/save_lora_adapter.py @@ -0,0 +1,56 @@ +import argparse +import importlib +import os + +import torch +from transformers import AutoModelForCausalLM, AutoConfig +from peft import PeftModel + +from sllm_store.transformers import save_lora + +parser = argparse.ArgumentParser( + description="Save a lora adapter from HuggingFace model hub." +) +parser.add_argument( + "--model-name", + type=str, + required=True, + help="Model name from HuggingFace model hub.", +) +parser.add_argument( + "--adapter-name", + type=str, + required=True, + help="Lora adapter name from HuggingFace model hub.", +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path to save the model.", +) + +args = parser.parse_args() + +base_model_name = args.model_name +lora_adapter_name = args.adapter_name +storage_path = args.storage_path + +config = AutoConfig.from_pretrained( + os.path.join(storage_path, "transformers", base_model_name), + trust_remote_code=True, +) +config.torch_dtype = torch.float16 +module = importlib.import_module("transformers") +hf_model_cls = getattr(module, AutoModelForCausalLM) +base_model = hf_model_cls.from_config( + config, + trust_remote_code=True, +).to(config.torch_dtype) + +# Load a lora adapter from HuggingFace model hub. +model = PeftModel.from_pretrained(base_model, lora_adapter_name) + +# Save the model to the local path. +model_path = os.path.join(storage_path, lora_adapter_name) +save_lora(model, model_path) diff --git a/sllm_store/examples/save_transformers_model.py b/sllm_store/examples/save_transformers_model.py new file mode 100644 index 0000000..17a5ac9 --- /dev/null +++ b/sllm_store/examples/save_transformers_model.py @@ -0,0 +1,37 @@ +import argparse +import os + +import torch +from transformers import AutoModelForCausalLM + +from sllm_store.transformers import save_model + +parser = argparse.ArgumentParser( + description="Save a model from HuggingFace model hub." +) +parser.add_argument( + "--model-name", + type=str, + required=True, + help="Model name from HuggingFace model hub.", +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path to save the model.", +) + +args = parser.parse_args() + +model_name = args.model_name +storage_path = args.storage_path + +# Load a model from HuggingFace model hub. +model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.float16 +) + +# Save the model to the local path. +model_path = os.path.join(storage_path, model_name) +save_model(model, model_path) diff --git a/sllm_store/examples/save_vllm_model.py b/sllm_store/examples/save_vllm_model.py new file mode 100644 index 0000000..5d5e099 --- /dev/null +++ b/sllm_store/examples/save_vllm_model.py @@ -0,0 +1,135 @@ +import argparse +import os +import shutil +from typing import Optional + + +class VllmModelDownloader: + def __init__(self): + pass + + def download_vllm_model( + self, + model_name: str, + torch_dtype: str, + tensor_parallel_size: int = 1, + storage_path: str = "./models", + local_model_path: Optional[str] = None, + pattern: Optional[str] = None, + max_size: Optional[int] = None, + ): + import gc + from tempfile import TemporaryDirectory + + import torch + from huggingface_hub import snapshot_download + from vllm import LLM + + # set the model storage path + storage_path = os.getenv("STORAGE_PATH", storage_path) + + def _run_writer(input_dir, model_name): + # load models from the input directory + llm_writer = LLM( + model=input_dir, + download_dir=input_dir, + dtype=torch_dtype, + tensor_parallel_size=tensor_parallel_size, + num_gpu_blocks_override=1, + enforce_eager=True, + max_model_len=1, + ) + model_path = os.path.join(storage_path, model_name) + model_executer = llm_writer.llm_engine.model_executor + # save the models in the ServerlessLLM format + model_executer.save_serverless_llm_state( + path=model_path, pattern=pattern, max_size=max_size + ) + for file in os.listdir(input_dir): + # Copy the metadata files into the output directory + if os.path.splitext(file)[1] not in ( + ".bin", + ".pt", + ".safetensors", + ): + src_path = os.path.join(input_dir, file) + dest_path = os.path.join(model_path, file) + if os.path.isdir(src_path): + shutil.copytree(src_path, dest_path) + else: + shutil.copy(src_path, dest_path) + del model_executer + del llm_writer + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + try: + with TemporaryDirectory() as cache_dir: + input_dir = local_model_path + # download from huggingface + if local_model_path is None: + input_dir = snapshot_download( + model_name, + cache_dir=cache_dir, + allow_patterns=[ + "*.safetensors", + "*.bin", + "*.json", + "*.txt", + ], + ) + _run_writer(input_dir, model_name) + except Exception as e: + print(f"An error occurred while saving the model: {e}") + # remove the output dir + shutil.rmtree(os.path.join(storage_path, model_name)) + raise RuntimeError( + f"Failed to save {model_name} for vllm backend: {e}" + ) from e + + +parser = argparse.ArgumentParser( + description="Save a model from HuggingFace model hub." +) +parser.add_argument( + "--model-name", + type=str, + required=True, + help="Model name from HuggingFace model hub.", +) +parser.add_argument( + "--local-model-path", + type=str, + required=False, + help="Local path to the model snapshot.", +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path to save the model.", +) +parser.add_argument( + "--tensor-parallel-size", + type=int, + default=1, + help="Tensor parallel size.", +) + +args = parser.parse_args() + +model_name = args.model_name +local_model_path = args.local_model_path +storage_path = args.storage_path +tensor_parallel_size = args.tensor_parallel_size + +downloader = VllmModelDownloader() +downloader.download_vllm_model( + model_name, + "float16", + tensor_parallel_size=tensor_parallel_size, + storage_path=storage_path, + local_model_path=local_model_path, +) diff --git a/sllm_store/proto/storage.proto b/sllm_store/proto/storage.proto new file mode 100644 index 0000000..f2f2d32 --- /dev/null +++ b/sllm_store/proto/storage.proto @@ -0,0 +1,94 @@ +syntax = "proto3"; + +package storage; + +service Storage { + rpc LoadModelAsync (LoadModelRequest) returns (LoadModelResponse) {} + rpc ConfirmModel (ConfirmModelRequest) returns (ConfirmModelResponse) {} + rpc UnloadModel (UnloadModelRequest) returns (UnloadModelResponse) {} + + rpc ClearMem (ClearMemRequest) returns (ClearMemResponse) {} + + rpc RegisterModel (RegisterModelRequest) returns (RegisterModelResponse) {} + rpc GetServerConfig (GetServerConfigRequest) returns (GetServerConfigResponse) {} +} + +message GetServerConfigRequest { +} + +message GetServerConfigResponse { + int64 mem_pool_size = 1; + int64 chunk_size = 2; +} + +message RegisterModelRequest { + string model_path = 1; +} + +message RegisterModelResponse { + string model_path = 1; + int64 model_size = 2; +} + +enum DeviceType { + DEVICE_TYPE_DISK = 0; + DEVICE_TYPE_CPU = 1; + DEVICE_TYPE_GPU = 2; +} + +message MemCopyChunk { + uint64 src_offset = 1; + uint64 size = 2; + uint64 dst_offset = 3; + uint64 handle_idx = 4; +} +message MemCopyChunkList { + repeated MemCopyChunk chunks = 1; +} +message MemCopyHandle { + bytes cuda_ipc_handle = 1; +} +message MemCopyHandleList { + repeated MemCopyHandle handles = 1; +} + +// If the handles has one item, all chunks share the same handle, other each chunk should have one handle +message LoadModelRequest { + string model_path = 1; + string replica_uuid = 2; + map chunks = 3; + map handles = 4; + DeviceType target_device_type = 5; +} + +message LoadModelResponse { + string model_path = 1; +} + +message ConfirmModelRequest { + string model_path = 1; + string replica_uuid = 2; + DeviceType target_device_type = 3; +} + +message ConfirmModelResponse { + string model_path = 1; + int32 code = 2; +} + +message UnloadModelRequest { + string model_path = 1; + string replica_uuid = 2; + DeviceType target_device_type = 3; +} + +message UnloadModelResponse { + string model_path = 1; + int32 code = 2; +} + +message ClearMemRequest { +} + +message ClearMemResponse { +} \ No newline at end of file diff --git a/sllm_store/py.typed b/sllm_store/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/sllm_store/pyproject.toml b/sllm_store/pyproject.toml new file mode 100644 index 0000000..e87d5e8 --- /dev/null +++ b/sllm_store/pyproject.toml @@ -0,0 +1,39 @@ +[build-system] +requires = [ + "setuptools", + "wheel", + "cmake>=3.20,<4.0.0", + "ninja", + "torch==2.7.0", + "numpy", +] +build-backend = "setuptools.build_meta" + + +[tool.ruff] +line-length = 80 +exclude = [ + "sllm_store/proto", +] + +[tool.ruff.lint] +select = [ + # pycodestyle + "E", + # Pyflakes + "F", + # pyupgrade + # "UP", + # flake8-bugbear + "B", + # flake8-simplify + "SIM", + # isort + # "I", +] +ignore = [] + + +[tool.isort] +use_parentheses = true +skip_gitignore = true diff --git a/sllm_store/requirements-build.txt b/sllm_store/requirements-build.txt new file mode 100644 index 0000000..7ed6a59 --- /dev/null +++ b/sllm_store/requirements-build.txt @@ -0,0 +1,7 @@ +cmake>=3.20,<4.0.0 +ninja +numpy +peft==0.15.2 +setuptools +torch==2.7.0 +wheel diff --git a/sllm_store/requirements.txt b/sllm_store/requirements.txt new file mode 100644 index 0000000..a227888 --- /dev/null +++ b/sllm_store/requirements.txt @@ -0,0 +1,8 @@ +accelerate==1.7.0 +bitsandbytes==0.46.0 +click>=8.1.7 +grpcio==1.72.1 +grpcio-tools==1.64.0 +peft==0.15.2 +torch==2.7.0 +transformers==4.52.4 diff --git a/sllm_store/setup.py b/sllm_store/setup.py new file mode 100644 index 0000000..aa653b1 --- /dev/null +++ b/sllm_store/setup.py @@ -0,0 +1,246 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import io +import os +import subprocess +import sys +from pathlib import Path +from typing import Dict + +from setuptools import Extension, setup +from setuptools.command.build_ext import build_ext +from setuptools.command.install import install + + +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import ROCM_HOME + + +ROOT_DIR = os.path.dirname(__file__) + + +def check_nvcc_installed(cuda_home: str) -> None: + """Check if nvcc (NVIDIA CUDA compiler) is installed.""" + try: + _ = subprocess.check_output( + [cuda_home + "/bin/nvcc", "-V"], universal_newlines=True + ) + except Exception as e: + raise RuntimeError( + "nvcc is not installed or not found in your PATH. " + "Please ensure that the CUDA toolkit is installed and nvcc is available in your PATH." # noqa: E501 + f" Error: {e}" + ) from None + + +def check_hipcc_installed(rocm_home: str) -> None: + """Check if hipcc (AMD HIP compiler) is installed.""" + # can be either /hip/bin/hipcc or /bin/hipcc + hipcc_paths = [rocm_home + "/bin/hipcc", rocm_home + "/hip/bin/hipcc"] + for hipcc in hipcc_paths: + try: + _ = subprocess.check_output( + [hipcc, "--version"], universal_newlines=True + ) + return + except Exception: + continue + raise RuntimeError( + "hipcc is not installed or not found in your PATH. " + "Please ensure that the HIP toolkit is installed and hipcc is available in your PATH." # noqa: E501 + ) from None + + +if CUDA_HOME is not None: + check_nvcc_installed(CUDA_HOME) +elif ROCM_HOME is not None: + check_hipcc_installed(ROCM_HOME) +else: + raise RuntimeError( + "CUDA_HOME or ROCM_HOME environment variable must be set to compile CUDA or HIP extensions." # noqa: E501 + ) + + +def is_ninja_available() -> bool: + try: + subprocess.run(["ninja", "--version"], stdout=subprocess.PIPE) + except FileNotFoundError: + return False + return True + + +class CustomInstall(install): + """Custom installation to ensure proto files are compiled + and extensions are built before installation.""" + + def run(self): + self.run_command("build_ext") + super().run() + + +def fetch_requirements(path): + with open(path, "r") as fd: + return [r.strip() for r in fd.readlines()] + + +def remove_prefix(text, prefix): + if text.startswith(prefix): + return text[len(prefix) :] + return text + + +def get_path(*filepath) -> str: + return os.path.join(ROOT_DIR, *filepath) + + +def read_readme() -> str: + """Read the README file if present.""" + p = get_path("README.md") + if os.path.isfile(p): + return io.open(get_path("README.md"), "r", encoding="utf-8").read() + else: + return "" + + +install_requires = fetch_requirements("requirements.txt") + +extras = {} + +extras["test"] = [ + "pytest", + "accelerate>=0.27.2", + "transformers>=4.37.2", + "parameterized", +] + +sys.path.append(Path.cwd().as_posix()) + + +class CMakeExtension(Extension): + def __init__(self, name: str, cmake_lists_dir: str = ".", **kwa) -> None: + super().__init__(name, sources=[], **kwa) + self.cmake_lists_dir = os.path.abspath(cmake_lists_dir) + + +# Adapted from https://github.com/vllm-project/vllm/blob/a1242324c99ff8b1e29981006dfb504da198c7c3/setup.py +class cmake_build_ext(build_ext): + did_config: Dict[str, bool] = {} + + def configure(self, ext: CMakeExtension) -> None: + if ext.cmake_lists_dir in cmake_build_ext.did_config: + return + + cmake_build_ext.did_config[ext.cmake_lists_dir] = True + + default_cfg = "Debug" if self.debug else "Release" + cfg = os.getenv("CMAKE_BUILD_TYPE", default_cfg) + + outdir = os.path.abspath( + os.path.dirname(self.get_ext_fullpath(ext.name)) + ) + + cmake_args = [ + "-DCMAKE_BUILD_TYPE={}".format(cfg), + "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY={}".format(outdir), + "-DCMAKE_RUNTIME_OUTPUT_DIRECTORY={}".format(outdir), + "-DCMAKE_ARCHIVE_OUTPUT_DIRECTORY={}".format(self.build_temp), + ] + + # verbose = bool(int(os.getenv('VERBOSE', '1'))) + verbose = True + if verbose: + cmake_args += ["-DCMAKE_VERBOSE_MAKEFILE=ON"] + + cmake_args += [ + "-DSLLM_STORE_PYTHON_EXECUTABLE={}".format(sys.executable) + ] + + if is_ninja_available(): + build_tool = ["-G", "Ninja"] + cmake_args += [ + "-DCMAKE_JOB_POOL_COMPILE:STRING=compile", + "-DCMAKE_JOB_POOLS:STRING=compile={}".format(8), + ] + else: + # Default build tool to whatever cmake picks. + build_tool = [] + + subprocess.check_call( + ["cmake", ext.cmake_lists_dir, *build_tool, *cmake_args], + cwd=self.build_temp, + ) + + def build_extensions(self) -> None: + # Ensure that CMake is present and working + try: + subprocess.check_output(["cmake", "--version"]) + except OSError as e: + raise RuntimeError("Cannot find CMake executable") from e + + # Create build directory if it does not exist. + if not os.path.exists(self.build_temp): + os.makedirs(self.build_temp) + + # Build all the extensions + for ext in self.extensions: + self.configure(ext) + + ext_target_name = remove_prefix(ext.name, "sllm_store.") + # num_jobs = 32 + + build_args = [ + "--build", + ".", + "--target", + ext_target_name, + "-j", + # str(num_jobs) + ] + + subprocess.check_call(["cmake", *build_args], cwd=self.build_temp) + print(self.build_temp, ext_target_name) + + +cmdclass = { + "build_ext": cmake_build_ext, + "install": CustomInstall, +} + +setup( + name="serverless-llm-store", + version="0.7.0", + ext_modules=[ + CMakeExtension(name="sllm_store._C"), + CMakeExtension(name="sllm_store._checkpoint_store"), + ], + install_requires=install_requires, + long_description=read_readme(), + long_description_content_type="text/markdown", + extras_require=extras, + entry_points={ + "console_scripts": ["sllm-store=sllm_store.cli:main"], + }, + package_data={ + "sllm_store": ["py.typed", "*.so"], + }, + packages=[ + "sllm_store", + "sllm_store.proto", + ], + cmdclass=cmdclass, +) diff --git a/sllm_store/sllm_store/__init__.py b/sllm_store/sllm_store/__init__.py new file mode 100644 index 0000000..314e503 --- /dev/null +++ b/sllm_store/sllm_store/__init__.py @@ -0,0 +1,17 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # diff --git a/sllm_store/sllm_store/cli.py b/sllm_store/sllm_store/cli.py new file mode 100644 index 0000000..c9b4a87 --- /dev/null +++ b/sllm_store/sllm_store/cli.py @@ -0,0 +1,484 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +import asyncio +import importlib +import os +import shutil +import sys +import logging +import time +from typing import Optional +import torch +import click + +from sllm_store.server import serve +from sllm_store.logger import init_logger +from sllm_store.utils import to_num_bytes +from sllm_store.transformers import save_model, save_lora, load_model, load_lora +from transformers import ( + AutoModelForCausalLM, + AutoConfig, + AutoTokenizer, + BitsAndBytesConfig, +) +from peft import PeftModel + + +class VllmModelDownloader: + def __init__(self): + pass + + def download_vllm_model( + self, + model_name: str, + torch_dtype: str, + tensor_parallel_size: int = 1, + storage_path: str = "./models", + local_model_path: Optional[str] = None, + pattern: Optional[str] = None, + max_size: Optional[int] = None, + ): + import gc + from tempfile import TemporaryDirectory + + import torch + from huggingface_hub import snapshot_download + from vllm import LLM + + # set the model storage path + storage_path = os.getenv("STORAGE_PATH", storage_path) + + def _run_writer(input_dir, model_name): + # load models from the input directory + llm_writer = LLM( + model=input_dir, + download_dir=input_dir, + dtype=torch_dtype, + tensor_parallel_size=tensor_parallel_size, + num_gpu_blocks_override=1, + enforce_eager=True, + max_model_len=1, + ) + model_path = os.path.join(storage_path, model_name) + model_executer = llm_writer.llm_engine.engine_core # For engine V1 + # save the models in the ServerlessLLM format + model_executer.save_serverless_llm_state( + path=model_path, pattern=pattern, max_size=max_size + ) + for file in os.listdir(input_dir): + # Copy the metadata files into the output directory + if os.path.splitext(file)[1] not in ( + ".bin", + ".pt", + ".safetensors", + ): + src_path = os.path.join(input_dir, file) + dest_path = os.path.join(model_path, file) + if os.path.isdir(src_path): + shutil.copytree(src_path, dest_path) + else: + shutil.copy(src_path, dest_path) + del model_executer + del llm_writer + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + try: + with TemporaryDirectory() as cache_dir: + input_dir = local_model_path + # download from huggingface + if local_model_path is None: + input_dir = snapshot_download( + model_name, + cache_dir=cache_dir, + allow_patterns=[ + "*.safetensors", + "*.bin", + "*.json", + "*.txt", + ], + ) + _run_writer(input_dir, model_name) + except Exception as e: + print(f"An error occurred while saving the model: {e}") + # remove the output dir + shutil.rmtree(os.path.join(storage_path, model_name)) + raise RuntimeError( + f"Failed to save {model_name} for vllm backend: {e}" + ) from e + + +logger = init_logger(__name__) + + +def check_vllm(): + """Simple check if ServerlessLLM patch is applied.""" + try: + # Check if the LoadFormat enum has SERVERLESS_LLM + from vllm.config import LoadFormat + + if not hasattr(LoadFormat, "SERVERLESS_LLM"): + return False + + # Check if ExecutorBase has the save method + from vllm.executor.executor_base import ExecutorBase + + return hasattr(ExecutorBase, "save_serverless_llm_state") + except ImportError: + return False + + +@click.group() +def cli(): + """sllm-store CLI""" + pass + + +@cli.command() +@click.option("--host", default="0.0.0.0", help="Host") +@click.option("--port", default=8073, help="Port") +@click.option("--storage-path", default="./models", help="Storage path") +@click.option("--num-thread", default=4, help="Number of I/O threads") +@click.option( + "--chunk-size", default="32MB", help="Chunk size, e.g., 4KB, 1MB, 1GB" +) +@click.option( + "--mem-pool-size", + default="4GB", + help="Memory pool size, e.g., 1GB, 4GB, 1TB", +) +@click.option( + "--disk-size", default="128GB", help="Disk size, e.g., 1GB, 4GB, 1TB" +) +@click.option( + "--registration-required", + default=False, + help="Require registration before loading model", +) +def start( + host, + port, + storage_path, + num_thread, + chunk_size, + mem_pool_size, + disk_size, + registration_required, +): + """Start the gRPC server.""" + # Convert the chunk size to bytes + chunk_size = to_num_bytes(chunk_size) + + # Convert the memory pool size to bytes + mem_pool_size = to_num_bytes(mem_pool_size) + + try: + logger.info("Starting gRPC server") + asyncio.run( + serve( + host=host, + port=port, + storage_path=storage_path, + num_thread=num_thread, + chunk_size=chunk_size, + mem_pool_size=mem_pool_size, + # disk size is not used + # disk_size=disk_size, + registration_required=registration_required, + ) + ) + except KeyboardInterrupt: + logging.info("Server stopped by user") + sys.exit(0) + + +@cli.command() +@click.option( + "--model", + "model_name", + type=str, + required=True, + help="Model name from HuggingFace model hub", +) +@click.option("--backend", type=str, required=True, help="Backend") +@click.option("--adapter-name", type=str, help="Name of the LoRA adapter") +@click.option( + "--tensor-parallel-size", type=int, default=1, help="Tensor parallel size" +) +@click.option( + "--local-model-path", type=str, help="Local path to the model snapshot" +) +@click.option( + "--storage-path", + default="./models", + help="Local path to save the model", +) +def save( + model_name, + backend, + adapter_name, + tensor_parallel_size, + local_model_path, + storage_path, +): + """ + Saves a model to the sllm-store's storage. + + This command is for adding new models to the sllm-store's local storage. + """ + + logger.info( + f"Saving model {adapter_name if adapter_name else model_name} " + f"to {storage_path}" + ) + + try: + if backend == "vllm": + if not check_vllm(): + logger.error( + "vLLM is not patched. Please run " + "`./sllm_store/vllm_patch/patch.sh` first." + ) + sys.exit(1) + downloader = VllmModelDownloader() + downloader.download_vllm_model( + model_name, + "float16", + tensor_parallel_size=tensor_parallel_size, + storage_path=storage_path, + local_model_path=local_model_path, + ) + elif backend == "transformers": + if adapter_name: + config = AutoConfig.from_pretrained( + model_name, + trust_remote_code=True, + ) + config.torch_dtype = torch.float16 + module = importlib.import_module("transformers") + hf_model_cls = module.AutoModelForCausalLM + base_model = hf_model_cls.from_config( + config, + trust_remote_code=True, + ).to(config.torch_dtype) + + # Load a lora adapter from HuggingFace model hub + model = PeftModel.from_pretrained(base_model, adapter_name) + + # Save the model to the local path + model_path = os.path.join(storage_path, adapter_name) + save_lora(model, model_path) + else: + # Load a model from HuggingFace model hub + model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.float16 + ) + + # Save the model to the local path + model_path = os.path.join(storage_path, model_name) + save_model(model, model_path) + else: + logger.error(f"Unsupported backend '{backend}'") + sys.exit(1) + except Exception as e: + logger.error(f"Failed to save model {model_name}: {e}", exc_info=True) + sys.exit(1) + + logger.info( + f"Model {adapter_name if adapter_name else model_name} " + f"saved successfully to {storage_path}" + ) + + +@cli.command() +@click.option( + "--model", + "model_name", + type=str, + required=True, + help="Model name from HuggingFace model hub", +) +@click.option("--backend", type=str, required=True, help="Backend") +@click.option("--adapter-name", type=str, help="Name of the LoRA adapter") +@click.option( + "--precision", + type=click.Choice(["int8", "fp4", "nf4"]), + help="Precision of quantized model.", +) +@click.option( + "--storage-path", + type=str, + default="./models", + help="Local path where model is saved", +) +def load( + model_name, + backend, + adapter_name, + precision, + storage_path, +): + """ + Loads a model from the sllm-store's storage. + + This command is for loading new models from the sllm-store's local storage. + """ + + logger.info( + f"Loading model {adapter_name if adapter_name else model_name} " + f"from {storage_path}" + ) + + quantization_config = None + if precision: + if precision == "int8": + quantization_config = BitsAndBytesConfig(load_in_8bit=True) + elif precision == "fp4": + quantization_config = BitsAndBytesConfig(load_in_4bit=True) + elif precision == "nf4": + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, bnb_4bit_quant_type="nf4" + ) + + try: + start_load_time = time.time() + + if backend == "vllm": + from vllm import LLM + + if not check_vllm(): + logger.error( + "vLLM is not patched. Please run " + "`./sllm_store/vllm_patch/patch.sh` first." + ) + sys.exit(1) + model_full_path = os.path.join(storage_path, model_name) + llm = LLM( + model=model_full_path, + load_format="serverless_llm", + dtype="float16", + ) + logger.info( + f"Model loading time: {time.time() - start_load_time:.2f}s" + ) + + example_inferences("vllm", model=llm) + + elif backend == "transformers": + # warm up the GPU + num_gpus = torch.cuda.device_count() + for i in range(num_gpus): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + if adapter_name: + model = load_model( + model_name, + device_map="auto", + torch_dtype=torch.float16, + storage_path=storage_path, + fully_parallel=True, + ) + + model = load_lora( + model, + adapter_name, + adapter_path=adapter_name, + device_map="auto", + storage_path=storage_path, + torch_dtype=torch.float16, + ) + else: + model = load_model( + model_name, + device_map="auto", + torch_dtype=torch.float16, + storage_path=storage_path, + fully_parallel=True, + quantization_config=quantization_config, + ) + logger.info( + f"Model loading time: {time.time() - start_load_time:.2f}s" + ) + + example_inferences( + "transformers", + model=model, + model_name=model_name, + adapter_name=adapter_name, + ) + + else: + logger.error(f"Unsupported backend '{backend}'") + sys.exit(1) + + except Exception as e: + logger.error( + f"Failed to load model or perform inference: {e}", exc_info=True + ) + sys.exit(1) + + logger.info( + f"Model {adapter_name if adapter_name else model_name} " + f"loaded successfully from {storage_path}" + ) + + +def example_inferences(backend, model=None, model_name=None, adapter_name=None): + if backend == "vllm": + prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", + ] + from vllm import SamplingParams + + sampling_params = SamplingParams(temperature=0.8, top_p=0.95) + outputs = model.generate(prompts, sampling_params) + + # Print the outputs. + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + elif backend == "transformers": + from pathlib import Path + + parts = Path(model_name).parts + if len(parts) >= 2: + model_name = f"{parts[-2]}/{parts[-1]}" + tokenizer = AutoTokenizer.from_pretrained(model_name) + inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to( + "cuda" + ) + generate_kwargs = {} + if adapter_name: + generate_kwargs["adapter_names"] = [adapter_name] + outputs = model.generate(**inputs, **generate_kwargs) + print(tokenizer.decode(outputs[0], skip_special_tokens=True)) + else: + logger.error(f"Unsupported backend '{backend}' for example inferences") + sys.exit(1) + + +# Entry point for the 'sllm-store' CLI +def main(): + cli() diff --git a/sllm_store/sllm_store/client.py b/sllm_store/sllm_store/client.py new file mode 100644 index 0000000..8efead6 --- /dev/null +++ b/sllm_store/sllm_store/client.py @@ -0,0 +1,157 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +import grpc +import sllm_store.proto.storage_pb2 as storage_pb2 +import sllm_store.proto.storage_pb2_grpc as storage_pb2_grpc +from sllm_store.logger import init_logger + +logger = init_logger(__name__) + + +# This is a singleton class that manages the checkpoint +class SllmStoreClient: + def __init__(self, server_address="127.0.0.1:8073"): + self.server_address = server_address + self.channel = grpc.insecure_channel(server_address) + self.stub = storage_pb2_grpc.StorageStub(self.channel) + self.checkpoints_in_gpu = {} + + def __del__(self): + # TODO: cleanup + pass + + def load_into_cpu(self, model_path): + request = storage_pb2.LoadModelRequest( + model_path=model_path, + target_device_type=storage_pb2.DeviceType.DEVICE_TYPE_CPU, + ) + try: + response = self.stub.LoadModelAsync(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.CANCELLED: + logger.error(f"Model not loaded {e}") + return False + else: + logger.error(f"Error: {e}") + return False + else: + return response + + def unload_from_cpu(self, model_path): + request = storage_pb2.UnloadModelRequest( + model_path=model_path, + target_device_type=storage_pb2.DeviceType.DEVICE_TYPE_CPU, + ) + try: + response = self.stub.UnloadModel(request) + except grpc.RpcError as e: + logger.error(f"Error: {e}") + return False + else: + return response + + def load_into_gpu( + self, model_path, replica_uuid, tensor_copy_chunks, cuda_memory_handles + ): + logger.debug(f"load_into_gpu: {model_path}, {replica_uuid}") + + gpu_chunk_map = {} + for device_uuid, chunks in tensor_copy_chunks.items(): + gpu_chunk_map[device_uuid] = storage_pb2.MemCopyChunkList( + chunks=[ + storage_pb2.MemCopyChunk( + src_offset=chunk[0], + size=chunk[1], + dst_offset=chunk[2], + handle_idx=chunk[3], + ) + for chunk in chunks + ] + ) + cuda_handle_map = {} + for device_uuid, handles in cuda_memory_handles.items(): + cuda_handle_map[device_uuid] = storage_pb2.MemCopyHandleList( + handles=[ + storage_pb2.MemCopyHandle( + cuda_ipc_handle=handle_str, + ) + for handle_str in handles + ] + ) + request = storage_pb2.LoadModelRequest( + model_path=model_path, + replica_uuid=replica_uuid, + chunks=gpu_chunk_map, + handles=cuda_handle_map, + target_device_type=storage_pb2.DeviceType.DEVICE_TYPE_GPU, + ) + try: + response = self.stub.LoadModelAsync(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.CANCELLED: + logger.error(f"Model not loaded {e}") + else: + logger.error(f"Error: {e}") + return False + else: + logger.info(f"Model loaded: {model_path}, {replica_uuid}") + return response + + def confirm_model_loaded(self, model_path, replica_uuid): + logger.info(f"confirm_model_loaded: {model_path}, {replica_uuid}") + request = storage_pb2.ConfirmModelRequest( + model_path=model_path, + replica_uuid=replica_uuid, + target_device_type=storage_pb2.DeviceType.DEVICE_TYPE_GPU, + ) + try: + _ = self.stub.ConfirmModel(request) + logger.info("Model loaded") + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.CANCELLED: + logger.error("Model not loaded") + return False + else: + logger.error(f"Error: {e}") + return False + + def register_model(self, model_path) -> int: + logger.info(f"register_model: {model_path}") + request = storage_pb2.RegisterModelRequest(model_path=model_path) + try: + response = self.stub.RegisterModel(request) + except grpc.RpcError as e: + logger.error(f"Error: {e}") + return -1 + else: + logger.info("Model registered") + return response.model_size + + def get_server_config(self): + request = storage_pb2.GetServerConfigRequest() + try: + response = self.stub.GetServerConfig(request) + except grpc.RpcError as e: + logger.error(f"Error: {e}") + return None + else: + return { + "chunk_size": response.chunk_size, + "mem_pool_size": response.mem_pool_size, + } diff --git a/sllm_store/sllm_store/device_map_utils.py b/sllm_store/sllm_store/device_map_utils.py new file mode 100644 index 0000000..df31c58 --- /dev/null +++ b/sllm_store/sllm_store/device_map_utils.py @@ -0,0 +1,309 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +from typing import Dict, List, Optional, Tuple, Union + +import torch +from accelerate import infer_auto_device_map +from accelerate.utils import get_balanced_memory, get_max_memory +from sllm_store.logger import init_logger + +logger = init_logger(__name__) + +DeviceMapType = Union[ + str, Dict[str, Union[int, str, torch.device]], int, torch.device +] + + +def _transform_device_map_to_dict( + device_map: DeviceMapType, +) -> Dict[str, Union[int, torch.device]]: + """Transforms the device_map to a dictionary if it is not already a dictionary.""" # noqa: E501 + + if isinstance(device_map, torch.device): + device_map = {"": device_map} + elif isinstance(device_map, str) and device_map not in [ + "auto", + "balanced", + "balanced_low_0", + "sequential", + ]: + try: + device_map = {"": torch.device(device_map)} + except RuntimeError: + raise ValueError( + "When passing device_map as a string, the value needs to be a device name (e.g. cpu, cuda:0) or " # noqa: E501 + f"'auto', 'balanced', 'balanced_low_0', 'sequential' but found {device_map}." # noqa: E501 + ) from None + elif isinstance(device_map, int): + if device_map < 0: + raise ValueError( + "You can't pass device_map as a negative int. If you want to put the model on the cpu, pass device_map = 'cpu' " # noqa: E501 + ) + else: + device_map = {"": device_map} + return device_map + + +def _expand_tensor_name( + device_map: DeviceMapType, tensor_names: List[str] +) -> Dict[str, Union[int, torch.device]]: + if "" in device_map and len(device_map) != 1: + raise RuntimeError( + f"Device map {device_map} is invalid. If you want to specify the default device, use key ''." # noqa: E501 + ) + + expanded_device_map = {} + for tensor_or_module, device_id in device_map.items(): + if tensor_or_module == "": + return {k: device_id for k in tensor_names} + + for name in tensor_names: + # TODO: use trie to speed up prefix match + if name.startswith(tensor_or_module): + expanded_device_map[name] = device_id + + return expanded_device_map + + +def _compute_device_placement_from_map( + model: torch.nn.Module, + device_map: DeviceMapType, + target_dtype: torch.dtype, + max_memory: Optional[int] = None, +) -> Dict[str, Union[int, torch.device]]: + """ + Computes the device placement for the model based on the device_map. + """ + + if isinstance(device_map, str): + no_split_modules = model._get_no_split_modules(device_map) + if device_map not in [ + "auto", + "balanced", + "balanced_low_0", + "sequential", + ]: + raise ValueError( + "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " # noqa: E501 + "'sequential'." + ) + + device_map_kwargs = {"no_split_module_classes": no_split_modules} + + if device_map != "sequential": + max_memory = get_balanced_memory( + model, + dtype=target_dtype, + low_zero=(device_map == "balanced_low_0"), + max_memory=max_memory, + **device_map_kwargs, + ) + else: + max_memory = get_max_memory(max_memory) + device_map_kwargs["max_memory"] = max_memory + + # Make sure tied weights are tied before creating the device map. + model.tie_weights() + device_map = infer_auto_device_map( + model, dtype=target_dtype, **device_map_kwargs + ) + + return device_map + + +def _compute_device_placement_from_map_fast( + no_split_modules: Dict[str, int], + tied_modules: List[Tuple[List[str], int]], + device_map: DeviceMapType, +) -> Dict[str, Union[int, torch.device]]: + """ + Computes the device placement for no split modules based on the device_map. + """ + + if isinstance(device_map, str): + if device_map not in [ + "auto", + "balanced", + "balanced_low_0", + "sequential", + ]: + raise ValueError( + "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " # noqa: E501 + "'sequential'." + ) + + max_memory = get_max_memory() + # we don't support loading to cpu + max_memory.pop("cpu") + + # tied modules are treated as a single module + for tied_groups, shared_size in tied_modules: + modules = list(no_split_modules.keys()) + for module in modules: + if module in tied_groups: + tied_size = no_split_modules[module] + for m in modules: + if m in tied_groups and m != module: + tied_size += no_split_modules[m] - shared_size + no_split_modules.pop(m) + no_split_modules[module] = tied_size + break + + while next(iter(no_split_modules.values())) > next( + iter(max_memory.values()) + ): + device_id, memory = max_memory.popitem() + logger.warning( + f"Device {device_id} has insufficient memory {memory} for the first module." # noqa: E501 + ) + + total_size = sum(no_split_modules.values()) + + if total_size > sum(max_memory.values()): + raise RuntimeError( + "The total size of the model is greater than the maximum memory available." # noqa: E501 + ) + + placement = None + if device_map == "auto" or device_map == "balanced": + # 1. use dynamic programming to find the best balanced placement + placement = _get_balanced_placement(no_split_modules, max_memory) + elif device_map == "balanced_low_0": + # 2.1 decide minimum modules on device 0 + # 2.2 use dynamic programming to find the best balanced placement for the rest on other devices # noqa: E501 + raise NotImplementedError + else: + # 3. use greedy algorithm to find the best sequential placement + placement = _get_sequential_placement(no_split_modules, max_memory) + + if placement is None: + raise RuntimeError( + "Failed to find a valid placement for the model." + ) + + # reassign tied modules to the same device + for tied_groups, shared_size in tied_modules: # noqa: B007 + modules = list(placement.keys()) + for module in modules: + if module in tied_groups: + for m in tied_groups: + if m != module: + placement[m] = placement[module] + + return placement + + return device_map + + +def _get_balanced_placement( + module_size: Dict[str, int], + device_memory: Dict[torch.device, int], +) -> Dict[str, Union[int, torch.device]]: + """ + Computes the balanced placement for no split modules based on the given device_memory. + """ # noqa: E501 + + module_names = list(module_size.keys()) + assert len(module_names) > 0 and len(module_names) >= len(device_memory) + + length = len(module_names) + n = len(device_memory) + if n <= 0 or length == 0: + logger.error("No device memory or no modules to place.") + return None + + # "balanced" means that the gap between the sums of the partitions is minimized # noqa: E501 + # Initialize DP table + dp = [ + [[float("inf"), float("inf"), 0, []] for _ in range(n + 1)] + for _ in range(length + 1) + ] + dp[0][0] = [ + 0, + float("inf"), + 0, + [[] for _ in range(n)], + ] # [gap, min_size, max_size, partitions] + + # Fill DP table + for i in range(1, length + 1): + for k in range(1, n + 1): + for j in range(i): + current_partition = module_names[j:i] + current_size = sum( + [module_size[module] for module in current_partition] + ) + # check if this partition can fit in the device memory + if current_size > device_memory[k - 1]: + # print(f"Partition {current_partition} is too large for device {k-1}, memory {device_memory[k-1]}") # noqa: E501 + continue + if dp[j][k - 1][0] < float("inf"): + if dp[j][k - 1][1] == float("inf"): + max_gap = 0 + else: + max_gap = max( + abs(dp[j][k - 1][1] - current_size), + abs(dp[j][k - 1][2] - current_size), + ) + if max_gap < dp[i][k][0]: + dp[i][k][0] = max_gap + dp[i][k][1] = min(dp[j][k - 1][1], current_size) + dp[i][k][2] = max(dp[j][k - 1][2], current_size) + dp[i][k][-1] = dp[j][k - 1][-1][:] # Copy partitions + dp[i][k][-1][k - 1] = ( + current_partition # Update last partition + ) + + # Result + result = dp[length][n][-1] + result_device_map = {} + for i, partition in enumerate(result): + for module in partition: + result_device_map[module] = i + return result_device_map + + +def _get_sequential_placement( + module_size: Dict[str, int], + device_memory: Dict[torch.device, int], +) -> Dict[str, Union[int, torch.device]]: + """ + Computes the sequential placement for no split modules based on the given device_memory. + """ # noqa: E501 + + module_names = list(module_size.keys()) + assert len(module_names) > 0 and len(module_names) >= len(device_memory) + + length = len(module_names) + n = len(device_memory) + if n <= 0 or length == 0: + return None + + result_device_map = {} + current_device = 0 + for module, size in module_size.items(): + while current_device < n and size > device_memory[current_device]: + current_device += 1 + if size <= device_memory[current_device]: + result_device_map[module] = current_device + else: + raise RuntimeError( + f"Module {module} is too large for device {current_device}, memory {device_memory[current_device]}" # noqa: E501 + ) + + return result_device_map diff --git a/sllm_store/sllm_store/logger.py b/sllm_store/sllm_store/logger.py new file mode 100644 index 0000000..118538d --- /dev/null +++ b/sllm_store/sllm_store/logger.py @@ -0,0 +1,81 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # + +# Adapted from https://github.com/vllm-project/vllm/blob/0ce0539d4750f9ebcd9b19d7085ca3b934b9ec67/vllm/logger.py +"""Logging configuration for sllm_store.""" + +import logging +import os +import sys + +_FORMAT = "%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s" +_DATE_FORMAT = "%m-%d %H:%M:%S" + + +class NewLineFormatter(logging.Formatter): + """Adds logging prefix to newlines to align multi-line messages.""" + + def __init__(self, fmt, datefmt=None): + logging.Formatter.__init__(self, fmt, datefmt) + + def format(self, record): + msg = logging.Formatter.format(self, record) + if record.message != "": + parts = msg.split(record.message) + msg = msg.replace("\n", "\r\n" + parts[0]) + return msg + + +_root_logger = logging.getLogger("sllm_store") +_default_handler = None + + +def _setup_logger(): + _root_logger.setLevel(logging.DEBUG) + global _default_handler + + _default_handler = logging.StreamHandler(sys.stdout) + _default_handler.flush = sys.stdout.flush # type: ignore + _default_handler.setLevel(logging.DEBUG) + _root_logger.addHandler(_default_handler) + + fmt = NewLineFormatter(_FORMAT, datefmt=_DATE_FORMAT) + _default_handler.setFormatter(fmt) + # Setting this will avoid the message + # being propagated to the parent logger. + _root_logger.propagate = False + + +_setup_logger() + + +def init_logger(name: str): + # Use the same settings as above for root logger + logger = logging.getLogger(name) + + log_level = os.getenv("LOG_LEVEL", "DEBUG").upper() + logger.setLevel(log_level) + + # Ensure the handler's level matches the logger's level + if _default_handler: + _default_handler.setLevel(log_level) + + logger.addHandler(_default_handler) + logger.propagate = False + + return logger diff --git a/sllm_store/sllm_store/proto/__init__.py b/sllm_store/sllm_store/proto/__init__.py new file mode 100644 index 0000000..314e503 --- /dev/null +++ b/sllm_store/sllm_store/proto/__init__.py @@ -0,0 +1,17 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # diff --git a/sllm_store/sllm_store/proto/storage.proto b/sllm_store/sllm_store/proto/storage.proto new file mode 120000 index 0000000..6772c53 --- /dev/null +++ b/sllm_store/sllm_store/proto/storage.proto @@ -0,0 +1 @@ +../../proto/storage.proto \ No newline at end of file diff --git a/sllm_store/sllm_store/proto/storage_pb2.py b/sllm_store/sllm_store/proto/storage_pb2.py new file mode 100644 index 0000000..fabbbc3 --- /dev/null +++ b/sllm_store/sllm_store/proto/storage_pb2.py @@ -0,0 +1,68 @@ +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: sllm_store/proto/storage.proto +"""Generated protocol buffer code.""" + +from google.protobuf.internal import builder as _builder +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile( + b'\n\x1esllm_store/proto/storage.proto\x12\x07storage"\x18\n\x16GetServerConfigRequest"D\n\x17GetServerConfigResponse\x12\x15\n\rmem_pool_size\x18\x01 \x01(\x03\x12\x12\n\nchunk_size\x18\x02 \x01(\x03"*\n\x14RegisterModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t"?\n\x15RegisterModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x12\n\nmodel_size\x18\x02 \x01(\x03"X\n\x0cMemCopyChunk\x12\x12\n\nsrc_offset\x18\x01 \x01(\x04\x12\x0c\n\x04size\x18\x02 \x01(\x04\x12\x12\n\ndst_offset\x18\x03 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\x01(\x05"\x11\n\x0f\x43learMemRequest"\x12\n\x10\x43learMemResponse*L\n\nDeviceType\x12\x14\n\x10\x44\x45VICE_TYPE_DISK\x10\x00\x12\x13\n\x0f\x44\x45VICE_TYPE_CPU\x10\x01\x12\x13\n\x0f\x44\x45VICE_TYPE_GPU\x10\x02\x32\xdc\x03\n\x07Storage\x12I\n\x0eLoadModelAsync\x12\x19.storage.LoadModelRequest\x1a\x1a.storage.LoadModelResponse"\x00\x12M\n\x0c\x43onfirmModel\x12\x1c.storage.ConfirmModelRequest\x1a\x1d.storage.ConfirmModelResponse"\x00\x12J\n\x0bUnloadModel\x12\x1b.storage.UnloadModelRequest\x1a\x1c.storage.UnloadModelResponse"\x00\x12\x41\n\x08\x43learMem\x12\x18.storage.ClearMemRequest\x1a\x19.storage.ClearMemResponse"\x00\x12P\n\rRegisterModel\x12\x1d.storage.RegisterModelRequest\x1a\x1e.storage.RegisterModelResponse"\x00\x12V\n\x0fGetServerConfig\x12\x1f.storage.GetServerConfigRequest\x1a .storage.GetServerConfigResponse"\x00\x62\x06proto3' +) + +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals()) +_builder.BuildTopDescriptorsAndMessages( + DESCRIPTOR, "sllm_store.proto.storage_pb2", globals() +) +if _descriptor._USE_C_DESCRIPTORS == False: + DESCRIPTOR._options = None + _LOADMODELREQUEST_CHUNKSENTRY._options = None + _LOADMODELREQUEST_CHUNKSENTRY._serialized_options = b"8\001" + _LOADMODELREQUEST_HANDLESENTRY._options = None + _LOADMODELREQUEST_HANDLESENTRY._serialized_options = b"8\001" + _DEVICETYPE._serialized_start = 1297 + _DEVICETYPE._serialized_end = 1373 + _GETSERVERCONFIGREQUEST._serialized_start = 43 + _GETSERVERCONFIGREQUEST._serialized_end = 67 + _GETSERVERCONFIGRESPONSE._serialized_start = 69 + _GETSERVERCONFIGRESPONSE._serialized_end = 137 + _REGISTERMODELREQUEST._serialized_start = 139 + _REGISTERMODELREQUEST._serialized_end = 181 + _REGISTERMODELRESPONSE._serialized_start = 183 + _REGISTERMODELRESPONSE._serialized_end = 246 + _MEMCOPYCHUNK._serialized_start = 248 + _MEMCOPYCHUNK._serialized_end = 336 + _MEMCOPYCHUNKLIST._serialized_start = 338 + _MEMCOPYCHUNKLIST._serialized_end = 395 + _MEMCOPYHANDLE._serialized_start = 397 + _MEMCOPYHANDLE._serialized_end = 437 + _MEMCOPYHANDLELIST._serialized_start = 439 + _MEMCOPYHANDLELIST._serialized_end = 499 + _LOADMODELREQUEST._serialized_start = 502 + _LOADMODELREQUEST._serialized_end = 873 + _LOADMODELREQUEST_CHUNKSENTRY._serialized_start = 725 + _LOADMODELREQUEST_CHUNKSENTRY._serialized_end = 797 + _LOADMODELREQUEST_HANDLESENTRY._serialized_start = 799 + _LOADMODELREQUEST_HANDLESENTRY._serialized_end = 873 + _LOADMODELRESPONSE._serialized_start = 875 + _LOADMODELRESPONSE._serialized_end = 914 + _CONFIRMMODELREQUEST._serialized_start = 916 + _CONFIRMMODELREQUEST._serialized_end = 1028 + _CONFIRMMODELRESPONSE._serialized_start = 1030 + _CONFIRMMODELRESPONSE._serialized_end = 1086 + _UNLOADMODELREQUEST._serialized_start = 1088 + _UNLOADMODELREQUEST._serialized_end = 1199 + _UNLOADMODELRESPONSE._serialized_start = 1201 + _UNLOADMODELRESPONSE._serialized_end = 1256 + _CLEARMEMREQUEST._serialized_start = 1258 + _CLEARMEMREQUEST._serialized_end = 1275 + _CLEARMEMRESPONSE._serialized_start = 1277 + _CLEARMEMRESPONSE._serialized_end = 1295 + _STORAGE._serialized_start = 1376 + _STORAGE._serialized_end = 1852 +# @@protoc_insertion_point(module_scope) diff --git a/sllm_store/sllm_store/proto/storage_pb2_grpc.py b/sllm_store/sllm_store/proto/storage_pb2_grpc.py new file mode 100644 index 0000000..3090d18 --- /dev/null +++ b/sllm_store/sllm_store/proto/storage_pb2_grpc.py @@ -0,0 +1,307 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" + +import grpc + +from sllm_store.proto import ( + storage_pb2 as sllm__store_dot_proto_dot_storage__pb2, +) + + +class StorageStub(object): + """Missing associated documentation comment in .proto file.""" + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.LoadModelAsync = channel.unary_unary( + "/storage.Storage/LoadModelAsync", + request_serializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.FromString, + ) + self.ConfirmModel = channel.unary_unary( + "/storage.Storage/ConfirmModel", + request_serializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.FromString, + ) + self.UnloadModel = channel.unary_unary( + "/storage.Storage/UnloadModel", + request_serializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.FromString, + ) + self.ClearMem = channel.unary_unary( + "/storage.Storage/ClearMem", + request_serializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.FromString, + ) + self.RegisterModel = channel.unary_unary( + "/storage.Storage/RegisterModel", + request_serializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.FromString, + ) + self.GetServerConfig = channel.unary_unary( + "/storage.Storage/GetServerConfig", + request_serializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.FromString, + ) + + +class StorageServicer(object): + """Missing associated documentation comment in .proto file.""" + + def LoadModelAsync(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details("Method not implemented!") + raise NotImplementedError("Method not implemented!") + + def ConfirmModel(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details("Method not implemented!") + raise NotImplementedError("Method not implemented!") + + def UnloadModel(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details("Method not implemented!") + raise NotImplementedError("Method not implemented!") + + def ClearMem(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details("Method not implemented!") + raise NotImplementedError("Method not implemented!") + + def RegisterModel(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details("Method not implemented!") + raise NotImplementedError("Method not implemented!") + + def GetServerConfig(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details("Method not implemented!") + raise NotImplementedError("Method not implemented!") + + +def add_StorageServicer_to_server(servicer, server): + rpc_method_handlers = { + "LoadModelAsync": grpc.unary_unary_rpc_method_handler( + servicer.LoadModelAsync, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.SerializeToString, + ), + "ConfirmModel": grpc.unary_unary_rpc_method_handler( + servicer.ConfirmModel, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.SerializeToString, + ), + "UnloadModel": grpc.unary_unary_rpc_method_handler( + servicer.UnloadModel, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.SerializeToString, + ), + "ClearMem": grpc.unary_unary_rpc_method_handler( + servicer.ClearMem, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.SerializeToString, + ), + "RegisterModel": grpc.unary_unary_rpc_method_handler( + servicer.RegisterModel, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.SerializeToString, + ), + "GetServerConfig": grpc.unary_unary_rpc_method_handler( + servicer.GetServerConfig, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + "storage.Storage", rpc_method_handlers + ) + server.add_generic_rpc_handlers((generic_handler,)) + + +# This class is part of an EXPERIMENTAL API. +class Storage(object): + """Missing associated documentation comment in .proto file.""" + + @staticmethod + def LoadModelAsync( + request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None, + ): + return grpc.experimental.unary_unary( + request, + target, + "/storage.Storage/LoadModelAsync", + sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + ) + + @staticmethod + def ConfirmModel( + request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None, + ): + return grpc.experimental.unary_unary( + request, + target, + "/storage.Storage/ConfirmModel", + sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + ) + + @staticmethod + def UnloadModel( + request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None, + ): + return grpc.experimental.unary_unary( + request, + target, + "/storage.Storage/UnloadModel", + sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + ) + + @staticmethod + def ClearMem( + request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None, + ): + return grpc.experimental.unary_unary( + request, + target, + "/storage.Storage/ClearMem", + sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + ) + + @staticmethod + def RegisterModel( + request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None, + ): + return grpc.experimental.unary_unary( + request, + target, + "/storage.Storage/RegisterModel", + sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + ) + + @staticmethod + def GetServerConfig( + request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None, + ): + return grpc.experimental.unary_unary( + request, + target, + "/storage.Storage/GetServerConfig", + sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + ) diff --git a/sllm_store/sllm_store/server.py b/sllm_store/sllm_store/server.py new file mode 100644 index 0000000..ef80d6b --- /dev/null +++ b/sllm_store/sllm_store/server.py @@ -0,0 +1,238 @@ +import asyncio +import grpc +import sllm_store +from sllm_store.proto import storage_pb2, storage_pb2_grpc +from sllm_store.logger import init_logger + +# this is necessary to avoid libtorch.so not found error +import torch # noqa: F401 + +import ctypes +import os + +ctypes.CDLL(os.path.join(sllm_store.__path__[0], "libglog.so")) + +from sllm_store._checkpoint_store import ( # noqa: E402 + CheckpointStore, + MemCopyChunk, +) + +logger = init_logger(__name__) + + +class StorageServicer(storage_pb2_grpc.StorageServicer): + def __init__( + self, + storage_path, + mem_pool_size, + num_thread, + chunk_size, + registration_required, + ): + if not storage_path: + logger.error("storage_path is empty") + raise ValueError("storage_path is empty") + + if mem_pool_size <= 0: + logger.error("mem_pool_size must be greater than 0") + raise ValueError("Invalid mem_pool_size") + + logger.info( + f"StorageServicer: storage_path={storage_path}, " + f"mem_pool_size={mem_pool_size}, num_thread={num_thread}, " + f"chunk_size={chunk_size}, " + f"registration_required={registration_required}" + ) + + self.storage = CheckpointStore( + storage_path, mem_pool_size, num_thread, chunk_size + ) + self.registration_required = registration_required + + async def LoadModelAsync(self, request, context): + model_path = request.model_path + if not model_path: + logger.error("model_path is empty") + context.set_code(grpc.StatusCode.INVALID_ARGUMENT) + return storage_pb2.LoadModelResponse() + + if not self.registration_required: + model_size = self.storage.register_model_info(model_path) + if model_size < 0: + logger.error("RegisterModel failed") + context.set_code(grpc.StatusCode.INTERNAL) + return storage_pb2.LoadModelResponse() + + device_type = request.target_device_type + if device_type == storage_pb2.DEVICE_TYPE_CPU: + ret = self.storage.load_model_from_disk_async(model_path) + elif device_type == storage_pb2.DEVICE_TYPE_GPU: + replica_uuid = request.replica_uuid + if not replica_uuid: + logger.error("replica_uuid is empty") + context.set_code(grpc.StatusCode.INVALID_ARGUMENT) + return storage_pb2.LoadModelResponse() + + gpu_memory_handles = { + device_uuid: [ + handle.cuda_ipc_handle for handle in handle_list.handles + ] + for device_uuid, handle_list in request.handles.items() + } + + def create_mem_copy_chunk(chunk): + mem_copy_chunk = MemCopyChunk() + mem_copy_chunk.src_offset = chunk.src_offset + mem_copy_chunk.size = chunk.size + mem_copy_chunk.dst_offset = chunk.dst_offset + mem_copy_chunk.handle_idx = chunk.handle_idx + return mem_copy_chunk + + mem_copy_chunks = { + device_uuid: [ + create_mem_copy_chunk(chunk) for chunk in chunk_list.chunks + ] + for device_uuid, chunk_list in request.chunks.items() + } + # logger.debug( + # f"LoadModelAsync: {model_path}, {replica_uuid}, " + # f"{gpu_memory_handles}, {mem_copy_chunks}" + # ) + ret = self.storage.load_model_from_mem_async( + model_path, replica_uuid, gpu_memory_handles, mem_copy_chunks + ) + else: + logger.error(f"Unsupported device type: {device_type}") + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + return storage_pb2.LoadModelResponse() + + if ret != 0: + logger.error("LoadModel failed") + context.set_code(grpc.StatusCode.INTERNAL) + return storage_pb2.LoadModelResponse() + + logger.info( + f"LoadModel: success {model_path} with target {device_type}" + ) + return storage_pb2.LoadModelResponse(model_path=model_path) + + async def ConfirmModel(self, request, context): + model_path = request.model_path + replica_uuid = request.replica_uuid + device_type = request.target_device_type + + if not model_path: + logger.error("model_path is empty") + context.set_code(grpc.StatusCode.INVALID_ARGUMENT) + return storage_pb2.ConfirmModelResponse() + + if device_type != storage_pb2.DEVICE_TYPE_GPU: + logger.error(f"Unsupported device type: {device_type}") + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + return storage_pb2.ConfirmModelResponse() + + for i in range(5): + ret = self.storage.wait_model_in_gpu(model_path, replica_uuid) + if ret == 0: + logger.info( + f"Confirm model {model_path} replica {replica_uuid} success" + ) + return storage_pb2.ConfirmModelResponse(model_path=model_path) + logger.info(f"Confirm model failed, retry {i + 1}") + + await asyncio.sleep(0.05) + + logger.error( + f"Confirm model {model_path} replica {replica_uuid} failed" + ) + context.set_code(grpc.StatusCode.INTERNAL) + return storage_pb2.ConfirmModelResponse() + + async def UnloadModel(self, request, context): + model_path = request.model_path + device_type = request.target_device_type + + if not model_path: + logger.error("model_path is empty") + context.set_code(grpc.StatusCode.INVALID_ARGUMENT) + return storage_pb2.UnloadModelResponse() + + if device_type != storage_pb2.DEVICE_TYPE_CPU: + logger.error(f"Unsupported device type: {device_type}") + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + return storage_pb2.UnloadModelResponse() + + for i in range(5): + ret = self.storage.unload_model_from_host(model_path) + if ret == 0: + logger.info(f"UnloadModel: success {model_path}") + return storage_pb2.UnloadModelResponse(model_path=model_path) + logger.info(f"UnloadModel failed, retry {i + 1}") + + await asyncio.sleep(0.01) + + logger.error(f"UnloadModel failed for model {model_path}") + context.set_code(grpc.StatusCode.INTERNAL) + return storage_pb2.UnloadModelResponse() + + async def ClearMem(self, request, context): + ret = self.storage.clear_mem() + if ret != 0: + logger.error("ClearMem failed") + context.set_code(grpc.StatusCode.INTERNAL) + return storage_pb2.ClearMemResponse() + + async def RegisterModel(self, request, context): + model_path = request.model_path + if not model_path: + logger.error("model_path is empty") + context.set_code(grpc.StatusCode.INVALID_ARGUMENT) + return storage_pb2.RegisterModelResponse() + + model_size = self.storage.register_model_info(model_path) + if model_size < 0: + logger.error("RegisterModel failed") + context.set_code(grpc.StatusCode.INTERNAL) + return storage_pb2.RegisterModelResponse() + + return storage_pb2.RegisterModelResponse( + model_path=model_path, model_size=model_size + ) + + async def GetServerConfig(self, request, context): + return storage_pb2.GetServerConfigResponse( + mem_pool_size=self.storage.get_mem_pool_size(), + chunk_size=self.storage.get_chunk_size(), + ) + + +async def serve( + host, + port, + storage_path, + num_thread, + chunk_size, + mem_pool_size, + registration_required, +): + server = grpc.aio.server() + storage_pb2_grpc.add_StorageServicer_to_server( + StorageServicer( + storage_path, + mem_pool_size, + num_thread, + chunk_size, + registration_required, + ), + server, + ) + listen_addr = f"{host}:{port}" + server.add_insecure_port(listen_addr) + logger.info(f"Starting gRPC server on {listen_addr}") + await server.start() + + try: + await server.wait_for_termination() + except (KeyboardInterrupt, asyncio.CancelledError): + logger.info("Shutting down gRPC server") + await server.stop(5) diff --git a/sllm_store/sllm_store/torch.py b/sllm_store/sllm_store/torch.py new file mode 100644 index 0000000..8516530 --- /dev/null +++ b/sllm_store/sllm_store/torch.py @@ -0,0 +1,162 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import json +import os +import time +import uuid +from typing import Dict, Optional, Union + +import torch + +# from accelerate.hooks import add_hook_to_module +from sllm_store._C import ( + allocate_cuda_memory, + get_cuda_memory_handles, + get_device_uuid_map, + restore_tensors, + save_tensors, +) +from sllm_store.client import SllmStoreClient +from sllm_store.device_map_utils import _expand_tensor_name +from sllm_store.logger import init_logger +from sllm_store.utils import ( + calculate_device_memory, + calculate_tensor_device_offsets, +) + +logger = init_logger(__name__) + + +def _get_uuid(): + return str(uuid.uuid4()) + + +def save_dict( + state_dict: Dict[str, torch.Tensor], model_path: Union[str, os.PathLike] +): + tensor_names = list(state_dict.keys()) + tensor_data_index = {} + for name, param in state_dict.items(): + param_storage = param.untyped_storage() + data_ptr = param_storage.data_ptr() + size = param_storage.size() + tensor_data_index[name] = (data_ptr, size) + + if not os.path.exists(model_path): + os.makedirs(model_path, exist_ok=True) + + # save tensors + tensor_offsets = save_tensors(tensor_names, tensor_data_index, model_path) + + # create tensor index + tensor_index = {} + for name, param in state_dict.items(): + # name: offset, size + tensor_index[name] = ( + tensor_offsets[name], + tensor_data_index[name][1], + tuple(param.shape), + tuple(param.stride()), + str(param.dtype), + ) + + # save tensor index + with open(os.path.join(model_path, "tensor_index.json"), "w") as f: + json.dump(tensor_index, f) + + +def load_dict( + model_path: Union[str, os.PathLike], + device_map: Dict[str, int], + storage_path: Optional[str] = None, +): + replica_uuid, state_dict = load_dict_non_blocking( + model_path, device_map, storage_path + ) + + client = SllmStoreClient("127.0.0.1:8073") + client.confirm_model_loaded(model_path, replica_uuid) + + return state_dict + + +def load_dict_non_blocking( + model_path: Optional[Union[str, os.PathLike]], + device_map: Dict[str, int], + storage_path: Optional[str] = None, +): + client = SllmStoreClient("127.0.0.1:8073") + ret = client.load_into_cpu(model_path) + if not ret: + raise ValueError(f"Failed to load model {model_path} into CPU") + + if not storage_path: + storage_path = os.getenv("STORAGE_PATH", "./models") + with open( + os.path.join(storage_path, model_path, "tensor_index.json"), "r" + ) as f: + tensor_index = json.load(f) + + tensor_meta_index = {} + tensor_data_index = {} + for name, (offset, size, shape, stride, dtype) in tensor_index.items(): + tensor_meta_index[name] = (shape, stride, dtype) + tensor_data_index[name] = (offset, size) + + start = time.time() + expanded_device_map = _expand_tensor_name( + device_map, list(tensor_index.keys()) + ) + device_memory = calculate_device_memory( + expanded_device_map, tensor_data_index + ) + # logger.debug(f"calculate_device_memory {device_memory}") + cuda_memory_ptrs = allocate_cuda_memory(device_memory) + # cuda_memory_ptrs = { k: [v] for k,v in cuda_memory_ptrs.items()} + cuda_memory_handles = get_cuda_memory_handles(cuda_memory_ptrs) + device_uuid_map = get_device_uuid_map() + # logger.debug(f"determine device_uuid_map {device_uuid_map}") + tensor_device_offsets, tensor_copy_chunks = calculate_tensor_device_offsets( + expanded_device_map, tensor_data_index + ) + logger.debug(f"allocate_cuda_memory takes {time.time() - start} seconds") + + replica_uuid = _get_uuid() + ret = client.load_into_gpu( + model_path, + replica_uuid, + { + device_uuid_map[device_id]: v + for device_id, v in tensor_copy_chunks.items() + }, + { + device_uuid_map[device_id]: [v] + for device_id, v in cuda_memory_handles.items() + }, + ) + if not ret: + raise ValueError(f"Failed to load model {model_path} into GPU") + + # load model state_dict + start = time.time() + state_dict = restore_tensors( + tensor_meta_index, cuda_memory_ptrs, tensor_device_offsets + ) + logger.info(f"restore state_dict takes {time.time() - start} seconds") + + return replica_uuid, state_dict diff --git a/sllm_store/sllm_store/transformers.py b/sllm_store/sllm_store/transformers.py new file mode 100644 index 0000000..2809165 --- /dev/null +++ b/sllm_store/sllm_store/transformers.py @@ -0,0 +1,498 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import concurrent.futures +import json +import os +import time +import uuid +from typing import Optional, Union, Dict, Any + +import torch +from accelerate import dispatch_model, init_empty_weights + +# from accelerate.hooks import add_hook_to_module +from accelerate.utils import set_module_tensor_to_device +from sllm_store._C import ( + allocate_cuda_memory, + get_cuda_memory_handles, + get_device_uuid_map, + restore_tensors, +) +from sllm_store.client import SllmStoreClient +from sllm_store.device_map_utils import ( + DeviceMapType, + _compute_device_placement_from_map, + _compute_device_placement_from_map_fast, + _expand_tensor_name, + _transform_device_map_to_dict, +) +from sllm_store.logger import init_logger +from sllm_store.torch import load_dict_non_blocking, save_dict +from sllm_store.utils import ( + calculate_device_memory, + calculate_tensor_device_offsets, + get_no_split_modules, + get_tied_no_split_modules, + send_module_buffers_to_device, + quantize, +) +from torch import nn +from transformers import AutoConfig +import importlib +from peft import ( + PeftModel, + get_peft_model, + get_peft_model_state_dict, + LoraConfig, +) +from peft.utils import set_peft_model_state_dict +from transformers.utils.quantization_config import ( + QuantizationConfigMixin, +) + +logger = init_logger(__name__) + + +def _get_uuid(): + return str(uuid.uuid4()) + + +def save_model(model: nn.Module, model_path: str): + """ + Args: + model: nn.Module + a model to be saved + storage_path: str + a local path to save the converted model + """ + if not os.path.exists(model_path): + os.makedirs(model_path, exist_ok=True) + + model = model.cpu() + model_state_dict = model.state_dict() + + save_dict(model_state_dict, model_path) + + # This section of code was adopted from the Hugging Face Transformers project under Apache-2.0 License. # noqa: E501 + # Source: https://github.com/huggingface/transformers/blob/241c04d36867259cdf11dbb4e9d9a60f9cb65ebc/src/transformers/modeling_utils.py#L2812-L2856 + # Modifications made: Removed the support for '_hf_peft_config_loaded' + # + # Save the config + model.config.save_pretrained(model_path) + if model.can_generate(): + model.generation_config.save_pretrained(model_path) + + # save module index + no_split_modules = get_no_split_modules(model, model._no_split_modules) + with open(os.path.join(model_path, "no_split_modules.json"), "w") as f: + json.dump(no_split_modules, f) + + # save tied parameters + tied_no_split_modules = get_tied_no_split_modules(model, no_split_modules) + with open(os.path.join(model_path, "tied_no_split_modules.json"), "w") as f: + json.dump(tied_no_split_modules, f) + + +def save_lora(model: PeftModel, lora_path: str): + if not os.path.exists(lora_path): + os.makedirs(lora_path, exist_ok=True) + + model = model.to("cpu") + + lora_state_dict = get_peft_model_state_dict(model) + + save_dict(lora_state_dict, lora_path) + + # save the config + if hasattr(model, "peft_config") and model.peft_config: + adapter_name = getattr(model, "active_adapter", None) + if adapter_name is None: + logger.warning("No active_adapter found") + return + + config = model.peft_config.get(adapter_name, None) + if config is None: + logger.warning(f"No config found for adapter: {adapter_name}") + return + + config.save_pretrained(lora_path) + logger.info( + f"Saved LoRA config for adapter: {adapter_name} to {lora_path}" + ) + + +def load_model( + model_path: Optional[Union[str, os.PathLike]], + device_map: DeviceMapType = "auto", + torch_dtype: Optional[torch.dtype] = None, + quantization_config: Optional[ + Union[QuantizationConfigMixin, Dict[str, Any]] + ] = None, + storage_path: Optional[str] = None, + fully_parallel: bool = False, + hf_model_class: str = "AutoModelForCausalLM", +): + if fully_parallel: + return fully_parallel_load( + model_path=model_path, + hf_model_class=hf_model_class, + device_map=device_map, + torch_dtype=torch_dtype, + quantization_config=quantization_config, + storage_path=storage_path, + ) + # if fully_parallel is disabled, we still try to parallelize the model + # initialization and data loading in the best effort + return best_effort_load( + model_path=model_path, + hf_model_class=hf_model_class, + device_map=device_map, + torch_dtype=torch_dtype, + quantization_config=quantization_config, + storage_path=storage_path, + ) + + +def fully_parallel_load( + model_path: Optional[Union[str, os.PathLike]], + hf_model_class: str, + device_map: DeviceMapType = "auto", + torch_dtype: Optional[torch.dtype] = None, + quantization_config: Optional[ + Union[QuantizationConfigMixin, Dict[str, Any]] + ] = None, + storage_path: Optional[str] = None, +): + if not storage_path: + storage_path = os.getenv("STORAGE_PATH", "./models") + + start = time.time() + device_map = _transform_device_map_to_dict(device_map) + with open( + os.path.join(storage_path, model_path, "tied_no_split_modules.json"), + "r", + ) as f: + tied_no_split_modules = json.load(f) + + if isinstance(device_map, str): + with open( + os.path.join(storage_path, model_path, "no_split_modules.json"), + "r", + ) as f: + no_split_modules = json.load(f) + device_map = _compute_device_placement_from_map_fast( + no_split_modules, tied_no_split_modules, device_map + ) + + # TODO: offload `load_dict_non_blocking` to c++ for real parallelism + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit( + load_dict_non_blocking, model_path, device_map, storage_path + ) + logger.debug( + f"load_dict_non_blocking takes {time.time() - start} seconds" + ) + + start = time.time() + config = AutoConfig.from_pretrained( + f"{os.path.join(storage_path, model_path)}", trust_remote_code=True + ) + if torch_dtype is not None: + config.torch_dtype = torch_dtype + logger.debug(f"load config takes {time.time() - start} seconds") + + start = time.time() + with init_empty_weights(): + module = importlib.import_module("transformers") + _class = getattr(module, hf_model_class) + model = _class.from_config( + config, + trust_remote_code=True, + ).to(config.torch_dtype) + model.tie_weights() + logger.debug(f"load model takes {time.time() - start} seconds") + + replica_uuid, state_dict = future.result() + + with torch.no_grad(): + if quantization_config and torch.cuda.is_available(): + model = quantize( + model, + state_dict, + quantization_config, + torch_dtype, + device_map, + model_path, + replica_uuid, + logger, + ) + else: + if quantization_config is not None: + logger.debug( + "Quantization on current device is not supported yet" + ) + + for name, param in state_dict.items(): + set_module_tensor_to_device(model, name, param.device, param) + send_module_buffers_to_device(model, device_map) + + dispatch_model( + model, device_map, skip_keys=model._skip_keys_device_placement + ) + client = SllmStoreClient("127.0.0.1:8073") + client.confirm_model_loaded(model_path, replica_uuid) + model.hf_device_map = device_map + model.eval() + return model + + +def best_effort_load( + model_path: Optional[Union[str, os.PathLike]], + hf_model_class: str, + device_map: DeviceMapType = "auto", + torch_dtype: Optional[torch.dtype] = None, + quantization_config: Optional[ + Union[QuantizationConfigMixin, Dict[str, Any]] + ] = None, + storage_path: Optional[str] = None, +): + client = SllmStoreClient("127.0.0.1:8073") + ret = client.load_into_cpu(model_path) + if not ret: + raise ValueError(f"Failed to load model {model_path} into CPU") + + replica_uuid = _get_uuid() + device_map = _transform_device_map_to_dict(device_map) + + if isinstance(device_map, dict) and ( + torch.device("cpu") in device_map.values() + or "cpu" in device_map.values() + ): + raise ValueError("CPU is not supported in device_map.") + + if not storage_path: + storage_path = os.getenv("STORAGE_PATH", "./models") + start = time.time() + config = AutoConfig.from_pretrained( + f"{os.path.join(storage_path, model_path)}", trust_remote_code=True + ) + if torch_dtype is not None: + config.torch_dtype = torch_dtype + + logger.debug(f"load config takes {time.time() - start} seconds") + start = time.time() + with init_empty_weights(): + module = importlib.import_module("transformers") + _class = getattr(module, hf_model_class) + model = _class.from_config(config, trust_remote_code=True).to( + config.torch_dtype + ) + + model.tie_weights() + logger.debug(f"load model takes {time.time() - start} seconds") + + start = time.time() + if isinstance(device_map, str): + device_map = _compute_device_placement_from_map( + model, device_map, config.torch_dtype + ) + logger.debug(f"device_map: {device_map}") + # check if 'cpu' is in device_map values and raise an exception + if "cpu" in device_map.values(): + raise ValueError( + "The GPUs are either unavailable or do not have enough memory. Please ensure they are available and ready for use." # noqa: E501 + ) + + logger.debug( + f"compute_device_placement takes {time.time() - start} seconds" + ) + + with open( + os.path.join(storage_path, model_path, "tensor_index.json"), "r" + ) as f: + tensor_index = json.load(f) + + tensor_meta_index = {} + tensor_data_index = {} + for name, (offset, size, shape, stride, dtype) in tensor_index.items(): + tensor_meta_index[name] = (shape, stride, dtype) + tensor_data_index[name] = (offset, size) + + start = time.time() + expanded_device_map = _expand_tensor_name( + device_map, list(tensor_index.keys()) + ) + device_memory = calculate_device_memory( + expanded_device_map, tensor_data_index + ) + # logger.debug(f"calculate_device_memory {device_memory}") + cuda_memory_ptrs = allocate_cuda_memory(device_memory) + # cuda_memory_ptrs = { k: [v] for k,v in cuda_memory_ptrs.items()} + cuda_memory_handles = get_cuda_memory_handles(cuda_memory_ptrs) + device_uuid_map = get_device_uuid_map() + # logger.debug(f"determine device_uuid_map {device_uuid_map}") + tensor_device_offsets, tensor_copy_chunks = calculate_tensor_device_offsets( + expanded_device_map, tensor_data_index + ) + logger.debug(f"allocate_cuda_memory takes {time.time() - start} seconds") + + ret = client.load_into_gpu( + model_path, + replica_uuid, + { + device_uuid_map[device_id]: v + for device_id, v in tensor_copy_chunks.items() + }, + { + device_uuid_map[device_id]: [v] + for device_id, v in cuda_memory_handles.items() + }, + ) + if not ret: + raise ValueError(f"Failed to load model {model_path} into GPU") + + # load model state_dict + start = time.time() + state_dict = restore_tensors( + tensor_meta_index, cuda_memory_ptrs, tensor_device_offsets + ) + logger.info(f"restore state_dict takes {time.time() - start} seconds") + + with torch.no_grad(): + if quantization_config and torch.cuda.is_available(): + model = quantize( + model, + state_dict, + quantization_config, + torch_dtype, + device_map, + model_path, + replica_uuid, + logger, + ) + else: + if quantization_config is not None: + logger.debug( + "Quantization on current device is not supported yet" + ) + + for name, param in state_dict.items(): + set_module_tensor_to_device(model, name, param.device, param) + send_module_buffers_to_device(model, device_map) + + dispatch_model( + model, device_map, skip_keys=model._skip_keys_device_placement + ) + + client.confirm_model_loaded(model_path, replica_uuid) + model.eval() + model.hf_device_map = device_map + + return model + + +def load_lora( + model: nn.Module, + adapter_name: str, + adapter_path: Optional[Union[str, os.PathLike]], + device_map: DeviceMapType = "auto", + storage_path: Optional[str] = None, + is_trainable: bool = False, + torch_dtype: Optional[torch.dtype] = None, +): + if not storage_path: + storage_path = os.getenv("STORAGE_PATH", "./models") + + config_path = os.path.join( + storage_path, adapter_path, "adapter_config.json" + ) + with open(config_path, "r") as f: + config_dict = json.load(f) + lora_config = LoraConfig(**config_dict) + + if lora_config.is_prompt_learning and is_trainable: + raise ValueError( + "Cannot set a prompt learning adapter to trainable\ + when loading pretrained adapter." + ) + + lora_config.inference_mode = not is_trainable + + client = SllmStoreClient("127.0.0.1:8073") + client.register_model(adapter_path) + + model.add_adapter(lora_config, adapter_name=adapter_name) + + replica_uuid, state_dict = load_dict_non_blocking( + adapter_path, {"": 0}, storage_path + ) + + # https://github.com/huggingface/transformers/blob/de182ba2690fe6c3466f6463c7f4b3a61694b885/src/transformers/integrations/peft.py#L228-L265 + processed_adapter_state_dict = {} + prefix = "base_model.model." + for key, value in state_dict.items(): + new_key = key[len(prefix) :] if key.startswith(prefix) else key + processed_adapter_state_dict[new_key] = value + + incompatible_keys = set_peft_model_state_dict( + model, processed_adapter_state_dict, adapter_name + ) + if incompatible_keys is not None: + err_msg = "" + origin_name = adapter_path if adapter_path is not None else "state_dict" + # Check for unexpected keys. + if ( + hasattr(incompatible_keys, "unexpected_keys") + and len(incompatible_keys.unexpected_keys) > 0 + ): + err_msg = ( + f"Loading adapter weights from {origin_name} led to \ + unexpected keys not found in the model: " + f"{', '.join(incompatible_keys.unexpected_keys)}. " + ) + + # Check for missing keys. + missing_keys = getattr(incompatible_keys, "missing_keys", None) + if missing_keys: + # Filter missing keys specific to the current adapter, \ + # as missing base model keys are expected. + lora_missing_keys = [ + k for k in missing_keys if "lora_" in k and adapter_name in k + ] + if lora_missing_keys: + err_msg += ( + f"Loading adapter weights from {origin_name} led to \ + missing keys in the model: " + f"{', '.join(lora_missing_keys)}" + ) + + if err_msg: + logger.warning(err_msg) + + # convert base model to PeftModel + peft_model = get_peft_model(model, lora_config) + + # synchronize + client.confirm_model_loaded(adapter_path, replica_uuid) + + if lora_config.inference_mode: + peft_model.eval() + + logger.info(f"Available adapters: {peft_model.peft_config.keys()}") + + return peft_model diff --git a/sllm_store/sllm_store/utils.py b/sllm_store/sllm_store/utils.py new file mode 100644 index 0000000..565f71a --- /dev/null +++ b/sllm_store/sllm_store/utils.py @@ -0,0 +1,349 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import re +from functools import reduce +from contextlib import suppress + +import torch +from torch import nn +from transformers import BitsAndBytesConfig +from accelerate.utils import find_tied_parameters, set_module_tensor_to_device +from transformers.quantizers.auto import AutoHfQuantizer +from transformers.utils.quantization_config import ( + QuantizationConfigMixin, +) + +from sllm_store.client import SllmStoreClient + + +def set_module_buffer_to_device( + module: nn.Module, + target: str, + device: torch.device, +): + module_path, _, buffer_name = target.rpartition(".") + + mod: torch.nn.Module = module.get_submodule(module_path) + + if not hasattr(mod, buffer_name): + raise AttributeError( + mod._get_name() + " has no attribute `" + buffer_name + "`" + ) + + buffer = mod._buffers[buffer_name] + mod._buffers[buffer_name] = buffer.to(device) + + +def send_module_buffers_to_device( + module: nn.Module, + device_map: dict, +): + if "" in device_map and len(device_map) != 1: + raise RuntimeError( + f"Device map {device_map} is invalid. If you want to specify the default device, use key ''." # noqa: E501 + ) + + buffer_names = [name for name, _ in module.named_buffers()] + for tensor_or_module, device_id in device_map.items(): + if tensor_or_module == "": + for buffer_name in buffer_names: + set_module_buffer_to_device(module, buffer_name, device_id) + else: + for buffer_name in buffer_names: + if buffer_name.startswith(tensor_or_module): + set_module_buffer_to_device(module, buffer_name, device_id) + + +def calculate_device_memory(device_map, tensor_index): + device_memory = {} + tensor_record = {} + for tensor_name, device in device_map.items(): + if tensor_name in tensor_index: + if device not in device_memory: + device_memory[device] = 0 + offset, size = tensor_index[tensor_name] + if (offset, size) in tensor_record: + continue # Skip duplicate tensors + tensor_record[(offset, size)] = True + device_memory[device] += tensor_index[tensor_name][1] + else: + raise ValueError(f"Tensor {tensor_name} not found in tensor_index.") + + return device_memory + + +def calculate_tensor_device_offsets(device_map, tensor_index): + tensor_device_offsets = {} + tensor_copy_chunks = {} + device_offset = {} + tensor_record = {} + for tensor_name, device in device_map.items(): + if device not in tensor_device_offsets: + tensor_device_offsets[device] = {} + tensor_copy_chunks[device] = [] + device_offset[device] = 0 + if tensor_name in tensor_index: + offset, size = tensor_index[tensor_name] + if (offset, size) in tensor_record: + tensor_device_offsets[device][tensor_name] = tensor_record[ + (offset, size) + ] + else: + tensor_record[(offset, size)] = device_offset[device] + tensor_device_offsets[device][tensor_name] = device_offset[ + device + ] + tensor_copy_chunks[device].append( + (offset, size, device_offset[device], 0) + ) + device_offset[device] += size + else: + raise ValueError(f"Tensor {tensor_name} not found in tensor_index.") + + return tensor_device_offsets, tensor_copy_chunks + + +def get_total_parameter_size(module): + total_param_size = 0 + for param in module.parameters(): + total_param_size += param.numel() * dtype_byte_size(param.dtype) + return total_param_size + + +def get_parameter_size(model: nn.Module, param_path: str): + # Split the parameter path by dots + attributes = param_path.split(".") + + # Use reduce to traverse the model's attributes + param = reduce(getattr, attributes, model) + + # Return the size of the parameter + return param.numel() * dtype_byte_size(param.dtype) + + +def get_no_split_modules(model, no_split_modules_list, parent_name=""): + no_split_modules = {} + for name, submodule in model.named_children(): + full_name = f"{parent_name}.{name}" if parent_name else name + module_class_name = submodule.__class__.__name__ + # If the module is a leaf module or in the no_split_modules_list, we don't split it # noqa: E501 + if ( + not list(submodule.children()) + or module_class_name in no_split_modules_list + ): + no_split_modules[full_name] = get_total_parameter_size(submodule) + continue + no_split_modules.update( + get_no_split_modules(submodule, no_split_modules_list, full_name) + ) + + return no_split_modules + + +def get_tied_no_split_modules(model, no_split_modules): + tied_parameters = find_tied_parameters(model) + tied_modules = [] + for tied_param_group in tied_parameters: + tied_module_group = [] + shared_size = None + for tied_param in tied_param_group: + param_size = get_parameter_size(model, tied_param) + if shared_size is None: + shared_size = param_size + else: + assert ( + shared_size == param_size + ), f"Parameter {tied_param} does not have the same size as the other parameters in the group" # noqa: E501 + tied_module = None + while "." in tied_param: + tied_param = tied_param.rsplit(".", 1)[0] + if tied_param in no_split_modules: + tied_module = tied_param + break + if tied_module is None: + raise ValueError( + f"Parameter {tied_param} is not in the no_split_modules list" # noqa: E501 + ) + tied_module_group.append(tied_module) + tied_modules.append((tied_module_group, shared_size)) + + return tied_modules + + +def dtype_byte_size(dtype: torch.dtype) -> int: + return torch.finfo(dtype).bits // 8 + + +def to_num_bytes(value: str) -> int: + """ + Convert a string representing a data size to its equivalent number of bytes. + + The input must strictly follow the format: + + + - : A positive integer. + - : One of the following case-sensitive units: + B, KB, MB, GB, TB, PB, EB, ZB, YB + + No leading, trailing, or middle spaces or other characters are allowed. + + Examples: + "1GB" -> 1073741824 + "512MB" -> 536870912 + + Args: + value (str): The string to convert. + + Returns: + int: The equivalent number of bytes. + + Raises: + ValueError: If the input format is incorrect. + """ + # Define the regular expression pattern for validation + pattern = r"^(\d+)(B|KB|MB|GB|TB|PB|EB|ZB|YB)$" + match = re.fullmatch(pattern, value) + + if not match: + error_message = ( + "Invalid format. The input must be a positive integer " + "followed immediately by a unit " + "(B, KB, MB, GB, TB, PB, EB, ZB, YB), case sensitive, " + "with no spaces or other characters." + ) + raise ValueError(error_message) + + number_str, unit = match.groups() + number = int(number_str) + + # Define the multiplier for each unit + unit_multipliers = { + "B": 1, + "KB": 1024, + "MB": 1024**2, + "GB": 1024**3, + "TB": 1024**4, + "PB": 1024**5, + "EB": 1024**6, + "ZB": 1024**7, + "YB": 1024**8, + } + + bytes_value = number * unit_multipliers[unit] + return bytes_value + + +def quantize( + model, + state_dict, + quantization_config, + torch_dtype, + device_map, + model_path, + replica_uuid, + logger, +): + if isinstance(quantization_config, dict): + try: + quantization_config = BitsAndBytesConfig.from_dict( + quantization_config + ) + except (TypeError, ValueError) as e: + logger.error(f"Invalid quantization_config dictionary: {e}") + raise ValueError( + f"Invalid quantization_config dictionary: {e}" + ) from e + + if not isinstance(quantization_config, QuantizationConfigMixin): + raise ValueError(f"Invalid config type: {type(quantization_config)}") + + quant_method = quantization_config.quant_method + if quant_method in [ + "aqlm", + "awq", + "bitnet", + "quanto", + "vptq", + "quark", + "higgs", + "hqq", + "eetq", + "torchao", + "spqr", + "fp8", + "auto-round", + "fbgemm_fp8", + "compressed_tensors", + ]: + raise ValueError( + f"{quant_method} (requires pre-quantization) " "is not supported." + ) + + logger.info(f"Using quantization method: {quant_method}") + if quantization_config.quant_method == "bitsandbytes": + precision = quantization_config.quantization_method() + logger.info(f"Using precision: {precision}") + + if quantization_config.llm_int8_enable_fp32_cpu_offload: + logger.debug("Offloading is not supported yet") + quantization_config.llm_int8_enable_fp32_cpu_offload = False + else: + with suppress(Exception): + logger.info(f"Using precision: {quantization_config.bits}") + + torch_dtype = torch_dtype or torch.float16 + hf_quantizer = AutoHfQuantizer.from_config( + quantization_config, pre_quantized=False + ) + model.hf_device_map = device_map + model.hf_quantizer = hf_quantizer + hf_quantizer.validate_environment(device_map=device_map) + hf_quantizer.preprocess_model(model, device_map=device_map) + + # synchronize + client = SllmStoreClient("127.0.0.1:8073") + client.confirm_model_loaded(model_path, replica_uuid) + + for name, param in state_dict.items(): + if param.is_floating_point(): + param = param.to(torch_dtype) + if hf_quantizer.check_quantized_param(model, param, name, state_dict): + final_device = param.device + hf_quantizer.create_quantized_param( + model, + param, + name, + final_device, + state_dict, + unexpected_keys=[], + ) + + else: + set_module_tensor_to_device(model, name, param.device, param) + + # converting new biases + for module in model.modules(): + b = getattr(module, "bias", None) + if b is not None and b.dtype != torch_dtype: + b.data = b.data.to(torch_dtype) + + if quant_method == "gptq": + model = model.to("cuda") + + hf_quantizer.postprocess_model(model) + return model diff --git a/sllm_store/tests/.DS_Store b/sllm_store/tests/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..0dd6a6e7800457d9b93e8820a0696fc155769405 GIT binary patch literal 6148 zcmeHKJ5B>J5S<|rT8Snl6>>=%PGF+V3336HL?T6&-2w_acSxLoV{i+4Bo4w2c;gw8 zH7t|}!Hi@-?|i*aT6>3xcy_a%5>1Gx!VqLx42Y-)O$X*IMwT4Y#j0((ffT=2C3_#y ziaP4Hm;L>pKJP6}Ti45NyM(5CI7|2GK-_fHSaTK+cDNAs7vl zVm>-BQTR**?+_eGY0z*Xn pk}M!t7e4`bkaOfX8`U0U4ZmoZ6eWx3b2!j{1TrB$IRihyz#G3OM#BIA literal 0 HcmV?d00001 diff --git a/sllm_store/tests/cpp/CMakeLists.txt b/sllm_store/tests/cpp/CMakeLists.txt new file mode 100644 index 0000000..6806708 --- /dev/null +++ b/sllm_store/tests/cpp/CMakeLists.txt @@ -0,0 +1,32 @@ +# Find GTest +FetchContent_Declare( + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + GIT_TAG v1.13.0 +) +FetchContent_MakeAvailable(googletest) + +# Specify the test source file +set(TEST_SOURCES test_load_to_host.cpp test_pinned_memory.cpp) + +# Create an executable for the tests +add_executable(runTests ${TEST_SOURCES}) + +# set CUDA or HIP +find_package(CUDAToolkit QUIET) +find_package(HIP QUIET) + +if (CUDAToolkit_FOUND) + message(STATUS "CUDA found") +elseif (HIP_FOUND) + message(STATUS "HIP found") + target_compile_definitions(runTests PRIVATE USE_HIP) +else() + message(FATAL_ERROR "Neither CUDA nor HIP found") +endif() + +# Link the test executable with GTest and the main project +target_link_libraries(runTests GTest::gtest_main sllm_store) + +# Register the test +add_test(NAME runTests COMMAND runTests) diff --git a/sllm_store/tests/cpp/test_load_to_host.cpp b/sllm_store/tests/cpp/test_load_to_host.cpp new file mode 100644 index 0000000..7cdc19b --- /dev/null +++ b/sllm_store/tests/cpp/test_load_to_host.cpp @@ -0,0 +1,98 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef USE_HIP +#include "checkpoint_store_hip.h" +#else +#include "checkpoint_store.h" +#endif + +bool WriteBytesToFile(const std::string& file_path, + const std::vector& data) { + // Ensure the directory exists + std::filesystem::create_directories( + std::filesystem::path(file_path).parent_path()); + + std::ofstream file(file_path, std::ios::binary); + if (!file) { + return false; // Failed to open file + } + file.write(reinterpret_cast(data.data()), data.size()); + return file.good(); // Return true if the write was successful +} + +class CheckpointStoreTest : public ::testing::Test { + protected: + static CheckpointStore* storage; + static size_t mem_pool_size; + static std::string storage_path; + static int num_thread; + static size_t chunk_size; + + static void SetUpTestSuite() { + mem_pool_size = 4L * 1024 * 1024 * 1024; // 4GB + storage_path = "./test_models"; + num_thread = 4; + chunk_size = 4 * 1024 * 1024; // 4MB + + storage = new CheckpointStore(storage_path, mem_pool_size, num_thread, + chunk_size); + } + + static void TearDownTestSuite() { delete storage; } +}; + +CheckpointStore* CheckpointStoreTest::storage = nullptr; +size_t CheckpointStoreTest::mem_pool_size = 0; +std::string CheckpointStoreTest::storage_path = ""; +int CheckpointStoreTest::num_thread = 0; +size_t CheckpointStoreTest::chunk_size = 0; + +TEST_F(CheckpointStoreTest, LoadModelFromDisk) { + std::string model_path = "facebook/opt-1.3b"; + size_t model_size = 256 * 1024 * 1024; // 256MB + + std::vector model_data(model_size, 0xFF); + std::string data_path = storage_path + "/" + model_path + "/tensor.data_0"; + + bool write_success = WriteBytesToFile(data_path, model_data); + ASSERT_TRUE(write_success) << "Failed to write test data to file"; + + // Register model + size_t registered_model_size = storage->RegisterModelInfo(model_path); + EXPECT_EQ(registered_model_size, model_size); + + // Load model from disk + EXPECT_EQ(storage->LoadModelFromDisk(model_path), 0); + + // Clear all models + EXPECT_EQ(storage->ClearMem(), 0); + + // Remove the model file + std::filesystem::remove_all(storage_path + "/*"); +} diff --git a/sllm_store/tests/cpp/test_pinned_memory.cpp b/sllm_store/tests/cpp/test_pinned_memory.cpp new file mode 100644 index 0000000..8da15a3 --- /dev/null +++ b/sllm_store/tests/cpp/test_pinned_memory.cpp @@ -0,0 +1,194 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include + +#include +#include +#include +#include +#include +#include + +#include "pinned_memory.h" +#include "pinned_memory_pool.h" + +class PinnedMemoryPoolTest : public ::testing::Test { + protected: + static size_t mem_pool_size; + static size_t chunk_size; + + static void SetUpTestSuite() { + mem_pool_size = 1L * 1024 * 1024 * 1024; // 1GB + chunk_size = 4 * 1024 * 1024; // 4MB + } +}; + +size_t PinnedMemoryPoolTest::mem_pool_size = 0; +size_t PinnedMemoryPoolTest::chunk_size = 0; + +TEST_F(PinnedMemoryPoolTest, InitializePinnedMemoryPool) { + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + EXPECT_EQ(pinned_memory_pool->chunk_size(), chunk_size); +} + +TEST_F(PinnedMemoryPoolTest, RegularAllocateDeallocate) { + size_t size = 1024; + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + + std::vector buffers; + EXPECT_EQ(pinned_memory_pool->Allocate(size, buffers), 0); + EXPECT_EQ(buffers.size(), 1); + ASSERT_NE(buffers[0], nullptr); + EXPECT_EQ(pinned_memory_pool->Deallocate(buffers), 0); +} + +TEST_F(PinnedMemoryPoolTest, IrregularAllocateDeallocate) { + size_t size = 17 * 1024 * 1024; // 17MB + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + + std::vector buffers; + EXPECT_EQ(pinned_memory_pool->Allocate(size, buffers), 0); + EXPECT_EQ(buffers.size(), 5); + for (auto buffer : buffers) { + ASSERT_NE(buffer, nullptr); + } + EXPECT_EQ(pinned_memory_pool->Deallocate(buffers), 0); +} + +TEST_F(PinnedMemoryPoolTest, OutOfMemoryAllocate) { + size_t size = 1024 * 1024 * 1024; // 1GB + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + + std::vector buffers; + EXPECT_EQ(pinned_memory_pool->Allocate(size, buffers), 0); + EXPECT_EQ(buffers.size(), 256); + for (auto buffer : buffers) { + ASSERT_NE(buffer, nullptr); + } + // When out of memory, Allocate() should return the number of buffers needed + EXPECT_EQ(pinned_memory_pool->Allocate(size, buffers), 256); + EXPECT_EQ(pinned_memory_pool->Deallocate(buffers), 0); + + // Allocate again + EXPECT_EQ(pinned_memory_pool->Allocate(size, buffers), 0); + EXPECT_EQ(buffers.size(), 256); + for (auto buffer : buffers) { + ASSERT_NE(buffer, nullptr); + } + EXPECT_EQ(pinned_memory_pool->Deallocate(buffers), 0); +} + +TEST_F(PinnedMemoryPoolTest, ZeroSizeAllocate) { + size_t size = 0; + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + + std::vector buffers; + EXPECT_NE(pinned_memory_pool->Allocate(size, buffers), 0); +} + +TEST_F(PinnedMemoryPoolTest, DeallocateNonExistentBuffer) { + size_t size = 1024; + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + + std::vector buffers; + EXPECT_EQ(pinned_memory_pool->Allocate(size, buffers), 0); + EXPECT_EQ(pinned_memory_pool->Deallocate(buffers), 0); + EXPECT_NE(pinned_memory_pool->Deallocate(buffers), 0); +} + +TEST_F(PinnedMemoryPoolTest, ConcurrentAllocateDeallocate) { + size_t size = 1024; + auto pinned_memory_pool = + std::make_unique(mem_pool_size, chunk_size); + + std::vector> futures; + for (int i = 0; i < 100; ++i) { + futures.push_back( + std::async(std::launch::async, [size, &pinned_memory_pool]() -> int { + std::vector buffers; + if (pinned_memory_pool->Allocate(size, buffers) == 0) { + if (buffers.size() != 1 || buffers[0] == nullptr) { + return -1; + } + // sleep for a random time + std::this_thread::sleep_for( + std::chrono::milliseconds(rand() % 100)); + return pinned_memory_pool->Deallocate(buffers); + } else { + return -1; + } + })); + } + + for (auto& future : futures) { + EXPECT_EQ(future.get(), 0); + } +} + +class PinnedMemoryTest : public ::testing::Test { + protected: + static size_t mem_pool_size; + static size_t chunk_size; + + static void SetUpTestSuite() { + mem_pool_size = 1L * 1024 * 1024 * 1024; // 1GB + chunk_size = 4 * 1024 * 1024; // 4MB + } +}; + +size_t PinnedMemoryTest::mem_pool_size = 0; +size_t PinnedMemoryTest::chunk_size = 0; + +TEST_F(PinnedMemoryTest, AllocatePinMemory) { + size_t size = 1024; + auto pinned_memory_pool = + std::make_shared(mem_pool_size, chunk_size); + std::unique_ptr pinned_memory = + std::make_unique(); + EXPECT_EQ(pinned_memory->Allocate(size, pinned_memory_pool), 0); + EXPECT_EQ(pinned_memory->num_chunks(), 1); + EXPECT_EQ(pinned_memory->chunk_size(), chunk_size); + auto buffers = pinned_memory->get(); + EXPECT_EQ(buffers.size(), 1); + ASSERT_NE(buffers[0], nullptr); +} + +TEST_F(PinnedMemoryTest, DeallocatePinMemory) { + size_t size = 1024 * 1024 * 1024; // 1GB + auto pinned_memory_pool = + std::make_shared(mem_pool_size, chunk_size); + // PinnedMemory should deallocate memory when it goes out of scope + for (int i = 0; i < 100; ++i) { + std::unique_ptr pinned_memory = + std::make_unique(); + EXPECT_EQ(pinned_memory->Allocate(size, pinned_memory_pool), 0); + EXPECT_EQ(pinned_memory->num_chunks(), 256); + EXPECT_EQ(pinned_memory->chunk_size(), chunk_size); + auto buffers = pinned_memory->get(); + EXPECT_EQ(buffers.size(), 256); + for (auto buffer : buffers) { + ASSERT_NE(buffer, nullptr); + } + } +} \ No newline at end of file diff --git a/sllm_store/tests/python/test_cli.py b/sllm_store/tests/python/test_cli.py new file mode 100644 index 0000000..bb482c2 --- /dev/null +++ b/sllm_store/tests/python/test_cli.py @@ -0,0 +1,605 @@ +import os +import shutil +import unittest +from pathlib import Path +from click.testing import CliRunner +from sllm_store.cli import cli +from huggingface_hub import snapshot_download + +TEST_MODEL_VLLM = "facebook/opt-125m" +TEST_MODEL_TRANSFORMERS = "facebook/opt-125m" +TEST_ADAPTER_MODEL = ( + "jeanlucmarsh/opt-125m-pattern-based_finetuning_with_lora-mnli-mm-d3_fs2" +) +TEST_DIRECTORY_SAVE = "./test_save_models" +TEST_DIRECTORY_LOAD = "./models" + + +class TestCliCommands(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.runner = CliRunner() + + result = cls.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_VLLM, + "--backend", + "vllm", + "--storage-path", + TEST_DIRECTORY_LOAD + "/vllm", + ], + ) + assert result.exit_code == 0, f"Initial save failed: {result.output}" + + result = cls.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--storage-path", + TEST_DIRECTORY_LOAD + "/transformers", + ], + ) + assert result.exit_code == 0, f"Initial save failed: {result.output}" + + result = cls.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--adapter-name", + TEST_ADAPTER_MODEL, + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + assert result.exit_code == 0, f"Initial save failed: {result.output}" + + def tearDown(self): + path = Path(TEST_DIRECTORY_SAVE) + if path.exists() and path.is_dir(): + try: + shutil.rmtree(path) + print(f"Removed old test folder: {path}") + except OSError as e: + print(f"Error removing {path}: {e}") + + @classmethod + def tearDownClass(cls): + path = Path(TEST_DIRECTORY_LOAD) + if path.exists() and path.is_dir(): + try: + shutil.rmtree(path) + print(f"[tearDownClass] Removed load folder: {path}") + except OSError as e: + print(f"[tearDownClass] Error removing {path}: {e}") + + # SAVE + + def test_save_vllm_model_default(self): + result = self.runner.invoke( + cli, ["save", "--model", TEST_MODEL_VLLM, "--backend", "vllm"] + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + expected_folder = Path("models") / TEST_MODEL_VLLM + self.assertTrue( + expected_folder.is_dir(), + f"Folder does not exist: {expected_folder}", + ) + + # Check if each partition directory was created + tensor_parallel_size = 1 + for i in range(tensor_parallel_size): + self.assertTrue(os.path.exists(f"{expected_folder}/rank_{i}")) + self.assertFalse( + os.path.exists(f"{expected_folder}/rank_{tensor_parallel_size}") + ) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "tensor_index.json", + "tensor.data_0", + ] + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for i in range(tensor_parallel_size): + for filename in expected_files: + self.assertTrue( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + + def test_save_vllm_model_params(self): + tensor_parallel_size = ( + 1 # Change to 2 if you have multiple GPUs and want to test it + ) + result = self.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_VLLM, + "--backend", + "vllm", + "--tensor-parallel-size", + str(tensor_parallel_size), + "--storage-path", + TEST_DIRECTORY_SAVE, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + expected_folder = Path(TEST_DIRECTORY_SAVE) / TEST_MODEL_VLLM + self.assertTrue( + expected_folder.is_dir(), + f"Folder does not exist: {expected_folder}", + ) + + # Check if each partition directory was created + for i in range(tensor_parallel_size): + self.assertTrue(os.path.exists(f"{expected_folder}/rank_{i}")) + self.assertFalse( + os.path.exists(f"{expected_folder}/rank_{tensor_parallel_size}") + ) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "tensor_index.json", + "tensor.data_0", + ] + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for i in range(tensor_parallel_size): + for filename in expected_files: + self.assertTrue( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + + def test_save_vllm_model_local_path(self): + local_dummy_model_path = Path(TEST_DIRECTORY_SAVE) / "dummy_local_model" + snapshot_download( + repo_id=TEST_MODEL_VLLM, + local_dir=local_dummy_model_path, + local_dir_use_symlinks=False, + ) + + result = self.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_VLLM, + "--backend", + "vllm", + "--local-model-path", + str(local_dummy_model_path), + "--storage-path", + TEST_DIRECTORY_SAVE, + ], + ) + + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + expected_folder = Path(TEST_DIRECTORY_SAVE) / TEST_MODEL_VLLM + self.assertTrue( + expected_folder.is_dir(), + f"Folder does not exist: {expected_folder}", + ) + + # Check if each partition directory was created + tensor_parallel_size = 1 + for i in range(tensor_parallel_size): + self.assertTrue(os.path.exists(f"{expected_folder}/rank_{i}")) + self.assertFalse( + os.path.exists(f"{expected_folder}/rank_{tensor_parallel_size}") + ) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "tensor_index.json", + "tensor.data_0", + ] + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for i in range(tensor_parallel_size): + for filename in expected_files: + self.assertTrue( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(expected_folder, f"rank_{i}", filename) + ) + ) + + def test_save_transformers_model_default(self): + result = self.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + expected_folder = Path("./models") / TEST_MODEL_TRANSFORMERS + self.assertTrue( + expected_folder.is_dir(), + f"Folder does not exist: {expected_folder}", + ) + self.assertTrue( + any(expected_folder.iterdir()), + f"Folder is empty: {expected_folder}", + ) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "config.json", + "generation_config.json", + "no_split_modules.json", + "tensor_index.json", + "tensor.data_0", + "tied_no_split_modules.json", + ] + for filename in expected_files: + self.assertTrue( + os.path.isfile(os.path.join(expected_folder, filename)) + ) + + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for filename in unexpected_files: + self.assertFalse( + os.path.isfile(os.path.join(expected_folder, filename)) + ) + + def test_save_transformers_adapter_valid(self): + result = self.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--storage-path", + TEST_DIRECTORY_SAVE, + ], + ) + + result = self.runner.invoke( + cli, + [ + "save", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--adapter-name", + TEST_ADAPTER_MODEL, + "--storage-path", + TEST_DIRECTORY_SAVE, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + expected_folder = ( + Path(TEST_DIRECTORY_SAVE) / TEST_ADAPTER_MODEL + ) # Adapters save under adapter_name + self.assertTrue( + expected_folder.is_dir(), + f"Adapter folder does not exist: {expected_folder}", + ) + self.assertTrue( + any(expected_folder.iterdir()), + f"Adapter folder is empty: {expected_folder}", + ) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "tensor_index.json", + "tensor.data_0", + ] + for filename in expected_files: + self.assertTrue( + os.path.isfile(os.path.join(expected_folder, filename)) + ) + + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for filename in unexpected_files: + self.assertFalse( + os.path.isfile(os.path.join(expected_folder, filename)) + ) + + def test_save_invalid_backend(self): + result = self.runner.invoke( + cli, + [ + "save", + "--model", + "facebook/opt-125m", + "--backend", + "unsupported_backend", + ], + ) + self.assertNotEqual(result.exit_code, 0) + self.assertFalse( + (Path(TEST_DIRECTORY_SAVE) / "facebook/opt-125m").exists() + ) + + def test_save_missing_model_name(self): + result = self.runner.invoke(cli, ["save", "--backend", "vllm"]) + self.assertNotEqual(result.exit_code, 0) + self.assertIn("Missing option '--model'", result.stderr) + + def test_save_non_existent_hf_model(self): + non_existent_model = "nonexistent/model-xyz-123" + result = self.runner.invoke( + cli, + [ + "save", + "--model", + non_existent_model, + "--backend", + "transformers", + "--storage-path", + TEST_DIRECTORY_SAVE, + ], + ) + self.assertNotEqual(result.exit_code, 0) + self.assertFalse( + (Path(TEST_DIRECTORY_SAVE) / non_existent_model).exists() + ) + + # LOAD + + def test_load_vllm_model_default(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + TEST_MODEL_VLLM, + "--backend", + "vllm", + "--storage-path", + TEST_DIRECTORY_LOAD + "/vllm", + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + self.assertIn("Prompt:", result.output) + self.assertIn("Generated text:", result.output) + + def test_load_transformers_model_default(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + "transformers/" + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + self.assertIn("Hello, my dog is cute", result.output) + + def test_load_transformers_precision_int8(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + "transformers/" + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--precision", + "int8", + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + self.assertIn("Hello, my dog is cute", result.output) + + def test_load_transformers_precision_fp4(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + "transformers/" + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--precision", + "fp4", + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + self.assertIn("Hello, my dog is cute", result.output) + + def test_load_transformers_precision_nf4(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + "transformers/" + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--precision", + "nf4", + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + self.assertIn("Hello, my dog is cute", result.output) + + def test_load_transformers_precision_invalid(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--precision", + "invalid_precision", + ], + ) + self.assertNotEqual(result.exit_code, 0) + + def test_load_transformers_adapter_valid(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + "transformers/" + TEST_MODEL_TRANSFORMERS, + "--backend", + "transformers", + "--adapter-name", + TEST_ADAPTER_MODEL, + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + self.assertEqual( + result.exit_code, + 0, + f"Command failed with output: {result.output}\n" + f"Stderr: {result.stderr}", + ) + self.assertIn("Hello, my dog is cute", result.output) + + def test_load_invalid_backend(self): + result = self.runner.invoke( + cli, + [ + "load", + "--model", + TEST_MODEL_TRANSFORMERS, + "--backend", + "unsupported_backend", + ], + ) + self.assertNotEqual(result.exit_code, 0) + + def test_load_non_existent_model(self): + non_existent_model = "nonexistent/model-xyz-123" + result = self.runner.invoke( + cli, + [ + "load", + "--model", + "transformers/" + non_existent_model, + "--backend", + "transformers", + "--storage-path", + TEST_DIRECTORY_LOAD, + ], + ) + self.assertNotEqual(result.exit_code, 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/sllm_store/tests/python/test_save_lora.py b/sllm_store/tests/python/test_save_lora.py new file mode 100644 index 0000000..13be9b1 --- /dev/null +++ b/sllm_store/tests/python/test_save_lora.py @@ -0,0 +1,78 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +import shutil +import unittest + +from peft import LoraConfig, get_peft_model +from sllm_store.transformers import save_lora +from transformers import AutoModelForCausalLM + + +class TestSaveLoraIntegration(unittest.TestCase): + def setUp(self): + # Set up a temporary directory for the test + self.model_name = "bigscience/bloomz-560m" + self.save_dir = "./test_models" + self.model_path = os.path.join(self.save_dir, self.model_name) + + # Ensure the save directory is clean before the test + if os.path.exists(self.save_dir): + shutil.rmtree(self.save_dir) + os.makedirs(self.save_dir) + + os.environ["STORAGE_PATH"] = self.save_dir + + def tearDown(self): + # Clean up by deleting the directory after the test + if os.path.exists(self.save_dir): + shutil.rmtree(self.save_dir) + + def test_save_lora(self): + # Fine-tuning the actual peft model + base_model = AutoModelForCausalLM.from_pretrained(self.model_name) + lora_config = LoraConfig( + r=4, + lora_alpha=16, + target_modules=["query_key_value"], + lora_dropout=0.1, + bias="lora_only", + task_type="CAUSAL_LM", + ) + peft_model = get_peft_model(base_model, lora_config) + # Save the model + save_lora(peft_model, self.model_path) + + # Check if the model directory was created + self.assertTrue(os.path.exists(self.model_path)) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "tensor_index.json", + "tensor.data_0", + ] + for filename in expected_files: + self.assertTrue( + os.path.isfile(os.path.join(self.model_path, filename)) + ) + + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for filename in unexpected_files: + self.assertFalse( + os.path.isfile(os.path.join(self.model_path, filename)) + ) diff --git a/sllm_store/tests/python/test_save_model.py b/sllm_store/tests/python/test_save_model.py new file mode 100644 index 0000000..ac683cc --- /dev/null +++ b/sllm_store/tests/python/test_save_model.py @@ -0,0 +1,76 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +import shutil +import unittest + +import torch +from sllm_store.transformers import save_model +from transformers import AutoModelForCausalLM + + +class TestSaveModelIntegration(unittest.TestCase): + def setUp(self): + # Set up a temporary directory for the test + self.model_name = "facebook/opt-1.3b" + self.save_dir = "./test_models" + self.model_path = os.path.join(self.save_dir, self.model_name) + + # Ensure the save directory is clean before the test + if os.path.exists(self.save_dir): + shutil.rmtree(self.save_dir) + os.makedirs(self.save_dir) + + os.environ["STORAGE_PATH"] = self.save_dir + + def tearDown(self): + # Clean up by deleting the directory after the test + if os.path.exists(self.save_dir): + shutil.rmtree(self.save_dir) + + def test_save_model(self): + # Load the actual model + model = AutoModelForCausalLM.from_pretrained( + self.model_name, torch_dtype=torch.float16 + ) + + # Save the model + save_model(model, self.model_path) + + # Check if the model directory was created + self.assertTrue(os.path.exists(self.model_path)) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "config.json", + "generation_config.json", + "no_split_modules.json", + "tensor_index.json", + "tensor.data_0", + "tied_no_split_modules.json", + ] + for filename in expected_files: + self.assertTrue( + os.path.isfile(os.path.join(self.model_path, filename)) + ) + + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for filename in unexpected_files: + self.assertFalse( + os.path.isfile(os.path.join(self.model_path, filename)) + ) diff --git a/sllm_store/tests/python/test_save_vllm_model.py b/sllm_store/tests/python/test_save_vllm_model.py new file mode 100644 index 0000000..bd09540 --- /dev/null +++ b/sllm_store/tests/python/test_save_vllm_model.py @@ -0,0 +1,114 @@ +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +import os +import shutil +import unittest + +import torch +from huggingface_hub import snapshot_download +from vllm import LLM + + +class TestSaveModelIntegration(unittest.TestCase): + def setUp(self): + # Set up a temporary directory for the test + self.model_name = "facebook/opt-1.3b" + self.torch_dtype = "float16" + self.tensor_parallel_size = 1 + self.save_dir = "./test_models" + self.model_path = os.path.join(self.save_dir, self.model_name) + + # Check if at least 2 GPUs are available + if ( + not torch.cuda.is_available() + or torch.cuda.device_count() < self.tensor_parallel_size + ): + raise unittest.SkipTest("Not enough GPUs available for this test") + + # Ensure the save directory is clean before the test + if os.path.exists(self.save_dir): + shutil.rmtree(self.save_dir) + os.makedirs(self.save_dir) + + os.environ["STORAGE_PATH"] = self.save_dir + + def tearDown(self): + # Clean up by deleting the directory after the test + if os.path.exists(self.save_dir): + shutil.rmtree(self.save_dir) + + def test_save_model(self): + # with TemporaryDirectory() as cache_dir: + # cache_dir = "./test_models" + # download model from huggingface + input_dir = snapshot_download( + self.model_name, + # cache_dir=cache_dir, + allow_patterns=["*.safetensors", "*.bin", "*.json", "*.txt"], + ) + # load models from the input directory + model_executer = LLM( + model=input_dir, + download_dir=input_dir, + dtype=self.torch_dtype, + tensor_parallel_size=self.tensor_parallel_size, + num_gpu_blocks_override=1, + enforce_eager=True, + max_model_len=1, + ).llm_engine.engine_core # For engine V1 + # save the models in the ServerlessLLM format + model_executer.save_serverless_llm_state(path=self.model_path) + + # Check if the model directory was created + self.assertTrue(os.path.exists(self.model_path)) + + # Check if each partition directory was created + for i in range(self.tensor_parallel_size): + self.assertTrue(os.path.exists(f"{self.model_path}/rank_{i}")) + self.assertFalse( + os.path.exists( + f"{self.model_path}/rank_{self.tensor_parallel_size}" + ) + ) + + # Check if certain files exist to verify that the model was saved + expected_files = [ + "tensor_index.json", + "tensor.data_0", + ] + unexpected_files = ["tensor.data_1", "*.bin", "*.safetensors"] + for i in range(self.tensor_parallel_size): + for filename in expected_files: + self.assertTrue( + os.path.isfile( + os.path.join(self.model_path, f"rank_{i}", filename) + ) + ) + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(self.model_path, f"rank_{i}", filename) + ) + ) + + for filename in unexpected_files: + self.assertFalse( + os.path.isfile( + os.path.join(self.model_path, f"rank_{i}", filename) + ) + ) diff --git a/sllm_store/tests/python/test_utils.py b/sllm_store/tests/python/test_utils.py new file mode 100644 index 0000000..46e60ee --- /dev/null +++ b/sllm_store/tests/python/test_utils.py @@ -0,0 +1,129 @@ +import unittest + +from sllm_store.utils import to_num_bytes + + +class TestToNumBytes(unittest.TestCase): + """Unit tests for the to_num_bytes function.""" + + # ------------------- + # Valid Input Tests + # ------------------- + def test_valid_bytes(self): + self.assertEqual(to_num_bytes("1B"), 1) + self.assertEqual(to_num_bytes("0B"), 0) + self.assertEqual(to_num_bytes("1024B"), 1024) + + def test_valid_kilobytes(self): + self.assertEqual(to_num_bytes("1KB"), 1024) + self.assertEqual(to_num_bytes("512KB"), 512 * 1024) + + def test_valid_megabytes(self): + self.assertEqual(to_num_bytes("1MB"), 1024**2) + self.assertEqual(to_num_bytes("256MB"), 256 * 1024**2) + + def test_valid_gigabytes(self): + self.assertEqual(to_num_bytes("1GB"), 1024**3) + self.assertEqual(to_num_bytes("2GB"), 2 * 1024**3) + + def test_valid_terabytes(self): + self.assertEqual(to_num_bytes("1TB"), 1024**4) + self.assertEqual(to_num_bytes("5TB"), 5 * 1024**4) + + def test_valid_petabytes(self): + self.assertEqual(to_num_bytes("1PB"), 1024**5) + self.assertEqual(to_num_bytes("3PB"), 3 * 1024**5) + + def test_valid_exabytes(self): + self.assertEqual(to_num_bytes("1EB"), 1024**6) + self.assertEqual(to_num_bytes("7EB"), 7 * 1024**6) + + def test_valid_zettabytes(self): + self.assertEqual(to_num_bytes("1ZB"), 1024**7) + self.assertEqual(to_num_bytes("9ZB"), 9 * 1024**7) + + def test_valid_yottabytes(self): + self.assertEqual(to_num_bytes("1YB"), 1024**8) + self.assertEqual(to_num_bytes("2YB"), 2 * 1024**8) + + # ------------------- + # Invalid Input Tests + # ------------------- + def test_invalid_unit_lowercase(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1gb") + self.assertIn("Invalid format", str(context.exception)) + + def test_invalid_unit_mixed_case(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1Gb") + self.assertIn("Invalid format", str(context.exception)) + + def test_invalid_unit_unknown(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1ABC") + self.assertIn("Invalid format", str(context.exception)) + + def test_leading_space(self): + with self.assertRaises(ValueError) as context: + to_num_bytes(" 1GB") + self.assertIn("Invalid format", str(context.exception)) + + def test_trailing_space(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1GB ") + self.assertIn("Invalid format", str(context.exception)) + + def test_middle_space(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1 GB") + self.assertIn("Invalid format", str(context.exception)) + + def test_leading_zero(self): + # Depending on requirements, leading zeros might be allowed. + self.assertEqual(to_num_bytes("0001GB"), 1024**3) + + def test_non_numeric_value(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("GB") + self.assertIn("Invalid format", str(context.exception)) + + def test_decimal_number(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1.5GB") + self.assertIn("Invalid format", str(context.exception)) + + def test_negative_number(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("-1GB") + self.assertIn("Invalid format", str(context.exception)) + + def test_empty_string(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("") + self.assertIn("Invalid format", str(context.exception)) + + def test_additional_characters(self): + with self.assertRaises(ValueError) as context: + to_num_bytes("1GBs") + self.assertIn("Invalid format", str(context.exception)) + + # ------------------- + # Boundary Tests + # ------------------- + def test_large_number(self): + self.assertEqual(to_num_bytes("999999999YB"), 999999999 * 1024**8) + + def test_minimal_input(self): + self.assertEqual(to_num_bytes("0B"), 0) + + def test_maximum_unit(self): + self.assertEqual(to_num_bytes("1YB"), 1024**8) + + +# ------------------- +# Running the Tests +# ------------------- + +if __name__ == "__main__": + unittest.main(argv=[""], exit=False) diff --git a/sllm_store/using_existing_torch.py b/sllm_store/using_existing_torch.py new file mode 100644 index 0000000..7e49500 --- /dev/null +++ b/sllm_store/using_existing_torch.py @@ -0,0 +1,20 @@ +# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.6/use_existing_torch.py + +import glob + +requires_files = glob.glob("requirements*.txt") +requires_files += ["pyproject.toml"] +for file in requires_files: + print(f">>> cleaning {file}") + with open(file) as f: + lines = f.readlines() + if "torch" in "".join(lines).lower(): + print("removed:") + with open(file, "w") as f: + for line in lines: + if "torch" not in line.lower(): + f.write(line) + else: + print(line.strip()) + print(f"<<< done cleaning {file}") + print() diff --git a/sllm_store/vllm_patch/check_patch.sh b/sllm_store/vllm_patch/check_patch.sh new file mode 100755 index 0000000..1799256 --- /dev/null +++ b/sllm_store/vllm_patch/check_patch.sh @@ -0,0 +1,44 @@ +#!/bin/bash +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +set -e + +SCRIPT_DIR=$(cd "$(dirname "$0")"; pwd) +PATCH_FILE="$SCRIPT_DIR/sllm_load.patch" +if [ ! -f "$PATCH_FILE" ]; then + echo "File does not exist: $PATCH_FILE" + exit 1 +fi + +VLLM_PATH_OUTPUT=$(python -c "import vllm; import os; print(os.path.dirname(os.path.abspath(vllm.__file__)))" 2>/dev/null) +VLLM_PATH=$(echo "$VLLM_PATH_OUTPUT" | tail -n 1) + +# Sanity check the path +echo "Detected VLLM_PATH: '$VLLM_PATH'" +if [ ! -d "$VLLM_PATH" ]; then + echo "Error: Detected VLLM_PATH is not a valid directory: '$VLLM_PATH'" + echo "Full output from python command was:" + echo "$VLLM_PATH_OUTPUT" + exit 1 +fi + +if patch -p2 --dry-run -d $VLLM_PATH < $PATCH_FILE > /dev/null 2>&1; then + echo "vLLM patch is not applied" +else + echo "vLLM patch has been applied" +fi diff --git a/sllm_store/vllm_patch/patch.sh b/sllm_store/vllm_patch/patch.sh new file mode 100755 index 0000000..c3a2166 --- /dev/null +++ b/sllm_store/vllm_patch/patch.sh @@ -0,0 +1,51 @@ +#!/bin/bash +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +set -e + +SCRIPT_DIR=$(cd "$(dirname "$0")"; pwd) +PATCH_FILE="$SCRIPT_DIR/sllm_load.patch" + +# Check if the patch file exists +if [ ! -f "$PATCH_FILE" ]; then + echo "File does not exist: $PATCH_FILE" + exit 1 +fi + +# Get the vLLM installation path +VLLM_PATH_OUTPUT=$(python -c "import vllm; import os; print(os.path.dirname(os.path.abspath(vllm.__file__)))" 2>/dev/null) +VLLM_PATH=$(echo "$VLLM_PATH_OUTPUT" | tail -n 1) + +# Sanity check the path +echo "Detected VLLM_PATH: '$VLLM_PATH'" +if [ ! -d "$VLLM_PATH" ]; then + echo "Error: Detected VLLM_PATH is not a valid directory: '$VLLM_PATH'" + echo "Full output from python command was:" + echo "$VLLM_PATH_OUTPUT" + exit 1 +fi + +# Attempt a dry run of the patch to check if it's already applied +if patch -p2 --dry-run -d "$VLLM_PATH" < "$PATCH_FILE"; then + echo "vLLM patch is not applied. Applying the patch now..." + # Apply the patch + patch -p2 -d "$VLLM_PATH" < "$PATCH_FILE" + echo "Patch applied successfully." +else + echo "vLLM patch has already been applied. Skipping..." +fi \ No newline at end of file diff --git a/sllm_store/vllm_patch/remove_patch.sh b/sllm_store/vllm_patch/remove_patch.sh new file mode 100755 index 0000000..bc7c3d8 --- /dev/null +++ b/sllm_store/vllm_patch/remove_patch.sh @@ -0,0 +1,40 @@ +#!/bin/bash +# ---------------------------------------------------------------------------- # +# serverlessllm # +# copyright (c) serverlessllm team 2024 # +# # +# licensed under the apache license, version 2.0 (the "license"); # +# you may not use this file except in compliance with the license. # +# # +# you may obtain a copy of the license at # +# # +# http://www.apache.org/licenses/license-2.0 # +# # +# unless required by applicable law or agreed to in writing, software # +# distributed under the license is distributed on an "as is" basis, # +# without warranties or conditions of any kind, either express or implied. # +# see the license for the specific language governing permissions and # +# limitations under the license. # +# ---------------------------------------------------------------------------- # +set -e + +SCRIPT_DIR=$(cd "$(dirname "$0")"; pwd) +PATCH_FILE="$SCRIPT_DIR/sllm_load.patch" +if [ ! -f "$PATCH_FILE" ]; then + echo "File does not exist: $PATCH_FILE" + exit 1 +fi + +VLLM_PATH_OUTPUT=$(python -c "import vllm; import os; print(os.path.dirname(os.path.abspath(vllm.__file__)))" 2>/dev/null) +VLLM_PATH=$(echo "$VLLM_PATH_OUTPUT" | tail -n 1) + +# Sanity check the path +echo "Detected VLLM_PATH: '$VLLM_PATH'" +if [ ! -d "$VLLM_PATH" ]; then + echo "Error: Detected VLLM_PATH is not a valid directory: '$VLLM_PATH'" + echo "Full output from python command was:" + echo "$VLLM_PATH_OUTPUT" + exit 1 +fi + +patch -p2 -d $VLLM_PATH -R < $PATCH_FILE \ No newline at end of file diff --git a/sllm_store/vllm_patch/sllm_load.patch b/sllm_store/vllm_patch/sllm_load.patch new file mode 100644 index 0000000..5b5eac0 --- /dev/null +++ b/sllm_store/vllm_patch/sllm_load.patch @@ -0,0 +1,426 @@ +diff --git a/vllm/config.py b/vllm/config.py +index db35c848b..2c39d9a6b 100644 +--- a/vllm/config.py ++++ b/vllm/config.py +@@ -1586,6 +1586,7 @@ class LoadFormat(str, enum.Enum): + RUNAI_STREAMER = "runai_streamer" + RUNAI_STREAMER_SHARDED = "runai_streamer_sharded" + FASTSAFETENSORS = "fastsafetensors" ++ SERVERLESS_LLM = "serverless_llm" + + + @config +diff --git a/vllm/executor/executor_base.py b/vllm/executor/executor_base.py +index 40ca1d299..5771b5d24 100644 +--- a/vllm/executor/executor_base.py ++++ b/vllm/executor/executor_base.py +@@ -144,7 +144,18 @@ class ExecutorBase(ABC): + def stop_remote_worker_execution_loop(self) -> None: + """Releases parallel workers from model loop.""" + return +- ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ ++ self.driver_worker.save_serverless_llm_state( ++ path=path, pattern=pattern, max_size=max_size ++ ) ++ + def add_lora(self, lora_request: LoRARequest) -> bool: + assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." + return all(self.collective_rpc("add_lora", args=(lora_request, ))) +diff --git a/vllm/executor/mp_distributed_executor.py b/vllm/executor/mp_distributed_executor.py +index d1f8c36fb..0d6d94c57 100644 +--- a/vllm/executor/mp_distributed_executor.py ++++ b/vllm/executor/mp_distributed_executor.py +@@ -132,6 +132,17 @@ class MultiprocessingDistributedExecutor(DistributedExecutorBase): + if (worker_monitor := getattr(self, "worker_monitor", + None)) is not None: + worker_monitor.close() ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self._run_workers("save_serverless_llm_state", ++ path=path, ++ pattern=pattern, ++ max_size=max_size) + + def _driver_execute_model( + self, execute_model_req: Optional[ExecuteModelRequest] +diff --git a/vllm/executor/ray_distributed_executor.py b/vllm/executor/ray_distributed_executor.py +index 8e67c7a41..01dc4b9cc 100644 +--- a/vllm/executor/ray_distributed_executor.py ++++ b/vllm/executor/ray_distributed_executor.py +@@ -427,6 +427,17 @@ class RayDistributedExecutor(DistributedExecutorBase): + self.tp_driver_workers.append(worker) + else: + self.non_driver_workers.append(worker) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self._run_workers("save_serverless_llm_state", ++ path=path, ++ pattern=pattern, ++ max_size=max_size) + + def _driver_execute_model( + self, execute_model_req: Optional[ExecuteModelRequest] +diff --git a/vllm/model_executor/model_loader/__init__.py b/vllm/model_executor/model_loader/__init__.py +index a443a652d..c1869a54a 100644 +--- a/vllm/model_executor/model_loader/__init__.py ++++ b/vllm/model_executor/model_loader/__init__.py +@@ -11,6 +11,7 @@ from vllm.model_executor.model_loader.bitsandbytes_loader import ( + from vllm.model_executor.model_loader.default_loader import DefaultModelLoader + from vllm.model_executor.model_loader.dummy_loader import DummyModelLoader + from vllm.model_executor.model_loader.gguf_loader import GGUFModelLoader ++from vllm.model_executor.model_loader.sllm_loader import ServerlessLLMLoader + from vllm.model_executor.model_loader.runai_streamer_loader import ( + RunaiModelStreamerLoader) + from vllm.model_executor.model_loader.sharded_state_loader import ( +@@ -45,6 +46,9 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader: + + if load_config.load_format == LoadFormat.RUNAI_STREAMER_SHARDED: + return ShardedStateLoader(load_config, runai_model_streamer=True) ++ ++ if load_config.load_format == LoadFormat.SERVERLESS_LLM: ++ return ServerlessLLMLoader(load_config) + + return DefaultModelLoader(load_config) + +diff --git a/vllm/model_executor/model_loader/sllm_loader.py b/vllm/model_executor/model_loader/sllm_loader.py +new file mode 100644 +index 000000000..9bd2fd7f9 +--- /dev/null ++++ b/vllm/model_executor/model_loader/sllm_loader.py +@@ -0,0 +1,150 @@ ++import torch ++from torch import nn ++import collections ++import os ++import gc ++from typing import Dict, Optional ++from vllm.config import LoadConfig, VllmConfig, ModelConfig ++from vllm.model_executor.model_loader.base_loader import BaseModelLoader ++from vllm.model_executor.model_loader.utils import ( ++ initialize_model, set_default_torch_dtype) ++ ++class ServerlessLLMLoader(BaseModelLoader): ++ # DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors" ++ ++ def __init__(self, load_config: LoadConfig): ++ super().__init__(load_config) ++ extra_config = ({} if load_config.model_loader_extra_config is None ++ else load_config.model_loader_extra_config.copy()) ++ # self.pattern = extra_config.pop("pattern", self.DEFAULT_PATTERN) ++ if extra_config: ++ raise ValueError(f"Unexpected extra config keys for load format " ++ f"{load_config.load_format}: " ++ f"{load_config.model_loader_extra_config.keys()}") ++ ++ @staticmethod ++ def _filter_subtensors( ++ tensors: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: ++ """ ++ Filter out all tensors that share the same memory or a subset of the ++ memory of another tensor. ++ """ ++ same_storage_groups = collections.defaultdict(list) ++ for key, tensor in tensors.items(): ++ if tensor.numel(): ++ ptr = tensor.untyped_storage().data_ptr() ++ same_storage_groups[tensor.device, ptr].append((key, tensor)) ++ ++ def get_end_ptr(tensor: torch.Tensor) -> int: ++ return tensor.view(-1)[-1].data_ptr() + tensor.element_size() ++ ++ result = {} ++ for group in same_storage_groups.values(): ++ for k, t in group: ++ a, b = t.data_ptr(), get_end_ptr(t) ++ for k2, t2 in group: ++ if not t2.is_contiguous(): ++ continue ++ a2, b2 = t2.data_ptr(), get_end_ptr(t2) ++ if a < a2 or b2 < b: ++ continue ++ if a2 < a or b < b2 or not t.is_contiguous(): ++ break # t2 covers strictly more memory than t. ++ if k2 > k: ++ # Same tensors, keep the one with the longer key. ++ break ++ else: ++ result[k] = t ++ return result ++ ++ def load_model(self, *, vllm_config: VllmConfig, **kwargs) -> nn.Module: ++ from sllm_store.torch import load_dict ++ from vllm.distributed import get_tensor_model_parallel_rank ++ ++ assert os.path.isdir(vllm_config.model_config.model) ++ ++ rank = get_tensor_model_parallel_rank() ++ ++ local_model_path = vllm_config.model_config.model ++ local_model_path = os.path.join(local_model_path, f"rank_{rank}") ++ ++ def remove_prefix(path, prefix): ++ # Normalize the paths to ensure consistency across different platforms ++ path = os.path.normpath(path) ++ prefix = os.path.normpath(prefix) ++ ++ # Check if the path starts with the prefix ++ if path.startswith(prefix): ++ # Return the path without the prefix ++ return path[len(prefix):].lstrip(os.sep) ++ ++ # Return the original path if the prefix doesn't exist ++ return path ++ ++ # vLLM needs a local model path to read model config but ++ # ServerlessLLM Store requires a global model path as the model ID ++ storage_path = os.getenv("STORAGE_PATH", "./models") ++ model_path = remove_prefix(local_model_path, storage_path) ++ ++ with set_default_torch_dtype(vllm_config.model_config.dtype): ++ # with torch.device(device_config.device): ++ with torch.device("cpu"): ++ model = initialize_model(vllm_config=vllm_config) ++ model = model.eval() ++ # set all parameters to meta device ++ state_dict = self._filter_subtensors(model.state_dict()) ++ key_list = list(state_dict.keys()) ++ ++ for key, param in model.named_parameters(recurse=True): ++ if key in key_list: ++ param.data = torch.empty(1, device="cuda") ++ gc.collect() ++ ++ device_id = torch.cuda.current_device() ++ device_map = {"": device_id} ++ # Note: storage path is already included in the local model path ++ sllm_state_dict = load_dict(model_path, device_map) ++ ++ for key, param in model.named_parameters(recurse=True): ++ if key in key_list: ++ tensor = sllm_state_dict[key] ++ param.data = tensor ++ state_dict.pop(key) ++ if state_dict: ++ raise ValueError( ++ f"Missing keys {tuple(state_dict)} in loaded state!") ++ ++ for name, buffer in model.named_buffers(recurse=True): ++ if buffer.device.type != 'cuda': ++ buffer.data = buffer.data.to(f'cuda:{device_id}') ++ ++ return model ++ ++ def download_model(self, model_config: ModelConfig) -> None: ++ pass ++ ++ def load_weights(self, model, model_config): ++ pass ++ ++ @staticmethod ++ def save_model( ++ model: torch.nn.Module, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ from vllm.distributed import get_tensor_model_parallel_rank ++ from sllm_store.torch import save_dict ++ ++ rank = get_tensor_model_parallel_rank() ++ state_dict = ServerlessLLMLoader._filter_subtensors(model.state_dict()) ++ ++ # move all tensors to CPU ++ for key, tensor in state_dict.items(): ++ state_dict[key] = tensor.cpu().contiguous() ++ ++ save_path = os.path.join(path, f"rank_{rank}") ++ if not os.path.exists(save_path): ++ os.makedirs(save_path) ++ ++ save_dict(state_dict, save_path) +\ No newline at end of file +diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py +index 740ba60fe..1071cb71f 100644 +--- a/vllm/v1/engine/core.py ++++ b/vllm/v1/engine/core.py +@@ -331,6 +331,16 @@ class EngineCore: + self.model_executor.save_sharded_state(path=path, + pattern=pattern, + max_size=max_size) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.model_executor.save_serverless_llm_state(path=path, ++ pattern=pattern, ++ max_size=max_size) + + def collective_rpc(self, + method: Union[str, Callable[..., _R]], +diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py +index 0d52bc9a6..95adf9af8 100644 +--- a/vllm/v1/engine/core_client.py ++++ b/vllm/v1/engine/core_client.py +@@ -256,6 +256,14 @@ class InprocClient(EngineCoreClient): + pattern: Optional[str] = None, + max_size: Optional[int] = None) -> None: + self.engine_core.save_sharded_state(path, pattern, max_size) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.engine_core.save_serverless_llm_state(path, pattern, max_size) + + def collective_rpc(self, + method: Union[str, Callable[..., _R]], +@@ -724,6 +732,14 @@ class SyncMPClient(MPClient): + pattern: Optional[str] = None, + max_size: Optional[int] = None) -> None: + self.call_utility("save_sharded_state", path, pattern, max_size) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.call_utility("save_serverless_llm_state", path, pattern, max_size) + + + class AsyncMPClient(MPClient): +@@ -906,6 +922,15 @@ class AsyncMPClient(MPClient): + await self.call_utility_async("save_sharded_state", path, pattern, + max_size) + ++ async def save_serverless_llm_state_async( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ await self.call_utility_async("save_serverless_llm_state", path, pattern, ++ max_size) ++ + async def collective_rpc_async( + self, + method: Union[str, Callable[..., _R]], +diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py +index 910c0e80b..d13645e3b 100644 +--- a/vllm/v1/worker/gpu_model_runner.py ++++ b/vllm/v1/worker/gpu_model_runner.py +@@ -1560,6 +1560,20 @@ class GPUModelRunner(LoRAModelRunnerMixin): + tensorizer_config=tensorizer_config, + ) + ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ from vllm.model_executor.model_loader import ServerlessLLMLoader ++ ServerlessLLMLoader.save_model( ++ self.model, ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) ++ + def _get_prompt_logprobs_dict( + self, + hidden_states: torch.Tensor, +diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py +index bce5cbb5f..55d65c7d3 100644 +--- a/vllm/v1/worker/gpu_worker.py ++++ b/vllm/v1/worker/gpu_worker.py +@@ -325,6 +325,18 @@ class Worker(WorkerBase): + pattern=pattern, + max_size=max_size, + ) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.model_runner.save_serverless_llm_state( ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) + + def save_tensorized_model( + self, +diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py +index 8c968faa7..932c943ab 100644 +--- a/vllm/worker/model_runner.py ++++ b/vllm/worker/model_runner.py +@@ -1236,6 +1236,20 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]): + pattern=pattern, + max_size=max_size, + ) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ from vllm.model_executor.model_loader import ServerlessLLMLoader ++ ServerlessLLMLoader.save_model( ++ self.model, ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) + + def save_tensorized_model( + self, +diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py +index 6e45b8423..d87e5e9c6 100644 +--- a/vllm/worker/worker.py ++++ b/vllm/worker/worker.py +@@ -218,6 +218,18 @@ class Worker(LocalOrDistributedWorkerBase): + max_size=max_size, + ) + ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.model_runner.save_serverless_llm_state( ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) ++ + def save_tensorized_model( + self, + tensorizer_config: TensorizerConfig, diff --git a/tests/.DS_Store b/tests/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c257b4db0587cc32d9751a40c708cebdbfe0e8ed GIT binary patch literal 8196 zcmeHMyGjE=6uqM<0>+rqO4i20%79?IhFA)Le1PVq2J=!AMXLp|v9YkV$Orfz7B;r} zHJ&>&Y-ZW)CTT?RUYI${?me?-4#RBjgospW)GQLM6Hyk8v9N+}O7L@Tu~@Qqn5@7j zYEh4xbVNg3Nr(gDfH)uyhy&ulZ*&0PY>sTgci%jfUK|hy{-p!FKX_=2zQw?x-a63f z5&)RNZ5_Bq9iTa$Mc-mzP*X9csUDQ2DqCVGL&tNE(}BLlz@VX%GIUb*&B|6NO1~XE zPp6ae4N5N#hy!s4xOUG`oodve^GN-EMZ?Z%vD)Z&2Zr{%mzLY@m&^4|zmA;jZZmic zHs1Er^P3Ch56)aIV+@TQqd^^N+w+E)-iO0!Ij+vn`%q_WtZsjzFD);s>w|UWp{YkF zaPv|-+CXp4L}D7(csonYclB;Qg?eXjCfeuaMCY-?^T=TqExLjBRG+ur({f$?-Iq{* z;@Gb5^W;Z$S6KHdUC;IRIkr^v4-XQ&qhdy1AoYYWutYS>;Hq~^Z!3IBatc& zhy#l{pfcrZxdfK2tz&$ywLP?BG!D)S3~CBGU5*3w4*_8Khas*#q%z017#PGIgd7BD L8>ABle$;_)z9s?s literal 0 HcmV?d00001 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..314e503 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,17 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # diff --git a/tests/backend_test/transformers_backend_test.py b/tests/backend_test/transformers_backend_test.py new file mode 100644 index 0000000..35a59e6 --- /dev/null +++ b/tests/backend_test/transformers_backend_test.py @@ -0,0 +1,809 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import os +from unittest.mock import patch + +import pytest +import torch +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer + +from sllm.serve.backends.backend_utils import BackendStatus +from sllm.serve.backends.transformers_backend import ( + TransformersBackend, +) + + +@pytest.fixture +def model_name(): + return "facebook/opt-125m" + + +@pytest.fixture +def encoder_model_name(): + return "BAAI/bge-small-en-v1.5" + + +@pytest.fixture +def backend_config(): + return { + "pretrained_model_name_or_path": "facebook/opt-125m", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM", + } + + +@pytest.fixture +def encoder_config(): + return { + "pretrained_model_name_or_path": "BAAI/bge-small-en-v1.5", + "torch_dtype": "float16", + "hf_model_class": "AutoModel", + } + + +@pytest.fixture +def transformers_backend(model_name, backend_config): + yield TransformersBackend(model_name, backend_config) + + +@pytest.fixture +def encoder_backend(encoder_model_name, encoder_config): + yield TransformersBackend(encoder_model_name, encoder_config) + + +def test_init(transformers_backend, model_name, backend_config): + assert transformers_backend.model_name == model_name + assert transformers_backend.backend_config == backend_config + assert transformers_backend.status == BackendStatus.UNINITIALIZED + + +def test_init_encoder(encoder_backend, encoder_model_name, encoder_config): + assert encoder_backend.model_name == encoder_model_name + assert encoder_backend.backend_config == encoder_config + assert encoder_backend.status == BackendStatus.UNINITIALIZED + + +def test_init_backend(transformers_backend, backend_config): + with patch( + "sllm.serve.backends.transformers_backend.load_model" + ) as mock_load_model: + transformers_backend.init_backend() + mock_load_model.assert_called_once() + storage_path = os.getenv("STORAGE_PATH", "./models") + model_path = os.path.join( + "transformers", + backend_config["pretrained_model_name_or_path"], + ) + device_map = backend_config.get("device_map", "auto") + torch_dtype = backend_config.get("torch_dtype", torch.float16) + torch_dtype = getattr(torch, torch_dtype) + hf_model_class = backend_config.get("hf_model_class", None) + quantization_config = backend_config.get("quantization_config", None) + mock_load_model.assert_called_once_with( + model_path, + device_map=device_map, + torch_dtype=torch_dtype, + storage_path=storage_path, + hf_model_class=hf_model_class, + quantization_config=quantization_config, + ) + + +def test_init_encoder_backend(encoder_backend, encoder_config): + with patch( + "sllm.serve.backends.transformers_backend.load_model" + ) as mock_load_model: + encoder_backend.init_backend() + mock_load_model.assert_called_once() + storage_path = os.getenv("STORAGE_PATH", "./models") + model_path = os.path.join( + "transformers", + encoder_config["pretrained_model_name_or_path"], + ) + device_map = encoder_config.get("device_map", "auto") + torch_dtype = encoder_config.get("torch_dtype", torch.float16) + torch_dtype = getattr(torch, torch_dtype) + hf_model_class = encoder_config.get("hf_model_class", None) + quantization_config = encoder_config.get("quantization_config", None) + mock_load_model.assert_called_once_with( + model_path, + device_map=device_map, + torch_dtype=torch_dtype, + storage_path=storage_path, + hf_model_class=hf_model_class, + quantization_config=quantization_config, + ) + + +@pytest.fixture +def model(): + model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m").to("cpu") + yield model + + +@pytest.fixture +def tokenizer(): + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m") + yield tokenizer("test_prompt", return_tensors="pt") + + +@pytest.fixture +def encoder(): + encoder = AutoModel.from_pretrained("BAAI/bge-small-en-v1.5").to("cpu") + yield encoder + + +@pytest.fixture +def encoder_tokenizer(): + encoder_tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5") + yield encoder_tokenizer( + ["test_prompt"], + max_length=4096, + padding=True, + truncation=True, + return_tensors="pt", + ) + + +def test_generate(transformers_backend, model, tokenizer): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize", + return_value=tokenizer, + ): + transformers_backend.init_backend() + input = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you? I am fine, thank you!", + } + ], + "temperature": 0.7, + "max_tokens": 10, + } + result = transformers_backend.generate(input) + assert "error" not in result + assert "choices" in result and len(result["choices"]) == 1 + + +def test_get_current_tokens(transformers_backend, model, tokenizer): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize", + return_value=tokenizer, + ): + transformers_backend.init_backend() + input = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you? I am fine, thank you!", + } + ], + "temperature": 0.7, + "max_tokens": 128, + } + import threading + + # Create a thread to call the generate method + thread = threading.Thread( + target=transformers_backend.generate, args=(input,) + ) + thread.start() + # Sleep for 1 second to allow the thread to start + import time + + time.sleep(1) + # Get the current tokens + current_tokens = transformers_backend.get_current_tokens() + assert current_tokens + thread.join() + + +def test_resume_kv_cache(transformers_backend, model, tokenizer): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize", + return_value=tokenizer, + ): + transformers_backend.init_backend() + inputs = transformers_backend._tokenize("") + intermediate_tokens = inputs["input_ids"].tolist() + try: + transformers_backend.resume_kv_cache(intermediate_tokens) + except Exception as e: + assert False, f"Failed to resume kv cache: {e}" + assert transformers_backend.past_key_values + assert ( + len(transformers_backend.past_key_values) + == model.config.num_hidden_layers + ) + assert len(transformers_backend.past_key_values[0]) == 2 + assert transformers_backend.past_key_values[0][0].shape == ( + len(intermediate_tokens), + model.config.num_attention_heads, + len(intermediate_tokens[0]), + model.config.hidden_size // model.config.num_attention_heads, + ) + + +def test_resume_generate(transformers_backend, model, tokenizer): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize", + return_value=tokenizer, + ): + transformers_backend.init_backend() + input = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you? I am fine, thank you!", + } + ], + "temperature": 0.7, + "max_tokens": 128, + } + intermediate_inputs = transformers_backend._tokenize("") + intermediate_tokens = intermediate_inputs["input_ids"].tolist() + try: + transformers_backend.resume_kv_cache(intermediate_tokens) + except Exception as e: + assert False, f"Failed to resume kv cache: {e}" + + result = transformers_backend.resume_generate( + input, intermediate_tokens + ) + assert result + assert "error" not in result + + +def test_shutdown(transformers_backend, model, tokenizer): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize", + return_value=tokenizer, + ): + transformers_backend.init_backend() + input = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you? I am fine, thank you!", + } + ], + "temperature": 0.7, + "max_tokens": 128, + } + import threading + + # Create a thread to call the generate method + thread = threading.Thread( + target=transformers_backend.generate, args=(input,) + ) + thread.start() + # Sleep for 1 second to allow the thread to start + import time + + time.sleep(1) + # Shutdown the backend + transformers_backend.shutdown() + thread.join() + + +def test_encode(encoder_backend, encoder, encoder_tokenizer): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=encoder, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._encoder_tokenize", + return_value=encoder_tokenizer, + ): + encoder_backend.init_backend() + input = { + "model": "BAAI/bge-small-en-v1.5", + "task_instruct": "Given a question, retrieve passages that answer the question", + "input": ["Hi, How are you?"], + } + result = encoder_backend.encode(input) + assert "error" not in result + assert "data" in result and len(result["data"]) == 1 + + +def test_generate_without_init(transformers_backend): + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=model, + ): + request_data = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you? I am fine, thank you!", + } + ], + "temperature": 0.7, + "max_tokens": 10, + } + response = transformers_backend.generate(request_data) + assert "error" in response + + +# LoRA-related fixtures and tests +@pytest.fixture +def base_model_name(): + return "facebook/opt-125m" + + +@pytest.fixture +def lora_model_name(): + return "peft-internal-testing/opt-125m-dummy-lora" + + +@pytest.fixture +def lora_model_name_2(): + return "monsterapi/opt125M_alpaca" + + +@pytest.fixture +def lora_backend_config(): + return { + "pretrained_model_name_or_path": "facebook/opt-125m", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM", + "device_map": "cpu", # Use CPU for testing to avoid GPU memory issues + } + + +@pytest.fixture +def lora_backend(base_model_name, lora_backend_config): + backend = TransformersBackend(base_model_name, lora_backend_config) + yield backend + + +@pytest.fixture +def mock_peft_model(): + """Create a mock PEFT model with the necessary attributes.""" + from unittest.mock import MagicMock + + model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m").to("cpu") + + # Add PEFT-like attributes + model.peft_config = {} + model.add_adapter = MagicMock() + model.set_adapter = MagicMock() + + return model + + +def test_load_lora_adapter_success(lora_backend, mock_peft_model): + """Test successful loading of a LoRA adapter.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.load_lora", + return_value=mock_peft_model, + ) as mock_load_lora: + lora_backend.init_backend() + + # Simulate successful LoRA loading by updating peft_config + def mock_load_lora_side_effect(*args, **kwargs): + mock_peft_model.peft_config["test_lora"] = { + "adapter_name": "test_lora" + } + return mock_peft_model + + mock_load_lora.side_effect = mock_load_lora_side_effect + + # Load the LoRA adapter + result = lora_backend.load_lora_adapter("test_lora", "dummy_path") + + # Verify the adapter is loaded + assert hasattr(lora_backend.model, "peft_config") + assert "test_lora" in lora_backend.model.peft_config + mock_load_lora.assert_called_once() + + +def test_load_lora_adapter_repeated(lora_backend, mock_peft_model): + """Test that loading the same LoRA adapter multiple times doesn't cause issues.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.load_lora", + return_value=mock_peft_model, + ) as mock_load_lora: + lora_backend.init_backend() + + # Simulate LoRA already loaded + mock_peft_model.peft_config["test_lora"] = {"adapter_name": "test_lora"} + + # Load the LoRA adapter first time (should be skipped) + result = lora_backend.load_lora_adapter("test_lora", "dummy_path") + + # Load the same adapter again - should not call load_lora + result = lora_backend.load_lora_adapter("test_lora", "dummy_path") + + # Verify load_lora was not called since adapter already exists + mock_load_lora.assert_not_called() + + # Verify the adapter is still loaded + assert hasattr(lora_backend.model, "peft_config") + assert "test_lora" in lora_backend.model.peft_config + + +def test_load_multiple_lora_adapters(lora_backend, mock_peft_model): + """Test loading multiple LoRA adapters.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.load_lora", + return_value=mock_peft_model, + ) as mock_load_lora: + lora_backend.init_backend() + + # Simulate successful LoRA loading by updating peft_config + def mock_load_lora_side_effect(*args, **kwargs): + adapter_name = args[1] # Second argument is adapter_name + mock_peft_model.peft_config[adapter_name] = { + "adapter_name": adapter_name + } + return mock_peft_model + + mock_load_lora.side_effect = mock_load_lora_side_effect + + # Load first LoRA adapter + lora_backend.load_lora_adapter("test_lora_1", "dummy_path_1") + + # Load second LoRA adapter + lora_backend.load_lora_adapter("test_lora_2", "dummy_path_2") + + # Verify both adapters are loaded + assert hasattr(lora_backend.model, "peft_config") + assert "test_lora_1" in lora_backend.model.peft_config + assert "test_lora_2" in lora_backend.model.peft_config + assert mock_load_lora.call_count == 2 + + +def test_generate_with_lora_adapter(lora_backend, mock_peft_model): + """Test generation with a loaded LoRA adapter.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.load_lora", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize" + ) as mock_tokenize: + lora_backend.init_backend() + + # Setup tokenizer mock + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m") + mock_inputs = tokenizer("test prompt", return_tensors="pt") + mock_tokenize.return_value = mock_inputs + + # Simulate LoRA adapter loaded + mock_peft_model.peft_config["test_lora"] = {"adapter_name": "test_lora"} + + # Mock model generation + with patch.object(mock_peft_model, "generate") as mock_generate: + # Create mock output tokens + mock_output = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + mock_generate.return_value = mock_output + + # Generate with LoRA adapter + input_data = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + "temperature": 0.7, + "max_tokens": 10, + "lora_adapter_name": "test_lora", + } + + result = lora_backend.generate(input_data) + + # Verify successful generation + assert "error" not in result + assert "choices" in result and len(result["choices"]) == 1 + assert "message" in result["choices"][0] + assert "content" in result["choices"][0]["message"] + + # Verify generate was called with adapter_names + mock_generate.assert_called_once() + call_kwargs = mock_generate.call_args[1] + assert "adapter_names" in call_kwargs + assert call_kwargs["adapter_names"] == ["test_lora"] + + +def test_generate_with_unloaded_lora_adapter(lora_backend, mock_peft_model): + """Test generation with an unloaded LoRA adapter should fail.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ): + lora_backend.init_backend() + + input_data = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + "temperature": 0.7, + "max_tokens": 10, + "lora_adapter_name": "nonexistent_lora", + } + + result = lora_backend.generate(input_data) + + # Verify error is returned + assert "error" in result + assert "LoRA adapter nonexistent_lora not found" in result["error"] + + +def test_generate_base_model_without_lora(lora_backend, mock_peft_model): + """Test generation with base model (no LoRA adapter specified).""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize" + ) as mock_tokenize: + lora_backend.init_backend() + + # Setup tokenizer mock + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m") + mock_inputs = tokenizer("test prompt", return_tensors="pt") + mock_tokenize.return_value = mock_inputs + + # Mock model generation + with patch.object(mock_peft_model, "generate") as mock_generate: + # Create mock output tokens + mock_output = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + mock_generate.return_value = mock_output + + input_data = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + "temperature": 0.7, + "max_tokens": 10, + } + + result = lora_backend.generate(input_data) + + # Verify successful generation + assert "error" not in result + assert "choices" in result and len(result["choices"]) == 1 + assert "message" in result["choices"][0] + assert "content" in result["choices"][0]["message"] + + # Verify generate was called without adapter_names + mock_generate.assert_called_once() + call_kwargs = mock_generate.call_args[1] + assert call_kwargs.get("adapter_names") is None + + +def test_generate_with_lora_on_base_model_error(lora_backend, mock_peft_model): + """Test that requesting LoRA generation on a base model without LoRA loaded fails.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ): + lora_backend.init_backend() + + # Don't load any LoRA adapter (peft_config is empty) + + input_data = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + "temperature": 0.7, + "max_tokens": 10, + "lora_adapter_name": "test_lora", + } + + result = lora_backend.generate(input_data) + + # Verify error is returned + assert "error" in result + assert "LoRA adapter test_lora not found" in result["error"] + + +def test_load_lora_adapter_uninitialized_backend(): + """Test that loading LoRA adapter on uninitialized backend fails.""" + backend_config = { + "pretrained_model_name_or_path": "facebook/opt-125m", + "torch_dtype": "float16", + "hf_model_class": "AutoModelForCausalLM", + } + backend = TransformersBackend("facebook/opt-125m", backend_config) + + # Try to load LoRA without initializing backend + result = backend.load_lora_adapter("test_lora", "dummy_path") + + # Verify error is returned + assert result is not None + assert "error" in result + assert "Model not initialized" in result["error"] + + +def test_generate_with_different_lora_adapters(lora_backend, mock_peft_model): + """Test generation with different LoRA adapters.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.load_lora", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize" + ) as mock_tokenize: + lora_backend.init_backend() + + # Setup tokenizer mock + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m") + mock_inputs = tokenizer("test prompt", return_tensors="pt") + mock_tokenize.return_value = mock_inputs + + # Simulate both LoRA adapters loaded + mock_peft_model.peft_config["test_lora_1"] = { + "adapter_name": "test_lora_1" + } + mock_peft_model.peft_config["test_lora_2"] = { + "adapter_name": "test_lora_2" + } + + # Mock model generation + with patch.object(mock_peft_model, "generate") as mock_generate: + # Create mock output tokens + mock_output = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + mock_generate.return_value = mock_output + + input_data_base = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + "temperature": 0.0, # Use temperature 0 for deterministic output + "max_tokens": 10, + } + + # Generate with first LoRA adapter + input_data_1 = input_data_base.copy() + input_data_1["lora_adapter_name"] = "test_lora_1" + result_1 = lora_backend.generate(input_data_1) + + # Generate with second LoRA adapter + input_data_2 = input_data_base.copy() + input_data_2["lora_adapter_name"] = "test_lora_2" + result_2 = lora_backend.generate(input_data_2) + + # Generate with base model (no LoRA) + result_base = lora_backend.generate(input_data_base) + + # Verify all generations are successful + assert "error" not in result_1 + assert "error" not in result_2 + assert "error" not in result_base + + # Extract generated content + content_1 = result_1["choices"][0]["message"]["content"] + content_2 = result_2["choices"][0]["message"]["content"] + content_base = result_base["choices"][0]["message"]["content"] + + # Verify they're all valid strings + assert isinstance(content_1, str) + assert isinstance(content_2, str) + assert isinstance(content_base, str) + + # Verify generate was called 3 times + assert mock_generate.call_count == 3 + + +def test_lora_adapter_persistence_across_generations( + lora_backend, mock_peft_model +): + """Test that LoRA adapter remains loaded across multiple generations.""" + with patch( + "sllm.serve.backends.transformers_backend.load_model", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.load_lora", + return_value=mock_peft_model, + ), patch( + "sllm.serve.backends.transformers_backend.TransformersBackend._tokenize" + ) as mock_tokenize: + lora_backend.init_backend() + + # Setup tokenizer mock + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m") + mock_inputs = tokenizer("test prompt", return_tensors="pt") + mock_tokenize.return_value = mock_inputs + + # Simulate LoRA adapter loaded + mock_peft_model.peft_config["test_lora"] = {"adapter_name": "test_lora"} + + # Mock model generation + with patch.object(mock_peft_model, "generate") as mock_generate: + # Create mock output tokens + mock_output = torch.tensor([[1, 2, 3, 4, 5, 6]]) + mock_generate.return_value = mock_output + + input_data = { + "model": "facebook/opt-125m", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + "temperature": 0.7, + "max_tokens": 5, + "lora_adapter_name": "test_lora", + } + + # Generate multiple times + for i in range(3): + result = lora_backend.generate(input_data) + assert "error" not in result + assert "choices" in result and len(result["choices"]) == 1 + + # Verify adapter is still loaded + assert hasattr(lora_backend.model, "peft_config") + assert "test_lora" in lora_backend.model.peft_config + + # Verify generate was called 3 times + assert mock_generate.call_count == 3 diff --git a/tests/backend_test/vllm_backend_test.py b/tests/backend_test/vllm_backend_test.py new file mode 100644 index 0000000..a3fd3d0 --- /dev/null +++ b/tests/backend_test/vllm_backend_test.py @@ -0,0 +1,275 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import asyncio +from typing import AsyncIterator +from unittest.mock import AsyncMock, patch + +import pytest +from vllm import ( + CompletionOutput, + EmbeddingOutput, + EmbeddingRequestOutput, + RequestOutput, +) + +from sllm.serve.backends.vllm_backend import ( + BackendStatus, + VllmBackend, +) + + +@pytest.fixture +def model_name(): + return "test-model" + + +@pytest.fixture +def backend_config(): + return { + "pretrained_model_name_or_path": "test-model", + "trace_debug": False, + "load_format": "sharded_state", + "torch_dtype": None, + } + + +async def generate( + inputs, sampling_params, request_id +) -> AsyncIterator[RequestOutput]: + prompt = inputs + tokens = [1, 2, 3] + + yield RequestOutput( + request_id, + prompt, + tokens, + None, + [CompletionOutput(0, "test", [4], 0.5, None)], + True, + ) + await asyncio.sleep(1) + yield RequestOutput( + request_id, + prompt, + tokens, + None, + [CompletionOutput(0, "test output", [4, 5], 0.5, None)], + True, + ) + await asyncio.sleep(1) + yield RequestOutput( + request_id, + prompt, + tokens, + None, + [CompletionOutput(0, "test output result", [4, 5, 6], 0.5, None)], + True, + ) + + +# Mock the encode method +async def encode( + inputs, pooling_params, request_id +) -> AsyncIterator[EmbeddingRequestOutput]: + # Simulate the behavior of real encode with multiple embeddings + yield EmbeddingRequestOutput( + request_id=request_id, + outputs=EmbeddingOutput(embedding=[0.1, 0.2, 0.3]), + prompt_token_ids=[101, 102, 103], + finished=True, + ) + await asyncio.sleep(1) + yield EmbeddingRequestOutput( + request_id=request_id, + outputs=EmbeddingOutput(embedding=[0.4, 0.5, 0.6]), + prompt_token_ids=[104, 105, 106], + finished=True, + ) + + +@pytest.fixture +def async_llm_engine(): + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine" + ) as MockAsyncLLMEngine: + async_llm_engine_obj = MockAsyncLLMEngine.return_value + async_llm_engine_obj.from_engine_args.return_value = ( + async_llm_engine_obj + ) + async_llm_engine_obj.abort = AsyncMock() + async_llm_engine_obj.generate.side_effect = generate + async_llm_engine_obj.encode.side_effect = encode + yield async_llm_engine_obj + + +@pytest.fixture +def vllm_backend(model_name, backend_config, async_llm_engine): + yield VllmBackend(model_name, backend_config) + + +def test_init(vllm_backend, backend_config): + assert vllm_backend.backend_config == backend_config + assert vllm_backend.status == BackendStatus.UNINITIALIZED + + +@pytest.mark.asyncio +async def test_init_backend(vllm_backend, async_llm_engine): + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + assert vllm_backend.status == BackendStatus.RUNNING + + +@pytest.mark.asyncio +async def test_generate_without_init(vllm_backend): + request_data = { + "model": "test-model", + "prompt": "user: Hello", + "request_id": "test-request-id", + } + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=AsyncMock(), + ): + response = await vllm_backend.generate(request_data) + assert "error" in response + + +@pytest.mark.asyncio +async def test_generate(vllm_backend, async_llm_engine): + request_data = { + "model": "test-model", + "prompt": "user: Hello", + "request_id": "test-request-id", + } + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + response = await vllm_backend.generate(request_data) + assert "error" not in response + assert "model" in response and response["model"] == "test-model" + assert "id" in response and response["id"] == "test-request-id" + + +@pytest.mark.asyncio +async def test_shutdown(model_name, backend_config, async_llm_engine): + # Open trace debug to avoid clean the finished request in record map + backend_config["trace_debug"] = True + vllm_backend = VllmBackend(model_name, backend_config) + request_data = { + "model": "test-model", + "prompt": "user: Hello", + "request_id": "test-request-id", + } + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + await vllm_backend.generate(request_data) + await vllm_backend.shutdown() + assert vllm_backend.status == BackendStatus.DELETING + # Since open trace debug, will try to abort the already done task + assert async_llm_engine.abort.call_count == 1 + + +@pytest.mark.asyncio +async def test_stop(vllm_backend, async_llm_engine): + request_data = { + "model": "test-model", + "prompt": "user: Hello", + "request_id": "test-request-id", + } + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + await vllm_backend.generate(request_data) + with patch( + "sllm.serve.backends.vllm_backend.VllmBackend.shutdown", + new_callable=AsyncMock, + ) as mock_shutdown: + await vllm_backend.stop() + assert vllm_backend.status == BackendStatus.STOPPING + # stop will not call abort + assert async_llm_engine.abort.call_count == 0 + # stop will call shutdown after stop done + assert mock_shutdown.call_count == 1 + + +@pytest.mark.asyncio +async def test_get_current_tokens(model_name, backend_config, async_llm_engine): + # Open trace debug to avoid clean the finished request in record map + backend_config["trace_debug"] = True + vllm_backend = VllmBackend(model_name, backend_config) + request_data = [ + { + "model": "test-model", + "prompt": "user: Hello", + "request_id": f"test-request-id{i}", + } + for i in range(3) + ] + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + for request in request_data: + await vllm_backend.generate(request) + current_tokens = await vllm_backend.get_current_tokens() + assert len(current_tokens) == 3 + + +@pytest.mark.asyncio +async def test_resume_kv_cache(vllm_backend): + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + vllm_backend.generate = AsyncMock() + request_datas = [[1, 2, 3], [4, 5, 6]] + await vllm_backend.resume_kv_cache(request_datas) + assert vllm_backend.generate.call_count == len(request_datas) + + +@pytest.mark.asyncio +async def test_encode(backend_config, async_llm_engine, model_name): + backend_config["enforce_eager"] = True + backend_config["enable_prefix_caching"] = False + vllm_backend = VllmBackend(model_name, backend_config) + + request_data = {"model": "test-model", "input": ["Hi, How are you?"]} + + with patch( + "sllm.serve.backends.vllm_backend.AsyncLLMEngine.from_engine_args", + return_value=async_llm_engine, + ): + await vllm_backend.init_backend() + + result = await vllm_backend.encode(request_data) + + assert "error" not in result + assert "data" in result + assert len(result["data"]) == 2 diff --git a/tests/cli_test/__init__.py b/tests/cli_test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/cli_test/delete_test.py b/tests/cli_test/delete_test.py new file mode 100644 index 0000000..43ccf8d --- /dev/null +++ b/tests/cli_test/delete_test.py @@ -0,0 +1,122 @@ +import unittest +from argparse import Namespace +from unittest.mock import MagicMock, patch + +from sllm.cli.delete import DeleteCommand + + +class TestDeleteCommand(unittest.TestCase): + @patch("sllm.cli.delete.requests.post") + def test_delete_single_model_success(self, mock_post): + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 200 + mock_post.return_value = mock_response + + args = Namespace(models=["facebook/opt-1.3b"]) + command = DeleteCommand(args) + command.run() + + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/delete", + headers={"Content-Type": "application/json"}, + json={"model": "facebook/opt-1.3b"}, + ) + self.assertEqual(mock_post.return_value.status_code, 200) + + @patch("sllm.cli.delete.requests.post") + def test_delete_multiple_models_success(self, mock_post): + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 200 + mock_post.return_value = mock_response + + args = Namespace(models=["facebook/opt-1.3b", "facebook/opt-2.7b"]) + command = DeleteCommand(args) + command.run() + + self.assertEqual(mock_post.call_count, 2) + + expected_calls = [ + ( + "http://127.0.0.1:8343/delete", + {"Content-Type": "application/json"}, + {"model": "facebook/opt-1.3b"}, + ), + ( + "http://127.0.0.1:8343/delete", + {"Content-Type": "application/json"}, + {"model": "facebook/opt-2.7b"}, + ), + ] + for call, expected in zip(mock_post.call_args_list, expected_calls): + args, kwargs = call + self.assertEqual(args[0], expected[0]) + self.assertEqual(kwargs["headers"], expected[1]) + self.assertEqual(kwargs["json"], expected[2]) + + @patch("sllm.cli.delete.requests.post") + def test_delete_model_failure(self, mock_post): + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 500 + mock_post.return_value = mock_response + + args = Namespace(models=["facebook/opt-1.3b"]) + command = DeleteCommand(args) + command.run() + + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/delete", + headers={"Content-Type": "application/json"}, + json={"model": "facebook/opt-1.3b"}, + ) + self.assertEqual(mock_post.return_value.status_code, 500) + + @patch("sllm.cli.delete.requests.post") + def test_delete_multiple_lora_adapters_success(self, mock_post): + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 200 + mock_post.return_value = mock_response + + args = Namespace( + models=["facebook/opt-1.3b"], lora_adapters=["adapter1", "adapter2"] + ) + command = DeleteCommand(args) + command.run() + + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/delete", + headers={"Content-Type": "application/json"}, + json={ + "model": "facebook/opt-1.3b", + "lora_adapters": ["adapter1", "adapter2"], + }, + ) + self.assertEqual(mock_post.return_value.status_code, 200) + + @patch("sllm.cli.delete.requests.post") + def test_delete_lora_adapter_failure(self, mock_post): + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 500 + mock_response.text = "Failed to delete LoRA adapters" + mock_post.return_value = mock_response + + args = Namespace( + models=["facebook/opt-1.3b"], lora_adapters=["adapter1"] + ) + command = DeleteCommand(args) + command.run() + + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/delete", + headers={"Content-Type": "application/json"}, + json={"model": "facebook/opt-1.3b", "lora_adapters": ["adapter1"]}, + ) + self.assertEqual(mock_post.return_value.status_code, 500) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/cli_test/deploy_test.py b/tests/cli_test/deploy_test.py new file mode 100644 index 0000000..07d6f49 --- /dev/null +++ b/tests/cli_test/deploy_test.py @@ -0,0 +1,273 @@ +import unittest +from argparse import Namespace +from unittest.mock import MagicMock, patch + +from sllm.cli.deploy import DeployCommand + + +class TestDeployCommand(unittest.TestCase): + @patch("sllm.cli.deploy.read_config") + @patch("sllm.cli.deploy.requests.post") + def test_deploy_with_model_only(self, mock_post, mock_read_config): + # Mock read_config to return a default configuration + mock_read_config.return_value = { + "model": "", + "backend": "vllm", + "num_gpus": 1, + "auto_scaling_config": { + "target": 1, + "min_instances": 1, + "max_instances": 5, + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + }, + } + + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 200 + mock_post.return_value = mock_response + + args = Namespace( + model="facebook/opt-1.3b", + config=None, + backend=None, + num_gpus=None, + target=None, + min_instances=None, + max_instances=None, + enable_lora=False, + lora_adapters=None, + ) + command = DeployCommand(args) + command.run() + + mock_read_config.assert_called_once() + mock_post.assert_called_once() + self.assertEqual( + mock_post.call_args[1]["json"]["model"], "facebook/opt-1.3b" + ) + + @patch("sllm.cli.deploy.read_config") + @patch("sllm.cli.deploy.requests.post") + def test_deploy_with_custom_config(self, mock_post, mock_read_config): + # Mock the default config + default_config = { + "model": "", + "backend": "vllm", + "num_gpus": 1, + "auto_scaling_config": { + "target": 1, + "min_instances": 1, + "max_instances": 5, + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + }, + } + mock_read_config.side_effect = [ + default_config, + {"model": "custom-model", "backend": "transformers"}, + ] + + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 200 + mock_post.return_value = mock_response + + args = Namespace( + model=None, + config="path/to/config.json", + backend=None, + num_gpus=None, + target=None, + min_instances=None, + max_instances=None, + enable_lora=False, + lora_adapters=None, + ) + command = DeployCommand(args) + command.run() + + expected_config = default_config + expected_config.update( + {"model": "custom-model", "backend": "transformers"} + ) + + mock_read_config.assert_called() + mock_post.assert_called_once() + self.assertEqual( + mock_post.call_args[1]["json"]["model"], "custom-model" + ) + self.assertEqual( + mock_post.call_args[1]["json"]["backend"], "transformers" + ) + + @patch("sllm.cli.deploy.read_config") + def test_validate_config(self, mock_read_config): + # Mocked valid configuration + mock_read_config.return_value = { + "model": "facebook/opt-1.3b", + "backend": "transformers", + "num_gpus": 1, + "auto_scaling_config": { + "target": 1, + "min_instances": 1, + "max_instances": 5, + }, + "backend_config": { + "pretrained_model_name_or_path": "facebook/opt-1.3b", + "device_map": "auto", + "torch_dtype": "float16", + }, + } + + # Initialize DeployCommand with valid arguments + args = Namespace( + model="facebook/opt-1.3b", + config=None, + backend=None, + num_gpus=None, + target=None, + min_instances=None, + max_instances=None, + enable_lora=False, + lora_adapters=None, + ) + command = DeployCommand(args) + + # Test with valid config (should not raise any exception) + valid_config = mock_read_config.return_value + command.validate_config(valid_config) + + # Test with invalid num_gpus < 1 + invalid_config_num_gpus = valid_config.copy() + invalid_config_num_gpus["num_gpus"] = -1 + with self.assertRaises(ValueError): + command.validate_config(invalid_config_num_gpus) + + # Test with invalid target < 1 + invalid_config_target = valid_config.copy() + invalid_config_target["auto_scaling_config"]["target"] = 0 + with self.assertRaises(ValueError): + command.validate_config(invalid_config_target) + + # Test with min_instances < 0 + invalid_config_min_instances = valid_config.copy() + invalid_config_min_instances["auto_scaling_config"][ + "min_instances" + ] = -1 + with self.assertRaises(ValueError): + command.validate_config(invalid_config_min_instances) + + # Test with max_instances < 0 + invalid_config_max_instances = valid_config.copy() + invalid_config_max_instances["auto_scaling_config"][ + "max_instances" + ] = -1 + with self.assertRaises(ValueError): + command.validate_config(invalid_config_max_instances) + + # Test with min_instances > max_instances + invalid_config_min_greater_than_max = valid_config.copy() + invalid_config_min_greater_than_max["auto_scaling_config"][ + "min_instances" + ] = 6 + invalid_config_min_greater_than_max["auto_scaling_config"][ + "max_instances" + ] = 5 + with self.assertRaises(ValueError): + command.validate_config(invalid_config_min_greater_than_max) + + @patch("sllm.cli.deploy.read_config") + def test_update_config(self, mock_read_config): + default_config = { + "model": "", + "backend": "vllm", + "num_gpus": 1, + "auto_scaling_config": { + "target": 1, + "min_instances": 1, + "max_instances": 5, + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + }, + } + provided_config = { + "model": "facebook/opt-2.7b", + "backend": "transformers", + } + + args = Namespace( + model=None, + config="path/to/config.json", + backend=None, + num_gpus=None, + target=None, + min_instances=None, + max_instances=None, + enable_lora=False, + lora_adapters=None, + ) + command = DeployCommand(args) + updated_config = command.update_config(default_config, provided_config) + + self.assertEqual(updated_config["model"], "facebook/opt-2.7b") + self.assertEqual(updated_config["backend"], "transformers") + self.assertEqual( + updated_config["auto_scaling_config"]["target"], 1 + ) # Should remain as default + + @patch("sllm.cli.deploy.requests.post") + def test_deploy_model_success(self, mock_post): + # Mock the response of the requests.post + mock_response = MagicMock() + mock_response.status_code = 200 + mock_post.return_value = mock_response + + config_data = { + "model": "facebook/opt-2.7b", + "backend": "transformers", + "num_gpus": 2, + "auto_scaling_config": { + "target": 2, + "min_instances": 1, + "max_instances": 4, + }, + "backend_config": { + "pretrained_model_name_or_path": "facebook/opt-2.7b", + "device_map": "auto", + "torch_dtype": "float16", + }, + } + + args = Namespace( + model="facebook/opt-2.7b", + config=None, + backend=None, + num_gpus=None, + target=None, + min_instances=None, + max_instances=None, + enable_lora=False, + lora_adapters=None, + ) + command = DeployCommand(args) + command.deploy_model(config_data) + + mock_post.assert_called_once() + self.assertEqual( + mock_post.call_args[1]["json"]["model"], "facebook/opt-2.7b" + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/cli_test/fine_tuning_test.py b/tests/cli_test/fine_tuning_test.py new file mode 100644 index 0000000..439611a --- /dev/null +++ b/tests/cli_test/fine_tuning_test.py @@ -0,0 +1,111 @@ +import unittest +from argparse import Namespace +from unittest.mock import MagicMock, patch + +from sllm.cli.fine_tuning import FineTuningCommand + + +class TestFineTuningCommand(unittest.TestCase): + @patch("sllm.cli.fine_tuning.requests.post") + @patch("sllm.cli.fine_tuning.read_config") + def test_fine_tuning_with_model_only(self, mock_read_config, mock_post): + # Mock read_config to return a sample fine-tuning configuration + mock_read_config.return_value = { + "model": "facebook/opt-125m", + "ft_backend": "peft", + "dataset_config": { + "dataset_source": "hf_hub", + "hf_dataset_name": "fka/awesome-chatgpt-prompts", + "tokenization_field": "prompt", + "split": "train[:10%]", + "data_files": "", + "extension_type": "", + }, + "lora_config": { + "r": 4, + "lora_alpha": 1, + "target_modules": ["query_key_value"], + "lora_dropout": 0.05, + "bias": "lora_only", + "task_type": "CAUSAL_LM", + }, + "training_config": { + "auto_find_batch_size": True, + "num_train_epochs": 2, + "learning_rate": 0.0001, + "use_cpu": False, + }, + } + + # Mock a successful POST response + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "model": "facebook/opt-125m", + "lora_save_path": "./models/ft_facebook/opt-125m", + } + mock_post.return_value = mock_response + + args = Namespace(base_model="facebook/opt-125m", config=None) + command = FineTuningCommand(args) + command.run() + + mock_read_config.assert_called_once_with(command.config_path) + mock_post.assert_called_once() + self.assertEqual( + mock_post.call_args[1]["json"]["model"], "facebook/opt-125m" + ) + self.assertEqual( + mock_response.json.return_value["lora_save_path"], + "./models/ft_facebook/opt-125m", + ) + + @patch("sllm.cli.fine_tuning.read_config") + def test_validate_config_missing_key(self, mock_read_config): + # Missing required key in the configuration + mock_read_config.return_value = { + "ft_backend": "peft", + "dataset_config": {"dataset_source": "hf_hub"}, + } + args = Namespace(base_model="facebook/opt-125m", config=None) + command = FineTuningCommand(args) + + with self.assertRaises(ValueError) as context: + command.validate_config(mock_read_config.return_value) + self.assertIn("Missing key", str(context.exception)) + + @patch("sllm.cli.fine_tuning.requests.post") + @patch("sllm.cli.fine_tuning.read_config") + def test_fine_tuning_request_failure(self, mock_read_config, mock_post): + # Simulate configuration + mock_read_config.return_value = { + "model": "facebook/opt-125m", + "ft_backend": "peft", + "dataset_config": { + "dataset_source": "hf_hub", + "hf_dataset_name": "fka/awesome-chatgpt-prompts", + "tokenization_field": "prompt", + "split": "train[:10%]", + }, + "lora_config": {"r": 4}, + "training_config": {"num_train_epochs": 2}, + } + + # Simulate a failed request + mock_response = MagicMock() + mock_response.status_code = 500 + mock_response.text = "Internal Server Error" + mock_post.return_value = mock_response + + args = Namespace(base_model="facebook/opt-125m", config=None) + command = FineTuningCommand(args) + + with self.assertLogs("sllm.cli.fine_tuning", level="ERROR") as log: + result = command.fine_tuning(mock_read_config.return_value) + self.assertIsNone(result) + mock_post.assert_called_once() + self.assertIn("Failed to do fine-tuning", log.output[0]) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/cli_test/generate_test.py b/tests/cli_test/generate_test.py new file mode 100644 index 0000000..8d18a7b --- /dev/null +++ b/tests/cli_test/generate_test.py @@ -0,0 +1,107 @@ +import unittest +from argparse import Namespace +from unittest.mock import patch + +from sllm.cli.generate import GenerateCommand + + +class TestGenerateCommand(unittest.TestCase): + @patch("sllm.cli.generate.requests.post") + @patch("sllm.cli.generate.read_config") + def test_generate_single_thread_success(self, mock_read_config, mock_post): + # Mock the configuration reading and the POST request + mock_read_config.return_value = { + "model": "facebook/opt-1.3b", + "messages": [{"role": "user", "content": "Hello!"}], + "temperature": 0.3, + "max_tokens": 50, + } + mock_post.return_value.status_code = 200 + mock_post.return_value.json.return_value = { + "choices": [{"message": {"content": "Hi!"}}] + } + + args = Namespace(input_path="/path/to/input.json", threads=1) + command = GenerateCommand(args) + command.run() + self.assertEqual(mock_post.return_value.status_code, 200) + + mock_read_config.assert_called_once_with("/path/to/input.json") + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/v1/chat/completions", + headers={"Content-Type": "application/json"}, + json={ + "model": "facebook/opt-1.3b", + "messages": [{"role": "user", "content": "Hello!"}], + "temperature": 0.3, + "max_tokens": 50, + }, + ) + + @patch("sllm.cli.generate.requests.post") + @patch("sllm.cli.generate.read_config") + def test_generate_single_thread_failure(self, mock_read_config, mock_post): + # Mock the configuration reading and the POST request with failure + mock_read_config.return_value = { + "model": "facebook/opt-1.3b", + "messages": [{"role": "user", "content": "Hello!"}], + "temperature": 0.3, + "max_tokens": 50, + } + mock_post.return_value.status_code = 500 + mock_post.return_value.text = "Internal Server Error" + + args = Namespace(input_path="/path/to/input.json", threads=1) + command = GenerateCommand(args) + command.run() + + mock_read_config.assert_called_once_with("/path/to/input.json") + mock_post.assert_called_once() + self.assertEqual(mock_post.return_value.status_code, 500) + + @patch("sllm.cli.generate.requests.post") + @patch("sllm.cli.generate.read_config") + def test_generate_multi_thread_success(self, mock_read_config, mock_post): + # Mock the configuration reading and the POST request + mock_read_config.return_value = { + "model": "facebook/opt-1.3b", + "messages": [{"role": "user", "content": "Hello!"}], + "temperature": 0.3, + "max_tokens": 50, + } + mock_post.return_value.status_code = 200 + mock_post.return_value.json.return_value = { + "choices": [{"message": {"content": "Hi!"}}] + } + + args = Namespace(input_path="/path/to/input.json", threads=2) + command = GenerateCommand(args) + command.run() + + mock_read_config.assert_called_once_with("/path/to/input.json") + self.assertEqual(mock_post.call_count, 2) + + @patch("sllm.cli.generate.requests.post") + @patch("sllm.cli.generate.read_config") + def test_generate_multi_thread_failure(self, mock_read_config, mock_post): + # Mock the configuration reading and the POST request with failure + mock_read_config.return_value = { + "model": "facebook/opt-1.3b", + "messages": [{"role": "user", "content": "Hello!"}], + "temperature": 0.3, + "max_tokens": 50, + } + mock_post.return_value.status_code = 500 + mock_post.return_value.text = "Internal Server Error" + + args = Namespace(input_path="/path/to/input.json", threads=2) + command = GenerateCommand(args) + command.run() + + mock_read_config.assert_called_once_with("/path/to/input.json") + self.assertEqual(mock_post.call_count, 2) + self.assertEqual(mock_post.return_value.status_code, 500) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/cli_test/sllm_cli_test.py b/tests/cli_test/sllm_cli_test.py new file mode 100644 index 0000000..a134dd5 --- /dev/null +++ b/tests/cli_test/sllm_cli_test.py @@ -0,0 +1,119 @@ +import sys +import unittest +from unittest.mock import patch + +from sllm.cli.sllm_cli import main + + +class TestSllmCLI(unittest.TestCase): + @patch("sllm.cli.deploy.DeployCommand") + def test_deploy_command(self, mock_deploy_command): + # Simulate command-line input + test_args = ["sllm-cli", "deploy", "--model", "facebook/opt-1.3b"] + with patch.object(sys, "argv", test_args): + main() + + # Check that DeployCommand was called with the correct arguments + mock_deploy_command.assert_called_once() + self.assertEqual( + mock_deploy_command.call_args[0][0].model, "facebook/opt-1.3b" + ) + + @patch("sllm.cli.generate.GenerateCommand") + def test_generate_command(self, mock_generate_command): + # Simulate command-line input + test_args = ["sllm-cli", "generate", "input.json"] + with patch.object(sys, "argv", test_args): + main() + + # Check that GenerateCommand was called with the correct arguments + mock_generate_command.assert_called_once() + self.assertEqual( + mock_generate_command.call_args[0][0].input_path, "input.json" + ) + + @patch("sllm.cli.fine_tuning.FineTuningCommand") + def test_fine_tuning_command(self, mock_fine_tuning_command): + # Simulate command-line input + test_args = [ + "sllm-cli", + "fine-tuning", + "--base-model", + "facebook/opt-125m", + ] + with patch.object(sys, "argv", test_args): + main() + + # Check that GenerateCommand was called with the correct arguments + mock_fine_tuning_command.assert_called_once() + self.assertEqual( + mock_fine_tuning_command.call_args[0][0].base_model, + "facebook/opt-125m", + ) + + @patch("sllm.cli.replay.ReplayCommand") + def test_replay_command(self, mock_replay_command): + # Simulate command-line input + test_args = [ + "sllm-cli", + "replay", + "--workload", + "workload.json", + "--dataset", + "dataset.json", + ] + with patch.object(sys, "argv", test_args): + main() + + # Check that ReplayCommand was called with the correct arguments + mock_replay_command.assert_called_once() + self.assertEqual( + mock_replay_command.call_args[0][0].workload, "workload.json" + ) + self.assertEqual( + mock_replay_command.call_args[0][0].dataset, "dataset.json" + ) + + @patch("sllm.cli.delete.DeleteCommand") + def test_delete_command(self, mock_delete_command): + # Simulate command-line input + test_args = [ + "sllm-cli", + "delete", + "facebook/opt-1.3b", + "facebook/opt-2.7b", + ] + with patch.object(sys, "argv", test_args): + main() + + # Check that DeleteCommand was called with the correct arguments + mock_delete_command.assert_called_once() + self.assertEqual( + mock_delete_command.call_args[0][0].models, + ["facebook/opt-1.3b", "facebook/opt-2.7b"], + ) + + @patch("sllm.cli.update.UpdateCommand") + def test_update_command(self, mock_update_command): + # Simulate command-line input + test_args = ["sllm-cli", "update", "--model", "facebook/opt-1.3b"] + with patch.object(sys, "argv", test_args): + main() + + # Check that UpdateCommand was called with the correct arguments + mock_update_command.assert_called_once() + self.assertEqual( + mock_update_command.call_args[0][0].model, "facebook/opt-1.3b" + ) + + @patch("argparse.ArgumentParser.print_help") + def test_no_command(self, mock_print_help): + with patch("sys.argv", ["sllm-cli"]), self.assertRaises(SystemExit): + main() + + # Check that the help message was printed + mock_print_help.assert_called_once() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/cli_test/status_test.py b/tests/cli_test/status_test.py new file mode 100644 index 0000000..e3b5df3 --- /dev/null +++ b/tests/cli_test/status_test.py @@ -0,0 +1,155 @@ +import sys +import unittest +from argparse import Namespace +from unittest.mock import MagicMock, patch + +# import os +# sys.path.insert(0, os.path.abspath(os.path.dirname(__file__))) +from sllm.cli.status import StatusCommand + + +class TestStatusCommand(unittest.TestCase): + @patch("sllm.cli.status.requests.get") + def test_query_status_success(self, mock_get): + # Mock a successful response + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = {"data": "mocked_model_status"} + mock_get.return_value = mock_response + + args = Namespace() + command = StatusCommand(args) + # execute query_status method + status = command.query_status() + mock_get.assert_called_once_with( + "http://127.0.0.1:8343/v1/models", + headers={"Content-Type": "application/json"}, + ) + self.assertEqual(status, {"data": "mocked_model_status"}) + + @patch("sllm.cli.status.requests.get") + def test_query_status_failure(self, mock_get): + # Mock a failed response + mock_response = MagicMock() + mock_response.status_code = 500 + mock_response.text = "Internal Server Error" + mock_get.return_value = mock_response + + # Initialize StatusCommand with dummy args + args = Namespace() + command = StatusCommand(args) + + # Execute query_status + status = command.query_status() + + # Assertions + mock_get.assert_called_once_with( + "http://127.0.0.1:8343/v1/models", + headers={"Content-Type": "application/json"}, + ) + self.assertIsNone(status) + + @patch("sllm.cli.status.requests.get") + def test_query_status_invalid_json(self, mock_get): + """Test query_status when the API responds with non-JSON data.""" + # Mock invalid JSON response + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.side_effect = ValueError("Invalid JSON") + mock_response.text = "Not a valid JSON" + mock_get.return_value = mock_response + + # Initialize StatusCommand with dummy args + args = Namespace() + command = StatusCommand(args) + + # Execute query_status + status = command.query_status() + + # Assertions + mock_get.assert_called_once_with( + "http://127.0.0.1:8343/v1/models", + headers={"Content-Type": "application/json"}, + ) + self.assertIsNone(status) + + @patch("sllm.cli.status.requests.get") + def test_query_status_custom_url(self, mock_get): + """Test query_status with a custom server URL.""" + # Mock successful response + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = {"data": "mocked_model_status"} + mock_get.return_value = mock_response + + # Set custom environment variable for server URL + custom_url = "http://custom-server-url:1234" + with patch.dict("os.environ", {"LLM_SERVER_URL": custom_url}): + args = Namespace() + command = StatusCommand(args) + + # Execute query_status + status = command.query_status() + + # Assertions + mock_get.assert_called_once_with( + custom_url + "/v1/models", + headers={"Content-Type": "application/json"}, + ) + self.assertEqual(status, {"data": "mocked_model_status"}) + + @patch("builtins.print") + @patch.object( + StatusCommand, + "query_status", + return_value={ + "object": "list", + "data": [ + { + "id": "facebook/opt-1.3b", + "object": "model", + "created": 1738960470, + # etc. – the rest of the dict... + } + ], + }, + ) + def test_run_success(self, mock_query_status, mock_print): + """ + Test that StatusCommand.run() logs a success if query_status returns a non-None result. + """ + args = Namespace() + cmd = StatusCommand(args) + cmd.run() + # Check that we actually called query_status + mock_query_status.assert_called_once() + + # Extract all 'print(...)' calls. + printed_lines = [args[0] for args, kwargs in mock_print.call_args_list] + + # You probably have only one print call in this scenario, but let's be safe and check them all: + # We expect something like: "Model status: {'object': 'list', 'data': [...]}" + found_line = any("Model status: {" in line for line in printed_lines) + self.assertTrue( + found_line, + f"Expected a printed line containing 'Model status: {{', got: {printed_lines}", + ) + + @patch("sllm.cli.status.logger.error") + @patch.object(StatusCommand, "query_status", return_value=None) + def test_run_failure(self, mock_query_status, mock_logger_error): + """ + Test that StatusCommand.run() logs an error if query_status returns None. + """ + args = Namespace() + cmd = StatusCommand(args) + cmd.run() + + mock_query_status.assert_called_once() + mock_logger_error.assert_called_once_with( + "Failed to fetch model status." + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/cli_test/update_test.py b/tests/cli_test/update_test.py new file mode 100644 index 0000000..653b5ed --- /dev/null +++ b/tests/cli_test/update_test.py @@ -0,0 +1,94 @@ +import unittest +from argparse import Namespace +from unittest.mock import patch + +from sllm.cli.update import UpdateCommand + + +class TestUpdateCommand(unittest.TestCase): + @patch("sllm.cli.update.requests.post") + @patch("sllm.cli.update.read_config") + @patch("sllm.cli.update.validate_config") + def test_update_with_config_file( + self, mock_validate, mock_read_config, mock_post + ): + # Mock the configuration reading and validation + mock_read_config.return_value = { + "model": "facebook/opt-1.3b", + "backend_config": { + "pretrained_model_name_or_path": "facebook/opt-1.3b" + }, + } + mock_post.return_value.status_code = 200 + + args = Namespace(model=None, config="/path/to/config.json") + command = UpdateCommand(args) + command.run() + + mock_read_config.assert_called_once_with("/path/to/config.json") + mock_validate.assert_called_once() + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/update", + headers={"Content-Type": "application/json"}, + json={ + "model": "facebook/opt-1.3b", + "backend_config": { + "pretrained_model_name_or_path": "facebook/opt-1.3b" + }, + }, + ) + + @patch("sllm.cli.update.requests.post") + @patch("sllm.cli.update.read_config") + def test_update_with_model_name(self, mock_read_config, mock_post): + # Mock the default configuration reading + mock_read_config.return_value = { + "model": "", + "backend_config": {"pretrained_model_name_or_path": ""}, + } + mock_post.return_value.status_code = 200 + + args = Namespace(model="facebook/opt-1.3b", config=None) + command = UpdateCommand(args) + command.run() + + mock_read_config.assert_called_once() + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/update", + headers={"Content-Type": "application/json"}, + json={ + "model": "facebook/opt-1.3b", + "backend_config": { + "pretrained_model_name_or_path": "facebook/opt-1.3b" + }, + }, + ) + + @patch("sllm.cli.update.requests.post") + @patch("sllm.cli.update.read_config") + def test_update_model_failure(self, mock_read_config, mock_post): + # Mock the default configuration reading + mock_read_config.return_value = { + "model": "", + "backend_config": {"pretrained_model_name_or_path": ""}, + } + mock_post.return_value.status_code = 500 + + args = Namespace(model="facebook/opt-1.3b", config=None) + command = UpdateCommand(args) + command.run() + + mock_read_config.assert_called_once() + mock_post.assert_called_once() + self.assertEqual(mock_post.return_value.status_code, 500) + + def test_update_missing_arguments(self): + args = Namespace(model=None, config=None) + command = UpdateCommand(args) + + with self.assertRaises(SystemExit): + command.run() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..5a04a10 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,25 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +# tests/conftest.py +import os +import sys + +# Add the project root directory to the Python path +sys.path.insert( + 0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +) diff --git a/tests/inference_test/inference_test.py b/tests/inference_test/inference_test.py new file mode 100644 index 0000000..d4524c6 --- /dev/null +++ b/tests/inference_test/inference_test.py @@ -0,0 +1,47 @@ +import json +import os +import pathlib + +import pytest +import torch +from transformers import AutoTokenizer + +from sllm_store.transformers import load_model + +with open("tests/inference_test/supported_models.json") as fh: + models = list(json.load(fh).keys()) + +try: + with open("tests/inference_test/failed_models.json") as fh: + _FAILED = {f["model"] for f in json.load(fh)} +except Exception: + _FAILED = set() + + +@pytest.fixture(scope="session") +def storage_path(tmp_path_factory): + env = os.getenv("MODEL_FOLDER") + return pathlib.Path(env) if env else tmp_path_factory.mktemp("models") + + +@pytest.fixture(scope="session", params=models, ids=models) +def model_name(request): + return request.param + + +def test_inference(model_name, storage_path): + if model_name in _FAILED: + pytest.skip("storage failure") + model = load_model( + model_name, + storage_path=storage_path, + device_map="auto", + torch_dtype=torch.float16, + fully_parallel=True, + ) + tokenizer = AutoTokenizer.from_pretrained(model_name) + inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to("cuda") + outputs = model.generate(**inputs) + print( + f"{model_name} output: {tokenizer.decode(outputs[0], skip_special_tokens=True)}" + ) diff --git a/tests/inference_test/store_test.py b/tests/inference_test/store_test.py new file mode 100644 index 0000000..737cc4d --- /dev/null +++ b/tests/inference_test/store_test.py @@ -0,0 +1,76 @@ +import json +import os +import pathlib + +import pytest +import torch +from transformers import AutoModelForCausalLM + +from sllm_store.transformers import load_model, save_model + +with open("tests/inference_test/supported_models.json") as fh: + models = list(json.load(fh).keys()) + + +@pytest.fixture(scope="session", params=models, ids=models) +def model_name(request): + return request.param + + +@pytest.fixture(scope="session") +def storage_path(tmp_path_factory): + env = os.getenv("MODEL_FOLDER") + return pathlib.Path(env) if env else tmp_path_factory.mktemp("models") + + +def store_and_compare(model_name, storage_path): + try: + os.makedirs(storage_path, exist_ok=True) + cache_dir = storage_path / model_name + hf_model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.float16, trust_remote_code=True + ) + save_model(hf_model, str(cache_dir)) + + test_model = load_model( + model_name, + storage_path=storage_path, + device_map="auto", + torch_dtype=torch.float16, + fully_parallel=True, + ) + + for name, param in test_model.named_parameters(): + ref = hf_model.state_dict()[name] + if param.dtype != ref.dtype: + return ( + f"dtype mismatch for {name}: {param.dtype} vs {ref.dtype}" + ) + if param.shape != ref.shape: + return ( + f"shape mismatch for {name}: {param.shape} vs {ref.shape}" + ) + if not torch.allclose(param.cpu(), ref.cpu(), atol=1e-6): + return f"value mismatch for {name}" + + del hf_model, test_model + torch.cuda.empty_cache() + return None + + except Exception as exc: + return str(exc) + + +def test_model_can_be_stored(model_name, storage_path, request): + error = store_and_compare(model_name, storage_path) + if error: + failures = request.session.__dict__.setdefault("_model_failures", []) + failures.append({"model": model_name, "error": error}) + assert error is None, error + + +def pytest_sessionfinish(session, exitstatus): + failures = session.__dict__.get("_model_failures", []) + if failures: + with open("tests/inference_test/failed_models.json", "w") as fh: + json.dump(failures, fh, indent=2) diff --git a/tests/inference_test/supported_models.json b/tests/inference_test/supported_models.json new file mode 100644 index 0000000..d9e8ed9 --- /dev/null +++ b/tests/inference_test/supported_models.json @@ -0,0 +1,10 @@ +{ + "facebook/opt-1.3b": { + "can_store": true, + "can_inference": true + }, + "Qwen/Qwen2.5-1.5B": { + "can_store": true, + "can_inference": true + } +} diff --git a/tests/new_cli_test/test_delete.py b/tests/new_cli_test/test_delete.py new file mode 100644 index 0000000..475b06b --- /dev/null +++ b/tests/new_cli_test/test_delete.py @@ -0,0 +1,63 @@ +# tests/new_cli_test/test_delete.py + +import unittest +from unittest import mock + +from click.testing import CliRunner + +from sllm.clic import cli + + +class TestDeleteCommand(unittest.TestCase): + def setUp(self): + self.runner = CliRunner() + + @mock.patch("sllm._cli_utils.requests.post") + def test_delete_success(self, mock_post): + mock_post.return_value.status_code = 200 + mock_post.return_value.text = "OK" + + result = self.runner.invoke(cli, ["delete", "foo", "bar"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Model 'foo' deleted successfully.", result.output) + self.assertIn("Model 'bar' deleted successfully.", result.output) + + self.assertEqual(mock_post.call_count, 2) + + expected_calls = [ + mock.call( + "http://127.0.0.1:8343/delete/", + headers={"Content-Type": "application/json"}, + json={"model": "foo"}, + ), + mock.call( + "http://127.0.0.1:8343/delete/", + headers={"Content-Type": "application/json"}, + json={"model": "bar"}, + ), + ] + mock_post.assert_has_calls(expected_calls, any_order=False) + + @mock.patch("sllm._cli_utils.requests.post") + def test_delete_failure(self, mock_post): + mock_post.return_value.status_code = 404 + mock_post.return_value.text = "Not Found" + + result = self.runner.invoke(cli, ["delete", "nonexistent"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Failed to delete model 'nonexistent'", result.output) + + mock_post.assert_called_once_with( + "http://127.0.0.1:8343/delete/", + headers={"Content-Type": "application/json"}, + json={"model": "nonexistent"}, + ) + + def test_delete_no_models(self): + result = self.runner.invoke(cli, ["delete"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("No model names provided for deletion", result.output) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/new_cli_test/test_deploy.py b/tests/new_cli_test/test_deploy.py new file mode 100644 index 0000000..7c049b8 --- /dev/null +++ b/tests/new_cli_test/test_deploy.py @@ -0,0 +1,135 @@ +# tests/new_cli_test/test_deploy.py + +import json +import tempfile +import unittest +from unittest import mock + +from click.testing import CliRunner + +from sllm.clic import cli + + +class TestDeployCommand(unittest.TestCase): + def setUp(self): + self.runner = CliRunner() + + @mock.patch("sllm._cli_utils.requests.post") + @mock.patch("sllm._cli_utils.read_config") + @mock.patch("sllm._cli_utils.os.path.exists", return_value=True) + def test_deploy_success_with_config( + self, mock_exists, mock_read, mock_post + ): + default_config = { + "model": "", + "backend": "vllm", + "num_gpus": 1, + "auto_scaling_config": { + "target": 1, + "min_instances": 1, + "max_instances": 5, + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + }, + } + user_config = { + "backend": "transformers", + } + + mock_read.side_effect = [default_config, user_config] + mock_post.return_value.status_code = 200 + mock_post.return_value.text = "OK" + + with tempfile.NamedTemporaryFile("w+", suffix=".json") as f: + json.dump(user_config, f) + f.flush() + result = self.runner.invoke( + cli, + ["deploy", "--model", "facebook/opt-2.7b", "--config", f.name], + ) + + self.assertEqual(result.exit_code, 0) + self.assertIn( + "Model 'facebook/opt-2.7b' deployed successfully.", result.output + ) + + request_json = mock_post.call_args[1]["json"] + self.assertEqual(request_json["model"], "facebook/opt-2.7b") + self.assertEqual(request_json["backend"], "transformers") + self.assertEqual( + request_json["auto_scaling_config"]["min_instances"], 1 + ) + + @mock.patch("sllm._cli_utils.requests.post") + @mock.patch("sllm._cli_utils.read_config") + @mock.patch("sllm._cli_utils.os.path.exists", return_value=True) + def test_deploy_failure_status(self, mock_exists, mock_read, mock_post): + mock_read.return_value = { + "model": "", + "backend": "vllm", + } + mock_post.return_value.status_code = 500 + mock_post.return_value.text = "Internal Error" + + result = self.runner.invoke(cli, ["deploy", "--model", "m"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Deploy failed with status 500", result.output) + + self.assertEqual(mock_post.call_args[1]["json"]["model"], "m") + + @mock.patch("sllm._cli_utils.os.path.exists", return_value=False) + def test_deploy_missing_default_config(self, mock_exists): + result = self.runner.invoke(cli, ["deploy", "--model", "x"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Default config not found", result.output) + + @mock.patch("sllm._cli_utils.requests.post") + @mock.patch("sllm._cli_utils.read_config") + @mock.patch("sllm._cli_utils.os.path.exists", return_value=True) + def test_deploy_with_extra_flags(self, mock_exists, mock_read, mock_post): + mock_read.return_value = { + "model": "", + "backend": "vllm", + "auto_scaling_config": {}, + "backend_config": {}, + } + mock_post.return_value.status_code = 200 + + result = self.runner.invoke( + cli, + [ + "deploy", + "--model", + "facebook/opt-1.3b", + "--backend", + "transformers", + "--num-gpus", + "2", + "--target", + "3", + "--min-instances", + "1", + "--max-instances", + "5", + ], + ) + self.assertEqual(result.exit_code, 0) + + request_data = mock_post.call_args[1]["json"] + self.assertEqual(request_data["model"], "facebook/opt-1.3b") + self.assertEqual(request_data["backend"], "transformers") + self.assertEqual(request_data["num_gpus"], 2) + self.assertEqual(request_data["auto_scaling_config"]["target"], 3) + self.assertEqual( + request_data["auto_scaling_config"]["min_instances"], 1 + ) + self.assertEqual( + request_data["auto_scaling_config"]["max_instances"], 5 + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/new_cli_test/test_start.py b/tests/new_cli_test/test_start.py new file mode 100644 index 0000000..878ee5b --- /dev/null +++ b/tests/new_cli_test/test_start.py @@ -0,0 +1,39 @@ +# tests/new_cli_test/test_start.py + +import unittest +from unittest import mock + +from click.testing import CliRunner + +from sllm.clic import cli + + +class TestStartCommand(unittest.TestCase): + def setUp(self): + self.runner = CliRunner() + + @mock.patch("sllm._cli_utils.subprocess.run") + @mock.patch("sllm._cli_utils.os.path.exists", return_value=True) + def test_start_success(self, mock_exists, mock_run): + result = self.runner.invoke(cli, ["start"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("SLLM server started successfully.", result.output) + + @mock.patch("sllm._cli_utils.os.path.exists", return_value=False) + def test_start_missing_compose(self, mock_exists): + result = self.runner.invoke(cli, ["start"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Cannot find docker-compose.yml", result.output) + + @mock.patch( + "sllm._cli_utils.subprocess.run", side_effect=Exception("docker error") + ) + @mock.patch("sllm._cli_utils.os.path.exists", return_value=True) + def test_start_subprocess_exception(self, mock_exists, mock_run): + result = self.runner.invoke(cli, ["start"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Failed to start services: docker error", result.output) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/new_cli_test/test_status.py b/tests/new_cli_test/test_status.py new file mode 100644 index 0000000..9f82117 --- /dev/null +++ b/tests/new_cli_test/test_status.py @@ -0,0 +1,61 @@ +# tests/new_cli_test/test_status.py + +import unittest +from unittest import mock + +import requests +from click.testing import CliRunner + +from sllm.clic import cli + + +class TestStatusCommand(unittest.TestCase): + def setUp(self): + self.runner = CliRunner() + + @mock.patch("sllm._cli_utils.requests.get") + def test_status_with_models(self, mock_get): + mock_resp = mock_get.return_value + mock_resp.status_code = 200 + mock_resp.json.return_value = {"models": [{"id": "a1"}, {"id": "b2"}]} + + result = self.runner.invoke(cli, ["status"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Model status retrieved", result.output) + self.assertIn("- a1", result.output) + self.assertIn("- b2", result.output) + + @mock.patch("sllm._cli_utils.requests.get") + def test_status_no_models(self, mock_get): + mock_resp = mock_get.return_value + mock_resp.status_code = 200 + mock_resp.json.return_value = {"models": []} + + result = self.runner.invoke(cli, ["status"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("No models currently deployed.", result.output) + + @mock.patch("sllm._cli_utils.requests.get") + def test_status_http_error(self, mock_get): + mock_resp = mock_get.return_value + mock_resp.status_code = 500 + mock_resp.text = "Internal Error" + + result = self.runner.invoke(cli, ["status"]) + self.assertEqual(result.exit_code, 0) + self.assertIn("Failed with status 500", result.output) + + @mock.patch( + "sllm._cli_utils.requests.get", + side_effect=requests.exceptions.RequestException("ConnErr"), + ) + def test_status_exception(self, mock_get): + result = self.runner.invoke(cli, ["status"]) + self.assertEqual(result.exit_code, 0) + self.assertIn( + "[EXCEPTION] Failed to query status: ConnErr", result.output + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/quantize_test/quantize_test.py b/tests/quantize_test/quantize_test.py new file mode 100644 index 0000000..e64cbc7 --- /dev/null +++ b/tests/quantize_test/quantize_test.py @@ -0,0 +1,132 @@ +import json +import os +import unittest + +import pytest +import torch +from transformers import ( + AutoModelForCausalLM, + BitsAndBytesConfig, +) + +from sllm_store.transformers import load_model, save_model + + +@pytest.fixture(scope="session") +def model_name(): + return "facebook/opt-1.3b" + + +@pytest.fixture(scope="session") +def storage_path(): + model_folder = os.getenv("MODEL_FOLDER") + if model_folder: + return model_folder + return pytest.tmp_path_factory.mktemp("models") + + +@pytest.fixture +def model_path(model_name, storage_path): + return os.path.join(storage_path, model_name) + + +@pytest.fixture(scope="session", autouse=True) +def setup_models(storage_path): + """Save the original model before tests.""" + os.makedirs(storage_path, exist_ok=True) + model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b") + save_model(model, os.path.join(storage_path, "facebook/opt-1.3b")) + + +# quantization configs +@pytest.fixture( + params=[ + BitsAndBytesConfig(load_in_4bit=True), + BitsAndBytesConfig(load_in_8bit=True), + BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_quant_type="nf4", + ), + ], + ids=["fp4", "int8", "nf4"], +) +def get_quantization_config(request): + return request.param + + +# model configs +@pytest.fixture +def hf_model(get_quantization_config, model_name): + model = AutoModelForCausalLM.from_pretrained( + model_name, + quantization_config=get_quantization_config, + torch_dtype=torch.float16, + device_map="auto", + ) + yield model + del model + torch.cuda.empty_cache() + + +@pytest.fixture(params=[True, False], ids=["fully_parallel", "best_effort"]) +def sllm_model(get_quantization_config, model_name, storage_path, request): + model = load_model( + model_name, + storage_path=storage_path, + quantization_config=get_quantization_config, + torch_dtype=torch.float16, + fully_parallel=request.param, + device_map="auto", + ) + yield model + del model + torch.cuda.empty_cache() + + +# tests +def compare_state_dicts(transformers_model, sllm_model): + """Compares model state dicts with support for partial quantization.""" + transformers_params = transformers_model.state_dict() + sllm_params = sllm_model.state_dict() + + # ignore lm_head + ignore_candidates = {"lm_head", "lm_head.weight"} + ignore_keys = { + k + for k in ignore_candidates + if k in transformers_params and k in sllm_params + } + + # get comparable keys + transformers_keys = set(transformers_params.keys()) - ignore_keys + sllm_keys = set(sllm_params.keys()) - ignore_keys + assert ( + transformers_keys == sllm_keys + ), f"Key mismatch. Diff: {transformers_keys.symmetric_difference(sllm_keys)}" + + for key in transformers_keys: + t_param = transformers_params[key] + s_param = sllm_params[key] + + # Shape check + assert t_param.shape == s_param.shape, ( + f"Shape mismatch for {key}: " + f"Transformers={t_param.shape}, SLLM={s_param.shape}" + ) + + # Dtype check (accounts for mixed quantization) + assert s_param.dtype == t_param.dtype, ( + f"Dtype mismatch for {key}: " + f"Transformers={t_param.dtype}, SLLM={s_param.dtype}" + ) + + # individual parameter check + assert torch.allclose(t_param, s_param, rtol=1e-2, atol=1e-3), ( + f"Param mismatch for {key}: " + f"Transformers={t_param.dtype}, SLLM={s_param.dtype}", + ) + + +def test_valid_quantization(hf_model, sllm_model): + compare_state_dicts(hf_model, sllm_model) diff --git a/tests/replay_test/README.md b/tests/replay_test/README.md new file mode 100644 index 0000000..0080f83 --- /dev/null +++ b/tests/replay_test/README.md @@ -0,0 +1,62 @@ +# How to test replay command + +## Run the server + +1. Set the `MODEL_FOLDER` environment variable to specify the folder to store the models: + + ```sh + cd path/to/your-folder/ + mkdir models + export MODEL_FOLDER=$PWD/models/ + ``` + +2. Run the server: + + ```sh + bash examples/clean.sh + bash examples/run.sh + ``` + +## Run the test + +In another terminal: + +1. Configuration: + + Set the `LLM_SERVER_URL` environment variable to specify the server URL: + + ```sh + export LLM_SERVER_URL=http://127.0.0.1:8343 + ``` + + Install the package: + + ```sh + pip install . + pip install datasets + ``` + + +2. Run the test: + + Change directory to the test folder: + + ```sh + cd tests/replay_test/ + ``` + + Prepare the gsm8k dataset: + + ```sh + python convert_gsm8k.py + ``` + + Run the test: + ```sh + python deploy_dummy_models.py --num-models 7 + python generate_random_workload.py --num-models 7 --request-rate 1 --duration-minutes 1 + The `replay` subcommand is not implemented in the current version of `sllm`. + You can manually process the workload and dataset files using custom scripts or tools. + Example: + python process_workload.py --workload workload.json --dataset gsm8k_dataset.json + ``` diff --git a/tests/replay_test/convert_gsm8k.py b/tests/replay_test/convert_gsm8k.py new file mode 100644 index 0000000..9a1dece --- /dev/null +++ b/tests/replay_test/convert_gsm8k.py @@ -0,0 +1,64 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import json + +from datasets import load_dataset + + +def convert_gsm8k_to_custom_format(config="main", split="train"): + # Load the gsm8k dataset with the specified config + dataset = load_dataset("gsm8k", config, split=split) + + input_texts = [] + output_lengths = [] + + # Extract questions and answers, and compute output lengths + for example in dataset: + question = example["question"] + answer = example["answer"] + input_texts.append(question) + output_lengths.append(calculate_tokens(answer)) + + # Create the final format + formatted_data = { + "input_text": input_texts, + "output_length": output_lengths, + } + + return formatted_data + + +def calculate_tokens(answer): + return len( + answer.split() + ) # TODO: undecided on how to calculate output tokens + + +def main(): + # Convert the dataset + formatted_data = convert_gsm8k_to_custom_format() + + # Save to a JSON file + with open("gsm8k_dataset.json", "w") as f: + json.dump(formatted_data, f, indent=4) + + print("Converted dataset saved as gsm8k_dataset.json") + + +if __name__ == "__main__": + main() diff --git a/tests/replay_test/deploy_dummy_models.py b/tests/replay_test/deploy_dummy_models.py new file mode 100644 index 0000000..531fa5f --- /dev/null +++ b/tests/replay_test/deploy_dummy_models.py @@ -0,0 +1,112 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import argparse +import os +import random +import time + +import requests + +# Configuration template +config_template = { + "model": "", + "backend": "dummy", + "num_gpus": 1, + "auto_scaling_config": { + "metric": "concurrency", + "target": 1, + "min_instances": 0, + "max_instances": 10, + }, + "backend_config": { + "pretrained_model_name_or_path": "", + "device_map": "auto", + "torch_dtype": "float16", + }, +} + + +# Function to generate random auto scaling configuration +def generate_auto_scaling_config(): + target = random.randint(1, 10) + min_instances = 0 + max_instances = random.randint( + 10, 20 + ) # Ensure max_instances is greater than min_instances + return { + "metric": "concurrency", + "target": target, + "min_instances": min_instances, + "max_instances": max_instances, + } + + +# Function to generate configuration +def generate_config(model_name, pretrained_model_path): + config = config_template.copy() + config["model"] = model_name + config["backend_config"]["pretrained_model_name_or_path"] = ( + pretrained_model_path + ) + config["auto_scaling_config"] = generate_auto_scaling_config() + return config + + +# Function to deploy model using generated configuration +def deploy_model(config, url): + headers = {"Content-Type": "application/json"} + response = requests.post(url, headers=headers, json=config) + + if response.status_code == 200: + print(f"Successfully deployed model with config: {config['model']}") + else: + print(f"Failed to deploy model with config: {config['model']}") + print(response.text) + + +# Main script to generate and deploy models +def main(num_models, register_url): + models = [f"dummy_model_{i}" for i in range(1, num_models + 1)] + pretrained_paths = [f"dummy_path_{i}" for i in range(1, num_models + 1)] + + for index, (model_name, pretrained_path) in enumerate( + zip(models, pretrained_paths), start=1 + ): + config = generate_config(model_name, pretrained_path) + deploy_model(config, register_url) + time.sleep( + 1 + ) # Sleep for 1 second to avoid deploying two models at the same time + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Deploy multiple models with generated configurations." + ) + parser.add_argument( + "-n", + "--num-models", + type=int, + required=True, + help="Number of models to deploy.", + ) + args = parser.parse_args() + register_url = ( + os.getenv("LLM_SERVER_URL", "http://127.0.0.1:8343") + "/register" + ) + main(args.num_models, register_url) diff --git a/tests/replay_test/generate_random_workload.py b/tests/replay_test/generate_random_workload.py new file mode 100644 index 0000000..5b22c36 --- /dev/null +++ b/tests/replay_test/generate_random_workload.py @@ -0,0 +1,77 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import argparse +import json +import random + + +def generate_random_workload(num_models, total_requests, duration_seconds): + workload = {f"dummy_model_{i}": [] for i in range(1, num_models + 1)} + + for _ in range(total_requests): + model_name = random.choice(list(workload.keys())) + timestamp = int(random.uniform(0, duration_seconds)) + workload[model_name].append(timestamp) + + # Sort timestamps for each model to simulate a real timeline of requests + for model in workload: + workload[model].sort() + + return workload + + +def main(): + parser = argparse.ArgumentParser( + description="Generate random workload.json for sllm-cli" + ) + parser.add_argument( + "--num-models", type=int, required=True, help="Number of models" + ) + parser.add_argument( + "--request-rate", + type=int, + required=True, + help="Request rate per second", + ) + parser.add_argument( + "--duration-minutes", + type=int, + required=True, + help="Duration of the workload in minutes", + ) + + args = parser.parse_args() + + num_models = args.num_models + request_rate = args.request_rate + duration_minutes = args.duration_minutes + duration_seconds = duration_minutes * 60 + total_requests = request_rate * duration_seconds + + workload = generate_random_workload( + num_models, total_requests, duration_seconds + ) + + with open("workload.json", "w") as f: + json.dump(workload, f, indent=4) + + print("Generated workload.json") + + +if __name__ == "__main__": + main() diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..9351aaf --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,30 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import unittest + +import torch + + +def is_cuda_available() -> bool: + return torch.cuda.is_available() + + +def require_cuda(test_case: unittest.TestCase): + return unittest.skipUnless(is_cuda_available(), "test requires a GPU")( + test_case + ) -- Gitee From 8dda9cc7e7d77df7b6d1aba74cc5f1e41834600d Mon Sep 17 00:00:00 2001 From: Jack Su Date: Sat, 2 Aug 2025 17:04:25 +0100 Subject: [PATCH 2/2] integrate npu migration code in sllm_store --- .gitignore | 2 + README.en.md | 12 + README.md | 12 + benchmarks/cann_benchmark_utils.py | 130 ++ sllm_store/CMakeLists.txt | 412 +++-- sllm_store/cmake/utils.cmake | 120 +- sllm_store/csrc/checkpoint/cann_ipc.cpp | 563 +++++++ sllm_store/csrc/checkpoint/cann_ipc.h | 111 ++ sllm_store/csrc/checkpoint/checkpoint.cpp | 469 ++++-- sllm_store/csrc/checkpoint/checkpoint.h | 39 +- sllm_store/csrc/checkpoint/checkpoint_py.cpp | 96 +- .../csrc/sllm_store/cann_error_handling.cpp | 37 + .../csrc/sllm_store/cann_error_handling.h | 44 + sllm_store/csrc/sllm_store/cann_memory.cpp | 81 + sllm_store/csrc/sllm_store/cann_memory.h | 49 + .../csrc/sllm_store/cann_memory_pool.cpp | 97 ++ sllm_store/csrc/sllm_store/cann_memory_pool.h | 45 + .../csrc/sllm_store/checkpoint_store.cpp | 633 +++++--- sllm_store/csrc/sllm_store/checkpoint_store.h | 46 +- sllm_store/csrc/sllm_store/error_handling.h | 5 + sllm_store/csrc/sllm_store/gpu_replica.cpp | 73 +- sllm_store/csrc/sllm_store/model.cpp | 1415 +++++++++++------ sllm_store/csrc/sllm_store/model.h | 97 +- .../csrc/sllm_store/pinned_memory_pool.cpp | 154 +- sllm_store/examples/cann_save_vllm_model.py | 136 ++ sllm_store/proto/storage.proto | 8 + sllm_store/requirements-build.txt | 3 +- sllm_store/requirements.txt | 15 +- sllm_store/setup.py | 170 +- sllm_store/sllm_store/cann_utils.py | 146 ++ sllm_store/sllm_store/cli.py | 425 +---- sllm_store/sllm_store/client.py | 11 + sllm_store/sllm_store/proto/storage_pb2.py | 100 +- .../sllm_store/proto/storage_pb2_grpc.py | 339 ++-- sllm_store/sllm_store/server.py | 9 +- sllm_store/sllm_store/torch.py | 66 +- sllm_store/sllm_store/transformers.py | 161 +- sllm_store/tests/python/test_cann_basic.py | 81 + .../tests/python/test_cann_load_model.py | 278 ++++ .../tests/python/test_cann_load_vllm_model.py | 29 + .../tests/python/test_cann_save_model.py | 25 + sllm_store/vllm_patch/sllm_load_npu.patch | 271 ++++ sllm_store/vllm_patch/vllm_ascend.patch | 46 + 43 files changed, 5102 insertions(+), 1959 deletions(-) create mode 100644 .gitignore create mode 100644 benchmarks/cann_benchmark_utils.py create mode 100644 sllm_store/csrc/checkpoint/cann_ipc.cpp create mode 100644 sllm_store/csrc/checkpoint/cann_ipc.h create mode 100644 sllm_store/csrc/sllm_store/cann_error_handling.cpp create mode 100644 sllm_store/csrc/sllm_store/cann_error_handling.h create mode 100644 sllm_store/csrc/sllm_store/cann_memory.cpp create mode 100644 sllm_store/csrc/sllm_store/cann_memory.h create mode 100644 sllm_store/csrc/sllm_store/cann_memory_pool.cpp create mode 100644 sllm_store/csrc/sllm_store/cann_memory_pool.h create mode 100644 sllm_store/examples/cann_save_vllm_model.py create mode 100644 sllm_store/sllm_store/cann_utils.py create mode 100644 sllm_store/tests/python/test_cann_basic.py create mode 100644 sllm_store/tests/python/test_cann_load_model.py create mode 100644 sllm_store/tests/python/test_cann_load_vllm_model.py create mode 100644 sllm_store/tests/python/test_cann_save_model.py create mode 100644 sllm_store/vllm_patch/sllm_load_npu.patch create mode 100644 sllm_store/vllm_patch/vllm_ascend.patch diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1bc2205 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +# vscode +.vscode/ \ No newline at end of file diff --git a/README.en.md b/README.en.md index cc01193..8fa0b8f 100644 --- a/README.en.md +++ b/README.en.md @@ -15,6 +15,18 @@ ServerlessLLM (`sllm`, pronounced "slim") is an open-source serverless framework Curious about how it works under the hood? Check out our [System Walkthrough](https://github.com/ServerlessLLM/ServerlessLLM/tree/main/blogs/serverless-llm-architecture) for a deep dive into the technical design—perfect if you're exploring your own research or building with ServerlessLLM. +## Features supported on GPU/NPU + +| Feature | GPU Support | NPU Support | +|--------------------------------------|-------------|----------------------| +| Storage Aware Scheduling with Docker Compose | ✅ | To Be Done | +| Live Migration of Inference Instances | ✅ | To Be Done | +| LoRA Serving | ✅ | To Be Done | +| Quantization | ✅ | To Be Done | +| sllm | ✅ | To Be Done | +| sllm_store | ✅ | ✅ | +| vLLM Support | ✅ | ✅ | + ## News - **[03/25]** We're excited to share that we'll be giving a ServerlessLLM tutorial at the SESAME workshop, co-located with ASPLOS/EuroSys 2025 in Rotterdam, Netherlands, on March 31. [Slides](https://docs.google.com/presentation/d/1ioGCVpsg0x3oCxX19EiE820aMiY22X5MG6jgImZ1W18/edit?usp=sharing) | [More info](https://sesame25.github.io/) diff --git a/README.md b/README.md index cc01193..8fa0b8f 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,18 @@ ServerlessLLM (`sllm`, pronounced "slim") is an open-source serverless framework Curious about how it works under the hood? Check out our [System Walkthrough](https://github.com/ServerlessLLM/ServerlessLLM/tree/main/blogs/serverless-llm-architecture) for a deep dive into the technical design—perfect if you're exploring your own research or building with ServerlessLLM. +## Features supported on GPU/NPU + +| Feature | GPU Support | NPU Support | +|--------------------------------------|-------------|----------------------| +| Storage Aware Scheduling with Docker Compose | ✅ | To Be Done | +| Live Migration of Inference Instances | ✅ | To Be Done | +| LoRA Serving | ✅ | To Be Done | +| Quantization | ✅ | To Be Done | +| sllm | ✅ | To Be Done | +| sllm_store | ✅ | ✅ | +| vLLM Support | ✅ | ✅ | + ## News - **[03/25]** We're excited to share that we'll be giving a ServerlessLLM tutorial at the SESAME workshop, co-located with ASPLOS/EuroSys 2025 in Rotterdam, Netherlands, on March 31. [Slides](https://docs.google.com/presentation/d/1ioGCVpsg0x3oCxX19EiE820aMiY22X5MG6jgImZ1W18/edit?usp=sharing) | [More info](https://sesame25.github.io/) diff --git a/benchmarks/cann_benchmark_utils.py b/benchmarks/cann_benchmark_utils.py new file mode 100644 index 0000000..39d842b --- /dev/null +++ b/benchmarks/cann_benchmark_utils.py @@ -0,0 +1,130 @@ +# ---------------------------------------------------------------------------- # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # +# # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # +# # +# You may obtain a copy of the License at # +# # +# http://www.apache.org/licenses/LICENSE-2.0 # +# # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # +# ---------------------------------------------------------------------------- # +import gc +import os +import time + +import torch +from torch import nn +import torch_npu +from tqdm import tqdm +from transformers import AutoModelForCausalLM, AutoTokenizer + +from sllm_store.transformers import load_model + + +def _warmup_npu(): + num_npus = torch_npu.npu.device_count() + print(f"Warming up {num_npus} NPUs") + for i in tqdm(range(num_npus)): + torch.ones(1).to(f"npu:{i}") + torch_npu.npu.synchronize() + + +def _warmup_inference(): + print("Warming up inference") + model_name = "facebook/opt-6.7b" + model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.float16, device_map="auto" + ) + prompts = [ + "The quick brown fox jumps over the lazy dog.", + ] + tokenizer = AutoTokenizer.from_pretrained(model_name) + inputs = tokenizer(prompts, return_tensors="pt").to("npu") + with torch.no_grad(): + outputs = model.generate(**inputs, max_new_tokens=50) + del outputs, tokenizer, inputs, model + gc.collect() + torch_npu.npu.empty_cache() + + +def benchmark_inference(model: nn.Module, model_path: str): + # Inference + prompts = [ + "The quick brown fox jumps over the lazy dog.", + ] + tokenizer = AutoTokenizer.from_pretrained(model_path) + inputs = tokenizer(prompts, return_tensors="pt").to("npu") + with torch.no_grad(): + start_time = time.time() + outputs = model.generate(**inputs, max_new_tokens=50) + end_time = time.time() + output_text = tokenizer.decode(outputs[0], skip_special_tokens=True) + end_to_end_time = end_time - start_time + throughput = outputs.shape[1] / end_to_end_time + + del outputs, tokenizer, inputs + gc.collect() + torch_npu.npu.empty_cache() + + return end_to_end_time, throughput, output_text + + +def measure( + model_name: str, model_format: str, model_dir: str, loading_order: list +): + results = [] + print( + f"Measuring loading time for {model_format} model={model_name}, repeating {len(loading_order)} times" + ) + # loading_order = torch.randperm(num_replicas) + for model_idx in loading_order: + print(f"Loading {model_name}_{model_idx}") + model_record = {"model_name": f"{model_name}_{model_idx}"} + + # Model Loading + if model_format == "sllm": + model_path = os.path.join(model_dir, f"{model_name}_{model_idx}") + start_time = time.time() + model = load_model( + f"{model_name}_{model_idx}", + storage_path=model_dir, + device_map="auto", + torch_dtype=torch.float16, + ) + end_time = time.time() + elif model_format == "safetensors": + model_path = os.path.join( + model_dir, f"{model_name}_safetensors_{model_idx}" + ) + start_time = time.time() + model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch.float16, + device_map="auto", + ) + end_time = time.time() + model_record["loading_time"] = end_time - start_time + + # Inference + end_to_end_time, throughput, output_text = benchmark_inference( + model, model_path + ) + + model_record["end_to_end_time"] = end_to_end_time + model_record["throughput"] = throughput + model_record["output_text"] = output_text + + results.append(model_record) + + del model + gc.collect() + torch_npu.npu.empty_cache() + + return results \ No newline at end of file diff --git a/sllm_store/CMakeLists.txt b/sllm_store/CMakeLists.txt index 65eb0f0..9359d61 100644 --- a/sllm_store/CMakeLists.txt +++ b/sllm_store/CMakeLists.txt @@ -1,5 +1,27 @@ cmake_minimum_required(VERSION 3.20) +# Find torch directory - hard coded conda environment +set(Torch_DIR /root/miniconda3/envs/sllm-worker/lib/python3.10/site-packages/torch/share/cmake/Torch) +find_package(Torch REQUIRED) +include_directories(${TORCH_INCLUDE_DIRS}) +link_directories(${TORCH_LIBRARY_DIRS}) + +# Find torch-npu directory - hard coded conda environment +find_library(TORCH_NPU_LIB torch_npu + PATHS ${TORCH_INSTALL_PREFIX}/lib + /root/miniconda3/envs/sllm-worker/lib/python3.10/site-packages/torch_npu/lib # Adjust to your env + NO_DEFAULT_PATH) + +if(NOT TORCH_NPU_LIB) + message(FATAL_ERROR "libtorch_npu.so not found. Ensure torch_npu is installed.") +endif() + +if(TORCH_NPU_LIB) + get_filename_component(TORCH_NPU_LIB_DIR ${TORCH_NPU_LIB} DIRECTORY) + set(TORCH_NPU_DIR ${TORCH_NPU_LIB_DIR}/..) + include_directories(${TORCH_NPU_DIR}/include) +endif() + # Set the project name project(storage LANGUAGES CXX) @@ -7,6 +29,11 @@ project(storage LANGUAGES CXX) set(CMAKE_CXX_STANDARD 17) option(BUILD_SLLM_TESTS "Build tests" OFF) +# Build backend options +option(USE_CANN "Enable CANN support" OFF) +option(USE_CUDA "Enable CUDA support" OFF) +option(USE_ROCM "Enable ROCm support" OFF) +option(USE_CPU_ONLY "Build CPU-only version" OFF) # Disable other tests set(BUILD_TESTING OFF) @@ -26,24 +53,99 @@ set(FETCHCONTENT_QUIET OFF) FetchContent_MakeAvailable(glog) FetchContent_MakeAvailable(googletest) -find_package(CUDAToolkit QUIET) -find_package(HIP QUIET) - -# set CUDA or HIP -if (CUDAToolkit_FOUND) - message(STATUS "CUDA found") - set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.9;9.0") - set(SLLM_STORE_GPU_LANG "CUDA") - enable_language(CUDA) -elseif (HIP_FOUND) - message(STATUS "HIP found") - set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100") - set(SLLM_STORE_GPU_LANG "HIP") - enable_language(HIP) +# Initialize backend variables +set(BACKEND_FOUND FALSE) +set(SLLM_STORE_GPU_LANG "NONE") + +# Check for explicit backend selection +if(USE_CPU_ONLY) + message(STATUS "Building CPU-only version") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "CPU") +elseif(USE_CANN) + # Check for CANN toolkit + if(DEFINED ASCEND_TOOLKIT_HOME) + set(CANN_TOOLKIT_ROOT ${ASCEND_TOOLKIT_HOME}) + else() + set(CANN_TOOLKIT_ROOT $ENV{ASCEND_TOOLKIT_HOME}) + endif() + + if(NOT CANN_TOOLKIT_ROOT) + set(CANN_TOOLKIT_ROOT "/usr/local/Ascend/ascend-toolkit/8.0.0") + endif() + + if(EXISTS "${CANN_TOOLKIT_ROOT}/include/acl/acl.h") + message(STATUS "CANN found at ${CANN_TOOLKIT_ROOT}") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "CANN") + add_definitions(-DUSE_CANN) + include_directories(${CANN_TOOLKIT_ROOT}/include) + link_directories(${CANN_TOOLKIT_ROOT}/lib64) + else() + message(FATAL_ERROR "CANN toolkit not found at ${CANN_TOOLKIT_ROOT}") + endif() +elseif(USE_CUDA) + find_package(CUDAToolkit QUIET) + if(CUDAToolkit_FOUND) + message(STATUS "CUDA found") + set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.9;9.0") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "CUDA") + enable_language(CUDA) + else() + message(FATAL_ERROR "CUDA toolkit not found") + endif() +elseif(USE_ROCM) + find_package(HIP QUIET) + if(HIP_FOUND) + message(STATUS "HIP found") + set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "HIP") + enable_language(HIP) + else() + message(FATAL_ERROR "ROCm/HIP not found") + endif() else() - message(FATAL_ERROR "Neither CUDA nor HIP found") + # Auto-detection fallback (legacy behavior) + find_package(CUDAToolkit QUIET) + find_package(HIP QUIET) + + # Check for CANN toolkit + set(CANN_TOOLKIT_ROOT $ENV{ASCEND_TOOLKIT_HOME}) + if(NOT CANN_TOOLKIT_ROOT) + set(CANN_TOOLKIT_ROOT "/usr/local/Ascend/ascend-toolkit/latest") + endif() + + if(EXISTS "${CANN_TOOLKIT_ROOT}/include/acl/acl.h") + message(STATUS "CANN found at ${CANN_TOOLKIT_ROOT} (auto-detected)") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "CANN") + add_definitions(-DUSE_CANN) + include_directories(${CANN_TOOLKIT_ROOT}/include) + link_directories(${CANN_TOOLKIT_ROOT}/lib64) + elseif (CUDAToolkit_FOUND) + message(STATUS "CUDA found (auto-detected)") + set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.9;9.0") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "CUDA") + enable_language(CUDA) + elseif (HIP_FOUND) + message(STATUS "HIP found (auto-detected)") + set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "HIP") + enable_language(HIP) + else() + message(STATUS "No GPU backend found, building CPU-only version") + set(BACKEND_FOUND TRUE) + set(SLLM_STORE_GPU_LANG "CPU") + endif() endif() +if(NOT BACKEND_FOUND) + message(FATAL_ERROR "No backend found or enabled") +endif() # Adapted from https://github.com/vllm-project/vllm/blob/a1242324c99ff8b1e29981006dfb504da198c7c3/CMakeLists.txt include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake) @@ -52,7 +154,7 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake) # Supported python versions. These versions will be searched in order, the # first match will be selected. These should be kept in sync with setup.py. # -set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12") +set(PYTHON_SUPPORTED_VERSIONS "3.8" "3.9" "3.10" "3.11") # # Try to find python package with an executable that exactly matches @@ -66,47 +168,78 @@ else() " before running cmake configure.") endif() -# -# Update cmake's `CMAKE_PREFIX_PATH` with torch location. -# -append_cmake_prefix_path("torch" "torch.utils.cmake_prefix_path") - -# -# Import torch cmake configuration. -# Torch also imports CUDA (and partially HIP) languages with some customizations, -# so there is no need to do this explicitly with check_language/enable_language, -# etc. -# -find_package(Torch REQUIRED) - -# -# Normally `torch.utils.cpp_extension.CUDAExtension` would add -# `libtorch_python.so` for linking against an extension. Torch's cmake -# configuration does not include this library (presumably since the cmake -# config is used for standalone C++ binaries that link against torch). -# The `libtorch_python.so` library defines some of the glue code between -# torch/python via pybind and is required by SLLM_STORE extensions for this -# reason. So, add it by manually with `find_library` using torch's -# installed library path. -# -find_library(torch_python_LIBRARY torch_python PATHS - "${TORCH_INSTALL_PREFIX}/lib") - -# -# Override the GPU architectures detected by cmake/torch and filter them by -# the supported versions for the current language. -# The final set of arches is stored in `SLLM_STORE_GPU_ARCHES`. -# -override_gpu_arches(SLLM_STORE_GPU_ARCHES -${SLLM_STORE_GPU_LANG} -"${${SLLM_STORE_GPU_LANG}_SUPPORTED_ARCHS}") - -# -# Query torch for additional GPU compilation flags for the given -# `SLLM_STORE_GPU_LANG`. -# The final set of arches is stored in `SLLM_STORE_GPU_FLAGS`. -# -get_torch_gpu_compiler_flags(SLLM_STORE_GPU_FLAGS ${SLLM_STORE_GPU_LANG}) +# Only try to find and configure torch for GPU backends +if(NOT SLLM_STORE_GPU_LANG STREQUAL "CPU") + # + # Update cmake's `CMAKE_PREFIX_PATH` with torch location. + # + append_cmake_prefix_path("torch" "torch.utils.cmake_prefix_path") + + # + # Import torch cmake configuration. + # Torch also imports CUDA (and partially HIP) languages with some customizations, + # so there is no need to do this explicitly with check_language/enable_language, + # etc. + # + find_package(Torch REQUIRED) + + # + # Normally `torch.utils.cpp_extension.CUDAExtension` would add + # `libtorch_python.so` for linking against an extension. Torch's cmake + # configuration does not include this library (presumably since the cmake + # config is used for standalone C++ binaries that link against torch). + # The `libtorch_python.so` library defines some of the glue code between + # torch/python via pybind and is required by SLLM_STORE extensions for this + # reason. So, add it by manually with `find_library` using torch's + # installed library path. + # + find_library(torch_python_LIBRARY torch_python PATHS + "${TORCH_INSTALL_PREFIX}/lib") + + # + # Override the GPU architectures detected by cmake/torch and filter them by + # the supported versions for the current language. + # The final set of arches is stored in `SLLM_STORE_GPU_ARCHES`. + # + override_gpu_arches(SLLM_STORE_GPU_ARCHES + ${SLLM_STORE_GPU_LANG} + "${${SLLM_STORE_GPU_LANG}_SUPPORTED_ARCHS}") + + # + # Query torch for additional GPU compilation flags for the given + # `SLLM_STORE_GPU_LANG`. + # The final set of arches is stored in `SLLM_STORE_GPU_FLAGS`. + # + get_torch_gpu_compiler_flags(SLLM_STORE_GPU_FLAGS ${SLLM_STORE_GPU_LANG}) +else() + # For CPU-only builds, we still need basic torch for some functionality + # but skip GPU-specific configuration + find_package(Torch QUIET) + if(NOT Torch_FOUND) + message(WARNING "Torch not found, some functionality may be limited") + endif() + + # Find pybind11 for CPU-only builds + find_package(pybind11 QUIET) + if(NOT pybind11_FOUND) + # Try to find pybind11 via Python + execute_process( + COMMAND ${Python_EXECUTABLE} -c "import pybind11; print(pybind11.get_cmake_dir())" + OUTPUT_VARIABLE pybind11_DIR + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + if(pybind11_DIR) + find_package(pybind11 PATHS ${pybind11_DIR}) + endif() + endif() + + if(NOT pybind11_FOUND) + message(FATAL_ERROR "pybind11 is required for CPU-only builds") + endif() + + set(SLLM_STORE_GPU_ARCHES "") + set(SLLM_STORE_GPU_FLAGS "") +endif() # # Define extension targets @@ -122,6 +255,7 @@ get_torch_gpu_compiler_flags(SLLM_STORE_GPU_FLAGS ${SLLM_STORE_GPU_LANG}) set(SLLM_STORE_EXT_SRC "csrc/checkpoint/aligned_buffer.cpp" + "csrc/checkpoint/cann_ipc.cpp" "csrc/checkpoint/checkpoint.cpp" "csrc/checkpoint/checkpoint_py.cpp" "csrc/checkpoint/tensor_writer.cpp" @@ -140,17 +274,57 @@ set(SLLM_STORE_CXX_EXT_SRC "csrc/checkpoint/aligned_buffer.cpp" "csrc/checkpoint/checkpoint_py.cpp" "csrc/checkpoint/tensor_writer.cpp" + "csrc/checkpoint/cann_ipc.cpp" ) -define_gpu_extension_target( - _C - DESTINATION sllm_store - LANGUAGE ${SLLM_STORE_GPU_LANG} - SOURCES ${SLLM_STORE_EXT_SRC} - CXX_SRCS ${SLLM_STORE_CXX_EXT_SRC} - COMPILE_FLAGS ${SLLM_STORE_GPU_FLAGS} - ARCHITECTURES ${SLLM_STORE_GPU_ARCHES} - WITH_SOABI) +# Find CANN libraries if using CANN backend +if(SLLM_STORE_GPU_LANG STREQUAL "CANN") + find_library(ASCENDCL_LIB ascendcl PATHS ${CMAKE_LIBRARY_PATH} $ENV{ASCEND_TOOLKIT_HOME}/lib64 /usr/local/Ascend/ascend-toolkit/latest/lib64) + find_library(ACL_RETR_LIB acl_retr PATHS ${CMAKE_LIBRARY_PATH} $ENV{ASCEND_TOOLKIT_HOME}/lib64 /usr/local/Ascend/ascend-toolkit/latest/lib64) + find_library(ACL_CBLAS_LIB acl_cblas PATHS ${CMAKE_LIBRARY_PATH} $ENV{ASCEND_TOOLKIT_HOME}/lib64 /usr/local/Ascend/ascend-toolkit/latest/lib64) + find_library(ACL_DVPP_LIB acl_dvpp PATHS ${CMAKE_LIBRARY_PATH} $ENV{ASCEND_TOOLKIT_HOME}/lib64 /usr/local/Ascend/ascend-toolkit/latest/lib64) + find_library(RUNTIME_LIB runtime PATHS ${CMAKE_LIBRARY_PATH} $ENV{ASCEND_TOOLKIT_HOME}/lib64 /usr/local/Ascend/ascend-toolkit/latest/lib64) + + if(NOT ASCENDCL_LIB) + message(FATAL_ERROR "Could not find libascendcl.so. Please set ASCEND_TOOLKIT_HOME or add CANN lib path to CMAKE_LIBRARY_PATH") + endif() +endif() + +# Define libraries for _C extension based on GPU language +if(SLLM_STORE_GPU_LANG STREQUAL "CANN") + set(SLLM_STORE_C_LIBRARIES + glog::glog + ${ASCENDCL_LIB} + ${RUNTIME_LIB} + ${TORCH_NPU_LIB} + ) +else() + set(SLLM_STORE_C_LIBRARIES + glog::glog + ) +endif() + +# Only define GPU extension if not CPU-only +if(NOT SLLM_STORE_GPU_LANG STREQUAL "CPU") + define_gpu_extension_target( + _C + DESTINATION sllm_store + LANGUAGE ${SLLM_STORE_GPU_LANG} + SOURCES ${SLLM_STORE_EXT_SRC} + CXX_SRCS ${SLLM_STORE_CXX_EXT_SRC} + COMPILE_FLAGS ${SLLM_STORE_GPU_FLAGS} + ARCHITECTURES ${SLLM_STORE_GPU_ARCHES} + LIBRARIES ${SLLM_STORE_C_LIBRARIES} + WITH_SOABI) +else() + # For CPU-only build, create a simple library target + pybind11_add_module(_C ${SLLM_STORE_EXT_SRC}) + target_link_libraries(_C PRIVATE glog::glog) + set_target_properties(_C PROPERTIES + OUTPUT_NAME "_C" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/sllm_store" + ) +endif() # # _checkpoint_store extension @@ -163,18 +337,63 @@ find_package(Threads REQUIRED) # Define source files used in CHECKPOINT_STORE # -set(CHECKPOINT_STORE_SOURCES - "csrc/sllm_store/binary_utils.cpp" - "csrc/sllm_store/checkpoint_store.cpp" - "csrc/sllm_store/checkpoint_store_py.cpp" - "csrc/sllm_store/cuda_memory.cpp" - "csrc/sllm_store/cuda_memory_pool.cpp" - "csrc/sllm_store/gpu_replica.cpp" - "csrc/sllm_store/memory_state.cpp" - "csrc/sllm_store/model.cpp" - "csrc/sllm_store/pinned_memory.cpp" - "csrc/sllm_store/pinned_memory_pool.cpp" -) +if (SLLM_STORE_GPU_LANG STREQUAL "CANN") + set(CHECKPOINT_STORE_SOURCES + "csrc/sllm_store/binary_utils.cpp" + "csrc/sllm_store/checkpoint_store.cpp" + "csrc/sllm_store/checkpoint_store_py.cpp" + "csrc/sllm_store/cann_memory.cpp" + "csrc/sllm_store/cann_memory_pool.cpp" + "csrc/sllm_store/gpu_replica.cpp" + "csrc/sllm_store/memory_state.cpp" + "csrc/sllm_store/model.cpp" + "csrc/sllm_store/pinned_memory.cpp" + "csrc/sllm_store/pinned_memory_pool.cpp" + "csrc/checkpoint/cann_ipc.cpp" + ) + + set(CHECKPOINT_STORE_LIBRARIES + Threads::Threads + glog::glog + ${ASCENDCL_LIB} + ${ACL_RETR_LIB} + ${ACL_CBLAS_LIB} + ${ACL_DVPP_LIB} + ${RUNTIME_LIB} + ${TORCH_NPU_LIB} + ) +elseif(SLLM_STORE_GPU_LANG STREQUAL "CPU") + set(CHECKPOINT_STORE_SOURCES + "csrc/sllm_store/binary_utils.cpp" + "csrc/sllm_store/checkpoint_store.cpp" + "csrc/sllm_store/checkpoint_store_py.cpp" + "csrc/sllm_store/memory_state.cpp" + "csrc/sllm_store/model.cpp" + "csrc/sllm_store/pinned_memory.cpp" + "csrc/sllm_store/pinned_memory_pool.cpp" + ) + + set(CHECKPOINT_STORE_LIBRARIES + Threads::Threads + glog::glog) +else() + set(CHECKPOINT_STORE_SOURCES + "csrc/sllm_store/binary_utils.cpp" + "csrc/sllm_store/checkpoint_store.cpp" + "csrc/sllm_store/checkpoint_store_py.cpp" + "csrc/sllm_store/cuda_memory.cpp" + "csrc/sllm_store/cuda_memory_pool.cpp" + "csrc/sllm_store/gpu_replica.cpp" + "csrc/sllm_store/memory_state.cpp" + "csrc/sllm_store/model.cpp" + "csrc/sllm_store/pinned_memory.cpp" + "csrc/sllm_store/pinned_memory_pool.cpp" + ) + + set(CHECKPOINT_STORE_LIBRARIES + Threads::Threads + glog::glog) +endif() # # Define pure CXX files used in CHECKPOINT_STORE (files without CUDA code) @@ -185,22 +404,31 @@ set(CHECKPOINT_STORE_CXX_SOURCES "csrc/sllm_store/binary_utils.cpp" "csrc/sllm_store/memory_state.cpp" "csrc/sllm_store/pinned_memory.cpp" + "csrc/checkpoint/cann_ipc.cpp" ) -set(CHECKPOINT_STORE_LIBRARIES - Threads::Threads - glog::glog) - -define_gpu_extension_target( - _checkpoint_store - DESTINATION sllm_store - LANGUAGE ${SLLM_STORE_GPU_LANG} - SOURCES ${CHECKPOINT_STORE_SOURCES} - CXX_SRCS ${CHECKPOINT_STORE_CXX_SOURCES} - COMPILE_FLAGS ${SLLM_STORE_GPU_FLAGS} - ARCHITECTURES ${SLLM_STORE_GPU_ARCHES} - LIBRARIES ${CHECKPOINT_STORE_LIBRARIES} - WITH_SOABI) +# Define checkpoint store extension +if(NOT SLLM_STORE_GPU_LANG STREQUAL "CPU") + define_gpu_extension_target( + _checkpoint_store + DESTINATION sllm_store + LANGUAGE ${SLLM_STORE_GPU_LANG} + SOURCES ${CHECKPOINT_STORE_SOURCES} + CXX_SRCS ${CHECKPOINT_STORE_CXX_SOURCES} + COMPILE_FLAGS ${SLLM_STORE_GPU_FLAGS} + ARCHITECTURES ${SLLM_STORE_GPU_ARCHES} + LIBRARIES ${CHECKPOINT_STORE_LIBRARIES} + WITH_SOABI) + target_link_directories(_checkpoint_store PRIVATE ascendcl) +else() + # For CPU-only build, create a simple library target + pybind11_add_module(_checkpoint_store ${CHECKPOINT_STORE_SOURCES}) + target_link_libraries(_checkpoint_store PRIVATE ${CHECKPOINT_STORE_LIBRARIES}) + set_target_properties(_checkpoint_store PROPERTIES + OUTPUT_NAME "_checkpoint_store" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/sllm_store" + ) +endif() # Enable testing if the option is ON if(BUILD_SLLM_TESTS) diff --git a/sllm_store/cmake/utils.cmake b/sllm_store/cmake/utils.cmake index 2fac3d2..0ed3f95 100644 --- a/sllm_store/cmake/utils.cmake +++ b/sllm_store/cmake/utils.cmake @@ -3,7 +3,7 @@ # Attempt to find the python package that uses the same python executable as # `EXECUTABLE` and is one of the `SUPPORTED_VERSIONS`. # -macro (find_python_from_executable EXECUTABLE SUPPORTED_VERSIONS) +macro(find_python_from_executable EXECUTABLE SUPPORTED_VERSIONS) file(REAL_PATH ${EXECUTABLE} EXECUTABLE) set(Python_EXECUTABLE ${EXECUTABLE}) find_package(Python COMPONENTS Interpreter Development.Module) @@ -21,11 +21,11 @@ macro (find_python_from_executable EXECUTABLE SUPPORTED_VERSIONS) endmacro() # -# Run `EXPR` in python. The standard output of python is stored in `OUT` and -# has trailing whitespace stripped. If an error is encountered when running +# Run `EXPR` in python. The standard output of python is stored in `OUT` and +# has trailing whitespace stripped. If an error is encountered when running # python, a fatal message `ERR_MSG` is issued. # -function (run_python OUT EXPR ERR_MSG) +function(run_python OUT EXPR ERR_MSG) execute_process( COMMAND "${Python_EXECUTABLE}" "-c" "${EXPR}" @@ -42,7 +42,7 @@ endfunction() # Run `EXPR` in python after importing `PKG`. Use the result of this to extend # `CMAKE_PREFIX_PATH` so the torch cmake configuration can be imported. -macro (append_cmake_prefix_path PKG EXPR) +macro(append_cmake_prefix_path PKG EXPR) run_python(_PREFIX_PATH "import ${PKG}; print(${EXPR})" "Failed to locate ${PKG} path") list(APPEND CMAKE_PREFIX_PATH ${_PREFIX_PATH}) @@ -56,7 +56,7 @@ endmacro() # CXX_SRCS - List of C++ source files that should not be hipified. (e.g., no CUDA code) # CXX_SRCS are removed from the list of ORIG_SRCS, in order to fix issue #206. # -function (hipify_sources_target OUT_SRCS NAME ORIG_SRCS CXX_SRCS) +function(hipify_sources_target OUT_SRCS NAME ORIG_SRCS CXX_SRCS) # # Split into C++ and non-C++ (i.e. CUDA) sources. # @@ -73,12 +73,12 @@ function (hipify_sources_target OUT_SRCS NAME ORIG_SRCS CXX_SRCS) # `CMAKE_CURRENT_BINARY_DIR` directory rather than the original csrc dir. # set(HIP_SRCS) - foreach (SRC ${SRCS}) + foreach(SRC ${SRCS}) set(ORIGINAL_SRC ${SRC}) - string(REGEX REPLACE "\.cu$" "\.hip" SRC ${SRC}) + string(REGEX REPLACE "\\.cu$" "\\.hip" SRC ${SRC}) string(REGEX REPLACE "cuda" "hip" SRC ${SRC}) if(${SRC} STREQUAL ${ORIGINAL_SRC}) - string(REGEX REPLACE "\.cpp$" "_hip\.cpp" SRC ${SRC}) + string(REGEX REPLACE "\\.cpp$" "_hip\\.cpp" SRC ${SRC}) endif() list(APPEND HIP_SRCS "${CMAKE_CURRENT_BINARY_DIR}/${SRC}") endforeach() @@ -99,8 +99,8 @@ endfunction() # # Get additional GPU compiler flags from torch. # -function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG) - if (${GPU_LANG} STREQUAL "CUDA") +function(get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG) + if(${GPU_LANG} STREQUAL "CUDA") # # Get common NVCC flags from torch. # @@ -108,10 +108,10 @@ function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG) "from torch.utils.cpp_extension import COMMON_NVCC_FLAGS; print(';'.join(COMMON_NVCC_FLAGS))" "Failed to determine torch nvcc compiler flags") - if (CUDA_VERSION VERSION_GREATER_EQUAL 11.8) + if(CUDA_VERSION VERSION_GREATER_EQUAL 11.8) list(APPEND GPU_FLAGS "-DENABLE_FP8") endif() - if (CUDA_VERSION VERSION_GREATER_EQUAL 12.0) + if(CUDA_VERSION VERSION_GREATER_EQUAL 12.0) list(REMOVE_ITEM GPU_FLAGS "-D__CUDA_NO_HALF_OPERATORS__" "-D__CUDA_NO_HALF_CONVERSIONS__" @@ -140,7 +140,7 @@ endfunction() # Macro for converting a `gencode` version number to a cmake version number. macro(string_to_ver OUT_VER IN_STR) - string(REGEX REPLACE "\([0-9]+\)\([0-9]\)" "\\1.\\2" ${OUT_VER} ${IN_STR}) + string(REGEX REPLACE "([0-9]+)([0-9])" "\\1.\\2" ${OUT_VER} ${IN_STR}) endmacro() # @@ -154,7 +154,7 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) set(_GPU_SUPPORTED_ARCHES_LIST ${GPU_SUPPORTED_ARCHES} ${ARGN}) message(STATUS "${GPU_LANG} supported arches: ${_GPU_SUPPORTED_ARCHES_LIST}") - if (${GPU_LANG} STREQUAL "HIP") + if(${GPU_LANG} STREQUAL "HIP") # # `GPU_ARCHES` controls the `--offload-arch` flags. # `CMAKE_HIP_ARCHITECTURES` is set up by torch and can be controlled @@ -166,8 +166,8 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) # set the module architecture flags. # set(${GPU_ARCHES}) - foreach (_ARCH ${CMAKE_HIP_ARCHITECTURES}) - if (_ARCH IN_LIST _GPU_SUPPORTED_ARCHES_LIST) + foreach(_ARCH ${CMAKE_HIP_ARCHITECTURES}) + if(_ARCH IN_LIST _GPU_SUPPORTED_ARCHES_LIST) list(APPEND ${GPU_ARCHES} ${_ARCH}) endif() endforeach() @@ -183,13 +183,13 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) # Setup/process CUDA arch flags. # # The torch cmake setup hardcodes the detected architecture flags in - # `CMAKE_CUDA_FLAGS`. Since `CMAKE_CUDA_FLAGS` is a "global" variable, it + # `CMAKE_CUDA_FLAGS`. Since `CMAKE_CUDA_FLAGS` is a "global" variable, it # can't modified on a per-target basis, e.g. for the `punica` extension. # So, all the `-gencode` flags need to be extracted and removed from # `CMAKE_CUDA_FLAGS` for processing so they can be passed by another method. # Since it's not possible to use `target_compiler_options` for adding target # specific `-gencode` arguments, the target's `CUDA_ARCHITECTURES` property - # must be used instead. This requires repackaging the architecture flags + # must be used instead. This requires repackaging the architecture flags # into a format that cmake expects for `CUDA_ARCHITECTURES`. # # This is a bit fragile in that it depends on torch using `-gencode` as opposed @@ -211,7 +211,7 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) # If this error is triggered, it might mean that torch has changed how it sets # up nvcc architecture code generation flags. - if (NOT _CUDA_ARCH_FLAGS) + if(NOT _CUDA_ARCH_FLAGS) message(FATAL_ERROR "Could not find any architecture related code generation flags in " "CMAKE_CUDA_FLAGS. (${CMAKE_CUDA_FLAGS})") @@ -230,34 +230,34 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) # Note: if a regex matches then `CMAKE_MATCH_1` holds the binding # for that match. - string(REGEX MATCH "arch=compute_\([0-9]+a?\)" _COMPUTE ${_ARCH}) - if (_COMPUTE) + string(REGEX MATCH "arch=compute_([0-9]+a?)" _COMPUTE ${_ARCH}) + if(_COMPUTE) set(_COMPUTE ${CMAKE_MATCH_1}) endif() - string(REGEX MATCH "code=sm_\([0-9]+a?\)" _SM ${_ARCH}) - if (_SM) + string(REGEX MATCH "code=sm_([0-9]+a?)" _SM ${_ARCH}) + if(_SM) set(_SM ${CMAKE_MATCH_1}) endif() - string(REGEX MATCH "code=compute_\([0-9]+a?\)" _CODE ${_ARCH}) - if (_CODE) + string(REGEX MATCH "code=compute_([0-9]+a?)" _CODE ${_ARCH}) + if(_CODE) set(_CODE ${CMAKE_MATCH_1}) endif() # Make sure the virtual architecture can be matched. - if (NOT _COMPUTE) + if(NOT _COMPUTE) message(FATAL_ERROR "Could not determine virtual architecture from: ${_ARCH}.") endif() # One of sm_ or compute_ must exist. - if ((NOT _SM) AND (NOT _CODE)) + if((NOT _SM) AND (NOT _CODE)) message(FATAL_ERROR "Could not determine a codegen architecture from: ${_ARCH}.") endif() - if (_SM) + if(_SM) # -real suffix let CMake to only generate elf code for the kernels. # we want this, otherwise the added ptx (default) will increase binary size. set(_VIRT "-real") @@ -270,7 +270,7 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES) # Check if the current version is in the supported arch list. string_to_ver(_CODE_VER ${_CODE_ARCH}) - if (NOT _CODE_VER IN_LIST _GPU_SUPPORTED_ARCHES_LIST) + if(NOT _CODE_VER IN_LIST _GPU_SUPPORTED_ARCHES_LIST) message(STATUS "discarding unsupported CUDA arch ${_VER}.") continue() endif() @@ -286,30 +286,30 @@ endmacro() # Define a target named `GPU_MOD_NAME` for a single extension. The # arguments are: # -# DESTINATION - Module destination directory. -# LANGUAGE - The GPU language for this module, e.g CUDA, HIP, -# etc. -# SOURCES - List of source files relative to CMakeLists.txt -# directory. +# DESTINATION - Module destination directory. +# LANGUAGE - The GPU language for this module, e.g CUDA, HIP, +# etc. +# SOURCES - List of source files relative to CMakeLists.txt +# directory. # # Optional arguments: # -# ARCHITECTURES - A list of target GPU architectures in cmake -# format. -# Refer `CMAKE_CUDA_ARCHITECTURES` documentation -# and `CMAKE_HIP_ARCHITECTURES` for more info. -# ARCHITECTURES will use cmake's defaults if -# not provided. -# COMPILE_FLAGS - Extra compiler flags passed to NVCC/hip. +# ARCHITECTURES - A list of target GPU architectures in cmake +# format. +# Refer `CMAKE_CUDA_ARCHITECTURES` documentation +# and `CMAKE_HIP_ARCHITECTURES` for more info. +# ARCHITECTURES will use cmake's defaults if +# not provided. +# COMPILE_FLAGS - Extra compiler flags passed to NVCC/hip. # INCLUDE_DIRECTORIES - Extra include directories. -# LIBRARIES - Extra link libraries. -# WITH_SOABI - Generate library with python SOABI suffix name. -# CXX_SRCS - List of C++ sources files to not be hipified. -# Usually means they shouldn't contain CUDA code. +# LIBRARIES - Extra link libraries. +# WITH_SOABI - Generate library with python SOABI suffix name. +# CXX_SRCS - List of C++ sources files to not be hipified. +# Usually means they shouldn't contain CUDA code. # # Note: optimization level/debug info is set via cmake build type. # -function (define_gpu_extension_target GPU_MOD_NAME) +function(define_gpu_extension_target GPU_MOD_NAME) cmake_parse_arguments(PARSE_ARGV 1 GPU "WITH_SOABI" @@ -318,11 +318,11 @@ function (define_gpu_extension_target GPU_MOD_NAME) ) # Add hipify preprocessing step when building with HIP/ROCm. - if (GPU_LANGUAGE STREQUAL "HIP") + if(GPU_LANGUAGE STREQUAL "HIP") hipify_sources_target(GPU_SOURCES ${GPU_MOD_NAME} "${GPU_SOURCES}" "${GPU_CXX_SRCS}") endif() - if (GPU_WITH_SOABI) + if(GPU_WITH_SOABI) set(GPU_WITH_SOABI WITH_SOABI) else() set(GPU_WITH_SOABI) @@ -330,12 +330,12 @@ function (define_gpu_extension_target GPU_MOD_NAME) Python_add_library(${GPU_MOD_NAME} MODULE "${GPU_SOURCES}" ${GPU_WITH_SOABI}) - if (GPU_LANGUAGE STREQUAL "HIP") + if(GPU_LANGUAGE STREQUAL "HIP") # Make this target dependent on the hipify preprocessor step. add_dependencies(${GPU_MOD_NAME} hipify${GPU_MOD_NAME}) endif() - if (GPU_ARCHITECTURES) + if(GPU_ARCHITECTURES) set_target_properties(${GPU_MOD_NAME} PROPERTIES ${GPU_LANGUAGE}_ARCHITECTURES "${GPU_ARCHITECTURES}") endif() @@ -356,12 +356,26 @@ function (define_gpu_extension_target GPU_MOD_NAME) # Don't use `TORCH_LIBRARIES` for CUDA since it pulls in a bunch of # dependencies that are not necessary and may not be installed. - if (GPU_LANGUAGE STREQUAL "CUDA") + if(GPU_LANGUAGE STREQUAL "CUDA") target_link_libraries(${GPU_MOD_NAME} PRIVATE ${CUDA_CUDA_LIB} ${CUDA_LIBRARIES}) + elseif(GPU_LANGUAGE STREQUAL "CANN") + # Set RPATH for CANN libraries + if(DEFINED ENV{ASCEND_TOOLKIT_HOME}) + set_target_properties(${GPU_MOD_NAME} PROPERTIES + BUILD_RPATH "$ENV{ASCEND_TOOLKIT_HOME}/lib64" + INSTALL_RPATH "$ENV{ASCEND_TOOLKIT_HOME}/lib64" + INSTALL_RPATH_USE_LINK_PATH TRUE) + else() + set_target_properties(${GPU_MOD_NAME} PROPERTIES + BUILD_RPATH "/usr/local/Ascend/ascend-toolkit/latest/lib64" + INSTALL_RPATH "/usr/local/Ascend/ascend-toolkit/latest/lib64" + INSTALL_RPATH_USE_LINK_PATH TRUE) + endif() + target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES} ${GPU_LIBRARIES} ) else() target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES}) endif() install(TARGETS ${GPU_MOD_NAME} LIBRARY DESTINATION ${GPU_DESTINATION}) -endfunction() +endfunction() \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/cann_ipc.cpp b/sllm_store/csrc/checkpoint/cann_ipc.cpp new file mode 100644 index 0000000..0ae6b09 --- /dev/null +++ b/sllm_store/csrc/checkpoint/cann_ipc.cpp @@ -0,0 +1,563 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// Licensed under the Apache License, Version 2.0 +// ---------------------------------------------------------------------------- +#ifdef USE_CANN +#include "cann_ipc.h" // Includes the declarations of helper functions and CannIpcManager + +#include // For errno +#include // For open, O_RDWR, O_CREAT etc. +#include +#include // For std::ifstream, std::ofstream +#include +#include +#include +#include + +// System includes for FIFO operations - MUST be in the .cpp where functions are defined +#include // For mkfifo +#include // For unlink (optional, for cleanup), though not used in helper fn, often used with FIFOs + +// * FIFO mechanism is only used for testing purpose, in actual development the pid is using proto to transfer +// Helper function to open a FIFO robustly +int open_fifo_fd(const std::string& fifo_path, int flags) +{ + // Create the FIFO if it doesn't exist. Permissions 0666 allow read/write for all. + if (mkfifo(fifo_path.c_str(), 0666) == -1 && errno != EEXIST) + { + std::cerr << "[ERROR][IPC_UTIL] Failed to create FIFO: " << fifo_path + << ", errno: " << errno << " : " << strerror(errno) << std::endl; + return -1; + } + + // We open as file descriptor first to handle blocking correctly. + // A simple std::fstream will block. + int fd = open(fifo_path.c_str(), flags, 0666); + if (fd == -1) + { + std::cerr << "[ERROR][IPC_UTIL] Failed to open FIFO: " << fifo_path + << ", errno: " << errno << " : " << strerror(errno) << std::endl; + // Consider retrying for transient errors like EINTR or EAGAIN (if O_NONBLOCK were used) + return -1; + } + return fd; +} + +// Helper functions for inter-process communication (using named pipes) +// These are the definitions. + +void send_int32(int32_t value, const std::string& fifo_path) +{ + int fd = open_fifo_fd(fifo_path, O_WRONLY | O_SYNC); // Open for writing, with sync + if (fd == -1) + { + return; + } + + if (write(fd, &value, sizeof(value)) == -1) + { + std::cerr << "[ERROR][IPC_UTIL] Failed to write int32_t to FIFO: " << fifo_path + << ", errno: " << errno << " : " << strerror(errno) << std::endl; + } + close(fd); + std::cout << "[INFO][IPC_UTIL] Sent int32_t value " << value << " to " + << fifo_path << std::endl; +} + +int32_t receive_int32(const std::string& fifo_path) +{ + int fd = open_fifo_fd(fifo_path, O_RDONLY | O_SYNC); // Open for reading, with sync + if (fd == -1) + { + return -1; + } + + int32_t received_value = -1; + ssize_t bytes_read = read(fd, &received_value, sizeof(received_value)); + if (bytes_read == -1) + { + std::cerr << "[ERROR][IPC_UTIL] Failed to read from FIFO: " << fifo_path + << ", errno: " << errno << " : " << strerror(errno) << std::endl; + received_value = -1; + } + else if (bytes_read == 0) + { + std::cerr << "[WARNING][IPC_UTIL] Reached EOF on FIFO: " << fifo_path + << " (no data or writer closed)" << std::endl; + received_value = -1; // Or handle as specific empty state + } + close(fd); + std::cout << "[INFO][IPC_UTIL] Received int32_t value " << received_value + << " from " << fifo_path << std::endl; + return received_value; +} + +void send_handle_info(uint64_t handle_id, size_t size, + const std::string& fifo_path) +{ + int fd = open_fifo_fd(fifo_path, O_WRONLY | O_SYNC); + if (fd == -1) + { + return; + } + + if (write(fd, &handle_id, sizeof(handle_id)) == -1 || + write(fd, &size, sizeof(size)) == -1) + { + std::cerr << "[ERROR][IPC_UTIL] Failed to write handle info to FIFO: " + << fifo_path << ", errno: " << errno << " : " << strerror(errno) + << std::endl; + } + close(fd); + std::cout << "[INFO][IPC_UTIL] Sent handle_id " << std::hex << handle_id + << ", size " << std::dec << size << " to " << fifo_path + << std::endl; +} + +std::pair receive_handle_info( + const std::string& fifo_path) +{ + int fd = open_fifo_fd(fifo_path, O_RDONLY | O_SYNC); + if (fd == -1) + { + return {0, 0}; + } + + uint64_t received_handle_id = 0; + size_t received_size = 0; + ssize_t bytes_read_handle = + read(fd, &received_handle_id, sizeof(received_handle_id)); + ssize_t bytes_read_size = read(fd, &received_size, sizeof(received_size)); + + if (bytes_read_handle == -1 || bytes_read_size == -1) + { + std::cerr << "[ERROR][IPC_UTIL] Failed to read handle info from FIFO: " + << fifo_path << ", errno: " << errno << " : " << strerror(errno) + << std::endl; + received_handle_id = 0; + received_size = 0; + } + else if (bytes_read_handle == 0 || bytes_read_size == 0) + { + std::cerr << "[WARNING][IPC_UTIL] Reached EOF on FIFO for handle info: " + << fifo_path << " (incomplete data or writer closed)" + << std::endl; + received_handle_id = 0; + received_size = 0; + } + close(fd); + std::cout << "[INFO][IPC_UTIL] Received handle_id " << std::hex + << received_handle_id << ", size " << std::dec << received_size + << " from " << fifo_path << std::endl; + return {received_handle_id, received_size}; +} + +// Real CANN IPC implementation using native CANN APIs + +aclError CannIpcManager::createIpcHandle(void** device_ptr, size_t size, + int device_id, uint64_t* handle_id, + int32_t process_b_pid) +{ + std::lock_guard lock(mutex_); + + if (device_ptr == nullptr || *device_ptr == nullptr) + { + std::cerr << "[ERROR] Device pointer is null, cannot create IPC handle" + << std::endl; + return ACL_ERROR_INVALID_PARAM; + } + + void* original_ptr = *device_ptr; // Store the original pointer + + // Set device context + aclError ret = aclrtSetDevice(device_id); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to set device " << device_id << ": " << ret + << std::endl; + return ret; + } + + // Align size to 2 MB (huge page size) + const size_t granularity = 2 * 1024 * 1024; // 2 MB + size_t aligned_size = ((size + granularity - 1) / granularity) * granularity; + + // First reserve virtual address space + void* vir_ptr = nullptr; + ret = aclrtReserveMemAddress(&vir_ptr, aligned_size, 0, nullptr, 0); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to reserve memory address: " << ret + << std::endl; + return ret; + } + + // Configure physical memory properties properly + aclrtPhysicalMemProp prop = {}; + prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; + prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; + prop.memAttr = ACL_HBM_MEM_HUGE; // Use 2MB pages + prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = static_cast(device_id); + prop.reserve = 0; + + // Allocate physical memory for IPC with proper properties + aclrtDrvMemHandle physical_handle; + ret = aclrtMallocPhysical(&physical_handle, aligned_size, &prop, 0); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to allocate physical memory for IPC: " << ret + << std::endl; + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + // Map the physical memory to the reserved virtual address + ret = aclrtMapMem(vir_ptr, aligned_size, 0, physical_handle, 0); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to map physical memory: " << ret << std::endl; + aclrtFreePhysical(physical_handle); + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + // Export physical memory handle for sharing + uint64_t shareable_handle; + ret = aclrtMemExportToShareableHandle(physical_handle, ACL_MEM_HANDLE_TYPE_NONE, + 0, &shareable_handle); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to export shareable handle: " << ret + << std::endl; + aclrtUnmapMem(vir_ptr); + aclrtFreePhysical(physical_handle); + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + // --- IPC LOGIC FOR PID EXCHANGE (Process A - Sender of Handle, Receiver of PID) --- + + // // 1. Send shareable_handle and its aligned_size to Process B + // std::cout << "[INFO] Process A sending handle info to Process B..." << std::endl; + // send_handle_info(shareable_handle, aligned_size, FIFO_PATH_A_TO_B); + + // // 2. Wait to receive Process B's PID + // std::cout << "[INFO] Process A waiting for Process B's PID on " << FIFO_PATH_B_TO_A << "..." << std::endl; + // int32_t process_b_pid = receive_int32(FIFO_PATH_B_TO_A); + + if (process_b_pid == -1) + { + std::cerr << "[ERROR] Failed to receive Process B's PID. Aborting IPC setup." + << std::endl; + // Perform cleanup for the created resources before returning + aclrtUnmapMem(vir_ptr); + aclrtFreePhysical(physical_handle); + aclrtReleaseMemAddress(vir_ptr); + return ACL_ERROR_INVALID_PARAM; // Or a more appropriate error code + } + + std::cout << "[INFO] Process A received Process B's PID: " << process_b_pid + << std::endl; + + // 3. Allow Process B (identified by process_b_pid) to import the handle + // aclrtMemSetPidToShareableHandle expects an array of PIDs. + int32_t pids_to_whitelist[] = {process_b_pid}; + ret = aclrtMemSetPidToShareableHandle(shareable_handle, pids_to_whitelist, 1); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to set PID whitelist for shareable handle: " + << ret << std::endl; + // Cleanup on failure + aclrtUnmapMem(vir_ptr); + aclrtFreePhysical(physical_handle); + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + std::cout << "[INFO] Successfully set pid to whitelist: " << process_b_pid + << std::endl; + + // Use shareable handle as the handle ID + *handle_id = shareable_handle; + + // Store memory info for cleanup (store the mapped pointer, not original) + MemoryInfo mem_info; + mem_info.ptr = vir_ptr; // Store original device_ptr for lookup + mem_info.size = aligned_size; + mem_info.device_id = device_id; + mem_info.physical_handle = physical_handle; + mem_info.shareable_handle = shareable_handle; + mem_info.vir_ptr = vir_ptr; // Store virtual address for release + mem_info.ipc_device_ptr = vir_ptr; // Store the mapped IPC pointer (vir_ptr) + + handle_to_memory_[*handle_id] = mem_info; + // ptr_to_handle_[original_ptr] = *handle_id; // Keep original mapping for lookup + ptr_to_handle_[vir_ptr] = *handle_id; // Also map the shared VA for cleanup + + // Return the shared virtual address to the caller + *device_ptr = vir_ptr; + + std::cout << "[INFO] Created CANN IPC handle " << std::hex << *handle_id + << " for device ptr " << original_ptr << " -> shared VA " << vir_ptr << " on device " << device_id + << ", size: " << aligned_size << std::endl; + + return ACL_ERROR_NONE; +} + +aclError CannIpcManager::openIpcHandle(uint64_t handle_id, int device_id, + void** device_ptr) +{ + std::lock_guard lock(mutex_); + + std::cout << "[INFO] Opening CANN IPC handle " << std::hex << handle_id + << " on device " << device_id << std::endl; + + // Get size from pending imports (this map is populated by stringToHandle when Process B receives the handle string) + auto pending_it = pending_imports_.find(handle_id); + if (pending_it == pending_imports_.end()) + { + std::cerr << "[ERROR] Size information not found for handle " << std::hex + << handle_id << ". Make sure stringToHandle was called." + << std::endl; + return ACL_ERROR_INVALID_PARAM; + } + size_t aligned_size = pending_it->second; // Use aligned_size as stored by stringToHandle + pending_imports_.erase(pending_it); // Clear after use + + // Set device context + aclError ret = aclrtSetDevice(device_id); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to set device " << device_id << ": " << ret + << std::endl; + return ret; + } + + // Reserve virtual address space first + void* vir_ptr = nullptr; + ret = aclrtReserveMemAddress(&vir_ptr, aligned_size, 0, nullptr, 0); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to reserve memory address: " << ret + << std::endl; + return ret; + } + + // // Get current process id (Process B's PID) + // int32_t pid_value; + // ret = aclrtDeviceGetBareTgid(&pid_value); + // if (ret != ACL_ERROR_NONE) { + // std::cerr << "[ERROR] Failed to get current process id: " << ret << std::endl; + // aclrtReleaseMemAddress(vir_ptr); + // return ret; + // } + + // --- IPC LOGIC FOR PID EXCHANGE (Process B - Receiver of Handle, Sender of PID) --- + + // ! do this in grpc server, beforehand + // // Send Process B's PID back to Process A + // std::cout << "[INFO] Process B sending its PID (" << pid_value << ") to Process A on " << FIFO_PATH_B_TO_A << "..." << std::endl; + // send_int32(pid_value, FIFO_PATH_B_TO_A); + // Process A should now have received this PID and called aclrtMemSetPidToShareableHandle. + + // Import the shareable handle + aclrtDrvMemHandle imported_handle; + ret = aclrtMemImportFromShareableHandle(handle_id, device_id, &imported_handle); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to import shareable handle " << std::hex + << handle_id << ": " << ret << std::endl; + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + // Map the imported physical memory to reserved virtual address + ret = aclrtMapMem(vir_ptr, aligned_size, 0, imported_handle, 0); + if (ret != ACL_ERROR_NONE) + { + std::cerr << "[ERROR] Failed to map imported memory: " << ret << std::endl; + aclrtFreePhysical(imported_handle); + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + // MOD: Sync after map to ensure data visibility on client + ret = aclrtSynchronizeDevice(); + if (ret != ACL_ERROR_NONE) { + std::cerr << "[ERROR] Failed to synchronize device after IPC map: " << ret << std::endl; + // Cleanup + aclrtUnmapMem(vir_ptr); + aclrtFreePhysical(imported_handle); + aclrtReleaseMemAddress(vir_ptr); + return ret; + } + + // Return the mapped device pointer + *device_ptr = vir_ptr; + + // Store the mapping for cleanup + MemoryInfo mem_info; + mem_info.ptr = vir_ptr; // Store the mapped IPC pointer + mem_info.size = aligned_size; // Store the aligned size for consistency + mem_info.device_id = device_id; + mem_info.physical_handle = imported_handle; + mem_info.shareable_handle = handle_id; + mem_info.vir_ptr = vir_ptr; + mem_info.ipc_device_ptr = vir_ptr; // This is the actual mapped pointer + + handle_to_memory_[handle_id] = mem_info; + ptr_to_handle_[vir_ptr] = handle_id; // Map the *imported* pointer to its handle + + std::cout << "[INFO] Successfully opened CANN IPC handle " << std::hex + << handle_id << " -> device ptr " << *device_ptr << " on device " + << device_id << std::endl; + + return ACL_ERROR_NONE; +} + +aclError CannIpcManager::closeIpcHandle(void* device_ptr) +{ + std::lock_guard lock(mutex_); + + auto it = ptr_to_handle_.find(device_ptr); + if (it == ptr_to_handle_.end()) + { + std::cerr << "[WARNING] Device pointer " << device_ptr + << " not found in IPC registry" << std::endl; + return ACL_ERROR_INVALID_PARAM; + } + + uint64_t handle_id = it->second; + auto mem_it = handle_to_memory_.find(handle_id); + if (mem_it == handle_to_memory_.end()) + { + std::cerr << "[WARNING] Handle " << std::hex << handle_id + << " not found in memory map" << std::endl; + return ACL_ERROR_INVALID_PARAM; + } + + const MemoryInfo& mem_info = mem_it->second; + + // Unmap the memory first + if (mem_info.vir_ptr != nullptr) + { // Use vir_ptr for unmapping + aclrtUnmapMem(mem_info.vir_ptr); + } + + // Free the physical memory handle + if (mem_info.physical_handle) + { + aclrtFreePhysical(mem_info.physical_handle); + } + + // Release the virtual address space + if (mem_info.vir_ptr != nullptr) + { + aclrtReleaseMemAddress(mem_info.vir_ptr); + } + + // std::cout << "[INFO] Closed CANN IPC handle " << std::hex << handle_id + // << " for device ptr " << device_ptr << std::endl; + + handle_to_memory_.erase(handle_id); + ptr_to_handle_.erase(it); + + return ACL_ERROR_NONE; +} + +std::string CannIpcManager::handleToString(uint64_t handle_id) +{ + std::lock_guard lock(mutex_); + + auto it = handle_to_memory_.find(handle_id); + if (it == handle_to_memory_.end()) + { + std::cerr << "[ERROR] Invalid handle ID: " << std::hex << handle_id + << std::endl; + return ""; + } + + const MemoryInfo& mem_info = it->second; + std::ostringstream oss; + // Store the shareable handle and the *aligned* size for consistent import + oss << std::hex << mem_info.shareable_handle << ":" << std::dec + << mem_info.size; + return oss.str(); +} + +uint64_t CannIpcManager::stringToHandle(const std::string& handle_str) +{ + size_t colon_pos = handle_str.find(':'); + if (colon_pos == std::string::npos) + { + std::cerr << "[ERROR] Invalid handle format - missing size: " << handle_str + << std::endl; + return 0; + } + + try + { + // Parse shareable handle (hex) + uint64_t shareable_handle = + std::stoull(handle_str.substr(0, colon_pos), nullptr, 16); + + // Parse buffer size (decimal) - this should be the ALIGNED size from the sender + size_t size = std::stoul(handle_str.substr(colon_pos + 1), nullptr, 10); + + // Store size for import operation + // This is crucial for openIpcHandle to know the correct aligned_size for aclrtReserveMemAddress + pending_imports_[shareable_handle] = size; + + return shareable_handle; + } + catch (const std::exception& e) + { + std::cerr << "[ERROR] Failed to parse handle string '" << handle_str + << "': " << e.what() << std::endl; + return 0; + } +} + +// Convenience functions that mimic CUDA IPC API exactly +aclError cannIpcGetMemHandle(std::string* handle_str, void** device_ptr, + size_t size, int device_id, + int32_t target_process_id) +{ + CannIpcManager& manager = CannIpcManager::getInstance(); + uint64_t handle_id; + + aclError ret = manager.createIpcHandle(device_ptr, size, device_id, + &handle_id, target_process_id); + if (ret != ACL_ERROR_NONE) + { + return ret; + } + + *handle_str = manager.handleToString(handle_id); + return ACL_ERROR_NONE; +} + +aclError cannIpcOpenMemHandle(void** device_ptr, const std::string& handle_str, + int device_id) +{ + CannIpcManager& manager = CannIpcManager::getInstance(); + uint64_t handle_id = manager.stringToHandle(handle_str); + + if (handle_id == 0) + { + std::cerr << "[ERROR] Invalid handle string: " << handle_str << std::endl; + return ACL_ERROR_INVALID_PARAM; + } + + return manager.openIpcHandle(handle_id, device_id, device_ptr); +} + +aclError cannIpcCloseMemHandle(void* device_ptr) +{ + CannIpcManager& manager = CannIpcManager::getInstance(); + return manager.closeIpcHandle(device_ptr); +} + +#endif // USE_CANN \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/cann_ipc.h b/sllm_store/csrc/checkpoint/cann_ipc.h new file mode 100644 index 0000000..ae3e7e6 --- /dev/null +++ b/sllm_store/csrc/checkpoint/cann_ipc.h @@ -0,0 +1,111 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// Licensed under the Apache License, Version 2.0 +// ---------------------------------------------------------------------------- +#pragma once + +#ifdef USE_CANN +#include "acl/acl.h" +#include +#include +#include +#include +#include +#include // For int32_t, uint64_t +#include // For std::pair + +// Required for named pipes (FIFOs) - needed for declarations +// We only include and if we specifically declare functions that use them. +// For the declarations, these might not be strictly necessary if the types are fundamental. +// However, including them ensures errno and other system-level types are known if helper functions +// were inline or templated (which they are not here). For external functions, it's generally fine. +// and for usage within the helper function definitions. + +// Define FIFO paths for inter-process communication +#define FIFO_PATH_A_TO_B "/tmp/cann_ipc_a_to_b" // Process A sends handle info to Process B +#define FIFO_PATH_B_TO_A "/tmp/cann_ipc_b_to_a" // Process B sends its PID to Process A + + +// Helper functions for inter-process communication (using named pipes) +// These are declarations ONLY. Implementations go into a .cpp file. + +/** + * @brief Sends an int32_t (e.g., a PID) through a named pipe. + * @param value The int32_t value to send. + * @param fifo_path The path to the named pipe. + */ +void send_int32(int32_t value, const std::string& fifo_path); + +/** + * @brief Receives an int32_t (e.g., a PID) from a named pipe. + * @param fifo_path The path to the named pipe. + * @return The received int32_t value, or -1 on error. + */ +int32_t receive_int32(const std::string& fifo_path); + +/** + * @brief Sends a uint64_t handle ID and size_t through a named pipe. + * @param handle_id The uint64_t handle ID to send. + * @param size The size_t value to send. + * @param fifo_path The path to the named pipe. + */ +void send_handle_info(uint64_t handle_id, size_t size, const std::string& fifo_path); + +/** + * @brief Receives a uint64_t handle ID and size_t from a named pipe. + * @param fifo_path The path to the named pipe. + * @return A pair containing the received handle ID and size, or {0, 0} on error. + */ +std::pair receive_handle_info(const std::string& fifo_path); + + +// Real CANN IPC Manager using native CANN APIs +// Uses aclrtMemExportToShareableHandle and aclrtMemImportFromShareableHandle +// Based on CANN documentation: https://www.hiascend.com/document/detail/zh/canncommercial/80RC22/apiref/appdevgapi/aclcppdevg_03_0117.html +class CannIpcManager { +public: + static CannIpcManager& getInstance() { + static CannIpcManager instance; + return instance; + } + + struct MemoryInfo { + void* ptr; // Original or imported device pointer + size_t size; // Memory size + int device_id; // Device ID + aclrtDrvMemHandle physical_handle; // Physical memory handle for CANN IPC + void* ipc_device_ptr; // Mapped IPC device pointer (can be removed if not used) + uint64_t shareable_handle; // CANN shareable handle for inter-process sharing + void* vir_ptr; // Virtual address pointer for cleanup + }; + + // Create IPC handle - mimics cudaIpcGetMemHandle + aclError createIpcHandle(void** device_ptr, size_t size, int device_id, uint64_t* handle_id, int32_t process_b_pid); + + // Open IPC handle - mimics cudaIpcOpenMemHandle + aclError openIpcHandle(uint64_t handle_id, int device_id, void** device_ptr); + + // Close IPC handle - mimics cudaIpcCloseMemHandle + aclError closeIpcHandle(void* device_ptr); + + // Convert handle to/from string for compatibility (enhanced with size info) + std::string handleToString(uint64_t handle_id); + uint64_t stringToHandle(const std::string& handle_str); + +private: + CannIpcManager() : next_handle_id_(1) {} + + std::mutex mutex_; + uint64_t next_handle_id_; + std::unordered_map handle_to_memory_; + std::unordered_map ptr_to_handle_; + std::unordered_map pending_imports_; // Store size info for imports +}; + +// Convenience functions that mimic CUDA IPC API exactly +aclError cannIpcGetMemHandle(std::string* handle_str, void** device_ptr, size_t size, int device_id, int32_t target_process_id); +aclError cannIpcOpenMemHandle(void** device_ptr, const std::string& handle_str, int device_id); +aclError cannIpcCloseMemHandle(void* device_ptr); + +#endif // USE_CANN diff --git a/sllm_store/csrc/checkpoint/checkpoint.cpp b/sllm_store/csrc/checkpoint/checkpoint.cpp index 58c3b6b..a623fd8 100644 --- a/sllm_store/csrc/checkpoint/checkpoint.cpp +++ b/sllm_store/csrc/checkpoint/checkpoint.cpp @@ -2,21 +2,36 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #include "checkpoint.h" +#include +#include +#include +#include + +#ifdef USE_CANN +#include "acl/acl.h" +#include +#include "torch_npu/csrc/aten/common/from_blob.h" +#include "torch_npu/csrc/core/npu/NPUCachingAllocator.h" +#include // One-stop header. +#include +#include "cann_ipc.h" +#include // For uintptr_t +#else #include #include #include @@ -25,20 +40,24 @@ #include #include #include -#include // One-stop header. +#include // One-stop header. #include #include +#endif +#include +#include #include #include #include #include -#include +#include #include -#include #include +#include #include +#include "aligned_buffer.h" #include "progress_bar.h" #include "tensor_writer.h" @@ -48,65 +67,65 @@ std::unordered_map SaveTensors( std::vector tensor_names, std::unordered_map>& tensor_data, const std::string& path) { - std::string tensor_filename = std::filesystem::path(path) / "tensor.data"; - // make a tensor writer - TensorWriter writer(tensor_filename); - // make a tensor index - std::unordered_map tensor_offsets; - // Some tensors may share the same data, so we need to record the data to - // avoid duplication - std::unordered_map data_record; - - int total = tensor_names.size(); - int count = 0; - - for (const auto& name : tensor_names) { - const auto& [base, size] = tensor_data[name]; - const char* data_ptr = reinterpret_cast(base); - if (data_record.find(data_ptr) != data_record.end()) { - tensor_offsets[name] = tensor_offsets[data_record[data_ptr]]; - continue; - } - data_record[data_ptr] = name; + std::string tensor_filename = std::filesystem::path(path) / "tensor.data"; + // make a tensor writer + TensorWriter writer(tensor_filename); + // make a tensor index + std::unordered_map tensor_offsets; + // Some tensors may share the same data, so we need to record the data to + // avoid duplication + std::unordered_map data_record; - uint64_t offset = writer.writeRecord(data_ptr, size); - tensor_offsets[name] = offset; + int total = tensor_names.size(); + int count = 0; - // Update progress bar - count++; - float progress = static_cast(count) / total; - showProgressBar(progress, "Saving tensors: "); - } + for (const auto& name : tensor_names) { + const auto& [base, size] = tensor_data[name]; + const char* data_ptr = reinterpret_cast(base); + if (data_record.find(data_ptr) != data_record.end()) { + tensor_offsets[name] = tensor_offsets[data_record[data_ptr]]; + continue; + } + data_record[data_ptr] = name; - return tensor_offsets; + uint64_t offset = writer.writeRecord(data_ptr, size); + tensor_offsets[name] = offset; + + // Update progress bar + count++; + float progress = static_cast(count) / total; + showProgressBar(progress, "Saving tensors: "); + } + + return tensor_offsets; } // Function to print the binary array in hexadecimal format void printBinaryArrayInHex(const unsigned char* data, size_t size) { - std::cout << "Data in Hex: "; - for (size_t i = 0; i < size; ++i) { - std::cout << std::hex << std::setw(2) << std::setfill('0') - << static_cast(data[i]) << " "; - } - std::cout << std::dec - << std::endl; // Switch back to decimal for any future output + std::cout << "Data in Hex: "; + for (size_t i = 0; i < size; ++i) { + std::cout << std::hex << std::setw(2) << std::setfill('0') + << static_cast(data[i]) << " "; + } + std::cout << std::dec + << std::endl; // Switch back to decimal for any future output } // Mapping from string to at::ScalarType at::ScalarType stringToScalarType(const std::string& dtype_str) { - static const std::unordered_map dtype_map = { - {"torch.float16", torch::kFloat16}, {"torch.float32", torch::kFloat32}, - {"torch.float64", torch::kFloat64}, {"torch.int16", torch::kInt16}, - {"torch.int32", torch::kInt32}, {"torch.int64", torch::kInt64}, - {"torch.uint8", torch::kUInt8}, {"torch.int8", torch::kInt8}, - {"torch.bfloat16", torch::kBFloat16}}; - - auto it = dtype_map.find(dtype_str); - if (it != dtype_map.end()) { - return it->second; - } else { - throw std::invalid_argument("Unknown dtype string: " + dtype_str); - } + static const std::unordered_map dtype_map = { + {"torch.float16", torch::kFloat16}, {"torch.float32", torch::kFloat32}, + {"torch.float64", torch::kFloat64}, {"torch.int16", torch::kInt16}, + {"torch.int32", torch::kInt32}, {"torch.int64", torch::kInt64}, + {"torch.uint8", torch::kUInt8}, {"torch.int8", torch::kInt8}, + {"torch.bfloat16", torch::kBFloat16}}; + + auto it = dtype_map.find(dtype_str); + if (it != dtype_map.end()) { + return it->second; + } else { + throw std::invalid_argument("Unknown dtype string: " + dtype_str); + } } std::unordered_map RestoreTensors( @@ -118,6 +137,63 @@ std::unordered_map RestoreTensors( tensor_device_offsets) { std::unordered_map state_dict; std::unordered_set handled_memory; + +#ifdef USE_CANN + std::cout << "=== RestoreTensors DEBUG START ===" << std::endl; + std::cout << "Number of devices: " << tensor_device_offsets.size() << std::endl; + std::cout << "Number of base addresses: " << memory_base_address.size() << std::endl; + + for (const auto& [device_id, tensor_offsets] : tensor_device_offsets) { + std::cout << "Processing device " << device_id << " with " << tensor_offsets.size() << " tensors" << std::endl; + + // Check if base address exists for device + auto memory_it = memory_base_address.find(device_id); + if (memory_it == memory_base_address.end()) { + std::cout << "No memory base address found for device " << device_id << std::endl; + continue; + } + + void* base_address = memory_it->second; + std::cout << "Device " << device_id << " base address: " << base_address << std::endl; + + // Synchronize device to ensure transfers are complete + // Note: Ideally called once per device externally; kept here for compatibility + aclrtSynchronizeDevice(); + std::cout << "NPU device " << device_id << " synchronized - all transfers complete" << std::endl; + + for (const auto& [tensor_name, tensor_offset] : tensor_offsets) { + auto meta_it = meta_state_dict.find(tensor_name); + if (meta_it == meta_state_dict.end()) { + std::cout << "Tensor " << tensor_name << " not found in meta_state_dict" << std::endl; + continue; + } + + auto& [sizes, strides, type_str] = meta_it->second; + void* tensor_addr = static_cast(base_address) + tensor_offset; + + // Create tensor + auto torch_dtype = stringToScalarType(type_str); + auto device = torch::Device(c10::DeviceType::PrivateUse1, device_id); + + // Use storage_offset=0 since offset is applied in tensor_addr + torch::Tensor real_tensor = at_npu::native::from_blob( + tensor_addr, + c10::IntArrayRef(sizes), + c10::IntArrayRef(strides), + 0, // storage_offset + torch::TensorOptions().dtype(torch_dtype).device(device), + device); + state_dict[tensor_name] = real_tensor; + + // Track base address for external cleanup (no deleter in NPU from_blob) + if (tensor_offset == 0) { + handled_memory.insert(base_address); + } + } + } + + std::cout << "=== RestoreTensors DEBUG END ===" << std::endl; +#else for (const auto& [device, tensor_offset] : tensor_device_offsets) { for (const auto& p : tensor_offset) { std::string name = p.first; @@ -152,99 +228,226 @@ std::unordered_map RestoreTensors( } } } +#endif return state_dict; } std::unordered_map GetGpuUUID() { - int deviceCount = 0; - cudaGetDeviceCount(&deviceCount); // Get the number of CUDA devices - std::unordered_map uuidToDeviceIdMap; - - for (int devId = 0; devId < deviceCount; ++devId) { - cudaDeviceProp props; - cudaGetDeviceProperties(&props, devId); // Get properties for each device - - // Convert UUID bytes to string with unsigned char casting - char uuidStr[80]; - snprintf( - uuidStr, sizeof(uuidStr), - "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", - (unsigned char)props.uuid.bytes[0], (unsigned char)props.uuid.bytes[1], - (unsigned char)props.uuid.bytes[2], (unsigned char)props.uuid.bytes[3], - (unsigned char)props.uuid.bytes[4], (unsigned char)props.uuid.bytes[5], - (unsigned char)props.uuid.bytes[6], (unsigned char)props.uuid.bytes[7], - (unsigned char)props.uuid.bytes[8], (unsigned char)props.uuid.bytes[9], - (unsigned char)props.uuid.bytes[10], - (unsigned char)props.uuid.bytes[11], - (unsigned char)props.uuid.bytes[12], - (unsigned char)props.uuid.bytes[13], - (unsigned char)props.uuid.bytes[14], - (unsigned char)props.uuid.bytes[15]); - - uuidToDeviceIdMap[std::string(uuidStr)] = devId; - } + int deviceCount = 0; +#ifdef USE_CANN + uint32_t device_count = 0; + aclrtGetDeviceCount(&device_count); + deviceCount = static_cast(device_count); +#else + cudaGetDeviceCount(&deviceCount); +#endif + + std::unordered_map uuidToDeviceIdMap; + + for (int devId = 0; devId < deviceCount; ++devId) { +#ifdef USE_CANN + // For CANN, generate simplified NPU UUIDs + char uuidStr[80]; + snprintf(uuidStr, sizeof(uuidStr), "npu-%02d", devId); + uuidToDeviceIdMap[std::string(uuidStr)] = devId; +#else + cudaDeviceProp props; + cudaGetDeviceProperties(&props, devId); // Get properties for each device + + // Convert UUID bytes to string with unsigned char casting + char uuidStr[80]; + snprintf( + uuidStr, sizeof(uuidStr), + "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + (unsigned char)props.uuid.bytes[0], (unsigned char)props.uuid.bytes[1], + (unsigned char)props.uuid.bytes[2], (unsigned char)props.uuid.bytes[3], + (unsigned char)props.uuid.bytes[4], (unsigned char)props.uuid.bytes[5], + (unsigned char)props.uuid.bytes[6], (unsigned char)props.uuid.bytes[7], + (unsigned char)props.uuid.bytes[8], (unsigned char)props.uuid.bytes[9], + (unsigned char)props.uuid.bytes[10], + (unsigned char)props.uuid.bytes[11], + (unsigned char)props.uuid.bytes[12], + (unsigned char)props.uuid.bytes[13], + (unsigned char)props.uuid.bytes[14], + (unsigned char)props.uuid.bytes[15]); + + uuidToDeviceIdMap[std::string(uuidStr)] = devId; +#endif + } + + return uuidToDeviceIdMap; +} - return uuidToDeviceIdMap; +#ifdef USE_CANN +// Modify the return type and content of AllocateCannMemory +std::unordered_map> AllocateCannMemory( + const std::unordered_map& tensor_sizes) { + std::unordered_map> memory_info_map; // Changed return type + for (const auto& p : tensor_sizes) { + int device = p.first; + size_t size = p.second; // This is the size! + void* ptr = nullptr; + aclrtSetDevice(device); + aclError ret = aclrtMalloc(&ptr, size, ACL_MEM_MALLOC_HUGE_FIRST); + if (ret != ACL_ERROR_NONE) { + std::cerr << "ACL Error: Failed to allocate " << size << " bytes on device " << device << ": " << ret << std::endl; + // Handle allocation failure appropriately (e.g., throw exception, return empty map) + // For now, we'll continue but this might lead to nullptr in map. + } + memory_info_map[device] = {ptr, size}; // Store both ptr and size + } + return memory_info_map; } +std::unordered_map GetCannMemoryHandles( + // Change input parameter type to match the new AllocateCannMemory return + std::unordered_map>& memory_info_map, + int32_t target_process_id) { + std::unordered_map memory_handles; + for (auto& p : memory_info_map) { // Note: auto& instead of const auto& + int device = p.first; + void* ptr = p.second.first; // The allocated pointer + size_t actual_size = p.second.second; // The actual size! + + std::string handle_str; + + // Use the actual_size directly with target_process_id + // Pass &ptr so it can be updated with shared VA + aclError ret = cannIpcGetMemHandle(&handle_str, &ptr, actual_size, device, target_process_id); + if (ret != ACL_ERROR_NONE) { + // Placeholder for proper logging + // LOG(ERROR) << "Failed to create CANN IPC handle for device " << device + // << " ptr " << ptr << " with size " << actual_size + // << " target_pid " << target_process_id + // << ", error: " << ret; + std::cerr << "ERROR: Failed to create CANN IPC handle for device " << device + << " ptr " << ptr << " with size " << actual_size + << " target_pid " << target_process_id + << ", error: " << ret << std::endl; + // Fallback to old method for compatibility + std::ostringstream oss; + oss << std::hex << reinterpret_cast(ptr) << ":" << std::dec << actual_size; + handle_str = oss.str(); + // LOG(WARNING) << "Using fallback handle format: " << handle_str; + std::cerr << "WARNING: Using fallback handle format: " << handle_str << std::endl; + } else { + // Update the memory map with the shared VA pointer + p.second.first = ptr; + std::cout << "Updated pointer for device " << device << " to shared VA: " << ptr << std::endl; + } + + std::cout << "IPC handle created: " << handle_str << " for target PID: " << target_process_id << std::endl; + memory_handles[device] = handle_str; + } + return memory_handles; +} + +// overloaded function, modify it similarly +// create IPC handle +std::unordered_map> GetCannMemoryHandles( + std::unordered_map>>& memory_info_vectors, // Changed input type + int32_t target_process_id) { + std::unordered_map> memory_handles; + for (auto& p : memory_info_vectors) { // Note: auto& instead of const auto& + auto device = p.first; + auto& ptr_size_pairs = p.second; // This is now a vector of pairs + + std::vector handles; + for (auto& pair_entry : ptr_size_pairs) { // Note: auto& instead of const auto& + void* ptr = pair_entry.first; + size_t actual_size = pair_entry.second; // Get the actual size + + std::string handle_str; + // Pass &ptr so it can be updated with shared VA + aclError ret = cannIpcGetMemHandle(&handle_str, &ptr, actual_size, device, target_process_id); + if (ret != ACL_ERROR_NONE) { + // Placeholder for proper logging + // LOG(ERROR) << "Failed to create CANN IPC handle for device " << device + // << " ptr " << ptr << " with size " << actual_size + // << " target_pid " << target_process_id + // << ", error: " << ret; + std::cerr << "ERROR: Failed to create CANN IPC handle for device " << device + << " ptr " << ptr << " with size " << actual_size + << " target_pid " << target_process_id + << ", error: " << ret << std::endl; + // Fallback to old method for compatibility + std::ostringstream oss; + oss << std::hex << reinterpret_cast(ptr) << ":" << std::dec << actual_size; + handle_str = oss.str(); + // LOG(WARNING) << "Using fallback handle format: " << handle_str; + std::cerr << "WARNING: Using fallback handle format: " << handle_str << std::endl; + } else { + // Update the pair with the shared VA pointer + pair_entry.first = ptr; + std::cout << "Updated pointer for device " << device << " to shared VA: " << ptr << std::endl; + } + + handles.push_back(handle_str); + } + memory_handles[device] = handles; + } + return memory_handles; +} +#else std::unordered_map AllocateCudaMemory( const std::unordered_map& tensor_sizes) { - std::unordered_map memory_ptrs; - for (const auto& p : tensor_sizes) { - int device = p.first; - size_t size = p.second; - void* ptr = nullptr; - cudaSetDevice(device); - cudaMalloc(&ptr, size); - memory_ptrs[device] = ptr; - } - return memory_ptrs; + std::unordered_map memory_ptrs; + for (const auto& p : tensor_sizes) { + int device = p.first; + size_t size = p.second; + void* ptr = nullptr; + cudaSetDevice(device); + cudaMalloc(&ptr, size); + memory_ptrs[device] = ptr; + } + return memory_ptrs; } std::unordered_map GetCudaMemoryHandles( const std::unordered_map& memory_ptrs) { - std::unordered_map memory_handles; - for (const auto& p : memory_ptrs) { - int device = p.first; - void* ptr = p.second; - cudaIpcMemHandle_t handle; - cudaSetDevice(device); - cudaIpcGetMemHandle(&handle, ptr); - memory_handles[device] = std::string(reinterpret_cast(&handle), - sizeof(cudaIpcMemHandle_t)); - } - return memory_handles; + std::unordered_map memory_handles; + for (const auto& p : memory_ptrs) { + int device = p.first; + void* ptr = p.second; + cudaIpcMemHandle_t handle; + cudaSetDevice(device); + cudaIpcGetMemHandle(&handle, ptr); + memory_handles[device] = std::string(reinterpret_cast(&handle), + sizeof(cudaIpcMemHandle_t)); + } + return memory_handles; } std::unordered_map> GetCudaMemoryHandles( const std::unordered_map>& memory_ptrs) { - std::unordered_map> memory_handles; - for (const auto& p : memory_ptrs) { - auto device = p.first; - const auto& ptrs = p.second; - cudaIpcMemHandle_t handle; - cudaSetDevice(device); - - std::vector handles; - for (const auto& ptr : ptrs) { - cudaIpcGetMemHandle(&handle, ptr); - handles.push_back(std::string(reinterpret_cast(&handle), - sizeof(cudaIpcMemHandle_t))); + std::unordered_map> memory_handles; + for (const auto& p : memory_ptrs) { + auto device = p.first; + const auto& ptrs = p.second; + cudaIpcMemHandle_t handle; + cudaSetDevice(device); + + std::vector handles; + for (const auto& ptr : ptrs) { + cudaIpcGetMemHandle(&handle, ptr); + handles.push_back(std::string(reinterpret_cast(&handle), + sizeof(cudaIpcMemHandle_t))); + } + memory_handles[device] = handles; } - memory_handles[device] = handles; - } - return memory_handles; + return memory_handles; } +#endif std::unordered_map GetDeviceUuidMap() { - std::unordered_map gpu_uuid = GetGpuUUID(); - std::unordered_map device_uuid_map; - for (const auto& p : gpu_uuid) { - if (device_uuid_map.find(p.second) != device_uuid_map.end()) { - std::cerr << "Duplicate device id: " << p.second << std::endl; - exit(1); + std::unordered_map gpu_uuid = GetGpuUUID(); + std::unordered_map device_uuid_map; + for (const auto& p : gpu_uuid) { + if (device_uuid_map.find(p.second) != device_uuid_map.end()) { + std::cerr << "Duplicate device id: " << p.second << std::endl; + exit(1); + } + device_uuid_map[p.second] = p.first; } - device_uuid_map[p.second] = p.first; - } - return device_uuid_map; + return device_uuid_map; } \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/checkpoint.h b/sllm_store/csrc/checkpoint/checkpoint.h index 692365e..c9a3853 100644 --- a/sllm_store/csrc/checkpoint/checkpoint.h +++ b/sllm_store/csrc/checkpoint/checkpoint.h @@ -2,26 +2,28 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #pragma once #include -#include // One-stop header. +#include // One-stop header. #include #include +#include +#include std::unordered_map SaveTensors( std::vector tensor_names, @@ -36,13 +38,26 @@ std::unordered_map RestoreTensors( const std::unordered_map>& tensor_device_offsets); -// {dev_id: ptr} +// Memory allocation and handle functions for both CUDA and CANN +#ifdef USE_CANN +#include "cann_ipc.h" + +// Function declarations only - implementations are in checkpoint.cpp +std::unordered_map> AllocateCannMemory( + const std::unordered_map& tensor_sizes); +std::unordered_map GetCannMemoryHandles( + std::unordered_map>& memory_info_map, int32_t target_process_id = -1); +std::unordered_map> GetCannMemoryHandles( + std::unordered_map>>& memory_info_vectors, int32_t target_process_id = -1); +#else +// CUDA function declarations std::unordered_map AllocateCudaMemory( const std::unordered_map& tensor_sizes); std::unordered_map GetCudaMemoryHandles( const std::unordered_map& memory_ptrs); std::unordered_map> GetCudaMemoryHandles( const std::unordered_map>& memory_ptrs); -std::unordered_map GetDeviceUuidMap(); +#endif +std::unordered_map GetDeviceUuidMap(); std::unordered_map GetGpuUUID(); \ No newline at end of file diff --git a/sllm_store/csrc/checkpoint/checkpoint_py.cpp b/sllm_store/csrc/checkpoint/checkpoint_py.cpp index 5157289..e139673 100644 --- a/sllm_store/csrc/checkpoint/checkpoint_py.cpp +++ b/sllm_store/csrc/checkpoint/checkpoint_py.cpp @@ -2,30 +2,96 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #include #include "checkpoint.h" +#include +#include // For getpid() and sleep() + namespace py = pybind11; +void start_profiling() { + std::cout << "PID: " << getpid() << std::endl; + sleep(30); // Pause for 30 seconds to allow perf recording +} + // define pybind11 module PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("start_profiling", &start_profiling, "Prints PID and pauses for profiling"); + m.def("save_tensors", &SaveTensors, "Save a state dict") - .def("restore_tensors", &RestoreTensors, "Restore a state dict") - .def("allocate_cuda_memory", &AllocateCudaMemory, "Allocate cuda memory") + .def("restore_tensors", &RestoreTensors, "Restore a state dict"); + +#ifdef USE_CANN + // CANN-specific bindings + m.def("allocate_cann_memory", &AllocateCannMemory, "Allocate CANN memory") + .def( + "get_cann_memory_handles", + [](std::unordered_map>& memory_info_map, + int32_t target_process_id = -1) { + std::unordered_map memory_handles = + GetCannMemoryHandles(memory_info_map, target_process_id); + + std::unordered_map py_memory_handles; + for (const auto& kv : memory_handles) { + py_memory_handles[kv.first] = py::bytes(kv.second); + } + + py::dict updated_ptrs; + for (const auto& kv : memory_info_map) { + updated_ptrs[py::int_(kv.first)] = py::int_(reinterpret_cast(kv.second.first)); + } + + return py::make_tuple(py_memory_handles, updated_ptrs); + }, + py::arg("memory_info_map"), py::arg("target_process_id") = -1, "Get CANN memory handles") + .def( + "get_cann_memory_handles", + [](std::unordered_map>>& memory_info_vectors, + int32_t target_process_id = -1) { + auto memory_handles = GetCannMemoryHandles(memory_info_vectors, target_process_id); + + std::unordered_map> py_memory_handles; + for (const auto& kv : memory_handles) { + std::vector handles; + for (const auto& handle : kv.second) { + handles.push_back(py::bytes(handle)); + } + py_memory_handles[kv.first] = handles; + } + + py::dict updated_ptrs; + for (const auto& kv : memory_info_vectors) { + py::list ptr_list; + for (const auto& pair : kv.second) { + ptr_list.append(py::int_(reinterpret_cast(pair.first))); + } + updated_ptrs[py::int_(kv.first)] = ptr_list; + } + + return py::make_tuple(py_memory_handles, updated_ptrs); + }, + py::arg("memory_info_vectors"), py::arg("target_process_id") = -1, "Get CANN memory handles") + .def("create_pointer_capsule", [](uintptr_t ptr) -> py::capsule { + return py::capsule(reinterpret_cast(ptr)); + }, "Create a capsule from uintptr_t"); +#else + // CUDA-specific bindings + m.def("allocate_cuda_memory", &AllocateCudaMemory, "Allocate cuda memory") .def( "get_cuda_memory_handles", [](const std::unordered_map& memory_ptrs) { @@ -58,7 +124,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { .def( "get_cuda_memory_handles", [](const std::unordered_map>& - memory_ptrs) { + memory_ptrs) { std::unordered_map> memory_ptrs_void; for (const auto& kv : memory_ptrs) { std::vector ptrs; @@ -79,6 +145,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { } return py_memory_handles; }, - py::arg("memory_ptrs"), "Get cuda memory handles") - .def("get_device_uuid_map", &GetDeviceUuidMap, "Get device uuid map"); + py::arg("memory_ptrs"), "Get cuda memory handles"); +#endif + + m.def("get_device_uuid_map", &GetDeviceUuidMap, "Get device uuid map"); } \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/cann_error_handling.cpp b/sllm_store/csrc/sllm_store/cann_error_handling.cpp new file mode 100644 index 0000000..bd5ee5d --- /dev/null +++ b/sllm_store/csrc/sllm_store/cann_error_handling.cpp @@ -0,0 +1,37 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#ifdef USE_CANN +#include "acl/acl.h" +#endif +#include +#include +#include + +#ifdef USE_CANN +#define CANN_CHECK(x, msg) { aclError ret = (x); if (ret != ACL_ERROR_NONE) { LOG(ERROR) << msg << " CANN error: " << ret << std::endl; return -1; } } +#else +#define CANN_CHECK(x, msg) { LOG(ERROR) << "CANN not available: " << msg << std::endl; return -1; } +#endif + +#define CHECK_POSIX(x, msg) { if ((x) < 0) { LOG(ERROR) << msg << " errno: " << errno << "msg: " << strerror(errno); return -1; } } + +#define WAIT_FUTURES(futures, msg) { for (auto& future : futures) { int ret = future.get(); if (ret != 0) { LOG(ERROR) << msg; return ret; } } } + +#define CHECK_RETURN(x, msg) { if ((x) != 0) { LOG(ERROR) << msg; return -1; } } \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/cann_error_handling.h b/sllm_store/csrc/sllm_store/cann_error_handling.h new file mode 100644 index 0000000..54e7529 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cann_error_handling.h @@ -0,0 +1,44 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#ifdef USE_CANN +#include "acl/acl.h" +#endif +#include +#include +#include + +#ifdef USE_CANN +// Macro to check CANN API call results. If an error occurs, it logs the error and returns -1. +#define CANN_CHECK(x, msg) { aclError ret = (x); if (ret != ACL_ERROR_NONE) { LOG(ERROR) << msg << " CANN error: " << ret << std::endl; return -1; } } +#else +// Placeholder macro when CANN is not enabled. It logs a message indicating CANN is unavailable. +#define CANN_CHECK(x, msg) { LOG(ERROR) << "CANN not available: " << msg << std::endl; return -1; } +#endif + +// Macro to check POSIX system call results. If an error occurs (return value < 0), +// it logs the error message, errno, and strerror, then returns -1. +#define CHECK_POSIX(x, msg) { if ((x) < 0) { LOG(ERROR) << msg << " errno: " << errno << "msg: " << strerror(errno); return -1; } } + +// Macro to wait for a vector of futures. It iterates through each future, +// gets its result, and if any result is non-zero, it logs an error and returns that result. +#define WAIT_FUTURES(futures, msg) { for (auto& future : futures) { int ret = future.get(); if (ret != 0) { LOG(ERROR) << msg; return ret; } } } + +// Macro to check a return value. If the value is non-zero, it logs an error and returns -1. +#define CHECK_RETURN(x, msg) { if ((x) != 0) { LOG(ERROR) << msg; return -1; } } diff --git a/sllm_store/csrc/sllm_store/cann_memory.cpp b/sllm_store/csrc/sllm_store/cann_memory.cpp new file mode 100644 index 0000000..e9593bf --- /dev/null +++ b/sllm_store/csrc/sllm_store/cann_memory.cpp @@ -0,0 +1,81 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "cann_memory.h" + +#include +#include +#include + +CannMemory::CannMemory() : data_(nullptr), size_(0), device_(-1) {} + +CannMemory::~CannMemory() { + if (data_) { // Ensure we have data to free +#ifdef USE_CANN + aclError ret = aclrtFree(data_); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to free CANN memory: " << ret; + } +#endif + } +} + +int CannMemory::Allocate(size_t size, int device) { + if (data_) { + LOG(ERROR) << "Memory already allocated\n"; + return 1; // Indicate error + } + +#ifdef USE_CANN + // Check if device and size are valid + uint32_t deviceCount = 0; + aclError ret = aclrtGetDeviceCount(&deviceCount); + if (ret != ACL_ERROR_NONE || device >= static_cast(deviceCount) || size == 0) { + LOG(ERROR) << "Invalid device or size\n"; + return 1; // Indicate error + } + + // Set device and allocate memory on it + ret = aclrtSetDevice(device); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to set device " << device << ": " << ret << "\n"; + return ret; + } + + ret = aclrtMalloc(&data_, size, ACL_MEM_MALLOC_HUGE_FIRST); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to allocate memory on device " << device << ": " << ret << "\n"; + return ret; + } + device_ = device; + size_ = size; + + // Generate IPC handle (CANN uses pointer address as handle string) + std::ostringstream oss; + oss << std::hex << reinterpret_cast(data_); + handle_ = oss.str(); + + return 0; // Indicate success +#else + LOG(ERROR) << "CANN support not compiled\n"; + return 1; +#endif +} + +void* CannMemory::get() const { return data_; } + +std::string CannMemory::getHandle() const { return handle_; } \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/cann_memory.h b/sllm_store/csrc/sllm_store/cann_memory.h new file mode 100644 index 0000000..8c57fa4 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cann_memory.h @@ -0,0 +1,49 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +// CANN headers - these will be available when building with CANN toolkit +#ifdef USE_CANN +#include "acl/acl.h" +#include "hccl/hccl.h" +#endif + +// #include "cann_memory_pool.h" + +class CannMemory { + public: + CannMemory(); + ~CannMemory(); + + // Disable copying and moving + CannMemory(const CannMemory&) = delete; + CannMemory& operator=(const CannMemory&) = delete; + CannMemory(CannMemory&&) = delete; + CannMemory& operator=(CannMemory&&) = delete; + + int Allocate(size_t size, int device); + void* get() const; + std::string getHandle() const; // CANN uses string-based handles + + private: + void* data_; + std::string handle_; // CANN IPC handle as string + size_t size_; + int device_; +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/cann_memory_pool.cpp b/sllm_store/csrc/sllm_store/cann_memory_pool.cpp new file mode 100644 index 0000000..e59e3b2 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cann_memory_pool.cpp @@ -0,0 +1,97 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#include "cann_memory_pool.h" + +#include +#include +#include + +CannMemoryPool::CannMemoryPool(int device_count, size_t size_per_device) + : device_count_(device_count), size_per_device_(size_per_device), + pool_(device_count), handles_(device_count), offsets_(device_count, 0) { + LOG(INFO) << "Creating CannMemoryPool with " << device_count + << " devices and " << size_per_device << " bytes per device"; +#ifdef USE_CANN + for (int i = 0; i < device_count; ++i) { + aclError ret = aclrtSetDevice(i); + if (ret != ACL_ERROR_NONE) { + LOG(FATAL) << "Failed to set device: " << ret; + } + void* ptr; + ret = aclrtMalloc(&ptr, size_per_device, ACL_MEM_MALLOC_HUGE_FIRST); + if (ret != ACL_ERROR_NONE) { + LOG(FATAL) << "Failed to allocate memory on device " << i << ": " << ret; + } + + // Generate IPC handle (CANN uses pointer address as handle string) + std::ostringstream oss; + oss << std::hex << reinterpret_cast(ptr); + handles_[i] = oss.str(); + + pool_[i] = ptr; + } +#else + LOG(FATAL) << "CANN support not compiled"; +#endif +} + +CannMemoryPool::~CannMemoryPool() { +#ifdef USE_CANN + for (int i = 0; i < device_count_; ++i) { + aclError ret = aclrtSetDevice(i); + if (ret != ACL_ERROR_NONE) { + LOG(FATAL) << "Failed to set device: " << ret; + } + ret = aclrtFree(pool_[i]); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to free memory on device " << i << ": " << ret; + } + } +#endif +} + +int CannMemoryPool::Allocate(size_t size, int device_id, void*& ptr, + std::string& handle) { + if (device_id >= device_count_ || device_id < 0) { + LOG(ERROR) << "Invalid device_id: " << device_id; + return -1; + } + + if (offsets_[device_id] + size > size_per_device_) { + LOG(ERROR) << "Not enough memory on device " << device_id; + return -1; + } + + ptr = static_cast(pool_[device_id]) + offsets_[device_id]; + handle = handles_[device_id]; + offsets_[device_id] += size; + + return 0; +} + +int CannMemoryPool::Deallocate(int device_id, void* ptr) { + if (device_id >= device_count_ || device_id < 0) { + LOG(ERROR) << "Invalid device_id: " << device_id; + return -1; + } + + // Note: Simple pool implementation doesn't actually deallocate individual chunks + // This would need more sophisticated implementation for production use + LOG(WARNING) << "CannMemoryPool deallocate is a no-op in simple implementation"; + return 0; +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/cann_memory_pool.h b/sllm_store/csrc/sllm_store/cann_memory_pool.h new file mode 100644 index 0000000..aa10f57 --- /dev/null +++ b/sllm_store/csrc/sllm_store/cann_memory_pool.h @@ -0,0 +1,45 @@ +// ---------------------------------------------------------------------------- +// ServerlessLLM +// Copyright (c) ServerlessLLM Team 2024 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ---------------------------------------------------------------------------- +#pragma once + +#include +#include + +// CANN headers - these will be available when building with CANN toolkit +#ifdef USE_CANN +#include "acl/acl.h" +#endif + +class CannMemoryPool { + public: + CannMemoryPool(int device_count, size_t size_per_device); + CannMemoryPool(const CannMemoryPool&) = delete; + CannMemoryPool& operator=(const CannMemoryPool&) = delete; + ~CannMemoryPool(); + + int Allocate(size_t size, int device_id, void*& ptr, + std::string& handle); // CANN uses string handles + int Deallocate(int device_id, void* ptr); + + private: + int device_count_; + size_t size_per_device_; + std::vector pool_; + std::vector handles_; // CANN IPC handles as strings + std::vector offsets_; +}; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/checkpoint_store.cpp b/sllm_store/csrc/sllm_store/checkpoint_store.cpp index a32ae44..11e8004 100644 --- a/sllm_store/csrc/sllm_store/checkpoint_store.cpp +++ b/sllm_store/csrc/sllm_store/checkpoint_store.cpp @@ -2,18 +2,18 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #include "checkpoint_store.h" @@ -26,8 +26,14 @@ #include #include #include +#include +#ifdef USE_CANN +#include "cann_error_handling.h" +#include "../checkpoint/cann_ipc.h" +#else #include "error_handling.h" +#endif CheckpointStore::CheckpointStore(const std::string& storage_path, size_t memory_pool_size, int num_thread, @@ -36,210 +42,232 @@ CheckpointStore::CheckpointStore(const std::string& storage_path, memory_pool_size_(memory_pool_size), num_thread_(num_thread), chunk_size_(chunk_size) { - // Get number of GPUs - cudaGetDeviceCount(&num_gpus_); - LOG(INFO) << "Number of GPUs: " << num_gpus_; - - LOG(INFO) << "I/O threads: " << num_thread - << ", chunk size: " << chunk_size / MB << "MB"; - LOG(INFO) << "Storage path: " << storage_path_; - - for (size_t i = 0; i < num_gpus_; ++i) { - cudaSetDevice(i); - - cudaDeviceProp props; - cudaGetDeviceProperties(&props, i); - - // Get GPU UUID - char uuidStr[80]; - snprintf( - uuidStr, sizeof(uuidStr), - "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", - (unsigned char)props.uuid.bytes[0], (unsigned char)props.uuid.bytes[1], - (unsigned char)props.uuid.bytes[2], (unsigned char)props.uuid.bytes[3], - (unsigned char)props.uuid.bytes[4], (unsigned char)props.uuid.bytes[5], - (unsigned char)props.uuid.bytes[6], (unsigned char)props.uuid.bytes[7], - (unsigned char)props.uuid.bytes[8], (unsigned char)props.uuid.bytes[9], - (unsigned char)props.uuid.bytes[10], - (unsigned char)props.uuid.bytes[11], - (unsigned char)props.uuid.bytes[12], - (unsigned char)props.uuid.bytes[13], - (unsigned char)props.uuid.bytes[14], - (unsigned char)props.uuid.bytes[15]); - - gpu_info_map_[i].uuid_ = std::string(uuidStr); - LOG(INFO) << "GPU " << i << " UUID: " << gpu_info_map_[i].uuid_; - - // create stream - cudaError_t err = cudaStreamCreate(&gpu_info_map_[i].stream_); - if (err != cudaSuccess) { - LOG(FATAL) << "cudaStreamCreate error: " << cudaGetErrorString(err); + // Get number of GPUs/NPUs +#ifdef USE_CANN + uint32_t device_count = 0; + aclrtGetDeviceCount(&device_count); + num_gpus_ = static_cast(device_count); +#else + cudaGetDeviceCount(&num_gpus_); +#endif + LOG(INFO) << "Number of GPUs/NPUs: " << num_gpus_; + + LOG(INFO) << "I/O threads: " << num_thread + << ", chunk size: " << chunk_size / MB << "MB"; + LOG(INFO) << "Storage path: " << storage_path_; + + for (size_t i = 0; i < num_gpus_; ++i) { +#ifdef USE_CANN + aclrtSetDevice(i); + + // Get NPU UUID (simplified version for CANN) + char uuidStr[80]; + snprintf(uuidStr, sizeof(uuidStr), "npu-%02zu", i); + gpu_info_map_[i].uuid_ = std::string(uuidStr); + LOG(INFO) << "NPU " << i << " UUID: " << gpu_info_map_[i].uuid_; + + // create stream + aclError ret = aclrtCreateStream(&gpu_info_map_[i].stream_); + if (ret != ACL_ERROR_NONE) { + LOG(FATAL) << "aclrtCreateStream error: " << ret; + } +#else + cudaSetDevice(i); + + cudaDeviceProp props; + cudaGetDeviceProperties(&props, i); + + // Get GPU UUID + char uuidStr[80]; + snprintf( + uuidStr, sizeof(uuidStr), + "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + (unsigned char)props.uuid.bytes[0], (unsigned char)props.uuid.bytes[1], + (unsigned char)props.uuid.bytes[2], (unsigned char)props.uuid.bytes[3], + (unsigned char)props.uuid.bytes[4], (unsigned char)props.uuid.bytes[5], + (unsigned char)props.uuid.bytes[6], (unsigned char)props.uuid.bytes[7], + (unsigned char)props.uuid.bytes[8], (unsigned char)props.uuid.bytes[9], + (unsigned char)props.uuid.bytes[10], + (unsigned char)props.uuid.bytes[11], + (unsigned char)props.uuid.bytes[12], + (unsigned char)props.uuid.bytes[13], + (unsigned char)props.uuid.bytes[14], + (unsigned char)props.uuid.bytes[15]); + + gpu_info_map_[i].uuid_ = std::string(uuidStr); + LOG(INFO) << "GPU " << i << " UUID: " << gpu_info_map_[i].uuid_; + + // create stream + cudaError_t err = cudaStreamCreate(&gpu_info_map_[i].stream_); + if (err != cudaSuccess) { + LOG(FATAL) << "cudaStreamCreate error: " << cudaGetErrorString(err); + } +#endif } - } - // Create a memory pool - memory_pool_ = - std::make_shared(memory_pool_size_, chunk_size_); - LOG(INFO) << "Memory pool created with " << memory_pool_size_ / GB << "GB"; + // Create a memory pool + memory_pool_ = + std::make_shared(memory_pool_size_, chunk_size_); + LOG(INFO) << "Memory pool created with " << memory_pool_size_ / GB << "GB"; } CheckpointStore::~CheckpointStore() { ClearMem(); } int64_t CheckpointStore::RegisterModelInfo(const std::string& model_path) { - std::unique_lock lock_info(model_info_mutex_); - if (model_map_.find(model_path) != model_map_.end()) { - // LOG(WARNING) << "Model " << model_path << " is already regfistered"; - auto model = model_map_.at(model_path); - return model->GetModelSize(); - } + std::unique_lock lock_info(model_info_mutex_); + if (model_map_.find(model_path) != model_map_.end()) { + // LOG(WARNING) << "Model " << model_path << " is already regfistered"; + auto model = model_map_.at(model_path); + return model->GetModelSize(); + } - auto model = std::make_shared(model_path); + auto model = std::make_shared(model_path); - int ret = model->Initialize(storage_path_); - if (ret != 0) { - LOG(ERROR) << "Failed to initialize model " << model_path; - return ret; - } + int ret = model->Initialize(storage_path_); + if (ret != 0) { + LOG(ERROR) << "Failed to initialize model " << model_path; + return ret; + } - model_map_[model_path] = model; + model_map_[model_path] = model; - LOG(INFO) << "Model " << model_path << " is registered"; + LOG(INFO) << "Model " << model_path << " is registered"; - return model->GetModelSize(); + return model->GetModelSize(); } int CheckpointStore::LoadModelFromDisk(const std::string& model_path) { - std::unique_lock lock_info(model_info_mutex_); - auto model = GetModelPtr(model_path); - if (model == nullptr) { - LOG(ERROR) << "Model " << model_path << " is not registered"; - return -1; - } - lock_info.unlock(); - - // Allocate memory - lock_info.lock(); - int remaining_size = model->AllocatePinnedMemory(memory_pool_); - if (remaining_size < 0) { - LOG(ERROR) << "Failed to allocate memory for model " << model_path; - return -1; - } else if (remaining_size > 0) { - int mem_chunks_needed = remaining_size; - std::vector>> - model_last_access_time(model_last_access_time_.begin(), - model_last_access_time_.end()); - std::sort(model_last_access_time.begin(), model_last_access_time.end(), - [](const auto& a, const auto& b) { return a.second < b.second; }); - for (const auto& [model_path, last_access_time] : model_last_access_time) { - auto& model = model_map_[model_path]; - int freed_chunks = - model->TryFreeHost(); // try to free memory, non-blocking - LOG(INFO) << "Free " << freed_chunks << " chunks, remaining " - << mem_chunks_needed; - if (freed_chunks > 0) { - LOG(INFO) << "Model " << model_path << " is freed from memory"; - mem_chunks_needed -= freed_chunks; - if (mem_chunks_needed <= 0) { - break; - } - } - } - if (mem_chunks_needed > 0) { - LOG(ERROR) << "Failed to free enough memory for model " << model_path; - return -1; + std::unique_lock lock_info(model_info_mutex_); + auto model = GetModelPtr(model_path); + if (model == nullptr) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return -1; } - ssize_t remaining_size = model->AllocatePinnedMemory(memory_pool_); + lock_info.unlock(); + + // Allocate memory + lock_info.lock(); + int remaining_size = model->AllocatePinnedMemory(memory_pool_); if (remaining_size < 0) { - LOG(ERROR) << "Failed to allocate memory for model " << model_path; - return -1; + LOG(ERROR) << "Failed to allocate memory for model " << model_path; + return -1; + } else if (remaining_size > 0) { + int mem_chunks_needed = remaining_size; + std::vector>> + model_last_access_time(model_last_access_time_.begin(), + model_last_access_time_.end()); + std::sort(model_last_access_time.begin(), model_last_access_time.end(), + [](const auto& a, const auto& b) { return a.second < b.second; }); + for (const auto& [model_path, last_access_time] : model_last_access_time) { + auto& model = model_map_[model_path]; + int freed_chunks = + model->TryFreeHost(); // try to free memory, non-blocking + LOG(INFO) << "Free " << freed_chunks << " chunks, remaining " + << mem_chunks_needed; + if (freed_chunks > 0) { + LOG(INFO) << "Model " << model_path << " is freed from memory"; + mem_chunks_needed -= freed_chunks; + if (mem_chunks_needed <= 0) { + break; + } + } + } + if (mem_chunks_needed > 0) { + LOG(ERROR) << "Failed to free enough memory for model " << model_path; + return -1; + } + ssize_t remaining_size = model->AllocatePinnedMemory(memory_pool_); + if (remaining_size < 0) { + LOG(ERROR) << "Failed to allocate memory for model " << model_path; + return -1; + } } - } - model_last_access_time_[model_path] = std::chrono::system_clock::now(); - lock_info.unlock(); + model_last_access_time_[model_path] = std::chrono::system_clock::now(); + lock_info.unlock(); - int ret = model->ToHost(num_thread_); + int ret = model->ToHost(num_thread_); - if (ret != 0) { - LOG(ERROR) << "Failed to load model " << model_path << " to host"; - if (model->FreeHost() != 0) { - LOG(ERROR) << "Failed to free memory for model " << model_path; + if (ret != 0) { + LOG(ERROR) << "Failed to load model " << model_path << " to host"; + if (model->FreeHost() != 0) { + LOG(ERROR) << "Failed to free memory for model " << model_path; + } + return ret; } - return ret; - } - return ret; + return ret; } int CheckpointStore::LoadModelFromDiskAsync(const std::string& model_path) { - std::unique_lock lock_info(model_info_mutex_); - async_tasks_.emplace(std::async(std::launch::async, [this, model_path]() { - return LoadModelFromDisk(model_path); - })); + std::unique_lock lock_info(model_info_mutex_); + async_tasks_.emplace(std::async(std::launch::async, [this, model_path]() { + return LoadModelFromDisk(model_path); + })); - return 0; + return 0; } int CheckpointStore::LoadModelFromMem( const std::string& model_path, const std::string& replica_uuid, const MemCopyHandleListMap& gpu_memory_handles, const MemCopyChunkListMap& mem_copy_chunks) { - // Sanity check - if (model_path.empty() || replica_uuid.empty()) { - LOG(ERROR) << "Model name or replica uuid is empty"; - return -1; - } else if (mem_copy_chunks.empty()) { - LOG(ERROR) << "No memory copy chunk provided"; - return -1; - } else if (gpu_memory_handles.size() != mem_copy_chunks.size()) { - LOG(ERROR) << "Mismatch between memory handles " - << gpu_memory_handles.size() << " and memory copy chunks " - << mem_copy_chunks.size(); - return -1; - } - std::unique_lock lock_info(model_info_mutex_); - auto model = GetModelPtr(model_path); - if (model == nullptr) { - LOG(ERROR) << "Model " << model_path << " is not registered"; - return -1; - } - model_last_access_time_[model_path] = std::chrono::system_clock::now(); - lock_info.unlock(); + // Sanity check + if (model_path.empty() || replica_uuid.empty()) { + LOG(ERROR) << "Model name or replica uuid is empty"; + return -1; + } else if (mem_copy_chunks.empty()) { + LOG(ERROR) << "No memory copy chunk provided"; + return -1; + } else if (gpu_memory_handles.size() != mem_copy_chunks.size()) { + LOG(ERROR) << "Mismatch between memory handles " + << gpu_memory_handles.size() << " and memory copy chunks " + << mem_copy_chunks.size(); + return -1; + } + std::unique_lock lock_info(model_info_mutex_); + auto model = GetModelPtr(model_path); + if (model == nullptr) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return -1; + } + model_last_access_time_[model_path] = std::chrono::system_clock::now(); + lock_info.unlock(); - LOG(INFO) << "Loading model " << model_path; + LOG(INFO) << "Loading model " << model_path; - // Convert device uuid to device id - std::unordered_map converted_mem_copy_chunks; - for (auto& [device_id, gpu_info] : gpu_info_map_) { - if (mem_copy_chunks.find(gpu_info.uuid_) == mem_copy_chunks.end()) { - continue; + // Convert device uuid to device id + std::unordered_map converted_mem_copy_chunks; + for (auto& [device_id, gpu_info] : gpu_info_map_) { + if (mem_copy_chunks.find(gpu_info.uuid_) == mem_copy_chunks.end()) { + continue; + } + converted_mem_copy_chunks[device_id] = mem_copy_chunks.at(gpu_info.uuid_); } - converted_mem_copy_chunks[device_id] = mem_copy_chunks.at(gpu_info.uuid_); - } - std::unordered_map converted_mem_copy_handles; - for (auto& [device_id, gpu_info] : gpu_info_map_) { - if (gpu_memory_handles.find(gpu_info.uuid_) == gpu_memory_handles.end()) { - continue; + std::unordered_map converted_mem_copy_handles; + for (auto& [device_id, gpu_info] : gpu_info_map_) { + if (gpu_memory_handles.find(gpu_info.uuid_) == gpu_memory_handles.end()) { + continue; + } + converted_mem_copy_handles[device_id] = + gpu_memory_handles.at(gpu_info.uuid_); } - converted_mem_copy_handles[device_id] = - gpu_memory_handles.at(gpu_info.uuid_); - } - // Convert memory handles to device pointers - auto device_ptrs = GetDevicePtrsFromMemHandles(gpu_memory_handles); + // Convert memory handles to device pointers + auto device_ptrs = GetDevicePtrsFromMemHandles(converted_mem_copy_handles, converted_mem_copy_chunks); - auto ret = model->ToGpu(replica_uuid, device_ptrs, converted_mem_copy_chunks, - converted_mem_copy_handles); + auto ret = model->ToGpu(replica_uuid, device_ptrs, converted_mem_copy_chunks, + converted_mem_copy_handles); - // TODO: check if the model is loaded successfully - if (ret != 0) { - LOG(ERROR) << "Failed to load model " << model_path << " to GPU"; - if (model->FreeGpu(replica_uuid) != 0) { - LOG(ERROR) << "Failed to free memory for model " << model_path; + // TODO: check if the model is loaded successfully + if (ret != 0) { + LOG(ERROR) << "Failed to load model " << model_path << " to GPU"; + if (model->FreeGpu(replica_uuid) != 0) { + LOG(ERROR) << "Failed to free memory for model " << model_path; + } } - } - return ret; + return ret; } int CheckpointStore::LoadModelFromMemAsync( @@ -247,93 +275,218 @@ int CheckpointStore::LoadModelFromMemAsync( const std::unordered_map& gpu_memory_handles, const std::unordered_map& mem_copy_chunks) { - std::unique_lock lock_info(model_info_mutex_); - async_tasks_.emplace(std::async( - std::launch::async, - [this, model_path, replica_uuid, gpu_memory_handles, mem_copy_chunks]() { - return LoadModelFromMem(model_path, replica_uuid, gpu_memory_handles, - mem_copy_chunks); - })); - - return 0; + std::unique_lock lock_info(model_info_mutex_); + async_tasks_.emplace(std::async( + std::launch::async, + [this, model_path, replica_uuid, gpu_memory_handles, mem_copy_chunks]() { + return LoadModelFromMem(model_path, replica_uuid, gpu_memory_handles, + mem_copy_chunks); + })); + + return 0; } int CheckpointStore::WaitModelInGpu(const std::string& model_path, const std::string& replica_uuid) { - // check if the model is in memory - std::shared_ptr model; - std::unique_lock lock_info(model_info_mutex_); - if (model_map_.find(model_path) == model_map_.end()) { - LOG(ERROR) << "Model " << model_path << " is not registered"; - return 1; - } - model = model_map_[model_path]; - lock_info.unlock(); + // check if the model is in memory + std::shared_ptr model; + std::unique_lock lock_info(model_info_mutex_); + if (model_map_.find(model_path) == model_map_.end()) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return 1; + } + model = model_map_[model_path]; + lock_info.unlock(); - return model->WaitInGpu(replica_uuid); + return model->WaitInGpu(replica_uuid); } int CheckpointStore::ClearMem() { - std::unique_lock lock_info(model_info_mutex_); - for (auto& [model_path, model] : model_map_) { - LOG(INFO) << "Unloading model " << model_path; - int ret = model->FreeHost(); - if (ret != 0) { - LOG(ERROR) << "Failed to free memory for model " << model_path; + std::unique_lock lock_info(model_info_mutex_); + for (auto& [model_path, model] : model_map_) { + LOG(INFO) << "Unloading model " << model_path; + int ret = model->FreeHost(); + if (ret != 0) { + LOG(ERROR) << "Failed to free memory for model " << model_path; + } } - } - model_map_.clear(); - LOG(INFO) << "All models unloaded from memory\n"; - return 0; + model_map_.clear(); + LOG(INFO) << "All models unloaded from memory\n"; + return 0; } int CheckpointStore::UnloadModelFromHost(const std::string& model_path) { - std::unique_lock lock_info(model_info_mutex_); - if (model_map_.find(model_path) == model_map_.end()) { - LOG(ERROR) << "Model " << model_path << " is not registered"; - return 1; - } - auto model = model_map_.at(model_path); - lock_info.unlock(); + std::unique_lock lock_info(model_info_mutex_); + if (model_map_.find(model_path) == model_map_.end()) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return 1; + } + auto model = model_map_.at(model_path); + lock_info.unlock(); - return model->FreeHost(); + return model->FreeHost(); } ModelPtr CheckpointStore::GetModelPtr(const std::string& model_path) { - if (model_map_.find(model_path) == model_map_.end()) { - LOG(ERROR) << "Model " << model_path << " is not registered"; - return nullptr; - } - return model_map_.at(model_path); + if (model_map_.find(model_path) == model_map_.end()) { + LOG(ERROR) << "Model " << model_path << " is not registered"; + return nullptr; + } + return model_map_.at(model_path); } MemPtrListMap CheckpointStore::GetDevicePtrsFromMemHandles( - const MemCopyHandleListMap& memory_handles) { - MemPtrListMap gpu_ptrs; - for (const auto& [device_id, gpu_info] : gpu_info_map_) { - const std::string& uuid = gpu_info.uuid_; - if (memory_handles.find(uuid) == memory_handles.end()) { - continue; - } - auto& handle_list = memory_handles.at(uuid); - for (const auto& handle : handle_list) { - // Convert handle string to cuda handle - cudaIpcMemHandle_t* cuda_handle = - reinterpret_cast(const_cast( - reinterpret_cast(handle.cuda_ipc_handle_.data()))); - void* ptr = nullptr; - - cudaSetDevice(device_id); - cudaError_t err = cudaIpcOpenMemHandle(&ptr, *cuda_handle, - cudaIpcMemLazyEnablePeerAccess); - if (err != cudaSuccess || ptr == nullptr) { - LOG(ERROR) << "Failed to open cuda handle on device " << device_id - << " error: " << cudaGetErrorString(err); - exit(1); - } - - gpu_ptrs[device_id].push_back(ptr); + // Parameter is now correctly keyed by int device_id + const std::unordered_map& memory_handles, + const std::unordered_map& mem_copy_chunks) { + MemPtrListMap gpu_ptrs; + + LOG(INFO) << "=== GetDevicePtrsFromMemHandles DEBUG START ==="; + // Log message corrected to reflect the actual key type + LOG(INFO) << "Total memory handle entries (by device ID): " << memory_handles.size(); + + // Iterate directly over the 'memory_handles' map, as its keys are already device IDs. + // This removes the need for `gpu_info_map_` lookup for finding handles. + for (const auto& [device_id, handle_list] : memory_handles) { + // If you still need the UUID or stream for logging or other operations specific to gpu_info_map_, + // you would look it up using device_id. + // For example: + // auto it = gpu_info_map_.find(device_id); + // if (it == gpu_info_map_.end()) { + // LOG(WARNING) << "GPU info not found for device ID: " << device_id << ". Skipping."; + // continue; + // } + // const std::string& uuid = it->second.uuid_; // If UUID is still needed for context/logging + // aclrtStream stream = it->second.stream_; // If stream is needed for ACL calls + + // Set NPU device context for the current thread/operation +#ifdef USE_CANN + aclrtSetDevice(device_id); + LOG(INFO) << "Set NPU device " << device_id << " context."; +#else + // For CUDA, ensure device is set. + cudaSetDevice(device_id); + LOG(INFO) << "Set CUDA device " << device_id << " context."; +#endif + // Corrected log for device ID being processed + LOG(INFO) << "Processing device " << device_id; + LOG(INFO) << "Found " << handle_list.size() << " handles for device " << device_id; + + for (size_t i = 0; i < handle_list.size(); ++i) { + const auto& handle = handle_list[i]; // This `handle` is a MemCopyHandle object +#ifdef USE_CANN + // Extract the handle string directly from the MemCopyHandle object + std::string handle_str = handle.cuda_ipc_handle_; + LOG(INFO) << "Handle [" << i << "] string: '" << handle_str << "' (length: " << handle_str.length() << ")"; + + if (handle_str.empty()) { + LOG(ERROR) << "Empty CANN IPC handle string for device " << device_id << ". Skipping."; + continue; + } + + // Open IPC handle directly using the handle string + void* ptr = nullptr; + aclError ret = cannIpcOpenMemHandle(&ptr, handle_str, device_id); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to open CANN IPC handle '" << handle_str + << "' on device " << device_id << ", error: " << ret; + continue; + } + + if (ptr == nullptr) { + LOG(ERROR) << "CANN IPC returned null pointer for handle '" << handle_str << "' on device " << device_id << ". Skipping."; + continue; + } + + LOG(INFO) << "Successfully opened CANN IPC handle '" << handle_str + << "' to device pointer: " << ptr << " on device " << device_id; + + // Optional: Verify the memory is accessible by sampling it + char sample_data[32]; + size_t sample_size = 32; + + // Parse handle string to get size for sampling + size_t actual_shared_size_for_sampling = 0; + size_t colon_pos = handle_str.find(':'); + if (colon_pos != std::string::npos) { + try { + actual_shared_size_for_sampling = std::stoull(handle_str.substr(colon_pos + 1)); + } catch (const std::exception& e) { + LOG(ERROR) << "Failed to parse handle_str '" << handle_str << "': " << e.what(); + actual_shared_size_for_sampling = 0; + } + } else { + LOG(ERROR) << "Invalid handle_str format (missing colon): " << handle_str; + actual_shared_size_for_sampling = 0; + } + + if (actual_shared_size_for_sampling > 0) { + sample_size = std::min(sample_size, actual_shared_size_for_sampling); + } else { + LOG(WARNING) << "Could not determine actual shared memory size for handle '" << handle_str << "'. Sampling with default " << sample_size << " bytes."; + } + + + // * comment it out to increase loading speed + // if (sample_size > 0) { + // aclError sample_ret = aclrtMemcpy( + // sample_data, + // sample_size, + // ptr, + // sample_size, + // ACL_MEMCPY_DEVICE_TO_HOST); + + // if (sample_ret == ACL_ERROR_NONE) { + // LOG(INFO) << "Memory accessibility verified for imported pointer " << ptr; + // std::stringstream ss; + // ss << "Sample data (first " << sample_size << " bytes):" << std::endl; + // for (size_t j = 0; j < sample_size; j += 16) { + // ss << " " << std::hex << std::setfill('0') << std::setw(4) << j << ": "; + // for (size_t k = 0; k < 16 && (j + k) < sample_size; k++) { + // ss << std::setw(2) << static_cast(static_cast(sample_data[j + k])) << " "; + // } + // ss << std::endl; + // } + // LOG(INFO) << ss.str(); + // } else { + // LOG(ERROR) << "Memory accessibility check failed for imported pointer " << ptr + // << ", error: " << sample_ret; + // } + // } else { + // LOG(WARNING) << "Skipping memory accessibility check for handle '" << handle_str << "' as sample size is 0."; + // } + + gpu_ptrs[device_id].push_back(ptr); +#else + // Original CUDA implementation + // Convert handle string to cuda handle + cudaIpcMemHandle_t* cuda_handle = + reinterpret_cast(const_cast( + reinterpret_cast(handle.cuda_ipc_handle_.data()))); + void* ptr = nullptr; + + cudaSetDevice(device_id); + cudaError_t err = cudaIpcOpenMemHandle(&ptr, *cuda_handle, + cudaIpcMemLazyEnablePeerAccess); + if (err != cudaSuccess || ptr == nullptr) { + LOG(ERROR) << "Failed to open cuda handle on device " << device_id + << " error: " << cudaGetErrorString(err); + exit(1); + } + + gpu_ptrs[device_id].push_back(ptr); +#endif } } - return gpu_ptrs; + + LOG(INFO) << "Final gpu_ptrs map size: " << gpu_ptrs.size(); + for (const auto& [dev_id, ptr_list] : gpu_ptrs) { + LOG(INFO) << "Device " << dev_id << " has " << ptr_list.size() << " pointers"; + for (size_t i = 0; i < ptr_list.size(); ++i) { + LOG(INFO) << " Pointer " << i << ": " << ptr_list[i]; + } + } + LOG(INFO) << "=== GetDevicePtrsFromMemHandles DEBUG END ==="; + + return gpu_ptrs; } \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/checkpoint_store.h b/sllm_store/csrc/sllm_store/checkpoint_store.h index 14a004a..f784350 100644 --- a/sllm_store/csrc/sllm_store/checkpoint_store.h +++ b/sllm_store/csrc/sllm_store/checkpoint_store.h @@ -2,18 +2,18 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #pragma once @@ -27,11 +27,20 @@ #include // Third-party library headers +#ifdef USE_CANN +#include "acl/acl.h" +#else #include +#endif // Own Headers -#include "cuda_memory.h" +#ifdef USE_CANN +#include "cann_memory.h" +// #include "cann_memory_pool.h" +#else +#include "cuda_memory.h_ // #include "cuda_memory_pool.h" +#endif #include "model.h" #include "pinned_memory.h" #include "pinned_memory_pool.h" @@ -72,7 +81,11 @@ class CheckpointStore { // int device_id_; size_t total_memory_ = 0; size_t free_memory_ = 0; - cudaStream_t stream_; +#ifdef USE_CANN + aclrtStream stream_; // CANN stream +#else + cudaStream_t stream_; // CUDA stream +#endif }; const std::filesystem::path storage_path_; @@ -97,11 +110,18 @@ class CheckpointStore { int InitializeModel(const std::shared_ptr& model); int AllocatePinnedMemory(const std::shared_ptr& model); std::vector> CalculateChunks(size_t offset, - size_t size); + size_t size); +#ifdef USE_CANN + int AllocateCannMemory( + const std::shared_ptr& gpu_replica, + std::vector> gpu_memory_sizes); +#else int AllocateCudaMemory( const std::shared_ptr& gpu_replica, std::vector> gpu_memory_sizes); +#endif ModelPtr GetModelByName(const std::string& model_path); MemPtrListMap GetDevicePtrsFromMemHandles( - const MemCopyHandleListMap& memory_handles); + const std::unordered_map& memory_handles, // Mapped by int (device_id) + const std::unordered_map& mem_copy_chunks); }; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/error_handling.h b/sllm_store/csrc/sllm_store/error_handling.h index 596e7fa..dcacf69 100644 --- a/sllm_store/csrc/sllm_store/error_handling.h +++ b/sllm_store/csrc/sllm_store/error_handling.h @@ -17,7 +17,12 @@ // ---------------------------------------------------------------------------- #pragma once +#ifdef USE_CANN +#include +#include +#else #include +#endif #include #include #include diff --git a/sllm_store/csrc/sllm_store/gpu_replica.cpp b/sllm_store/csrc/sllm_store/gpu_replica.cpp index 694935f..1434b08 100644 --- a/sllm_store/csrc/sllm_store/gpu_replica.cpp +++ b/sllm_store/csrc/sllm_store/gpu_replica.cpp @@ -2,35 +2,66 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at // -// You may obtain a copy of the License at +// http://www.apache.org/licenses/LICENSE-2.0 // -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #include "gpu_replica.h" +#ifdef USE_CANN +#include "acl/acl.h" +#include "../checkpoint/cann_ipc.h" +#else #include +#endif #include void GpuReplica::Clear() { - for (auto& [device_id, device_ptr] : device_ptrs_) { - cudaSetDevice(device_id); - cudaError_t err = cudaIpcCloseMemHandle(device_ptr); - if (err != cudaSuccess) { - LOG(ERROR) << "Failed to close memory handle for device " << device_id - << " error: " << cudaGetErrorString(err); + // Iterate through all device IDs and their corresponding lists of device pointers. + for (auto& [device_id, device_ptr] : device_ptrs_) { +#ifdef USE_CANN + // Set the current NPU device context for the current thread. + aclError ret = aclrtSetDevice(device_id); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to set device " << device_id << " error: " << ret; + } + + // Iterate through each device pointer that was opened via IPC and close its handle. + aclError close_ret = cannIpcCloseMemHandle(device_ptr); + if (close_ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to close CANN IPC memory handle for device " << device_id + << " pointer " << device_ptr << ", error: " << close_ret; + } else { + LOG(INFO) << "Successfully closed CANN IPC memory handle for device " << device_id + << " pointer " << device_ptr; + } +#else + // For CUDA, set the device and close IPC memory handles. + cudaSetDevice(device_id); + cudaError_t err = cudaIpcCloseMemHandle(device_ptr); + if (err != cudaSuccess) { + LOG(ERROR) << "Failed to close memory handle for device " << device_id + << " error: " << cudaGetErrorString(err); + } +#endif } - } - gpu_loading_queue_.clear(); - tensor_offsets_.clear(); - state_ = MemoryState::INTERRUPTED; - cv_.notify_all(); + // Clear various internal data structures associated with the GPU replica. + gpu_loading_queue_.clear(); // Clear the queue of tensors waiting to be loaded to GPU. + tensor_offsets_.clear(); // Clear the map storing tensor offsets within the GPU memory. + + // Update the memory state to INTERRUPTED, indicating that the replica's memory is no longer valid. + state_ = MemoryState::INTERRUPTED; + + // Notify all waiting threads that the state has changed. This is typically used in conjunction + // with a condition variable (cv_) to unblock threads that might be waiting for the GPU replica + // to be in a specific state (e.g., loaded or ready). + cv_.notify_all(); } \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/model.cpp b/sllm_store/csrc/sllm_store/model.cpp index 2d8a62c..577f416 100644 --- a/sllm_store/csrc/sllm_store/model.cpp +++ b/sllm_store/csrc/sllm_store/model.cpp @@ -2,23 +2,24 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #include "model.h" #include #include +#include // for posix_memalign <- this fix O_DIRECT issue #include #include @@ -26,585 +27,969 @@ #include #include #include +#include +#include #include #include #include #include // Third-party library headers +#ifdef USE_CANN +#include "acl/acl.h" +#else #include +#endif #include +#ifdef USE_CANN +#include "cann_error_handling.h" +#include "../checkpoint/cann_ipc.h" +#else #include "error_handling.h" +#endif int Model::Initialize(const std::filesystem::path storage_path) { - std::lock_guard lock(mutex_); - if (state_ != MemoryState::UNINITIALIZED) { + std::lock_guard lock(mutex_); + if (state_ != MemoryState::UNINITIALIZED) { + return 0; + } + model_size_ = 0; + partition_sizes_.clear(); + partition_paths_.clear(); + // Attempt to read from 0 until the file is not found + for (int partition_id = 0;; ++partition_id) { + auto tensor_path = storage_path / model_path_ / + ("tensor.data_" + std::to_string(partition_id)); + if (access(tensor_path.c_str(), F_OK) == -1) { + LOG(INFO) << "Tensor file " << tensor_path << " does not exist"; + break; + } + struct stat st; + if (stat(tensor_path.c_str(), &st) != 0) { + LOG(ERROR) << "Failed to get file size of " << tensor_path; + return -1; + } + model_size_ += st.st_size; + partition_sizes_.push_back(st.st_size); + partition_paths_.push_back(tensor_path); + } + if (model_size_ == 0) { + LOG(ERROR) << "Model " << model_path_ << " does not exist"; + return -1; + } + state_ = MemoryState::UNALLOCATED; + return 0; - } - model_size_ = 0; - partition_sizes_.clear(); - partition_paths_.clear(); - // Attempt to read from 0 until the file is not found - for (int partition_id = 0;; ++partition_id) { - auto tensor_path = storage_path / model_path_ / - ("tensor.data_" + std::to_string(partition_id)); - if (access(tensor_path.c_str(), F_OK) == -1) { - LOG(INFO) << "Tensor file " << tensor_path << " does not exist"; - break; - } - struct stat st; - if (stat(tensor_path.c_str(), &st) != 0) { - LOG(ERROR) << "Failed to get file size of " << tensor_path; - return -1; - } - model_size_ += st.st_size; - partition_sizes_.push_back(st.st_size); - partition_paths_.push_back(tensor_path); - } - if (model_size_ == 0) { - LOG(ERROR) << "Model " << model_path_ << " does not exist"; - return -1; - } - state_ = MemoryState::UNALLOCATED; - - return 0; } int Model::ToHost(int num_threads) { - std::unique_lock lock(mutex_); - if (state_ != MemoryState::ALLOCATED) { - if (state_ == MemoryState::LOADING || state_ == MemoryState::LOADED) { - return 0; - } else { - LOG(ERROR) << "Model " << model_path_ << " is at state " << state_; - return -1; - } - } - - std::vector file_descriptors; - // Attempt to read from 0 until the file is not found - for (int partition_id = 0; partition_id < partition_sizes_.size(); - ++partition_id) { - auto tensor_path = partition_paths_[partition_id]; - if (access(tensor_path.c_str(), F_OK) == -1) { - LOG(ERROR) << "File " << tensor_path << " does not exist"; - return -1; - } - - // Open file - int fd = open(tensor_path.c_str(), O_DIRECT | O_RDONLY); - if (fd < 0) { - std::string err = "open() failed for file: " + tensor_path.string() + - ", error: " + strerror(errno); - LOG(ERROR) << err; - return -1; - } - - file_descriptors.push_back(fd); - } - - LOG(INFO) << "Loading model " << model_path_ << " size " << model_size_ - << " to host"; - if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { - LOG(ERROR) << "CPU memory not allocated"; - return 1; - } - - auto host_buffers = pinned_mem_->get(); - size_t num_chunks = pinned_mem_->num_chunks(); - size_t chunk_size = pinned_mem_->chunk_size(); - host_ptr_vector_ = std::make_shared(); - host_ptr_vector_->init("queue_name", num_chunks); - std::vector> futures; - size_t chunk_per_thread = (num_chunks + num_threads - 1) / num_threads; - LOG(INFO) << "Loading model " << model_path_ << " to host with " - << num_threads << " threads, " << num_chunks << " chunks, " - << chunk_size << " chunk size, " << chunk_per_thread - << " chunks per thread"; - - state_ = MemoryState::LOADING; - lock.unlock(); - - for (int thread_idx = 0; thread_idx < num_threads; ++thread_idx) { - futures.emplace_back(std::async(std::launch::async, [&, thread_idx]() { - size_t partition_id = 0; - size_t file_offset = thread_idx * chunk_per_thread * chunk_size; - while (partition_id < partition_sizes_.size() && - file_offset >= partition_sizes_.at(partition_id)) { - file_offset -= partition_sizes_.at(partition_id); - partition_id += 1; - } - if (partition_id >= partition_sizes_.size()) { - LOG(INFO) << "Thread " << thread_idx << " early exits"; - return 0; - } - LOG(INFO) << "Thread " << thread_idx << " starting from partition " - << partition_id << " offset " << file_offset; - for (size_t chunk_idx = thread_idx * chunk_per_thread; - chunk_idx < (thread_idx + 1) * chunk_per_thread && - chunk_idx < num_chunks; - ++chunk_idx) { - size_t size = - std::min(chunk_size, model_size_ - chunk_idx * chunk_size); - if (host_buffers[chunk_idx] == nullptr) { - LOG(ERROR) << "Host buffer not allocated"; - return -1; + std::unique_lock lock(mutex_); + if (state_ != MemoryState::ALLOCATED) { + if (state_ == MemoryState::LOADING || state_ == MemoryState::LOADED) { + return 0; + } else { + LOG(ERROR) << "Model " << model_path_ << " is at state " << state_; + return -1; } + } - if (state_ == MemoryState::CANCELLED) { - LOG(INFO) << "Loading from disk for model " << model_path_ - << " is cancelled"; - return 0; + LOG(INFO) << "Loading model " << model_path_ << " size " << model_size_ + << " to host"; + if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { + LOG(ERROR) << "CPU memory not allocated"; + return 1; + } + + auto host_buffers = pinned_mem_->get(); + size_t num_chunks = pinned_mem_->num_chunks(); + size_t chunk_size = pinned_mem_->chunk_size(); + host_ptr_vector_ = std::make_shared(); + host_ptr_vector_->init("queue_name", num_chunks); + std::vector> futures; + size_t chunk_per_thread = (num_chunks + num_threads - 1) / num_threads; + LOG(INFO) << "Loading model " << model_path_ << " to host with " + << num_threads << " threads, " << num_chunks << " chunks, " + << chunk_size << " chunk size, " << chunk_per_thread + << " chunks per thread"; + + state_ = MemoryState::LOADING; + lock.unlock(); + + for (int thread_idx = 0; thread_idx < num_threads; ++thread_idx) { + futures.emplace_back(std::async(std::launch::async, [&, thread_idx]() { + LOG(INFO) << "=== Thread " << thread_idx << " START ==="; + + // Each thread opens its own file descriptors + std::vector thread_fds; + LOG(INFO) << "Thread " << thread_idx << ": Opening " + << partition_sizes_.size() << " partition files"; + + for (int partition_id = 0; partition_id < partition_sizes_.size(); + ++partition_id) { + auto tensor_path = partition_paths_[partition_id]; + LOG(INFO) << "Thread " << thread_idx << ": Opening partition " + << partition_id << ": " << tensor_path; + + // Check file exists first + if (access(tensor_path.c_str(), F_OK) == -1) { + LOG(ERROR) << "Thread " << thread_idx << ": File " << tensor_path + << " does not exist (access check failed)"; + for (int cleanup_fd : thread_fds) close(cleanup_fd); + return -1; + } + + // * If using O_DIRECT, will cause pread() FILE EXISTS error + int fd = open(tensor_path.c_str(), O_DIRECT | O_RDONLY); + if (fd < 0) { + int err_no = errno; + LOG(ERROR) << "Thread " << thread_idx << ": open() failed for " + << tensor_path << ", errno: " << err_no + << ", error: " << strerror(err_no); + for (int cleanup_fd : thread_fds) close(cleanup_fd); + return -1; + } + + // // Get block size for alignment + // struct stat st; + // if (fstat(fd, &st) != 0) { + // int err_no = errno; + // LOG(ERROR) << "Thread " << thread_idx << ": fstat failed for " + // << tensor_path << ", errno: " << err_no + // << ", error: " << strerror(err_no); + // close(fd); + // for (int cleanup_fd : thread_fds) close(cleanup_fd); + // return -1; + // } + // size_t alignment = st.st_blksize; + // if (alignment == 0) { + // alignment = 4096; // Fallback to common page size + // LOG(WARNING) << "Thread " << thread_idx << ": fstat returned 0 blksize, using fallback 4096"; + // } + // LOG(INFO) << "Thread " << thread_idx << ": Using alignment/block size=" << alignment << " for " << tensor_path; + + // // Test pread with aligned buffer and size + // void* test_buf; + // if (posix_memalign(&test_buf, alignment, alignment) != 0) { + // LOG(ERROR) << "Thread " << thread_idx << ": posix_memalign failed for test buffer"; + // close(fd); + // for (int cleanup_fd : thread_fds) close(cleanup_fd); + // return -1; + // } + // ssize_t test_ret = pread(fd, test_buf, alignment, 0); + // free(test_buf); + // if (test_ret < 0) { + // int err_no = errno; + // LOG(ERROR) << "Thread " << thread_idx << ": immediate pread() failed for " + // << tensor_path << ", errno: " << err_no + // << ", error: " << strerror(err_no); + // close(fd); // Clean up this fd before returning + // for (int cleanup_fd : thread_fds) close(cleanup_fd); + // return -1; + // } else { + // LOG(INFO) << "Thread " << thread_idx << ": immediate pread() succeeded, read " + // << test_ret << " bytes"; + // } + + LOG(INFO) << "Thread " << thread_idx << ": Successfully opened " + << "partition " << partition_id << " with fd=" << fd + << ", file size=" << partition_sizes_[partition_id] + << " bytes"; + thread_fds.push_back(fd); + } + + size_t partition_id = 0; + size_t file_offset = thread_idx * chunk_per_thread * chunk_size; + LOG(INFO) << "Thread " << thread_idx + << ": Initial file_offset=" << file_offset; + + // Calculate starting partition + size_t original_offset = file_offset; + while (partition_id < partition_sizes_.size() && + file_offset >= partition_sizes_.at(partition_id)) { + LOG(INFO) << "Thread " << thread_idx << ": Skipping partition " + << partition_id << " (size=" << partition_sizes_.at(partition_id) + << "), remaining offset=" << file_offset; + file_offset -= partition_sizes_.at(partition_id); + partition_id += 1; + } + + if (partition_id >= partition_sizes_.size()) { + LOG(INFO) << "Thread " << thread_idx << " early exits (offset " + << original_offset << " beyond all partitions)"; + for (int fd : thread_fds) close(fd); + return 0; + } + + LOG(INFO) << "Thread " << thread_idx << " starting from partition " + << partition_id << " offset " << file_offset + << " (adjusted from " << original_offset << ")"; + + size_t total_bytes_read = 0; + size_t chunks_processed = 0; + + for (size_t chunk_idx = thread_idx * chunk_per_thread; + chunk_idx < (thread_idx + 1) * chunk_per_thread && + chunk_idx < num_chunks; + ++chunk_idx) { + size_t size = std::min(chunk_size, model_size_ - chunk_idx * chunk_size); + + LOG(INFO) << "Thread " << thread_idx << ": Processing chunk " << chunk_idx + << "/" << num_chunks << ", size=" << size << " bytes"; + + if (host_buffers[chunk_idx] == nullptr) { + LOG(ERROR) << "Thread " << thread_idx << ": Host buffer[" << chunk_idx + << "] is null!"; + for (int fd : thread_fds) close(fd); + return -1; + } + + if (state_ == MemoryState::CANCELLED) { + LOG(INFO) << "Thread " << thread_idx << ": Loading cancelled, processed " + << chunks_processed << " chunks"; + for (int fd : thread_fds) close(fd); + return 0; + } + + size_t remaining_bytes = size; + size_t buffer_offset = 0; + size_t chunk_bytes_read = 0; + + while (remaining_bytes > 0) { + if (partition_id >= partition_sizes_.size()) { + LOG(ERROR) << "Thread " << thread_idx + << ": Unexpected end of partitions at partition " + << partition_id << " while reading chunk " << chunk_idx; + for (int fd : thread_fds) close(fd); + return -1; + } + + int fd = thread_fds[partition_id]; + + // Get alignment again (in case per-partition, but it's per-fd) + struct stat st; + if (fstat(fd, &st) != 0) { + int err_no = errno; + LOG(ERROR) << "Thread " << thread_idx << ": fstat failed during read"; + for (int fd : thread_fds) close(fd); + return -1; + } + size_t alignment = st.st_blksize; + if (alignment == 0) alignment = 4096; + + size_t bytes_left_in_partition = partition_sizes_.at(partition_id) - file_offset; + size_t read_size = std::min(remaining_bytes, bytes_left_in_partition); + + LOG(INFO) << "Thread " << thread_idx << ": Reading " << read_size + << " bytes from partition " << partition_id << " at offset " + << file_offset << " (fd=" << fd << ")"; + + // O_DIRECT aligned read using temp buffer + size_t offset_misalign = file_offset % alignment; + size_t aligned_offset = file_offset - offset_misalign; + size_t aligned_read_size = ((read_size + offset_misalign + alignment - 1) / alignment) * alignment; + + void* temp_buf; + if (posix_memalign(&temp_buf, alignment, aligned_read_size) != 0) { + LOG(ERROR) << "Thread " << thread_idx << ": posix_memalign failed for read buffer"; + for (int fd : thread_fds) close(fd); + return -1; + } + + ssize_t ret = pread(fd, temp_buf, aligned_read_size, aligned_offset); + if (ret < 0) { + int err_no = errno; + auto tensor_path = partition_paths_[partition_id]; + LOG(ERROR) << "Thread " << thread_idx << ": pread() failed for file: " + << tensor_path << ", errno: " << err_no + << ", error: " << strerror(err_no) << ", fd=" << fd + << ", offset=" << aligned_offset << ", size=" << aligned_read_size; + free(temp_buf); + for (int fd : thread_fds) close(fd); + return -1; + } + if (ret < (ssize_t)(read_size + offset_misalign)) { + LOG(ERROR) << "Thread " << thread_idx << ": Short read for file " + << partition_paths_[partition_id] << ": expected at least " + << (read_size + offset_misalign) << " bytes, got " << ret << " (fd=" << fd + << ", offset=" << aligned_offset << ")"; + free(temp_buf); + for (int fd : thread_fds) close(fd); + return -1; + } + + // Copy the relevant part to the host buffer + memcpy((void*)(host_buffers[chunk_idx] + buffer_offset), + (char*)temp_buf + offset_misalign, + read_size); + free(temp_buf); + + // Simulate ret as read_size for the rest of the code + ret = read_size; + + // Sample first few bytes to verify data is non-zero + if (buffer_offset == 0 && chunk_idx % 10 == 0) { + unsigned char* data_ptr = (unsigned char*)(host_buffers[chunk_idx]); + LOG(INFO) << "Thread " << thread_idx << ": Chunk " << chunk_idx + << " first 16 bytes: " << std::hex << std::setfill('0'); + for (int i = 0; i < std::min(16, (int)ret); i++) { + std::cout << std::setw(2) << (int)data_ptr[i] << " "; + } + std::cout << std::dec << std::endl; + } + + remaining_bytes -= ret; + buffer_offset += ret; + file_offset += ret; + chunk_bytes_read += ret; + total_bytes_read += ret; + + // Move to next partition if current one is exhausted + if (file_offset >= partition_sizes_.at(partition_id)) { + LOG(INFO) << "Thread " << thread_idx << ": Finished partition " + << partition_id << ", moving to partition " + << (partition_id + 1); + partition_id += 1; + file_offset = 0; + } + } + + LOG(INFO) << "Thread " << thread_idx << ": Chunk " << chunk_idx + << " completed, read " << chunk_bytes_read << " bytes"; + + host_ptr_vector_->enqueue(chunk_idx, Batch{chunk_idx, size}); + chunks_processed++; + } + + // Close thread-specific file descriptors + LOG(INFO) << "Thread " << thread_idx << ": Closing " + << thread_fds.size() << " file descriptors"; + for (int i = 0; i < thread_fds.size(); i++) { + close(thread_fds[i]); + LOG(INFO) << "Thread " << thread_idx << ": Closed fd=" << thread_fds[i] + << " for partition " << i; + } + + LOG(INFO) << "=== Thread " << thread_idx << " COMPLETE: processed " + << chunks_processed << " chunks, read " << total_bytes_read + << " bytes total ==="; + return 0; + })); + } + + bool error = false; + for (auto& future : futures) { + int ret = future.get(); + if (ret != 0) { + LOG(ERROR) << "Error reading from disk, ret " << ret; + error = true; } + } - int fd = file_descriptors[partition_id]; - ssize_t ret = - pread(fd, (void*)host_buffers[chunk_idx], size, file_offset); - if (ret < 0) { - auto tensor_path = partition_paths_[partition_id]; - LOG(ERROR) << "pread() failed for file: " << tensor_path - << ", error: " << strerror(errno); - return -1; - } else if (ret != size) { - if (ret < size && partition_id + 1 < file_descriptors.size()) { - partition_id += 1; - file_offset = 0; - size_t remaining_size = size - ret; - int fd = file_descriptors[partition_id]; - ret = pread(fd, (void*)(host_buffers[chunk_idx] + ret), - remaining_size, file_offset); - if (ret != remaining_size) { - auto tensor_path = partition_paths_[partition_id]; - LOG(ERROR) << "Failed to read file: " << tensor_path - << " read: " << ret << " expected: " << remaining_size; - return -1; + lock.lock(); + if (error) { + state_ = MemoryState::INTERRUPTED; + // Deal with gpu replicas + for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { + if (gpu_replica->state_ == MemoryState::LOADING) { + gpu_replica->state_ = MemoryState::CANCELLED; + gpu_replica->cv_.notify_all(); } - } else { - auto tensor_path = partition_paths_[partition_id]; - LOG(ERROR) << "Failed to read file: " << tensor_path - << " read: " << ret << " expected: " << size; - return -1; - } + // wait for gpu replicas to finish + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + // Note: gpu replicas will be handled by the caller } - file_offset += ret; + // release pinned memory + pinned_mem_.reset(); + state_ = MemoryState::UNALLOCATED; - host_ptr_vector_->enqueue(chunk_idx, Batch{chunk_idx, size}); - } + return -1; + } - return 0; - })); - } + // === HOST MEMORY VALIDATION DEBUG === + LOG(INFO) << "=== HOST MEMORY VALIDATION START ==="; + LOG(INFO) << "Validating host memory after file loading completion"; + LOG(INFO) << "Total chunks: " << num_chunks << ", chunk size: " << chunk_size; - bool error = false; - for (auto& future : futures) { - int ret = future.get(); - if (ret != 0) { - LOG(ERROR) << "Error reading from disk, ret " << ret; - error = true; - } - } + size_t total_non_zero_bytes = 0; + size_t total_zero_bytes = 0; - // close file - for (int fd : file_descriptors) { - close(fd); - } + // Sample multiple chunks across the entire model + std::vector sample_chunks; + sample_chunks.push_back(0); // First chunk + sample_chunks.push_back(num_chunks / 4); // 25% through + sample_chunks.push_back(num_chunks / 2); // 50% through + sample_chunks.push_back(3 * num_chunks / 4); // 75% through + sample_chunks.push_back(num_chunks - 1); // Last chunk - lock.lock(); - if (error) { - state_ = MemoryState::INTERRUPTED; - // Deal with gpu replicas - for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { - if (gpu_replica->state_ == MemoryState::LOADING) { - gpu_replica->state_ = MemoryState::CANCELLED; - gpu_replica->cv_.notify_all(); - } - // wait for gpu replicas to finish - gpu_replica->cv_.wait(lock, [&gpu_replica] { - return gpu_replica->state_ == MemoryState::LOADED || - gpu_replica->state_ == MemoryState::INTERRUPTED; - }); - // Note: gpu replicas will be handled by the caller - } - // release pinned memory - pinned_mem_.reset(); - state_ = MemoryState::UNALLOCATED; + for (size_t chunk_idx : sample_chunks) { + if (chunk_idx >= num_chunks) continue; - return -1; - } + LOG(INFO) << "Sampling chunk " << chunk_idx << "/" << num_chunks; + unsigned char* chunk_data = (unsigned char*)host_buffers[chunk_idx]; - state_ = MemoryState::LOADED; - LOG(INFO) << "Finished loading model " << model_path_ << " from disk"; + if (chunk_data == nullptr) { + LOG(ERROR) << "Host buffer[" << chunk_idx << "] is null!"; + continue; + } - return 0; -} + size_t actual_chunk_size = std::min(chunk_size, model_size_ - chunk_idx * chunk_size); + LOG(INFO) << "Chunk " << chunk_idx << " size: " << actual_chunk_size << " bytes"; -int Model::ToGpu( - const std::string& replica_uuid, const MemPtrListMap& device_ptrs, - const std::unordered_map& mem_copy_chunks, - const std::unordered_map& mem_copy_handles) { - std::unique_lock lock(mutex_); - if (state_ == MemoryState::UNINITIALIZED) { - LOG(ERROR) << "Model " << model_path_ << " is not initialized"; - return -1; - } - - if (gpu_replicas_.find(replica_uuid) != gpu_replicas_.end()) { - LOG(ERROR) << "Replica " << replica_uuid << " already exists"; - return -1; - } - LOG(INFO) << "Creating replica " << replica_uuid; - gpu_replicas_.emplace(replica_uuid, std::make_shared()); - GpuReplicaPtr gpu_replica = gpu_replicas_.at(replica_uuid); - for (const auto& [device_id, _] : device_ptrs) { - LOG(INFO) << "Creating queue for device " << device_id; - gpu_replica->gpu_loading_queue_.emplace(device_id, - std::make_shared()); - } - gpu_replica->device_ptrs_ = device_ptrs; - gpu_replica->state_ = MemoryState::LOADING; - LOG(INFO) << "Created replica " << replica_uuid; - cv_.notify_all(); - lock.unlock(); - - // Start a dispatcher first - auto dispatch_future = std::async( - std::launch::async, - [this, gpu_replica, mem_copy_chunks, mem_copy_handles]() { - return DispatchToGpu(gpu_replica, mem_copy_chunks, mem_copy_handles); - }); - - LOG(INFO) << "Dispatcher started for model " << model_path_; - - std::unordered_map> futures; - for (auto& [device_id, device_ptr_list] : device_ptrs) { - futures.emplace( - device_id, std::async(std::launch::async, [this, gpu_replica, device_id, - device_ptr_list]() { - auto gpu_loading_queue = - gpu_replica->gpu_loading_queue_.at(device_id); - if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { - LOG(ERROR) << "CPU memory not allocated"; - return 1; - } - - cudaError_t err = cudaSetDevice(device_id); - if (err != cudaSuccess) { - LOG(ERROR) << "Error setting device " << cudaGetErrorString(err); - return 1; - } - - auto& host_buffers = pinned_mem_->get(); - - size_t loaded_size = 0; - while (true) { - auto [chunk_id, chunk_offset, size, gpu_offset, handle_idx] = - gpu_loading_queue->dequeue(); - if (size == 0) { - break; + // Count zero vs non-zero bytes in first 1KB + size_t sample_size = std::min((size_t)1024, actual_chunk_size); + size_t chunk_zero_bytes = 0; + size_t chunk_non_zero_bytes = 0; + + for (size_t i = 0; i < sample_size; i++) { + if (chunk_data[i] == 0) { + chunk_zero_bytes++; + } else { + chunk_non_zero_bytes++; + } + } + + total_zero_bytes += chunk_zero_bytes; + total_non_zero_bytes += chunk_non_zero_bytes; + + LOG(INFO) << "Chunk " << chunk_idx << " sample (first " << sample_size + << " bytes): " << chunk_non_zero_bytes << " non-zero, " + << chunk_zero_bytes << " zero bytes"; + + // Show hex dump of first 64 bytes + LOG(INFO) << "Chunk " << chunk_idx << " first 64 bytes (hex):"; + for (size_t i = 0; i < std::min((size_t)64, actual_chunk_size); i += 16) { + std::cout << " " << std::hex << std::setfill('0') << std::setw(8) << i << ": "; + for (size_t j = 0; j < 16 && (i + j) < std::min((size_t)64, actual_chunk_size); j++) { + std::cout << std::setw(2) << (int)chunk_data[i + j] << " "; } - if (gpu_replica->state_ == MemoryState::CANCELLED) { - LOG(INFO) << "Loading from mem for model " << model_path_ - << " is cancelled," - << " chunk " << chunk_id << " offset " - << " size " << size; - return 0; + std::cout << std::dec << std::endl; + } + + // Show as float16 values (first 32 bytes = 16 float16 values) + if (actual_chunk_size >= 32) { + LOG(INFO) << "Chunk " << chunk_idx << " first 16 float16 values:"; + uint16_t* float16_data = (uint16_t*)chunk_data; + for (int i = 0; i < 16; i++) { + // Convert float16 to float32 for display + uint32_t f32_bits = ((uint32_t)(float16_data[i] & 0x8000)) << 16; // Sign + if ((float16_data[i] & 0x7c00) != 0) { // Not zero/denormal + f32_bits |= (((uint32_t)(float16_data[i] & 0x7c00) + 0x1c000)) << 13; // Exponent + f32_bits |= ((uint32_t)(float16_data[i] & 0x03ff)) << 13; // Mantissa + } + float f32_val = *(float*)&f32_bits; + std::cout << f32_val << " "; } + std::cout << std::endl; + } + } - CUDA_CHECK( - cudaMemcpy( - (void*)((char*)device_ptr_list[handle_idx] + gpu_offset), - (void*)(host_buffers[chunk_id] + chunk_offset), size, - cudaMemcpyHostToDevice), - "cudaMemcpy Error"); - loaded_size += size; - } + LOG(INFO) << "HOST MEMORY SUMMARY:"; + LOG(INFO) << " Total sampled bytes: " << (total_zero_bytes + total_non_zero_bytes); + LOG(INFO) << " Non-zero bytes: " << total_non_zero_bytes; + LOG(INFO) << " Zero bytes: " << total_zero_bytes; + LOG(INFO) << " Non-zero percentage: " + << (100.0 * total_non_zero_bytes / (total_zero_bytes + total_non_zero_bytes)) + << "%"; + + if (total_non_zero_bytes == 0) { + LOG(ERROR) << "*** CRITICAL: ALL HOST MEMORY IS ZERO! File loading failed! ***"; + } else if (total_non_zero_bytes < (total_zero_bytes + total_non_zero_bytes) / 10) { + LOG(WARNING) << "*** WARNING: Less than 10% non-zero data in host memory ***"; + } else { + LOG(INFO) << "*** HOST MEMORY VALIDATION PASSED: Contains non-zero data ***"; + } - LOG(INFO) << "Finished loading tensor from memory to device " - << device_id; + LOG(INFO) << "=== HOST MEMORY VALIDATION END ==="; - return 0; - })); - } - - LOG(INFO) << "Waiting for model " << model_path_ << " num tasks " - << futures.size() << " state " << gpu_replica->state_; - dispatch_future.wait(); - bool error = false; - for (auto& [device_id, future] : futures) { - int ret = future.get(); - if (ret != 0) { - LOG(ERROR) << "Error copying to device " << device_id; - error = true; - } - } - - lock.lock(); - futures.clear(); - - if (error) { - LOG(ERROR) << "Failed to load model " << model_path_; - gpu_replica->state_ = MemoryState::INTERRUPTED; - } else { - gpu_replica->state_ = MemoryState::LOADED; - } - gpu_replica->cv_.notify_all(); - - // TODO: move to background thread - for (auto& [device_id, device_ptr_list] : gpu_replica->device_ptrs_) { - cudaSetDevice(device_id); - for (auto device_ptr : device_ptr_list) { - cudaError_t err = cudaIpcCloseMemHandle(device_ptr); - if (err != cudaSuccess) { - LOG(ERROR) << "Failed to close memory handle for device " << device_id - << " error: " << cudaGetErrorString(err); - } - } - } - - if (gpu_replica->state_ == MemoryState::INTERRUPTED) { - LOG(ERROR) << "Model " << model_path_ << " replica " << replica_uuid - << " is interrupted"; - return -1; - } - - return 0; + state_ = MemoryState::LOADED; + LOG(INFO) << "Finished loading model " << model_path_ << " from disk"; + + return 0; +} + +int Model::ToGpu(const std::string& replica_uuid, + const MemPtrListMap& device_ptrs, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles) { + std::unique_lock lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(ERROR) << "Model " << model_path_ << " is not initialized"; + return -1; + } + + if (gpu_replicas_.find(replica_uuid) != gpu_replicas_.end()) { + LOG(ERROR) << "Replica " << replica_uuid << " already exists"; + return -1; + } + LOG(INFO) << "Creating replica " << replica_uuid; + gpu_replicas_.emplace(replica_uuid, std::make_shared()); + GpuReplicaPtr gpu_replica = gpu_replicas_.at(replica_uuid); + for (const auto& [device_id, _] : device_ptrs) { + LOG(INFO) << "Creating queue for device " << device_id; + gpu_replica->gpu_loading_queue_.emplace(device_id, + std::make_shared()); + } + gpu_replica->device_ptrs_ = device_ptrs; + gpu_replica->state_ = MemoryState::LOADING; + LOG(INFO) << "Created replica " << replica_uuid; + cv_.notify_all(); + lock.unlock(); + + // Start a dispatcher first + auto dispatch_future = std::async( + std::launch::async, + [this, gpu_replica, mem_copy_chunks, mem_copy_handles]() { + return DispatchToGpu(gpu_replica, mem_copy_chunks, mem_copy_handles); + }); + + LOG(INFO) << "Dispatcher started for model " << model_path_; + + std::unordered_map> futures; + for (auto& [device_id, device_ptr_list] : device_ptrs) { + futures.emplace( + device_id, std::async(std::launch::async, [this, gpu_replica, device_id, + device_ptr_list]() { + auto gpu_loading_queue = + gpu_replica->gpu_loading_queue_.at(device_id); + if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { + LOG(ERROR) << "CPU memory not allocated"; + return 1; + } + +#ifdef USE_CANN + aclError ret = aclrtSetDevice(device_id); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Error setting device " << ret; + return 1; + } +#else + cudaError_t err = cudaSetDevice(device_id); + if (err != cudaSuccess) { + LOG(ERROR) << "Error setting device " << cudaGetErrorString(err); + return 1; + } +#endif + + auto& host_buffers = pinned_mem_->get(); + + size_t loaded_size = 0; + size_t total_chunks_processed = 0; + LOG(INFO) << "=== NPU LOADING DEBUG: Device " << device_id << " START ==="; + + while (true) { + auto [chunk_id, chunk_offset, size, gpu_offset, handle_idx] = + gpu_loading_queue->dequeue(); + if (size == 0) { + LOG(INFO) << "=== NPU LOADING DEBUG: Device " << device_id + << " - End signal received, processed " << total_chunks_processed << " chunks ==="; + break; + } + if (gpu_replica->state_ == MemoryState::CANCELLED) { + LOG(INFO) << "Loading from mem for model " << model_path_ + << " is cancelled," + << " chunk " << chunk_id << " offset " + << " size " << size; + return 0; + } + +#ifdef USE_CANN + aclrtSetDevice(device_id); + + if (device_ptr_list[handle_idx] == nullptr) { + LOG(ERROR) << "Device pointer is null for handle " << handle_idx << " on Device " << device_id; + return 1; + } + + // Debug: Sample host data before copying + if (total_chunks_processed % 50 == 0) { // Sample every 50th chunk + LOG(INFO) << "NPU COPY DEBUG: Chunk " << chunk_id << ", offset " << chunk_offset + << ", size " << size << ", gpu_offset " << gpu_offset; + + unsigned char* host_data = (unsigned char*)(host_buffers[chunk_id] + chunk_offset); + LOG(INFO) << "Host data before copy (first 32 bytes):"; + for (int i = 0; i < std::min(32, (int)size); i += 16) { + std::cout << " " << std::hex << std::setfill('0') << std::setw(8) << i << ": "; + for (int j = 0; j < 16 && (i + j) < std::min(32, (int)size); j++) { + std::cout << std::setw(2) << (int)host_data[i + j] << " "; + } + std::cout << std::dec << std::endl; + } + } + + aclError ret = aclrtMemcpy( + (void*)((char*)device_ptr_list[handle_idx] + gpu_offset), + size, + (const void*)(host_buffers[chunk_id] + chunk_offset), + size, + ACL_MEMCPY_HOST_TO_DEVICE); + if (ret != 0) { + LOG(ERROR) << "device_ptr_list[handle_idx]" << (void*)((char*)device_ptr_list[handle_idx] + gpu_offset) + << "host buffer" << (const void*)(host_buffers[chunk_id] + chunk_offset) << "size" << size; + + LOG(ERROR) << "Failed to copy memory from host to device " + << device_id << " error: " << ret; + return 1; + } + + // // Debug: Verify NPU data after copying + // if (total_chunks_processed % 50 == 0) { // Sample every 50th chunk + // LOG(INFO) << "NPU COPY SUCCESS: Host→NPU copy completed for chunk " << chunk_id; + + // // Sample NPU memory to verify the copy worked + // char npu_sample[32]; + // aclError sample_ret = aclrtMemcpy( + // npu_sample, + // sizeof(npu_sample), + // (void*)((char*)device_ptr_list[handle_idx] + gpu_offset), + // std::min((size_t)32, size), + // ACL_MEMCPY_DEVICE_TO_HOST); + + // if (sample_ret == ACL_ERROR_NONE) { + // LOG(INFO) << "NPU data after copy (first 32 bytes):"; + // for (int i = 0; i < std::min(32, (int)size); i += 16) { + // std::cout << " " << std::hex << std::setfill('0') << std::setw(8) << i << ": "; + // for (int j = 0; j < 16 && (i + j) < std::min(32, (int)size); j++) { + // std::cout << std::setw(2) << (int)(unsigned char)npu_sample[i + j] << " "; + // } + // std::cout << std::dec << std::endl; + // } + + // // Compare host vs NPU data + // unsigned char* host_data = (unsigned char*)(host_buffers[chunk_id] + chunk_offset); + // bool data_matches = true; + // for (int i = 0; i < std::min(32, (int)size); i++) { + // if (host_data[i] != (unsigned char)npu_sample[i]) { + // data_matches = false; + // break; + // } + // } + + // if (data_matches) { + // LOG(INFO) << "✅ HOST → NPU DATA VERIFICATION PASSED for chunk " << chunk_id; + // } else { + // LOG(ERROR) << "❌ HOST → NPU DATA MISMATCH for chunk " << chunk_id; + // } + // } else { + // LOG(ERROR) << "Failed to sample NPU memory for verification, error: " << sample_ret; + // } + // } +#else + CUDA_CHECK( + cudaMemcpy( + (void*)((char*)device_ptr_list[handle_idx] + gpu_offset), + (void*)(host_buffers[chunk_id] + chunk_offset), size, + cudaMemcpyHostToDevice), + "cudaMemcpy Error"); +#endif + loaded_size += size; + total_chunks_processed++; + } + + LOG(INFO) << "=== NPU LOADING COMPLETE: Device " << device_id + << " - Loaded " << loaded_size << " bytes in " << total_chunks_processed << " chunks ==="; + LOG(INFO) << "Finished loading tensor from memory to device " + << device_id; + + return 0; + })); + } + + LOG(INFO) << "Waiting for model " << model_path_ << " num tasks " + << futures.size() << " state " << gpu_replica->state_; + dispatch_future.wait(); + bool error = false; + for (auto& [device_id, future] : futures) { + int ret = future.get(); + if (ret != 0) { + LOG(ERROR) << "Error copying to device " << device_id; + error = true; + } + } + + lock.lock(); + futures.clear(); + + if (error) { + LOG(ERROR) << "Failed to load model " << model_path_; + gpu_replica->state_ = MemoryState::INTERRUPTED; + } else { + gpu_replica->state_ = MemoryState::LOADED; + } + gpu_replica->cv_.notify_all(); + + // TODO: move to background thread + for (auto& [device_id, device_ptr_list] : gpu_replica->device_ptrs_) { +#ifdef USE_CANN + aclrtSetDevice(device_id); + for (auto device_ptr : device_ptr_list) { + // CRITICAL FIX: Use real CANN IPC cleanup now that we have proper IPC + aclError ret = cannIpcCloseMemHandle(device_ptr); + if (ret != ACL_ERROR_NONE) { + LOG(ERROR) << "Failed to close CANN IPC handle for device " << device_id + << " pointer " << device_ptr << ", error: " << ret; + } else { + LOG(INFO) << "Successfully closed CANN IPC handle for device " << device_id + << " pointer " << device_ptr; + } + } +#else + cudaSetDevice(device_id); + for (auto device_ptr : device_ptr_list) { + cudaError_t err = cudaIpcCloseMemHandle(device_ptr); + if (err != cudaSuccess) { + LOG(ERROR) << "Failed to close memory handle for device " << device_id + << " error: " << cudaGetErrorString(err); + } + } +#endif + } + + if (gpu_replica->state_ == MemoryState::INTERRUPTED) { + LOG(ERROR) << "Model " << model_path_ << " replica " << replica_uuid + << " is interrupted"; + return -1; + } + + return 0; } int Model::WaitInHost() { - std::unique_lock lock(mutex_); - if (state_ < MemoryState::LOADED) { - cv_.wait(lock, [this] { - return state_ == MemoryState::LOADED || - state_ == MemoryState::INTERRUPTED; - }); - } - - if (state_ >= MemoryState::INTERRUPTED) { - LOG(INFO) << "Model " << model_path_ << " is interrupted"; - return 1; - } - - return 0; + std::unique_lock lock(mutex_); + if (state_ < MemoryState::LOADED) { + cv_.wait(lock, [this] { + return state_ == MemoryState::LOADED || + state_ == MemoryState::INTERRUPTED; + }); + } + + if (state_ >= MemoryState::INTERRUPTED) { + LOG(INFO) << "Model " << model_path_ << " is interrupted"; + return 1; + } + + return 0; } int Model::WaitInGpu(const std::string& replica_uuid) { - std::unique_lock lock(mutex_); - if (gpu_replicas_.find(replica_uuid) == gpu_replicas_.end()) { - cv_.wait(lock, [this, replica_uuid] { - return gpu_replicas_.find(replica_uuid) != gpu_replicas_.end(); - }); - } - - auto& gpu_replica = gpu_replicas_.at(replica_uuid); - - if (gpu_replica->state_ < MemoryState::LOADED) { - gpu_replica->cv_.wait(lock, [&gpu_replica] { - return gpu_replica->state_ == MemoryState::LOADED || - gpu_replica->state_ == MemoryState::INTERRUPTED; - }); - } - - if (gpu_replica->state_ >= MemoryState::INTERRUPTED) { - LOG(INFO) << "Model " << model_path_ << " is interrupted"; - return 1; - } - - return 0; + std::unique_lock lock(mutex_); + if (gpu_replicas_.find(replica_uuid) == gpu_replicas_.end()) { + cv_.wait(lock, [this, replica_uuid] { + return gpu_replicas_.find(replica_uuid) != gpu_replicas_.end(); + }); + } + + auto& gpu_replica = gpu_replicas_.at(replica_uuid); + + if (gpu_replica->state_ < MemoryState::LOADED) { + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + } + + if (gpu_replica->state_ >= MemoryState::INTERRUPTED) { + LOG(INFO) << "Model " << model_path_ << " is interrupted"; + return 1; + } + + return 0; } int Model::FreeGpu(const std::string& replica_uuid) { - std::unique_lock lock(mutex_); - if (gpu_replicas_.find(replica_uuid) == gpu_replicas_.end()) { - LOG(ERROR) << "Model " << model_path_ << " replica " << replica_uuid - << " is not registered"; - return -1; - } - - auto& gpu_replica = gpu_replicas_.at(replica_uuid); - if (gpu_replica->state_ == MemoryState::UNINITIALIZED) { - LOG(WARNING) << "Model " << model_path_ << " replica " << replica_uuid - << " is not initialized"; + std::unique_lock lock(mutex_); + if (gpu_replicas_.find(replica_uuid) == gpu_replicas_.end()) { + LOG(ERROR) << "Model " << model_path_ << " replica " << replica_uuid + << " is not registered"; + return -1; + } + + auto& gpu_replica = gpu_replicas_.at(replica_uuid); + if (gpu_replica->state_ == MemoryState::UNINITIALIZED) { + LOG(WARNING) << "Model " << model_path_ << " replica " << replica_uuid + << " is not initialized"; + gpu_replicas_.erase(replica_uuid); + return 0; + } + + if (gpu_replica->state_ == MemoryState::LOADING) { + LOG(INFO) << "Waiting for model " << model_path_ << " replica " + << replica_uuid << " to be loaded"; + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + } + gpu_replicas_.erase(replica_uuid); return 0; - } - - if (gpu_replica->state_ == MemoryState::LOADING) { - LOG(INFO) << "Waiting for model " << model_path_ << " replica " - << replica_uuid << " to be loaded"; - gpu_replica->cv_.wait(lock, [&gpu_replica] { - return gpu_replica->state_ == MemoryState::LOADED || - gpu_replica->state_ == MemoryState::INTERRUPTED; - }); - } - - gpu_replicas_.erase(replica_uuid); - return 0; } int Model::FreeHost() { - std::unique_lock lock(mutex_); - if (state_ == MemoryState::UNINITIALIZED) { - LOG(WARNING) << "Model " << model_path_ << " is not initialized"; - return 1; - } - - if (state_ == MemoryState::UNALLOCATED) { - LOG(WARNING) << "Model " << model_path_ << " is not allocated"; - return 1; - } - - if (state_ == MemoryState::LOADING) { - LOG(INFO) << "Waiting for model " << model_path_ << " to be loaded"; - cv_.wait(lock, [this] { - return state_ == MemoryState::LOADED || - state_ == MemoryState::INTERRUPTED; - }); - } - - // make sure no gpu replicas are loading - for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { - if (gpu_replica->state_ == MemoryState::LOADING) { - LOG(INFO) << "Waiting for replica " << replica_uuid << " to be loaded"; - gpu_replica->cv_.wait(lock, [&gpu_replica] { - return gpu_replica->state_ == MemoryState::LOADED || - gpu_replica->state_ == MemoryState::INTERRUPTED; - }); + std::unique_lock lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(WARNING) << "Model " << model_path_ << " is not initialized"; + return 1; + } + + if (state_ == MemoryState::UNALLOCATED) { + LOG(WARNING) << "Model " << model_path_ << " is not allocated"; + return 1; + } + + if (state_ == MemoryState::LOADING) { + LOG(INFO) << "Waiting for model " << model_path_ << " to be loaded"; + cv_.wait(lock, [this] { + return state_ == MemoryState::LOADED || + state_ == MemoryState::INTERRUPTED; + }); + } + + // make sure no gpu replicas are loading + for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { + if (gpu_replica->state_ == MemoryState::LOADING) { + LOG(INFO) << "Waiting for replica " << replica_uuid << " to be loaded"; + gpu_replica->cv_.wait(lock, [&gpu_replica] { + return gpu_replica->state_ == MemoryState::LOADED || + gpu_replica->state_ == MemoryState::INTERRUPTED; + }); + } } - } - // free pinned memory - int freed_chunks = pinned_mem_->num_chunks(); - pinned_mem_.reset(); - state_ = MemoryState::UNALLOCATED; + // free pinned memory + int freed_chunks = pinned_mem_->num_chunks(); + pinned_mem_.reset(); + state_ = MemoryState::UNALLOCATED; - return 0; + return 0; } int Model::TryFreeHost() { - std::unique_lock lock(mutex_); - if (state_ == MemoryState::UNINITIALIZED) { - LOG(WARNING) << "Model " << model_path_ << " is not initialized"; - return 0; - } + std::unique_lock lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(WARNING) << "Model " << model_path_ << " is not initialized"; + return 0; + } - if (state_ == MemoryState::UNALLOCATED) { - LOG(WARNING) << "Model " << model_path_ << " is not allocated"; - return 0; - } + if (state_ == MemoryState::UNALLOCATED) { + LOG(WARNING) << "Model " << model_path_ << " is not allocated"; + return 0; + } - if (state_ == MemoryState::LOADING) { - return -1; - } + if (state_ == MemoryState::LOADING) { + return -1; + } - // make sure no gpu replicas are loading - for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { - if (gpu_replica->state_ == MemoryState::LOADING) { - return -1; + // make sure no gpu replicas are loading + for (auto& [replica_uuid, gpu_replica] : gpu_replicas_) { + if (gpu_replica->state_ == MemoryState::LOADING) { + return -1; + } } - } - // free pinned memory - int freed_chunks = pinned_mem_->num_chunks(); - pinned_mem_.reset(); - state_ = MemoryState::UNALLOCATED; + // free pinned memory + int freed_chunks = pinned_mem_->num_chunks(); + pinned_mem_.reset(); + state_ = MemoryState::UNALLOCATED; - return freed_chunks; + return freed_chunks; } int Model::DispatchToGpu( const std::shared_ptr& gpu_replica, const std::unordered_map& mem_copy_chunks, const std::unordered_map& mem_copy_handles) { - // device_id, chunk_offset, size, gpu_offset - - size_t num_chunks = pinned_mem_->num_chunks(); - std::vector> chunk_id_to_gpu_chunks(num_chunks); - for (const auto& [device_id, mem_copy_chunk_list] : mem_copy_chunks) { - const auto& device_handles = mem_copy_handles.at(device_id); - std::vector handle_offsets(device_handles.size(), 0); - - for (auto [host_offset, size, gpu_offset, handle_idx] : - mem_copy_chunk_list) { - handle_offsets[handle_idx] = gpu_offset; - - std::vector> chunks = - MapDataToChunks(host_offset, size, pinned_mem_->chunk_size()); - for (const auto& [chunk_id, chunk_offset, size] : chunks) { - chunk_id_to_gpu_chunks[chunk_id].push_back( - std::make_tuple(device_id, chunk_offset, size, - handle_offsets[handle_idx], handle_idx)); - handle_offsets[handle_idx] += size; - } - } - } - - for (int i = 0; i < host_ptr_vector_->capacity(); i++) { - auto data_chunk = host_ptr_vector_->dequeue(i); - auto chunk_id = data_chunk.chunk_id_; - auto& gpu_chunks = chunk_id_to_gpu_chunks[chunk_id]; - for (const auto& [device_id, chunk_offset, size, gpu_offset, handle_idx] : - gpu_chunks) { - auto& gpu_loading_queue = gpu_replica->gpu_loading_queue_.at(device_id); - // LOG(INFO) << "Enqueueing chunk " << chunk_id << " offset " << - // chunk_offset - // << " size " << size << " to device " << device_id; - gpu_loading_queue->enqueue( - GpuBatch{chunk_id, chunk_offset, size, gpu_offset, handle_idx}); - } - } - - // notify end of loading - for (auto& [device_id, gpu_loading_queue] : gpu_replica->gpu_loading_queue_) { - gpu_loading_queue->enqueue(GpuBatch{}); - } - - return 0; + // device_id, chunk_offset, size, gpu_offset + + size_t num_chunks = pinned_mem_->num_chunks(); + std::vector> chunk_id_to_gpu_chunks(num_chunks); + for (const auto& [device_id, mem_copy_chunk_list] : mem_copy_chunks) { + const auto& device_handles = mem_copy_handles.at(device_id); + std::vector handle_offsets(device_handles.size(), 0); + + for (auto [host_offset, size, gpu_offset, handle_idx] : + mem_copy_chunk_list) { + handle_offsets[handle_idx] = gpu_offset; + + std::vector> chunks = + MapDataToChunks(host_offset, size, pinned_mem_->chunk_size()); + for (const auto& [chunk_id, chunk_offset, size] : chunks) { + chunk_id_to_gpu_chunks[chunk_id].push_back( + std::make_tuple(device_id, chunk_offset, size, + handle_offsets[handle_idx], handle_idx)); + handle_offsets[handle_idx] += size; + } + } + } + + for (int i = 0; i < host_ptr_vector_->capacity(); i++) { + auto data_chunk = host_ptr_vector_->dequeue(i); + auto chunk_id = data_chunk.chunk_id_; + auto& gpu_chunks = chunk_id_to_gpu_chunks[chunk_id]; + for (const auto& [device_id, chunk_offset, size, gpu_offset, handle_idx] : + gpu_chunks) { + auto& gpu_loading_queue = gpu_replica->gpu_loading_queue_.at(device_id); + LOG(INFO) << "Enqueueing chunk " << chunk_id << " offset " << chunk_offset + << " size " << size << " to device " << device_id; + gpu_loading_queue->enqueue( + GpuBatch{chunk_id, chunk_offset, size, gpu_offset, handle_idx}); + } + } + + // notify end of loading + for (auto& [device_id, gpu_loading_queue] : gpu_replica->gpu_loading_queue_) { + gpu_loading_queue->enqueue(GpuBatch{}); + } + + return 0; } std::vector> Model::MapDataToChunks( size_t offset, size_t size, size_t chunk_size) { - int start_chunk = offset / chunk_size; - size_t offset_in_start_chunk = offset % chunk_size; - size_t remaining_data = size; - std::vector> output; - - for (int chunk_id = start_chunk; remaining_data > 0; ++chunk_id) { - const size_t chunk_data_size = - (chunk_id == start_chunk) - ? std::min(chunk_size - offset_in_start_chunk, remaining_data) - : std::min(chunk_size, remaining_data); - output.emplace_back(chunk_id, - chunk_id == start_chunk ? offset_in_start_chunk : 0, - chunk_data_size); - remaining_data -= chunk_data_size; - } - - return output; + int start_chunk = offset / chunk_size; + size_t offset_in_start_chunk = offset % chunk_size; + size_t remaining_data = size; + std::vector> output; + + for (int chunk_id = start_chunk; remaining_data > 0; ++chunk_id) { + const size_t chunk_data_size = + (chunk_id == start_chunk) + ? std::min(chunk_size - offset_in_start_chunk, remaining_data) + : std::min(chunk_size, remaining_data); + output.emplace_back(chunk_id, + chunk_id == start_chunk ? offset_in_start_chunk : 0, + chunk_data_size); + remaining_data -= chunk_data_size; + } + + return output; } int Model::AllocatePinnedMemory(std::shared_ptr pool) { - std::lock_guard lock(mutex_); - if (state_ == MemoryState::UNINITIALIZED) { - LOG(ERROR) << "Model " << model_path_ << " is not initialized"; - return -1; - } - if (state_ != MemoryState::UNALLOCATED) { + std::lock_guard lock(mutex_); + if (state_ == MemoryState::UNINITIALIZED) { + LOG(ERROR) << "Model " << model_path_ << " is not initialized"; + return -1; + } + if (state_ != MemoryState::UNALLOCATED) { + return 0; + } + pinned_mem_ = std::make_shared(); + int ret = pinned_mem_->Allocate(model_size_, pool); + if (ret < 0) { + LOG(ERROR) << "Error allocating CPU memory for model " << model_path_; + return ret; + } else if (ret > 0) { + LOG(WARNING) << "Not enough memory for model " << model_path_; + return ret; + } else if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { + LOG(ERROR) << "CPU memory not allocated"; + return -1; + } + + state_ = MemoryState::ALLOCATED; return 0; - } - pinned_mem_ = std::make_shared(); - int ret = pinned_mem_->Allocate(model_size_, pool); - if (ret < 0) { - LOG(ERROR) << "Error allocating CPU memory for model " << model_path_; - return ret; - } else if (ret > 0) { - LOG(WARNING) << "Not enough memory for model " << model_path_; - return ret; - } else if (!pinned_mem_ || pinned_mem_->num_chunks() == 0) { - LOG(ERROR) << "CPU memory not allocated"; - return -1; - } - - state_ = MemoryState::ALLOCATED; - return 0; -}; \ No newline at end of file +} \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/model.h b/sllm_store/csrc/sllm_store/model.h index b60af26..441ae0e 100644 --- a/sllm_store/csrc/sllm_store/model.h +++ b/sllm_store/csrc/sllm_store/model.h @@ -2,18 +2,18 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. // -// You may obtain a copy of the License at +// You may obtain a copy of the License at // -// http://www.apache.org/licenses/LICENSE-2.0 +// http://www.apache.org/licenses/LICENSE-2.0 // -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #pragma once @@ -32,7 +32,12 @@ #include // Third-party library headers +#ifdef USE_CANN +#include +#include +#else #include +#endif #include #include "error_handling.h" @@ -40,53 +45,53 @@ #include "types_and_defs.h" struct GpuReplica { - std::condition_variable cv_; - MemoryState state_ = MemoryState::UNINITIALIZED; + std::condition_variable cv_; + MemoryState state_ = MemoryState::UNINITIALIZED; - std::unordered_map> gpu_loading_queue_; - MemPtrListMap device_ptrs_; + std::unordered_map> gpu_loading_queue_; + MemPtrListMap device_ptrs_; }; using GpuReplicaPtr = std::shared_ptr; class Model { - public: - Model(const std::filesystem::path& model_path) : model_path_(model_path) {} - int Initialize(const std::filesystem::path storage_path); - int AllocatePinnedMemory(std::shared_ptr pool); - int ToHost(int num_threads); - int ToGpu(const std::string& replica_uuid, const MemPtrListMap& device_ptrs, - const std::unordered_map& mem_copy_chunks, - const std::unordered_map& mem_copy_handles); - int WaitInHost(); - int WaitInGpu(const std::string& replica_uuid); - int FreeGpu(const std::string& replica_uuid); - int FreeHost(); - int TryFreeHost(); - uint64_t GetModelSize() const { return model_size_; } +public: + Model(const std::filesystem::path& model_path) : model_path_(model_path) {} + int Initialize(const std::filesystem::path storage_path); + int AllocatePinnedMemory(std::shared_ptr pool); + int ToHost(int num_threads); + int ToGpu(const std::string& replica_uuid, const MemPtrListMap& device_ptrs, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles); + int WaitInHost(); + int WaitInGpu(const std::string& replica_uuid); + int FreeGpu(const std::string& replica_uuid); + int FreeHost(); + int TryFreeHost(); + uint64_t GetModelSize() const { return model_size_; } - private: - std::mutex mutex_; - std::condition_variable cv_; - MemoryState state_ = MemoryState::UNINITIALIZED; +private: + std::mutex mutex_; + std::condition_variable cv_; + MemoryState state_ = MemoryState::UNINITIALIZED; - // Model path - const std::string model_path_; + // Model path + const std::string model_path_; - // Model info needs to be initialized - size_t model_size_; - std::vector partition_sizes_; - std::vector partition_paths_; - std::shared_ptr pinned_mem_; + // Model info needs to be initialized + size_t model_size_; + std::vector partition_sizes_; + std::vector partition_paths_; + std::shared_ptr pinned_mem_; - std::unordered_map gpu_replicas_; + std::unordered_map gpu_replicas_; - std::shared_ptr host_ptr_vector_; + std::shared_ptr host_ptr_vector_; - std::vector> MapDataToChunks( - size_t offset, size_t size, size_t chunk_size); - int DispatchToGpu( - const std::shared_ptr& gpu_replica, - const std::unordered_map& mem_copy_chunks, - const std::unordered_map& mem_copy_handles); + std::vector> MapDataToChunks( + size_t offset, size_t size, size_t chunk_size); + int DispatchToGpu( + const std::shared_ptr& gpu_replica, + const std::unordered_map& mem_copy_chunks, + const std::unordered_map& mem_copy_handles); }; using ModelPtr = std::shared_ptr; \ No newline at end of file diff --git a/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp b/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp index 488c7c1..0851b56 100644 --- a/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp +++ b/sllm_store/csrc/sllm_store/pinned_memory_pool.cpp @@ -2,99 +2,115 @@ // ServerlessLLM // Copyright (c) ServerlessLLM Team 2024 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at // -// You may obtain a copy of the License at +// http://www.apache.org/licenses/LICENSE-2.0 // -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. // ---------------------------------------------------------------------------- #include "pinned_memory_pool.h" +#ifdef USE_CANN +#include "acl/acl.h" +#else #include +#endif #include PinnedMemoryPool::PinnedMemoryPool(size_t total_size, size_t chunk_size) : chunk_size_(chunk_size) { - size_t num_buffers = (total_size + chunk_size - 1) / chunk_size; - if (num_buffers * chunk_size != total_size) { - LOG(ERROR) << "PinnedMemoryPool size not multiple of chunk_size"; - } - LOG(INFO) << "Creating PinnedMemoryPool with " << num_buffers - << " buffers of " << chunk_size << " bytes"; - - for (size_t i = 0; i < num_buffers; ++i) { - char* buffer = static_cast(aligned_alloc(4096, chunk_size_)); - if (buffer == nullptr) { - LOG(FATAL) << "Malloc failed"; + size_t num_buffers = (total_size + chunk_size - 1) / chunk_size; + if (num_buffers * chunk_size != total_size) { + LOG(ERROR) << "PinnedMemoryPool size not multiple of chunk_size"; } + LOG(INFO) << "Creating PinnedMemoryPool with " << num_buffers + << " buffers of " << chunk_size << " bytes"; + + for (size_t i = 0; i < num_buffers; ++i) { +#ifdef USE_CANN + // For CANN, allocate pinned host memory directly + char* buffer = nullptr; + aclError ret = aclrtMallocHost((void**)&buffer, chunk_size_); + if (ret != ACL_ERROR_NONE) { + LOG(FATAL) << "aclrtMallocHost failed: " << ret; + } +#else + char* buffer = static_cast(aligned_alloc(4096, chunk_size_)); + if (buffer == nullptr) { + LOG(FATAL) << "Malloc failed"; + } - cudaError_t err = - cudaHostRegister(buffer, chunk_size_, cudaHostRegisterDefault); - if (err != cudaSuccess) { - LOG(FATAL) << "cudaHostRegister failed: " << cudaGetErrorString(err); + cudaError_t err = + cudaHostRegister(buffer, chunk_size_, cudaHostRegisterDefault); + if (err != cudaSuccess) { + LOG(FATAL) << "cudaHostRegister failed: " << cudaGetErrorString(err); + } +#endif + pool_.insert(buffer); + free_list_.insert(buffer); } - pool_.insert(buffer); - free_list_.insert(buffer); - } } PinnedMemoryPool::~PinnedMemoryPool() { - for (char* buffer : pool_) { - cudaHostUnregister(buffer); - free(buffer); - } + for (char* buffer : pool_) { +#ifdef USE_CANN + aclrtFreeHost(buffer); +#else + cudaHostUnregister(buffer); + free(buffer); +#endif + } } int PinnedMemoryPool::Allocate(size_t size, std::vector& buffers) { - std::lock_guard lock(mutex_); - if (size == 0) { - LOG(ERROR) << "PinnedMemoryPool Allocate size is zero"; - return -1; - } + std::lock_guard lock(mutex_); + if (size == 0) { + LOG(ERROR) << "PinnedMemoryPool Allocate size is zero"; + return -1; + } - int num_buffers_needed = (size + chunk_size_ - 1) / chunk_size_; - if (num_buffers_needed > free_list_.size()) { - LOG(ERROR) << "PinnedMemoryPool out of memory (" << free_list_.size() - << " buffers available, " << num_buffers_needed - << " buffers needed)"; - return num_buffers_needed - free_list_.size(); - } + int num_buffers_needed = (size + chunk_size_ - 1) / chunk_size_; + if (num_buffers_needed > free_list_.size()) { + LOG(ERROR) << "PinnedMemoryPool out of memory (" << free_list_.size() + << " buffers available, " << num_buffers_needed + << " buffers needed)"; + return num_buffers_needed - free_list_.size(); + } - buffers.clear(); - buffers.resize(num_buffers_needed); - auto it = free_list_.begin(); - for (size_t i = 0; i < num_buffers_needed; ++i) { - buffers[i] = *it; - it = free_list_.erase(it); - } + buffers.clear(); + buffers.resize(num_buffers_needed); + auto it = free_list_.begin(); + for (size_t i = 0; i < num_buffers_needed; ++i) { + buffers[i] = *it; + it = free_list_.erase(it); + } - LOG(INFO) << "PinnedMemoryPool Allocate " << buffers.size() << " buffers" - << " free buffers " << free_list_.size() << " total buffers " - << pool_.size(); + LOG(INFO) << "PinnedMemoryPool Allocate " << buffers.size() << " buffers" + << " free buffers " << free_list_.size() << " total buffers " + << pool_.size(); - return 0; // Success + return 0; // Success } int PinnedMemoryPool::Deallocate(std::vector& buffers) { - std::lock_guard lock(mutex_); - for (char* buffer : buffers) { - if (pool_.find(buffer) == pool_.end()) { - LOG(ERROR) << "Buffer not found in pool"; - return -1; + std::lock_guard lock(mutex_); + for (char* buffer : buffers) { + if (pool_.find(buffer) == pool_.end()) { + LOG(ERROR) << "Buffer not found in pool"; + return -1; + } + if (free_list_.find(buffer) != free_list_.end()) { + LOG(ERROR) << "Buffer already in free list"; + return -1; + } + free_list_.insert(buffer); } - if (free_list_.find(buffer) != free_list_.end()) { - LOG(ERROR) << "Buffer already in free list"; - return -1; - } - free_list_.insert(buffer); - } - LOG(INFO) << "Deallocated " << buffers.size() << " buffers"; - return 0; // Success -} + LOG(INFO) << "Deallocated " << buffers.size() << " buffers"; + return 0; // Success +} \ No newline at end of file diff --git a/sllm_store/examples/cann_save_vllm_model.py b/sllm_store/examples/cann_save_vllm_model.py new file mode 100644 index 0000000..102b693 --- /dev/null +++ b/sllm_store/examples/cann_save_vllm_model.py @@ -0,0 +1,136 @@ +import argparse +import os +import shutil +from typing import Optional + + +class VllmModelDownloader: + def __init__(self): + pass + + def download_vllm_model( + self, + model_name: str, + torch_dtype: str, + tensor_parallel_size: int = 1, + storage_path: str = "./models", + local_model_path: Optional[str] = None, + pattern: Optional[str] = None, + max_size: Optional[int] = None, + ): + import gc + from tempfile import TemporaryDirectory + + import torch + import torch_npu + from huggingface_hub import snapshot_download + from vllm import LLM + + # set the model storage path + storage_path = os.getenv("STORAGE_PATH", storage_path) + + def _run_writer(input_dir, model_name): + # load models from the input directory + llm_writer = LLM( + model=input_dir, + download_dir=input_dir, + dtype=torch_dtype, + tensor_parallel_size=tensor_parallel_size, + num_gpu_blocks_override=1, + enforce_eager=True, + max_model_len=1, + ) + model_path = os.path.join(storage_path, model_name) + model_executer = llm_writer.llm_engine.model_executor + # save the models in the ServerlessLLM format + model_executer.save_serverless_llm_state( + path=model_path, pattern=pattern, max_size=max_size + ) + for file in os.listdir(input_dir): + # Copy the metadata files into the output directory + if os.path.splitext(file)[1] not in ( + ".bin", + ".pt", + ".safetensors", + ): + src_path = os.path.join(input_dir, file) + dest_path = os.path.join(model_path, file) + if os.path.isdir(src_path): + shutil.copytree(src_path, dest_path) + else: + shutil.copy(src_path, dest_path) + del model_executer + del llm_writer + gc.collect() + if torch.npu.is_available(): + torch.npu.empty_cache() + torch.npu.synchronize() + + try: + with TemporaryDirectory() as cache_dir: + input_dir = local_model_path + # download from huggingface + if local_model_path is None: + input_dir = snapshot_download( + model_name, + cache_dir=cache_dir, + allow_patterns=[ + "*.safetensors", + "*.bin", + "*.json", + "*.txt", + ], + ) + _run_writer(input_dir, model_name) + except Exception as e: + print(f"An error occurred while saving the model: {e}") + # remove the output dir + shutil.rmtree(os.path.join(storage_path, model_name)) + raise RuntimeError( + f"Failed to save {model_name} for vllm backend: {e}" + ) from e + + +parser = argparse.ArgumentParser( + description="Save a model from HuggingFace model hub." +) +parser.add_argument( + "--model-name", + type=str, + required=True, + help="Model name from HuggingFace model hub.", +) +parser.add_argument( + "--local-model-path", + type=str, + required=False, + help="Local path to the model snapshot.", +) +parser.add_argument( + "--storage-path", + type=str, + default="./models", + help="Local path to save the model.", +) +parser.add_argument( + "--tensor-parallel-size", + type=int, + default=1, + help="Tensor parallel size.", +) + +args = parser.parse_args() + +model_name = args.model_name +local_model_path = args.local_model_path +storage_path = args.storage_path +tensor_parallel_size = args.tensor_parallel_size + +downloader = VllmModelDownloader() +downloader.download_vllm_model( + model_name, + "float16", + tensor_parallel_size=tensor_parallel_size, + storage_path=storage_path, + local_model_path=local_model_path, +) \ No newline at end of file diff --git a/sllm_store/proto/storage.proto b/sllm_store/proto/storage.proto index f2f2d32..20c5973 100644 --- a/sllm_store/proto/storage.proto +++ b/sllm_store/proto/storage.proto @@ -11,6 +11,14 @@ service Storage { rpc RegisterModel (RegisterModelRequest) returns (RegisterModelResponse) {} rpc GetServerConfig (GetServerConfigRequest) returns (GetServerConfigResponse) {} + rpc GetPid (GetPidRequest) returns (GetPidResponse) {} +} + +message GetPidRequest { +} + +message GetPidResponse { + int32 pid = 1; } message GetServerConfigRequest { diff --git a/sllm_store/requirements-build.txt b/sllm_store/requirements-build.txt index 7ed6a59..bae9656 100644 --- a/sllm_store/requirements-build.txt +++ b/sllm_store/requirements-build.txt @@ -3,5 +3,6 @@ ninja numpy peft==0.15.2 setuptools -torch==2.7.0 +torch==2.4.0 +torch-npu==2.4.0.post2 wheel diff --git a/sllm_store/requirements.txt b/sllm_store/requirements.txt index a227888..9f48da3 100644 --- a/sllm_store/requirements.txt +++ b/sllm_store/requirements.txt @@ -1,8 +1,11 @@ -accelerate==1.7.0 -bitsandbytes==0.46.0 +accelerate==1.2.1 +bitsandbytes>=0.41.0,<=0.45.1 click>=8.1.7 -grpcio==1.72.1 -grpcio-tools==1.64.0 +grpcio==1.49.1 +grpcio-tools==1.49.1 peft==0.15.2 -torch==2.7.0 -transformers==4.52.4 +torch==2.5.1 +torch-npu==2.5.1 +transformers==4.47.1 +decorator +attrs==22.2.0 \ No newline at end of file diff --git a/sllm_store/setup.py b/sllm_store/setup.py index aa653b1..f75fa6a 100644 --- a/sllm_store/setup.py +++ b/sllm_store/setup.py @@ -17,6 +17,7 @@ # ---------------------------------------------------------------------------- # import io import os +import platform import subprocess import sys from pathlib import Path @@ -26,29 +27,105 @@ from setuptools import Extension, setup from setuptools.command.build_ext import build_ext from setuptools.command.install import install - -from torch.utils.cpp_extension import CUDA_HOME -from torch.utils.cpp_extension import ROCM_HOME +try: + torch_available = True + # The assert is not needed since Github CI does not use GPU server, + # install cuda library is sufficient + # assert torch.cuda.is_available() == True + from torch.utils.cpp_extension import CUDA_HOME + from torch.utils.cpp_extension import ROCM_HOME + from torch_npu.utils.cpp_extension import NpuExtension +except Exception: + torch_available = False + CUDA_HOME = None + ROCM_HOME = None + print( + "[WARNING] Unable to import torch, pre-compiling ops will be disabled. " + "Please visit https://pytorch.org/ to see how to properly install torch on your system." # noqa: E501 + ) ROOT_DIR = os.path.dirname(__file__) +# Determine build backend based on environment and platform +def detect_build_backend(): + """Detect which backend to build based on environment variables and platform.""" + + # Check for explicit backend selection + use_cann = os.getenv("USE_CANN", "0").lower() in ("1", "true", "yes") + use_cuda = os.getenv("USE_CUDA", "auto").lower() + + # CANN support (Linux only) + ascend_toolkit_home = os.getenv("ASCEND_TOOLKIT_HOME") + print("Reached") + print(f"HOME: {ascend_toolkit_home}") + print(f"USA_CANN {use_cann}") + + if use_cann and ascend_toolkit_home and platform.system() == "Linux": + if check_cann_available(ascend_toolkit_home): + print("Reached") + return "cann", ascend_toolkit_home + else: + print("[WARNING] CANN requested but not available, falling back...") + + # CUDA support + if use_cuda != "false" and CUDA_HOME is not None: + if check_nvcc_installed(CUDA_HOME): + return "cuda", CUDA_HOME + else: + print("[WARNING] CUDA requested but nvcc not available, falling back...") + + # ROCm support + if ROCM_HOME is not None: + if check_hipcc_installed(ROCM_HOME): + return "rocm", ROCM_HOME + else: + print("[WARNING] ROCm available but hipcc not working, falling back...") + + # CPU-only fallback + print("[INFO] Building in CPU-only mode") + return "cpu", None + + +def check_cann_available(ascend_toolkit_home: str) -> bool: + """Check if CANN toolkit is available.""" + try: + # Check for essential CANN headers + include_dir = os.path.join(ascend_toolkit_home, "include") + acl_header = os.path.join(include_dir, "acl", "acl.h") + + if not os.path.exists(acl_header): + print(f"[WARNING] CANN header not found: {acl_header}") + return False + + # Check for CANN libraries + lib_dir = os.path.join(ascend_toolkit_home, "lib64") + acl_lib = os.path.join(lib_dir, "libascendcl.so") + + if not os.path.exists(acl_lib): + print(f"[WARNING] CANN library not found: {acl_lib}") + return False + + print(f"[INFO] CANN toolkit found at: {ascend_toolkit_home}") + return True + except Exception as e: + print(f"[WARNING] Error checking CANN availability: {e}") + return False + -def check_nvcc_installed(cuda_home: str) -> None: +def check_nvcc_installed(cuda_home: str) -> bool: """Check if nvcc (NVIDIA CUDA compiler) is installed.""" try: _ = subprocess.check_output( [cuda_home + "/bin/nvcc", "-V"], universal_newlines=True ) - except Exception as e: - raise RuntimeError( - "nvcc is not installed or not found in your PATH. " - "Please ensure that the CUDA toolkit is installed and nvcc is available in your PATH." # noqa: E501 - f" Error: {e}" - ) from None + return True + except Exception: + print("[WARNING] nvcc is not installed or not found in your PATH.") + return False -def check_hipcc_installed(rocm_home: str) -> None: +def check_hipcc_installed(rocm_home: str) -> bool: """Check if hipcc (AMD HIP compiler) is installed.""" # can be either /hip/bin/hipcc or /bin/hipcc hipcc_paths = [rocm_home + "/bin/hipcc", rocm_home + "/hip/bin/hipcc"] @@ -57,29 +134,24 @@ def check_hipcc_installed(rocm_home: str) -> None: _ = subprocess.check_output( [hipcc, "--version"], universal_newlines=True ) - return + return True except Exception: continue - raise RuntimeError( - "hipcc is not installed or not found in your PATH. " - "Please ensure that the HIP toolkit is installed and hipcc is available in your PATH." # noqa: E501 - ) from None - - -if CUDA_HOME is not None: - check_nvcc_installed(CUDA_HOME) -elif ROCM_HOME is not None: - check_hipcc_installed(ROCM_HOME) -else: - raise RuntimeError( - "CUDA_HOME or ROCM_HOME environment variable must be set to compile CUDA or HIP extensions." # noqa: E501 - ) + print("[WARNING] hipcc is not installed or not found in your PATH.") + return False + +# Detect build backend +BUILD_BACKEND, BACKEND_HOME = detect_build_backend() +print(f"[INFO] Building with backend: {BUILD_BACKEND}") def is_ninja_available() -> bool: + # Allow disabling ninja via environment variable + if os.getenv("DISABLE_NINJA", "0").lower() in ("1", "true", "yes"): + return False try: subprocess.run(["ninja", "--version"], stdout=subprocess.PIPE) - except FileNotFoundError: + except (FileNotFoundError, OSError): return False return True @@ -138,6 +210,7 @@ class CMakeExtension(Extension): # Adapted from https://github.com/vllm-project/vllm/blob/a1242324c99ff8b1e29981006dfb504da198c7c3/setup.py +# Pass -DUSE_CANN=ON to the cmake command to build the extension with CANN support. class cmake_build_ext(build_ext): did_config: Dict[str, bool] = {} @@ -161,6 +234,27 @@ class cmake_build_ext(build_ext): "-DCMAKE_ARCHIVE_OUTPUT_DIRECTORY={}".format(self.build_temp), ] + # Add backend-specific arguments + if BUILD_BACKEND == "cann": + cmake_args.extend([ + "-DUSE_CANN=ON", + "-DASCEND_TOOLKIT_HOME={}".format(BACKEND_HOME), + ]) + elif BUILD_BACKEND == "cuda": + cmake_args.extend([ + "-DUSE_CUDA=ON", + "-DCUDA_TOOLKIT_ROOT_DIR={}".format(BACKEND_HOME), + ]) + elif BUILD_BACKEND == "rocm": + cmake_args.extend([ + "-DUSE_ROCM=ON", + "-DROCM_ROOT={}".format(BACKEND_HOME), + ]) + else: # cpu + cmake_args.extend([ + "-DUSE_CPU_ONLY=ON", + ]) + # verbose = bool(int(os.getenv('VERBOSE', '1'))) verbose = True if verbose: @@ -180,10 +274,15 @@ class cmake_build_ext(build_ext): # Default build tool to whatever cmake picks. build_tool = [] - subprocess.check_call( - ["cmake", ext.cmake_lists_dir, *build_tool, *cmake_args], - cwd=self.build_temp, - ) + try: + subprocess.check_call( + ["cmake", ext.cmake_lists_dir, *build_tool, *cmake_args], + cwd=self.build_temp, + ) + except subprocess.CalledProcessError as e: + print(f"[ERROR] CMake configuration failed with backend {BUILD_BACKEND}") + print(f"[ERROR] Command: cmake {ext.cmake_lists_dir} {' '.join(build_tool)} {' '.join(cmake_args)}") + raise e def build_extensions(self) -> None: # Ensure that CMake is present and working @@ -212,7 +311,12 @@ class cmake_build_ext(build_ext): # str(num_jobs) ] - subprocess.check_call(["cmake", *build_args], cwd=self.build_temp) + try: + subprocess.check_call(["cmake", *build_args], cwd=self.build_temp) + print(f"[INFO] Successfully built extension: {ext_target_name}") + except subprocess.CalledProcessError as e: + print(f"[ERROR] Failed to build extension: {ext_target_name}") + raise e print(self.build_temp, ext_target_name) @@ -243,4 +347,4 @@ setup( "sllm_store.proto", ], cmdclass=cmdclass, -) +) \ No newline at end of file diff --git a/sllm_store/sllm_store/cann_utils.py b/sllm_store/sllm_store/cann_utils.py new file mode 100644 index 0000000..51bf2f3 --- /dev/null +++ b/sllm_store/sllm_store/cann_utils.py @@ -0,0 +1,146 @@ +"""CANN utilities for ServerlessLLM""" +import torch + +# Global variable to track torch_npu import +_torch_npu_imported = False + +def is_cann_available(): + """Check if CANN/NPU is available""" + global _torch_npu_imported + try: + # Check if torch_npu is available and import it + import torch_npu + _torch_npu_imported = True + return torch.npu.is_available() + except ImportError: + return False + +def ensure_torch_npu_imported(): + """Ensure torch_npu is imported before any tensor operations""" + global _torch_npu_imported + if not _torch_npu_imported and is_cann_available(): + try: + import torch_npu + _torch_npu_imported = True + print("torch_npu imported and PrivateUse1HooksInterface registered") + except ImportError: + pass + return _torch_npu_imported + +def get_device_type(): + """Get the device type (cuda or npu)""" + if is_cann_available(): + return "npu" + else: + return "cuda" + +def get_memory_functions(): + """Get the appropriate memory allocation functions""" + if is_cann_available(): + try: + from sllm_store._C import allocate_cann_memory, get_cann_memory_handles + return allocate_cann_memory, _get_cann_memory_handles_with_pid + except ImportError: + raise ImportError("CANN functions not available in compiled extension") + else: + from sllm_store._C import allocate_cuda_memory, get_cuda_memory_handles + return allocate_cuda_memory, get_cuda_memory_handles + +def _get_cann_memory_handles_with_pid(memory_info_map): + """ + Wrapper for get_cann_memory_handles that automatically gets the server PID + """ + from sllm_store._C import get_cann_memory_handles + from sllm_store.client import SllmStoreClient + + try: + # Get server PID via gRPC + client = SllmStoreClient("127.0.0.1:8073") + server_pid = client.get_pid() + + if server_pid is None: + print("Warning: Could not get server PID via gRPC, using default (-1)") + server_pid = -1 + else: + print(f"Got server PID via gRPC: {server_pid}") + + # Call the C++ function with the server PID + return get_cann_memory_handles(memory_info_map, server_pid) + + except Exception as e: + print(f"Error getting server PID: {e}") + print("Falling back to default PID (-1)") + return get_cann_memory_handles(memory_info_map, -1) + +def init_npu_backend(): + """Initialize NPU backend and register with PyTorch""" + if not is_cann_available(): + return False + + try: + import torch_npu + + # Register NPU as a custom backend for PrivateUse1 + # This is essential for PyTorch to recognize NPU devices + try: + # Try the newer API first (PyTorch 2.1+) + if hasattr(torch.utils, 'rename_privateuse1_backend'): + torch.utils.rename_privateuse1_backend("npu") + elif hasattr(torch, 'rename_privateuse1_backend'): + torch.rename_privateuse1_backend("npu") + else: + print("Warning: rename_privateuse1_backend not available, using fallback initialization") + except Exception as e: + print(f"Warning: Failed to register NPU backend name: {e}") + + # Initialize NPU context + # This ensures NPU devices are properly initialized + if torch_npu.npu.device_count() > 0: + # Initialize NPU runtime + try: + # Try to register the NPU module with torch + if hasattr(torch, '_register_device_module'): + torch._register_device_module('npu', torch_npu.npu) + except Exception as e: + print(f"Warning: Failed to register NPU module: {e}") + + # Warm up NPU devices to ensure proper initialization + for i in range(torch_npu.npu.device_count()): + try: + # Create a small tensor to initialize the device + x = torch.empty(1, device=f"npu:{i}") + del x # Clean up + torch_npu.npu.synchronize() + except Exception as e: + print(f"Warning: Failed to initialize NPU device {i}: {e}") + continue + + print(f"NPU backend initialized with {torch_npu.npu.device_count()} devices") + return True + else: + print("No NPU devices found") + return False + + except Exception as e: + print(f"Failed to initialize NPU backend: {e}") + import traceback + traceback.print_exc() + return False + +# Initialize NPU backend when module is imported +_npu_initialized = False + +def ensure_npu_initialized(): + """Ensure NPU backend is initialized""" + global _npu_initialized + if not _npu_initialized and is_cann_available(): + _npu_initialized = init_npu_backend() + return _npu_initialized + +# Automatically initialize NPU when module is imported +if is_cann_available(): + try: + ensure_npu_initialized() + except Exception as e: + print(f"Warning: Failed to auto-initialize NPU backend: {e}") + pass \ No newline at end of file diff --git a/sllm_store/sllm_store/cli.py b/sllm_store/sllm_store/cli.py index c9b4a87..41d924f 100644 --- a/sllm_store/sllm_store/cli.py +++ b/sllm_store/sllm_store/cli.py @@ -17,135 +17,18 @@ # ---------------------------------------------------------------------------- # import asyncio -import importlib -import os -import shutil import sys import logging -import time -from typing import Optional -import torch import click from sllm_store.server import serve from sllm_store.logger import init_logger from sllm_store.utils import to_num_bytes -from sllm_store.transformers import save_model, save_lora, load_model, load_lora -from transformers import ( - AutoModelForCausalLM, - AutoConfig, - AutoTokenizer, - BitsAndBytesConfig, -) -from peft import PeftModel - - -class VllmModelDownloader: - def __init__(self): - pass - - def download_vllm_model( - self, - model_name: str, - torch_dtype: str, - tensor_parallel_size: int = 1, - storage_path: str = "./models", - local_model_path: Optional[str] = None, - pattern: Optional[str] = None, - max_size: Optional[int] = None, - ): - import gc - from tempfile import TemporaryDirectory - - import torch - from huggingface_hub import snapshot_download - from vllm import LLM - - # set the model storage path - storage_path = os.getenv("STORAGE_PATH", storage_path) - - def _run_writer(input_dir, model_name): - # load models from the input directory - llm_writer = LLM( - model=input_dir, - download_dir=input_dir, - dtype=torch_dtype, - tensor_parallel_size=tensor_parallel_size, - num_gpu_blocks_override=1, - enforce_eager=True, - max_model_len=1, - ) - model_path = os.path.join(storage_path, model_name) - model_executer = llm_writer.llm_engine.engine_core # For engine V1 - # save the models in the ServerlessLLM format - model_executer.save_serverless_llm_state( - path=model_path, pattern=pattern, max_size=max_size - ) - for file in os.listdir(input_dir): - # Copy the metadata files into the output directory - if os.path.splitext(file)[1] not in ( - ".bin", - ".pt", - ".safetensors", - ): - src_path = os.path.join(input_dir, file) - dest_path = os.path.join(model_path, file) - if os.path.isdir(src_path): - shutil.copytree(src_path, dest_path) - else: - shutil.copy(src_path, dest_path) - del model_executer - del llm_writer - gc.collect() - if torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.synchronize() - - try: - with TemporaryDirectory() as cache_dir: - input_dir = local_model_path - # download from huggingface - if local_model_path is None: - input_dir = snapshot_download( - model_name, - cache_dir=cache_dir, - allow_patterns=[ - "*.safetensors", - "*.bin", - "*.json", - "*.txt", - ], - ) - _run_writer(input_dir, model_name) - except Exception as e: - print(f"An error occurred while saving the model: {e}") - # remove the output dir - shutil.rmtree(os.path.join(storage_path, model_name)) - raise RuntimeError( - f"Failed to save {model_name} for vllm backend: {e}" - ) from e - +# Initialize logger for this module logger = init_logger(__name__) -def check_vllm(): - """Simple check if ServerlessLLM patch is applied.""" - try: - # Check if the LoadFormat enum has SERVERLESS_LLM - from vllm.config import LoadFormat - - if not hasattr(LoadFormat, "SERVERLESS_LLM"): - return False - - # Check if ExecutorBase has the save method - from vllm.executor.executor_base import ExecutorBase - - return hasattr(ExecutorBase, "save_serverless_llm_state") - except ImportError: - return False - - @click.group() def cli(): """sllm-store CLI""" @@ -153,25 +36,26 @@ def cli(): @cli.command() -@click.option("--host", default="0.0.0.0", help="Host") -@click.option("--port", default=8073, help="Port") -@click.option("--storage-path", default="./models", help="Storage path") -@click.option("--num-thread", default=4, help="Number of I/O threads") +@click.option("--host", default="0.0.0.0", help="Host for the gRPC server") +@click.option("--port", default=8073, help="Port for the gRPC server") +@click.option("--storage-path", default="./models", help="Local path to store models") +@click.option("--num-thread", default=4, help="Number of I/O threads for the server") @click.option( - "--chunk-size", default="32MB", help="Chunk size, e.g., 4KB, 1MB, 1GB" + "--chunk-size", default="32MB", help="Chunk size for data transfer, e.g., 4KB, 1MB, 1GB" ) @click.option( "--mem-pool-size", default="4GB", - help="Memory pool size, e.g., 1GB, 4GB, 1TB", + help="Memory pool size for the server, e.g., 1GB, 4GB, 1TB", ) @click.option( - "--disk-size", default="128GB", help="Disk size, e.g., 1GB, 4GB, 1TB" + "--disk-size", default="128GB", help="Total disk size available, e.g., 1GB, 4GB, 1TB (Note: currently not used in 'serve' function)" ) @click.option( "--registration-required", + is_flag=True, # Changed to is_flag for boolean option without explicit value default=False, - help="Require registration before loading model", + help="Require registration before loading model (set this flag to enable)", ) def start( host, @@ -180,18 +64,19 @@ def start( num_thread, chunk_size, mem_pool_size, - disk_size, + disk_size, # This parameter is passed but commented out in the serve call below. registration_required, ): - """Start the gRPC server.""" - # Convert the chunk size to bytes + """Start the gRPC server for ServerlessLLM Store""" + # Convert the chunk size string to bytes (e.g., "32MB" -> 33554432) chunk_size = to_num_bytes(chunk_size) - # Convert the memory pool size to bytes + # Convert the memory pool size string to bytes mem_pool_size = to_num_bytes(mem_pool_size) try: - logger.info("Starting gRPC server") + logger.info(f"Starting gRPC server on {host}:{port}") + # Run the asynchronous gRPC server asyncio.run( serve( host=host, @@ -200,285 +85,23 @@ def start( num_thread=num_thread, chunk_size=chunk_size, mem_pool_size=mem_pool_size, - # disk size is not used + # disk_size is not currently used by the serve function, as indicated in the original code. # disk_size=disk_size, registration_required=registration_required, ) ) except KeyboardInterrupt: - logging.info("Server stopped by user") + logging.info("Server stopped by user (KeyboardInterrupt)") sys.exit(0) - - -@cli.command() -@click.option( - "--model", - "model_name", - type=str, - required=True, - help="Model name from HuggingFace model hub", -) -@click.option("--backend", type=str, required=True, help="Backend") -@click.option("--adapter-name", type=str, help="Name of the LoRA adapter") -@click.option( - "--tensor-parallel-size", type=int, default=1, help="Tensor parallel size" -) -@click.option( - "--local-model-path", type=str, help="Local path to the model snapshot" -) -@click.option( - "--storage-path", - default="./models", - help="Local path to save the model", -) -def save( - model_name, - backend, - adapter_name, - tensor_parallel_size, - local_model_path, - storage_path, -): - """ - Saves a model to the sllm-store's storage. - - This command is for adding new models to the sllm-store's local storage. - """ - - logger.info( - f"Saving model {adapter_name if adapter_name else model_name} " - f"to {storage_path}" - ) - - try: - if backend == "vllm": - if not check_vllm(): - logger.error( - "vLLM is not patched. Please run " - "`./sllm_store/vllm_patch/patch.sh` first." - ) - sys.exit(1) - downloader = VllmModelDownloader() - downloader.download_vllm_model( - model_name, - "float16", - tensor_parallel_size=tensor_parallel_size, - storage_path=storage_path, - local_model_path=local_model_path, - ) - elif backend == "transformers": - if adapter_name: - config = AutoConfig.from_pretrained( - model_name, - trust_remote_code=True, - ) - config.torch_dtype = torch.float16 - module = importlib.import_module("transformers") - hf_model_cls = module.AutoModelForCausalLM - base_model = hf_model_cls.from_config( - config, - trust_remote_code=True, - ).to(config.torch_dtype) - - # Load a lora adapter from HuggingFace model hub - model = PeftModel.from_pretrained(base_model, adapter_name) - - # Save the model to the local path - model_path = os.path.join(storage_path, adapter_name) - save_lora(model, model_path) - else: - # Load a model from HuggingFace model hub - model = AutoModelForCausalLM.from_pretrained( - model_name, torch_dtype=torch.float16 - ) - - # Save the model to the local path - model_path = os.path.join(storage_path, model_name) - save_model(model, model_path) - else: - logger.error(f"Unsupported backend '{backend}'") - sys.exit(1) except Exception as e: - logger.error(f"Failed to save model {model_name}: {e}", exc_info=True) + logger.error(f"An unexpected error occurred: {e}") sys.exit(1) - logger.info( - f"Model {adapter_name if adapter_name else model_name} " - f"saved successfully to {storage_path}" - ) - -@cli.command() -@click.option( - "--model", - "model_name", - type=str, - required=True, - help="Model name from HuggingFace model hub", -) -@click.option("--backend", type=str, required=True, help="Backend") -@click.option("--adapter-name", type=str, help="Name of the LoRA adapter") -@click.option( - "--precision", - type=click.Choice(["int8", "fp4", "nf4"]), - help="Precision of quantized model.", -) -@click.option( - "--storage-path", - type=str, - default="./models", - help="Local path where model is saved", -) -def load( - model_name, - backend, - adapter_name, - precision, - storage_path, -): - """ - Loads a model from the sllm-store's storage. - - This command is for loading new models from the sllm-store's local storage. - """ - - logger.info( - f"Loading model {adapter_name if adapter_name else model_name} " - f"from {storage_path}" - ) - - quantization_config = None - if precision: - if precision == "int8": - quantization_config = BitsAndBytesConfig(load_in_8bit=True) - elif precision == "fp4": - quantization_config = BitsAndBytesConfig(load_in_4bit=True) - elif precision == "nf4": - quantization_config = BitsAndBytesConfig( - load_in_4bit=True, bnb_4bit_quant_type="nf4" - ) - - try: - start_load_time = time.time() - - if backend == "vllm": - from vllm import LLM - - if not check_vllm(): - logger.error( - "vLLM is not patched. Please run " - "`./sllm_store/vllm_patch/patch.sh` first." - ) - sys.exit(1) - model_full_path = os.path.join(storage_path, model_name) - llm = LLM( - model=model_full_path, - load_format="serverless_llm", - dtype="float16", - ) - logger.info( - f"Model loading time: {time.time() - start_load_time:.2f}s" - ) - - example_inferences("vllm", model=llm) - - elif backend == "transformers": - # warm up the GPU - num_gpus = torch.cuda.device_count() - for i in range(num_gpus): - torch.ones(1).to(f"cuda:{i}") - torch.cuda.synchronize() - if adapter_name: - model = load_model( - model_name, - device_map="auto", - torch_dtype=torch.float16, - storage_path=storage_path, - fully_parallel=True, - ) - - model = load_lora( - model, - adapter_name, - adapter_path=adapter_name, - device_map="auto", - storage_path=storage_path, - torch_dtype=torch.float16, - ) - else: - model = load_model( - model_name, - device_map="auto", - torch_dtype=torch.float16, - storage_path=storage_path, - fully_parallel=True, - quantization_config=quantization_config, - ) - logger.info( - f"Model loading time: {time.time() - start_load_time:.2f}s" - ) - - example_inferences( - "transformers", - model=model, - model_name=model_name, - adapter_name=adapter_name, - ) - - else: - logger.error(f"Unsupported backend '{backend}'") - sys.exit(1) - - except Exception as e: - logger.error( - f"Failed to load model or perform inference: {e}", exc_info=True - ) - sys.exit(1) - - logger.info( - f"Model {adapter_name if adapter_name else model_name} " - f"loaded successfully from {storage_path}" - ) - - -def example_inferences(backend, model=None, model_name=None, adapter_name=None): - if backend == "vllm": - prompts = [ - "Hello, my name is", - "The president of the United States is", - "The capital of France is", - "The future of AI is", - ] - from vllm import SamplingParams - - sampling_params = SamplingParams(temperature=0.8, top_p=0.95) - outputs = model.generate(prompts, sampling_params) - - # Print the outputs. - for output in outputs: - prompt = output.prompt - generated_text = output.outputs[0].text - print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") - elif backend == "transformers": - from pathlib import Path - - parts = Path(model_name).parts - if len(parts) >= 2: - model_name = f"{parts[-2]}/{parts[-1]}" - tokenizer = AutoTokenizer.from_pretrained(model_name) - inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to( - "cuda" - ) - generate_kwargs = {} - if adapter_name: - generate_kwargs["adapter_names"] = [adapter_name] - outputs = model.generate(**inputs, **generate_kwargs) - print(tokenizer.decode(outputs[0], skip_special_tokens=True)) - else: - logger.error(f"Unsupported backend '{backend}' for example inferences") - sys.exit(1) - - -# Entry point for the 'sllm-store' CLI +# Entry point for the 'sllm-store' command line tool def main(): + """Main function to run the CLI""" cli() + +if __name__ == "__main__": + main() diff --git a/sllm_store/sllm_store/client.py b/sllm_store/sllm_store/client.py index 8efead6..114a8ff 100644 --- a/sllm_store/sllm_store/client.py +++ b/sllm_store/sllm_store/client.py @@ -155,3 +155,14 @@ class SllmStoreClient: "chunk_size": response.chunk_size, "mem_pool_size": response.mem_pool_size, } + + def get_pid(self): + """Get the server process ID for CANN IPC setup""" + request = storage_pb2.GetPidRequest() + try: + response = self.stub.GetPid(request) + logger.info(f"Server PID {response.pid}") + return response.pid + except grpc.RpcError as e: + logger.error(f"Error getting server PID: {e}") + return None \ No newline at end of file diff --git a/sllm_store/sllm_store/proto/storage_pb2.py b/sllm_store/sllm_store/proto/storage_pb2.py index fabbbc3..6186bc1 100644 --- a/sllm_store/sllm_store/proto/storage_pb2.py +++ b/sllm_store/sllm_store/proto/storage_pb2.py @@ -1,7 +1,7 @@ +# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! -# source: sllm_store/proto/storage.proto +# source: storage.proto """Generated protocol buffer code.""" - from google.protobuf.internal import builder as _builder from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool @@ -11,9 +11,9 @@ from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile( - b'\n\x1esllm_store/proto/storage.proto\x12\x07storage"\x18\n\x16GetServerConfigRequest"D\n\x17GetServerConfigResponse\x12\x15\n\rmem_pool_size\x18\x01 \x01(\x03\x12\x12\n\nchunk_size\x18\x02 \x01(\x03"*\n\x14RegisterModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t"?\n\x15RegisterModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x12\n\nmodel_size\x18\x02 \x01(\x03"X\n\x0cMemCopyChunk\x12\x12\n\nsrc_offset\x18\x01 \x01(\x04\x12\x0c\n\x04size\x18\x02 \x01(\x04\x12\x12\n\ndst_offset\x18\x03 \x01(\x04\x12\x12\n\nhandle_idx\x18\x04 \x01(\x04"9\n\x10MemCopyChunkList\x12%\n\x06\x63hunks\x18\x01 \x03(\x0b\x32\x15.storage.MemCopyChunk"(\n\rMemCopyHandle\x12\x17\n\x0f\x63uda_ipc_handle\x18\x01 \x01(\x0c"<\n\x11MemCopyHandleList\x12\'\n\x07handles\x18\x01 \x03(\x0b\x32\x16.storage.MemCopyHandle"\xf3\x02\n\x10LoadModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x14\n\x0creplica_uuid\x18\x02 \x01(\t\x12\x35\n\x06\x63hunks\x18\x03 \x03(\x0b\x32%.storage.LoadModelRequest.ChunksEntry\x12\x37\n\x07handles\x18\x04 \x03(\x0b\x32&.storage.LoadModelRequest.HandlesEntry\x12/\n\x12target_device_type\x18\x05 \x01(\x0e\x32\x13.storage.DeviceType\x1aH\n\x0b\x43hunksEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12(\n\x05value\x18\x02 \x01(\x0b\x32\x19.storage.MemCopyChunkList:\x02\x38\x01\x1aJ\n\x0cHandlesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12)\n\x05value\x18\x02 \x01(\x0b\x32\x1a.storage.MemCopyHandleList:\x02\x38\x01"\'\n\x11LoadModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t"p\n\x13\x43onfirmModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x14\n\x0creplica_uuid\x18\x02 \x01(\t\x12/\n\x12target_device_type\x18\x03 \x01(\x0e\x32\x13.storage.DeviceType"8\n\x14\x43onfirmModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x0c\n\x04\x63ode\x18\x02 \x01(\x05"o\n\x12UnloadModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x14\n\x0creplica_uuid\x18\x02 \x01(\t\x12/\n\x12target_device_type\x18\x03 \x01(\x0e\x32\x13.storage.DeviceType"7\n\x13UnloadModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x0c\n\x04\x63ode\x18\x02 \x01(\x05"\x11\n\x0f\x43learMemRequest"\x12\n\x10\x43learMemResponse*L\n\nDeviceType\x12\x14\n\x10\x44\x45VICE_TYPE_DISK\x10\x00\x12\x13\n\x0f\x44\x45VICE_TYPE_CPU\x10\x01\x12\x13\n\x0f\x44\x45VICE_TYPE_GPU\x10\x02\x32\xdc\x03\n\x07Storage\x12I\n\x0eLoadModelAsync\x12\x19.storage.LoadModelRequest\x1a\x1a.storage.LoadModelResponse"\x00\x12M\n\x0c\x43onfirmModel\x12\x1c.storage.ConfirmModelRequest\x1a\x1d.storage.ConfirmModelResponse"\x00\x12J\n\x0bUnloadModel\x12\x1b.storage.UnloadModelRequest\x1a\x1c.storage.UnloadModelResponse"\x00\x12\x41\n\x08\x43learMem\x12\x18.storage.ClearMemRequest\x1a\x19.storage.ClearMemResponse"\x00\x12P\n\rRegisterModel\x12\x1d.storage.RegisterModelRequest\x1a\x1e.storage.RegisterModelResponse"\x00\x12V\n\x0fGetServerConfig\x12\x1f.storage.GetServerConfigRequest\x1a .storage.GetServerConfigResponse"\x00\x62\x06proto3' -) + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rstorage.proto\x12\x07storage\"\x0f\n\rGetPidRequest\"\x1d\n\x0eGetPidResponse\x12\x0b\n\x03pid\x18\x01 \x01(\x05\"\x18\n\x16GetServerConfigRequest\"D\n\x17GetServerConfigResponse\x12\x15\n\rmem_pool_size\x18\x01 \x01(\x03\x12\x12\n\nchunk_size\x18\x02 \x01(\x03\"*\n\x14RegisterModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\"?\n\x15RegisterModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x12\n\nmodel_size\x18\x02 \x01(\x03\"X\n\x0cMemCopyChunk\x12\x12\n\nsrc_offset\x18\x01 \x01(\x04\x12\x0c\n\x04size\x18\x02 \x01(\x04\x12\x12\n\ndst_offset\x18\x03 \x01(\x04\x12\x12\n\nhandle_idx\x18\x04 \x01(\x04\"9\n\x10MemCopyChunkList\x12%\n\x06chunks\x18\x01 \x03(\x0b\x32\x15.storage.MemCopyChunk\"(\n\rMemCopyHandle\x12\x17\n\x0f\x63uda_ipc_handle\x18\x01 \x01(\x0c\"<\n\x11MemCopyHandleList\x12\'\n\x07handles\x18\x01 \x03(\x0b\x32\x16.storage.MemCopyHandle\"\xf3\x02\n\x10LoadModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x14\n\x0creplica_uuid\x18\x02 \x01(\t\x12\x35\n\x06chunks\x18\x03 \x03(\x0b\x32%.storage.LoadModelRequest.ChunksEntry\x12\x37\n\x07handles\x18\x04 \x03(\x0b\x32&.storage.LoadModelRequest.HandlesEntry\x12/\n\x12target_device_type\x18\x05 \x01(\x0e\x32\x13.storage.DeviceType\x1aH\n\x0bChunksEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12(\n\x05value\x18\x02 \x01(\x0b\x32\x19.storage.MemCopyChunkList:\x02\x38\x01\x1aJ\n\x0cHandlesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12)\n\x05value\x18\x02 \x01(\x0b\x32\x1a.storage.MemCopyHandleList:\x02\x38\x01\"\'\n\x11LoadModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\"p\n\x13ConfirmModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x14\n\x0creplica_uuid\x18\x02 \x01(\t\x12/\n\x12target_device_type\x18\x03 \x01(\x0e\x32\x13.storage.DeviceType\"8\n\x14ConfirmModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x0c\n\x04code\x18\x02 \x01(\x05\"o\n\x12UnloadModelRequest\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x14\n\x0creplica_uuid\x18\x02 \x01(\t\x12/\n\x12target_device_type\x18\x03 \x01(\x0e\x32\x13.storage.DeviceType\"7\n\x13UnloadModelResponse\x12\x12\n\nmodel_path\x18\x01 \x01(\t\x12\x0c\n\x04code\x18\x02 \x01(\x05\"\x11\n\x0fClearMemRequest\"\x12\n\x10ClearMemResponse*L\n\nDeviceType\x12\x14\n\x10DEVICE_TYPE_DISK\x10\x00\x12\x13\n\x0fDEVICE_TYPE_CPU\x10\x01\x12\x13\n\x0fDEVICE_TYPE_GPU\x10\x022\x99\x04\n\x07Storage\x12I\n\x0eLoadModelAsync\x12\x19.storage.LoadModelRequest\x1a\x1a.storage.LoadModelResponse\"\x00\x12M\n\x0cConfirmModel\x12\x1c.storage.ConfirmModelRequest\x1a\x1d.storage.ConfirmModelResponse\"\x00\x12J\n\x0bUnloadModel\x12\x1b.storage.UnloadModelRequest\x1a\x1c.storage.UnloadModelResponse\"\x00\x12\x41\n\x08ClearMem\x12\x18.storage.ClearMemRequest\x1a\x19.storage.ClearMemResponse\"\x00\x12P\n\rRegisterModel\x12\x1d.storage.RegisterModelRequest\x1a\x1e.storage.RegisterModelResponse\"\x00\x12V\n\x0fGetServerConfig\x12\x1f.storage.GetServerConfigRequest\x1a .storage.GetServerConfigResponse\"\x00\x12;\n\x06GetPid\x12\x16.storage.GetPidRequest\x1a\x17.storage.GetPidResponse\"\x00b\x06proto3') _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals()) _builder.BuildTopDescriptorsAndMessages( @@ -22,47 +22,51 @@ _builder.BuildTopDescriptorsAndMessages( if _descriptor._USE_C_DESCRIPTORS == False: DESCRIPTOR._options = None _LOADMODELREQUEST_CHUNKSENTRY._options = None - _LOADMODELREQUEST_CHUNKSENTRY._serialized_options = b"8\001" + _LOADMODELREQUEST_CHUNKSENTRY._serialized_options = b'8\001' _LOADMODELREQUEST_HANDLESENTRY._options = None - _LOADMODELREQUEST_HANDLESENTRY._serialized_options = b"8\001" - _DEVICETYPE._serialized_start = 1297 - _DEVICETYPE._serialized_end = 1373 - _GETSERVERCONFIGREQUEST._serialized_start = 43 - _GETSERVERCONFIGREQUEST._serialized_end = 67 - _GETSERVERCONFIGRESPONSE._serialized_start = 69 - _GETSERVERCONFIGRESPONSE._serialized_end = 137 - _REGISTERMODELREQUEST._serialized_start = 139 - _REGISTERMODELREQUEST._serialized_end = 181 - _REGISTERMODELRESPONSE._serialized_start = 183 - _REGISTERMODELRESPONSE._serialized_end = 246 - _MEMCOPYCHUNK._serialized_start = 248 - _MEMCOPYCHUNK._serialized_end = 336 - _MEMCOPYCHUNKLIST._serialized_start = 338 - _MEMCOPYCHUNKLIST._serialized_end = 395 - _MEMCOPYHANDLE._serialized_start = 397 - _MEMCOPYHANDLE._serialized_end = 437 - _MEMCOPYHANDLELIST._serialized_start = 439 - _MEMCOPYHANDLELIST._serialized_end = 499 - _LOADMODELREQUEST._serialized_start = 502 - _LOADMODELREQUEST._serialized_end = 873 - _LOADMODELREQUEST_CHUNKSENTRY._serialized_start = 725 - _LOADMODELREQUEST_CHUNKSENTRY._serialized_end = 797 - _LOADMODELREQUEST_HANDLESENTRY._serialized_start = 799 - _LOADMODELREQUEST_HANDLESENTRY._serialized_end = 873 - _LOADMODELRESPONSE._serialized_start = 875 - _LOADMODELRESPONSE._serialized_end = 914 - _CONFIRMMODELREQUEST._serialized_start = 916 - _CONFIRMMODELREQUEST._serialized_end = 1028 - _CONFIRMMODELRESPONSE._serialized_start = 1030 - _CONFIRMMODELRESPONSE._serialized_end = 1086 - _UNLOADMODELREQUEST._serialized_start = 1088 - _UNLOADMODELREQUEST._serialized_end = 1199 - _UNLOADMODELRESPONSE._serialized_start = 1201 - _UNLOADMODELRESPONSE._serialized_end = 1256 - _CLEARMEMREQUEST._serialized_start = 1258 - _CLEARMEMREQUEST._serialized_end = 1275 - _CLEARMEMRESPONSE._serialized_start = 1277 - _CLEARMEMRESPONSE._serialized_end = 1295 - _STORAGE._serialized_start = 1376 - _STORAGE._serialized_end = 1852 -# @@protoc_insertion_point(module_scope) + _LOADMODELREQUEST_HANDLESENTRY._serialized_options = b'8\001' + _DEVICETYPE._serialized_start=1328 + _DEVICETYPE._serialized_end=1404 + _GETPIDREQUEST._serialized_start=26 + _GETPIDREQUEST._serialized_end=41 + _GETPIDRESPONSE._serialized_start=43 + _GETPIDRESPONSE._serialized_end=72 + _GETSERVERCONFIGREQUEST._serialized_start=74 + _GETSERVERCONFIGREQUEST._serialized_end=98 + _GETSERVERCONFIGRESPONSE._serialized_start=100 + _GETSERVERCONFIGRESPONSE._serialized_end=168 + _REGISTERMODELREQUEST._serialized_start=170 + _REGISTERMODELREQUEST._serialized_end=212 + _REGISTERMODELRESPONSE._serialized_start=214 + _REGISTERMODELRESPONSE._serialized_end=277 + _MEMCOPYCHUNK._serialized_start=279 + _MEMCOPYCHUNK._serialized_end=367 + _MEMCOPYCHUNKLIST._serialized_start=369 + _MEMCOPYCHUNKLIST._serialized_end=426 + _MEMCOPYHANDLE._serialized_start=428 + _MEMCOPYHANDLE._serialized_end=468 + _MEMCOPYHANDLELIST._serialized_start=470 + _MEMCOPYHANDLELIST._serialized_end=530 + _LOADMODELREQUEST._serialized_start=533 + _LOADMODELREQUEST._serialized_end=904 + _LOADMODELREQUEST_CHUNKSENTRY._serialized_start=756 + _LOADMODELREQUEST_CHUNKSENTRY._serialized_end=828 + _LOADMODELREQUEST_HANDLESENTRY._serialized_start=830 + _LOADMODELREQUEST_HANDLESENTRY._serialized_end=904 + _LOADMODELRESPONSE._serialized_start=906 + _LOADMODELRESPONSE._serialized_end=945 + _CONFIRMMODELREQUEST._serialized_start=947 + _CONFIRMMODELREQUEST._serialized_end=1059 + _CONFIRMMODELRESPONSE._serialized_start=1061 + _CONFIRMMODELRESPONSE._serialized_end=1117 + _UNLOADMODELREQUEST._serialized_start=1119 + _UNLOADMODELREQUEST._serialized_end=1230 + _UNLOADMODELRESPONSE._serialized_start=1232 + _UNLOADMODELRESPONSE._serialized_end=1287 + _CLEARMEMREQUEST._serialized_start=1289 + _CLEARMEMREQUEST._serialized_end=1306 + _CLEARMEMRESPONSE._serialized_start=1308 + _CLEARMEMRESPONSE._serialized_end=1326 + _STORAGE._serialized_start=1407 + _STORAGE._serialized_end=1944 +# @@protoc_insertion_point(module_scope) \ No newline at end of file diff --git a/sllm_store/sllm_store/proto/storage_pb2_grpc.py b/sllm_store/sllm_store/proto/storage_pb2_grpc.py index 3090d18..f43549d 100644 --- a/sllm_store/sllm_store/proto/storage_pb2_grpc.py +++ b/sllm_store/sllm_store/proto/storage_pb2_grpc.py @@ -1,6 +1,5 @@ # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" - import grpc from sllm_store.proto import ( @@ -18,35 +17,40 @@ class StorageStub(object): channel: A grpc.Channel. """ self.LoadModelAsync = channel.unary_unary( - "/storage.Storage/LoadModelAsync", - request_serializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.SerializeToString, - response_deserializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.FromString, - ) + '/storage.Storage/LoadModelAsync', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.FromString, + ) self.ConfirmModel = channel.unary_unary( - "/storage.Storage/ConfirmModel", - request_serializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.SerializeToString, - response_deserializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.FromString, - ) + '/storage.Storage/ConfirmModel', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.FromString, + ) self.UnloadModel = channel.unary_unary( - "/storage.Storage/UnloadModel", - request_serializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.SerializeToString, - response_deserializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.FromString, - ) + '/storage.Storage/UnloadModel', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.FromString, + ) self.ClearMem = channel.unary_unary( - "/storage.Storage/ClearMem", - request_serializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.SerializeToString, - response_deserializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.FromString, - ) + '/storage.Storage/ClearMem', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.FromString, + ) self.RegisterModel = channel.unary_unary( - "/storage.Storage/RegisterModel", - request_serializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.SerializeToString, - response_deserializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.FromString, - ) + '/storage.Storage/RegisterModel', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.FromString, + ) self.GetServerConfig = channel.unary_unary( - "/storage.Storage/GetServerConfig", - request_serializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.SerializeToString, - response_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.FromString, - ) + '/storage.Storage/GetServerConfig', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.FromString, + ) + self.GetPid = channel.unary_unary( + '/storage.Storage/GetPid', + request_serializer=sllm__store_dot_proto_dot_storage__pb2.GetPidRequest.SerializeToString, + response_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetPidResponse.FromString, + ) class StorageServicer(object): @@ -55,253 +59,208 @@ class StorageServicer(object): def LoadModelAsync(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def ConfirmModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def UnloadModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def ClearMem(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def RegisterModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def GetServerConfig(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details("Method not implemented!") - raise NotImplementedError("Method not implemented!") + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetPid(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def add_StorageServicer_to_server(servicer, server): rpc_method_handlers = { - "LoadModelAsync": grpc.unary_unary_rpc_method_handler( + 'LoadModelAsync': grpc.unary_unary_rpc_method_handler( servicer.LoadModelAsync, request_deserializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.FromString, response_serializer=sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.SerializeToString, ), - "ConfirmModel": grpc.unary_unary_rpc_method_handler( + 'ConfirmModel': grpc.unary_unary_rpc_method_handler( servicer.ConfirmModel, request_deserializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.FromString, response_serializer=sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.SerializeToString, ), - "UnloadModel": grpc.unary_unary_rpc_method_handler( + 'UnloadModel': grpc.unary_unary_rpc_method_handler( servicer.UnloadModel, request_deserializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.FromString, response_serializer=sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.SerializeToString, ), - "ClearMem": grpc.unary_unary_rpc_method_handler( + 'ClearMem': grpc.unary_unary_rpc_method_handler( servicer.ClearMem, request_deserializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.FromString, response_serializer=sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.SerializeToString, ), - "RegisterModel": grpc.unary_unary_rpc_method_handler( + 'RegisterModel': grpc.unary_unary_rpc_method_handler( servicer.RegisterModel, request_deserializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.FromString, response_serializer=sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.SerializeToString, ), - "GetServerConfig": grpc.unary_unary_rpc_method_handler( + 'GetServerConfig': grpc.unary_unary_rpc_method_handler( servicer.GetServerConfig, request_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.FromString, response_serializer=sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.SerializeToString, ), + 'GetPid': grpc.unary_unary_rpc_method_handler( + servicer.GetPid, + request_deserializer=sllm__store_dot_proto_dot_storage__pb2.GetPidRequest.FromString, + response_serializer=sllm__store_dot_proto_dot_storage__pb2.GetPidResponse.SerializeToString, + ), } generic_handler = grpc.method_handlers_generic_handler( - "storage.Storage", rpc_method_handlers - ) + 'storage.Storage', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) -# This class is part of an EXPERIMENTAL API. + # This class is part of an EXPERIMENTAL API. class Storage(object): """Missing associated documentation comment in .proto file.""" @staticmethod - def LoadModelAsync( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, + def LoadModelAsync(request, target, - "/storage.Storage/LoadModelAsync", + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/LoadModelAsync', sllm__store_dot_proto_dot_storage__pb2.LoadModelRequest.SerializeToString, sllm__store_dot_proto_dot_storage__pb2.LoadModelResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod - def ConfirmModel( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, + def ConfirmModel(request, target, - "/storage.Storage/ConfirmModel", + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/ConfirmModel', sllm__store_dot_proto_dot_storage__pb2.ConfirmModelRequest.SerializeToString, sllm__store_dot_proto_dot_storage__pb2.ConfirmModelResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod - def UnloadModel( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, + def UnloadModel(request, target, - "/storage.Storage/UnloadModel", + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/UnloadModel', sllm__store_dot_proto_dot_storage__pb2.UnloadModelRequest.SerializeToString, sllm__store_dot_proto_dot_storage__pb2.UnloadModelResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod - def ClearMem( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, + def ClearMem(request, target, - "/storage.Storage/ClearMem", + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/ClearMem', sllm__store_dot_proto_dot_storage__pb2.ClearMemRequest.SerializeToString, sllm__store_dot_proto_dot_storage__pb2.ClearMemResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod - def RegisterModel( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, + def RegisterModel(request, target, - "/storage.Storage/RegisterModel", + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/RegisterModel', sllm__store_dot_proto_dot_storage__pb2.RegisterModelRequest.SerializeToString, sllm__store_dot_proto_dot_storage__pb2.RegisterModelResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod - def GetServerConfig( - request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None, - ): - return grpc.experimental.unary_unary( - request, + def GetServerConfig(request, target, - "/storage.Storage/GetServerConfig", + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/GetServerConfig', sllm__store_dot_proto_dot_storage__pb2.GetServerConfigRequest.SerializeToString, sllm__store_dot_proto_dot_storage__pb2.GetServerConfigResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - ) + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def GetPid(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/storage.Storage/GetPid', + sllm__store_dot_proto_dot_storage__pb2.GetPidRequest.SerializeToString, + sllm__store_dot_proto_dot_storage__pb2.GetPidResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) \ No newline at end of file diff --git a/sllm_store/sllm_store/server.py b/sllm_store/sllm_store/server.py index ef80d6b..99c9496 100644 --- a/sllm_store/sllm_store/server.py +++ b/sllm_store/sllm_store/server.py @@ -205,6 +205,13 @@ class StorageServicer(storage_pb2_grpc.StorageServicer): chunk_size=self.storage.get_chunk_size(), ) + async def GetPid(self, request, context): + """Return the current process ID for cann IPC setup""" + import acl + pid, ret = acl.rt.device_get_bare_tgid() + logger.info(f"GetPid called, returning PID: {pid}") + return storage_pb2.GetPidResponse(pid=pid) + async def serve( host, @@ -235,4 +242,4 @@ async def serve( await server.wait_for_termination() except (KeyboardInterrupt, asyncio.CancelledError): logger.info("Shutting down gRPC server") - await server.stop(5) + await server.stop(5) \ No newline at end of file diff --git a/sllm_store/sllm_store/torch.py b/sllm_store/sllm_store/torch.py index 8516530..7cc174d 100644 --- a/sllm_store/sllm_store/torch.py +++ b/sllm_store/sllm_store/torch.py @@ -1,19 +1,19 @@ # ---------------------------------------------------------------------------- # -# ServerlessLLM # -# Copyright (c) ServerlessLLM Team 2024 # +# ServerlessLLM # +# Copyright (c) ServerlessLLM Team 2024 # # # -# Licensed under the Apache License, Version 2.0 (the "License"); # -# you may not use this file except in compliance with the License. # +# Licensed under the Apache License, Version 2.0 (the "License"); # +# you may not use this file except in compliance with the License. # # # -# You may obtain a copy of the License at # +# You may obtain a copy of the License at # # # -# http://www.apache.org/licenses/LICENSE-2.0 # +# http://www.apache.org/licenses/LICENSE-2.0 # # # -# Unless required by applicable law or agreed to in writing, software # -# distributed under the License is distributed on an "AS IS" BASIS, # -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # -# See the License for the specific language governing permissions and # -# limitations under the License. # +# Unless required by applicable law or agreed to in writing, software # +# distributed under the License is distributed on an "AS IS" BASIS, # +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # +# See the License for the specific language governing permissions and # +# limitations under the License. # # ---------------------------------------------------------------------------- # import json import os @@ -23,10 +23,15 @@ from typing import Dict, Optional, Union import torch +# Import torch_npu BEFORE importing our C++ extension to ensure PrivateUse1HooksInterface is registered +try: + import torch_npu + print("torch_npu imported successfully - PrivateUse1HooksInterface should be registered") +except ImportError: + print("torch_npu not available, NPU operations may fail") + # from accelerate.hooks import add_hook_to_module from sllm_store._C import ( - allocate_cuda_memory, - get_cuda_memory_handles, get_device_uuid_map, restore_tensors, save_tensors, @@ -39,6 +44,16 @@ from sllm_store.utils import ( calculate_tensor_device_offsets, ) +try: + from sllm_store.cann_utils import get_memory_functions, get_device_type + allocate_memory, get_memory_handles = get_memory_functions() + device_type = get_device_type() + from sllm_store._C import create_pointer_capsule +except ImportError: + # Fallback to CUDA + from sllm_store._C import allocate_cuda_memory as allocate_memory, get_cuda_memory_handles as get_memory_handles + device_type = "cuda" + logger = init_logger(__name__) @@ -126,15 +141,22 @@ def load_dict_non_blocking( expanded_device_map, tensor_data_index ) # logger.debug(f"calculate_device_memory {device_memory}") - cuda_memory_ptrs = allocate_cuda_memory(device_memory) - # cuda_memory_ptrs = { k: [v] for k,v in cuda_memory_ptrs.items()} - cuda_memory_handles = get_cuda_memory_handles(cuda_memory_ptrs) + memory_ptrs = allocate_memory(device_memory) + if device_type == "npu": + memory_handles, updated_ptrs = get_memory_handles(memory_ptrs) + memory_ptrs_for_restore = updated_ptrs + else: + memory_handles = get_memory_handles(memory_ptrs) + memory_ptrs_for_restore = memory_ptrs + device_uuid_map = get_device_uuid_map() # logger.debug(f"determine device_uuid_map {device_uuid_map}") tensor_device_offsets, tensor_copy_chunks = calculate_tensor_device_offsets( expanded_device_map, tensor_data_index ) - logger.debug(f"allocate_cuda_memory takes {time.time() - start} seconds") + logger.debug( + f"allocate_{device_type}_memory takes {time.time() - start} seconds" + ) replica_uuid = _get_uuid() ret = client.load_into_gpu( @@ -146,7 +168,7 @@ def load_dict_non_blocking( }, { device_uuid_map[device_id]: [v] - for device_id, v in cuda_memory_handles.items() + for device_id, v in memory_handles.items() }, ) if not ret: @@ -154,9 +176,13 @@ def load_dict_non_blocking( # load model state_dict start = time.time() + if device_type == "npu": + memory_ptrs = {k: create_pointer_capsule(v) for k, v in memory_ptrs_for_restore.items()} + else: + memory_ptrs = memory_ptrs_for_restore state_dict = restore_tensors( - tensor_meta_index, cuda_memory_ptrs, tensor_device_offsets + tensor_meta_index, memory_ptrs, tensor_device_offsets ) logger.info(f"restore state_dict takes {time.time() - start} seconds") - return replica_uuid, state_dict + return replica_uuid, state_dict \ No newline at end of file diff --git a/sllm_store/sllm_store/transformers.py b/sllm_store/sllm_store/transformers.py index 2809165..ca41619 100644 --- a/sllm_store/sllm_store/transformers.py +++ b/sllm_store/sllm_store/transformers.py @@ -16,23 +16,24 @@ # limitations under the License. # # ---------------------------------------------------------------------------- # import concurrent.futures +import importlib import json import os import time import uuid -from typing import Optional, Union, Dict, Any +from typing import Optional, Union import torch +import torch_npu from accelerate import dispatch_model, init_empty_weights # from accelerate.hooks import add_hook_to_module from accelerate.utils import set_module_tensor_to_device -from sllm_store._C import ( - allocate_cuda_memory, - get_cuda_memory_handles, - get_device_uuid_map, - restore_tensors, -) +from peft import LoraConfig, PeftModel, get_peft_model, get_peft_model_state_dict +from peft.utils import set_peft_model_state_dict +from sllm_store._C import get_device_uuid_map, restore_tensors +from sllm_store._C import start_profiling + from sllm_store.client import SllmStoreClient from sllm_store.device_map_utils import ( DeviceMapType, @@ -49,22 +50,25 @@ from sllm_store.utils import ( get_no_split_modules, get_tied_no_split_modules, send_module_buffers_to_device, - quantize, ) from torch import nn from transformers import AutoConfig -import importlib -from peft import ( - PeftModel, - get_peft_model, - get_peft_model_state_dict, - LoraConfig, -) -from peft.utils import set_peft_model_state_dict -from transformers.utils.quantization_config import ( - QuantizationConfigMixin, +from transformers.integrations.bitsandbytes import ( + replace_with_bnb_linear, + set_module_quantized_tensor_to_device, ) +try: + from sllm_store.cann_utils import get_device_type, get_memory_functions + allocate_memory, get_memory_handles = get_memory_functions() + device_type = get_device_type() + from sllm_store._C import create_pointer_capsule +except ImportError: + from sllm_store._C import allocate_cuda_memory as allocate_memory + from sllm_store._C import get_cuda_memory_handles as get_memory_handles + + device_type = "cuda" + logger = init_logger(__name__) @@ -112,7 +116,7 @@ def save_lora(model: PeftModel, lora_path: str): if not os.path.exists(lora_path): os.makedirs(lora_path, exist_ok=True) - model = model.to("cpu") + model = model.cpu() lora_state_dict = get_peft_model_state_dict(model) @@ -140,9 +144,7 @@ def load_model( model_path: Optional[Union[str, os.PathLike]], device_map: DeviceMapType = "auto", torch_dtype: Optional[torch.dtype] = None, - quantization_config: Optional[ - Union[QuantizationConfigMixin, Dict[str, Any]] - ] = None, + quantization_config=None, storage_path: Optional[str] = None, fully_parallel: bool = False, hf_model_class: str = "AutoModelForCausalLM", @@ -173,9 +175,7 @@ def fully_parallel_load( hf_model_class: str, device_map: DeviceMapType = "auto", torch_dtype: Optional[torch.dtype] = None, - quantization_config: Optional[ - Union[QuantizationConfigMixin, Dict[str, Any]] - ] = None, + quantization_config=None, storage_path: Optional[str] = None, ): if not storage_path: @@ -228,19 +228,42 @@ def fully_parallel_load( logger.debug(f"load model takes {time.time() - start} seconds") replica_uuid, state_dict = future.result() + + # # synchronize + # client = SllmStoreClient("127.0.0.1:8073") + # client.confirm_model_loaded(model_path, replica_uuid) + # start_profiling() with torch.no_grad(): if quantization_config and torch.cuda.is_available(): - model = quantize( - model, - state_dict, - quantization_config, - torch_dtype, - device_map, - model_path, - replica_uuid, - logger, + from transformers import BitsAndBytesConfig + + if not isinstance(quantization_config, BitsAndBytesConfig): + raise ValueError( + f"Invalid config type: {type(quantization_config)}" + ) + + logger.debug( + f"using precision: {quantization_config.quantization_method()}" ) + + if quantization_config.llm_int8_enable_fp32_cpu_offload: + logger.debug("Offloading is not supported yet") + quantization_config.llm_int8_enable_fp32_cpu_offload = False + + has_torch_dtype = torch_dtype is not None + model = replace_with_bnb_linear( + model, quantization_config=quantization_config + ) + + for name, param in state_dict.items(): + final_device = param.device + if not has_torch_dtype: + param = param.to(torch.float16) + + set_module_quantized_tensor_to_device( + model, name, final_device, param.to("cpu") + ) else: if quantization_config is not None: logger.debug( @@ -254,9 +277,9 @@ def fully_parallel_load( dispatch_model( model, device_map, skip_keys=model._skip_keys_device_placement ) + client = SllmStoreClient("127.0.0.1:8073") client.confirm_model_loaded(model_path, replica_uuid) - model.hf_device_map = device_map model.eval() return model @@ -266,9 +289,7 @@ def best_effort_load( hf_model_class: str, device_map: DeviceMapType = "auto", torch_dtype: Optional[torch.dtype] = None, - quantization_config: Optional[ - Union[QuantizationConfigMixin, Dict[str, Any]] - ] = None, + quantization_config=None, storage_path: Optional[str] = None, ): client = SllmStoreClient("127.0.0.1:8073") @@ -341,9 +362,17 @@ def best_effort_load( expanded_device_map, tensor_data_index ) # logger.debug(f"calculate_device_memory {device_memory}") - cuda_memory_ptrs = allocate_cuda_memory(device_memory) + cuda_memory_ptrs = allocate_memory(device_memory) # cuda_memory_ptrs = { k: [v] for k,v in cuda_memory_ptrs.items()} - cuda_memory_handles = get_cuda_memory_handles(cuda_memory_ptrs) + if device_type == "npu": + cuda_memory_handles, updated_ptrs = get_memory_handles( + cuda_memory_ptrs + ) + memory_ptrs_for_restore = updated_ptrs + else: + cuda_memory_handles = get_memory_handles(cuda_memory_ptrs) + memory_ptrs_for_restore = cuda_memory_ptrs + device_uuid_map = get_device_uuid_map() # logger.debug(f"determine device_uuid_map {device_uuid_map}") tensor_device_offsets, tensor_copy_chunks = calculate_tensor_device_offsets( @@ -368,23 +397,53 @@ def best_effort_load( # load model state_dict start = time.time() + # Use the prepared memory_ptrs_for_restore + if device_type == "npu": + # For NPU, updated_ptrs is {int: int(uintptr)}, convert to capsules + memory_ptrs = { + k: create_pointer_capsule(v) + for k, v in memory_ptrs_for_restore.items() + } + else: + memory_ptrs = memory_ptrs_for_restore # CUDA returns map state_dict = restore_tensors( - tensor_meta_index, cuda_memory_ptrs, tensor_device_offsets + tensor_meta_index, memory_ptrs, tensor_device_offsets ) logger.info(f"restore state_dict takes {time.time() - start} seconds") with torch.no_grad(): if quantization_config and torch.cuda.is_available(): - model = quantize( - model, - state_dict, - quantization_config, - torch_dtype, - device_map, - model_path, - replica_uuid, - logger, + from transformers import BitsAndBytesConfig + + if not isinstance(quantization_config, BitsAndBytesConfig): + raise ValueError( + f"Invalid config type: {type(quantization_config)}" + ) + + logger.debug( + f"using precision: {quantization_config.quantization_method()}" + ) + + if quantization_config.llm_int8_enable_fp32_cpu_offload: + logger.debug("Offloading is not supported yet") + quantization_config.llm_int8_enable_fp32_cpu_offload = False + + model = replace_with_bnb_linear( + model, quantization_config=quantization_config ) + + client.confirm_model_loaded(model_path, replica_uuid) + + for name, param in state_dict.items(): + if ( + param.dtype not in [torch.uint8, torch.int8] + and torch_dtype is None + ): + param = param.to(torch.float16) + + set_module_quantized_tensor_to_device( + model, name, param.device, param + ) else: if quantization_config is not None: logger.debug( @@ -495,4 +554,4 @@ def load_lora( logger.info(f"Available adapters: {peft_model.peft_config.keys()}") - return peft_model + return peft_model \ No newline at end of file diff --git a/sllm_store/tests/python/test_cann_basic.py b/sllm_store/tests/python/test_cann_basic.py new file mode 100644 index 0000000..15149d3 --- /dev/null +++ b/sllm_store/tests/python/test_cann_basic.py @@ -0,0 +1,81 @@ +import os +import sys +import pytest +import torch + +# Add the parent directory to sys.path to import sllm_store +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +try: + import torch_npu + CANN_AVAILABLE = True + print("torch_npu successfully imported") +except ImportError: + CANN_AVAILABLE = False + print("torch_npu not available") + +def test_cann_basic_import(): + """Test basic CANN functionality""" + if not CANN_AVAILABLE: + pytest.skip("CANN not available") + + # Test device detection + try: + device_count = torch_npu.npu.device_count() + print(f"NPU devices detected: {device_count}") + assert device_count > 0, "No NPU devices found" + except Exception as e: + pytest.fail(f"Failed to get NPU device count: {e}") + +def test_sllm_store_import(): + """Test that sllm_store can be imported""" + try: + from sllm_store.transformers import load_model + print("sllm_store.transformers imported successfully") + except ImportError as e: + pytest.fail(f"Failed to import sllm_store.transformers: {e}") + +def test_cann_utils_import(): + """Test CANN utils import""" + if not CANN_AVAILABLE: + pytest.skip("CANN not available") + + try: + from sllm_store.cann_utils import get_memory_functions, get_device_type + allocate_memory, get_memory_handles = get_memory_functions() + device_type = get_device_type() + print(f"Device type: {device_type}") + assert device_type == "npu", f"Expected 'npu', got '{device_type}'" + except ImportError as e: + print(f"CANN utils not available (expected if not compiled with CANN): {e}") + +def test_basic_npu_operations(): + """Test basic NPU tensor operations""" + if not CANN_AVAILABLE: + pytest.skip("CANN not available") + + try: + # Test basic tensor creation and movement + device = torch_npu.npu.device(0) + x = torch.randn(2, 3) + x_npu = x.to(device) + + print(f"Tensor device: {x_npu.device}") + print(f"Tensor shape: {x_npu.shape}") + + # Test basic operation + y_npu = x_npu * 2 + y_cpu = y_npu.cpu() + + assert torch.allclose(y_cpu, x * 2), "NPU computation failed" + print("Basic NPU operations successful") + except Exception as e: + pytest.fail(f"NPU operations failed: {e}") + +if __name__ == "__main__": + print("Testing CANN basic functionality...") + test_cann_basic_import() + test_sllm_store_import() + test_cann_utils_import() + test_basic_npu_operations() + print("All basic tests completed!") \ No newline at end of file diff --git a/sllm_store/tests/python/test_cann_load_model.py b/sllm_store/tests/python/test_cann_load_model.py new file mode 100644 index 0000000..f60a986 --- /dev/null +++ b/sllm_store/tests/python/test_cann_load_model.py @@ -0,0 +1,278 @@ +import time +import torch +import torch_npu +# from torch_npu.contrib import transfer_to_npu +import os +from transformers import AutoTokenizer, AutoModelForCausalLM +from sllm_store.transformers import load_model +from sllm_store.cann_utils import is_cann_available +from sllm_store._C import start_profiling + +# start_profiling() + +# os.environ['ASCEND_RT_VISIBLE_DEVICES']='0,1' + +# --- Configuration --- +model_name = 'facebook/opt-1.3b' +storage_path = "/mnt/model_weights/models/" +target_dtype = torch.float16 +# --- End Configuration --- + +# --- Helper Functions for Weight Inspection --- +def inspect_weights(model_instance, stage_name, target_device=None): + """Prints statistics for key model weights.""" + print(f"\n--- {stage_name} Weight Inspection ---") + + if target_device: + print(f"Model current device: {next(model_instance.parameters()).device}") + + # For Qwen2.5, typically model.model.layers is the list of transformer blocks + # We'll check the first layer's self_attn.q_proj.weight as a representative + try: + # Access the first transformer block's query projection weight + # Path might be model.model.layers[0].self_attn.q_proj.weight for Qwen2.5 + # Or model.decoder.layers[0].self_attn.q_proj.weight for other architectures + + # A safer way to find a relevant weight + target_param = None + for name, param in model_instance.named_parameters(): + if "layers.0.self_attn.q_proj.weight" in name: + target_param = param + break + + if target_param is None: + print("Could not find a common transformer layer weight (e.g., q_proj).") + print("Attempting to inspect the first found trainable parameter.") + for name, param in model_instance.named_parameters(): + if param.requires_grad: + target_param = param + break + + if target_param is not None: + # Ensure the tensor is on CPU for statistical stability when comparing across devices + # and convert to float32 for accurate statistics if it's float16 + param_cpu_float32 = target_param.cpu().float() + + print(f"Inspecting weight: '{name}' (dtype: {target_param.dtype}, device: {target_param.device})") + print(f" Shape: {param_cpu_float32.shape}") + print(f" Mean: {param_cpu_float32.mean().item():.6f}") + print(f" Std: {param_cpu_float32.std().item():.6f}") + print(f" Min: {param_cpu_float32.min().item():.6f}") + print(f" Max: {param_cpu_float32.max().item():.6f}") + + # Check for NaNs/Infs + if torch.isnan(param_cpu_float32).any(): + print("!!! WARNING: NaN values detected in this weight !!!") + if torch.isinf(param_cpu_float32).any(): + print("!!! WARNING: Inf values detected in this weight !!!") + + # Print first few elements to spot obvious corruption patterns + print(" First 10 elements (flattened):") + print(param_cpu_float32.flatten()[:10].tolist()) + else: + print("No trainable parameters found in the model to inspect.") + + except Exception as e: + print(f"Error during weight inspection for '{stage_name}': {e}") + +def compare_weights(model1, model2, name1="Model 1", name2="Model 2"): + print(f"\n--- Comparing selected weights between {name1} and {name2} ---") + + param1 = None + param2 = None + + for n1, p1 in model1.named_parameters(): + if "layers.0.self_attn.q_proj.weight" in n1: + param1_name = n1 + param1 = p1 + break + + for n2, p2 in model2.named_parameters(): + if "layers.0.self_attn.q_proj.weight" in n2: + param2_name = n2 + param2 = p2 + break + + if param1 is None or param2 is None: + print("Could not find corresponding 'q_proj.weight' in both models for comparison.") + print("Falling back to comparing first trainable parameters if available.") + + # Fallback to first trainable parameter + for n1, p1 in model1.named_parameters(): + if p1.requires_grad: + param1_name = n1 + param1 = p1 + break + for n2, p2 in model2.named_parameters(): + if p2.requires_grad: + param2_name = n2 + param2 = p2 + break + + if param1 is not None and param2 is not None: + # Move both to CPU and convert to float32 for accurate comparison + p1_cpu_float32 = param1.cpu().float() + p2_cpu_float32 = param2.cpu().float() + + if p1_cpu_float32.shape != p2_cpu_float32.shape: + print(f"WARNING: Mismatched shapes: {param1_name}: {p1_cpu_float32.shape} vs {param2_name}: {p2_cpu_float32.shape}") + return + + diff = (p1_cpu_float32 - p2_cpu_float32).abs().mean().item() + print(f"Mean absolute difference for '{param1_name}' vs '{param2_name}': {diff:.6f}") + + if diff > 1e-4: # A small tolerance for floating point differences due to precision or operations + print(f"*** WARNING: Significant difference detected (diff > 1e-4) between {name1} and {name2} for this weight! ***") + else: + print(f"Weights are largely similar (diff <= 1e-4).") + else: + print("Could not find suitable parameters in both models for comparison.") + + +# --- Main Script --- + +# Check if NPU is available +device_type = "npu" if is_cann_available() else "cuda" +if not is_cann_available() and torch.cuda.is_available(): + device_type = "cuda" +elif not is_cann_available() and not torch.cuda.is_available(): + print("Neither NPU nor CUDA is available. This script requires an accelerator.") + exit() + +print(f"Using {device_type.upper()} devices") + +# Warm up the device +if device_type == "npu": + try: + num_npus = torch_npu.npu.device_count() + print(f"Number of NPU devices: {num_npus}") + for i in range(num_npus): + torch.ones(1).to(f"npu:{i}") + torch_npu.npu.synchronize() + print("NPU devices warmed up") + except Exception as e: + print(f"Error warming up NPU devices: {e}") + # Proceed with a single device if multi-NPU warm-up fails + device = torch.device("npu:0") +elif device_type == "cuda": + num_gpus = torch.cuda.device_count() + print(f"Number of CUDA devices: {num_gpus}") + for i in range(num_gpus): + torch.ones(1).to(f"cuda:{i}") + torch.cuda.synchronize() + print("CUDA devices warmed up") + +device = torch.device(f"{device_type}") +print(f"Using primary device: {device}") + +# --- 1. Baseline: Load with standard transformers to CPU --- +print("\n--- STEP 1: Loading model with standard transformers (CPU baseline) ---") +start_baseline = time.time() +try: + # Load to CPU first to rule out device-specific issues initially + original_model_cpu = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=target_dtype, + trust_remote_code=True # Qwen models often require this + ) + print(f"Original model loaded successfully to CPU. Time: {time.time() - start_baseline:.2f}s") + inspect_weights(original_model_cpu, "Original CPU Load") + +except Exception as e: + print(f"FAILED: Standard transformers model loading failed: {e}") + print("This indicates an issue with the base model files or Hugging Face setup.") + exit() + +# --- 2. Load model with sllm_store --- +print(f"\n--- STEP 2: Loading model with sllm_store.transformers.load_model ---") +sllm_model_initial = None +sllm_model_on_npu = None +try: + start_sllm_load = time.time() + sllm_model_initial = load_model( + model_name, + device_map="auto", # sllm_store will decide where to initially place + torch_dtype=target_dtype, + storage_path=storage_path, + fully_parallel=True + ) + # Print out the device map + if hasattr(sllm_model_initial, 'hf_device_map'): + print("Loaded model's hf_device_map:") + print(sllm_model_initial.hf_device_map) + else: + print("hf_device_map not attached to model.") + + print(f"sllm_store model loaded successfully. Time: {time.time() - start_sllm_load:.2f}s") + + # Inspect weights immediately after sllm_store load (may still be on CPU or mixed) + inspect_weights(sllm_model_initial, "After sllm_store Load (Initial Placement)") + + # Compare with the original CPU model + compare_weights(original_model_cpu, sllm_model_initial, "Original CPU", "sllm_store Initial") + + # --- 3. Move sllm_store model to target device (NPU/CUDA) --- + print(f"\n--- STEP 3: Skipping move as model is already dispatched via Accelerate ---") + sllm_model_on_npu = sllm_model_initial + start_to_device = time.time() + # sllm_model_on_npu = sllm_model_initial.to(device) + print(f"sllm_store model moved to {device.type.upper()}. Time: {time.time() - start_to_device:.2f}s") + + # Inspect weights after moving to the target device + inspect_weights(sllm_model_on_npu, f"After .to({device.type.upper()})") + + # Compare the sllm_store model after .to() with the original CPU model + compare_weights(original_model_cpu, sllm_model_on_npu, "Original CPU", "sllm_store on NPU") + + # Compare the sllm_store model before and after .to() (if it started on CPU) + if str(next(sllm_model_initial.parameters()).device).startswith('cpu'): + compare_weights(sllm_model_initial, sllm_model_on_npu, "sllm_store Initial (CPU)", "sllm_store on NPU") + +except Exception as e: + print(f"FAILED: sllm_store model loading or device transfer failed: {e}") + # If this fails, the issue is likely within sllm_store or torch_npu's handling + exit() + +# --- 4. Load the tokenizer --- +print("\n--- STEP 4: Loading tokenizer ---") +try: + tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) + print("Tokenizer loaded successfully.") +except Exception as e: + print(f"FAILED: Tokenizer loading failed: {e}") + exit() + +# --- 5. Prepare input and run inference --- +print("\n--- STEP 5: Preparing input and running inference ---") +inputs = tokenizer('Hello, my dog is cute', return_tensors='pt').to("npu") +print(f"Input tokens on device: {inputs['input_ids'].device}") +print(f"Input tokens decoded: {tokenizer.decode(inputs['input_ids'][0], skip_special_tokens=False)}") + +# for name, param in sllm_model_on_npu.named_parameters(): +# print(name) +# print(param) + +try: + start_inference = time.time() + outputs = sllm_model_on_npu.generate(**inputs, max_new_tokens=100) + print(f"Inference completed. Time: {time.time() - start_inference:.2f}s") + + # Check output tensor for issues + print(f"Output tensor shape: {outputs.shape}") + print(f"Output tensor dtype: {outputs.dtype}") + if torch.isnan(outputs).any(): + print("!!! WARNING: NaN values detected in output tensor !!!") + if torch.isinf(outputs).any(): + print("!!! WARNING: Inf values detected in output tensor !!!") + + # Decode and clean up output + print("\n--- Inference Response ---") + print("Response (raw decoded):", tokenizer.decode(outputs[0], skip_special_tokens=True)) + +except Exception as e: + print(f"FAILED: Inference failed: {e}") + print("This could be due to corrupted weights, NPU issues during computation, or other runtime errors.") + # If the output is "corrupted", this is where it would manifest. + # The weight checks above help determine if it was corrupted *before* inference. + +print("\n--- Testing Complete ---") \ No newline at end of file diff --git a/sllm_store/tests/python/test_cann_load_vllm_model.py b/sllm_store/tests/python/test_cann_load_vllm_model.py new file mode 100644 index 0000000..f8aff09 --- /dev/null +++ b/sllm_store/tests/python/test_cann_load_vllm_model.py @@ -0,0 +1,29 @@ +from vllm import LLM, SamplingParams + +import os + +storage_path = os.getenv("STORAGE_PATH", "/mnt/model_weights/vllm_models") +model_name = "facebook/opt-1.3b" +model_path = os.path.join(storage_path, model_name) + +llm = LLM( + model=model_path, + load_format="serverless_llm", + dtype="float16" +) + +prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", +] + +sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +outputs = llm.generate(prompts, sampling_params) + +# Print the outputs. +for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") \ No newline at end of file diff --git a/sllm_store/tests/python/test_cann_save_model.py b/sllm_store/tests/python/test_cann_save_model.py new file mode 100644 index 0000000..afb8e62 --- /dev/null +++ b/sllm_store/tests/python/test_cann_save_model.py @@ -0,0 +1,25 @@ +# Import necessary modules +from sllm_store.transformers import save_model +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +models = [ + # 'facebook/opt-1.3b', + 'facebook/opt-6.7b', + # 'deepseek-ai/DeepSeek-R1-Distill-Llama-8B', + # 'Qwen/Qwen2.5-Coder-14B-Instruct', + # 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', + # 'Qwen/Qwen2.5-72B-Instruct' +] + +for model_name in models: + print(model_name) + # Load the model from Hugging Face (as in your code) + loaded_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16) + + # Save the model to the NVMe-mounted path in ServerlessLLM's optimized format + save_model(loaded_model, f'/mnt/model_weights/models/{model_name}') + + # Load and save the tokenizer (using standard Transformers method, as ServerlessLLM focuses on model weights) + tokenizer = AutoTokenizer.from_pretrained(model_name) + tokenizer.save_pretrained(f'/mnt/model_weights/models/{model_name}') \ No newline at end of file diff --git a/sllm_store/vllm_patch/sllm_load_npu.patch b/sllm_store/vllm_patch/sllm_load_npu.patch new file mode 100644 index 0000000..8a67934 --- /dev/null +++ b/sllm_store/vllm_patch/sllm_load_npu.patch @@ -0,0 +1,271 @@ +diff --git a/vllm/config.py b/vllm/config.py +index 8b824a1f..140471eb 100644 +--- a/vllm/config.py ++++ b/vllm/config.py +@@ -1140,6 +1140,7 @@ class LoadFormat(str, enum.Enum): + BITSANDBYTES = "bitsandbytes" + MISTRAL = "mistral" + RUNAI_STREAMER = "runai_streamer" ++ SERVERLESS_LLM = "serverless_llm" + + + @dataclass +diff --git a/vllm/executor/distributed_gpu_executor.py b/vllm/executor/distributed_gpu_executor.py +index deb7cb1c..113c289c 100644 +--- a/vllm/executor/distributed_gpu_executor.py ++++ b/vllm/executor/distributed_gpu_executor.py +@@ -129,6 +129,17 @@ class DistributedGPUExecutor(GPUExecutor): + pattern=pattern, + max_size=max_size) + ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self._run_workers("save_serverless_llm_state", ++ path=path, ++ pattern=pattern, ++ max_size=max_size) ++ + @abstractmethod + def _driver_execute_model( + self, execute_model_req: Optional[ExecuteModelRequest] +diff --git a/vllm/executor/gpu_executor.py b/vllm/executor/gpu_executor.py +index 7fa34456..9d0cf881 100644 +--- a/vllm/executor/gpu_executor.py ++++ b/vllm/executor/gpu_executor.py +@@ -87,6 +87,16 @@ class GPUExecutor(ExecutorBase): + ) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]: + output = self.driver_worker.execute_model(execute_model_req) + return output ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.driver_worker.save_serverless_llm_state( ++ path=path, pattern=pattern, max_size=max_size ++ ) + + def add_lora(self, lora_request: LoRARequest) -> bool: + assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." +diff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py +index a9c1fa72..15267ed4 100644 +--- a/vllm/model_executor/model_loader/loader.py ++++ b/vllm/model_executor/model_loader/loader.py +@@ -20,10 +20,11 @@ import numpy as np + import torch + from huggingface_hub import HfApi + from torch import nn ++import gc + from transformers import AutoModelForCausalLM + from transformers.utils import SAFE_WEIGHTS_INDEX_NAME + +-from vllm.config import (LoadConfig, LoadFormat, ModelConfig, ParallelConfig, ++from vllm.config import (LoadConfig, LoadFormat, ModelConfig, ParallelConfig, + VllmConfig, set_current_vllm_config) + from vllm.distributed import (get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size) +@@ -690,6 +691,140 @@ class ShardedStateLoader(BaseModelLoader): + state_dict_part, + os.path.join(path, filename), + ) ++ ++class ServerlessLLMLoader(BaseModelLoader): ++ # DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors" ++ ++ def __init__(self, load_config: LoadConfig): ++ super().__init__(load_config) ++ extra_config = ({} if load_config.model_loader_extra_config is None ++ else load_config.model_loader_extra_config.copy()) ++ # self.pattern = extra_config.pop("pattern", self.DEFAULT_PATTERN) ++ if extra_config: ++ raise ValueError(f"Unexpected extra config keys for load format " ++ f"{load_config.load_format}: " ++ f"{load_config.model_loader_extra_config.keys()}") ++ ++ @staticmethod ++ def _filter_subtensors( ++ tensors: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: ++ """ ++ Filter out all tensors that share the same memory or a subset of the ++ memory of another tensor. ++ """ ++ same_storage_groups = collections.defaultdict(list) ++ for key, tensor in tensors.items(): ++ if tensor.numel(): ++ ptr = tensor.untyped_storage().data_ptr() ++ same_storage_groups[tensor.device, ptr].append((key, tensor)) ++ ++ def get_end_ptr(tensor: torch.Tensor) -> int: ++ return tensor.view(-1)[-1].data_ptr() + tensor.element_size() ++ ++ result = {} ++ for group in same_storage_groups.values(): ++ for k, t in group: ++ a, b = t.data_ptr(), get_end_ptr(t) ++ for k2, t2 in group: ++ if not t2.is_contiguous(): ++ continue ++ a2, b2 = t2.data_ptr(), get_end_ptr(t2) ++ if a < a2 or b2 < b: ++ continue ++ if a2 < a or b < b2 or not t.is_contiguous(): ++ break # t2 covers strictly more memory than t. ++ if k2 > k: ++ # Same tensors, keep the one with the longer key. ++ break ++ else: ++ result[k] = t ++ return result ++ ++ def load_model(self, *, vllm_config: VllmConfig) -> nn.Module: ++ from sllm_store.torch import load_dict ++ from vllm.distributed import get_tensor_model_parallel_rank ++ ++ assert os.path.isdir(vllm_config.model_config.model) ++ ++ rank = get_tensor_model_parallel_rank() ++ ++ local_model_path = vllm_config.model_config.model ++ local_model_path = os.path.join(local_model_path, f"rank_{rank}") ++ ++ def remove_prefix(path, prefix): ++ # Normalize the paths to ensure consistency across different platforms ++ path = os.path.normpath(path) ++ prefix = os.path.normpath(prefix) ++ ++ # Check if the path starts with the prefix ++ if path.startswith(prefix): ++ # Return the path without the prefix ++ return path[len(prefix):].lstrip(os.sep) ++ ++ # Return the original path if the prefix doesn't exist ++ return path ++ ++ # vLLM needs a local model path to read model config but ++ # ServerlessLLM Store requires a global model path as the model ID ++ storage_path = os.getenv("STORAGE_PATH", "./models") ++ model_path = remove_prefix(local_model_path, storage_path) ++ ++ with set_default_torch_dtype(vllm_config.model_config.dtype): ++ # with torch.device(device_config.device): ++ with torch.device("cpu"): ++ model = _initialize_model(vllm_config=vllm_config) ++ model = model.eval() ++ # set all parameters to meta device ++ state_dict = self._filter_subtensors(model.state_dict()) ++ key_list = list(state_dict.keys()) ++ ++ for key, param in model.named_parameters(recurse=True): ++ if key in key_list: ++ param.data = torch.empty(1, device="npu") ++ gc.collect() ++ ++ device_id = torch.npu.current_device() ++ device_map = {"": device_id} ++ # Note: storage path is already included in the local model path ++ sllm_state_dict = load_dict(model_path, device_map) ++ ++ for key, param in model.named_parameters(recurse=True): ++ if key in key_list: ++ tensor = sllm_state_dict[key] ++ param.data = tensor ++ state_dict.pop(key) ++ if state_dict: ++ raise ValueError( ++ f"Missing keys {tuple(state_dict)} in loaded state!") ++ ++ return model ++ ++ def download_model(self, model_config: ModelConfig) -> None: ++ pass ++ ++ @staticmethod ++ def save_model( ++ model: torch.nn.Module, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ from vllm.distributed import get_tensor_model_parallel_rank ++ from sllm_store.torch import save_dict ++ ++ rank = get_tensor_model_parallel_rank() ++ state_dict = ServerlessLLMLoader._filter_subtensors(model.state_dict()) ++ ++ # move all tensors to CPU ++ for key, tensor in state_dict.items(): ++ state_dict[key] = tensor.cpu().contiguous() ++ ++ save_path = os.path.join(path, f"rank_{rank}") ++ if not os.path.exists(save_path): ++ os.makedirs(save_path) ++ ++ save_dict(state_dict, save_path) ++ + + + class BitsAndBytesModelLoader(BaseModelLoader): +@@ -1365,6 +1500,9 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader: + + if load_config.load_format == LoadFormat.SHARDED_STATE: + return ShardedStateLoader(load_config) ++ ++ if load_config.load_format == LoadFormat.SERVERLESS_LLM: ++ return ServerlessLLMLoader(load_config) + + if load_config.load_format == LoadFormat.BITSANDBYTES: + return BitsAndBytesModelLoader(load_config) +diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py +index 1c6d1bbe..a5a1ff1d 100644 +--- a/vllm/worker/model_runner.py ++++ b/vllm/worker/model_runner.py +@@ -1186,6 +1186,20 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]): + pattern=pattern, + max_size=max_size, + ) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ from vllm.model_executor.model_loader.loader import ServerlessLLMLoader ++ ServerlessLLMLoader.save_model( ++ self.model, ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) + + def save_tensorized_model( + self, +diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py +index f51b51d4..8a5a2fc2 100644 +--- a/vllm/worker/worker.py ++++ b/vllm/worker/worker.py +@@ -165,6 +165,18 @@ class Worker(LocalOrDistributedWorkerBase): + pattern=pattern, + max_size=max_size, + ) ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.model_runner.save_serverless_llm_state( ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) + + def save_tensorized_model( + self, \ No newline at end of file diff --git a/sllm_store/vllm_patch/vllm_ascend.patch b/sllm_store/vllm_patch/vllm_ascend.patch new file mode 100644 index 0000000..2b830f4 --- /dev/null +++ b/sllm_store/vllm_patch/vllm_ascend.patch @@ -0,0 +1,46 @@ +--- a/vllm_ascend/worker/model_runner.py ++++ b/vllm_ascend/worker/model_runner.py +@@ -1156,6 +1156,20 @@ class NPUModelRunner(NPUModelRunnerBase[ModelInputForNPUWithSamplingMetadata]): + attn_backend=self.attn_backend, + ) + return model_input ++ ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ from vllm.model_executor.model_loader.loader import ServerlessLLMLoader ++ ServerlessLLMLoader.save_model( ++ self.model, ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) + + def prepare_model_input( + self, +diff --git a/vllm_ascend/worker/worker.py b/vllm_ascend/worker/worker.py +index 120d343..9b88c8d 100644 +--- a/vllm_ascend/worker/worker.py ++++ b/vllm_ascend/worker/worker.py +@@ -228,6 +228,18 @@ class NPUWorker(LocalOrDistributedWorkerBase): + max_size=max_size, + ) + ++ def save_serverless_llm_state( ++ self, ++ path: str, ++ pattern: Optional[str] = None, ++ max_size: Optional[int] = None, ++ ) -> None: ++ self.model_runner.save_serverless_llm_state( ++ path, ++ pattern=pattern, ++ max_size=max_size, ++ ) ++ + def save_tensorized_model( + self, + tensorizer_config: TensorizerConfig, \ No newline at end of file -- Gitee

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