From f557494cc45aff812a20c8102402eeefcbf4a943 Mon Sep 17 00:00:00 2001 From: "hongliang.yuan" Date: Thu, 10 Apr 2025 15:32:00 +0800 Subject: [PATCH] update all model config --- .gitignore | 1 - tests/all_deepsparkinference_model_info.json | 5386 ++++++++++++++++++ tests/models_igie.yaml | 734 --- tests/models_ixrt.yaml | 502 -- tests/models_trtllm.yaml | 33 - tests/models_vllm.yaml | 162 - tests/run_igie.py | 92 +- tests/run_ixrt.py | 90 +- tests/run_trtllm.py | 28 +- tests/run_vllm.py | 54 +- 10 files changed, 5518 insertions(+), 1564 deletions(-) create mode 100644 tests/all_deepsparkinference_model_info.json delete mode 100644 tests/models_igie.yaml delete mode 100644 tests/models_ixrt.yaml delete mode 100644 tests/models_trtllm.yaml delete mode 100644 tests/models_vllm.yaml diff --git a/.gitignore b/.gitignore index b774d469..2a53e187 100644 --- a/.gitignore +++ b/.gitignore @@ -59,7 +59,6 @@ cover/ # checkpoints and datasets checkpoints/ imagenet_val/ -*.json *.onnx *.pth *.engine 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https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/densenet169-b2777c0a.pth - name: densenet169 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/densenet169/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/densenet201-c1103571.pth - name: densenet201 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/densenet201/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth - name: efficientnetv2_rw_t - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnetv2_rw_t/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pth - name: efficientnet_b0 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_b0/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth - name: efficientnet_b1 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_b1/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pth - name: efficientnet_b2 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_b2/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pth - name: efficientnet_b3 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_b3/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth - name: efficientnet_v2 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_v2/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth - name: efficientnet_v2_s - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_v2_s/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/googlenet-1378be20.pth - name: googlenet - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/googlenet/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth - name: hrnet_w18 - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/hrnet_w18/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth - name: inception_v3 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/inception_v3/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth - name: mnasnet0_5 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/mnasnet0_5/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth - name: mobilenet_v2 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/mobilenet_v2/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth - name: mobilenet_v3 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/mobilenet_v3/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth - name: mobilenet_v3_large - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/mobilenet_v3_large/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth - name: mvitv2_base - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/mvitv2_base/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth - name: regnet_x_1_6gf - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/regnet_x_1_6gf/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/regnet_y_1_6gf-b11a554e.pth - name: regnet_y_1_6gf - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/regnet_y_1_6gf/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth - name: repvgg - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/repvgg/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth - name: res2net50 - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/res2net50/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest50-528c19ca.pth - name: resnest50 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/resnest50/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnet101-63fe2227.pth - name: resnet101 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/resnet101/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnet152-394f9c45.pth - name: resnet152 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/resnet152/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnet18-f37072fd.pth - name: resnet18 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/resnet18/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnet50-0676ba61.pth - name: resnet50 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/resnet50/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth - name: resnetv1d50 - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/resnetv1d50/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth - name: resnext101_64x4d - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/resnext101_64x4d/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth - name: resnext50_32x4d - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/resnext50_32x4d/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth - name: seresnet50 - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/seresnet50/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth - name: shufflenetv2_x0_5 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/shufflenetv2_x0_5/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth - name: shufflenetv2_x1_0 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/shufflenetv2_x1_0/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth - name: shufflenetv2_x1_5 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/shufflenetv2_x1_5/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://local/swin-tiny-patch4-window7-224 - name: swin_transformer - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/classification/swin_transformer/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/vgg16-397923af.pth - name: vgg16 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/vgg16/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth - name: wide_resnet50 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/wide_resnet50/igie - task_type: cv/classification -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth - name: atss - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/atss/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth - name: centernet - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/centernet/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth - name: fcos - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/fcos/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth - name: foveabox - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/foveabox/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth - name: fsaf - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/fsaf/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth - name: hrnet - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/hrnet/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth - name: paa - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/paa/igie - task_type: cv/detection -- datasets: local/widerface - download_url: https://github.com/biubug6/Face-Detector-1MB-with-landmark/raw/master/weights/mobilenet0.25_Final.pth - name: retinaface - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/retinaface/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth - name: retinanet - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/retinanet/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth - name: rtmdet - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/rtmdet/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt - name: yolov10 - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/detection/yolov10/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov3.pt - name: yolov3 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/detection/yolov3/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights - name: yolov4 - need_third_part: true - precisions: - - fp16 - - int8 - relative_path: models/cv/detection/yolov4/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt - name: yolov5 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/detection/yolov5/igie - task_type: cv/detection -- 3rd_party_repo: YOLOv6 - datasets: local/coco - download_url: https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6s.pt - name: yolov6 - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/detection/yolov6/igie - task_type: cv/detection -- 3rd_party_repo: yolov7 - datasets: local/coco - download_url: https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt - name: yolov7 - need_third_part: true - precisions: - - fp16 - - int8 - relative_path: models/cv/detection/yolov7/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt - name: yolov8 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/detection/yolov8/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9s.pt - name: yolov9 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/detection/yolov9/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_m.pth - name: yolox - need_third_part: true - precisions: - - fp16 - - int8 - relative_path: models/cv/detection/yolox/igie - task_type: cv/detection -- datasets: local/coco - download_url: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth - name: rtmpose - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/pose_estimation/rtmpose/igie - task_type: cv/pose_estimation -- datasets: local/market1501 - download_url: https://local/ckpt.t7 - name: deepsort - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/trace/deepsort/igie - task_type: cv/trace -- datasets: local/VehicleID - download_url: https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/vehicleid_bot_R50-ibn.pth - name: fastreid - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/trace/fastreid/igie - task_type: cv/trace -- datasets: local/VehicleID - download_url: https://local/epoch_14.pth - name: repnet - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/trace/repnet/igie - task_type: cv/trace - # http://s3.bmio.net/kashgari/china-people-daily-ner-corpus.tar.gz -- datasets: local/china-people-daily-ner-corpus - download_url: https://huggingface.co/bert-base-chinese - name: bert_base_ner - need_third_part: false - precisions: - - int8 - relative_path: models/nlp/language_model/bert_base_ner/igie - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://huggingface.co/csarron/bert-base-uncased-squad-v1 - name: bert_base_squad - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/language_model/bert_base_squad/igie - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://huggingface.co/neuralmagic/bert-large-uncased-finetuned-squadv1 - name: bert_large_squad - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/nlp/language_model/bert_large_squad/igie - task_type: nlp/language_model -- datasets: https://www.openslr.org/33/aishell.tar.gz - download_url: http://mobvoi-speech-public.ufile.ucloud.cn/public/wenet/aishell/20211025_conformer_exp.tar.gz - name: conformer - need_third_part: true - precisions: - - fp16 - relative_path: models/speech/speech_recognition/conformer/igie - task_type: speech/speech_recognition -- datasets: https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND - download_url: https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar - name: kie_layoutxlm - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/ocr/kie_layoutxlm/igie - task_type: cv/ocr -- datasets: https://www.dropbox.com/scl/fo/zf04eicju8vbo4s6wobpq/AMv9bzR7paSwCJcQ03egJyI/lmdb_evaluation - download_url: https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar - name: svtr - need_third_part: true - precisions: - - fp16 - relative_path: models/cv/ocr/svtr/igie - task_type: cv/ocr -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/vgg11-8a719046.pth - name: vgg11 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/vgg11/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pth - name: efficientnet_b4 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_b4/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/mnasnet0_75-7090bc5f.pth - name: mnasnet0_75 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/mnasnet0_75/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth - name: resnext101_32x8d - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/resnext101_32x8d/igie - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth - name: squeezenet_v1_0 - need_third_part: false - precisions: - - fp16 - relative_path: 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https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth - name: hrnetpose - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/pose_estimation/hrnetpose/igie - task_type: cv/pose_estimation \ No newline at end of file diff --git a/tests/models_ixrt.yaml b/tests/models_ixrt.yaml deleted file mode 100644 index 3c8b7625..00000000 --- a/tests/models_ixrt.yaml +++ /dev/null @@ -1,502 +0,0 @@ ---- -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/alexnet-owt-7be5be79.pth - name: alexnet - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/alexnet/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/convnext_small-0c510722.pth - name: convnext_small - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/convnext_small/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://local/cspdarknet53_3rdparty_8xb32_in1k_20220329-bd275287.pth - name: cspdarknet53 - need_third_part: true - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/cspdarknet53/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth - name: cspresnet50 - need_third_part: true - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/cspresnet50/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://local/densenet121.pth - name: densenet121 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/densenet121/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/densenet161-8d451a50.pth - name: densenet161 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/densenet161/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/densenet169-b2777c0a.pth - name: densenet169 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/densenet169/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth - name: efficientnet_b0 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/efficientnet_b0/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b1_rwightman-bac287d4.pth - name: efficientnet_b1 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/efficientnet_b1/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pth - name: efficientnet_b2 - need_third_part: false - precisions: - - fp16 - relative_path: models/cv/classification/efficientnet_b2/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://local/efficientnet_v2.pth - name: efficientnet_v2 - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/cv/classification/efficientnet_v2/ixrt - task_type: cv/classification -- datasets: https://www.image-net.org/download.php - download_url: https://download.pytorch.org/models/googlenet-1378be20.pth - name: googlenet - need_third_part: 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clip - need_third_part: false - precisions: - - fp16 - relative_path: models/multimodal/vision_language_understanding/clip/ixformer - task_type: multimodal/vision_language_understanding -- datasets: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_squad.tar - download_url: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_albert.tar - name: albert - need_third_part: true - precisions: - - fp16 - relative_path: models/nlp/language_model/albert/ixrt - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://huggingface.co/csarron/bert-base-uncased-squad-v1 - name: bert_base_squad - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/nlp/language_model/bert_base_squad/ixrt - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://huggingface.co/neuralmagic/bert-large-uncased-finetuned-squadv1 - name: bert_large_squad - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/nlp/language_model/bert_large_squad/ixrt - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_deberta.tar - name: deberta - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/language_model/deberta/ixrt - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_roberta.tar - name: roberta - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/language_model/roberta/ixrt - task_type: nlp/language_model -- datasets: local/SQuAD - download_url: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_roformer.tar - name: roformer - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/language_model/roformer/ixrt - task_type: nlp/language_model -- datasets: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/cifar-100-python.tar - download_url: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_videobert.tar - name: videobert - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/language_model/videobert/ixrt - task_type: nlp/language_model -- datasets: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/eval.csv - need_comfirm: true - download_url: https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_wide_deep_saved_model.tar - name: widedeep - need_third_part: false - precisions: - - fp16 - relative_path: models/recommendation/ctr-prediction/widedeep/ixrt - task_type: recommendation/ctr-prediction -- datasets: https://www.openslr.org/33/aishell.tar.gz - download_url: http://mobvoi-speech-public.ufile.ucloud.cn/public/wenet/aishell/20211025_conformer_exp.tar.gz - name: conformer - need_third_part: true - precisions: - - fp16 - relative_path: models/speech/speech_recognition/conformer/ixrt - task_type: speech/speech_recognition -- datasets: https://www.openslr.org/33/aishell.tar.gz - download_url: https://drive.google.com/drive/folders/1_2zN6lbu4zUc0-iq8XbABEm6fl9mohkv - name: transformer_asr - need_third_part: true - precisions: - - fp16 - relative_path: models/speech/speech_recognition/transformer_asr/ixrt - task_type: speech/speech_recognition diff --git a/tests/models_trtllm.yaml b/tests/models_trtllm.yaml deleted file mode 100644 index 7e5f7767..00000000 --- a/tests/models_trtllm.yaml +++ /dev/null @@ -1,33 +0,0 @@ ---- -- datasets: https://localhost - download_url: https://huggingface.co/meta-llama/llama2-7b-chat - name: llama2-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/llama2-7b/trtllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/llama2-13b-chat - name: llama2-13b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/llama2-13b/trtllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/llama2-70b-chat - name: llama2-70b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/llama2-70b/trtllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://modelscope.cn/models/qwen/Qwen1.5-7B - name: qwen1.5-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen1.5-7b/text-generation-inference - task_type: nlp/large_language_model diff --git a/tests/models_vllm.yaml b/tests/models_vllm.yaml deleted file mode 100644 index 5781866f..00000000 --- a/tests/models_vllm.yaml +++ /dev/null @@ -1,162 +0,0 @@ ---- -- datasets: https://localhost - download_url: https://huggingface.co/baichuan-inc/Baichuan2-7B-Base - name: baichuan2-7b - need_third_part: false - precisions: - - fp16 - - int8 - relative_path: models/nlp/large_language_model/baichuan2-7b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://huggingface.co/THUDM/chatglm3-6b - name: chatglm3-6b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/chatglm3-6b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://www.modelscope.cn/models/ZhipuAI/chatglm3-6b-32k - name: chatglm3-6b-32k - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/chatglm3-6b-32k/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/llama2-7b - name: llama2-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/llama2-7b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/Meta-Llama-3-70B-Instruct - name: llama3-70b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/llama3-70b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/qwen-7B - name: qwen-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen-7b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://modelscope.cn/models/qwen/Qwen1.5-7B - name: qwen1.5-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen1.5-7b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://modelscope.cn/models/qwen/Qwen1.5-14B - name: qwen1.5-14b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen1.5-14b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://modelscope.cn/models/Qwen/Qwen1.5-32B-Chat - name: qwen1.5-32b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen1.5-32b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://modelscope.cn/models/qwen/Qwen1.5-72B - name: qwen1.5-72b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen1.5-72b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://modelscope.cn/models/Qwen/Qwen2-7B-Instruct - name: qwen2-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen2-7b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/Qwen2-72B - name: qwen2-72b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/qwen2-72b/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://huggingface.co/stabilityai/stablelm-2-1_6b - name: stablelm - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/stablelm/vllm - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/MiniCPM-V-2 - name: minicpm-v-2 - need_third_part: false - precisions: - - fp16 - relative_path: models/multimodal/vision-language-understanding/minicpm-v-2/vllm/ - task_type: multimodal/vision-language-understanding -- datasets: https://localhost - download_url: https://localhost/DeepSeek-R1-Distill-Qwen-1.5B - name: deepseek-r1-distill-qwen-1.5b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/ - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/DeepSeek-R1-Distill-Qwen-7B - name: deepseek-r1-distill-qwen-7b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/ - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/DeepSeek-R1-Distill-Qwen-14B - name: deepseek-r1-distill-qwen-14b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/ - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/DeepSeek-R1-Distill-Qwen-32B - name: deepseek-r1-distill-qwen-32b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/ - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/DeepSeek-R1-Distill-Llama-8B - name: deepseek-r1-distill-llama-8b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/ - task_type: nlp/large_language_model -- datasets: https://localhost - download_url: https://localhost/DeepSeek-R1-Distill-Llama-70B - name: deepseek-r1-distill-llama-70b - need_third_part: false - precisions: - - fp16 - relative_path: models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/ - task_type: nlp/large_language_model \ No newline at end of file diff --git a/tests/run_igie.py b/tests/run_igie.py index a6003526..c621e31a 100644 --- a/tests/run_igie.py +++ b/tests/run_igie.py @@ -55,73 +55,73 @@ def main(): sys.exit(-1) result = {} - if model["task_type"] == "cv/classification": - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] == "cv/classification": + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_clf_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # 检测模型 - if model["task_type"] in ["cv/detection", "cv/pose_estimation"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["cv/object_detection", "cv/pose_estimation"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_detec_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # OCR模型 - if model["task_type"] in ["cv/ocr"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["cv/ocr"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_ocr_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # Trace模型 - if model["task_type"] in ["cv/trace"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["cv/trace"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_trace_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # Speech模型 - if model["task_type"] in ["speech/speech_recognition"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["audio/speech_recognition"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_speech_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # NLP模型 - if model["task_type"] in ["nlp/language_model"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["nlp/plm"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_nlp_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") logging.info(f"Full text result: {result}") def get_model_config(mode_name): - with open("models_igie.yaml", "r") as file: - models = yaml.safe_load(file) + with open("all_deepsparkinference_model_info.json", mode='r', encoding='utf-8') as file: + models = json.load(file) - for model in models: - if model["name"] == mode_name.lower(): + for model in models['models']: + if model["model_name"] == mode_name.lower() and model["framework"] == "igie": return model return @@ -135,7 +135,7 @@ def check_model_result(result): result["status"] = status def run_clf_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -143,7 +143,7 @@ def run_clf_testcase(model): d_url = model["download_url"] checkpoint_n = d_url.split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./ bash ci/prepare.sh ls -l | grep onnx @@ -160,7 +160,7 @@ def run_clf_testcase(model): logging.info(f"Start running {model_name} {prec} test case") script = f""" export DATASETS_DIR=/mnt/deepspark/data/datasets/imagenet-val - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash scripts/infer_{model_name}_{prec}_accuracy.sh bash scripts/infer_{model_name}_{prec}_performance.sh """ @@ -183,7 +183,7 @@ def run_clf_testcase(model): return result def run_detec_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -192,7 +192,7 @@ def run_detec_testcase(model): checkpoint_n = d_url.split("/")[-1] dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./ ln -s /mnt/deepspark/data/datasets/{dataset_n} ./ """ @@ -212,7 +212,7 @@ def run_detec_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export DATASETS_DIR=./{dataset_n}/ bash scripts/infer_{model_name}_{prec}_accuracy.sh bash scripts/infer_{model_name}_{prec}_performance.sh @@ -253,7 +253,7 @@ def run_detec_testcase(model): return result def run_ocr_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -262,7 +262,7 @@ def run_ocr_testcase(model): checkpoint_n = d_url.split("/")[-1] dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./ ln -s /mnt/deepspark/data/datasets/{dataset_n} ./ unzip /mnt/deepspark/data/3rd_party/PaddleOCR-release-2.6.zip -d ./PaddleOCR @@ -279,7 +279,7 @@ def run_ocr_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export DATASETS_DIR=./{dataset_n}/ bash scripts/infer_{model_name}_{prec}_accuracy.sh bash scripts/infer_{model_name}_{prec}_performance.sh @@ -309,7 +309,7 @@ def run_ocr_testcase(model): return result def run_trace_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -318,7 +318,7 @@ def run_trace_testcase(model): checkpoint_n = d_url.split("/")[-1] dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./ ln -s /mnt/deepspark/data/datasets/{dataset_n} ./ """ @@ -341,7 +341,7 @@ def run_trace_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export DATASETS_DIR=./{dataset_n}/ bash scripts/infer_{model_name}_{prec}_accuracy.sh bash scripts/infer_{model_name}_{prec}_performance.sh @@ -370,7 +370,7 @@ def run_trace_testcase(model): # BERT series models def run_nlp_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -385,7 +385,7 @@ def run_nlp_testcase(model): prepare_script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} {mkdir_script} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./{target_dir} export DATASETS_DIR=/mnt/deepspark/data/datasets/{dataset_n} @@ -408,7 +408,7 @@ def run_nlp_testcase(model): script = f""" set -x export DATASETS_DIR=/mnt/deepspark/data/datasets/{dataset_n} - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash scripts/infer_{model_name}_{prec}_accuracy.sh bash scripts/infer_{model_name}_{prec}_performance.sh """ @@ -429,7 +429,7 @@ def run_nlp_testcase(model): return result def run_speech_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -438,7 +438,7 @@ def run_speech_testcase(model): checkpoint_n = d_url.split("/")[-1] dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./ ln -s /mnt/deepspark/data/datasets/{dataset_n} ./ """ @@ -465,7 +465,7 @@ def run_speech_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export PYTHONPATH=./wenet:$PYTHONPATH echo $PYTHONPATH bash scripts/infer_{model_name}_{prec}_accuracy.sh diff --git a/tests/run_ixrt.py b/tests/run_ixrt.py index 63573efa..70457ac8 100644 --- a/tests/run_ixrt.py +++ b/tests/run_ixrt.py @@ -55,63 +55,63 @@ def main(): sys.exit(-1) result = {} - if model["task_type"] == "cv/classification": - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] == "cv/classification": + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_clf_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # 检测模型 - if model["task_type"] in ["cv/detection", "cv/pose_estimation"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["cv/object_detection", "cv/pose_estimation"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_detec_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # Segmentation模型 - if model["task_type"] in ["cv/segmentation", "cv/face", "multimodal/text_and_image"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["cv/segmentation", "cv/face_recognition", "multimodal/vision_language_model"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_segmentation_and_face_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # Speech模型 - if model["task_type"] in ["speech/speech_recognition"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["audio/speech_recognition"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_speech_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") # NLP模型 - if model["task_type"] in ["nlp/language_model", "recommendation/ctr-prediction"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["nlp/plm", "others/recommendation"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_nlp_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") logging.info(f"Full text result: {result}") def get_model_config(mode_name): - with open("models_ixrt.yaml", "r") as file: - models = yaml.safe_load(file) + with open("all_deepsparkinference_model_info.json", mode='r', encoding='utf-8') as file: + models = json.load(file) for model in models: - if model["name"] == mode_name.lower(): + if model["model_name"] == mode_name.lower() and model["framework"] == "ixrt": return model return @@ -125,7 +125,7 @@ def check_model_result(result): result["status"] = status def run_clf_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -133,7 +133,7 @@ def run_clf_testcase(model): d_url = model["download_url"] checkpoint_n = d_url.split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash ci/prepare.sh """ # add pip list info when in debug mode @@ -157,7 +157,7 @@ def run_clf_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export DATASETS_DIR=/root/data/datasets/imagenet-val export PROJ_DIR=./ export CHECKPOINTS_DIR=./checkpoints @@ -169,7 +169,7 @@ def run_clf_testcase(model): if model_name == "swin_transformer_large": script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export ORIGIN_ONNX_NAME=./swin-large-torch-fp32 export OPTIMIER_FILE=/root/data/3rd_party/iluvatar-corex-ixrt/tools/optimizer/optimizer.py export PROJ_PATH=./ @@ -197,7 +197,7 @@ def run_clf_testcase(model): return result def run_detec_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -206,7 +206,7 @@ def run_detec_testcase(model): checkpoint_n = d_url.split("/")[-1] dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /root/data/datasets/{dataset_n} ./ bash ci/prepare.sh """ @@ -223,7 +223,7 @@ def run_detec_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export DATASETS_DIR=./{dataset_n}/ export MODEL_PATH=./{model_name}.onnx @@ -241,7 +241,7 @@ def run_detec_testcase(model): if model_name == "rtmpose": script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 predict.py --model data/rtmpose/rtmpose_opt.onnx --precision fp16 --img_path demo/demo.jpg """ @@ -285,14 +285,14 @@ def run_detec_testcase(model): return result def run_segmentation_and_face_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, } dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash ci/prepare.sh ls -l | grep onnx """ @@ -307,7 +307,7 @@ def run_segmentation_and_face_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export DATASETS_DIR=./{dataset_n}/ export PROJ_DIR=./ export CHECKPOINTS_DIR=./checkpoints @@ -321,7 +321,7 @@ def run_segmentation_and_face_testcase(model): if model_name == "clip": script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 inference.py """ @@ -343,14 +343,14 @@ def run_segmentation_and_face_testcase(model): # BERT series models def run_nlp_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, } prepare_script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash ci/prepare.sh """ @@ -365,7 +365,7 @@ def run_nlp_testcase(model): logging.info(f"Start running {model_name} {prec} test case") script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export ORIGIN_ONNX_NAME=./data/open_{model_name}/{model_name} export OPTIMIER_FILE=/root/data/3rd_party/iluvatar-corex-ixrt/tools/optimizer/optimizer.py export PROJ_PATH=./ @@ -390,20 +390,20 @@ def run_nlp_testcase(model): if model_name == "bert_base_squad": script = f""" set -x - cd ../{model['relative_path']}/python + cd ../{model['deepsparkinference_path']}/python bash script/infer_{model_name}_{prec}_ixrt.sh """ elif model_name == "bert_large_squad": script = f""" set -x - cd ../{model['relative_path']}/python + cd ../{model['deepsparkinference_path']}/python bash script/build_engine.sh --bs 32 bash script/inference_squad.sh --bs 32 """ if prec == "int8": script = f""" set -x - cd ../{model['relative_path']}/python + cd ../{model['deepsparkinference_path']}/python bash script/build_engine.sh --bs 32 --int8 bash script/inference_squad.sh --bs 32 --int8 """ @@ -424,7 +424,7 @@ def run_nlp_testcase(model): return result def run_speech_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -433,7 +433,7 @@ def run_speech_testcase(model): checkpoint_n = d_url.split("/")[-1] dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash ci/prepare.sh ls -l | grep onnx """ @@ -448,14 +448,14 @@ def run_speech_testcase(model): for prec in model["precisions"]: logging.info(f"Start running {model_name} {prec} test case") script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash scripts/infer_{model_name}_{prec}_accuracy.sh bash scripts/infer_{model_name}_{prec}_performance.sh """ if model_name == "transformer_asr": script = f""" - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 inference.py hparams/train_ASR_transformer.yaml --data_folder=/home/data/speechbrain/aishell --engine_path transformer.engine """ diff --git a/tests/run_trtllm.py b/tests/run_trtllm.py index c3c68b76..4e2d75e2 100644 --- a/tests/run_trtllm.py +++ b/tests/run_trtllm.py @@ -56,23 +56,23 @@ def main(): result = {} # NLP模型 - if model["task_type"] in ["nlp/large_language_model"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["nlp/llm"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_nlp_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") logging.info(f"Full text result: {result}") def get_model_config(mode_name): - with open("models_trtllm.yaml", "r") as file: - models = yaml.safe_load(file) + with open("all_deepsparkinference_model_info.json", mode='r', encoding='utf-8') as file: + models = json.load(file) for model in models: - if model["name"] == mode_name.lower(): + if model["model_name"] == mode_name.lower() and model["framework"] == "trtllm": return model return @@ -86,7 +86,7 @@ def check_model_result(result): result["status"] = status def run_nlp_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -96,7 +96,7 @@ def run_nlp_testcase(model): dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash ci/prepare.sh """ @@ -111,19 +111,19 @@ def run_nlp_testcase(model): logging.info(f"Start running {model_name} {prec} test case") script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} """ if model_name == "llama2-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} bash scripts/test_trtllm_llama2_7b_gpu1_build.sh bash scripts/test_trtllm_llama2_7b_gpu1.sh """ elif model_name == "llama2-13b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1 bash scripts/test_trtllm_llama2_13b_gpu2_build.sh bash scripts/test_trtllm_llama2_13b_gpu2.sh @@ -131,7 +131,7 @@ def run_nlp_testcase(model): elif model_name == "llama2-70b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/test_trtllm_llama2_70b_gpu8_build.sh bash scripts/test_trtllm_llama2_70b_gpu8.sh @@ -139,7 +139,7 @@ def run_nlp_testcase(model): elif model_name == "qwen1.5-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=1 python3 offline_inference.py --model2path ./data/Qwen1.5-7B """ diff --git a/tests/run_vllm.py b/tests/run_vllm.py index 518ad79e..d5a62ea8 100644 --- a/tests/run_vllm.py +++ b/tests/run_vllm.py @@ -56,23 +56,23 @@ def main(): result = {} # NLP模型 - if model["task_type"] in ["nlp/large_language_model", "multimodal/vision-language-understanding"]: - logging.info(f"Start running {model['name']} test case:\n{json.dumps(model, indent=4)}") + if model["category"] in ["nlp/llm", "multimodal/vision_language_model"]: + logging.info(f"Start running {model['model_name']} test case:\n{json.dumps(model, indent=4)}") d_url = model["download_url"] if d_url is not None: result = run_nlp_testcase(model) check_model_result(result) - logging.debug(f"The result of {model['name']} is\n{json.dumps(result, indent=4)}") - logging.info(f"End running {model['name']} test case.") + logging.debug(f"The result of {model['model_name']} is\n{json.dumps(result, indent=4)}") + logging.info(f"End running {model['model_name']} test case.") logging.info(f"Full text result: {result}") def get_model_config(mode_name): - with open("models_vllm.yaml", "r") as file: - models = yaml.safe_load(file) + with open("all_deepsparkinference_model_info.json", mode='r', encoding='utf-8') as file: + models = json.load(file) for model in models: - if model["name"] == mode_name.lower(): + if model["model_name"] == mode_name.lower() and model["framework"] == "vllm": return model return @@ -86,7 +86,7 @@ def check_model_result(result): result["status"] = status def run_nlp_testcase(model): - model_name = model["name"] + model_name = model["model_name"] result = { "name": model_name, "result": {}, @@ -96,7 +96,7 @@ def run_nlp_testcase(model): dataset_n = model["datasets"].split("/")[-1] prepare_script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} ln -s /mnt/deepspark/data/checkpoints/{checkpoint_n} ./{model_name} bash ci/prepare.sh """ @@ -112,109 +112,109 @@ def run_nlp_testcase(model): logging.info(f"Start running {model_name} {prec} test case") script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} """ if model_name == "baichuan2-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./baichuan2-7b/ --max-tokens 256 --trust-remote-code --chat_template template_baichuan.jinja --temperature 0.0 """ if prec == "int8": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./baichuan2-7b/int8/ --chat_template template_baichuan.jinja --quantization w8a16 --max-num-seqs 1 --max-model-len 256 --trust-remote-code --temperature 0.0 --max-tokens 256 """ elif model_name == "chatglm3-6b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./chatglm3-6b --trust-remote-code --temperature 0.0 --max-tokens 256 """ elif model_name == "chatglm3-6b-32k": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./chatglm3-6b-32k --trust-remote-code --temperature 0.0 --max-tokens 256 """ elif model_name == "llama2-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./llama2-7b --max-tokens 256 -tp 1 --temperature 0.0 """ elif model_name == "llama3-70b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1,2,3 python3 offline_inference.py --model ./llama3-70b --max-tokens 256 -tp 4 --temperature 0.0 """ elif model_name == "qwen-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1 python3 offline_inference.py --model ./qwen-7b --max-tokens 256 -tp 2 --trust-remote-code --temperature 0.0 """ elif model_name == "qwen1.5-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./qwen1.5-7b --max-tokens 256 -tp 1 --temperature 0.0 --max-model-len 3096 """ elif model_name == "qwen1.5-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./qwen1.5-7b --max-tokens 256 -tp 1 --temperature 0.0 --max-model-len 3096 """ elif model_name == "qwen1.5-14b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./qwen1.5-14b --max-tokens 256 -tp 1 --temperature 0.0 --max-model-len 896 """ elif model_name == "qwen1.5-32b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1,2,3 python3 offline_inference.py --model ./qwen1.5-32b --max-tokens 256 -tp 4 --temperature 0.0 """ elif model_name == "qwen1.5-72b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1 python3 offline_inference.py --model ./qwen1.5-72b --max-tokens 256 -tp 2 --temperature 0.0 --max-model-len 3096 """ elif model_name == "qwen2-7b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0 python3 offline_inference.py --model ./qwen2-7b --max-tokens 256 -tp 1 --temperature 0.0 """ elif model_name == "qwen2-72b": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 offline_inference.py --model ./qwen2-72b --max-tokens 256 -tp 8 --temperature 0.0 --gpu-memory-utilization 0.98 --max-model-len 32768 """ elif model_name == "stablelm": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export CUDA_VISIBLE_DEVICES=0,1 python3 offline_inference.py --model ./stablelm --max-tokens 256 -tp 1 --temperature 0.0 """ elif model_name == "minicpm-v-2": script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} export PT_SDPA_ENABLE_HEAD_DIM_PADDING=1 export PATH=/usr/local/corex/bin:${PATH} export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64 @@ -223,7 +223,7 @@ def run_nlp_testcase(model): elif model_name.startswith("deepseek-r1-distill-"): script = f""" set -x - cd ../{model['relative_path']} + cd ../{model['deepsparkinference_path']} python3 offline_inference.py --model ./{model_name} --max-tokens 256 -tp 2 --temperature 0.0 --max-model-len 3096 """ -- Gitee