Merge remote-tracking branch 'origin/main' into hjhee/erfinv

.github/actions/inductor-xpu-e2e-test/action.yml

@@ -81,7 +81,6 @@ runs:
         cp .github/scripts/inductor_xpu_test.sh ../pytorch
         cd ../pytorch
         source /opt/intel/oneapi/pytorch-gpu-dev-0.5/oneapi-vars.sh
-        rm -f ${{ github.workspace }}/summary_accuracy.log
         # check param
         function contains() {
             contains_status="echo 'Start $2 ...'"
@@ -130,7 +129,7 @@ runs:
                         --mode $mode \
                         --dtype $dt \
                         --csv_file ${LOG_DIR}/inductor_${suite}_${dt}_${mode}_xpu_${scenario}.csv \
-                        2>&1 |tee -a ${{ github.workspace }}/summary_accuracy.log
+                        2>&1 |tee -a inductor_log/summary_accuracy.log
                 fi
               done
             done
@@ -150,7 +149,6 @@ runs:
           sed -i "s/$/,$(basename $var)/" $var
           cat $var >> inductor_log/summary_accuracy.csv
         done
-        cat ${{ github.workspace }}/summary_accuracy.log |tee -a inductor_log/summary_accuracy.log
 
         # check param
         function contains() {

.github/ci_expected_accuracy/check_expected.py

@@ -36,12 +36,15 @@
 new_models = []
 new_pass_models = []
 lost_models = []
+timeout_models = []
 for model_name in model_names:
 # for index, row in refer_data.iterrows():
     test_row = next(([i, line] for i, line in test_data.iterrows() if line["name"] == model_name), "N/A")
     refer_row = next(([i, line] for i, line in refer_data.iterrows() if line["name"] == model_name), "N/A")
     test_accuracy = test_row[1]["accuracy"] if test_row != "N/A" else "N/A"
     refer_accuracy = refer_row[1][args.dtype] if refer_row != "N/A" else "N/A"
+    test_accuracy = str(test_accuracy)
+    refer_accuracy = str(refer_accuracy)
     if test_accuracy == "N/A":
         lost_models.append([model_name, test_accuracy])
     elif 'pass' in test_accuracy:
@@ -54,6 +57,13 @@
         elif 'pass' not in refer_accuracy:
             new_pass_models.append([model_name, test_accuracy])
             refer_data.at[refer_row[0], args.dtype] = test_accuracy
+    elif 'timeout' in test_accuracy:
+        timeout_models.append([model_name, test_accuracy])
+        if refer_accuracy == "N/A":
+            new_models.append([model_name, test_accuracy])
+            refer_data.loc[len(refer_data),:] = "N/A"
+            refer_data.at[len(refer_data) - 1, "name"] = model_name
+            refer_data.at[len(refer_data) - 1, args.dtype] = test_accuracy
     else:
         if refer_accuracy == "N/A":
             new_models.append([model_name, test_accuracy])
@@ -72,8 +82,9 @@
 print("============ Summary for {} {} {} accuracy ============".format(args.suite, args.dtype, args.mode))
 print("Total models:", len(model_names))
 print("Passed models:", len(passed_models))
-print("Real failed: models:", len(real_failed_models), real_failed_models)
-print("Expected failed: models:", len(expected_failed_models), expected_failed_models)
+print("Real failed models:", len(real_failed_models), real_failed_models)
+print("Expected failed models:", len(expected_failed_models), expected_failed_models)
+print("Warning timeout models:", len(timeout_models), timeout_models)
 print("New models:", len(new_models), new_models)
 print("Failed to passed models:", len(new_pass_models), new_pass_models)
 print("Not run/in models:", len(lost_models), lost_models)

.github/ci_expected_accuracy/inductor_timm_models_training.csv

@@ -1,62 +1,62 @@
 name,float32,bfloat16,float16,amp_bf16,amp_fp16
-adv_inception_v3,pass,pass,fail_accuracy,pass,pass
+adv_inception_v3,pass,pass,pass,pass,pass
 beit_base_patch16_224,pass,pass,pass,pass,pass
-botnet26t_256,pass,pass,fail_accuracy,pass,pass
+botnet26t_256,pass,pass,pass,pass,pass
 cait_m36_384,pass,pass,pass,pass,pass
 coat_lite_mini,pass,pass,pass,pass,pass
 convit_base,pass,pass,pass,pass,pass
 convmixer_768_32,pass,pass,pass,pass,pass
-convnext_base,pass,pass,pass,pass,pass
+convnext_base,pass,pass,fail_accuracy,pass,pass
 crossvit_9_240,pass,pass,pass,pass,pass
-cspdarknet53,pass,pass,fail_accuracy,pass,pass
+cspdarknet53,pass,pass,pass,pass,pass
 deit_base_distilled_patch16_224,pass,pass,pass,pass,pass
-dla102,pass,pass,fail_accuracy,pass,pass
+dla102,pass,pass,pass,pass,pass
 dm_nfnet_f0,pass,pass,pass,pass,pass
-dpn107,pass,pass,fail_accuracy,pass,pass
-eca_botnext26ts_256,pass,pass,fail_accuracy,pass,pass
-eca_halonext26ts,pass,pass,fail_accuracy,pass,pass
-ese_vovnet19b_dw,pass,pass,fail_accuracy,pass,pass
-fbnetc_100,pass,pass,fail_accuracy,pass,pass
-fbnetv3_b,pass,pass,fail_accuracy,pass,pass
-gernet_l,pass,pass,fail_accuracy,pass,pass
-ghostnet_100,pass,pass,fail_accuracy,pass,pass
-gluon_inception_v3,pass,pass,fail_accuracy,pass,pass
+dpn107,pass,pass,pass,pass,pass
+eca_botnext26ts_256,pass,pass,pass,pass,pass
+eca_halonext26ts,pass,pass,fail_to_run,pass,pass
+ese_vovnet19b_dw,pass,pass,pass,pass,pass
+fbnetc_100,pass,pass,pass,pass,pass
+fbnetv3_b,pass,pass,pass,pass,pass
+gernet_l,pass,pass,pass,pass,pass
+ghostnet_100,pass,pass,pass,pass,pass
+gluon_inception_v3,pass,pass,pass,pass,pass
 gmixer_24_224,pass,pass,pass,pass,pass
 gmlp_s16_224,pass,pass,pass,pass,pass
-hrnet_w18,pass,pass,fail_accuracy,pass,pass
-inception_v3,pass,pass,fail_accuracy,pass,pass
-jx_nest_base,pass,pass,pass,pass,pass
+hrnet_w18,pass,pass,pass,pass,pass
+inception_v3,pass,pass,pass,pass,pass
+jx_nest_base,pass,pass,fail_accuracy,pass,pass
 lcnet_050,pass,pass,fail_accuracy,pass,pass
 levit_128,pass,pass,pass,pass,pass
 mixer_b16_224,pass,pass,pass,pass,pass
-mixnet_l,pass,pass,fail_accuracy,pass,pass
-mnasnet_100,pass,pass,fail_accuracy,pass,pass
-mobilenetv2_100,pass,pass,fail_accuracy,pass,pass
-mobilenetv3_large_100,pass,pass,fail_accuracy,pass,pass
-mobilevit_s,pass,pass,fail_accuracy,pass,pass
+mixnet_l,pass,pass,pass,pass,pass
+mnasnet_100,pass,pass,pass,pass,pass
+mobilenetv2_100,pass,pass,pass,pass,pass
+mobilenetv3_large_100,pass,pass,pass,pass,pass
+mobilevit_s,pass,pass,pass,pass,pass
 nfnet_l0,pass,pass,pass,pass,pass
 pit_b_224,pass,pass,pass,pass,pass
-pnasnet5large,pass,pass,pass,pass,fail_accuracy
+pnasnet5large,pass,pass,pass,pass,pass
 poolformer_m36,pass,pass,pass,pass,pass
-regnety_002,pass,pass,fail_accuracy,pass,pass
-repvgg_a2,pass,pass,fail_accuracy,pass,pass
-res2net101_26w_4s,pass,pass,fail_accuracy,pass,pass
-res2net50_14w_8s,pass,pass,fail_accuracy,pass,pass
-res2next50,pass,pass,fail_accuracy,pass,pass
+regnety_002,pass,pass,pass,pass,pass
+repvgg_a2,pass,pass,pass,pass,pass
+res2net101_26w_4s,pass,pass,pass,pass,pass
+res2net50_14w_8s,pass,pass,pass,pass,pass
+res2next50,pass,pass,pass,pass,pass
 resmlp_12_224,pass,pass,pass,pass,pass
-resnest101e,pass,pass,fail_accuracy,pass,pass
-rexnet_100,pass,pass,fail_accuracy,pass,pass
-sebotnet33ts_256,pass,pass,fail_accuracy,pass,pass
-selecsls42b,pass,pass,fail_accuracy,pass,pass
-spnasnet_100,pass,pass,fail_accuracy,pass,pass
+resnest101e,pass,pass,pass,pass,pass
+rexnet_100,pass,pass,pass,pass,pass
+sebotnet33ts_256,pass,pass,pass,pass,pass
+selecsls42b,pass,pass,pass,pass,pass
+spnasnet_100,pass,pass,pass,pass,pass
 swin_base_patch4_window7_224,pass,pass,pass,pass,pass
-swsl_resnext101_32x16d,pass,pass,fail_accuracy,pass,pass
-tf_efficientnet_b0,pass,pass,fail_accuracy,pass,pass
-tf_mixnet_l,pass,pass,fail_accuracy,pass,pass
-tinynet_a,pass,pass,fail_accuracy,pass,pass
+swsl_resnext101_32x16d,pass,pass,pass,pass,pass
+tf_efficientnet_b0,pass,pass,pass,pass,pass
+tf_mixnet_l,pass,pass,pass,pass,pass
+tinynet_a,pass,pass,pass,pass,pass
 tnt_s_patch16_224,pass,pass,pass,pass,pass
 twins_pcpvt_base,pass,pass,pass,pass,pass
 visformer_small,pass,pass,pass,pass,pass
 vit_base_patch16_224,pass,pass,pass,pass,pass
-volo_d1_224,pass,pass,fail_accuracy,pass,pass
+volo_d1_224,pass,pass,pass,pass,pass
 xcit_large_24_p8_224,pass_due_to_skip,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run

.github/ci_expected_accuracy/inductor_torchbench_inference.csv

@@ -1,11 +1,11 @@
 name,float32,bfloat16,float16,amp_bf16,amp_fp16
 torchrec_dlrm,pass,eager_fail_to_run,eager_fail_to_run,pass,pass
 BERT_pytorch,pass,pass,pass,pass,pass
-Background_Matting,pass_due_to_skip,pass_due_to_skip,eager_fail_to_run,pass_due_to_skip,eager_fail_to_run
+Background_Matting,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip
 DALLE2_pytorch,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 LearningToPaint,pass,pass,pass,pass,pass
 Super_SloMo,pass,pass,pass,pass,pass
-alexnet,eager_two_runs_differ,pass,pass,pass,eager_two_runs_differ
+alexnet,pass,pass,pass,pass,pass
 basic_gnn_edgecnn,pass,pass,pass,pass,pass
 basic_gnn_gcn,pass,pass,pass,pass,pass
 basic_gnn_gin,pass,pass,pass,pass,pass
@@ -21,11 +21,11 @@ detectron2_fasterrcnn_r_50_c4,pass,eager_fail_to_run,fail_accuracy,eager_fail_to
 detectron2_fasterrcnn_r_50_dc5,pass,eager_fail_to_run,fail_accuracy,eager_fail_to_run,fail_accuracy
 detectron2_fasterrcnn_r_50_fpn,pass,eager_fail_to_run,fail_accuracy,eager_fail_to_run,fail_accuracy
 detectron2_fcos_r_50_fpn,pass,pass,pass,pass,pass
-detectron2_maskrcnn,fail_accuracy,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
-detectron2_maskrcnn_r_101_c4,fail_accuracy,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
-detectron2_maskrcnn_r_101_fpn,fail_accuracy,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
-detectron2_maskrcnn_r_50_c4,pass,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
-detectron2_maskrcnn_r_50_fpn,fail_accuracy,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
+detectron2_maskrcnn,fail_to_run,eager_fail_to_run,fail_to_run,eager_fail_to_run,fail_to_run
+detectron2_maskrcnn_r_101_c4,fail_accuracy,eager_fail_to_run,fail_accuracy,eager_fail_to_run,fail_accuracy
+detectron2_maskrcnn_r_101_fpn,fail_accuracy,eager_fail_to_run,fail_accuracy,eager_fail_to_run,fail_accuracy
+detectron2_maskrcnn_r_50_c4,pass,eager_fail_to_run,fail_accuracy,eager_fail_to_run,fail_accuracy
+detectron2_maskrcnn_r_50_fpn,fail_accuracy,eager_fail_to_run,fail_accuracy,eager_fail_to_run,fail_accuracy
 dlrm,pass,pass,pass,pass,pass
 doctr_det_predictor,pass,pass,pass,eager_fail_to_run,pass
 doctr_reco_predictor,pass,pass,pass,pass,pass
@@ -61,7 +61,7 @@ mnasnet1_0,pass,pass,pass,pass,pass
 mobilenet_v2,pass,pass,pass,pass,pass
 mobilenet_v2_quantized_qat,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
 mobilenet_v3_large,pass,pass,pass,pass,pass
-moco,model_fail_to_load,model_fail_to_load,model_fail_to_load,eager_fail_to_run,model_fail_to_load
+moco,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
 moondream,pass,pass,pass,pass,pass
 nanogpt,pass,pass,pass,pass,pass
 nvidia_deeprecommender,pass,pass,pass,pass,pass
@@ -71,15 +71,15 @@ phlippe_resnet,pass,pass,pass,pass,pass
 pyhpc_equation_of_state,pass,pass,fail_accuracy,pass,pass
 pyhpc_isoneutral_mixing,pass,pass,pass,pass,pass
 pyhpc_turbulent_kinetic_energy,pass,pass,pass,pass,pass
-pytorch_CycleGAN_and_pix2pix,pass,pass,eager_fail_to_run,pass,eager_fail_to_run
+pytorch_CycleGAN_and_pix2pix,pass,pass,pass,pass,pass
 pytorch_stargan,pass,pass,pass,pass,pass
 pytorch_unet,pass,pass,pass,pass,pass
 resnet152,pass,pass,pass,pass,pass
 resnet18,pass,pass,pass,pass,pass
 resnet50,pass,pass,pass,pass,pass
 resnet50_quantized_qat,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
 resnext50_32x4d,pass,pass,pass,pass,pass
-sam,eager_two_runs_differ,pass,pass,pass,pass
+sam,pass,pass,pass,pass,pass
 sam_fast,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 shufflenet_v2_x1_0,pass,pass,pass,pass,pass
 simple_gpt,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
@@ -89,7 +89,7 @@ speech_transformer,pass,pass,pass,pass,pass
 squeezenet1_1,pass,fail_accuracy,fail_accuracy,pass,pass
 stable_diffusion_text_encoder,pass,pass,pass,pass,pass
 stable_diffusion_unet,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip
-tacotron2,pass,pass,pass,model_fail_to_load,fail_to_run
+tacotron2,pass,pass,pass,fail_to_run,fail_to_run
 timm_efficientdet,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
 timm_efficientnet,pass,pass,pass,pass,pass
 timm_nfnet,pass,pass,pass,pass,pass
@@ -100,6 +100,7 @@ timm_vision_transformer_large,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip
 timm_vovnet,pass,pass,pass,pass,pass
 torch_multimodal_clip,pass,pass,pass,eager_fail_to_run,eager_fail_to_run
 tts_angular,pass,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
-vgg16,eager_two_runs_differ,pass,pass,pass,pass
+vgg16,pass,pass,pass,pass,pass
 vision_maskrcnn,pass,pass,pass,eager_fail_to_run,eager_fail_to_run
 yolov3,pass,pass,pass,pass,pass
+hf_Roberta_base,pass,pass,pass,pass,pass

.github/ci_expected_accuracy/inductor_torchbench_training.csv

@@ -1,11 +1,11 @@
 name,float32,bfloat16,float16,amp_bf16,amp_fp16
-torchrec_dlrm,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,fail_to_run
+torchrec_dlrm,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 BERT_pytorch,pass,pass,pass,pass,pass
-Background_Matting,pass_due_to_skip,pass_due_to_skip,eager_fail_to_run,pass_due_to_skip,eager_fail_to_run
+Background_Matting,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip
 DALLE2_pytorch,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 LearningToPaint,pass,pass,pass,pass,pass
-Super_SloMo,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ
-alexnet,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ
+Super_SloMo,eager_two_runs_differ,pass,pass,eager_two_runs_differ,pass
+alexnet,eager_two_runs_differ,pass,pass,pass,pass
 basic_gnn_edgecnn,pass,pass,pass,pass,pass
 basic_gnn_gcn,pass,pass,pass,pass,pass
 basic_gnn_gin,pass,pass,pass,pass,pass
@@ -30,7 +30,7 @@ dlrm,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eag
 doctr_det_predictor,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 doctr_reco_predictor,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 drq,pass,pass,pass,pass,pass
-fastNLP_Bert,fail_accuracy,pass,pass,fail_accuracy,fail_accuracy
+fastNLP_Bert,pass,pass,pass,pass,pass
 functorch_dp_cifar10,fail_accuracy,fail_accuracy,fail_accuracy,pass,pass
 functorch_maml_omniglot,pass,pass,pass,fail_accuracy,pass
 hf_Albert,pass,pass,pass,pass,pass
@@ -61,7 +61,7 @@ mnasnet1_0,pass,pass,pass,pass,pass
 mobilenet_v2,pass,pass,pass,pass,pass
 mobilenet_v2_quantized_qat,fail_accuracy,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 mobilenet_v3_large,pass,pass,pass,pass,pass
-moco,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,eager_fail_to_run
+moco,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
 moondream,pass,pass,pass,pass,pass
 nanogpt,pass,pass,pass,pass,pass
 nvidia_deeprecommender,pass,pass,pass,pass,pass
@@ -71,7 +71,7 @@ phlippe_resnet,pass,fail_accuracy,pass,fail_accuracy,pass
 pyhpc_equation_of_state,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 pyhpc_isoneutral_mixing,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
 pyhpc_turbulent_kinetic_energy,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
-pytorch_CycleGAN_and_pix2pix,eager_two_runs_differ,eager_two_runs_differ,eager_fail_to_run,eager_two_runs_differ,eager_fail_to_run
+pytorch_CycleGAN_and_pix2pix,eager_two_runs_differ,eager_two_runs_differ,pass,eager_two_runs_differ,pass
 pytorch_stargan,pass,pass,pass,pass,pass
 pytorch_unet,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip
 resnet152,pass,pass,pass,pass,pass
@@ -93,13 +93,14 @@ tacotron2,fail_to_run,fail_to_run,fail_to_run,fail_to_run,fail_to_run
 timm_efficientdet,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load,model_fail_to_load
 timm_efficientnet,pass,pass,pass,pass,pass
 timm_nfnet,pass,pass,pass,pass,pass
-timm_regnet,pass,pass,pass,pass,pass
+timm_regnet,pass,fail_accuracy,pass,pass,pass
 timm_resnest,pass,pass,pass,pass,pass
 timm_vision_transformer,pass,pass,pass,pass,pass
 timm_vision_transformer_large,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip,pass_due_to_skip
 timm_vovnet,pass,pass,pass,pass,pass
 torch_multimodal_clip,pass,pass,pass,eager_fail_to_run,eager_fail_to_run
 tts_angular,pass,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run,eager_fail_to_run
-vgg16,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ,eager_two_runs_differ
+vgg16,pass,pass,pass,pass,pass
 vision_maskrcnn,pass,pass,pass,eager_fail_to_run,eager_fail_to_run
-yolov3,pass,pass,fail_accuracy,pass,pass
+yolov3,pass,pass,pass,pass,pass
+hf_Roberta_base,pass,pass,pass,pass,pass

.github/workflows/_linux_ut.yml

@@ -40,6 +40,11 @@ jobs:
       - name: Prepare Stock Pytorch
         run: |
           pwd
+          which conda && conda clean -ay
+          conda remove --all -y -n xpu_op_${ZE_AFFINITY_MASK} || \
+                rm -rf $(dirname ${CONDA_EXE})/../envs/xpu_op_${ZE_AFFINITY_MASK}
+          conda create -n xpu_op_${ZE_AFFINITY_MASK} python=${{ inputs.python }} cmake ninja -y
+          source activate xpu_op_${ZE_AFFINITY_MASK}
           cd ../ && rm -rf pytorch
           git clone https://github.com/pytorch/pytorch pytorch
           cd pytorch && git checkout ${{ inputs.pytorch }} 
@@ -57,10 +62,6 @@ jobs:
           fi
       - name: Build Pytorch XPU
         run: |
-          which conda && conda clean -ay
-          conda remove --all -y -n xpu_op_${ZE_AFFINITY_MASK} || \
-                rm -rf $(dirname ${CONDA_EXE})/../envs/xpu_op_${ZE_AFFINITY_MASK}
-          conda create -n xpu_op_${ZE_AFFINITY_MASK} python=${{ inputs.python }} cmake ninja -y
           source activate xpu_op_${ZE_AFFINITY_MASK}
           conda install -c intel mkl-static mkl-include -y
           cd ../pytorch