[MIGraphX EP] Set External Data Path #21598
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ping @PeixuanZuo @cloudhan @ytaous @snnn |
TedThemistokleous
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/azp run Big Models,Linux Android Emulator QNN CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline |
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/azp run Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU TensorRT CI Pipeline,Windows x64 QNN CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed |
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Azure Pipelines successfully started running 9 pipeline(s). |
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Azure Pipelines successfully started running 8 pipeline(s). |
tianleiwu
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Aug 2, 2024
onnxruntime/core/providers/migraphx/migraphx_execution_provider.cc
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/azp run Big Models,Linux Android Emulator QNN CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline |
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/azp run Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU TensorRT CI Pipeline,Windows x64 QNN CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed |
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Azure Pipelines successfully started running 9 pipeline(s). |
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Azure Pipelines successfully started running 8 pipeline(s). |
tianleiwu
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Aug 2, 2024
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I saw some build error in CI Pipleine: /onnxruntime_src/onnxruntime/core/providers/migraphx/migraphx_execution_provider.cc:1302:23: error: ‘struct migraphx::api::onnx_options’ has no member named ‘set_external_data_path’ |
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You need to update to the latest MIGraphX that's been released. |
PeixuanZuo
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Aug 7, 2024
This reverts commit 45b7c41.
tianleiwu
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Aug 9, 2024
### Description * Fix migraphx build error caused by #21598: Add a conditional compile on code block that depends on ROCm >= 6.2. Note that the pipeline uses ROCm 6.0. Unblock orttraining-linux-gpu-ci-pipeline and orttraining-ortmodule-distributed and orttraining-amd-gpu-ci-pipeline pipelines: * Disable a model test in linux GPU training ci pipelines caused by #19470: Sometime, cudnn frontend throws exception that cudnn graph does not support a Conv node of keras_lotus_resnet3D model on V100 GPU. Note that same test does not throw exception in other GPU pipelines. The failure might be related to cudnn 8.9 and V100 GPU used in the pipeline (Amper GPUs and cuDNN 9.x do not have the issue). The actual fix requires fallback logic, which will take time to implement, so we temporarily disable the test in training pipelines. * Force install torch for cuda 11.8. (The docker has torch 2.4.0 for cuda 12.1 to build torch extension, which it is not compatible cuda 11.8). Note that this is temporary walkround. More elegant fix is to make sure right torch version in docker build step, that might need update install_python_deps.sh and corresponding requirements.txt. * Skip test_gradient_correctness_conv1d since it causes segment fault. Root cause need more investigation (maybe due to cudnn frontend as well). * Skip test_aten_attention since it causes assert failure. Root cause need more investigation (maybe due to torch version). * Skip orttraining_ortmodule_distributed_tests.py since it has error that compiler for torch extension does not support c++17. One possible fix it to set the following compile argument inside setup.py of extension fused_adam: extra_compile_args['cxx'] = ['-std=c++17']. However, due to the urgency of unblocking the pipelines, just disable the test for now. * skip test_softmax_bf16_large. For some reason, torch.cuda.is_bf16_supported() returns True in V100 with torch 2.3.1, so the test was run in CI, but V100 does not support bf16 natively. * Fix typo of deterministic ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
prathikr
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Aug 9, 2024
* Fix migraphx build error caused by #21598: Add a conditional compile on code block that depends on ROCm >= 6.2. Note that the pipeline uses ROCm 6.0. Unblock orttraining-linux-gpu-ci-pipeline and orttraining-ortmodule-distributed and orttraining-amd-gpu-ci-pipeline pipelines: * Disable a model test in linux GPU training ci pipelines caused by #19470: Sometime, cudnn frontend throws exception that cudnn graph does not support a Conv node of keras_lotus_resnet3D model on V100 GPU. Note that same test does not throw exception in other GPU pipelines. The failure might be related to cudnn 8.9 and V100 GPU used in the pipeline (Amper GPUs and cuDNN 9.x do not have the issue). The actual fix requires fallback logic, which will take time to implement, so we temporarily disable the test in training pipelines. * Force install torch for cuda 11.8. (The docker has torch 2.4.0 for cuda 12.1 to build torch extension, which it is not compatible cuda 11.8). Note that this is temporary walkround. More elegant fix is to make sure right torch version in docker build step, that might need update install_python_deps.sh and corresponding requirements.txt. * Skip test_gradient_correctness_conv1d since it causes segment fault. Root cause need more investigation (maybe due to cudnn frontend as well). * Skip test_aten_attention since it causes assert failure. Root cause need more investigation (maybe due to torch version). * Skip orttraining_ortmodule_distributed_tests.py since it has error that compiler for torch extension does not support c++17. One possible fix it to set the following compile argument inside setup.py of extension fused_adam: extra_compile_args['cxx'] = ['-std=c++17']. However, due to the urgency of unblocking the pipelines, just disable the test for now. * skip test_softmax_bf16_large. For some reason, torch.cuda.is_bf16_supported() returns True in V100 with torch 2.3.1, so the test was run in CI, but V100 does not support bf16 natively. * Fix typo of deterministic <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
sumitsays
pushed a commit
that referenced
this pull request
Aug 9, 2024
* Fix migraphx build error caused by #21598: Add a conditional compile on code block that depends on ROCm >= 6.2. Note that the pipeline uses ROCm 6.0. Unblock orttraining-linux-gpu-ci-pipeline and orttraining-ortmodule-distributed and orttraining-amd-gpu-ci-pipeline pipelines: * Disable a model test in linux GPU training ci pipelines caused by #19470: Sometime, cudnn frontend throws exception that cudnn graph does not support a Conv node of keras_lotus_resnet3D model on V100 GPU. Note that same test does not throw exception in other GPU pipelines. The failure might be related to cudnn 8.9 and V100 GPU used in the pipeline (Amper GPUs and cuDNN 9.x do not have the issue). The actual fix requires fallback logic, which will take time to implement, so we temporarily disable the test in training pipelines. * Force install torch for cuda 11.8. (The docker has torch 2.4.0 for cuda 12.1 to build torch extension, which it is not compatible cuda 11.8). Note that this is temporary walkround. More elegant fix is to make sure right torch version in docker build step, that might need update install_python_deps.sh and corresponding requirements.txt. * Skip test_gradient_correctness_conv1d since it causes segment fault. Root cause need more investigation (maybe due to cudnn frontend as well). * Skip test_aten_attention since it causes assert failure. Root cause need more investigation (maybe due to torch version). * Skip orttraining_ortmodule_distributed_tests.py since it has error that compiler for torch extension does not support c++17. One possible fix it to set the following compile argument inside setup.py of extension fused_adam: extra_compile_args['cxx'] = ['-std=c++17']. However, due to the urgency of unblocking the pipelines, just disable the test for now. * skip test_softmax_bf16_large. For some reason, torch.cuda.is_bf16_supported() returns True in V100 with torch 2.3.1, so the test was run in CI, but V100 does not support bf16 natively. * Fix typo of deterministic <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
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Description
Changes to add in Set external data path for model weight files. Additional fixes to ensure this compiles off the latest v1.19 Onnxruntime
Motivation and Context
Separate weights used for larger models (like stable diffusion) is motivation for this change set