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* Add amd migraphx execution provider to onnx runtime * rename MiGraphX to MIGraphX * add migraphx EP to tests * support multiple program output * disable more tests * backup changes related to program multiple outputs * remove logging code * remove unnecessary changes in migraphx_execution_provider.cc * add migraphx EP to tests * add input requests of the batchnorm operator * add to support an onnx operator PRelu * update migrapx dockerfile and removed one unused line * chagnes related to support dynamic input shape * fix build error * code backup * code backup * version that has 106 models run correctly * code backup * code backup * remove unnecessary print info * code backup * code backup * code backup * code backup * code backup * code backup * changes corresponding to migraphx change * fix merge conflict * minor code cleanup * code cleanup * remove unnecessary code * remove unnecessary code * add to support more constant folding analysis * more constant folding checking for shape input * add env var to control whether fp16 is enabled. Modify docker file to use ROCM3.3 * fix function name to avoid build error * add build and execution instruction for migraphx execution provider * added more build instructions * fixed a small format error * a minor change * fix review comments * another minor change * additional refinement of the documents * additional changes * remove unnecessary changes in the dockfile * additional changes for the dockerfile * code change backup * fix errors related to a few unit tests * fix a build error related to api change * fix unit test errors by either disabling the test or fix related isssues * remove unnecessary log info * sync submodule tvm with master * remove unnecessary changes * remove an unnecessary code line * refine documents for addition example
43 lines
2.1 KiB
Markdown
43 lines
2.1 KiB
Markdown
# MIGraphX Execution Provider
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ONNX Runtime's [MIGraphX](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/) execution provider uses AMD's Deep Learning graph optimization engine to accelerate ONNX model on AMD GPUs.
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## Build
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For build instructions, please see the [BUILD page](../../BUILD.md#AMD-MIGraphX).
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## Using the MIGraphX execution provider
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### C/C++
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The MIGraphX execution provider needs to be registered with ONNX Runtime to enable in the inference session.
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```
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string log_id = "Foo";
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auto logging_manager = std::make_unique<LoggingManager>
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(std::unique_ptr<ISink>{new CLogSink{}},
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static_cast<Severity>(lm_info.default_warning_level),
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false,
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LoggingManager::InstanceType::Default,
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&log_id)
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Environment::Create(std::move(logging_manager), env)
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InferenceSession session_object{so,env};
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session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::MIGraphXExecutionProvider>());
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status = session_object.Load(model_file_name);
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```
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You can check [here](https://github.com/scxiao/ort_test/tree/master/char_rnn) for a specific c/c++ program.
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The C API details are [here](../C_API.md#c-api).
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### Python
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When using the Python wheel from the ONNX Runtime build with MIGraphX execution provider, it will be automatically
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prioritized over the default GPU or CPU execution providers. There is no need to separately register the execution
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provider. Python APIs details are [here](../python/api_summary.rst#api-summary).
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You can check [here](https://github.com/scxiao/ort_test/tree/master/python/run_onnx) for a python script to run an
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model on either the CPU or MIGraphX Execution Provider.
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## Performance Tuning
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For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../ONNX_Runtime_Perf_Tuning.md)
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When/if using [onnxruntime_perf_test](../../onnxruntime/test/perftest#onnxruntime-performance-test), use the flag `-e migraphx`
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## Configuring environment variables
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MIGraphX providers an environment variable ORT_MIGRAPHX_FP16_ENABLE to enable the FP16 mode.
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