[MIGraphX EP] Add migraphx ep save load compiles #20643
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TedThemistokleous
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Fixes GPU faults Seen when running Mixed Precision inferences workloads with Bert v1.1 (fp16 + int8 Quant of Conv + MatMul) Was hitting an edge case with mixed precision where the input parameters were not being populated and using uninitizlied values for the parameters which would "work" silently as no issue in inference arise. For bert though, segment_ids is pushed through a gather onnx operator which uses these as an index. Using uninitialized memory made this error such that it was non obvious why we were getting failures between our runs and we saw the issue intermittently between machines/cards/etc. Fixes here are as follows to the MIGraphX Execution Provider -Fp16 quantization after int8 -Additional debug logging for workflow of loading/quantization - Set input/output parameters as seperate run prior to int8 calibration - Set all dynamic data as input parameters for int8 static calibration to be performed with MIGraphX Without these changes models will fail to copy input parameters on mixed precision runs when we decided to quantize as MIGraphX assumes all inputs will be used for calibration not just the input data read in from a calibration table.
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Initial changes for the getting the flags into the state to save/load precompiled MIGraphX models in the EP.
TedThemistokleous
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Makes it easier to handle workflow if the input model needs to be recompiled or not.
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ping @cloudhan @ytaous @PeixuanZuo useful to speed up workflow when working with MIGraphX EP Should come after this is merged: #20982 |
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Validated with: microsoft/onnxruntime-inference-examples#441 |
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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 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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cloudhan
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Jun 17, 2024
TedThemistokleous
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Jun 17, 2024
Adds the ability for MIGraphX EP to save off or load compiled models to save time between inferences. Via Command line User should be able to set the save ability with ORT_MIGRAPHX_SAVE_COMPILED_MODEL ORT_MIGRAPHX_SAVE_COMPILE_PATH User should be able to set the load ability with ORT_MIGRAPHX_LOAD_COMPILED_MODEL ORT_MIGRAPHX_LOAD_COMPILE_PATH via Onnxruntime API migx_save_compiled_model migx_save_model_name migx_load_compiled_model migx_load_model_name The motivation for this is to leverage MIGraphX's existing API to save/load models after our compile step of graph optimization. For larger models or models which were compiled with additional tuning steps, this saves time after first compile and inference run, and thus speeds up the user experience in order to encourage development. --------- Co-authored-by: Ted Themistokleous <[email protected]>
TedThemistokleous
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Jun 17, 2024
Adds the ability for MIGraphX EP to save off or load compiled models to save time between inferences. Via Command line User should be able to set the save ability with ORT_MIGRAPHX_SAVE_COMPILED_MODEL ORT_MIGRAPHX_SAVE_COMPILE_PATH User should be able to set the load ability with ORT_MIGRAPHX_LOAD_COMPILED_MODEL ORT_MIGRAPHX_LOAD_COMPILE_PATH via Onnxruntime API migx_save_compiled_model migx_save_model_name migx_load_compiled_model migx_load_model_name The motivation for this is to leverage MIGraphX's existing API to save/load models after our compile step of graph optimization. For larger models or models which were compiled with additional tuning steps, this saves time after first compile and inference run, and thus speeds up the user experience in order to encourage development. --------- Co-authored-by: Ted Themistokleous <[email protected]>
cloudhan
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Jun 18, 2024
See #20643 ### Description Changes order of how we perform quantization to better support mixed precision and fixes a bug found with parameters of inputs for int8 quantization not being correctly handled. We now perform int8 quantization first on a full precision input model, before then quantizing the model to fp16 for remain ops that aren't quantized. The former case was causing us to use a low precision input which could cause larger values to be inserted than intended to the model when int8 quantization is perform. The symptom of this was a failure during quantization steps. Similar to the above input parameters were being uninitialized and resulting in similar failure during int8 quantization. GPU faults were intermittent but present as using uninitialized memory created undefined behavior when we started testing more complex models during mixed precision. ### Motivation and Context In some cases we've seen random data and/or invalid values entering into compiled onnx graphs. This is due to input parameters to the MIGraphX Graph not being set correctly when mixed precision (int8 + fp16) is used and ordering of quantization steps is causes a lower precision model to be used to perform int8 quantization. In most cases the failure is silent/intermittent. In some cases we've observed gpu faults due to out of bounds values being set. This change is required as a large input parameter to the MIGraphX graph is initialized to a large random value, and the next operator is using that for indexing, we get undefined behavior and a GPU fault.
TedThemistokleous
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Jun 21, 2024
Adds the ability for MIGraphX EP to save off or load compiled models to save time between inferences. Via Command line User should be able to set the save ability with ORT_MIGRAPHX_SAVE_COMPILED_MODEL ORT_MIGRAPHX_SAVE_COMPILE_PATH User should be able to set the load ability with ORT_MIGRAPHX_LOAD_COMPILED_MODEL ORT_MIGRAPHX_LOAD_COMPILE_PATH via Onnxruntime API migx_save_compiled_model migx_save_model_name migx_load_compiled_model migx_load_model_name The motivation for this is to leverage MIGraphX's existing API to save/load models after our compile step of graph optimization. For larger models or models which were compiled with additional tuning steps, this saves time after first compile and inference run, and thus speeds up the user experience in order to encourage development. --------- Co-authored-by: Ted Themistokleous <[email protected]>
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Description
Adds the ability for MIGraphX EP to save off or load compiled models to save time between inferences.
Via Command line
User should be able to set the save ability with
ORT_MIGRAPHX_SAVE_COMPILED_MODEL
ORT_MIGRAPHX_SAVE_COMPILE_PATH
User should be able to set the load ability with
ORT_MIGRAPHX_LOAD_COMPILED_MODEL
ORT_MIGRAPHX_LOAD_COMPILE_PATH
via Onnxruntime API
migx_save_compiled_model
migx_save_model_name
migx_load_compiled_model
migx_load_model_name
Motivation and Context
The motivation for this is to leverage MIGraphX's existing API to save/load models after our compile step of graph optimization. For larger models or models which were compiled with additional tuning steps, this saves time after first compile and inference run, and thus speeds up the user experience in order to encourage development.