mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-25 22:26:24 +00:00
5350abe19d
this is a big PR. we are going to move it up to layer_dev , which is still a L3 so we are still safe to do work there agile. we are going to move this into the L3 so that ryan can start doing intergration testing. we will pause for a full code review and integration test result prior to going into the L2. >>>> raw comments from previous commits >>> * LearningModelSession is cleaned up to use the adapter, and parts of binding are. * moved everything in the winmladapter made it all nano-com using, WRL to construct objects in the ORT side. base interfaces for everythign for winml to call cleaned up a bunch of winml to use the base interfaces. * more pieces * GetData across the abi. * renamed some namepsace cleaned up OrtValue cleaned up Tensor cleaned up custom ops. everything *but* learnignmodel should be clean * make sure it's building. winml.dll is still a monolith.
82 lines
No EOL
2.6 KiB
C++
82 lines
No EOL
2.6 KiB
C++
// Copyright (c) Microsoft Corporation.
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// Licensed under the MIT License.
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#include "pch.h"
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#include "inc/AbiCustomRegistryImpl.h"
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namespace Windows::AI::MachineLearning::Adapter {
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HRESULT STDMETHODCALLTYPE AbiCustomRegistryImpl::RegisterOperatorSetSchema(
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const MLOperatorSetId* opSetId,
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int baseline_version,
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const MLOperatorSchemaDescription* const* schema,
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uint32_t schemaCount,
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_In_opt_ IMLOperatorTypeInferrer* typeInferrer,
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_In_opt_ IMLOperatorShapeInferrer* shapeInferrer) const noexcept try {
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#ifdef LAYERING_DONE
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for (uint32_t i = 0; i < schemaCount; ++i) {
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telemetry_helper.RegisterOperatorSetSchema(
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schema[i]->name,
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schema[i]->inputCount,
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schema[i]->outputCount,
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schema[i]->typeConstraintCount,
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schema[i]->attributeCount,
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schema[i]->defaultAttributeCount);
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}
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#endif
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// Delegate to base class
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return AbiCustomRegistry::RegisterOperatorSetSchema(
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opSetId,
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baseline_version,
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schema,
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schemaCount,
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typeInferrer,
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shapeInferrer);
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}
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CATCH_RETURN();
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HRESULT STDMETHODCALLTYPE AbiCustomRegistryImpl::RegisterOperatorKernel(
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const MLOperatorKernelDescription* opKernel,
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IMLOperatorKernelFactory* operatorKernelFactory,
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_In_opt_ IMLOperatorShapeInferrer* shapeInferrer) const noexcept {
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return RegisterOperatorKernel(opKernel, operatorKernelFactory, shapeInferrer, false, false, false);
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}
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HRESULT STDMETHODCALLTYPE AbiCustomRegistryImpl::RegisterOperatorKernel(
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const MLOperatorKernelDescription* opKernel,
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IMLOperatorKernelFactory* operatorKernelFactory,
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_In_opt_ IMLOperatorShapeInferrer* shapeInferrer,
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bool isInternalOperator,
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bool canAliasFirstInput,
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bool supportsGraph,
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const uint32_t* requiredInputCountForGraph,
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bool requiresFloatFormatsForGraph,
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_In_reads_(constantCpuInputCount) const uint32_t* requiredConstantCpuInputs,
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uint32_t constantCpuInputCount) const noexcept try {
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#ifdef LAYERING_DONE
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// Log a custom op telemetry if the operator is not a built-in DML operator
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if (!isInternalOperator) {
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telemetry_helper.LogRegisterOperatorKernel(
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opKernel->name,
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opKernel->domain,
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static_cast<int>(opKernel->executionType));
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}
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#endif
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// Delegate to base class
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return AbiCustomRegistry::RegisterOperatorKernel(
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opKernel,
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operatorKernelFactory,
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shapeInferrer,
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isInternalOperator,
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canAliasFirstInput,
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supportsGraph,
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requiredInputCountForGraph,
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requiresFloatFormatsForGraph,
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requiredConstantCpuInputs,
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constantCpuInputCount);
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}
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CATCH_RETURN();
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} // namespace Windows::AI::MachineLearning::Adapter
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