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https://github.com/saymrwulf/onnxruntime.git
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43c45ddd66
* Merged PR 6093117: Fix test_DynamicQuantizedLinear_max_adjusted_expanded by allowing Identity operator to run on non-float inputs Motivation: As part of the OnnxConformance Backend tests, DynamicQuantizedLinear_max_adjusted_expanded is failing. Root Cause: - The test model has `Identity` operator as one of the node. The input of this node is of non-float data type. - In DML, `Identity` operator is registered as operator which requires floating input. - As per `DirectMLSchema.h`, support for non-float input has been added for `Identity` operator in DML but the same has not been reflected in the `OperatorRegistration.cpp`. Changes: - Removed all traces of the requiresFloatFormatsForGraph flag from it's definition and usage. This flag was only used for Identity and it's related operator. - Added null check for the graphOutput nodeArg in GraphDescBuilder.cpp to stop the crash of the test. Related work items: #33076298 * Merged PR 6103324: Remove usage of non-generic error code (FWP_E_NULL_POINTER) Motivation: Addressing Dwayne comment on the previous PR. [Ref: [6093117](https://dev.azure.com/microsoft/WindowsAI/_git/onnxruntime/pullrequest/6093117?discussionId=44292162&path=%2Fonnxruntime%2Fcore%2Fproviders%2Fdml%2FDmlExecutionProvider%2Fsrc%2FGraphPartitioner.cpp)] Changes: Inside the DML EP, we should not use some other platform specific error codes. Instead we should a appropriate generic error code. Related work items: #33076298 Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>
93 lines
3 KiB
C++
93 lines
3 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "pch.h"
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#ifdef USE_DML
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#include "abi_custom_registry_impl.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, nullptr, 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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_In_opt_ IMLOperatorSupportQueryPrivate* supportQuery,
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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 supportedWith64BitTensorsVia32BitStrides,
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bool supportedWith64BitTensorsVia32BitStridesFromAnyEp,
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bool prefer64BitTensorsDirectly,
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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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supportQuery,
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isInternalOperator,
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canAliasFirstInput,
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supportsGraph,
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requiredInputCountForGraph,
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supportedWith64BitTensorsVia32BitStrides,
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supportedWith64BitTensorsVia32BitStridesFromAnyEp,
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prefer64BitTensorsDirectly,
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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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#endif USE_DML
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