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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>
44 lines
1.8 KiB
C++
44 lines
1.8 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#ifdef USE_DML
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#include "core/providers/dml/DmlExecutionProvider/src/AbiCustomRegistry.h"
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namespace Windows::AI::MachineLearning::Adapter {
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// An implementation of AbiCustomRegistry that emits telemetry events when operator kernels or schemas are registered.
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class AbiCustomRegistryImpl : public AbiCustomRegistry {
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public:
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HRESULT STDMETHODCALLTYPE RegisterOperatorSetSchema(
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const MLOperatorSetId* op_set_id,
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int baseline_version,
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const MLOperatorSchemaDescription* const* schema,
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uint32_t schema_count,
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_In_opt_ IMLOperatorTypeInferrer* type_inferrer,
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_In_opt_ IMLOperatorShapeInferrer* shape_inferrer) const noexcept override;
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HRESULT STDMETHODCALLTYPE RegisterOperatorKernel(
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const MLOperatorKernelDescription* operator_kernel,
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IMLOperatorKernelFactory* operator_kernel_factory,
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_In_opt_ IMLOperatorShapeInferrer* shape_inferrer,
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_In_opt_ IMLOperatorSupportQueryPrivate* supportQuery,
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bool is_internal_operator,
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bool can_alias_first_input,
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bool supports_graph,
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const uint32_t* required_input_count_for_graph = nullptr,
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bool supports_64bit_directly = false,
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bool allows_64bit_via_strides = false,
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bool allows_64bit_via_strides_from_any_ep = false,
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_In_reads_(constant_cpu_input_count) const uint32_t* required_constant_cpu_inputs = nullptr,
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uint32_t constant_cpu_input_count = 0) const noexcept override;
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HRESULT STDMETHODCALLTYPE RegisterOperatorKernel(
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const MLOperatorKernelDescription* op_kernel,
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IMLOperatorKernelFactory* operator_kernel_factory,
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_In_opt_ IMLOperatorShapeInferrer* shape_inferrer) const noexcept override;
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};
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} // namespace Windows::AI::MachineLearning::Adapter
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#endif USE_DML
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