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Reduce ops for DNNL ep (#10056)
* Add Reduce Ops to DNNL ep Combine the Reduction ops into one class Add ReduceL1, ReduceL2, ReduceSum, ReduceMax, ReduceMin, and ReduceProd, ReduceSumSquare, ReduceLogSum, and ReduceLogSumExp Reduce code now also handles the keepdims attribute Also updated code to use HandleNegativeAxis function from the providers/common.h code instead of manually calculating. In code documentation exists to help explain complex reduction op code Add elementwise ops to Reduction op capability code removed keepdims check from the Reduction op capability code. Updated the error_tolerance for LogGrad(DNNL EP only) after finding a few instances that the tests were a little out of tolerance. Signed-off-by: George Nash <george.nash@intel.com> * Documentation cleanup in dnnl_qattention Cleaned up the Comments documenting the QAttention operator For some reason a bunch of new lines were introduced to the comment making it harder to read. Signed-off-by: George Nash <george.nash@intel.com>
This commit is contained in:
George Nash
GitHub
parent
44c701192b
commit
93636cbd20
12 changed files with 451 additions and 174 deletions
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@ -191,34 +191,31 @@ bool DnnlBatchNormalizationNodeCapability::IsDimensionSupported(const Node* node
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return true;
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}
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// DnnlReduceMeanNodeCapability class
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// DnnlReduceNodeCapability class
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//-------------------------------------
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bool DnnlReduceMeanNodeCapability::Supported(const Node* node, const GraphViewer& graph_viewer) const {
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ORT_UNUSED_PARAMETER(graph_viewer);
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bool DnnlReduceNodeCapability::Supported(const Node* node, const GraphViewer& graph_viewer) const {
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// These reduction operators use elementwise ops so elementwise operators must also be supported.
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if(node->OpType() == "ReduceLogSum" ||
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node->OpType() == "ReduceLogSumExp" ||
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node->OpType() == "ReduceSumSquare") {
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if(!_eltwise.Supported(node, graph_viewer)) return false;
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}
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if (!IsTypeSupported(node)) return false;
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if (!IsAttributeSupported(node)) return false;
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if (!IsDimensionSupported(node)) return false;
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return true;
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}
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bool DnnlReduceMeanNodeCapability::IsAttributeSupported(const Node* node) const {
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const NodeAttributes& attributes = node->GetAttributes();
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auto attr = attributes.find("keepdims");
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if (attr != attributes.end() && attr->second().i() == 0) {
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return false;
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}
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return true;
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}
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bool DnnlReduceMeanNodeCapability::IsDimensionSupported(const Node* node) const {
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bool DnnlReduceNodeCapability::IsDimensionSupported(const Node* node) const {
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auto node_inputs = node->InputDefs();
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if (node_inputs[0]->Shape() != nullptr && node_inputs[0]->Shape()->dim_size() == 0) {
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LOGS_DEFAULT(INFO) << "Reduction op not supported because input data is a scalar\n";
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return false;
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}
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return true;
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}
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// DnnlSoftmaxNodeCapability class
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//-------------------------------------
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bool DnnlSoftmaxNodeCapability::Supported(const Node* node, const GraphViewer& graph_viewer) const {
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ORT_UNUSED_PARAMETER(graph_viewer);
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if (!IsTypeSupported(node)) return false;
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@ -145,22 +145,6 @@ class DnnlBatchNormalizationNodeCapability : public DnnlDefaultNodeCapability {
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bool IsDimensionSupported(const Node* node) const;
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};
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/**
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* Decide if a ReduceMean op is supported by DnnlExecutionProvider
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*
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* Dnnl does not support the "keepdims" attribute when it is `0`
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*/
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class DnnlReduceMeanNodeCapability : public DnnlDefaultNodeCapability {
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public:
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DnnlReduceMeanNodeCapability() : DnnlDefaultNodeCapability({type_float32}) {}
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bool Supported(const Node* node, const GraphViewer& graph_viewer) const override;
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private:
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bool IsAttributeSupported(const Node* node) const;
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bool IsDimensionSupported(const Node* node) const;
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};
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/**
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* Decide if a Softmax op is supported by DnnlExecutionProvider
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*
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@ -249,6 +233,21 @@ class DnnlElementwiseCapability : public DnnlDefaultNodeCapability {
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bool IsDimensionSupported(const Node* node) const;
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};
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/**
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* Decide if a Reduce op is supported by DnnlExecutionProvider
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*/
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class DnnlReduceNodeCapability : public DnnlDefaultNodeCapability {
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public:
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DnnlReduceNodeCapability() : DnnlDefaultNodeCapability({type_float32}) {}
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bool Supported(const Node* node, const GraphViewer& graph_viewer) const override;
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private:
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bool IsDimensionSupported(const Node* node) const;
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DnnlElementwiseCapability _eltwise;
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};
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class DnnlPowNodeCapability : public DnnlDefaultMultiInputNodeCapability {
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public:
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DnnlPowNodeCapability()
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@ -31,7 +31,16 @@ DnnlOpManager::DnnlOpManager() {
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dnnl_ops_map_.emplace(std::make_pair("Mul", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("Pow", std::unique_ptr<DnnlNodeCapability>(new DnnlPowNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("QAttention", std::unique_ptr<DnnlNodeCapability>(new DnnlQAttentionNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceMean", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceMeanNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceL1", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceL2", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceLogSum", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceLogSumExp", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceMax", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceMean", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceMin", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceProd", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceSum", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("ReduceSumSquare", std::unique_ptr<DnnlNodeCapability>(new DnnlReduceNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("Relu", std::unique_ptr<DnnlNodeCapability>(new DnnlElementwiseCapability())));
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dnnl_ops_map_.emplace(std::make_pair("Reshape", std::unique_ptr<DnnlNodeCapability>(new DnnlReshapeNodeCapability())));
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dnnl_ops_map_.emplace(std::make_pair("Round", std::unique_ptr<DnnlNodeCapability>(new DnnlElementwiseCapability())));
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@ -45,97 +45,53 @@ dnnl::memory DnnlQAttention::ComputeTotalScale(DnnlSubgraphPrimitive& sp, DnnlNo
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}
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/*
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input_tensor weight_tensor
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input_tensor weight_tensor
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\ /
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\ /
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\ /
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\ /
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matmulinteger
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matmulinteger
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with input and weight zero point,
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input and weight scale and bias
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| QKV
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slice
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/ | \
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/ | \
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/ | \
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/ | \
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/ | \
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|Q |K |V
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| | |
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reshape reshape reshape
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| | |
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permute permute permute
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| | |
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| transpose |
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\ | |
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\ | |
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\ | |
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\ | |
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matmul |
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sqrt(head_dim) | |
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\ | |
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\ | |
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\ | |
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matmul |
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sqrt(head_dim) | |
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\ | |
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\ | |
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\ | |
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div |
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(mask) |
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| /
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softmax /
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| /
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(mask) /
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| /
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softmax /
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| /
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matmul
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permute
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reshape
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output
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*/
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/*
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342
onnxruntime/core/providers/dnnl/subgraph/dnnl_reduce.cc
Normal file
342
onnxruntime/core/providers/dnnl/subgraph/dnnl_reduce.cc
Normal file
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@ -0,0 +1,342 @@
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// Copyright(C) 2021 Intel Corporation
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// Licensed under the MIT License
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#include "dnnl_reduce.h"
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#include "dnnl_subgraph.h"
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#include "dnnl_subgraph_primitive.h"
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#include "core/providers/common.h"
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namespace onnxruntime {
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namespace ort_dnnl {
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DnnlReduce::DnnlReduce() {}
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// assume all dims are available
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void DnnlReduce::CreatePrimitive(DnnlSubgraphPrimitive& sp, DnnlNode& node) {
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using namespace dnnl;
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// get the engine, currently only support either single gpu or single cpu device
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auto dnnl_engine = sp.GetEngine();
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enum ReduceOp {
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ReduceL1,
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ReduceL2,
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ReduceLogSum,
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ReduceLogSumExp,
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ReduceMax,
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ReduceMean,
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ReduceMin,
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ReduceProd,
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ReduceSum,
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ReduceSumSquare
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};
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ReduceOp reduce_op = ReduceSum;
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dnnl::algorithm algo = dnnl::algorithm::reduction_sum;
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if (node.OpType() == "ReduceL1") {
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reduce_op = ReduceL1;
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algo = dnnl::algorithm::reduction_norm_lp_power_p_sum;
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} else if (node.OpType() == "ReduceL2") {
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reduce_op = ReduceL2;
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algo = dnnl::algorithm::reduction_norm_lp_sum;
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} else if(node.OpType() == "ReduceLogSum") {
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reduce_op = ReduceLogSum;
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algo = dnnl::algorithm::reduction_sum;
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} else if(node.OpType() == "ReduceLogSumExp") {
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reduce_op = ReduceLogSumExp;
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algo = dnnl::algorithm::reduction_sum;
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} else if (node.OpType() == "ReduceMax") {
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reduce_op = ReduceMax;
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algo = dnnl::algorithm::reduction_max;
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} else if (node.OpType() == "ReduceMean") {
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reduce_op = ReduceMean;
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algo = dnnl::algorithm::reduction_mean;
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} else if (node.OpType() == "ReduceMin") {
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reduce_op = ReduceMin;
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algo = dnnl::algorithm::reduction_min;
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} else if (node.OpType() == "ReduceProd") {
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reduce_op = ReduceProd;
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algo = dnnl::algorithm::reduction_mul;
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} else if (node.OpType() == "ReduceSum") {
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reduce_op = ReduceSum;
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algo = dnnl::algorithm::reduction_sum;
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} else if (node.OpType() == "ReduceSumSquare") {
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reduce_op = ReduceSumSquare;
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algo = dnnl::algorithm::reduction_sum;
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}
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auto opset = node.SinceVersion();
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dnnl::memory::dims axes;
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if (reduce_op == ReduceSum) {
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// in ReduceSum opset older than version 13 the Axes came in as an attribute
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// after version 13 the axis is an optional tensor input.
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if (opset < 13) {
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axes = ReadAxes(node);
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} else {
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if (node.Input(IN_AXES).Exists()) {
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auto axes_mem = sp.GetMemory(node.Input(IN_AXES));
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dnnl::memory::dims axes_dims = axes_mem.get_desc().dims();
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int64_t* p_axes_data = (int64_t*)axes_mem.get_data_handle();
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axes = std::vector<int64_t>(p_axes_data, p_axes_data + axes_dims[0]);
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}
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}
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} else {
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axes = ReadAxes(node);
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}
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auto src_mem = sp.GetMemoryInOrtFormat(node.Input(IN_DATA), dnnl_engine);
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auto src_md = src_mem.get_desc();
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if (reduce_op == ReduceSum) {
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// If axes is empty and the noop_with_empty_axes != 0 return the IN_DATA as the output.
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if (axes.empty()) {
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if (NoOpWithEmptyAxes(node)) {
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sp.SetMemory(node.Output(OUT_REDUCED), src_mem, true);
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return;
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}
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}
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}
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//We need to calculate output tensor shape
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//First we initialize it with input shape and then we modify it based on the attribute values
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//This is because the DNNL primitive functionality is determined by the input and output shapes.
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auto src_dims = src_md.dims();
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auto ndim = src_dims.size();
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// convert negative axis values to the positive axis
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for (size_t i = 0; i < axes.size(); ++i) {
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axes[i] = HandleNegativeAxis(axes[i], ndim);
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}
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// Handle out of order and repeating dims.
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std::sort(axes.begin(), axes.end());
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axes.erase(std::unique(axes.begin(), axes.end()), axes.end());
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// if axes is empty change all non-zero shape dims to 1
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if (axes.size() == 0) {
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for (size_t i = 0; i < ndim; ++i) {
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if (src_dims[i] != 0)
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src_dims[i] = 1;
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}
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//If there is axis, then make the respective dimensions 1, keeping the other dimension values untouched.
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} else {
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for (size_t i = 0; i < axes.size(); i++) {
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if (src_dims[axes[i]] != 0)
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src_dims[axes[i]] = 1;
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}
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}
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auto dst_shape = TensorShape(src_dims.data(), ndim);
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dnnl::memory::dims dst_dims_mkl(dst_shape.GetDims().begin(), dst_shape.GetDims().end());
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auto dst_md = dnnl::memory::desc({dst_dims_mkl}, src_md.data_type(), dnnl::memory::format_tag::any);
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// Check to see if the destination shape and source shape are the same.
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bool src_and_dst_dims_equal = true;
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if (src_md.dims().size() == dst_md.dims().size()) {
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for (size_t i = 0; i < src_md.dims().size(); ++i) {
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if (src_md.dims()[i] != dst_md.dims()[i]) {
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src_and_dst_dims_equal = false;
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break;
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}
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}
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}
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/*
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* OneDNN will return an error if a reduction algorithm is called that does not result in a
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* shape reduction. For this reason we have code paths that are taken if the source dimensions and
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* destination dimensions are equal that will not call the reduction op.
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*
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* "ReduceLogSum" is equivelent to Log(ReduceSum(input))
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* - if the reduction op is called then the eltwise_log post op will added to the reduction primitive.
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* - if the reduction op is not called then the eltwise_log primitive is added as its own primitive
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* - NOTE "ReduceLogSum" follows the code flow of "All other reduce ops" with the exception of the added
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* post op and an extra check if src_dims == dest_dims.
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* "ReduceLogSumExp" is equivelent to Log(ReduceSum(Exp(input)))
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* - if the reduction op is called then the eltwise_exp primitive is added before the reduction op
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* the eletwise_log post op will be added to the reduction primitive
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* - if the reduction op is not called then the input is not modified since Log(Exp(input) == input
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* "ReduceSumSquare" is equivelent to ReduceSum(Square(input))
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* - the eltwise_square primitive is added before the reduction op
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* - if the source and destination dimensions are not equal the reduction op is called
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* All other reduce ops
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* - if the source and destination dimensions are not equal call the reduction op
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* - otherwise don't modify the input.
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*
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* After the Reduction check the "KeepDims" attribute
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* - if KeepDims == 1 the output is the result of the reduction op
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* - if KeepDims == 0 we perform a squeeze operation on the output of the reduction op
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* - NOTE: Even if reduction op is not called KeepDims attribute can result in the output being modified
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*/
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dnnl::memory reduce_src_mem;
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dnnl::memory reduce_dst_mem;
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dnnl::primitive_attr dnnl_primitive_attr;
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if ((reduce_op == ReduceLogSum || reduce_op == ReduceLogSumExp ) && !src_and_dst_dims_equal) {
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dnnl::post_ops eltwise_post_op;
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eltwise_post_op.append_eltwise(1.0f, dnnl::algorithm::eltwise_log, 1.0f, 1.0f);
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dnnl_primitive_attr.set_post_ops(eltwise_post_op);
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}
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if (reduce_op == ReduceLogSumExp) {
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if (!src_and_dst_dims_equal) {
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auto elementwise_desc = dnnl::eltwise_forward::desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::eltwise_exp, src_md);
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auto elementwise_pd = dnnl::eltwise_forward::primitive_desc(elementwise_desc, dnnl_engine);
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auto elementwise_dst_mem = dnnl::memory(elementwise_pd.dst_desc(), dnnl_engine);
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auto elemenwise_primitive = dnnl::eltwise_forward(elementwise_pd);
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sp.AddPrimitive(elemenwise_primitive, {{DNNL_ARG_SRC, src_mem},
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||||
{DNNL_ARG_DST, elementwise_dst_mem}});
|
||||
auto reduce_desc = dnnl::reduction::desc(algo, src_md, dst_md, 0.f, 0.f);
|
||||
auto reduce_pd = dnnl::reduction::primitive_desc(reduce_desc, dnnl_primitive_attr, dnnl_engine);
|
||||
|
||||
reduce_dst_mem = dnnl::memory(reduce_pd.dst_desc(), dnnl_engine);
|
||||
|
||||
auto reducemean_op = dnnl::reduction(reduce_pd);
|
||||
sp.AddPrimitive(reducemean_op, {{DNNL_ARG_SRC, elementwise_dst_mem},
|
||||
{DNNL_ARG_DST, reduce_dst_mem}});
|
||||
} else {
|
||||
reduce_dst_mem = src_mem;
|
||||
}
|
||||
} else if(reduce_op == ReduceSumSquare) {
|
||||
auto elementwise_desc = dnnl::eltwise_forward::desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::eltwise_square, src_md);
|
||||
auto elementwise_pd = dnnl::eltwise_forward::primitive_desc(elementwise_desc, dnnl_engine);
|
||||
|
||||
auto elementwise_dst_mem = dnnl::memory(elementwise_pd.dst_desc(), dnnl_engine);
|
||||
|
||||
auto elemenwise_primitive = dnnl::eltwise_forward(elementwise_pd);
|
||||
sp.AddPrimitive(elemenwise_primitive, {{DNNL_ARG_SRC, src_mem},
|
||||
{DNNL_ARG_DST, elementwise_dst_mem}});
|
||||
if (!src_and_dst_dims_equal) {
|
||||
auto reduce_desc = dnnl::reduction::desc(algo, src_md, dst_md, 0.f, 0.f);
|
||||
auto reduce_pd = dnnl::reduction::primitive_desc(reduce_desc, dnnl_engine);
|
||||
|
||||
reduce_dst_mem = dnnl::memory(reduce_pd.dst_desc(), dnnl_engine);
|
||||
|
||||
auto reducemean_op = dnnl::reduction(reduce_pd);
|
||||
sp.AddPrimitive(reducemean_op, {{DNNL_ARG_SRC, elementwise_dst_mem},
|
||||
{DNNL_ARG_DST, reduce_dst_mem}});
|
||||
} else {
|
||||
reduce_dst_mem = elementwise_dst_mem;
|
||||
}
|
||||
} else {
|
||||
// If calculated source and destination shape are the same do not do the reduction operation.
|
||||
if (!src_and_dst_dims_equal) {
|
||||
float p_val = 0.f;
|
||||
if (reduce_op == ReduceL1) {
|
||||
p_val = 1.0f;
|
||||
} else if (reduce_op == ReduceL2) {
|
||||
p_val = 2.0f;
|
||||
}
|
||||
|
||||
auto reduce_desc = dnnl::reduction::desc(algo, src_md, dst_md, p_val, 0.f);
|
||||
auto reduce_pd = dnnl::reduction::primitive_desc(reduce_desc, dnnl_primitive_attr, dnnl_engine);
|
||||
|
||||
// If using GPU this will move the memory from the CPU to the GPU.
|
||||
reduce_src_mem = sp.GetMemoryAndReshape(node.Input(IN_DATA), reduce_pd.src_desc(), dnnl_engine);
|
||||
reduce_dst_mem = dnnl::memory(reduce_pd.dst_desc(), dnnl_engine);
|
||||
|
||||
auto reducemean_op = dnnl::reduction(reduce_pd);
|
||||
sp.AddPrimitive(reducemean_op, {{DNNL_ARG_SRC, reduce_src_mem},
|
||||
{DNNL_ARG_DST, reduce_dst_mem}});
|
||||
} else {
|
||||
if (reduce_op == ReduceLogSum) {
|
||||
auto elementwise_desc = dnnl::eltwise_forward::desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::eltwise_log, src_md);
|
||||
auto elementwise_pd = dnnl::eltwise_forward::primitive_desc(elementwise_desc, dnnl_engine);
|
||||
|
||||
reduce_dst_mem = dnnl::memory(elementwise_pd.dst_desc(), dnnl_engine);
|
||||
|
||||
auto elemenwise_primitive = dnnl::eltwise_forward(elementwise_pd);
|
||||
sp.AddPrimitive(elemenwise_primitive, {{DNNL_ARG_SRC, src_mem},
|
||||
{DNNL_ARG_DST, reduce_dst_mem}});
|
||||
} else {
|
||||
reduce_dst_mem = src_mem;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// If keepdims != 0 set the output to the reduce op results
|
||||
auto keepdims = Keepdims(node);
|
||||
if (keepdims) {
|
||||
if (src_and_dst_dims_equal) {
|
||||
sp.SetMemory(node.Output(OUT_REDUCED), reduce_dst_mem, true);
|
||||
} else {
|
||||
sp.SetMemory(node.Output(OUT_REDUCED), reduce_dst_mem);
|
||||
}
|
||||
// if keepdims == 0 we do a squeeze operation on reduce output shape.
|
||||
} else {
|
||||
std::vector<int64_t> output_shape;
|
||||
size_t j = 0;
|
||||
for (size_t i = 0; i < ndim; ++i) {
|
||||
if ((j < axes.size() && axes[j] == static_cast<int64_t>(i)) ||
|
||||
(axes.size() == 0 && src_dims[i] == 1)) {
|
||||
ORT_ENFORCE(src_dims[i] == 1, "Dimension of input ", i, " must be 1 instead of ", src_dims[i],
|
||||
". shape=", src_dims);
|
||||
++j;
|
||||
continue;
|
||||
}
|
||||
|
||||
if ((j < axes.size() && axes[j] == static_cast<int64_t>(i) && src_dims[i] == 0) ||
|
||||
(axes.size() == 0 && src_dims[i] == 0)) {
|
||||
ORT_ENFORCE(keepdims,
|
||||
"Can't reduce on dim with value of 0 if 'keepdims' is false. "
|
||||
"Invalid output shape would be produced. input_shape:",
|
||||
TensorShape(src_md.dims()));
|
||||
}
|
||||
output_shape.push_back(src_dims[i]);
|
||||
}
|
||||
|
||||
// OneDNN does not support scalar output if the output shape is {} change it to {1}
|
||||
bool is_scalar_output = false;
|
||||
if (output_shape.empty()) {
|
||||
output_shape.push_back(1);
|
||||
is_scalar_output = true;
|
||||
}
|
||||
dnnl::memory::desc squeeze_md(output_shape, node.Input(IN_DATA).Type(), sp.GetDnnlFormat(output_shape.size()));
|
||||
dnnl::memory squeeze_mem = dnnl::memory(squeeze_md, dnnl_engine, nullptr);
|
||||
// if the src and dst dims are equal then we will have a valid data handle here.
|
||||
// Otherwise we must get the data handle at runtime using the AddReshape function.
|
||||
// reading the data handle directy is more efficent if is it possible.
|
||||
if (!src_and_dst_dims_equal) {
|
||||
squeeze_mem.set_data_handle(reduce_dst_mem.get_data_handle());
|
||||
} else {
|
||||
sp.AddReshape(reduce_dst_mem, squeeze_mem);
|
||||
}
|
||||
sp.SetMemory(node.Output(OUT_REDUCED), squeeze_mem, true, is_scalar_output);
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<int64_t> DnnlReduce::ReadAxes(DnnlNode& node) {
|
||||
auto attr = node.Attributes().find("axes");
|
||||
std::vector<int64_t> axes;
|
||||
if (attr != node.Attributes().end()) {
|
||||
auto& proto = attr->second();
|
||||
axes.reserve(proto.ints_size());
|
||||
for (int i = 0; i < proto.ints_size(); i++) {
|
||||
axes.push_back(proto.ints(i));
|
||||
}
|
||||
}
|
||||
return axes;
|
||||
}
|
||||
|
||||
bool DnnlReduce::Keepdims(DnnlNode& node) {
|
||||
auto attr = node.Attributes().find("keepdims");
|
||||
if (attr != node.Attributes().end() &&
|
||||
attr->second().i() == 0) {
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool DnnlReduce::NoOpWithEmptyAxes(DnnlNode& node) {
|
||||
auto attr = node.Attributes().find("noop_with_empty_axes");
|
||||
if (attr != node.Attributes().end() &&
|
||||
attr->second().i() != 0) {
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
} // namespace ort_dnnl
|
||||
} // namespace onnxruntime
|
||||
|
|
@ -8,18 +8,21 @@
|
|||
namespace onnxruntime {
|
||||
namespace ort_dnnl {
|
||||
|
||||
class DnnlReduceMean {
|
||||
class DnnlReduce {
|
||||
public:
|
||||
enum InputTensors : int {
|
||||
IN_X = 0
|
||||
IN_DATA = 0,
|
||||
IN_AXES = 1
|
||||
};
|
||||
|
||||
enum OutputTensors : int {
|
||||
OUT_Y = 0
|
||||
OUT_REDUCED = 0
|
||||
};
|
||||
DnnlReduceMean();
|
||||
DnnlReduce();
|
||||
void CreatePrimitive(DnnlSubgraphPrimitive& sp, DnnlNode& node);
|
||||
std::vector<int64_t> ReadAxes(DnnlNode& node);
|
||||
bool Keepdims(DnnlNode& node);
|
||||
bool NoOpWithEmptyAxes(DnnlNode& node);
|
||||
};
|
||||
|
||||
} // namespace ort_dnnl
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
// Copyright(C) 2021 Intel Corporation
|
||||
// Licensed under the MIT License
|
||||
#include "dnnl_reducemean.h"
|
||||
#include "dnnl_subgraph.h"
|
||||
#include "dnnl_subgraph_primitive.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
namespace ort_dnnl {
|
||||
|
||||
|
||||
DnnlReduceMean::DnnlReduceMean() {}
|
||||
|
||||
// assume all dims are available
|
||||
void DnnlReduceMean::CreatePrimitive(DnnlSubgraphPrimitive& sp, DnnlNode& node) {
|
||||
|
||||
using namespace dnnl;
|
||||
|
||||
// get the engine, currently only support either single gpu or single cpu device
|
||||
auto dnnl_engine = sp.GetEngine();
|
||||
|
||||
auto axes = ReadAxes(node);
|
||||
|
||||
auto reducemean_src_mem = sp.GetMemory(node.Input(IN_X));
|
||||
auto src_md = reducemean_src_mem.get_desc();
|
||||
|
||||
//We need to calculate output tensor shape
|
||||
//First we initialize it with input shape and then we modify it based on the attribute values
|
||||
//This is because the DNNL primitive functionality is determined by the input and output shapes.
|
||||
|
||||
auto src_dims = src_md.dims();
|
||||
auto ndim = src_dims.size();
|
||||
for (unsigned long int i = 0; i < ndim; i++) {
|
||||
if (axes.size() == 0)
|
||||
src_dims[i] = 1; //If no axis is specified, then output shape is just all 1's
|
||||
else if (i < axes.size()) {
|
||||
if (axes[i] < 0)
|
||||
src_dims[ndim + axes[i]] = 1;
|
||||
else
|
||||
src_dims[axes[i]] = 1;
|
||||
} //If there is axis, then make the respective dimensions 1, keeping the other dimension values untouched.
|
||||
}
|
||||
|
||||
auto dst_shape = TensorShape(src_dims.data(), ndim);
|
||||
dnnl::memory::dims dst_dims_mkl(dst_shape.GetDims().begin(), dst_shape.GetDims().end());
|
||||
auto dst_md = dnnl::memory::desc({dst_dims_mkl}, src_md.data_type(), dnnl::memory::format_tag::any);
|
||||
|
||||
auto reducemean_desc = dnnl::reduction::desc(dnnl::algorithm::reduction_mean, src_md, dst_md, 0.f, 0.f);
|
||||
auto reducemean_pd = dnnl::reduction::primitive_desc(reducemean_desc, dnnl_engine);
|
||||
|
||||
// If using GPU this will move the memory from the CPU to the GPU.
|
||||
reducemean_src_mem = sp.GetMemoryAndReshape(node.Input(IN_X), reducemean_pd.src_desc(), dnnl_engine);
|
||||
auto reducemean_dst_mem = dnnl::memory(reducemean_pd.dst_desc(), dnnl_engine);
|
||||
|
||||
auto reducemean_op = dnnl::reduction(reducemean_pd);
|
||||
sp.AddPrimitive(reducemean_op, {{DNNL_ARG_SRC, reducemean_src_mem},
|
||||
{DNNL_ARG_DST, reducemean_dst_mem}});
|
||||
|
||||
sp.SetMemory(node.Output(OUT_Y), reducemean_dst_mem);
|
||||
}
|
||||
|
||||
std::vector<int64_t> DnnlReduceMean::ReadAxes(DnnlNode& node) {
|
||||
auto attr = node.Attributes().find("axes");
|
||||
std::vector<int64_t> axes;
|
||||
if (attr != node.Attributes().end()) {
|
||||
auto& proto = attr->second();
|
||||
axes.reserve(proto.ints_size());
|
||||
for (int i = 0; i < proto.ints_size(); i++) {
|
||||
axes.push_back(proto.ints(i));
|
||||
}
|
||||
}
|
||||
return axes;
|
||||
}
|
||||
|
||||
} // namespace ort_dnnl
|
||||
} // namespace onnxruntime
|
||||
|
|
@ -175,6 +175,10 @@ NodeAttributes& DnnlNode::Attributes() {
|
|||
return *attr_;
|
||||
}
|
||||
|
||||
int DnnlNode::SinceVersion() {
|
||||
return onnx_node_->SinceVersion();
|
||||
}
|
||||
|
||||
DnnlSubgraph::DnnlSubgraph(const GraphViewer& graph_viewer) : graph_viewer_(graph_viewer) {
|
||||
Build();
|
||||
is_dynamic_ = false;
|
||||
|
|
|
|||
|
|
@ -76,6 +76,7 @@ class DnnlNode {
|
|||
NodeAttributes& Attributes();
|
||||
std::vector<DnnlTensor*>& Inputs();
|
||||
std::vector<DnnlTensor*>& Outputs();
|
||||
int SinceVersion();
|
||||
|
||||
private:
|
||||
const Node* onnx_node_ = nullptr;
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@
|
|||
#include "dnnl_pool.h"
|
||||
#include "dnnl_pow.h"
|
||||
#include "dnnl_qattention.h"
|
||||
#include "dnnl_reducemean.h"
|
||||
#include "dnnl_reduce.h"
|
||||
#include "dnnl_reshape.h"
|
||||
#include "dnnl_softmax.h"
|
||||
#include "dnnl_softmaxgrad.h"
|
||||
|
|
@ -126,6 +126,7 @@ void DnnlSubgraphPrimitive::AddKernels() {
|
|||
std::unordered_set<std::string> binary_ops = {"Add", "Div", "Mul", "Sub"};
|
||||
std::unordered_set<std::string> elementwise_ops = {"Abs", "Elu", "Exp", "LeakyRelu", "Log", "Relu", "Round", "Sigmoid", "Softplus", "Sqrt", "Tanh"};
|
||||
std::unordered_set<std::string> pool_ops = {"AveragePool", "GlobalAveragePool", "GlobalMaxPool", "MaxPool"};
|
||||
std::unordered_set<std::string> reduce_ops = {"ReduceL1", "ReduceL2", "ReduceLogSum", "ReduceLogSumExp", "ReduceMax", "ReduceMean", "ReduceMin", "ReduceProd", "ReduceSum", "ReduceSumSquare"};
|
||||
|
||||
auto indices = subgraph_->GetDnnlNodesInTopologicalOrder();
|
||||
for (auto index : indices) {
|
||||
|
|
@ -158,8 +159,8 @@ void DnnlSubgraphPrimitive::AddKernels() {
|
|||
DnnlPow().CreatePrimitive(*this, node);
|
||||
} else if (node.OpType() == "QAttention") {
|
||||
DnnlQAttention().CreatePrimitive(*this, node);
|
||||
} else if (node.OpType() == "ReduceMean") {
|
||||
DnnlReduceMean().CreatePrimitive(*this, node);
|
||||
} else if (reduce_ops.count(node.OpType())) {
|
||||
DnnlReduce().CreatePrimitive(*this, node);
|
||||
} else if (node.OpType() == "Reshape") {
|
||||
DnnlReshape().CreatePrimitive(*this, node);
|
||||
} else if (node.OpType() == "Softmax") {
|
||||
|
|
|
|||
|
|
@ -557,6 +557,28 @@ TEST(ReductionOpTest, ReduceLogSumExp_double) {
|
|||
test.Run();
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceLogSumExp_float_no_reduction) {
|
||||
OpTester test("ReduceLogSumExp");
|
||||
test.AddAttribute("axes", std::vector<int64_t>{0});
|
||||
test.AddAttribute("keepdims", (int64_t)0);
|
||||
test.AddInput<float>("data", {1, 2, 2},
|
||||
{1.0f, 2.0f,
|
||||
3.0f, 4.0f});
|
||||
test.AddOutput<float>("reduced", {2, 2}, {1.f, 2.f, 3.f, 4.f});
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceLogSumExp_float_no_reduction_keepdims) {
|
||||
OpTester test("ReduceLogSumExp");
|
||||
test.AddAttribute("axes", std::vector<int64_t>{0});
|
||||
test.AddAttribute("keepdims", (int64_t)1);
|
||||
test.AddInput<float>("data", {1, 2, 2},
|
||||
{1.0f, 2.0f,
|
||||
3.0f, 4.0f});
|
||||
test.AddOutput<float>("reduced", {1, 2, 2}, {1.f, 2.f, 3.f, 4.f});
|
||||
test.Run();
|
||||
}
|
||||
|
||||
#if defined(USE_CUDA) || defined(USE_ROCM)
|
||||
TEST(ReductionOpTest, ReduceLogSumExp_half) {
|
||||
OpTester test("ReduceLogSumExp");
|
||||
|
|
@ -1082,6 +1104,24 @@ TEST(ReductionOpTest, ReduceMean0DTensor_double) {
|
|||
}
|
||||
#endif // !(defined USE_TVM)
|
||||
|
||||
TEST(ReductionOpTest, ReduceMean_keepdims_results_in_noop) {
|
||||
OpTester test("ReduceMean");
|
||||
test.AddAttribute("axes", std::vector<int64_t>{0});
|
||||
test.AddAttribute("keepdims", (int64_t)1);
|
||||
test.AddInput<float>("data", {1, 3}, {1.0, 2.0, 3.0});
|
||||
test.AddOutput<float>("reduced", {1, 3}, {1.0, 2.0, 3.0});
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceMean_keepdims_results_in_shape_change) {
|
||||
OpTester test("ReduceMean");
|
||||
test.AddAttribute("axes", std::vector<int64_t>{0});
|
||||
test.AddAttribute("keepdims", (int64_t)0);
|
||||
test.AddInput<float>("data", {1, 3}, {1.0, 2.0, 3.0});
|
||||
test.AddOutput<float>("reduced", {3}, {1.0, 2.0, 3.0});
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceMin_default_axes_keepdims) {
|
||||
OpTester test("ReduceMin");
|
||||
test.AddAttribute("keepdims", (int64_t)1);
|
||||
|
|
@ -2450,7 +2490,7 @@ TEST(ReductionOpTest, OptimizeShapeForFastReduce_ReduceDimWithZero3) {
|
|||
ASSERT_EQ(fast_kind, FastReduceKind::kKR);
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceDimWithZero3) {
|
||||
TEST(ReductionOpTest, ReduceSum_ReduceDimWithZero3) {
|
||||
auto run = [](OpTester& tester, const std::string& error_msg = "") {
|
||||
auto expect = error_msg.empty() ? OpTester::ExpectResult::kExpectSuccess
|
||||
: OpTester::ExpectResult::kExpectFailure;
|
||||
|
|
|
|||
|
|
@ -443,7 +443,7 @@ TEST(GradientCheckerTest, LogGrad) {
|
|||
|
||||
float max_error;
|
||||
#ifdef USE_DNNL
|
||||
float error_tolerance = 3e-3f;
|
||||
float error_tolerance = 4e-3f;
|
||||
#else
|
||||
float error_tolerance = 1e-3f;
|
||||
#endif
|
||||
|
|
|
|||
Loading…
Reference in a new issue