mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-27 20:02:15 +00:00
cf19c3697d
### Description
Run clang-format in CI. Formatted all c/c++, objective-c/c++ files.
Excluded
```
'onnxruntime/core/mlas/**',
'onnxruntime/contrib_ops/cuda/bert/tensorrt_fused_multihead_attention/**',
```
because they contain assembly or is data heavy
### Motivation and Context
Coding style consistency
101 lines
3.1 KiB
C++
101 lines
3.1 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "core/optimizer/div_mul_fusion.h"
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#include "core/graph/graph_utils.h"
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#include "core/optimizer/initializer.h"
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#include "core/optimizer/utils.h"
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using namespace ONNX_NAMESPACE;
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using namespace onnxruntime::common;
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namespace onnxruntime {
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/**
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Transform that fuses two Div -> Mul nodes to a single Div node
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when the first input to Div is 1.
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1 / x1 * x2 -> x2 / x1
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*/
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bool DivMulFusion::SatisfyCondition(const Graph& graph, const Node& node, const logging::Logger&) const {
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if (!graph_utils::IsSupportedOptypeVersionAndDomain(node, "Div", {7, 13, 14}) ||
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node.GetOutputEdgesCount() != 1) {
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return false;
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}
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const auto& next_node = *node.OutputNodesBegin();
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if (!graph_utils::IsSupportedOptypeVersionAndDomain(next_node, "Mul", {7, 13, 14}) ||
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// Make sure the two nodes do not span execution providers.
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next_node.GetExecutionProviderType() != node.GetExecutionProviderType()) {
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return false;
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}
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// Check that the appropriate input to the Div node is a constant.
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if (!graph_utils::NodeArgIsConstant(graph, *node.InputDefs()[0])) {
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return false;
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}
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const auto* initializer = graph_utils::GetConstantInitializer(graph, node.InputDefs()[0]->Name());
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if (!initializer) {
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return false;
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}
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int32_t data_type = initializer->data_type();
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Initializer div_A(*initializer, graph.ModelPath());
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if (div_A.size() > 1) {
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return false;
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}
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switch (data_type) {
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case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
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if (*div_A.data<float>() != 1.f) {
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return false;
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}
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break;
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case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
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if (math::halfToFloat(div_A.data<MLFloat16>()->val) != 1.f) {
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return false;
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}
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break;
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case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE:
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if (*div_A.data<double>() != static_cast<double>(1.f)) {
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return false;
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}
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break;
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case ONNX_NAMESPACE::TensorProto_DataType_INT32:
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if (*div_A.data<int32_t>() != static_cast<int32_t>(1)) {
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return false;
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}
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break;
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case ONNX_NAMESPACE::TensorProto_DataType_INT64:
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if (*div_A.data<int64_t>() != static_cast<int64_t>(1)) {
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return false;
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}
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break;
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default:
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return false;
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}
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if (graph.NodeProducesGraphOutput(node)) {
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return false;
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}
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return true;
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}
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Status DivMulFusion::Apply(Graph& graph, Node& node, RewriteRuleEffect& rule_effect, const logging::Logger&) const {
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auto& div_node = node;
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auto& mul_node = *graph.GetNode(div_node.OutputNodesBegin()->Index()); // get mutable next node
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const auto& div_output = div_node.OutputDefs();
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auto& mul_inputs = mul_node.MutableInputDefs();
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// get other input of mul
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auto& mul_other_input = mul_inputs[0] == div_output[0] ? mul_inputs[1] : mul_inputs[0];
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graph_utils::ReplaceNodeInput(div_node, 0, *mul_other_input);
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// move the output definition and edges from the mul_node to the div_node and delete the mul_node
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graph_utils::FinalizeNodeFusion(graph, div_node, mul_node);
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rule_effect = RewriteRuleEffect::kModifiedRestOfGraph;
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return Status::OK();
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}
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} // namespace onnxruntime
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