GPT2 Gelu Fusion & Test (#3009)

* GPT2 Gelu Fusion & Test

* change header path

* Refine code & add missing test onnx file

* Fix builds & refine float/double/fp16 compare.

* Fix builds

* Add Bias Check and UTs

* Fix build and uts

* Fuse with second formula & test

* minor change

* disable FastGelu to see whether the builds can pass

* Verify where is wrong

* disable for debugging

* Revert "disable for debugging"

This reverts commit 535c081.

* Revert "Verify where is wrong"

This reverts commit ffc43ec.

* disable the transformer for inference currently

* Enable FastGeluFusion and fix segement fault when run bertsquad10.onnx test

* Add more Unit tests convering Gelu subgraph use graph input/output

(cherry picked from commit 0739ab985240c6d9acdb8f0afd40c5fb316166af)

* Mode Bias Fusion in BiasGelu.cc

Co-authored-by: Changming Sun <chasun@microsoft.com>

onnxruntime/core/optimizer/bias_gelu_fusion.cc

@@ -64,20 +64,28 @@ Status BiasGelu::ApplyImpl(Graph& graph, bool& modified, int graph_level, const
     }
 
     const Node& next_node = (*next_node_itr);
-    if (!graph_utils::IsSupportedOptypeVersionAndDomain(next_node, "Gelu", {1}, kMSDomain) ||
+    if (!(graph_utils::IsSupportedOptypeVersionAndDomain(next_node, "Gelu", {1}, kMSDomain) ||
+          graph_utils::IsSupportedOptypeVersionAndDomain(next_node, "FastGelu", {1}, kMSDomain)) ||
         next_node.GetExecutionProviderType() != node.GetExecutionProviderType()) {
       continue;
     }
 
+    bool is_fast_gelu = next_node.OpType().compare("FastGelu") == 0;
+    if (is_fast_gelu && next_node.InputDefs().size() > 1) {
+      continue;
+    }
+
     if (!graph.GetNodeOutputsInGraphOutputs(node).empty()) {
       continue;
     }
 
     Node& add_node = node;
     Node& gelu_node = const_cast<Node&>(next_node);
+    std::string op_type = "BiasGelu";
+    if (is_fast_gelu) op_type = "FastGelu";
 
-    Node& gelu_add_fusion_node = graph.AddNode(graph.GenerateNodeName("BiasGelu"),
-                                               "BiasGelu",
+    Node& gelu_add_fusion_node = graph.AddNode(graph.GenerateNodeName(op_type),
+                                               op_type,
                                                "fused Add and Gelu",
                                                gelu_input,
                                                {},

onnxruntime/core/optimizer/fast_gelu_fusion.cc

@@ -0,0 +1,244 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/optimizer/initializer.h"
+#include "core/optimizer/fast_gelu_fusion.h"
+#include "core/optimizer/utils.h"
+#include "core/graph/graph_utils.h"
+#include "float.h"
+#include <deque>
+
+using namespace ONNX_NAMESPACE;
+using namespace onnxruntime::common;
+namespace onnxruntime {
+
+// FastGelu supports limited data types.
+static std::vector<std::string> supported_data_types{"tensor(float16)", "tensor(float)"};
+
+static bool CheckNode(const Node& node, const std::string& op_name, int32_t opset_version, ProviderType provider,
+  bool require_single_output=false){
+  return graph_utils::IsSupportedOptypeVersionAndDomain(node, op_name, {opset_version}) &&
+        node.GetExecutionProviderType() == provider &&
+        optimizer_utils::IsSupportedDataType(node, supported_data_types) &&
+        (!require_single_output || node.GetOutputEdgesCount() == 1);
+}
+
+MatchResult FastGeluFusion::CheckFirstFormula(Graph& graph, Node& mul1_node, 
+  std::vector<std::reference_wrapper<Node>>& nodes_to_fuse) const {
+  MatchResult matchResult{false, nullptr, nullptr};
+  if (!graph_utils::IsSupportedOptypeVersionAndDomain(mul1_node, "Mul", {7}) ||
+      !graph_utils::IsSupportedProvider(mul1_node, GetCompatibleExecutionProviders()) ||
+      mul1_node.GetOutputEdgesCount() != 1 ||
+      !optimizer_utils::IsSupportedDataType(mul1_node, supported_data_types)) {
+    return matchResult;
+  }
+
+  int32_t input_index = -1;
+  const float mul_val = 0.044715f;
+  for (auto i = 0; i < 2; i++) {
+    if (optimizer_utils::IsInitializerWithExpectedValue(graph, *(mul1_node.InputDefs()[i]), mul_val, true)){
+      input_index = i;
+      break;
+    }
+  }
+
+  if (input_index == -1) return matchResult;
+
+  NodeArg* gelu_without_bias_input_arg = mul1_node.MutableInputDefs()[(input_index + 1) % 2];
+  nodes_to_fuse.push_back(mul1_node);
+
+
+  Node& mul2_node = *graph.GetNode(mul1_node.OutputNodesBegin()->Index());
+  input_index = optimizer_utils::IndexOfNodeInput(mul2_node, *mul1_node.MutableOutputDefs()[0]);
+  if (!CheckNode(mul2_node, "Mul", 7,  mul1_node.GetExecutionProviderType(), true) ||
+      mul2_node.MutableInputDefs()[(input_index + 1) % 2]->Name() != gelu_without_bias_input_arg->Name()) {
+    return matchResult;;
+  }
+  nodes_to_fuse.push_back(mul2_node);
+
+
+  Node& add1_node = *graph.GetNode(mul2_node.OutputNodesBegin()->Index());
+  input_index = optimizer_utils::IndexOfNodeInput(add1_node, *mul2_node.MutableOutputDefs()[0]);
+  if (!CheckNode(add1_node, "Add", 7, mul1_node.GetExecutionProviderType(), true) ||
+      !optimizer_utils::IsInitializerWithExpectedValue(graph, *(add1_node.InputDefs()[(input_index + 1) % 2]), 1.0f, true)) {
+    return matchResult;
+  }
+  nodes_to_fuse.push_back(add1_node);
+
+
+  Node& mul3_node = *graph.GetNode(add1_node.OutputNodesBegin()->Index());
+  if (!CheckNode(mul3_node, "Mul", 7, mul1_node.GetExecutionProviderType(), true)) {
+    return matchResult;
+  }
+  nodes_to_fuse.push_back(mul3_node);
+
+  input_index = optimizer_utils::IndexOfNodeInput(mul3_node, *add1_node.MutableOutputDefs()[0]);
+  const Node* p_mul3_input_node = graph_utils::GetInputNode(mul3_node, (input_index + 1) % 2);
+  if (p_mul3_input_node == nullptr) return matchResult;
+  Node& mul4_node = const_cast<Node&>(*p_mul3_input_node);
+  if (!CheckNode(mul4_node, "Mul", 7, mul1_node.GetExecutionProviderType(), true)) {
+    return matchResult;
+  }
+
+  input_index = -1;
+  const float mul4_val = 0.7978845834732056f;
+  for (auto i = 0; i < 2; i++) {
+    if (optimizer_utils::IsInitializerWithExpectedValue(graph, *(mul4_node.InputDefs()[i]), mul4_val, true)){
+      input_index = i;
+      break;
+    }
+  }
+
+  if (input_index == -1 || mul4_node.InputDefs()[(input_index + 1) % 2]->Name() != gelu_without_bias_input_arg->Name())
+      return matchResult;
+  nodes_to_fuse.push_back(mul4_node);
+
+  matchResult.matched = true;
+  matchResult.gelu_without_bias_input_arg = gelu_without_bias_input_arg;
+  matchResult.tanh_input_node = &mul3_node;
+  return matchResult;
+}
+
+MatchResult FastGeluFusion::CheckSecondFormula(Graph& graph, Node& pow1_node, 
+  std::vector<std::reference_wrapper<Node>>& nodes_to_fuse) const {
+  MatchResult matchResult{false, nullptr, nullptr};
+  if (!graph_utils::IsSupportedOptypeVersionAndDomain(pow1_node, "Pow", {7}) ||
+      !graph_utils::IsSupportedProvider(pow1_node, GetCompatibleExecutionProviders()) ||
+      pow1_node.GetOutputEdgesCount() != 1 ||
+      !optimizer_utils::IsSupportedDataType(pow1_node, supported_data_types)) {
+    return matchResult;
+  }
+
+  if (!optimizer_utils::IsInitializerWithExpectedValue(graph, *(pow1_node.InputDefs()[1]), 3.0f, true)){
+    return matchResult;
+  }
+
+  NodeArg* pow_input_arg = pow1_node.MutableInputDefs()[0];
+  nodes_to_fuse.push_back(pow1_node);
+
+  Node& mul1_node = *graph.GetNode(pow1_node.OutputNodesBegin()->Index());
+  auto input_index = optimizer_utils::IndexOfNodeInput(mul1_node, *pow1_node.MutableOutputDefs()[0]);
+  if (!CheckNode(mul1_node, "Mul", 7,  pow1_node.GetExecutionProviderType(), true) ||
+      !optimizer_utils::IsInitializerWithExpectedValue(graph, *(mul1_node.InputDefs()[(input_index + 1) % 2]),
+        0.044714998453855515f, true)) {
+    return matchResult;
+  }
+  nodes_to_fuse.push_back(mul1_node);
+
+
+  Node& add1_node = *graph.GetNode(mul1_node.OutputNodesBegin()->Index());
+  input_index = optimizer_utils::IndexOfNodeInput(add1_node, *mul1_node.MutableOutputDefs()[0]);
+  if (!CheckNode(add1_node, "Add", 7, pow1_node.GetExecutionProviderType(), true) ||
+      add1_node.MutableInputDefs()[(input_index + 1) % 2]->Name() != pow_input_arg->Name()) {
+    return matchResult;
+  }
+  nodes_to_fuse.push_back(add1_node);
+
+
+  Node& mul2_node = *graph.GetNode(add1_node.OutputNodesBegin()->Index());
+  input_index = optimizer_utils::IndexOfNodeInput(mul2_node, *add1_node.MutableOutputDefs()[0]);
+  if (!CheckNode(mul2_node, "Mul", 7, pow1_node.GetExecutionProviderType(), true) ||
+      !optimizer_utils::IsInitializerWithExpectedValue(graph, *(mul2_node.InputDefs()[(input_index + 1) % 2]),
+        0.7978845834732056f, true)) {
+    return matchResult;
+  }
+  nodes_to_fuse.push_back(mul2_node);
+
+  matchResult.matched = true;
+  matchResult.gelu_without_bias_input_arg = pow_input_arg;
+  matchResult.tanh_input_node = &mul2_node;
+  return matchResult;
+}
+
+Status FastGeluFusion::ApplyImpl(Graph& graph, bool& modified, int graph_level, const logging::Logger& logger) const {
+  GraphViewer graph_viewer(graph);
+  const auto& node_topology_list = graph_viewer.GetNodesInTopologicalOrder();
+
+  for (auto node_index : node_topology_list) {
+    auto* p_node = graph.GetNode(node_index);
+    if (p_node == nullptr)
+      continue;
+
+    Node& node = *p_node;
+    ORT_RETURN_IF_ERROR(Recurse(node, modified, graph_level, logger));
+
+    std::vector<std::reference_wrapper<Node>> nodes_to_fuse;
+    MatchResult matchRet = CheckFirstFormula(graph, node, nodes_to_fuse);
+    if (!matchRet.matched) {
+      nodes_to_fuse.clear();
+      matchRet = CheckSecondFormula(graph, node, nodes_to_fuse);
+
+      if(!matchRet.matched) continue;
+    };
+
+    Node& tanh_node = *graph.GetNode(matchRet.tanh_input_node->OutputNodesBegin()->Index());
+    if (!CheckNode(tanh_node, "Tanh", 6, node.GetExecutionProviderType(), true)) {
+      continue;
+    }
+
+
+    Node& add2_node = *graph.GetNode(tanh_node.OutputNodesBegin()->Index());
+    if (!CheckNode(add2_node, "Add", 7, node.GetExecutionProviderType(), true)) {
+      continue;
+    }
+
+    auto input_index = optimizer_utils::IndexOfNodeInput(add2_node, *tanh_node.MutableOutputDefs()[0]);
+    if (!optimizer_utils::IsInitializerWithExpectedValue(graph, *(add2_node.InputDefs()[(input_index + 1) % 2]), 1.0f, true)) {
+      continue;
+    }
+
+    Node& mul5_node = *graph.GetNode(add2_node.OutputNodesBegin()->Index());
+    // This is the output of the Gelu subgraph, we don't need check it has single edge.
+    if (!CheckNode(mul5_node, "Mul", 7, node.GetExecutionProviderType(), false)) {
+      continue;
+    }
+
+    // ingnore the transformer if Gelu's output is the graph's output.
+    if (!graph.GetNodeOutputsInGraphOutputs(mul5_node).empty()) {
+      continue;
+    }
+
+    input_index = optimizer_utils::IndexOfNodeInput(mul5_node, *add2_node.MutableOutputDefs()[0]);
+    const Node* p_mul5_input_node = graph_utils::GetInputNode(mul5_node, (input_index + 1) % 2);
+    if (p_mul5_input_node == nullptr) continue;
+    Node& mul6_node = const_cast<Node&>(*p_mul5_input_node);
+    if (!CheckNode(mul6_node, "Mul", 7, node.GetExecutionProviderType(), false)) {
+      continue;
+    }
+
+    input_index = -1;
+    for (auto i = 0; i < 2; i++) {
+      if (optimizer_utils::IsInitializerWithExpectedValue(graph, *(mul6_node.InputDefs()[i]), 0.5f, true)){
+        input_index = i;
+        break;
+      }
+    }
+
+    if (input_index == -1 || mul6_node.InputDefs()[(input_index + 1) % 2]->Name() != matchRet.gelu_without_bias_input_arg->Name())
+      continue;
+
+    std::vector<NodeArg*> gelu_input_defs{matchRet.gelu_without_bias_input_arg};
+    nodes_to_fuse.insert(nodes_to_fuse.end(), {tanh_node, add2_node, mul6_node, mul5_node});
+
+    auto type_info = *node.MutableOutputDefs()[0]->TypeAsProto();
+    auto& shape_output = graph.GetOrCreateNodeArg(graph.GenerateNodeArgName("fast_gelu_output"), &type_info);
+    Node& fast_gelu_node = graph.AddNode(graph.GenerateNodeName("GPT2Gelu"),
+                                         "FastGelu",
+                                         "fused GPT2Gelu subgraphs ",
+                                         gelu_input_defs,
+                                         {&shape_output}, {}, kMSDomain);
+
+    // assign provider to this new node, provider should be same as the provider for old node.
+    fast_gelu_node.SetExecutionProviderType(node.GetExecutionProviderType());
+
+    // move input edges to node (first in list) across to the fast_gelu_node.
+    // move output definitions and output edges from mul5_node (last in list) to fast_gelu_node.
+    // remove all nodes.
+    graph_utils::FinalizeNodeFusion(graph, nodes_to_fuse, fast_gelu_node);
+
+    modified = true;
+  }
+
+  return Status::OK();
+}
+}  // namespace onnxruntime

onnxruntime/core/optimizer/fast_gelu_fusion.h

@@ -0,0 +1,39 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+#include "core/optimizer/graph_transformer.h"
+
+namespace onnxruntime {
+
+struct MatchResult {
+  public:
+    bool matched;
+    NodeArg* gelu_without_bias_input_arg; // The Gelu input arg if not considering bias node.
+    Node* tanh_input_node;
+};
+
+/**
+@Class FastGeluFusion
+
+Rewrite graph fusing Gelu activation subgraph to a single Gelu node.
+
+The formula corresponding to Gelu activation subgraph:
+x * 0.5 * (1.0 + tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x))) or
+x * 0.5 * (1.0 + tanh((sqrt(2 / pi) * (x + 0.044715 * pow(x, 3))))), where x is the input.
+
+*/
+class FastGeluFusion : public GraphTransformer {
+ public:
+  FastGeluFusion(const std::unordered_set<std::string>& compatible_execution_providers = {}) noexcept
+      : GraphTransformer("FastGeluFusion", compatible_execution_providers) {}
+
+  Status ApplyImpl(Graph& graph, bool& modified, int graph_level, const logging::Logger& logger) const override;
+
+  MatchResult CheckFirstFormula(Graph& graph, Node& node, std::vector<std::reference_wrapper<Node>>& nodes_to_fuse) const;
+
+  MatchResult CheckSecondFormula(Graph& graph, Node& nodes, std::vector<std::reference_wrapper<Node>>& nodes_to_fuse) const;
+};
+
+}  // namespace onnxruntime

onnxruntime/core/optimizer/graph_transformer_utils.cc

@@ -21,6 +21,7 @@
 #include "core/optimizer/bias_gelu_fusion.h"
 #include "core/optimizer/gelu_fusion.h"
 #include "core/optimizer/gelu_approximation.h"
+#include "core/optimizer/fast_gelu_fusion.h"
 #include "core/optimizer/layer_norm_fusion.h"
 #include "core/optimizer/skip_layer_norm_fusion.h"
 #include "core/optimizer/embed_layer_norm_fusion.h"
@@ -135,6 +136,7 @@ std::vector<std::unique_ptr<GraphTransformer>> GenerateTransformers(TransformerL
 
       std::unordered_set<std::string> cuda_execution_providers = {onnxruntime::kCudaExecutionProvider};
       transformers.emplace_back(onnxruntime::make_unique<GeluApproximation>(cuda_execution_providers));
+      transformers.emplace_back(onnxruntime::make_unique<FastGeluFusion>(cuda_execution_providers));
 #endif
     } break;