Cherry pick 3 fixes to rel-1.2.0 (#3158)

* Publish release symbols (#3152)

* Publish release symbols

* Publish symbols if IsReleaseBuild

* Disable delayload for cuda dlls (#3147)

This change fixes #3129. When running onnxruntime as dll on Windows, CUDA does some internal cleanups when process exits. After this, any call to CUDA would cause crash. Delayload makes thread_local destructor to happen after CUDA cleanup, thus the crash.

* Update Gelu Fusion to support new graph pattern from PyTorch 1.4 (#3148)

* update GeluFusion to support pattern from PyTorch 1.4; 
* Fix a bug that missing the check of an edge between mul2 and root.
* update script to fuse gelu from PyTorch 1.4
* Add test for python optimizer

Co-authored-by: Tiago Koji Castro Shibata <[email protected]>
Co-authored-by: KeDengMS <[email protected]>
Co-authored-by: Tianlei Wu <[email protected]>

Author: 4 people

cmake/CMakeLists.txt

@@ -724,12 +724,13 @@ if (onnxruntime_USE_CUDA)
   if (WIN32)
     link_directories(${onnxruntime_CUDNN_HOME}/lib/x64)
 
-    file(GLOB cuda_dll_paths "${onnxruntime_CUDA_HOME}/bin/cublas64_*" "${onnxruntime_CUDA_HOME}/bin/cudart64_*" "${onnxruntime_CUDA_HOME}/bin/curand64_*")
-    set(onnxruntime_DELAYLOAD_FLAGS "${onnxruntime_DELAYLOAD_FLAGS} /DELAYLOAD:cudnn64_7.dll")
-    foreach(cuda_dll_path ${cuda_dll_paths})
-        get_filename_component(cuda_dll_file_name ${cuda_dll_path} NAME)
-        set(onnxruntime_DELAYLOAD_FLAGS "${onnxruntime_DELAYLOAD_FLAGS} /DELAYLOAD:${cuda_dll_file_name}")
-    endforeach(cuda_dll_path)
+    # delayload causes crash on exit, so disable for now
+    #file(GLOB cuda_dll_paths "${onnxruntime_CUDA_HOME}/bin/cublas64_*" "${onnxruntime_CUDA_HOME}/bin/cudart64_*" "${onnxruntime_CUDA_HOME}/bin/curand64_*")
+    #set(onnxruntime_DELAYLOAD_FLAGS "${onnxruntime_DELAYLOAD_FLAGS} /DELAYLOAD:cudnn64_7.dll")
+    #foreach(cuda_dll_path ${cuda_dll_paths})
+    #    get_filename_component(cuda_dll_file_name ${cuda_dll_path} NAME)
+    #    set(onnxruntime_DELAYLOAD_FLAGS "${onnxruntime_DELAYLOAD_FLAGS} /DELAYLOAD:${cuda_dll_file_name}")
+    #endforeach(cuda_dll_path)
 
   else()
     link_directories(${onnxruntime_CUDNN_HOME}/lib64)

onnxruntime/core/optimizer/gelu_fusion.cc

@@ -24,7 +24,25 @@ static bool IsSupportedDataType(const Node& node) {
   }
   return true;
 }
-
+/*
+     This function fuses subgraph like the following into one Gelu node.
+     Subgraph pattern 1:
+                   +-------Mul(0.5)---------------------+
+                   |                                    |
+                   |                                    v
+                [root] --> Div -----> Erf  --> Add --> Mul ==>
+                          (B=1.4142...)        (1)
+
+      Subgraph pattern 2:
+                   +------------------------------------+
+                   |                                    |
+                   |                                    v
+                [root] --> Div -----> Erf  --> Add --> Mul -->Mul ==>
+                          (B=1.4142...)        (1)            (0.5)
+
+       After Fusion:
+                [root]--> Gelu ==>
+*/
 Status GeluFusion::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();
@@ -68,13 +86,9 @@ Status GeluFusion::ApplyImpl(Graph& graph, bool& modified, int graph_level, cons
       continue;
     }
 
-    // Check the other input node(e.g. not of type Erf) is 1.0f.
-    const Node& add_first_input_node = *(add_node.InputNodesBegin());
-    int add_const_input_index = 0;
-    if (add_first_input_node.OpType().compare("Erf") == 0) {
-      add_const_input_index = 1;
-    }
-    const auto& add_const_input_arg = add_node.InputDefs()[add_const_input_index];
+    // Check the other input node (e.g. not the Erf) is 1.0f.
+    bool is_erf_first_input = (add_node.InputDefs()[0]->Name() == erf_node.MutableOutputDefs()[0]->Name());
+    const auto& add_const_input_arg = add_node.InputDefs()[is_erf_first_input ? 1 : 0];
     if (!optimizer_utils::IsInitializerWithExpectedValue(graph, *add_const_input_arg, 1.0f, true)) {
       continue;
     }
@@ -87,35 +101,60 @@ Status GeluFusion::ApplyImpl(Graph& graph, bool& modified, int graph_level, cons
       continue;
     }
 
-    const Node* p_mul2_node = nullptr;
-    for (auto iter = mul_node.InputNodesBegin(); iter != mul_node.InputNodesEnd(); ++iter) {
-      if ((*iter).OpType().compare("Mul") == 0) {
-        // find the other input node of Mul
-        p_mul2_node = &(*iter);
-        break;
+    bool is_pattern_1 = true;
+    const Node* p_mul2_node = graph_utils::FirstParentByType(mul_node, "Mul");
+    if (p_mul2_node != nullptr) {
+      // Match subgraph pattern 1
+      Node& mul2_node = *graph.GetNode(p_mul2_node->Index());
+      if (!graph_utils::IsSupportedOptypeVersionAndDomain(mul2_node, "Mul", {7}) ||
+          mul2_node.GetExecutionProviderType() != div.GetExecutionProviderType() ||
+          mul2_node.GetOutputEdgesCount() != 1 ||
+          !IsSupportedDataType(mul2_node)) {
+        continue;
       }
-    }
-    if (p_mul2_node == nullptr) {
-      continue;
-    }
 
-    Node& mul2_node = *graph.GetNode(p_mul2_node->Index());
-    if (!graph_utils::IsSupportedOptypeVersionAndDomain(mul2_node, "Mul", {7}) ||
-        mul2_node.GetExecutionProviderType() != div.GetExecutionProviderType() ||
-        mul2_node.GetOutputEdgesCount() != 1 ||
-        !IsSupportedDataType(mul2_node)) {
-      continue;
-    }
+      // One input of mul2_node shall be the subgraph input
+      auto root_index = optimizer_utils::IndexOfNodeInput(*p_mul2_node, *div.InputDefs()[0]);
+      if (root_index < 0)
+        continue;
 
-    // Check the other input node(e.g. not of type Add) is 0.5f.
-    int mul_const_input_index = 0;
-    if (mul2_node.InputDefs()[0]->Name() == div.MutableInputDefs()[0]->Name()) {
-      mul_const_input_index = 1;
-    }
+      // Check the other input node is 0.5f.
+      int mul_const_input_index = (root_index == 0 ? 1 : 0);
 
-    const auto& mul_const_input_arg = mul2_node.InputDefs()[mul_const_input_index];
-    if (!optimizer_utils::IsInitializerWithExpectedValue(graph, *mul_const_input_arg, 0.5f, true)) {
-      continue;
+      const auto& mul_const_input_arg = mul2_node.InputDefs()[mul_const_input_index];
+      if (!optimizer_utils::IsInitializerWithExpectedValue(graph, *mul_const_input_arg, 0.5f, true)) {
+        continue;
+      }
+    } else {
+      is_pattern_1 = false;
+
+      // Match subgraph pattern 2
+      if (mul_node.GetOutputEdgesCount() != 1) {
+        continue;
+      }
+
+      // Another input of Mul node shall be the subgraph input.
+      auto root_index = optimizer_utils::IndexOfNodeInput(mul_node, *div.InputDefs()[0]);
+      if (root_index < 0)
+        continue;
+
+      Node& mul2_node = *graph.GetNode(mul_node.OutputNodesBegin()->Index());
+      if (!graph_utils::IsSupportedOptypeVersionAndDomain(mul2_node, "Mul", {7}) ||
+          mul_node.GetExecutionProviderType() != div.GetExecutionProviderType() ||
+          !IsSupportedDataType(mul_node)) {
+        continue;
+      }
+
+      int mul_const_input_index = 0;
+      if (mul2_node.InputDefs()[0]->Name() == mul_node.MutableOutputDefs()[0]->Name()) {
+        mul_const_input_index = 1;
+      }
+      const auto& mul_const_input_arg = mul2_node.InputDefs()[mul_const_input_index];
+      if (!optimizer_utils::IsInitializerWithExpectedValue(graph, *mul_const_input_arg, 0.5f, true)) {
+        continue;
+      }
+
+      p_mul2_node = &mul2_node;
     }
 
     const std::vector<NodeArg*> gelu_input_defs{div.MutableInputDefs()[0]};
@@ -131,7 +170,12 @@ Status GeluFusion::ApplyImpl(Graph& graph, bool& modified, int graph_level, cons
     // move input edges to div (first in list) across to the gelu_node.
     // move output definitions and output edges from mul_node (last in list) to gelu_node.
     // remove all the other nodes.
-    graph_utils::FinalizeNodeFusion(graph, {div, erf_node, add_node, mul2_node, mul_node}, gelu_node);
+    Node& mul2 = *graph.GetNode(p_mul2_node->Index());
+    if (is_pattern_1) {
+      graph_utils::FinalizeNodeFusion(graph, {div, erf_node, add_node, mul2, mul_node}, gelu_node);
+    } else {
+      graph_utils::FinalizeNodeFusion(graph, {div, erf_node, add_node, mul_node, mul2}, gelu_node);
+    }
 
     modified = true;
   }

onnxruntime/python/tools/bert/BertOnnxModel.py

@@ -234,15 +234,24 @@ def fuse_gelu(self, gelu_op_name):
 
     """
      Fuse Gelu with Erf into one node:
-                   +-------Mul(B=0.5)-------------------+
+     Pattern 1:
+                   +-------Mul(0.5)---------------------+
                    |                                    |
                    |                                    v
                 [root] --> Div -----> Erf  --> Add --> Mul -->
-                          (B=1.4142...)       (B=1)
+                          (B=1.4142...)       (1)
+
+      Pattern 2:
+                   +------------------------------------+
+                   |                                    |
+                   |                                    v
+                [root] --> Div -----> Erf  --> Add --> Mul -->Mul -->
+                          (B=1.4142...)       (1)            (0.5)
 
      Note that constant input for Add and Mul could be first or second input: like either A=0.5 or B=0.5 is fine.
     """
     def fuse_gelu_with_elf(self, gelu_op_name):
+        logger.debug(f"start fuse_gelu_with_elf({gelu_op_name})")
         input_name_to_nodes = self.input_name_to_nodes()
         output_name_to_node = self.output_name_to_node()
 
@@ -276,25 +285,38 @@ def fuse_gelu_with_elf(self, gelu_op_name):
             if self.find_constant_input(div, 1.4142, delta=0.001) != 1:
                 continue
 
-            root_node = self.get_parent(div, 0, output_name_to_node)
-            if root_node is None:
-                continue
+            subgraph_input = div.input[0]
 
-            mul_half = self.match_parent(mul_after_erf, 'Mul', None, output_name_to_node)
-            if mul_half is None:
-                continue
+            another = 1 if mul_after_erf.input[0] == add_after_erf.output[0] else 0
+            if subgraph_input == mul_after_erf.input[another]: # pattern 2
+                children = input_name_to_nodes[mul_after_erf.output[0]]
+                if len(children) != 1 or children[0].op_type != 'Mul':
+                    continue
+                mul_half = children[0]
+                if not self.has_constant_input(mul_half, 0.5):
+                    continue
+                subgraph_output = mul_half.output[0]
+            else: # pattern 1
+                mul_half = self.match_parent(mul_after_erf, 'Mul', another, output_name_to_node)
+                if mul_half is None:
+                    continue
 
-            if not self.has_constant_input(mul_half, 0.5):
-                continue
+                if not self.has_constant_input(mul_half, 0.5):
+                    continue
+
+                if subgraph_input not in mul_half.input:
+                    continue
+
+                subgraph_output = mul_after_erf.output[0]
 
             subgraph_nodes = [div, erf_node, add_after_erf, mul_after_erf, mul_half]
-            if not self.is_safe_to_fuse_nodes(subgraph_nodes, [mul_after_erf.output[0]], input_name_to_nodes, output_name_to_node):
+            if not self.is_safe_to_fuse_nodes(subgraph_nodes, [subgraph_output], input_name_to_nodes, output_name_to_node):
                 continue
 
             nodes_to_remove.extend(subgraph_nodes)
             gelu_node = onnx.helper.make_node(gelu_op_name,
-                inputs=[root_node.output[0]],
-                outputs=[mul_after_erf.output[0]])
+                inputs=[subgraph_input],
+                outputs=[subgraph_output])
             gelu_node.domain = "com.microsoft"
             nodes_to_add.append(gelu_node)
 

onnxruntime/python/tools/bert/test_bert_optimization.py

@@ -22,6 +22,7 @@
 BERT_TEST_MODELS = {
     "bert_pytorch_0": 'test_data\\bert_squad_pytorch1.4_opset11\\BertForQuestionAnswering_0.onnx',
     "bert_pytorch_1": 'test_data\\bert_squad_pytorch1.4_opset11\\BertForQuestionAnswering_1.onnx',
+    "bert_squad_pytorch1.4_opset10_fp32": 'test_data\\bert_squad_pytorch1.4_opset10_fp32\\BertForQuestionAnswering.onnx',
     "bert_keras_0": 'test_data\\bert_mrpc_tensorflow2.1_opset10\\TFBertForSequenceClassification_1.onnx'
 }
 
@@ -155,6 +156,13 @@ def test_pytorch_model_0_gpu(self):
             }
         self.verify_node_count(bert_model, expected_node_count)
 
+    def test_pytorch_model_2_cpu(self):
+        input = BERT_TEST_MODELS['bert_squad_pytorch1.4_opset10_fp32']
+        bert_model = optimize_model(input, 'bert', gpu_only=False,
+                                    num_heads=2, hidden_size=8, sequence_length=10,
+                                    input_int32=False, float16=False)
+        self.assertTrue(bert_model.is_fully_optimized())
+
     def test_keras_model_1_cpu(self):
         input = BERT_TEST_MODELS['bert_keras_0']
 

onnxruntime/python/tools/bert/test_data/bert_squad_pytorch1.4_opset10_fp32/BertForQuestionAnswering.onnx

@@ -0,0 +1,3 @@
+

+
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onnxruntime/python/tools/bert/test_data/bert_squad_pytorch1.4_opset10_fp32/test_data_set_0/input_0.pb

@@ -0,0 +1,2 @@
+

+
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