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Fuse Attention For One Input bert-base-dynamic Model (#3850)
match additional mask path for attention
This commit is contained in:
liuziyue
GitHub
parent
43b87de7d1
commit
914aaaa1c8
4 changed files with 252 additions and 15 deletions
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@ -220,13 +220,15 @@ static NodeArg* ProcessMask(Graph& graph, NodeArg* mask_input, ProviderType prov
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auto data_type = mask_input->TypeAsProto()->tensor_type().elem_type();
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if (data_type != ONNX_NAMESPACE::TensorProto_DataType_INT64 &&
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data_type != ONNX_NAMESPACE::TensorProto_DataType_INT32) {
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DEBUG_LOG("Mask data type is not int32 or int64");
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data_type != ONNX_NAMESPACE::TensorProto_DataType_INT32 &&
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data_type != ONNX_NAMESPACE::TensorProto_DataType_FLOAT) {
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DEBUG_LOG("Mask data type is not int32 or int64 or float32");
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return nullptr;
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}
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NodeArg* reduce_sum_input = mask_input;
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if (data_type == ONNX_NAMESPACE::TensorProto_DataType_INT64) {
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if (data_type == ONNX_NAMESPACE::TensorProto_DataType_INT64 ||
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data_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT) {
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NodeArg& cast_int32 = CastMaskToInt32(graph, mask_input, provider_type);
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reduce_sum_input = &cast_int32;
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}
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@ -343,7 +345,7 @@ Status AttentionFusion::ApplyImpl(Graph& graph, bool& modified, int graph_level,
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| (0,2,1,3) (0,2,3,1) (perm=0,2,1,3) |
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| \ / | mask_Unsqueeze(axes=2)
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| qk_MatMul | |
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| | [B=2] | [A=1] mask_Cast(to=1)
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| | [B=2] | ([A=1] mask_Cast(to=1))
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| | / | \ /
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| qk_Div | mask_Sub [A=1000]
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| \ | \ /
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@ -465,33 +467,56 @@ bool AttentionFusion::FuseSubGraph(Node& layer_norm, const Node& add_after_layer
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return false;
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}
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// path 2 to find mask
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// path 2 to find mask. Unsqueeze -> Unsqueeze -> (Cast) -> Sub -> Mul -> Add -> Softmax
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// The "Cast" node in parentheses is optional.
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std::vector<graph_utils::EdgeEndToMatch> mask_path{
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{0, 0, "Softmax", {1, 11}, kOnnxDomain},
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{0, 0, "Add", {7}, kOnnxDomain},
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{0, 1, "Mul", {7}, kOnnxDomain},
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{0, 0, "Sub", {7}, kOnnxDomain},
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{0, 1, "Cast", {9}, kOnnxDomain},
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{0, 0, "Unsqueeze", {1, 11}, kOnnxDomain},
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{0, 0, "Unsqueeze", {1, 11}, kOnnxDomain}};
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{0, 0, "Sub", {7}, kOnnxDomain}};
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if (!graph_utils::FindPath(qkv_matmul, true, mask_path, edges, logger)) {
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DEBUG_LOG("Failed to find path for mask");
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return false;
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}
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const Node& softmax = edges[0]->GetNode();
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const Node& mask_add = edges[1]->GetNode();
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const Node& mask_mul = edges[2]->GetNode();
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const Node& mask_sub = edges[3]->GetNode();
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const Node& mask_cast = edges[4]->GetNode();
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const Node& mask_unsqueeze_2 = edges[5]->GetNode();
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const Node& mask_unsqueeze_1 = edges[6]->GetNode();
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// Match optional mask cast node
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Node* p_mask_cast = nullptr;
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Node* p_mask_unsqueeze_2 = nullptr;
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Node* p_mask_unsqueeze_1 = nullptr;
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std::vector<graph_utils::EdgeEndToMatch> mask_path_format_1{
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{0, 1, "Cast", {9}, kOnnxDomain},
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{0, 0, "Unsqueeze", {1, 11}, kOnnxDomain},
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{0, 0, "Unsqueeze", {1, 11}, kOnnxDomain}};
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std::vector<graph_utils::EdgeEndToMatch> mask_path_format_2{
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{0, 1, "Unsqueeze", {1, 11}, kOnnxDomain},
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{0, 0, "Unsqueeze", {1, 11}, kOnnxDomain}};
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if (graph_utils::FindPath(mask_sub, true, mask_path_format_1, edges, logger)) {
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p_mask_cast = const_cast<Node*>(&edges[0]->GetNode());
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p_mask_unsqueeze_2 = const_cast<Node*>(&edges[1]->GetNode());
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p_mask_unsqueeze_1 = const_cast<Node*>(&edges[2]->GetNode());
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} else if (graph_utils::FindPath(mask_sub, true, mask_path_format_2, edges, logger)) {
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p_mask_unsqueeze_2 = const_cast<Node*>(&edges[0]->GetNode());
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p_mask_unsqueeze_1 = const_cast<Node*>(&edges[1]->GetNode());
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} else {
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DEBUG_LOG("Failed to find path for mask");
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return false;
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}
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const Node& mask_unsqueeze_2 = *p_mask_unsqueeze_2;
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const Node& mask_unsqueeze_1 = *p_mask_unsqueeze_1;
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if (softmax.GetOutputEdgesCount() != 1 ||
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mask_add.GetOutputEdgesCount() != 1 ||
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mask_sub.GetOutputEdgesCount() != 1 ||
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mask_cast.GetOutputEdgesCount() != 1 ||
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(p_mask_cast != nullptr && (*p_mask_cast).GetOutputEdgesCount() != 1) ||
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mask_unsqueeze_2.GetOutputEdgesCount() != 1 ||
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mask_unsqueeze_1.GetOutputEdgesCount() != 1) {
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DEBUG_LOG("Output edge count not expected for mask nodes");
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@ -691,7 +716,9 @@ bool AttentionFusion::FuseSubGraph(Node& layer_norm, const Node& add_after_layer
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if (mask_mul.GetOutputEdgesCount() == 1) {
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nodes_to_remove.push_back(mask_mul.Index());
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nodes_to_remove.push_back(mask_sub.Index());
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nodes_to_remove.push_back(mask_cast.Index());
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if (p_mask_cast != nullptr) {
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nodes_to_remove.push_back((*p_mask_cast).Index());
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}
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nodes_to_remove.push_back(mask_unsqueeze_2.Index());
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nodes_to_remove.push_back(mask_unsqueeze_1.Index());
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}
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@ -1437,6 +1437,33 @@ TEST_F(GraphTransformationTests, AttentionFusionInt64Test) {
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ValidateAttention(graph);
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}
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// Test Attention Fusion with float32 mask and no "cast" node in mask path
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TEST_F(GraphTransformationTests, AttentionFusionFloat32Test) {
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auto model_uri = MODEL_FOLDER "fusion/attention_mask_no_cast.onnx";
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std::shared_ptr<Model> p_model;
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ASSERT_STATUS_OK(Model::Load(model_uri, p_model, nullptr, *logger_));
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Graph& graph = p_model->MainGraph();
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onnxruntime::GraphTransformerManager graph_transformation_mgr{5};
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graph_transformation_mgr.Register(onnxruntime::make_unique<AttentionFusion>(), TransformerLevel::Level2);
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auto ret = graph_transformation_mgr.ApplyTransformers(graph, TransformerLevel::Level2, *logger_);
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ASSERT_TRUE(ret.IsOK());
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std::map<std::string, int> op_to_count = CountOpsInGraph(graph);
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EXPECT_EQ(op_to_count["MatMul"], 1);
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EXPECT_EQ(op_to_count["Add"], 2);
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EXPECT_EQ(op_to_count["Transpose"], 0);
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EXPECT_EQ(op_to_count["Reshape"], 0);
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EXPECT_EQ(op_to_count["Mul"], 0);
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EXPECT_EQ(op_to_count["Div"], 0);
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EXPECT_EQ(op_to_count["Sub"], 0);
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EXPECT_EQ(op_to_count["Unsqueeze"], 0);
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EXPECT_EQ(op_to_count["ReduceSum"], 1);
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EXPECT_EQ(op_to_count["Attention"], 1);
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ValidateAttention(graph);
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}
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TEST_F(GraphTransformationTests, GeluFusionTest) {
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auto model_uri = MODEL_FOLDER "fusion/gelu.onnx";
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std::shared_ptr<Model> p_model;
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183
onnxruntime/test/testdata/transform/fusion/attention_gen.py
vendored
Normal file
183
onnxruntime/test/testdata/transform/fusion/attention_gen.py
vendored
Normal file
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@ -0,0 +1,183 @@
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import onnx
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from onnx import helper
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from onnx import TensorProto
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from enum import Enum
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def GenerateModel(model_name):
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nodes = [ # Attention subgraph
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helper.make_node("LayerNormalization", ["input_1", "layer_norm_weight", "layer_norm_bias"],
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["layernorm_out"],
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"layernorm",
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axis=-1,
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epsion=0.000009999999747378752),
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# q nodes
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helper.make_node("MatMul", ["layernorm_out", "matmul_q_weight"], ["matmul_q_out"], "matmul_q"),
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helper.make_node("Add", ["matmul_q_out", "add_q_weight"], ["add_q_out"], "add_q"),
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helper.make_node("Reshape", ["add_q_out", "reshape_weight_1"], ["reshape_q_out"], "reshape_q"),
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helper.make_node("Transpose", ["reshape_q_out"], ["transpose_q_out"], "transpose_q",
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perm=[0,2,1,3]),
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# k nodes
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helper.make_node("MatMul", ["layernorm_out", "matmul_k_weight"], ["matmul_k_out"], "matmul_k"),
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helper.make_node("Add", ["matmul_k_out", "add_k_weight"], ["add_k_out"], "add_k"),
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helper.make_node("Reshape", ["add_k_out", "reshape_weight_1"], ["reshape_k_out"], "reshape_k"),
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helper.make_node("Transpose", ["reshape_k_out"], ["transpose_k_out"], "transpose_k",
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perm=[0,2,3,1]),
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# mask nodes
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helper.make_node("Constant", [], ["mask_input"], "constant",
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value=helper.make_tensor('mask', TensorProto.FLOAT,
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[1, 3], [0.0, 0.0, 0.0])),
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helper.make_node("Unsqueeze", ["mask_input"], ["unsqueeze0_out"], "unsqueeze0", axes=[1]),
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helper.make_node("Unsqueeze", ["unsqueeze0_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[2]),
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helper.make_node("Sub", ["sub_weight", "unsqueeze1_out"], ["sub_out"], "sub"),
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helper.make_node("Mul", ["sub_out", "mul_weight"], ["mul_mask_out"], "mul_mask"),
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# qk nodes
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helper.make_node("MatMul", ["transpose_q_out", "transpose_k_out"], ["matmul_qk_out"], "matmul_qk"),
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helper.make_node("Div", ["matmul_qk_out", "div_weight"], ["div_qk_out"], "div_qk"),
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helper.make_node("Add", ["div_qk_out", "mul_mask_out"], ["add_qk_out"], "add_qk"),
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helper.make_node("Softmax", ["add_qk_out"], ["softmax_qk_out"], "softmax_qk", axis=3),
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# v nodes
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helper.make_node("MatMul", ["layernorm_out", "matmul_v_weight"], ["matmul_v_out"], "matmul_v"),
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helper.make_node("Add", ["matmul_v_out", "add_v_weight"], ["add_v_out"], "add_v"),
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helper.make_node("Reshape", ["add_v_out", "reshape_weight_1"], ["reshape_v_out"], "reshape_v"),
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helper.make_node("Transpose", ["reshape_v_out"], ["transpose_v_out"], "transpose_v",
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perm=[0,2,1,3]),
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# qkv nodes
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helper.make_node("MatMul", ["softmax_qk_out", "transpose_v_out"], ["matmul_qkv_1_out"], "matmul_qkv_1"),
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helper.make_node("Transpose", ["matmul_qkv_1_out"], ["transpose_qkv_out"], "transpose_qkv",
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perm=[0,2,1,3]
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),
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helper.make_node("Reshape", ["transpose_qkv_out", "reshape_weight_2"], ["reshape_qkv_out"], "reshape_qkv"),
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helper.make_node("MatMul", ["reshape_qkv_out", "matmul_qkv_weight"], ["matmul_qkv_2_out"], "matmul_qkv_2"),
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helper.make_node("Add", ["matmul_qkv_2_out", "add_qkv_weight"], ["add_qkv_out"], "add_qkv"),
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helper.make_node("Add", ["add_qkv_out", "layernorm_out"], ["output"], "add"),
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]
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matmul_q_weights = [
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-0.10791015625, -0.04193115234375, 0.09051513671875, 0.025787353515625,
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-0.11572265625, -0.126953125, -0.043304443359375, -0.02984619140625,
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0.033538818359375, -0.05755615234375, -0.04986572265625, -0.01558685302734375,
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-0.0352783203125, 0.03546142578125, 0.05218505859375, 0.005565643310546875,
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-0.05950927734375, 0.0172119140625, 0.06646728515625, 0.046630859375,
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0.031524658203125, 0.048614501953125, -0.11102294921875, -0.018463134765625,
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-0.0352783203125, 0.037200927734375, 0.082763671875, 0.1260986328125,
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-0.1087646484375, 0.00566864013671875, -0.027191162109375, -0.0027103424072265625,
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-0.1256103515625, -0.0245361328125, 0.04437255859375, -0.05267333984375,
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-0.0606689453125, 0.009735107421875, 0.01100921630859375, 0.045928955078125,
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-0.036834716796875, 0.005405426025390625, 0.04571533203125, 0.11767578125,
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0.0286102294921875, -0.01071929931640625, -0.006378173828125, 0.0213470458984375,
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-0.1434326171875, -0.0975341796875, 0.031402587890625, 0.02880859375,
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0.048004150390625, -0.028289794921875, 0.018157958984375, 0.061981201171875,
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-0.126953125, -0.03350830078125, 0.1297607421875, -0.0093841552734375,
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-0.0258026123046875, -0.000560760498046875, 0.1123046875, -0.0560302734375
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]
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matmul_k_weights = [
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0.022125244140625, -0.017730712890625, -0.03265380859375, -0.05108642578125,
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0.0423583984375, 0.112060546875, 0.080810546875, 0.09375,
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-0.043182373046875, -0.05010986328125, -0.063720703125, -0.00824737548828125,
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0.1492919921875, 0.048431396484375, -0.0482177734375, -0.1123046875,
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-0.00719451904296875, -0.0229949951171875, -0.03424072265625, 0.0152435302734375,
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0.023468017578125, 0.0301513671875, -0.04656982421875, -0.043701171875,
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0.040313720703125, 0.00644683837890625, -0.0186614990234375, 0.0261383056640625,
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0.09063720703125, -0.078369140625, -0.05841064453125, -0.0743408203125,
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0.040130615234375, -0.0782470703125, 0.03729248046875, -0.07537841796875,
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-0.0006098747253417969, 0.0285186767578125, -0.0518798828125, -0.01404571533203125,
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-0.08001708984375, 0.015960693359375, -0.0357666015625, -0.048065185546875,
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0.01461029052734375, 0.06365966796875, 0.10125732421875, -0.00481414794921875,
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0.056182861328125, 0.072998046875, -0.06591796875, -0.035064697265625,
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-0.1356201171875, -0.055877685546875, 0.06793212890625, -0.1292724609375,
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0.054901123046875, -0.0021762847900390625, 0.059783935546875, -0.035430908203125,
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0.0528564453125, 0.035125732421875, -0.0186767578125, -0.062286376953125
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]
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matmul_v_weights = [
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-0.03643798828125, 0.02862548828125, 0.039764404296875, 0.06097412109375,
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-0.002288818359375, -0.10797119140625, -0.01171875, 0.041717529296875,
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0.032196044921875, 0.0135650634765625, 0.020233154296875, -0.05084228515625,
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-0.011260986328125, -0.1241455078125, -0.0101165771484375, -0.00490570068359375,
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-0.01361083984375, -0.01454925537109375, -0.000637054443359375, -0.01534271240234375,
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-0.0438232421875, 0.034332275390625, 0.011962890625, -0.0139617919921875,
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0.03363037109375, 0.0265350341796875, 0.039947509765625, -0.0268707275390625,
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0.03900146484375, 0.08172607421875, 0.015625, 0.010986328125,
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0.0240325927734375, -0.029022216796875, 0.01403045654296875, 0.0135650634765625,
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-0.0174102783203125, 0.07305908203125, -0.0231170654296875, 0.011444091796875,
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0.006130218505859375, 0.06268310546875, -0.05902099609375, -0.0109100341796875,
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0.0185089111328125, 0.0161590576171875, 0.0185546875, 0.032440185546875,
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||||
0.0011491775512695312, 0.01153564453125, 0.005832672119140625, -0.0538330078125,
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-0.008056640625, 0.01096343994140625, 0.037811279296875, 0.05902099609375,
|
||||
0.0394287109375, 0.00004678964614868164, -0.03778076171875, 0.004573822021484375,
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-0.0237274169921875, -0.0124969482421875, -0.045013427734375, -0.04217529296875
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]
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matmul_qkv_weights = [
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-0.04888916015625, 0.0143280029296875, 0.066650390625,-0.0343017578125,
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-0.0010356903076171875, -0.00048232078552246094, 0.07470703125, -0.04736328125,
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0.01454925537109375, -0.0086669921875, -0.051971435546875, -0.0201568603515625,
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0.040435791015625, -0.019256591796875, 0.0205078125, 0.0111541748046875,
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0.0071868896484375, -0.0298309326171875, -0.0306549072265625, -0.0225372314453125,
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-0.04193115234375, 0.07073974609375, -0.048065185546875, 0.0198822021484375,
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-0.035552978515625, -0.022796630859375, 0.03839111328125, 0.007099151611328125,
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-0.0080108642578125, -0.0017957687377929688, 0.0266265869140625,-0.028289794921875,
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0.0032901763916015625, 0.0208740234375, -0.01529693603515625, -0.046600341796875,
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-0.034637451171875, 0.011322021484375, -0.026458740234375, 0.04656982421875,
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||||
-0.0091705322265625, 0.017913818359375, -0.019256591796875, -0.001216888427734375,
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-0.08245849609375, -0.023162841796875, -0.04132080078125, -0.03363037109375,
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0.0029315948486328125, 0.03173828125, -0.004024505615234375, 0.04534912109375,
|
||||
-0.0036163330078125, -0.03912353515625, -0.00800323486328125, 0.058197021484375,
|
||||
0.05572509765625, 0.01165771484375, 0.06756591796875, 0.05816650390625,
|
||||
-0.0654296875, -0.0241851806640625, 0.0205535888671875, -0.031707763671875
|
||||
]
|
||||
|
||||
add_q_weight = [-0.23681640625, -0.16552734375, 0.2191162109375, -0.1756591796875,
|
||||
-0.03460693359375, -0.05316162109375, -0.336181640625, -0.253662109375]
|
||||
|
||||
add_k_weight = [0.0246734619140625, 0.011993408203125, 0.0178375244140625, 0.00998687744140625,
|
||||
0.0255126953125, 0.076416015625, -0.040771484375, 0.0107879638671875]
|
||||
|
||||
add_v_weight = [-0.005893707275390625, -0.00916290283203125, 0.04541015625, 0.0159454345703125,
|
||||
-0.0029163360595703125, -0.03472900390625, 0.0535888671875, 0.0091094970703125]
|
||||
|
||||
add_qkv_weight = [-0.1146240234375, -0.06768798828125, -0.10040283203125, -0.07012939453125,
|
||||
-0.08624267578125, 0.1507568359375, -0.06634521484375, -0.0194549560546875]
|
||||
|
||||
initializers = [ # initializers
|
||||
helper.make_tensor('layer_norm_weight', TensorProto.FLOAT, [8], [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0]),
|
||||
helper.make_tensor('layer_norm_bias', TensorProto.FLOAT, [8], [0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4]),
|
||||
helper.make_tensor('matmul_q_weight', TensorProto.FLOAT, [8, 8], matmul_q_weights),
|
||||
helper.make_tensor('matmul_k_weight', TensorProto.FLOAT, [8, 8], matmul_k_weights),
|
||||
helper.make_tensor('matmul_v_weight', TensorProto.FLOAT, [8, 8], matmul_v_weights),
|
||||
helper.make_tensor('matmul_qkv_weight', TensorProto.FLOAT, [8, 8], matmul_qkv_weights),
|
||||
helper.make_tensor('div_weight', TensorProto.FLOAT, [1], [2]),
|
||||
helper.make_tensor('sub_weight', TensorProto.FLOAT, [1], [1.0]),
|
||||
helper.make_tensor('mul_weight', TensorProto.FLOAT, [1], [-10000]),
|
||||
helper.make_tensor('add_q_weight', TensorProto.FLOAT, [8], add_q_weight),
|
||||
helper.make_tensor('add_k_weight', TensorProto.FLOAT, [8], add_k_weight),
|
||||
helper.make_tensor('add_v_weight', TensorProto.FLOAT, [8], add_v_weight),
|
||||
helper.make_tensor('add_qkv_weight', TensorProto.FLOAT, [8], add_qkv_weight),
|
||||
helper.make_tensor('reshape_weight_1', TensorProto.INT64, [4], [0, 0, 2, 4]),
|
||||
helper.make_tensor('reshape_weight_2', TensorProto.INT64, [3], [0, 0, 8]),
|
||||
]
|
||||
|
||||
graph = helper.make_graph(
|
||||
nodes,
|
||||
"AttentionFusionOneInput", #name
|
||||
[ # inputs
|
||||
helper.make_tensor_value_info('input_1', TensorProto.FLOAT, [1, 3, 8])
|
||||
],
|
||||
[ # outputs
|
||||
helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 3, 8]),
|
||||
],
|
||||
initializers)
|
||||
|
||||
model = helper.make_model(graph)
|
||||
onnx.save(model, model_name)
|
||||
|
||||
|
||||
GenerateModel('attention_mask_no_cast.onnx')
|
||||
|
||||
BIN
onnxruntime/test/testdata/transform/fusion/attention_mask_no_cast.onnx
vendored
Normal file
BIN
onnxruntime/test/testdata/transform/fusion/attention_mask_no_cast.onnx
vendored
Normal file
Binary file not shown.
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Reference in a new issue