[TRT] Transpose layernorm fusion with different input format #50082
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| /* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
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| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||
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| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
| #include "paddle/fluid/framework/ir/trans_layernorm_fuse_pass.h" | ||
| #include <string> | ||
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| #include "paddle/fluid/framework/ir/graph_pattern_detector.h" | ||
| #include "paddle/fluid/framework/op_version_registry.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
| namespace ir { | ||
| class Node; | ||
| } // namespace ir | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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| namespace paddle { | ||
| namespace framework { | ||
| namespace ir { | ||
| namespace patterns { | ||
| struct TransLayernormPattern : public PatternBase { | ||
| TransLayernormPattern(PDPattern *pattern, const std::string &name_scope) | ||
| : PatternBase(pattern, name_scope, "trans_layernorm") {} | ||
| void operator()(PDNode *x); | ||
| PATTERN_DECL_NODE(transpose); | ||
| PATTERN_DECL_NODE(transpose_output); | ||
| PATTERN_DECL_NODE(reshape); | ||
| PATTERN_DECL_NODE(reshape_output); | ||
| PATTERN_DECL_NODE(layernorm); | ||
| PATTERN_DECL_NODE(layernorm_scale); | ||
| PATTERN_DECL_NODE(layernorm_bias); | ||
| PATTERN_DECL_NODE(layernorm_output); | ||
| }; | ||
| void TransLayernormPattern::operator()(PDNode *x) { | ||
| std::unordered_set<std::string> reshape_ops{"reshape2", | ||
| "flatten_contiguous_range"}; | ||
| auto *transpose = | ||
| pattern->NewNode(transpose_repr())->assert_is_op("transpose2"); | ||
| auto *transpose_output = pattern->NewNode(transpose_output_repr()) | ||
| ->assert_is_op_output("transpose2") | ||
| ->assert_is_ops_input(reshape_ops, "X"); | ||
| transpose->LinksFrom({x}).LinksTo({transpose_output}); | ||
| auto *reshape = pattern->NewNode(reshape_repr())->assert_is_ops(reshape_ops); | ||
| auto *reshape_output = pattern->NewNode(reshape_output_repr()) | ||
| ->assert_is_ops_output(reshape_ops, "Out") | ||
| ->assert_is_op_input("layer_norm", "X") | ||
| ->AsOutput(); | ||
| reshape->LinksFrom({transpose_output}).LinksTo({reshape_output}); | ||
| auto *layernorm = | ||
| pattern->NewNode(layernorm_repr())->assert_is_op("layer_norm"); | ||
| auto *layernorm_scale = pattern->NewNode(layernorm_scale_repr()) | ||
| ->assert_is_op_input("layer_norm", "Scale") | ||
| ->AsInput(); | ||
| auto *layernorm_bias = pattern->NewNode(layernorm_bias_repr()) | ||
| ->assert_is_op_input("layer_norm", "Bias") | ||
| ->AsInput(); | ||
| auto *layernorm_output = pattern->NewNode(layernorm_output_repr()) | ||
| ->assert_is_op_output("layer_norm", "Y") | ||
| ->AsOutput(); | ||
| layernorm->LinksFrom({reshape_output, layernorm_scale, layernorm_bias}) | ||
| .LinksTo({layernorm_output}); | ||
| } | ||
| } // namespace patterns | ||
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| // this pass make a fusion as below: | ||
| // | ||
| // | | ||
| // transpose(axis= [0,2,3,1]) | ||
| // | | ||
| // reshape(n,h*w,c) | ||
| // | | | ||
| // out layernorm(begin_norm_axis=2 or -1) | ||
| // | | ||
| // layernorm_out | ||
| // | ||
| // ->fuse to | ||
| // | ||
| // | | ||
| // trans_layernorm | ||
| // | | | ||
| // out layernorm_out | ||
| // | ||
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| int TransLayernormFusePass::ApplyConvTransLayernormPattern( | ||
| ir::Graph *graph) const { | ||
| PADDLE_ENFORCE_NOT_NULL( | ||
| graph, platform::errors::PreconditionNotMet("graph should not be null.")); | ||
| FusePassBase::Init("trans_layernorm_fuse", graph); | ||
| int found_subgraph_count = 0; | ||
| GraphPatternDetector gpd; | ||
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| PDNode *x = nullptr; | ||
| x = gpd.mutable_pattern() | ||
| ->NewNode("trans_layernorm_fuse/x") | ||
| ->AsInput() | ||
| ->assert_var_not_persistable() | ||
| ->assert_is_op_input("transpose2", "X"); | ||
| patterns::TransLayernormPattern fused_pattern(gpd.mutable_pattern(), | ||
| "trans_layernorm_fuse"); | ||
| fused_pattern(x); | ||
| auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph, | ||
| Graph *graph) { | ||
| if (subgraph.count(x) <= 0) { | ||
| LOG(WARNING) << "The subgraph is empty."; | ||
| return; | ||
| } | ||
| VLOG(4) << "handle transpose layernorm fuse"; | ||
| GET_IR_NODE_FROM_SUBGRAPH(transpose, transpose, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH( | ||
| transpose_output, transpose_output, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH(reshape, reshape, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH(reshape_output, reshape_output, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH(layernorm, layernorm, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH(layernorm_scale, layernorm_scale, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH(layernorm_bias, layernorm_bias, fused_pattern); | ||
| GET_IR_NODE_FROM_SUBGRAPH( | ||
| layernorm_output, layernorm_output, fused_pattern); | ||
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| if (!IsCompat(subgraph, graph)) { | ||
| LOG(WARNING) << "transpose layernorm pass in op compat failed."; | ||
| return; | ||
| } | ||
| // trans_layernorm is suit for nchw-to-nhwc transpose before layernorm | ||
| // check for it | ||
| std::vector<int> trans_axis = | ||
| PADDLE_GET_CONST(std::vector<int>, transpose->Op()->GetAttr("axis")); | ||
| if (trans_axis != std::vector<int>{0, 2, 3, 1}) { | ||
| VLOG(1) << "transpose layernorm fuse pass, transpose axis check fail, " | ||
| "stop fusion"; | ||
| return; | ||
| } | ||
| if (reshape->Op()->Type() == "flatten_contiguous_range") { | ||
| int start_axis = | ||
| PADDLE_GET_CONST(int, reshape->Op()->GetAttr("start_axis")); | ||
| int stop_axis = | ||
| PADDLE_GET_CONST(int, reshape->Op()->GetAttr("stop_axis")); | ||
| if (!(start_axis == 1 && stop_axis == 2)) { | ||
| VLOG(1) << "transpose layernorm fuse pass, flatten axis check fail, " | ||
| "stop fusion"; | ||
| return; | ||
| } | ||
| } else if (reshape->Op()->Type() == "reshape2") { | ||
| std::vector<int> reshape_shape = | ||
| PADDLE_GET_CONST(std::vector<int>, reshape->Op()->GetAttr("shape")); | ||
| if (reshape_shape.size() != 3) { | ||
| VLOG(1) | ||
| << "transpose layernorm fuse pass, reshape check fail, stop fusion"; | ||
| return; | ||
| } | ||
| } | ||
| auto layernorm_begin_norm_axis = | ||
| PADDLE_GET_CONST(int, layernorm->Op()->GetAttr("begin_norm_axis")); | ||
| if (layernorm_begin_norm_axis != 2 && layernorm_begin_norm_axis != -1) { | ||
| VLOG(1) << "transpose layernorm fuse pass, layernorm begin norm axis " | ||
| "check fail, stop fusion"; | ||
| return; | ||
| } | ||
| std::unordered_set<const Node *> del_node_set; | ||
| // Create an preln_groupnorm_act op node | ||
| OpDesc new_desc(*layernorm->Op()); | ||
| new_desc.SetType("trans_layernorm"); | ||
| new_desc.SetInput("X", {subgraph.at(x)->Name()}); | ||
| new_desc.SetOutput("Out_reshape", {reshape_output->Name()}); | ||
| new_desc.SetOutput("Out_layernorm", {layernorm_output->Name()}); | ||
| new_desc.RemoveOutput("Y"); | ||
| new_desc.Flush(); | ||
| auto fused_node = graph->CreateOpNode(&new_desc); // OpDesc will be copied. | ||
| del_node_set.insert(transpose); | ||
| del_node_set.insert(transpose_output); | ||
| del_node_set.insert(reshape); | ||
| del_node_set.insert(layernorm); | ||
| GraphSafeRemoveNodes(graph, del_node_set); | ||
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| IR_NODE_LINK_TO(subgraph.at(x), fused_node); | ||
| IR_NODE_LINK_TO(layernorm_scale, fused_node); | ||
| IR_NODE_LINK_TO(layernorm_bias, fused_node); | ||
| IR_NODE_LINK_TO(fused_node, reshape_output); | ||
| IR_NODE_LINK_TO(fused_node, layernorm_output); | ||
| found_subgraph_count++; | ||
| }; | ||
| gpd(graph, handler); | ||
| return found_subgraph_count; | ||
| } | ||
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| void TransLayernormFusePass::ApplyImpl(ir::Graph *graph) const { | ||
| FusePassBase::Init("trans_layernorm_fuse_pass", graph); | ||
| int found_subgraph_count = ApplyConvTransLayernormPattern(graph); | ||
| AddStatis(found_subgraph_count); | ||
| } | ||
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| } // namespace ir | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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| REGISTER_PASS(trans_layernorm_fuse_pass, | ||
| paddle::framework::ir::TransLayernormFusePass); | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| /* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
|
|
||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
|
|
||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
|
|
||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
| #pragma once | ||
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| #include "paddle/fluid/framework/ir/fuse_pass_base.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
| namespace ir { | ||
| // This pass aim to fuse below structure | ||
| // | ||
| // | | ||
| // transpose(axis= [0,2,3,1]) | ||
| // | | ||
| // reshape(n,h*w,c) | ||
| // | | | ||
| // out layernorm(begin_norm_axis=2 or -1) | ||
| // | | ||
| // layernorm_out | ||
| // | ||
| // ->fuse to | ||
| // | ||
| // | | ||
| // trans_layernorm | ||
| // | | | ||
| // out layernorm_out | ||
|
There was a problem hiding this comment. 这里加点详细注释吧，说明这个Pass只对 transpose(0, 2, 3, 1) 且对最后一个维度做norm才生效 There was a problem hiding this comment. 以对pass相关说明进行详细注释,感谢review |
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| // | ||
| class Graph; | ||
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| class TransLayernormFusePass : public FusePassBase { | ||
| public: | ||
| TransLayernormFusePass() { | ||
| AddOpCompat(OpCompat("reshape2")) | ||
| .AddInput("X") | ||
| .IsTensor() | ||
| .End() | ||
| .AddInput("Shape") | ||
| .IsTensor() | ||
| .IsOptional() | ||
| .End() | ||
| .AddInput("ShapeTensor") | ||
| .IsOptional() | ||
| .End() | ||
| .AddOutput("Out") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("XShape") | ||
| .IsOptional() | ||
| .IsTensor() | ||
| .End() | ||
| .AddAttr("shape") | ||
| .IsType<std::vector<int>>() | ||
| .End(); | ||
| AddOpCompat(OpCompat("flatten_contiguous_range")) | ||
| .AddInput("X") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("Out") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("XShape") | ||
| .IsOptional() | ||
| .IsTensor() | ||
| .End() | ||
| .AddAttr("start_axis") | ||
| .IsNumEQ(1) | ||
| .End() | ||
| .AddAttr("stop_axis") | ||
| .IsNumEQ(2) | ||
| .End(); | ||
| AddOpCompat(OpCompat("transpose2")) | ||
| .AddInput("X") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("Out") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("XShape") | ||
| .IsOptional() | ||
| .IsTensor() | ||
| .End() | ||
| .AddAttr("axis") // {0, 2, 1, 3} | ||
| .IsType<std::vector<int>>() | ||
| .End(); | ||
| AddOpCompat(OpCompat("layer_norm")) | ||
| .AddInput("X") | ||
| .IsTensor() | ||
| .End() | ||
| .AddInput("Scale") | ||
| .IsTensor() | ||
| .End() | ||
| .AddInput("Bias") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("Y") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("Mean") | ||
| .IsTensor() | ||
| .End() | ||
| .AddOutput("Variance") | ||
| .IsTensor() | ||
| .End() | ||
| .AddAttr("begin_norm_axis") | ||
| .IsNumGT(0) | ||
| .End() | ||
| .AddAttr("epsilon") | ||
| .IsNumGE(0.0f) | ||
| .IsNumLE(1.0f) | ||
| .End(); | ||
| } | ||
| virtual ~TransLayernormFusePass() {} | ||
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| protected: | ||
| void ApplyImpl(ir::Graph* graph) const override; | ||
| int ApplyConvTransLayernormPattern(ir::Graph* graph) const; | ||
| }; | ||
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| } // namespace ir | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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注释
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以对pass相关说明进行详细注释,感谢review以对pass相关说明进行详细注释,感谢review