未验证 提交 bf653d8e 编写于 作者: W Wilber 提交者: GitHub

[Inference] Solve 2.0 trt performance reduce compare 1.8. (#29925) (#29964)

上级 acb29ff8
......@@ -88,6 +88,8 @@ pass_library(simplify_with_basic_ops_pass base)
pass_library(fc_elementwise_layernorm_fuse_pass base)
pass_library(skip_layernorm_fuse_pass base)
pass_library(multihead_matmul_fuse_pass inference)
pass_library(adaptive_pool2d_convert_global_pass inference)
pass_library(unsqueeze2_eltwise_fuse_pass inference)
if(WITH_GPU)
pass_library(cudnn_placement_pass base DEPS placement_pass_base)
pass_library(embedding_eltwise_layernorm_fuse_pass inference)
......@@ -141,7 +143,9 @@ cc_test(test_simplify_with_basic_ops_pass SRCS simplify_with_basic_ops_pass_test
cc_test(test_fc_elementwise_layernorm_fuse_pass SRCS fc_elementwise_layernorm_fuse_pass_tester.cc DEPS fc_elementwise_layernorm_fuse_pass)
cc_test(test_skip_layernorm_fuse_pass SRCS skip_layernorm_fuse_pass_tester.cc DEPS skip_layernorm_fuse_pass)
cc_test(test_multihead_matmul_fuse_pass SRCS multihead_matmul_fuse_pass_tester.cc DEPS multihead_matmul_fuse_pass)
cc_test(test_conv_bn_fuse_pass SRCS conv_bn_fuse_pass_tester.cc DEPS conv_bn_fuse_pass)
cc_test(test_conv_bn_fuse_pass_cc SRCS conv_bn_fuse_pass_tester.cc DEPS conv_bn_fuse_pass)
cc_test(test_adaptive_pool2d_convert_global_pass SRCS adaptive_pool2d_convert_global_pass_tester.cc DEPS adaptive_pool2d_convert_global_pass)
cc_test(test_unsqueeze2_eltwise_fuse_pass SRCS unsqueeze2_eltwise_fuse_pass_tester.cc DEPS unsqueeze2_eltwise_fuse_pass)
if(WITH_GPU)
cc_test(test_embedding_eltwise_layernorm_fuse_pass SRCS embedding_eltwise_layernorm_fuse_pass_tester.cc DEPS embedding_eltwise_layernorm_fuse_pass)
cc_test(test_cudnn_placement_pass SRCS cudnn_placement_pass_tester.cc DEPS cudnn_placement_pass)
......
/* Copyright (c) 2020 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. */
#include "paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
void AdaptivePool2dConvertGlobalPass::ApplyImpl(ir::Graph* graph) const {
std::string name_scope = "adaptive_pool2d_convert_global_pass";
FusePassBase::Init(name_scope, graph);
int num = 0;
for (const Node* n : graph->Nodes()) {
if (n->IsOp()) {
auto* op = n->Op();
if (op->HasAttr("adaptive") && op->HasAttr("ksize")) {
bool adaptive = BOOST_GET_CONST(bool, op->GetAttr("adaptive"));
std::vector<int> ksize =
BOOST_GET_CONST(std::vector<int>, op->GetAttr("ksize"));
if (adaptive && ksize.size() == 2 && ksize[0] == 1 && ksize[1] == 1) {
op->SetAttr("adaptive", false);
op->SetAttr("global_pooling", true);
++num;
}
}
}
}
// LOG(INFO) << "--- processed " << num << " nodes";
AddStatis(num);
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(adaptive_pool2d_convert_global_pass,
paddle::framework::ir::AdaptivePool2dConvertGlobalPass);
REGISTER_PASS_CAPABILITY(adaptive_pool2d_convert_global_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination().EQ(
"pool2d", 0));
/* Copyright (c) 2020 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
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class Graph;
/*
* Update pool2d's attr to speed up trt engine.
*
* when adaptive=true, ksize=[1,1], we turn to adaptive=false,
* global_pooling=true.
*/
class AdaptivePool2dConvertGlobalPass : public FusePassBase {
public:
virtual ~AdaptivePool2dConvertGlobalPass() {}
protected:
void ApplyImpl(ir::Graph* graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
/* Copyright (c) 2020 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. */
#include "paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
TEST(AdaptivePool2dConvertGlobalPass, basic) {
Layers layers;
auto* x = layers.data("x", {1, 92, 28, 28});
AttributeMap attrs;
attrs["adaptive"] = true;
attrs["ksize"] = std::vector<int>{1, 1};
layers.pool2d(x, false, &attrs);
std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
auto pass =
PassRegistry::Instance().Get("adaptive_pool2d_convert_global_pass");
VLOG(3) << DebugString(graph);
graph.reset(pass->Apply(graph.release()));
VLOG(3) << DebugString(graph);
bool global_pooling = false;
for (auto* node : graph->Nodes()) {
if (node->IsOp() && node->Op()->Type() == "pool2d") {
if (node->Op()->HasAttr("global_pooling")) {
global_pooling =
BOOST_GET_CONST(bool, node->Op()->GetAttr("global_pooling"));
}
}
}
PADDLE_ENFORCE_EQ(
global_pooling, true,
platform::errors::PreconditionNotMet(
"The attribute of pool2d global_pooling should be true after fuse"));
}
TEST(AdaptivePool2dConvertGlobalPass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("adaptive_pool2d_convert_global_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(adaptive_pool2d_convert_global_pass);
......@@ -81,18 +81,34 @@ struct Layers {
return out;
}
VarDesc* pool2d(VarDesc* x, bool use_cudnn) {
VarDesc* pool2d(VarDesc* x, bool use_cudnn,
const AttributeMap* attrs = nullptr) {
VarDesc* out = lod_tensor(unique_name());
OpDesc* op = program_.MutableBlock(0)->AppendOp();
op->SetType("pool2d");
op->SetInput("X", {x->Name()});
op->SetOutput("Out", {out->Name()});
op->SetAttr("use_cudnn", use_cudnn);
if (attrs) {
for (auto& iter : *attrs) {
op->SetAttr(iter.first, iter.second);
}
}
op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kForward));
return out;
}
VarDesc* unsqueeze2(VarDesc* x, const std::vector<int> axes) {
VarDesc* out = lod_tensor(unique_name());
OpDesc* op = program_.MutableBlock(0)->AppendOp();
op->SetType("unsqueeze2");
op->SetInput("X", {x->Name()});
op->SetOutput("Out", {out->Name()});
op->SetAttr("axes", axes);
return out;
}
VarDesc* relu(VarDesc* x, VarDesc* out = nullptr) {
return unary_op("relu", x, out);
}
......@@ -188,8 +204,9 @@ struct Layers {
return binary_op("elementwise_add", x, y, out);
}
VarDesc* elementwise_mul(VarDesc* x, VarDesc* y, VarDesc* out = nullptr) {
return binary_op("elementwise_mul", x, y, out);
VarDesc* elementwise_mul(VarDesc* x, VarDesc* y, VarDesc* out = nullptr,
const AttributeMap* attrs = nullptr) {
return binary_op("elementwise_mul", x, y, out, attrs);
}
VarDesc* dropout(VarDesc* x, float dropout_prob,
......
/* Copyright (c) 2019 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. */
#include "paddle/fluid/framework/ir/unsqueeze2_eltwise_fuse_pass.h"
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
namespace ir {
namespace patterns {
struct UnsqueezeEltwise : public PatternBase {
UnsqueezeEltwise(PDPattern *pattern, const std::string &name_scope)
: PatternBase(pattern, name_scope, "unsqueeze2_eltwise_fuse_pass") {}
PDNode *operator()(PDNode *x, PDNode *y);
// declare operator node's name
PATTERN_DECL_NODE(unsqz);
PATTERN_DECL_NODE(elementwise);
// declare variable node's name
PATTERN_DECL_NODE(eltwise_in_x);
PATTERN_DECL_NODE(unsqz_in);
PATTERN_DECL_NODE(unsqz_out);
PATTERN_DECL_NODE(eltwise_out);
};
PDNode *UnsqueezeEltwise::operator()(PDNode *x, PDNode *y) {
x->assert_is_op_input("elementwise_mul", "X");
y->assert_is_op_input("unsqueeze2", "X");
auto *unsqz = pattern->NewNode(unsqz_repr())->assert_is_op("unsqueeze2");
auto *unsqz_out = pattern->NewNode(unsqz_out_repr())
->assert_is_op_output("unsqueeze2", "Out")
->assert_is_op_input("elementwise_mul", "Y");
unsqz->LinksFrom({y}).LinksTo({unsqz_out});
auto *elementwise =
pattern->NewNode(elementwise_repr())->assert_is_op("elementwise_mul");
auto *eltwise_out = pattern->NewNode(eltwise_out_repr())
->AsOutput()
->assert_is_op_output("elementwise_mul");
elementwise->LinksFrom({x, unsqz_out}).LinksTo({eltwise_out});
return eltwise_out;
}
} // namespace patterns
void UnsqueezeEltwiseFusePass::ApplyImpl(ir::Graph *graph) const {
PADDLE_ENFORCE_NOT_NULL(
graph, platform::errors::PreconditionNotMet("graph should not be null."));
FusePassBase::Init("unsqueeze2_eltwise_fuse_pass", graph);
int found_subgraph_count = 0;
GraphPatternDetector gpd;
auto *x = gpd.mutable_pattern()
->NewNode("unsqueeze2_eltwise_fuse_pass/x")
->AsInput()
->assert_is_op_input("elementwise_mul", "X")
->assert_var_not_persistable();
auto *y = gpd.mutable_pattern()
->NewNode("unsqueeze2_eltwise_fuse_pass/y")
->AsInput()
->assert_is_op_input("unsqueeze2", "X")
->assert_var_not_persistable();
patterns::UnsqueezeEltwise fused_pattern(gpd.mutable_pattern(),
"unsqueeze2_eltwise_fuse_pass");
fused_pattern(x, y);
auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
Graph *graph) {
if (subgraph.count(x) <= 0 || subgraph.count(y) <= 0) {
LOG(WARNING) << "The subgraph is empty.";
return;
}
VLOG(4) << "handle UnsqueezeEltwise fuse";
GET_IR_NODE_FROM_SUBGRAPH(eltwise_op, elementwise, fused_pattern);
GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, fused_pattern);
GET_IR_NODE_FROM_SUBGRAPH(unsqz_op, unsqz, fused_pattern);
GET_IR_NODE_FROM_SUBGRAPH(unsqz_out, unsqz_out, fused_pattern);
size_t eltwise_in_x_rank = (subgraph.at(x)->Var()->GetShape()).size();
size_t unsqz_in_rank = (subgraph.at(y)->Var()->GetShape()).size();
std::vector<int> unsqz_op_axes =
BOOST_GET_CONST(std::vector<int>, unsqz_op->Op()->GetAttr("axes"));
int eltwise_op_axis =
BOOST_GET_CONST(int, eltwise_op->Op()->GetAttr("axis"));
if (eltwise_in_x_rank == 4 && unsqz_in_rank == 2 &&
unsqz_op_axes == std::vector<int>{2, 3} && eltwise_op_axis == -1) {
eltwise_op->Op()->SetAttr("axis", 0);
eltwise_op->Op()->SetInput("Y", {subgraph.at(y)->Name()});
IR_NODE_LINK_TO(subgraph.at(x), eltwise_op);
IR_NODE_LINK_TO(subgraph.at(y), eltwise_op);
IR_NODE_LINK_TO(eltwise_op, eltwise_out);
GraphSafeRemoveNodes(graph, {unsqz_op, unsqz_out});
found_subgraph_count++;
}
};
gpd(graph, handler);
AddStatis(found_subgraph_count);
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(unsqueeze2_eltwise_fuse_pass,
paddle::framework::ir::UnsqueezeEltwiseFusePass);
REGISTER_PASS_CAPABILITY(unsqueeze2_eltwise_fuse_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("unsqueeze2", 0)
.EQ("elementwise_mul", 0));
/* Copyright (c) 2020 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
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
namespace ir {
class Graph;
// |(rank 4) |(rank 2) |(rank 4) |(rank 2)
// | unsqueeze2(axes=[2,3]) | |
// | | fuse \ /
// |------elementwise_mul(axis=-1) -> elementwise_mul(axis=0)
// | |
// | |
//
// Notice:
// the rank of input is obtained from var_desc,
// it maybe change in runtime.
class UnsqueezeEltwiseFusePass : public FusePassBase {
public:
virtual ~UnsqueezeEltwiseFusePass() {}
protected:
void ApplyImpl(ir::Graph* graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
/* Copyright (c) 2020 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. */
#include "paddle/fluid/framework/ir/unsqueeze2_eltwise_fuse_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
namespace ir {
TEST(UnsqueezeEltwiseFusePass, basic) {
Layers layers;
auto* x = layers.data("x", {1, 92, 28, 28});
auto* y = layers.data("y", {1, 92});
std::vector<int> axes{2, 3};
auto* unsqz_out = layers.unsqueeze2(y, axes);
AttributeMap attrs;
attrs["axis"] = -1;
layers.elementwise_mul(x, unsqz_out, nullptr, &attrs);
std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
auto pass = PassRegistry::Instance().Get("unsqueeze2_eltwise_fuse_pass");
int num_nodes_before = graph->Nodes().size();
VLOG(3) << DebugString(graph);
graph.reset(pass->Apply(graph.release()));
int num_nodes_after = graph->Nodes().size();
int num_fused_nodes_after = GetNumOpNodes(graph, "elementwise_mul");
VLOG(3) << DebugString(graph);
PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after + 2,
platform::errors::PreconditionNotMet(
"The number of nodes before and after the fuse does "
"not meet expectations"));
PADDLE_ENFORCE_EQ(
num_fused_nodes_after, 1,
platform::errors::PreconditionNotMet(
"The number of fusion nodes does not meet expectations after fuse"));
}
TEST(UnsqueezeEltwiseFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("unsqueeze2_eltwise_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(unsqueeze2_eltwise_fuse_pass);
......@@ -71,7 +71,8 @@ void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
void PaddlePassBuilder::ClearPasses() { passes_.clear(); }
const std::vector<std::string> kTRTSubgraphPasses({
"conv_affine_channel_fuse_pass", //
"conv_affine_channel_fuse_pass", //
"adaptive_pool2d_convert_global_pass",
"conv_eltwiseadd_affine_channel_fuse_pass", //
"shuffle_channel_detect_pass", //
"quant_conv2d_dequant_fuse_pass", //
......@@ -81,10 +82,11 @@ const std::vector<std::string> kTRTSubgraphPasses({
"embedding_eltwise_layernorm_fuse_pass", //
"multihead_matmul_fuse_pass_v2", //
"skip_layernorm_fuse_pass", //
"conv_bn_fuse_pass", //
"fc_fuse_pass", //
"tensorrt_subgraph_pass", //
"conv_bn_fuse_pass", //
"unsqueeze2_eltwise_fuse_pass",
"conv_bn_fuse_pass", //
"fc_fuse_pass", //
"tensorrt_subgraph_pass", //
"conv_bn_fuse_pass", //
#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
// guaranteed at least v7
"conv_elementwise_add_act_fuse_pass", //
......@@ -207,6 +209,7 @@ void CpuPassStrategy::EnableMKLDNN() {
"matmul_transpose_reshape_fuse_pass", //
// Disabled due to topology-dependent speed-up
// "fc_mkldnn_pass",
// "fc_act_mkldnn_fuse_pass",
"batch_norm_act_fuse_pass",
"mkldnn_inplace_pass", // This pass should be activated after
// fuses
......
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