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未验证 提交 7c6835ca 编写于 作者: H hong 提交者： GitHub
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Revert "add common subexpression elimination (#44386)" (#47062) 

This reverts commit 166ff39a.
上级 776e80a6
隐藏空白更改
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Showing with 0 addition and 523 deletion
paddle/fluid/framework/ir/CMakeLists.txt 100644 → 100755
浏览文件 @ 7c6835ca
...@@ -115,7 +115,6 @@ pass_library(gpu_cpu_map_matmul_to_mul_pass inference) ...@@ -115,7 +115,6 @@ pass_library(gpu_cpu_map_matmul_to_mul_pass inference)
pass_library(dense_fc_to_sparse_pass inference) pass_library(dense_fc_to_sparse_pass inference)
pass_library(dense_multihead_matmul_to_sparse_pass inference) pass_library(dense_multihead_matmul_to_sparse_pass inference)
pass_library(generate_pass DEPS pass_desc_proto) pass_library(generate_pass DEPS pass_desc_proto)
pass_library(common_subexpression_elimination_pass inference)
target_link_libraries(generate_pass pass_desc_proto) target_link_libraries(generate_pass pass_desc_proto)
if(WITH_TENSORRT) if(WITH_TENSORRT)
...@@ -327,10 +326,6 @@ cc_test( ...@@ -327,10 +326,6 @@ cc_test(
test_generate_pass_cc test_generate_pass_cc
SRCS generate_pass_tester.cc SRCS generate_pass_tester.cc
DEPS generate_pass pass_desc_proto) DEPS generate_pass pass_desc_proto)
cc_test(
test_common_subexpression_elimination_pass_cc
SRCS common_subexpression_elimination_pass_tester.cc
DEPS common_subexpression_elimination_pass)
cc_test( cc_test(
test_delete_dropout_pass_cc test_delete_dropout_pass_cc
SRCS delete_dropout_op_pass_test.cc SRCS delete_dropout_op_pass_test.cc
......
paddle/fluid/framework/ir/common_subexpression_elimination_pass.cc 已删除 100644 → 0
浏览文件 @ 776e80a6
// Copyright (c) 2022 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/common_subexpression_elimination_pass.h"
#include <string>
#include <type_traits>
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/framework/type_defs.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/utils/variant.h"
namespace {
std::string NodeTypeToString(paddle::framework::ir::Node::Type type) {
if (type == paddle::framework::ir::Node::Type::kOperation) {
return "kOperation";
} else {
return "kVariable";
}
}
const std::unordered_set<std::string> commutative_operators{"mul",
"bitwise_and",
"bitwise_or",
"equal_all",
"equal",
"not_equal",
"logical_and",
"logical_or",
"elementwise_max",
"elementwise_fmax",
"elementwise_min",
"elementwise_fmin",
"elementwise_mul",
"elementwise_add",
"add_p",
"max_p",
"mul_p",
"eq_p",
"ne_p"};
const std::unordered_set<std::string> nondeterministic_operators{
"dropout",
"dropout_nd",
"gaussian_random_batch_size_like",
"gaussian_random",
"randint",
"random_crop",
"random_routing",
"randperm",
"uniform_random_batch_size_like",
"uniform_random_inplace",
"uniform_random",
"fused_bias_dropout_residual_layer_norm"};
const std::unordered_set<std::string> side_effect_operators{
"feed", "cast", "fetch", "fill_constant", "fill_constant_batch_size_like"};
template <class T>
inline void HashCombine(std::size_t *seed, const T &v) {
std::hash<T> hasher;
(*seed) ^= hasher(v) + 0x9e3779b9 + ((*seed) << 6) + ((*seed) >> 2);
}
} // namespace
namespace std {
#define HASH_ATTRIBUTE(attr, id, type) \
do { \
if (attr.index() == id) { \
return std::hash<type>{}(get<id>(attr)); \
} \
} while (0)
#define HASH_VECTOR_ATTRIBUTE(attr, id, type) \
do { \
if (attr.index() == id) { \
std::vector<type> vec = get<id>(attr); \
size_t seed = 0; \
for (const auto &v : vec) { \
HashCombine(&seed, v); \
} \
return seed; \
} \
} while (0)
template <>
struct hash<paddle::framework::proto::VarType_Type> {
size_t operator()(const paddle::framework::proto::VarType_Type &attr) const {
using type = typename std::underlying_type<
paddle::framework::proto::VarType_Type>::type;
return std::hash<type>()(static_cast<type>(attr));
}
};
template <>
struct hash<paddle::framework::Attribute> {
size_t operator()(const paddle::framework::Attribute &attr) const {
if (attr.index() == 0) {
return 0;
}
if (attr.index() == 7) {
return static_cast<size_t>(get<7>(attr));
}
HASH_ATTRIBUTE(attr, 1, int);
HASH_ATTRIBUTE(attr, 2, float);
HASH_ATTRIBUTE(attr, 3, std::string);
HASH_VECTOR_ATTRIBUTE(attr, 4, int);
HASH_VECTOR_ATTRIBUTE(attr, 5, float);
HASH_VECTOR_ATTRIBUTE(attr, 6, std::string);
HASH_ATTRIBUTE(attr, 8, std::vector<bool>);
HASH_ATTRIBUTE(attr, 9, paddle::framework::BlockDesc *);
HASH_ATTRIBUTE(attr, 10, int64_t);
HASH_VECTOR_ATTRIBUTE(attr, 11, paddle::framework::BlockDesc *);
HASH_VECTOR_ATTRIBUTE(attr, 12, int64_t);
HASH_VECTOR_ATTRIBUTE(attr, 13, double);
return 0;
}
};
} // namespace std
namespace paddle {
namespace framework {
namespace ir {
void CommonSubexpressionEliminationPass::ApplyImpl(ir::Graph *graph) const {
PADDLE_ENFORCE_EQ(
graph->IsMainGraph(),
true,
platform::errors::InvalidArgument(
"CommonSubexpressionEliminationPass only accepts main graph"));
CommonSubexpressionEliminate(
graph, graph, [](Node *) -> Node * { return nullptr; });
}
void CommonSubexpressionEliminationPass::CommonSubexpressionEliminate(
ir::Graph *main_graph,
ir::Graph *graph,
std::function<Node *(Node *)> parent_exist_nodes) const {
const char *kSubBlock = "sub_block";
std::unordered_set<ir::Node *, HashOpNode, EqualOpNode> exist_nodes;
std::vector<Node *> nodes = TopologySortOperations(*graph);
for (Node *node : nodes) {
if (node->inputs.empty()) {
continue;
}
if (side_effect_operators.count(node->Name()) != 0) {
continue;
}
if (nondeterministic_operators.count(node->Name()) != 0) {
continue;
}
if (node->Op()->HasAttr(kSubBlock)) {
auto sub_block_id =
node->Op()->GetAttrIfExists<BlockDesc *>(kSubBlock)->ID();
CommonSubexpressionEliminate(
main_graph,
main_graph->GetSubGraph(sub_block_id),
[&exist_nodes, &parent_exist_nodes](Node *node) -> Node * {
auto exist_node = exist_nodes.find(node);
if (exist_node != exist_nodes.end()) {
return *exist_node;
}
return parent_exist_nodes(node);
});
continue;
}
Node *exist_node = parent_exist_nodes(node);
if (exist_node == nullptr) {
auto res = exist_nodes.insert(node);
if (!res.second) {
exist_node = *res.first;
}
}
if (exist_node != nullptr) {
for (size_t i = 0; i < exist_node->outputs.size(); ++i) {
Node *exist_node_output = exist_node->outputs[i];
Node *current_node_output = node->outputs[i];
std::vector<Node *> current_node_output_outputs =
current_node_output->outputs;
for (size_t i = 0; i < current_node_output_outputs.size(); ++i) {
IR_NODE_LINK_TO(exist_node_output, current_node_output_outputs[i]);
}
}
GraphSafeRemoveNodes(graph,
std::unordered_set<const Node *>(
node->outputs.begin(), node->outputs.end()));
GraphSafeRemoveNodes(graph, {node});
}
}
}
size_t HashOpNode::operator()(const Node *node) const {
PADDLE_ENFORCE_EQ(node->IsOp(),
true,
platform::errors::InvalidArgument(
"HashOpNode only supports operation node type"));
size_t seed = 0;
std::vector<Node *> inputs(node->inputs);
if (commutative_operators.count(node->Name()) != 0) {
auto comparator = [](Node *a, Node *b) { return a->Name() > b->Name(); };
std::stable_sort(inputs.begin(), inputs.end(), comparator);
}
for (size_t i = 0; i < inputs.size(); ++i) {
HashCombine(&seed, inputs[i]->id());
HashCombine(&seed, node->GraphId());
}
const std::string kDepVarName = std::string(Node::kControlDepVarName);
for (size_t i = 0; i < node->outputs.size(); ++i) {
if (node->outputs[i] == nullptr) {
continue;
}
if (node->outputs[i]->IsCtrlVar()) {
HashCombine(&seed, kDepVarName);
} else if (node->outputs[i]->IsVar()) {
HashCombine(&seed, node->outputs[i]->Var()->GetType());
}
}
OpDesc *desc = node->Op();
std::vector<std::string> attributes = desc->AttrNames();
sort(attributes.begin(), attributes.end());
for (const std::string &attribute : attributes) {
HashCombine(&seed, desc->GetAttr(attribute));
}
return seed;
}
bool EqualOpNode::operator()(const Node *lhs, const Node *rhs) const {
PADDLE_ENFORCE_EQ(lhs->IsOp() && rhs->IsOp(),
true,
platform::errors::InvalidArgument(
"EqualOpNode only supports operation node type"));
if (lhs == nullptr && rhs == nullptr) {
return true;
}
if (lhs == nullptr || rhs == nullptr) {
return false;
}
if (lhs->NodeType() != rhs->NodeType()) {
return false;
}
if (lhs->Name() != rhs->Name()) {
return false;
}
std::vector<Node *> lhs_inputs(lhs->inputs);
std::vector<Node *> rhs_inputs(rhs->inputs);
if (commutative_operators.count(lhs->Name()) != 0) {
auto comparator = [](Node *a, Node *b) { return a->Name() > b->Name(); };
std::stable_sort(lhs_inputs.begin(), lhs_inputs.end(), comparator);
std::stable_sort(rhs_inputs.begin(), rhs_inputs.end(), comparator);
}
// compare inputs value
if (lhs_inputs.size() != rhs_inputs.size()) {
return false;
}
if (!std::equal(lhs_inputs.begin(), lhs_inputs.end(), rhs_inputs.begin())) {
return false;
}
// compare attribute
const OpDesc *lhs_desc = lhs->Op();
const OpDesc *rhs_desc = rhs->Op();
std::vector<std::string> lhs_attr_names = lhs_desc->AttrNames();
std::vector<std::string> rhs_attr_names = rhs_desc->AttrNames();
if (lhs_attr_names.size() != rhs_attr_names.size()) {
return false;
}
std::sort(lhs_attr_names.begin(), lhs_attr_names.end());
std::sort(rhs_attr_names.begin(), rhs_attr_names.end());
for (size_t i = 0; i < lhs_attr_names.size(); ++i) {
if (lhs_attr_names[i] != rhs_attr_names[i]) {
return false;
}
if (lhs_desc->GetAttr(lhs_attr_names[i]) !=
rhs_desc->GetAttr(rhs_attr_names[i])) {
return false;
}
}
// compare outputs value type
std::vector<Node *> lhs_outputs(lhs->outputs);
std::vector<Node *> rhs_outputs(rhs->outputs);
if (lhs_outputs.size() != rhs_outputs.size()) {
return false;
}
for (size_t i = 0; i < lhs_outputs.size(); ++i) {
if (!lhs_outputs[i]->IsVar() || !rhs_outputs[i]->IsVar()) {
return false;
}
if (lhs_outputs[i]->IsCtrlVar() != rhs_outputs[i]->IsCtrlVar()) {
return false;
}
if (lhs_outputs[i]->IsCtrlVar() && rhs_outputs[i]->IsCtrlVar()) {
continue;
}
if (lhs_outputs[i]->Var()->GetType() != rhs_outputs[i]->Var()->GetType()) {
return false;
}
}
return true;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(common_subexpression_elimination_pass,
paddle::framework::ir::CommonSubexpressionEliminationPass);
REGISTER_PASS_CAPABILITY(common_subexpression_elimination_pass);
paddle/fluid/framework/ir/common_subexpression_elimination_pass.h 已删除 100644 → 0
浏览文件 @ 776e80a6
// Copyright (c) 2022 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 <iostream>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
namespace ir {
class Graph;
class CommonSubexpressionEliminationPass : public FusePassBase {
public:
CommonSubexpressionEliminationPass() {}
protected:
void ApplyImpl(ir::Graph* graph) const override;
private:
void CommonSubexpressionEliminate(
ir::Graph* main_graph,
ir::Graph* graph,
std::function<Node*(Node*)> parent_exist_nodes) const;
};
struct HashOpNode {
size_t operator()(const Node* node) const;
};
struct EqualOpNode {
bool operator()(const Node* lhs, const Node* rhs) const;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/common_subexpression_elimination_pass_tester.cc 已删除 100644 → 0
浏览文件 @ 776e80a6
/* Copyright (c) 2022 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 <gtest/gtest.h>
#include <functional>
#include <sstream>
#include <string>
#include <unordered_set>
#include "paddle/fluid/framework/ir/common_subexpression_elimination_pass.h"
#include "paddle/fluid/framework/ir/graph_helper.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(CommonSubexpressionEliminationPass, basic_test) {
// inputs operator output
// --------------------------------------------------------------------
// (relu(a), b) elementwise_add -> d
// (relu(a), c) elementwise_add -> e
// (d, e) elementwise_add -> f
Layers layers;
auto* a = layers.data("a", {1024, 768});
auto* b = layers.data("b", {1024, 768});
auto* c = layers.data("c", {1024, 768});
auto* d = layers.elementwise_add(layers.relu(a), b);
auto* e = layers.elementwise_add(layers.relu(a), c);
auto* f = layers.data("f", {1024, 768});
layers.elementwise_add(d, e, f, 0);
std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
auto pass =
PassRegistry::Instance().Get("common_subexpression_elimination_pass");
graph.reset(pass->Apply(graph.release()));
int num_nodes_after = GetNumOpNodes(graph, "relu");
PADDLE_ENFORCE_EQ(num_nodes_after,
1,
platform::errors::InvalidArgument(
"Before the common subexpression elimination pass, "
"there should be 1 "
"relu op, but the result is %d",
num_nodes_after));
}
TEST(CommonSubexpressionEliminationPass, commutative_operator_test) {
// inputs operator output
// --------------------------------------------------------------------
// (a, b) elementwise_add -> e
// (b, a) elementwise_add -> f
// (e, c) elementwise_add -> g
// (f, d) elementwise_add -> h
Layers layers;
auto* a = layers.data("a", {1024, 768});
auto* b = layers.data("b", {1024, 768});
auto* c = layers.data("c", {1024, 768});
auto* d = layers.data("d", {1024, 768});
auto* e = layers.data("e", {1024, 768});
auto* f = layers.data("f", {1024, 768});
auto* g = layers.data("g", {1024, 768});
auto* h = layers.data("h", {1024, 768});
layers.elementwise_add(a, b, e, 0);
layers.elementwise_add(b, a, f, 0);
layers.elementwise_add(e, c, g, 0);
layers.elementwise_add(f, d, h, 0);
std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
auto pass =
PassRegistry::Instance().Get("common_subexpression_elimination_pass");
graph.reset(pass->Apply(graph.release()));
int num_nodes_after = GetNumOpNodes(graph, "elementwise_add");
PADDLE_ENFORCE_EQ(num_nodes_after,
3,
platform::errors::InvalidArgument(
"Before the common subexpression elimination pass, "
"there should be 3 "
"elementwise_add op, but the result is %d",
num_nodes_after));
}
TEST(CommonSubexpressionEliminationPass, nondeterministic_operator_test) {
// inputs operator output
// --------------------------------------------------------------------
// (dropout(a), b) elementwise_add -> d
// (dropout(a), c) elementwise_add -> e
// (d, e) elementwise_add -> f
Layers layers;
auto* a = layers.data("a", {1024, 768});
auto* b = layers.data("b", {1024, 768});
auto* c = layers.data("c", {1024, 768});
auto* d =
layers.elementwise_add(layers.dropout(a, 0.5, "downgrade_in_infer"), b);
auto* e =
layers.elementwise_add(layers.dropout(a, 0.5, "downgrade_in_infer"), c);
auto* f = layers.data("f", {1024, 768});
layers.elementwise_add(d, e, f, 0);
std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
auto pass =
PassRegistry::Instance().Get("common_subexpression_elimination_pass");
graph.reset(pass->Apply(graph.release()));
int num_nodes_after = GetNumOpNodes(graph, "dropout");
PADDLE_ENFORCE_EQ(num_nodes_after,
2,
platform::errors::InvalidArgument(
"After the common subexpression elimination pass, "
"there should still be 2 "
"dropout op, but the result is %d",
num_nodes_after));
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(common_subexpression_elimination_pass);
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @ 7c6835ca
...@@ -282,7 +282,6 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) { ...@@ -282,7 +282,6 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
"conv_eltwiseadd_bn_fuse_pass", // "conv_eltwiseadd_bn_fuse_pass", //
"conv_transpose_bn_fuse_pass", // "conv_transpose_bn_fuse_pass", //
"conv_transpose_eltwiseadd_bn_fuse_pass", // "conv_transpose_eltwiseadd_bn_fuse_pass", //
"common_subexpression_elimination_pass", //
"is_test_pass", // "is_test_pass", //
"constant_folding_pass", "constant_folding_pass",
// following pass should be located in the last, since // following pass should be located in the last, since
......
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