提交 056599a7 编写于 作者: L luotao1

add expected_kernel_cache_pass

test=develop
上级 bfdab00e
...@@ -71,6 +71,7 @@ pass_library(transpose_flatten_concat_fuse_pass inference) ...@@ -71,6 +71,7 @@ pass_library(transpose_flatten_concat_fuse_pass inference)
pass_library(identity_scale_op_clean_pass base) pass_library(identity_scale_op_clean_pass base)
pass_library(sync_batch_norm_pass base) pass_library(sync_batch_norm_pass base)
pass_library(runtime_context_cache_pass base) pass_library(runtime_context_cache_pass base)
pass_library(expected_kernel_cache_pass base)
# There may be many transpose-flatten structures in a model, and the output of # There may be many transpose-flatten structures in a model, and the output of
# these structures will be used as inputs to the concat Op. This pattern will # these structures will be used as inputs to the concat Op. This pattern will
......
/* 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/expected_kernel_cache_pass.h"
#include <memory>
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<ir::Graph> ExpectedKernelCachePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Applies Expected Kernel Cache strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp()) {
n->Op()->SetAttr(kEnableCacheExpectedKernel, true);
}
}
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(expected_kernel_cache_pass,
paddle::framework::ir::ExpectedKernelCachePass);
/* 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. */
#pragma once
#include <memory>
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class ExpectedKernelCachePass : public Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
...@@ -895,7 +895,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope, ...@@ -895,7 +895,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place); auto* dev_ctx = pool.Get(place);
if (!kernel_type_) { if (!HasAttr(kEnableCacheExpectedKernel) || !kernel_type_) {
ChooseKernel(*runtime_ctx, scope, place); ChooseKernel(*runtime_ctx, scope, place);
} }
......
...@@ -70,6 +70,12 @@ constexpr char kNewGradSuffix[] = "@NEWGRAD@"; ...@@ -70,6 +70,12 @@ constexpr char kNewGradSuffix[] = "@NEWGRAD@";
/// this Op's execution to save the elapsed time. /// this Op's execution to save the elapsed time.
constexpr char kEnableCacheRuntimeContext[] = "@ENABLE_CACHE_RUNTIME_CONTEXT@"; constexpr char kEnableCacheRuntimeContext[] = "@ENABLE_CACHE_RUNTIME_CONTEXT@";
/// If an Op has attribtue kEnableCacheExpectedKernel, it means that in a same
/// name scope and same place, since the expected kerenl of this Op does not
/// change in the execution, it could be recorded only at the first iteration of
/// this Op's execution to save the elapsed time.
constexpr char kEnableCacheExpectedKernel[] = "@ENABLE_CACHE_EXPECTED_KERNEL@";
/// If an Op has this attribute, all its kernels should calculate output /// If an Op has this attribute, all its kernels should calculate output
/// variable's shape in the corresponding Compute() function. And /// variable's shape in the corresponding Compute() function. And
/// OperatorWithKernel::RunImpl() would skip call this Op's InferShape() /// OperatorWithKernel::RunImpl() would skip call this Op's InferShape()
......
...@@ -203,6 +203,7 @@ void AnalysisConfig::Update() { ...@@ -203,6 +203,7 @@ void AnalysisConfig::Update() {
pass_builder()->InsertPass(3, "tensorrt_subgraph_pass"); pass_builder()->InsertPass(3, "tensorrt_subgraph_pass");
} }
pass_builder()->DeletePass("runtime_context_cache_pass"); pass_builder()->DeletePass("runtime_context_cache_pass");
pass_builder()->DeletePass("expected_kernel_cache_pass");
} }
if (use_mkldnn_) { if (use_mkldnn_) {
......
...@@ -81,6 +81,7 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) { ...@@ -81,6 +81,7 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
"conv_elementwise_add2_act_fuse_pass", // "conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", // "conv_elementwise_add_fuse_pass", //
"runtime_context_cache_pass", // "runtime_context_cache_pass", //
"expected_kernel_cache_pass", //
#endif #endif
}); });
...@@ -117,6 +118,7 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) { ...@@ -117,6 +118,7 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
"is_test_pass", // "is_test_pass", //
"identity_scale_op_clean_pass", // "identity_scale_op_clean_pass", //
"runtime_context_cache_pass", // "runtime_context_cache_pass", //
"expected_kernel_cache_pass", //
}); });
use_gpu_ = false; use_gpu_ = false;
} }
......
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