提交 5dd281f7 编写于 作者: X Xin Pan

polish

test=develop
上级 8d83e38a
...@@ -904,6 +904,16 @@ void OperatorWithKernel::RuntimeInferShape(const Scope& scope, ...@@ -904,6 +904,16 @@ void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
this->InferShape(&infer_shape_ctx); this->InferShape(&infer_shape_ctx);
} }
std::vector<KernelConfig>* OperatorWithKernel::GetKernelConfig(
const OpKernelType& key) const {
auto config_iter = kernel_configs_map_.find(key);
std::vector<KernelConfig>* kernel_configs = nullptr;
if (config_iter != kernel_configs_map_.end()) {
kernel_configs = &(config_iter->second);
}
return kernel_configs;
}
void OperatorWithKernel::RunImpl(const Scope& scope, void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const { const platform::Place& place) const {
RuntimeContext ctx(Inputs(), Outputs(), scope); RuntimeContext ctx(Inputs(), Outputs(), scope);
...@@ -940,11 +950,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope, ...@@ -940,11 +950,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
KernelTypeToString(expected_kernel_key)); KernelTypeToString(expected_kernel_key));
} }
auto config_iter = kernel_configs_map_.find(expected_kernel_key); std::vector<KernelConfig>* kernel_configs =
std::vector<KernelConfig>* kernel_configs = nullptr; GetKernelConfig(expected_kernel_key);
if (config_iter != kernel_configs_map_.end()) {
kernel_configs = &(config_iter->second);
}
// do data transformScope &transfer_scope; // do data transformScope &transfer_scope;
std::vector<std::string> transfered_inplace_vars; std::vector<std::string> transfered_inplace_vars;
......
...@@ -28,6 +28,7 @@ limitations under the License. */ ...@@ -28,6 +28,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_info.h" #include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h" #include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator_kernel_configs.h"
#include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h" #include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/framework/tensor.h"
...@@ -184,98 +185,6 @@ class OperatorBase { ...@@ -184,98 +185,6 @@ class OperatorBase {
const platform::Place& place) const = 0; const platform::Place& place) const = 0;
}; };
template <typename TAlgorithm>
class AlgorithmsCache {
public:
AlgorithmsCache() : search_times_(0) { hash_.clear(); }
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations,
int algorithmFlags, // can set for different data type
std::function<TAlgorithm()> gen_func);
TAlgorithm GetAlgorithm(int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func);
private:
std::unordered_map<int64_t, TAlgorithm> hash_;
int search_times_;
};
template <typename TAlgorithm>
TAlgorithm framework::AlgorithmsCache<TAlgorithm>::GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
int64_t seed = 0;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std::hash<int64_t> hashFn;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for (const auto num : dims1) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
for (const auto num : dims2) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
}
for (const auto num : strides) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 2;
}
for (const auto num : paddings) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 3;
}
for (const auto num : dilations) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 4;
}
seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
(seed << 6) + (seed >> 2) + 5;
if (seed == 0) return gen_func();
if (hash_.find(seed) == hash_.end()) {
TAlgorithm value = gen_func();
hash_[seed] = value;
}
return hash_[seed];
}
template <typename TAlgorithm>
TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
if (hash_.find(area) != hash_.end()) {
return hash_[area];
}
if (search_times_ < search_times) {
auto algo = gen_func();
hash_[area] = algo;
++search_times_;
return algo;
}
TAlgorithm algo;
int64_t min = static_cast<uint64_t>(INT_MAX);
for (const auto& m : hash_) {
if (m.first < min) {
min = m.first;
algo = m.second;
}
}
return algo;
}
#ifdef PADDLE_WITH_CUDA #ifdef PADDLE_WITH_CUDA
using KernelConfig = boost::variant< using KernelConfig = boost::variant<
std::shared_ptr<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>, std::shared_ptr<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>,
...@@ -602,6 +511,8 @@ class OperatorWithKernel : public OperatorBase { ...@@ -602,6 +511,8 @@ class OperatorWithKernel : public OperatorBase {
virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const; virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
std::vector<KernelConfig>* GetKernelConfig(const OpKernelType& key) const;
protected: protected:
virtual OpKernelType GetKernelTypeForVar( virtual OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor, const std::string& var_name, const Tensor& tensor,
......
/* Copyright (c) 2016 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 <algorithm>
#include <unordered_map>
#include <vector>
namespace paddle {
namespace framework {
// Not thread-safe. Should be owned per-kernel.
template <typename TAlgorithm>
class AlgorithmsCache {
public:
AlgorithmsCache() : search_times_(0) { hash_.clear(); }
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations,
int algorithmFlags, // can set for different data type
std::function<TAlgorithm()> gen_func);
TAlgorithm GetAlgorithm(int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func);
private:
std::unordered_map<int64_t, TAlgorithm> hash_;
int search_times_;
};
template <typename TAlgorithm>
TAlgorithm framework::AlgorithmsCache<TAlgorithm>::GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
int64_t seed = 0;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std::hash<int64_t> hashFn;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for (const auto num : dims1) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
for (const auto num : dims2) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
}
for (const auto num : strides) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 2;
}
for (const auto num : paddings) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 3;
}
for (const auto num : dilations) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 4;
}
seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
(seed << 6) + (seed >> 2) + 5;
if (seed == 0) return gen_func();
if (hash_.find(seed) == hash_.end()) {
TAlgorithm value = gen_func();
hash_[seed] = value;
}
return hash_[seed];
}
template <typename TAlgorithm>
TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
if (hash_.find(area) != hash_.end()) {
return hash_[area];
}
if (search_times_ < search_times) {
auto algo = gen_func();
hash_[area] = algo;
++search_times_;
return algo;
}
TAlgorithm algo;
int64_t min = static_cast<uint64_t>(INT_MAX);
for (const auto& m : hash_) {
if (m.first < min) {
min = m.first;
algo = m.second;
}
}
return algo;
}
} // namespace framework
} // namespace paddle
...@@ -44,8 +44,13 @@ class PreparedOp { ...@@ -44,8 +44,13 @@ class PreparedOp {
PreparedOp(const framework::OperatorBase& op, PreparedOp(const framework::OperatorBase& op,
const framework::RuntimeContext& ctx, const framework::RuntimeContext& ctx,
framework::OperatorWithKernel::OpKernelFunc func, framework::OperatorWithKernel::OpKernelFunc func,
platform::DeviceContext* dev_ctx) platform::DeviceContext* dev_ctx,
: op(op), ctx(ctx), func(func), dev_ctx(dev_ctx) {} std::vector<framework::KernelConfig>* kernel_configs)
: op(op),
ctx(ctx),
func(func),
dev_ctx(dev_ctx),
kernel_configs(kernel_configs) {}
static PreparedOp Prepare(const framework::RuntimeContext& ctx, static PreparedOp Prepare(const framework::RuntimeContext& ctx,
const framework::OperatorWithKernel& op, const framework::OperatorWithKernel& op,
...@@ -84,7 +89,9 @@ class PreparedOp { ...@@ -84,7 +89,9 @@ class PreparedOp {
PADDLE_THROW("op %s does not have kernel for %s", op.Type(), PADDLE_THROW("op %s does not have kernel for %s", op.Type(),
KernelTypeToString(expected_kernel_key)); KernelTypeToString(expected_kernel_key));
} }
return PreparedOp(op, ctx, kernel_iter->second, dev_ctx); std::vector<framework::KernelConfig>* kernel_configs =
op.GetKernelConfig(expected_kernel_key);
return PreparedOp(op, ctx, kernel_iter->second, dev_ctx, kernel_configs);
} }
inline platform::DeviceContext* GetDeviceContext() const { return dev_ctx; } inline platform::DeviceContext* GetDeviceContext() const { return dev_ctx; }
...@@ -93,6 +100,7 @@ class PreparedOp { ...@@ -93,6 +100,7 @@ class PreparedOp {
const framework::RuntimeContext& ctx; const framework::RuntimeContext& ctx;
framework::OperatorWithKernel::OpKernelFunc func; framework::OperatorWithKernel::OpKernelFunc func;
platform::DeviceContext* dev_ctx; platform::DeviceContext* dev_ctx;
std::vector<framework::KernelConfig>* kernel_configs;
}; };
class OpBase; class OpBase;
......
...@@ -138,8 +138,9 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, ...@@ -138,8 +138,9 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
op->place_ = GetExpectedPlace(expected_place, inputs); op->place_ = GetExpectedPlace(expected_place, inputs);
PreparedOp prepared_op = PreparedOp::Prepare(ctx, *op_kernel, op->place_); PreparedOp prepared_op = PreparedOp::Prepare(ctx, *op_kernel, op->place_);
prepared_op.op.RuntimeInferShape(scope, op->place_, ctx); prepared_op.op.RuntimeInferShape(scope, op->place_, ctx);
prepared_op.func(framework::ExecutionContext( prepared_op.func(
prepared_op.op, scope, *prepared_op.dev_ctx, prepared_op.ctx, nullptr)); framework::ExecutionContext(prepared_op.op, scope, *prepared_op.dev_ctx,
prepared_op.ctx, prepared_op.kernel_configs));
if (!stop_gradient) { if (!stop_gradient) {
std::unique_ptr<std::unordered_map<std::string, std::string>> grad_to_var( std::unique_ptr<std::unordered_map<std::string, std::string>> grad_to_var(
......
...@@ -154,8 +154,6 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> { ...@@ -154,8 +154,6 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
algo = algo_cache.GetAlgorithm(x_dims[2] * x_dims[3], search_times, 0, algo = algo_cache.GetAlgorithm(x_dims[2] * x_dims[3], search_times, 0,
search_func); search_func);
} else { } else {
// Cache searched algo in Var(kCUDNNFwdAlgoCache).
// all conv ops use the same kCUDNNFwdAlgoCache variable.
algo = algo_cache.GetAlgorithm(x_dims, f_dims, strides, paddings, algo = algo_cache.GetAlgorithm(x_dims, f_dims, strides, paddings,
dilations, 0, search_func); dilations, 0, search_func);
} }
......
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