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“fb63cd89d4343270ad96598aac177a7ad8d36c21”上不存在“paddle/phi/kernels/impl/unsqueeze_grad_kernel_impl.h”
未验证 提交 b937cdc5 编写于 作者: L limingshu 提交者： GitHub
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Autotune the workspace_size_limit in conv. (#40338) 

* Using the maximum workspace_size of all alogirhms to limit the workspace size in exhaustive search mode.

* Use the system cudaMalloc and cudaFree to allocate workspace during searching.

* Enable switch of two kind of workspace setting methods.
Co-authored-by: NLiu Yiqun <liuyiqun01@baidu.com>
上级 e1792a31
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paddle/fluid/framework/conv_search_cache.h
浏览文件 @ b937cdc5
......@@ -16,7 +16,6 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator_kernel_configs.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
namespace paddle {
......
paddle/fluid/operators/conv_base_helper.h 0 → 100644
浏览文件 @ b937cdc5
/* 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 <algorithm>
#include <array>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/conv_search_cache.h"
#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DataLayout = platform::DataLayout;
using framework::AlgorithmsCache;
using framework::ConvSearchCache;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
// As the basic for SearchAlgorithm struct.
template <typename PerfT>
struct SearchAlgorithm {};
// As the container of searchAlgorithm::Find() result.
template <typename AlgoT>
struct SearchResult {
public:
AlgoT algo = static_cast<AlgoT>(0);
float time = -1.f;
size_t workspace_size = 0;
};
// As the container of conv relevant descriptors.
template <typename HandleT, typename DataT>
struct ConvArgsBase {
HandleT handle;
platform::TensorDescriptor idesc, odesc;
platform::FilterDescriptor wdesc;
platform::ConvolutionDescriptor cdesc;
const framework::Tensor *x, *w, *o;
DataT cudnn_dtype;
// strides
std::vector<int> s;
// paddings
std::vector<int> p;
// dilations
std::vector<int> d;
ConvArgsBase(const framework::Tensor* x, const framework::Tensor* w,
const framework::Tensor* o, const std::vector<int> s,
const std::vector<int> p, const std::vector<int> d, DataT dtype)
: x(x), w(w), o(o), s(s), p(p), d(d), cudnn_dtype(dtype) {}
};
static inline void GetNCDHW(const framework::DDim& dims,
const DataLayout& layout, int* N, int* C, int* D,
int* H, int* W) {
*N = dims[0];
*C = layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
int i = layout == DataLayout::kNCHW ? 0 : 1;
if (dims.size() == 5) {
*D = dims[2 - i];
*H = dims[3 - i];
*W = dims[4 - i];
} else {
*D = 1;
*H = dims[2 - i];
*W = dims[3 - i];
}
}
template <typename T>
static std::ostream& operator<<(std::ostream& out, const std::vector<T>& v) {
out << "[";
for (auto const& tmp : v) out << tmp << ",";
out << "]";
return out;
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_cudnn_helper.h
浏览文件 @ b937cdc5
......@@ -14,44 +14,15 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <array>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/conv_search_cache.h"
#include "paddle/fluid/framework/operator_kernel_configs.h"
#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/conv_base_helper.h"
#include "paddle/fluid/platform/cuda_graph_with_memory_pool.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DataLayout = platform::DataLayout;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
using framework::AlgorithmsCache;
static inline void GetNCDHW(const framework::DDim& dims,
const DataLayout& layout, int* N, int* C, int* D,
int* H, int* W) {
*N = dims[0];
*C = layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
int i = layout == DataLayout::kNCHW ? 0 : 1;
if (dims.size() == 5) {
*D = dims[2 - i];
*H = dims[3 - i];
*W = dims[4 - i];
} else {
*D = 1;
*H = dims[2 - i];
*W = dims[3 - i];
}
}
using ConvArgs = ConvArgsBase<cudnnHandle_t, cudnnDataType_t>;
template <typename DeviceContext, typename T, size_t D>
static void RemovePaddingSlice(const phi::GPUContext& context,
......@@ -68,121 +39,103 @@ static void RemovePaddingSlice(const phi::GPUContext& context,
extents[i] = new_out_dims[i];
}
int start;
for (size_t i = 0; i < axes.size(); ++i) {
start = starts[i];
int start = starts[i];
if (start < 0) {
start = (start + in_dims[axes[i]]);
}
start = std::max(start, 0);
offsets[axes[i]] = start;
}
auto in_t =
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*input);
auto out_t =
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*out, new_out_dims);
EigenSlice<std::decay_t<decltype(place)>, T, D>::Eval(place, out_t, in_t,
offsets, extents);
phi::funcs::EigenSlice<std::decay_t<decltype(place)>, T, D>::Eval(
place, out_t, in_t, offsets, extents);
}
template <typename T>
std::ostream& operator<<(std::ostream& out, const std::vector<T>& v) {
out << "[";
for (auto const& tmp : v) out << tmp << ",";
out << "]";
return out;
static inline double ToMegaBytes(size_t bytes) {
return static_cast<double>(bytes) / (1 << 20);
}
inline int MaxBwdFilterAlgos(cudnnHandle_t cudnn_handle) {
int max_algos = 0;
#if CUDNN_VERSION_MIN(7, 0, 1)
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
cudnn_handle, &max_algos));
#endif
return max_algos;
static inline bool UseFixedWorkspace() {
return FLAGS_conv_workspace_size_limit >= 0;
}
template <typename PerfType, typename AlgoType>
void ChooseAlgoByWorkspace(PerfType* perf_results, size_t perf_num,
size_t workspace_byte, AlgoType* algo) {
for (size_t i = 0; i < perf_num; ++i) {
auto result = perf_results[i];
if (result.status == CUDNN_STATUS_SUCCESS &&
result.memory < workspace_byte) {
*algo = result.algo;
VLOG(3) << " algo: " << result.algo << ", time: " << result.time
<< " ms, wksp = " << result.memory
<< ", status = " << result.status;
return;
}
static size_t CaclWorkspaceLimitInBytes(const phi::GPUContext& ctx) {
if (!UseFixedWorkspace()) {
int device_id = platform::GetCurrentDeviceId();
int64_t allocated = memory::StatGetCurrentValue("Allocated", device_id);
int64_t reserved = memory::StatGetCurrentValue("Reserved", device_id);
int64_t availble = platform::GpuAvailableMemToAlloc();
int64_t cur_workspace_size = ctx.cudnn_workspace_handle().WorkspaceSize();
VLOG(3) << "[memory] allocated=" << ToMegaBytes(allocated)
<< " MB, reserved=" << ToMegaBytes(reserved)
<< " MB, available_to_alloc=" << ToMegaBytes(availble)
<< " MB, current_workspace_size=" << ToMegaBytes(cur_workspace_size)
<< " MB.";
return std::max(std::max(availble, cur_workspace_size),
reserved - allocated);
} else {
return FLAGS_conv_workspace_size_limit * 1024 * 1024;
}
VLOG(3) << "Can not find alog that requires memory < "
<< static_cast<double>(workspace_byte) / (1 << 20) << " MB";
}
template <typename PerfType, typename AlgoType>
void ChooseAlgo(const std::vector<PerfType>& perf_results,
size_t workspace_byte, AlgoType* algo) {
VLOG(3) << "=========BwdFilterAlgo Perf result=========";
for (const auto& result : perf_results) {
auto math_type_str = "False";
if (result.mathType == CUDNN_TENSOR_OP_MATH) {
math_type_str = "True";
}
VLOG(3) << " algo: " << result.algo << ", TensorCore: " << math_type_str
<< ", time: " << result.time << " ms"
<< ", wksp = " << result.memory << ", status = " << result.status;
template <typename PerfT>
std::string GetPerfResultString(std::string prefix,
const std::vector<PerfT>& perf_results,
int actual_algo_count, size_t workspace_limit) {
std::ostringstream out;
out << prefix << " (workspace limit=" << ToMegaBytes(workspace_limit)
<< " MB):\n";
for (int i = 0; i < actual_algo_count; ++i) {
const auto& result = perf_results[i];
auto math_type_str = (result.mathType == CUDNN_TENSOR_OP_MATH) ? "T" : "F";
out << " algo=" << result.algo << ": tensor_core=" << math_type_str
<< ", time=" << result.time
<< " ms, memory=" << ToMegaBytes(result.memory)
<< " MB, status=" << result.status << "\n";
}
return out.str();
}
for (size_t i = 0; i != perf_results.size(); ++i) {
const auto& result = perf_results[i];
template <typename PerfT, typename AlgoT>
void ChooseAlgoByWorkspace(const std::vector<PerfT>& perf_results,
size_t workspace_limit,
SearchResult<AlgoT>* algo_result) {
for (size_t i = 0; i < perf_results.size(); ++i) {
auto result = perf_results[i];
if (result.status == CUDNN_STATUS_SUCCESS &&
(result.memory <= workspace_byte)) {
if ((result.mathType == CUDNN_TENSOR_OP_MATH) &&
(i != perf_results.size() - 1)) {
const auto& next_result = perf_results[i + 1];
if (next_result.status == CUDNN_STATUS_SUCCESS &&
next_result.algo == result.algo &&
next_result.memory == result.memory &&
next_result.mathType != CUDNN_TENSOR_OP_MATH &&
next_result.time < 1.01 * result.time) {
// Skip over this result- it's not really a Tensor Core algo.
// Because it is only 1% performance difference.
// Prefer to choose the next equivalent non-Tensor Core algo.
continue;
}
}
*algo = result.algo;
auto math_type_str = "0";
if (result.mathType == CUDNN_TENSOR_OP_MATH) {
math_type_str = "1";
}
VLOG(3) << " choose algo: " << result.algo << ", TC: " << math_type_str
<< ", time: " << result.time << " ms"
<< ", wksp = " << result.memory << ", status = " << result.status;
break;
result.memory < workspace_limit) {
algo_result->algo = result.algo;
algo_result->time = result.time;
algo_result->workspace_size = result.memory;
VLOG(3) << " algo=" << result.algo << ", time=" << result.time
<< " ms, memory=" << ToMegaBytes(result.memory)
<< " MB (limit=" << ToMegaBytes(workspace_limit)
<< " MB), status=" << result.status;
return;
}
}
VLOG(3) << "Can not find an algorithm that requires memory < "
<< ToMegaBytes(workspace_limit) << " MB";
}
using framework::ConvSearchCache;
static void SetConvMathType(const phi::GPUContext& ctx, cudnnDataType_t dtype,
const platform::ConvolutionDescriptor& cdesc) {
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
auto& dev_ctx = ctx;
if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
if (ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cudnnSetConvolutionMathType(
cdesc.desc(), CUDNN_TENSOR_OP_MATH));
VLOG(5) << "use cudnn_tensor_op_math";
#if CUDA_VERSION >= 11000
#if CUDNN_VERSION_MIN(8, 1, 0)
} else if (dev_ctx.GetComputeCapability() >= 80 &&
dtype == CUDNN_DATA_BFLOAT16) {
} else if (ctx.GetComputeCapability() >= 80 && dtype == CUDNN_DATA_BFLOAT16) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cudnnSetConvolutionMathType(
cdesc.desc(), CUDNN_TENSOR_OP_MATH));
#endif // CUDNN_VERSION_MIN(8, 1, 0)
......@@ -198,76 +151,49 @@ static void SetConvMathType(const phi::GPUContext& ctx, cudnnDataType_t dtype,
#endif
}
struct ConvArgs {
cudnnHandle_t handle;
platform::TensorDescriptor idesc, odesc;
platform::FilterDescriptor wdesc;
platform::ConvolutionDescriptor cdesc;
const framework::Tensor *x, *w, *o;
cudnnDataType_t cudnn_dtype;
// strides
std::vector<int> s;
// paddings
std::vector<int> p;
// dilations
std::vector<int> d;
ConvArgs(const framework::Tensor* x, const framework::Tensor* w,
const framework::Tensor* o, const std::vector<int> s,
const std::vector<int> p, const std::vector<int> d,
cudnnDataType_t dtype)
: x(x), w(w), o(o), s(s), p(p), d(d), cudnn_dtype(dtype) {}
};
template <typename perf_t>
struct SearchAlgorithm {};
template <>
struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
using perf_t = cudnnConvolutionFwdAlgoPerf_t;
using algo_t = cudnnConvolutionFwdAlgo_t;
using PerfT = cudnnConvolutionFwdAlgoPerf_t;
using AlgoT = cudnnConvolutionFwdAlgo_t;
template <typename T>
static algo_t Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic, const phi::GPUContext& ctx) {
static SearchResult<AlgoT> Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic,
const phi::GPUContext& ctx) {
SearchResult<AlgoT> result;
auto dtype = platform::CudnnDataType<T>::type;
bool has_got_workspace_size = true;
size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
size_t workspace_size = 0;
algo_t algo;
size_t workspace_size_limit = CaclWorkspaceLimitInBytes(ctx);
SetConvMathType(ctx, dtype, args.cdesc);
if (!exhaustive_search && !deterministic) {
#if CUDNN_VERSION >= 7001
int perf_count;
int actual_perf_count;
int best_algo_idx = 0;
std::unique_ptr<perf_t[]> perf_results(new perf_t[kNUM_CUDNN_FWD_ALGS]);
std::vector<PerfT> perf_results(kNUM_CUDNN_FWD_ALGS);
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(), kNUM_CUDNN_FWD_ALGS,
&perf_count, perf_results.get()));
algo = (perf_results.get())[best_algo_idx].algo;
workspace_size = (perf_results.get())[best_algo_idx].memory;
&actual_perf_count, perf_results.data()));
result.algo = perf_results[best_algo_idx].algo;
result.workspace_size = perf_results[best_algo_idx].memory;
if (workspace_size > workspace_size_limit) {
if (result.workspace_size > workspace_size_limit) {
#if CUDNN_VERSION >= 8000
// cudnnGetConvolutionForwardAlgorithm is removed in CUDNN-8
ChooseAlgoByWorkspace<perf_t, algo_t>(perf_results.get(),
kNUM_CUDNN_FWD_ALGS,
workspace_size_limit, &algo);
ChooseAlgoByWorkspace<PerfT, AlgoT>(perf_results, workspace_size_limit,
&result);
#else
VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
"the workspace size request("
<< workspace_size << ") exceeds the limit("
VLOG(3) << "Fallback to non-v7 method to find conv algorithm "
"becasue the workspace size request("
<< result.workspace_size << ") exceeds the limit("
<< workspace_size_limit << ")";
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit, &(result.algo)));
#endif
}
#else
......@@ -276,30 +202,30 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit, &(result.algo)));
#endif
VLOG(3) << "choose algo " << algo;
} else if (deterministic) {
algo = static_cast<cudnnConvolutionFwdAlgo_t>(1);
result.algo = static_cast<AlgoT>(1);
} else {
auto& dev_ctx = ctx;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetForward());
auto workspace_handle = ctx.cudnn_workspace_handle();
auto x_dims = phi::vectorize(args.x->dims());
auto w_dims = phi::vectorize(args.w->dims());
VLOG(10) << "cudnnConvolutionFwdAlgoPerf_t:"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
AlgorithmsCache<AlgoT>& algo_cache =
*(framework::ConvSearchCache::Instance().GetForward());
result.algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
std::vector<PerfT> perf_results(kNUM_CUDNN_FWD_ALGS);
size_t max_workspace_size =
FindMaxWorkspaceSize(args, workspace_size_limit);
VLOG(4) << "max_workspace_size=" << ToMegaBytes(max_workspace_size)
<< " MB";
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
......@@ -308,25 +234,28 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
args.wdesc.desc(), args.w->data<T>(), args.cdesc.desc(),
args.odesc.desc(), const_cast<T*>(args.o->data<T>()),
kNUM_CUDNN_FWD_ALGS, &returned_algo_count,
perf_stat.data(), cudnn_workspace_ptr,
workspace_size_limit));
perf_results.data(), cudnn_workspace_ptr,
max_workspace_size));
};
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size_limit);
VLOG(3) << "FwdAlgo Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = perf_stat[i];
VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
<< " " << stat.memory;
}
return perf_stat[0].algo;
workspace_handle.RunFuncSync(cudnn_find_func, max_workspace_size,
UseFixedWorkspace());
VLOG(4) << GetPerfResultString<PerfT>(
"[Exhaustive Search] FwdAlgo Perf result", perf_results,
returned_algo_count, workspace_size_limit);
result.time = perf_results[0].time;
return perf_results[0].algo;
});
}
VLOG(3) << "choose algo " << algo;
return algo;
VLOG(3) << "[cuDNN Convoltion] exhaustive_search=" << exhaustive_search
<< ", deterministic=" << deterministic
<< ", choose algo=" << result.algo << ", workspace="
<< ToMegaBytes(GetWorkspaceSize(args, result.algo)) << " MB";
return result;
}
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
static size_t GetWorkspaceSize(const ConvArgs& args,
cudnnConvolutionFwdAlgo_t algo) {
size_t workspace_size = 0;
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
......@@ -334,68 +263,84 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
args.cdesc.desc(), args.odesc.desc(), algo, &workspace_size));
return workspace_size;
}
private:
static size_t FindMaxWorkspaceSize(const ConvArgs& args,
size_t workspace_size_limit) {
if (!UseFixedWorkspace()) {
size_t max_workspace_size = 0;
for (size_t algo = 0; algo < kNUM_CUDNN_FWD_ALGS; ++algo) {
size_t workspace_size = 0;
auto status =
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
static_cast<cudnnConvolutionFwdAlgo_t>(algo), &workspace_size);
if (status == CUDNN_STATUS_SUCCESS) {
max_workspace_size = std::max(workspace_size, max_workspace_size);
}
}
return std::min(max_workspace_size, workspace_size_limit);
} else {
return workspace_size_limit;
}
}
};
template <>
struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
using perf_t = cudnnConvolutionBwdDataAlgoPerf_t;
using algo_t = cudnnConvolutionBwdDataAlgo_t;
using PerfT = cudnnConvolutionBwdDataAlgoPerf_t;
using AlgoT = cudnnConvolutionBwdDataAlgo_t;
template <typename T>
static algo_t Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic, const phi::GPUContext& ctx) {
static SearchResult<AlgoT> Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic,
const phi::GPUContext& ctx) {
SearchResult<AlgoT> result;
auto dtype = platform::CudnnDataType<T>::type;
size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
size_t workspace_size = 0;
bool has_got_workspace_size = true;
algo_t algo;
size_t workspace_size_limit = CaclWorkspaceLimitInBytes(ctx);
SetConvMathType(ctx, dtype, args.cdesc);
if (!exhaustive_search && !deterministic) {
#if CUDNN_VERSION >= 7001
int perf_count;
int actual_perf_count;
int best_algo_idx = 0;
std::unique_ptr<perf_t[]> perf_results(
new perf_t[kNUM_CUDNN_BWD_DATA_ALGS]);
std::vector<PerfT> perf_results(kNUM_CUDNN_BWD_DATA_ALGS);
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm_v7(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(), kNUM_CUDNN_BWD_DATA_ALGS,
&perf_count, perf_results.get()));
algo = (perf_results.get())[best_algo_idx].algo;
&actual_perf_count, perf_results.data()));
result.algo = perf_results[best_algo_idx].algo;
#if CUDNN_VERSION < 7500
int stride_dim = args.x->dims().size() - 2;
bool blacklist = std::any_of(args.s.begin(), args.s.begin() + stride_dim,
[=](int n) { return n != 1; });
if (blacklist && (static_cast<cudnnConvolutionBwdDataAlgo_t>(
perf_results[best_algo_idx].algo) ==
if (blacklist && (perf_results[best_algo_idx].algo ==
CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING ||
static_cast<cudnnConvolutionBwdDataAlgo_t>(
perf_results[best_algo_idx].algo) ==
perf_results[best_algo_idx].algo ==
CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)) {
algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
result.algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
}
#endif
workspace_size = GetWorkspaceSize(args, algo);
if (workspace_size > workspace_size_limit) {
has_got_workspace_size = false;
result.workspace_size = GetWorkspaceSize(args, result.algo);
if (result.workspace_size > workspace_size_limit) {
#if CUDNN_VERSION >= 8000
// cudnnGetConvolutionBackwardDataAlgorithm is removed in CUDNN-8
ChooseAlgoByWorkspace<perf_t, algo_t>(perf_results.get(),
kNUM_CUDNN_BWD_DATA_ALGS,
workspace_size_limit, &algo);
ChooseAlgoByWorkspace<PerfT, AlgoT>(perf_results, workspace_size_limit,
&result);
#else
VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
"the workspace size request("
<< workspace_size << ") exceeds the limit("
<< result.workspace_size << ") exceeds the limit("
<< workspace_size_limit << ")";
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(),
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit, &(result.algo)));
#endif
}
#else
......@@ -404,29 +349,29 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(),
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit, &(result.algo)));
#endif
} else if (deterministic) {
return CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
result.algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
} else {
auto& dev_ctx = ctx;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardData());
auto workspace_handle = ctx.cudnn_workspace_handle();
auto x_dims = phi::vectorize(args.x->dims());
auto w_dims = phi::vectorize(args.w->dims());
VLOG(10) << "cudnnConvolutionFwdAlgoPerf_t"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
AlgorithmsCache<AlgoT>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardData());
result.algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_BWD_DATA_ALGS> perf_stat;
std::vector<PerfT> perf_results(kNUM_CUDNN_BWD_DATA_ALGS);
size_t max_workspace_size =
FindMaxWorkspaceSize(args, workspace_size_limit);
VLOG(3) << "max_workspace_size=" << ToMegaBytes(max_workspace_size)
<< " MB";
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
......@@ -437,26 +382,28 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
args.cdesc.desc(), args.idesc.desc(),
const_cast<T*>(args.x->data<T>()),
kNUM_CUDNN_BWD_DATA_ALGS, &returned_algo_count,
perf_stat.data(), cudnn_workspace_ptr,
workspace_size_limit));
perf_results.data(), cudnn_workspace_ptr,
max_workspace_size));
};
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size_limit);
VLOG(3) << "BwdDataAlgo Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = perf_stat[i];
VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
<< " " << stat.memory;
}
return perf_stat[0].algo;
workspace_handle.RunFuncSync(cudnn_find_func, max_workspace_size,
UseFixedWorkspace());
VLOG(3) << GetPerfResultString<PerfT>(
"[Exhaustive Search] BwdDataAlgo Perf result", perf_results,
returned_algo_count, workspace_size_limit);
result.time = perf_results[0].time;
return perf_results[0].algo;
});
}
VLOG(3) << "choose algo " << algo;
return algo;
VLOG(3) << "[cuDNN Convoltion] exhaustive_search=" << exhaustive_search
<< ", deterministic=" << deterministic
<< ", choose algo=" << result.algo << ", workspace="
<< ToMegaBytes(GetWorkspaceSize(args, result.algo)) << " MB";
return result;
}
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
static size_t GetWorkspaceSize(const ConvArgs& args,
cudnnConvolutionBwdDataAlgo_t algo) {
size_t workspace_size = 0;
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
......@@ -464,57 +411,75 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
args.cdesc.desc(), args.idesc.desc(), algo, &workspace_size));
return workspace_size;
}
private:
static size_t FindMaxWorkspaceSize(const ConvArgs& args,
size_t workspace_size_limit) {
if (!UseFixedWorkspace()) {
size_t max_workspace_size = 0;
for (size_t algo = 0; algo < kNUM_CUDNN_BWD_DATA_ALGS; ++algo) {
size_t workspace_size = 0;
auto status =
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(),
static_cast<cudnnConvolutionBwdDataAlgo_t>(algo),
&workspace_size);
if (status == CUDNN_STATUS_SUCCESS) {
max_workspace_size = std::max(workspace_size, max_workspace_size);
}
}
return std::min(max_workspace_size, workspace_size_limit);
} else {
return workspace_size_limit;
}
}
};
template <>
struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
using perf_t = cudnnConvolutionBwdFilterAlgoPerf_t;
using algo_t = cudnnConvolutionBwdFilterAlgo_t;
using PerfT = cudnnConvolutionBwdFilterAlgoPerf_t;
using AlgoT = cudnnConvolutionBwdFilterAlgo_t;
template <typename T>
static algo_t Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic, const phi::GPUContext& ctx) {
static SearchResult<AlgoT> Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic,
const phi::GPUContext& ctx) {
platform::CUDAGraphCaptureModeGuard guard;
SearchResult<AlgoT> result;
auto dtype = platform::CudnnDataType<T>::type;
size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
size_t workspace_size = 0;
bool has_got_workspace_size = true;
size_t workspace_size_limit = CaclWorkspaceLimitInBytes(ctx);
SetConvMathType(ctx, dtype, args.cdesc);
algo_t algo;
if (!exhaustive_search && !deterministic) {
#if CUDNN_VERSION >= 7001
using perf_t = cudnnConvolutionBwdFilterAlgoPerf_t;
int perf_count;
int actual_perf_count;
int best_algo_idx = 0;
std::unique_ptr<perf_t[]> perf_results(
new perf_t[kNUM_CUDNN_BWD_FILTER_ALGS]);
std::vector<PerfT> perf_results(kNUM_CUDNN_BWD_FILTER_ALGS);
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm_v7(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(), kNUM_CUDNN_BWD_FILTER_ALGS,
&perf_count, perf_results.get()));
algo = (perf_results.get())[best_algo_idx].algo;
workspace_size = (perf_results.get())[best_algo_idx].memory;
&actual_perf_count, perf_results.data()));
result.algo = perf_results[best_algo_idx].algo;
result.workspace_size = perf_results[best_algo_idx].memory;
if (workspace_size > workspace_size_limit) {
workspace_size = workspace_size_limit;
if (result.workspace_size > workspace_size_limit) {
#if CUDNN_VERSION >= 8000
// cudnnGetConvolutionBackwardFilterAlgorithm is removed in CUDNN-8
ChooseAlgoByWorkspace<perf_t, algo_t>(perf_results.get(),
kNUM_CUDNN_BWD_FILTER_ALGS,
workspace_size_limit, &algo);
ChooseAlgoByWorkspace<PerfT, AlgoT>(perf_results, workspace_size_limit,
&result);
#else
VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue "
"the workspace size request("
<< workspace_size << ") exceeds the limit("
<< result.workspace_size << ") exceeds the limit("
<< workspace_size_limit << ")";
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(),
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit, &(result.algo)));
#endif
}
#else
......@@ -523,28 +488,32 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(),
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
workspace_size_limit, &(result.algo)));
#endif
} else if (deterministic) {
return CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
result.algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
} else {
auto& dev_ctx = ctx;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardFilter());
auto workspace_handle = ctx.cudnn_workspace_handle();
auto x_dims = phi::vectorize(args.x->dims());
auto w_dims = phi::vectorize(args.w->dims());
VLOG(10) << "cudnnConvolutionFwdAlgoPerf_t:"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
AlgorithmsCache<AlgoT>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardFilter());
if (dtype != CUDNN_DATA_HALF) {
algo = algo_cache.GetAlgorithm(
result.algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_BWD_FILTER_ALGS> perf_stat;
std::vector<PerfT> perf_results(kNUM_CUDNN_BWD_FILTER_ALGS);
size_t max_workspace_size =
FindMaxWorkspaceSize(args, workspace_size_limit);
VLOG(3) << "max_workspace_size="
<< ToMegaBytes(max_workspace_size) << " MB";
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::
......@@ -554,29 +523,26 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
args.cdesc.desc(), args.wdesc.desc(),
const_cast<T*>(args.w->data<T>()),
kNUM_CUDNN_BWD_FILTER_ALGS, &returned_algo_count,
perf_stat.data(), cudnn_workspace_ptr,
workspace_size_limit));
perf_results.data(), cudnn_workspace_ptr,
max_workspace_size));
};
workspace_handle.RunFuncSync(cudnn_find_func,
workspace_size_limit);
VLOG(3)
<< "BwdFilterAlgo Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = perf_stat[i];
VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
<< " " << stat.memory;
}
return perf_stat[0].algo;
workspace_handle.RunFuncSync(cudnn_find_func, max_workspace_size,
UseFixedWorkspace());
VLOG(3) << GetPerfResultString<PerfT>(
"[Exhaustive Search] BwdFilterAlgo Perf result", perf_results,
returned_algo_count, workspace_size_limit);
result.time = perf_results[0].time;
return perf_results[0].algo;
});
} else {
auto max_algos = MaxBwdFilterAlgos(args.handle);
algo = algo_cache.GetAlgorithm(
result.algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
algo_t chosen_algo;
std::vector<perf_t> perf_results(max_algos);
SearchResult<AlgoT> algo_result;
int actual_algos = 0;
std::vector<PerfT> perf_results(kNUM_CUDNN_BWD_FILTER_ALGS);
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::
cudnnFindConvolutionBackwardFilterAlgorithm(
......@@ -585,17 +551,21 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
perf_results.size(), &actual_algos,
perf_results.data()));
perf_results.resize(actual_algos);
ChooseAlgo<perf_t, algo_t>(perf_results, workspace_size_limit,
&chosen_algo);
return chosen_algo;
ChooseAlgo(perf_results, workspace_size_limit, &algo_result);
result.time = algo_result.time;
return algo_result.algo;
});
}
}
VLOG(3) << "choose algo " << algo;
return algo;
VLOG(3) << "[cuDNN Convoltion] exhaustive_search=" << exhaustive_search
<< ", deterministic=" << deterministic
<< ", choose algo=" << result.algo << ", workspace="
<< ToMegaBytes(GetWorkspaceSize(args, result.algo)) << " MB";
return result;
}
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
static size_t GetWorkspaceSize(const ConvArgs& args,
cudnnConvolutionBwdFilterAlgo_t algo) {
platform::CUDAGraphCaptureModeGuard guard;
size_t workspace_size = 0;
PADDLE_ENFORCE_GPU_SUCCESS(
......@@ -604,6 +574,69 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
args.cdesc.desc(), args.wdesc.desc(), algo, &workspace_size));
return workspace_size;
}
private:
static size_t FindMaxWorkspaceSize(const ConvArgs& args,
size_t workspace_size_limit) {
if (!UseFixedWorkspace()) {
size_t max_workspace_size = 0;
for (size_t algo = 0; algo < kNUM_CUDNN_BWD_FILTER_ALGS; ++algo) {
size_t workspace_size = 0;
auto status =
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(),
static_cast<cudnnConvolutionBwdFilterAlgo_t>(algo),
&workspace_size);
if (status == CUDNN_STATUS_SUCCESS) {
max_workspace_size = std::max(workspace_size, max_workspace_size);
}
}
return std::min(max_workspace_size, workspace_size_limit);
} else {
return workspace_size_limit;
}
}
static void ChooseAlgo(const std::vector<PerfT>& perf_results,
size_t workspace_limit,
SearchResult<AlgoT>* algo_result) {
VLOG(3) << GetPerfResultString<PerfT>(
"[Exhaustive Search] BwdFilterAlgo Perf result", perf_results,
perf_results.size(), workspace_limit);
for (size_t i = 0; i != perf_results.size(); ++i) {
const auto& result = perf_results[i];
if (result.status == CUDNN_STATUS_SUCCESS &&
(result.memory <= workspace_limit)) {
if ((result.mathType == CUDNN_TENSOR_OP_MATH) &&
(i != perf_results.size() - 1)) {
const auto& next_result = perf_results[i + 1];
if (next_result.status == CUDNN_STATUS_SUCCESS &&
next_result.algo == result.algo &&
next_result.memory == result.memory &&
next_result.mathType != CUDNN_TENSOR_OP_MATH &&
next_result.time < 1.01 * result.time) {
// Skip over this result- it's not really a Tensor Core algo.
// Because it is only 1% performance difference.
// Prefer to choose the next equivalent non-Tensor Core algo.
continue;
}
}
algo_result->algo = result.algo;
algo_result->time = result.time;
auto math_type_str = "0";
if (result.mathType == CUDNN_TENSOR_OP_MATH) {
math_type_str = "1";
}
VLOG(3) << " choose algo: " << result.algo
<< ", TC: " << math_type_str << ", time: " << result.time
<< " ms, wksp = " << result.memory
<< ", status = " << result.status;
break;
}
}
}
};
} // namespace operators
......
paddle/fluid/operators/conv_cudnn_op_cache.h
浏览文件 @ b937cdc5
......@@ -20,7 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
DECLARE_uint64(conv_workspace_size_limit);
DECLARE_int64(conv_workspace_size_limit);
DECLARE_bool(cudnn_exhaustive_search);
DECLARE_int64(cudnn_exhaustive_search_times);
......
paddle/fluid/operators/conv_miopen_helper.h
浏览文件 @ b937cdc5
......@@ -14,42 +14,12 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <array>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/conv_search_cache.h"
#include "paddle/fluid/framework/operator_kernel_configs.h"
#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/fluid/operators/conv_base_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DataLayout = platform::DataLayout;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
using framework::AlgorithmsCache;
static inline void GetNCDHW(const framework::DDim& dims,
const DataLayout& layout, int* N, int* C, int* D,
int* H, int* W) {
*N = dims[0];
*C = layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
int i = layout == DataLayout::kNCHW ? 0 : 1;
if (dims.size() == 5) {
*D = dims[2 - i];
*H = dims[3 - i];
*W = dims[4 - i];
} else {
*D = 1;
*H = dims[2 - i];
*W = dims[3 - i];
}
}
using ConvArgs = ConvArgsBase<miopenHandle_t, miopenDataType_t>;
template <typename DeviceContext, typename T, size_t D>
static void RemovePaddingSlice(const phi::GPUContext& context,
......@@ -66,9 +36,8 @@ static void RemovePaddingSlice(const phi::GPUContext& context,
extents[i] = new_out_dims[i];
}
int start;
for (size_t i = 0; i < axes.size(); ++i) {
start = starts[i];
int start = starts[i];
if (start < 0) {
start = (start + in_dims[axes[i]]);
}
......@@ -85,41 +54,6 @@ static void RemovePaddingSlice(const phi::GPUContext& context,
out_t.device(place) = in_t.slice(offsets, extents);
}
template <typename T>
std::ostream& operator<<(std::ostream& out, const std::vector<T>& v) {
out << "[";
for (auto const& tmp : v) out << tmp << ",";
out << "]";
return out;
}
using framework::ConvSearchCache;
struct ConvArgs {
miopenHandle_t handle;
platform::TensorDescriptor idesc, odesc;
platform::FilterDescriptor wdesc;
platform::ConvolutionDescriptor cdesc;
const framework::Tensor *x, *w, *o;
miopenDataType_t cudnn_dtype;
// strides
std::vector<int> s;
// paddings
std::vector<int> p;
// dilations
std::vector<int> d;
ConvArgs(const framework::Tensor* x, const framework::Tensor* w,
const framework::Tensor* o, const std::vector<int> s,
const std::vector<int> p, const std::vector<int> d,
miopenDataType_t dtype)
: x(x), w(w), o(o), s(s), p(p), d(d), cudnn_dtype(dtype) {}
};
template <typename algo_t>
struct SearchAlgorithm {};
template <>
struct SearchAlgorithm<miopenConvFwdAlgorithm_t> {
using perf_t = miopenConvAlgoPerf_t;
......
paddle/fluid/operators/fused/fusion_conv_inception_op.cu
浏览文件 @ b937cdc5
......@@ -16,8 +16,6 @@ limitations under the License. */
#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
DECLARE_uint64(conv_workspace_size_limit);
namespace paddle {
namespace operators {
......
paddle/fluid/platform/device/gpu/gpu_info.cc
浏览文件 @ b937cdc5
......@@ -188,6 +188,8 @@ class RecordedGpuMallocHelper {
if (UNLIKELY(malloc_managed_memory)) {
result = cudaMallocManaged(ptr, size);
} else {
VLOG(10) << "[cudaMalloc] size=" << static_cast<double>(size) / (1 << 20)
<< " MB";
result = cudaMalloc(ptr, size);
}
#endif
......@@ -226,6 +228,8 @@ class RecordedGpuMallocHelper {
if (err != hipErrorDeinitialized) {
#else
auto err = cudaFree(ptr);
VLOG(10) << "[cudaFree] size=" << static_cast<double>(size) / (1 << 20)
<< " MB";
if (err != cudaErrorCudartUnloading) {
#endif
PADDLE_ENFORCE_GPU_SUCCESS(err);
......
paddle/fluid/platform/device_context.cc
浏览文件 @ b937cdc5
......@@ -522,8 +522,8 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place) : phi::GPUContext(place) {
cuda_stream_.reset(new stream::CUDAStream(phi::GPUContext::stream(), place));
auto& instance = memory::allocation::AllocatorFacade::Instance();
instance.SetDefaultStream(place, phi::GPUContext::stream());
workspace_.reset(
new phi::DnnWorkspaceHandle(instance.GetAllocator(place).get()));
workspace_.reset(new phi::DnnWorkspaceHandle(
instance.GetAllocator(place).get(), stream()));
}
CUDADeviceContext::~CUDADeviceContext() = default;
......@@ -623,7 +623,8 @@ phi::DnnWorkspaceHandle CUDADeviceContext::cudnn_workspace_handle() const {
return phi::DnnWorkspaceHandle(
memory::allocation::AllocatorFacade::Instance()
.GetAllocator(GetPlace())
.get());
.get(),
stream());
}
return phi::GPUContext::cudnn_workspace_handle();
}
......
paddle/fluid/platform/flags.cc
浏览文件 @ b937cdc5
......@@ -161,8 +161,7 @@ PADDLE_DEFINE_EXPORTED_bool(
* increased.
* Users need to balance memory and speed.
*/
PADDLE_DEFINE_EXPORTED_uint64(
conv_workspace_size_limit,
PADDLE_DEFINE_EXPORTED_int64(conv_workspace_size_limit,
paddle::platform::kDefaultConvWorkspaceSizeLimitMB,
"cuDNN convolution workspace limit in MB unit.");
......
paddle/phi/backends/gpu/gpu_context.cc
浏览文件 @ b937cdc5
......@@ -12,6 +12,7 @@ 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/phi/backends/gpu/gpu_context.h"
#include <algorithm>
#include <array>
......@@ -155,6 +156,39 @@ static void StreamCallbackFunc(gpuStream_t stream,
} // namespace internal
void DnnWorkspaceHandle::RunFuncSync(
const std::function<void(void*)>& cudnn_func,
size_t required_workspace_bytes,
bool use_cached_allocation) {
bool need_realloc = required_workspace_bytes > WorkspaceSize();
if (need_realloc && !use_cached_allocation) {
void* workspace_ptr = nullptr;
size_t size = ((required_workspace_bytes + 255) >> 8) << 8;
std::lock_guard<std::mutex> guard(*mtx_);
#ifdef PADDLE_WITH_HIP
auto status = hipMalloc(&workspace_ptr, size);
#else
auto status = cudaMalloc(&workspace_ptr, size);
#endif
if (status == gpuSuccess) {
cudnn_func(workspace_ptr);
phi::backends::gpu::GpuStreamSync(stream_);
#ifdef PADDLE_WITH_HIP
PADDLE_ENFORCE_GPU_SUCCESS(hipFree(workspace_ptr));
#else
PADDLE_ENFORCE_GPU_SUCCESS(cudaFree(workspace_ptr));
#endif
return;
}
}
RunFunc(cudnn_func, required_workspace_bytes);
if (need_realloc) {
// Release the workspace allocated in this running.
ResetWorkspace();
}
}
void DnnWorkspaceHandle::ResetWorkspace() { allocation_ = nullptr; }
void DnnWorkspaceHandle::ReallocWorkspace(size_t required_workspace_bytes) {
......@@ -295,13 +329,13 @@ struct GPUContext::Impl {
void InitDnnWorkspace() {
PD_CHECK(allocator_ != nullptr,
"the device allocator for gpu context is nullptr.");
workspace_ = new DnnWorkspaceHandle(allocator_);
workspace_ = new DnnWorkspaceHandle(allocator_, stream_);
}
void DestoryInternalWorkspace() {
if (owned_ && workspace_ != nullptr) {
delete workspace_;
stream_ = nullptr;
workspace_ = nullptr;
}
}
......@@ -313,7 +347,7 @@ struct GPUContext::Impl {
DnnWorkspaceHandle GetDnnWorkspace() {
PD_CHECK(allocator_ != nullptr,
"the device allocator for gpu context is nullptr.");
return DnnWorkspaceHandle(allocator_);
return DnnWorkspaceHandle(allocator_, stream_);
}
void InitStream() {
......
paddle/phi/backends/gpu/gpu_context.h
浏览文件 @ b937cdc5
......@@ -21,6 +21,7 @@ limitations under the License. */
#include "paddle/phi/backends/gpu/forwards.h"
#include "paddle/phi/backends/gpu/gpu_decls.h"
#include "paddle/phi/backends/gpu/gpu_helper.h"
#include "paddle/phi/backends/gpu/gpu_info.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/device_context.h"
......@@ -28,8 +29,8 @@ namespace phi {
class DnnWorkspaceHandle {
public:
explicit inline DnnWorkspaceHandle(Allocator* allocator)
: allocator_(allocator) {
inline DnnWorkspaceHandle(Allocator* allocator, gpuStream_t stream)
: allocator_(allocator), stream_(stream) {
mtx_.reset(new std::mutex());
}
......@@ -48,11 +49,9 @@ class DnnWorkspaceHandle {
* running the function. Currently this function is only used when cudnn
* exhaustive searching and callers have to guarantee that the input function
* is host blocking */
inline void RunFuncSync(const std::function<void(void*)>& cudnn_func,
size_t required_workspace_bytes) {
RunFunc(cudnn_func, required_workspace_bytes);
ResetWorkspace();
}
void RunFuncSync(const std::function<void(void*)>& cudnn_func,
size_t required_workspace_bytes,
bool use_cached_allocation = true);
inline size_t WorkspaceSize() {
if (allocation_ == nullptr) {
......@@ -70,7 +69,8 @@ class DnnWorkspaceHandle {
private:
Allocator::AllocationPtr allocation_{nullptr};
Allocator* allocator_{nullptr};
Allocator* allocator_{nullptr}; // Not owned
gpuStream_t stream_{nullptr}; // Not owned
std::unique_ptr<std::mutex> mtx_;
};
......
paddle/phi/kernels/gpudnn/conv_grad_grad_kernel.cu
浏览文件 @ b937cdc5
......@@ -289,21 +289,17 @@ void ConvCudnnGradGradKernel(
dtype};
#ifdef PADDLE_WITH_HIP
miopenConvFwdAlgorithm_t fwd_algo1 = static_cast<miopenConvFwdAlgorithm_t>(0);
miopenConvFwdAlgorithm_t fwd_algo2 = static_cast<miopenConvFwdAlgorithm_t>(0);
miopenConvBwdDataAlgorithm_t data_algo =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvBwdWeightsAlgorithm_t filter_algo =
static_cast<miopenConvBwdWeightsAlgorithm_t>(0);
paddle::operators::SearchResult<miopenConvFwdAlgorithm_t> fwd_result1;
paddle::operators::SearchResult<miopenConvFwdAlgorithm_t> fwd_result2;
paddle::operators::SearchResult<miopenConvBwdDataAlgorithm_t> data_result;
paddle::operators::SearchResult<miopenConvBwdWeightsAlgorithm_t>
filter_result;
#else
cudnnConvolutionFwdAlgo_t fwd_algo1 =
static_cast<cudnnConvolutionFwdAlgo_t>(0);
cudnnConvolutionFwdAlgo_t fwd_algo2 =
static_cast<cudnnConvolutionFwdAlgo_t>(0);
cudnnConvolutionBwdDataAlgo_t data_algo =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionBwdFilterAlgo_t filter_algo =
static_cast<cudnnConvolutionBwdFilterAlgo_t>(0);
paddle::operators::SearchResult<cudnnConvolutionFwdAlgo_t> fwd_result1;
paddle::operators::SearchResult<cudnnConvolutionFwdAlgo_t> fwd_result2;
paddle::operators::SearchResult<cudnnConvolutionBwdDataAlgo_t> data_result;
paddle::operators::SearchResult<cudnnConvolutionBwdFilterAlgo_t>
filter_result;
#endif
auto layout = paddle::platform::GetCudnnTensorFormat(
......@@ -332,13 +328,13 @@ void ConvCudnnGradGradKernel(
using search1 =
paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = search1::GetWorkspaceSize(args1);
fwd_algo1 = search1::Find<T>(
fwd_result1.algo = search1::Find<T>(
args1, exhaustive_search, false, workspace_size, ctx);
#else
using search1 =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
fwd_algo1 = search1::Find<T>(args1, exhaustive_search, false, ctx);
workspace_size = search1::GetWorkspaceSize(args1, fwd_algo1);
fwd_result1 = search1::Find<T>(args1, exhaustive_search, false, ctx);
workspace_size = search1::GetWorkspaceSize(args1, fwd_result1.algo);
#endif
}
......@@ -360,14 +356,14 @@ void ConvCudnnGradGradKernel(
paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
fwd_algo2 = search2::Find<T>(
fwd_result2.algo = search2::Find<T>(
args2, exhaustive_search, false, workspace_size, ctx);
#else
using search2 =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
fwd_algo2 = search2::Find<T>(args2, exhaustive_search, false, ctx);
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2, fwd_algo2));
fwd_result2 = search2::Find<T>(args2, exhaustive_search, false, ctx);
workspace_size = std::max(
workspace_size, search2::GetWorkspaceSize(args2, fwd_result2.algo));
#endif
}
}
......@@ -389,15 +385,15 @@ void ConvCudnnGradGradKernel(
using search3 =
paddle::operators::SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size = std::max(workspace_size, search3::GetWorkspaceSize(args3));
filter_algo = search3::Find<T>(
filter_result.algo = search3::Find<T>(
args3, exhaustive_search, deterministic, workspace_size, ctx);
#else
using search3 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo =
filter_result =
search3::Find<T>(args3, exhaustive_search, deterministic, ctx);
workspace_size =
std::max(workspace_size, search3::GetWorkspaceSize(args3, filter_algo));
workspace_size = std::max(
workspace_size, search3::GetWorkspaceSize(args3, filter_result.algo));
#endif
}
......@@ -419,14 +415,15 @@ void ConvCudnnGradGradKernel(
using search4 =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search4::GetWorkspaceSize(args4));
data_algo = search4::Find<T>(
data_result.algo = search4::Find<T>(
args4, exhaustive_search, deterministic, workspace_size, ctx);
#else
using search4 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
data_algo = search4::Find<T>(args4, exhaustive_search, deterministic, ctx);
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4, data_algo));
data_result =
search4::Find<T>(args4, exhaustive_search, deterministic, ctx);
workspace_size = std::max(
workspace_size, search4::GetWorkspaceSize(args4, data_result.algo));
#endif
}
......@@ -471,7 +468,7 @@ void ConvCudnnGradGradKernel(
args1.wdesc.desc(),
w,
args1.cdesc.desc(),
fwd_algo1,
fwd_result1.algo,
&beta,
args1.odesc.desc(),
transformed_ddy_channel,
......@@ -492,7 +489,7 @@ void ConvCudnnGradGradKernel(
args1.wdesc.desc(),
w + i * group_offset_filter,
args1.cdesc.desc(),
fwd_algo1,
fwd_result1.algo,
workspace_ptr,
workspace_size,
&beta,
......@@ -517,7 +514,7 @@ void ConvCudnnGradGradKernel(
args2.wdesc.desc(),
ddw,
args2.cdesc.desc(),
fwd_algo2,
fwd_result2.algo,
&beta,
args2.odesc.desc(),
transformed_ddy_channel,
......@@ -538,7 +535,7 @@ void ConvCudnnGradGradKernel(
args2.wdesc.desc(),
ddw + i * group_offset_filter,
args2.cdesc.desc(),
fwd_algo2,
fwd_result2.algo,
workspace_ptr,
workspace_size,
&alpha,
......@@ -568,7 +565,7 @@ void ConvCudnnGradGradKernel(
args3.idesc.desc(),
ddx,
args3.cdesc.desc(),
filter_algo,
filter_result.algo,
&beta,
args3.wdesc.desc(),
dw,
......@@ -589,7 +586,7 @@ void ConvCudnnGradGradKernel(
args3.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args3.cdesc.desc(),
filter_algo,
filter_result.algo,
workspace_ptr,
workspace_size,
&beta,
......@@ -615,7 +612,7 @@ void ConvCudnnGradGradKernel(
args4.wdesc.desc(),
ddw,
args4.cdesc.desc(),
data_algo,
data_result.algo,
&beta,
args4.idesc.desc(),
transformed_dx,
......@@ -636,7 +633,7 @@ void ConvCudnnGradGradKernel(
args4.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args4.cdesc.desc(),
data_algo,
data_result.algo,
workspace_ptr,
workspace_size,
&beta,
......
paddle/phi/kernels/gpudnn/conv_grad_kernel.cu
浏览文件 @ b937cdc5
......@@ -322,17 +322,16 @@ void ConvCudnnGradKernel(const Context& ctx,
int group_offset_in = i_c / groups * i_h * i_w * i_d;
int group_offset_out = o_c / groups * o_h * o_w * o_d;
int group_offset_filter = transformed_filter_channel.numel() / groups;
// ------------------- cudnn backward algorithm ---------------------
#ifdef PADDLE_WITH_HIP
miopenConvBwdDataAlgorithm_t data_algo =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvBwdWeightsAlgorithm_t filter_algo =
static_cast<miopenConvBwdWeightsAlgorithm_t>(0);
paddle::operators::SearchResult<miopenConvBwdDataAlgorithm_t> bwd_result;
paddle::operators::SearchResult<miopenConvBwdWeightsAlgorithm_t>
filter_result;
#else
cudnnConvolutionBwdDataAlgo_t data_algo =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionBwdFilterAlgo_t filter_algo =
static_cast<cudnnConvolutionBwdFilterAlgo_t>(0);
paddle::operators::SearchResult<cudnnConvolutionBwdDataAlgo_t> bwd_result;
paddle::operators::SearchResult<cudnnConvolutionBwdFilterAlgo_t>
filter_result;
#endif
// input data workspace_size
size_t workspace_size_d = 0;
......@@ -368,14 +367,14 @@ void ConvCudnnGradKernel(const Context& ctx,
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size_d =
std::max(workspace_size_d, search1::GetWorkspaceSize(args1));
data_algo = search1::Find<T>(
bwd_result.algo = search1::Find<T>(
args1, exhaustive_search, deterministic, workspace_size_d, ctx);
#else
using search1 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
data_algo = search1::Find<T>(args1, exhaustive_search, deterministic, ctx);
workspace_size_d =
std::max(workspace_size_d, search1::GetWorkspaceSize(args1, data_algo));
bwd_result = search1::Find<T>(args1, exhaustive_search, deterministic, ctx);
workspace_size_d = std::max(
workspace_size_d, search1::GetWorkspaceSize(args1, bwd_result.algo));
#endif
}
......@@ -397,15 +396,17 @@ void ConvCudnnGradKernel(const Context& ctx,
paddle::operators::SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size_w =
std::max(workspace_size_w, search2::GetWorkspaceSize(args2));
filter_algo = search2::Find<T>(
filter_result.algo = search2::Find<T>(
args2, exhaustive_search, deterministic, workspace_size_w, ctx);
#else
using search2 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo =
filter_result =
search2::Find<T>(args2, exhaustive_search, deterministic, ctx);
workspace_size_w = std::max(workspace_size_w,
search2::GetWorkspaceSize(args2, filter_algo));
VLOG(3) << "filter algo: " << filter_result.algo << ", time "
<< filter_result.time;
workspace_size_w = std::max(
workspace_size_w, search2::GetWorkspaceSize(args2, filter_result.algo));
#endif
}
......@@ -439,7 +440,7 @@ void ConvCudnnGradKernel(const Context& ctx,
args1.wdesc.desc(),
filter_data,
args1.cdesc.desc(),
data_algo,
bwd_result.algo,
&beta,
args1.idesc.desc(),
temp_tensor_data,
......@@ -471,7 +472,7 @@ void ConvCudnnGradKernel(const Context& ctx,
args1.wdesc.desc(),
filter_data,
args1.cdesc.desc(),
data_algo,
bwd_result.algo,
&beta,
args1.idesc.desc(),
transformed_input_grad_data,
......@@ -494,7 +495,7 @@ void ConvCudnnGradKernel(const Context& ctx,
args1.odesc.desc(),
output_grad_data + i * group_offset_out,
args1.cdesc.desc(),
data_algo,
bwd_result.algo,
cudnn_workspace_ptr,
workspace_size_d,
&beta,
......@@ -554,7 +555,7 @@ void ConvCudnnGradKernel(const Context& ctx,
args2.idesc.desc(),
input_data,
args2.cdesc.desc(),
filter_algo,
filter_result.algo,
&beta,
args2.wdesc.desc(),
filter_grad_data,
......@@ -575,7 +576,7 @@ void ConvCudnnGradKernel(const Context& ctx,
args2.odesc.desc(),
output_grad_data + i * group_offset_out,
args2.cdesc.desc(),
filter_algo,
filter_result.algo,
cudnn_workspace_ptr,
workspace_size_w,
&beta_filter,
......
paddle/phi/kernels/gpudnn/conv_kernel.cu
浏览文件 @ b937cdc5
......@@ -18,7 +18,6 @@
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/fluid/framework/eigen.h"
#ifdef PADDLE_WITH_HIP
#include "paddle/fluid/operators/conv_miopen_helper.h"
#else
......@@ -68,7 +67,6 @@ void ConvCudnnKernel(const Context& ctx,
"FLAGS_cudnn_deterministic True at same time."));
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
auto dtype = paddle::platform::CudnnDataType<T>::type;
#ifdef PADDLE_WITH_HIP
......@@ -309,17 +307,17 @@ void ConvCudnnKernel(const Context& ctx,
size_t workspace_size = 0; // final workspace to allocate.
// ------------------- cudnn conv algorithm ---------------------
#ifdef PADDLE_WITH_HIP
miopenConvFwdAlgorithm_t algo{};
paddle::operators::SearchResult<miopenConvFwdAlgorithm_t> fwd_result;
using search = paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = search::GetWorkspaceSize(args);
algo = search::Find<T>(
fwd_result.algo = search::Find<T>(
args, exhaustive_search, deterministic, workspace_size, ctx);
#else
cudnnConvolutionFwdAlgo_t algo{};
paddle::operators::SearchResult<cudnnConvolutionFwdAlgo_t> fwd_result;
using search =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
algo = search::Find<T>(args, exhaustive_search, deterministic, ctx);
workspace_size = search::GetWorkspaceSize(args, algo);
fwd_result = search::Find<T>(args, exhaustive_search, deterministic, ctx);
workspace_size = search::GetWorkspaceSize(args, fwd_result.algo);
#endif
#if defined(PADDLE_WITH_CUDA) && CUDNN_VERSION_MIN(7, 0, 1)
......@@ -328,7 +326,7 @@ void ConvCudnnKernel(const Context& ctx,
// in forward computation, so change the algorithm to CUDNN_CONVOLUTION_\
// FWD_ALGO_IMPLICIT_GEMM manually.
if (groups > 1) {
algo = static_cast<cudnnConvolutionFwdAlgo_t>(0);
fwd_result.algo = static_cast<cudnnConvolutionFwdAlgo_t>(0);
}
#endif
......@@ -352,7 +350,7 @@ void ConvCudnnKernel(const Context& ctx,
args.wdesc.desc(),
filter_data,
args.cdesc.desc(),
algo,
fwd_result.algo,
&beta,
args.odesc.desc(),
output_data,
......@@ -373,7 +371,7 @@ void ConvCudnnKernel(const Context& ctx,
args.wdesc.desc(),
filter_data + i * group_offset_filter,
args.cdesc.desc(),
algo,
fwd_result.algo,
workspace_ptr,
workspace_size,
&beta,
......
paddle/phi/kernels/gpudnn/conv_transpose_grad_kernel.cu
浏览文件 @ b937cdc5
......@@ -188,11 +188,13 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
dtype};
#ifdef PADDLE_WITH_HIP
miopenConvFwdAlgorithm_t data_algo{};
miopenConvBwdWeightsAlgorithm_t filter_algo{};
paddle::operators::SearchResult<miopenConvFwdAlgorithm_t> fwd_result;
paddle::operators::SearchResult<miopenConvBwdWeightsAlgorithm_t>
filter_result;
#else
cudnnConvolutionFwdAlgo_t data_algo{};
cudnnConvolutionBwdFilterAlgo_t filter_algo{};
paddle::operators::SearchResult<cudnnConvolutionFwdAlgo_t> fwd_result;
paddle::operators::SearchResult<cudnnConvolutionBwdFilterAlgo_t>
filter_result;
#endif
auto layout_tensor = paddle::platform::GetCudnnTensorFormat(layout);
......@@ -218,14 +220,14 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
using search1 =
paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = std::max(workspace_size, search1::GetWorkspaceSize(args1));
data_algo =
fwd_result.algo =
search1::Find<T>(args1, false, deterministic, workspace_size, ctx);
#else
using search1 =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
data_algo = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1, data_algo));
fwd_result = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size = std::max(
workspace_size, search1::GetWorkspaceSize(args1, fwd_result.algo));
#endif
}
......@@ -245,14 +247,14 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
using search2 =
paddle::operators::SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size = std::max(workspace_size, search2::GetWorkspaceSize(args2));
filter_algo =
filter_result.algo =
search2::Find<T>(args2, false, deterministic, workspace_size, ctx);
#else
using search2 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2, filter_algo));
filter_result = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size = std::max(
workspace_size, search2::GetWorkspaceSize(args2, filter_result.algo));
#endif
}
......@@ -278,7 +280,7 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
args1.wdesc.desc(),
filter_data + filter_offset * g,
args1.cdesc.desc(),
data_algo,
fwd_result.algo,
&beta,
args1.odesc.desc(),
dx_data + x_offset * g,
......@@ -295,7 +297,7 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
args1.wdesc.desc(),
filter_data + filter_offset * g,
args1.cdesc.desc(),
data_algo,
fwd_result.algo,
cudnn_workspace,
workspace_size,
&beta,
......@@ -338,7 +340,7 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
args2.idesc.desc(),
dout_data + dout_offset * g,
args2.cdesc.desc(),
filter_algo,
filter_result.algo,
&beta,
args2.wdesc.desc(),
dfilter_data + filter_offset * g,
......@@ -355,7 +357,7 @@ void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
args2.odesc.desc(),
x_data + x_offset * g,
args2.cdesc.desc(),
filter_algo,
filter_result.algo,
cudnn_workspace,
workspace_size,
&beta,
......@@ -653,22 +655,17 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
dilations_,
dtype};
#ifdef PADDLE_WITH_HIP
miopenConvBwdDataAlgorithm_t bwd_algo1 =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvBwdDataAlgorithm_t bwd_algo2 =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvFwdAlgorithm_t data_algo = static_cast<miopenConvFwdAlgorithm_t>(0);
miopenConvBwdWeightsAlgorithm_t filter_algo =
static_cast<miopenConvBwdWeightsAlgorithm_t>(0);
paddle::operators::SearchResult<miopenConvBwdDataAlgorithm_t> bwd_result1;
paddle::operators::SearchResult<miopenConvBwdDataAlgorithm_t> bwd_result2;
paddle::operators::SearchResult<miopenConvBwdWeightsAlgorithm_t>
filter_result;
paddle::operators::SearchResult<miopenConvFwdAlgorithm_t> fwd_result;
#else
cudnnConvolutionBwdDataAlgo_t bwd_algo1 =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionBwdDataAlgo_t bwd_algo2 =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionFwdAlgo_t data_algo =
static_cast<cudnnConvolutionFwdAlgo_t>(0);
cudnnConvolutionBwdFilterAlgo_t filter_algo =
static_cast<cudnnConvolutionBwdFilterAlgo_t>(0);
paddle::operators::SearchResult<cudnnConvolutionBwdDataAlgo_t> bwd_result1;
paddle::operators::SearchResult<cudnnConvolutionBwdDataAlgo_t> bwd_result2;
paddle::operators::SearchResult<cudnnConvolutionBwdFilterAlgo_t>
filter_result;
paddle::operators::SearchResult<cudnnConvolutionFwdAlgo_t> fwd_result;
#endif
auto layout = paddle::platform::GetCudnnTensorFormat(GPUDNNDataLayout::kNCHW);
......@@ -696,13 +693,13 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
using search1 =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = search1::GetWorkspaceSize(args1);
bwd_algo1 =
bwd_result1.algo =
search1::Find<T>(args1, false, deterministic, workspace_size, ctx);
#else
using search1 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
bwd_algo1 = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size = search1::GetWorkspaceSize(args1, bwd_algo1);
bwd_result1 = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size = search1::GetWorkspaceSize(args1, bwd_result1.algo);
#endif
ddfilter_ = ddfilter.data<T>();
......@@ -720,14 +717,14 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
using search2 =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search2::GetWorkspaceSize(args2));
bwd_algo2 =
bwd_result2.algo =
search2::Find<T>(args2, false, deterministic, workspace_size, ctx);
#else
using search2 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
bwd_algo2 = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2, bwd_algo2));
bwd_result2 = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size = std::max(
workspace_size, search2::GetWorkspaceSize(args2, bwd_result2.algo));
#endif
}
......@@ -736,9 +733,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args3.handle = handle;
args3.idesc.set(transformed_dout, iwo_group);
args3.wdesc.set(*dfilter, layout, iwo_group);
args3.odesc.set(transformed_ddx_channel, iwo_group);
args3.cdesc.set(dtype,
padding_common,
strides,
......@@ -749,14 +744,14 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
using search3 =
paddle::operators::SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size = std::max(workspace_size, search3::GetWorkspaceSize(args3));
filter_algo =
filter_result.algo =
search3::Find<T>(args3, false, deterministic, workspace_size, ctx);
#else
using search3 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo = search3::Find<T>(args3, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search3::GetWorkspaceSize(args3, filter_algo));
filter_result = search3::Find<T>(args3, false, deterministic, ctx);
workspace_size = std::max(
workspace_size, search3::GetWorkspaceSize(args3, filter_result.algo));
#endif
}
......@@ -777,14 +772,14 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
using search4 =
paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = std::max(workspace_size, search4::GetWorkspaceSize(args4));
data_algo =
fwd_result.algo =
search4::Find<T>(args4, false, deterministic, workspace_size, ctx);
#else
using search4 =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
data_algo = search4::Find<T>(args4, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4, data_algo));
fwd_result = search4::Find<T>(args4, false, deterministic, ctx);
workspace_size = std::max(
workspace_size, search4::GetWorkspaceSize(args4, fwd_result.algo));
#endif
}
......@@ -831,7 +826,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args1.wdesc.desc(),
filter_ + i * group_offset_filter,
args1.cdesc.desc(),
bwd_algo1,
bwd_result1.algo,
&beta,
args1.idesc.desc(),
transformed_ddout_channel_ + i * group_offset_out,
......@@ -850,7 +845,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args1.odesc.desc(),
ddx_ + i * group_offset_in,
args1.cdesc.desc(),
bwd_algo1,
bwd_result1.algo,
workspace_ptr,
workspace_size,
&beta,
......@@ -877,7 +872,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args2.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args2.cdesc.desc(),
bwd_algo2,
bwd_result2.algo,
&beta,
args2.idesc.desc(),
conv_x_ddfilter_data + i * group_offset_out,
......@@ -908,7 +903,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args2.odesc.desc(),
x_ + i * group_offset_in,
args2.cdesc.desc(),
bwd_algo2,
bwd_result2.algo,
workspace_ptr,
workspace_size,
&alpha,
......@@ -964,7 +959,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args3.idesc.desc(),
transformed_dout_channel_ + i * group_offset_out,
args3.cdesc.desc(),
filter_algo,
filter_result.algo,
&beta,
args3.wdesc.desc(),
dfilter_ + i * group_offset_filter,
......@@ -983,7 +978,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args3.odesc.desc(),
ddx_ + i * group_offset_in,
args3.cdesc.desc(),
filter_algo,
filter_result.algo,
workspace_ptr,
workspace_size,
&beta,
......@@ -1009,7 +1004,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args4.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args4.cdesc.desc(),
data_algo,
fwd_result.algo,
&beta,
args4.odesc.desc(),
transformed_dx_ + i * group_offset_in,
......@@ -1028,7 +1023,7 @@ void Conv2dTransposeDoubleGradGPUDNNKernel(
args4.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args4.cdesc.desc(),
data_algo,
fwd_result.algo,
workspace_ptr,
workspace_size,
&beta,
......
paddle/phi/kernels/gpudnn/conv_transpose_kernel.cu
浏览文件 @ b937cdc5
......@@ -217,16 +217,19 @@ void ConvTransposeRawGPUDNNKernel(const Context& ctx,
c_groups);
#ifdef PADDLE_WITH_HIP
paddle::operators::SearchResult<miopenConvBwdDataAlgorithm_t> bwd_result;
using search =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search::GetWorkspaceSize(args));
algo = search::Find<T>(args, false, deterministic, workspace_size, ctx);
bwd_result.algo =
search::Find<T>(args, false, deterministic, workspace_size, ctx);
#else
paddle::operators::SearchResult<cudnnConvolutionBwdDataAlgo_t> bwd_result;
using search =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
algo = search::Find<T>(args, false, deterministic, ctx);
bwd_result = search::Find<T>(args, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search::GetWorkspaceSize(args, algo));
std::max(workspace_size, search::GetWorkspaceSize(args, bwd_result.algo));
#endif
// ------------------- cudnn conv transpose forward ---------------------
......@@ -247,7 +250,7 @@ void ConvTransposeRawGPUDNNKernel(const Context& ctx,
args.wdesc.desc(),
filter_data + filter_offset * g,
args.cdesc.desc(),
algo,
bwd_result.algo,
&beta,
args.idesc.desc(),
transformed_out_data + out_offset * g,
......@@ -264,7 +267,7 @@ void ConvTransposeRawGPUDNNKernel(const Context& ctx,
args.odesc.desc(),
x_data + x_offset * g,
args.cdesc.desc(),
algo,
bwd_result.algo,
cudnn_workspace,
workspace_size,
&beta,
......
paddle/phi/kernels/impl/conv_cudnn_impl.h
浏览文件 @ b937cdc5
......@@ -36,7 +36,7 @@
#include "paddle/phi/kernels/funcs/batch_norm_utils.h"
DECLARE_bool(cudnn_deterministic);
DECLARE_uint64(conv_workspace_size_limit);
DECLARE_int64(conv_workspace_size_limit);
DECLARE_bool(cudnn_exhaustive_search);
namespace phi {
......
python/paddle/fluid/tests/unittests/test_switch_autotune.py
浏览文件 @ b937cdc5
......@@ -43,6 +43,16 @@ def static_program(net, data):
return loss
def set_flags(enable_autotune):
if paddle.is_compiled_with_cuda():
if enable_autotune:
paddle.set_flags({'FLAGS_conv_workspace_size_limit': -1})
paddle.set_flags({'FLAGS_cudnn_exhaustive_search': 1})
else:
paddle.set_flags({'FLAGS_conv_workspace_size_limit': 512})
paddle.set_flags({'FLAGS_cudnn_exhaustive_search': 0})
class TestAutoTune(unittest.TestCase):
def test_autotune(self):
paddle.fluid.core.disable_autotune()
......@@ -61,6 +71,7 @@ class TestAutoTune(unittest.TestCase):
class TestDygraphAutoTuneStatus(TestAutoTune):
def run_program(self, enable_autotune):
set_flags(enable_autotune)
if enable_autotune:
paddle.fluid.core.enable_autotune()
else:
......@@ -107,6 +118,7 @@ class TestDygraphAutoTuneStatus(TestAutoTune):
class TestStaticAutoTuneStatus(TestAutoTune):
def run_program(self, enable_autotune):
paddle.enable_static()
set_flags(enable_autotune)
if enable_autotune:
paddle.fluid.core.enable_autotune()
else:
......
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