未验证 提交 10dffc68 编写于 作者: W Wu Yi 提交者: GitHub

Merge pull request #13618 from typhoonzero/revert_13530

Revert "Some trivial optimization (#13530)"
......@@ -38,31 +38,27 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
std::string op_type_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
}
const proto::OpProto& Proto() const {
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator %s Proto has not been registered",
op_type_);
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator Proto has not been registered");
PADDLE_ENFORCE(proto_->IsInitialized(),
"Operator %s Proto must be initialized in op info",
op_type_);
"Operator Proto must be initialized in op info");
return *proto_;
}
const OpCreator& Creator() const {
PADDLE_ENFORCE_NOT_NULL(
creator_, "Operator %s Creator has not been registered", op_type_);
PADDLE_ENFORCE_NOT_NULL(creator_,
"Operator Creator has not been registered");
return creator_;
}
const GradOpMakerFN& GradOpMaker() const {
PADDLE_ENFORCE_NOT_NULL(grad_op_maker_,
"Operator %s GradOpMaker has not been registered.",
op_type_);
"Operator GradOpMaker has not been registered.");
return grad_op_maker_;
}
......@@ -77,9 +73,8 @@ class OpInfoMap {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string& type, OpInfo info) {
void Insert(const std::string& type, const OpInfo& info) {
PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
info.op_type_ = type;
map_.insert({type, info});
}
......
......@@ -45,12 +45,10 @@ class ReadInferVarType : public framework::VarTypeInference {
framework::VarDesc* reader = block->FindVarRecursive(reader_name);
auto dtypes = reader->GetDataTypes();
PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size());
auto lod_levels = reader->GetLoDLevels();
for (size_t i = 0; i < dtypes.size(); ++i) {
framework::VarDesc& out = block->FindRecursiveOrCreateVar(out_names[i]);
out.SetType(framework::proto::VarType::LOD_TENSOR);
out.SetDataType(dtypes[i]);
out.SetLoDLevel(lod_levels[i]);
}
}
};
......
......@@ -12,7 +12,7 @@ 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 <algorithm>
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/sgd_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -33,21 +33,22 @@ __global__ void SGDKernel(const T* g, const T* p, const T* learning_rate,
}
}
template <typename T>
template <typename T, int block_size>
__global__ void SparseSGDFunctorKernel(const T* selected_rows,
const int64_t* rows,
const T* learning_rate, T* tensor_out,
int64_t row_numel, int64_t limit) {
for (int64_t i = blockIdx.x; i < limit; i += gridDim.x) {
const T* selected_rows_ptr = selected_rows + i * row_numel;
T* tensor_out_ptr = tensor_out + rows[i] * row_numel;
for (int64_t index = threadIdx.x; index < row_numel; index += blockDim.x) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out_ptr + index,
-1.0 * learning_rate[0] * selected_rows_ptr[index]);
}
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out + index, -1.0 * learning_rate[0] * selected_rows[index]);
}
}
} // namespace
......@@ -96,15 +97,13 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
auto* in_data = in_value.data<T>();
auto* out_data = param_out->data<T>();
const int kThreadsPerBlock = 256;
int thread_x = kThreadsPerBlock;
int max_threads = ctx.cuda_device_context().GetMaxPhysicalThreadCount();
int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
SparseSGDFunctorKernel<<<max_blocks, thread_x, 0,
ctx.cuda_device_context().stream()>>>(
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in_rows.size());
SparseSGDFunctorKernel<
T, 256><<<grid, threads, 0, ctx.cuda_device_context().stream()>>>(
in_data, in_rows.CUDAData(ctx.GetPlace()), learning_rate->data<T>(),
out_data, in_row_numel, in_rows.size());
out_data, in_row_numel);
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
......
......@@ -52,26 +52,16 @@ class ShrinkRNNMemoryOp : public ArrayOp {
size_t height = dst_num_rows;
// do shrink for the top level LoD
if (x_tensor.lod().size() > 0 &&
x_tensor.lod()[0].size() > static_cast<size_t>(dst_num_rows)) {
if (x_tensor.lod().size() > 1) { // MultiLevel LoD
auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(
x_tensor.lod(), 0, dst_num_rows, 0);
height = lod_offset.second.second;
auto out_lod = out_tensor.mutable_lod();
framework::AppendLoD(out_lod, lod_offset.first);
} else {
// Shrink LoD
auto lod_item = x_tensor.lod()[0];
lod_item.resize(dst_num_rows + 1);
out_tensor.set_lod({lod_item});
const auto &const_lod_item = lod_item;
height = const_lod_item.back();
}
auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(x_tensor.lod(), 0,
dst_num_rows, 0);
height = lod_offset.second.second;
auto out_lod = out_tensor.mutable_lod();
framework::AppendLoD(out_lod, lod_offset.first);
}
if (height != 0) {
if (dst_num_rows != 0) {
out_tensor.mutable_data(place, x_tensor.type());
auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
framework::TensorCopy(x_tensor.Slice(0, height), place, *dev_ctx,
......@@ -144,11 +134,8 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
} else {
auto &dout_tensor = dout_var->Get<framework::LoDTensor>();
auto height = dout_tensor.dims()[0];
if (height != 0) {
auto slice = dx_tensor.Slice(0, static_cast<int>(height));
framework::TensorCopy(dout_tensor, dout_tensor.place(), dev_ctx,
&slice);
}
auto slice = dx_tensor.Slice(0, static_cast<int>(height));
framework::TensorCopy(dout_tensor, dout_tensor.place(), dev_ctx, &slice);
if (dx_tensor.dims()[0] > height) {
auto rest_tensor = dx_tensor.Slice(
static_cast<int>(height), static_cast<int>(dx_tensor.dims()[0]));
......
......@@ -201,7 +201,6 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
compute_capability = GetCUDAComputeCapability(place_.device);
multi_process = GetCUDAMultiProcessors(place_.device);
max_threads_per_mp = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
grid_max_dims_ = GpuMaxGridDim(place_.device);
PADDLE_ENFORCE(cudaStreamCreate(&stream_));
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
......@@ -240,10 +239,6 @@ int CUDADeviceContext::GetMaxPhysicalThreadCount() const {
return multi_process * max_threads_per_mp;
}
std::tuple<int, int, int> CUDADeviceContext::GetMaxGridDims() const {
return grid_max_dims_;
}
Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
return eigen_device_.get();
}
......
......@@ -13,7 +13,6 @@ limitations under the License. */
#include <memory>
#include <mutex> // NOLINT
#include <string>
#include <tuple>
#include <unordered_map>
#include <vector>
......@@ -92,8 +91,6 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return the max physical thread count in the device context */
int GetMaxPhysicalThreadCount() const;
std::tuple<int, int, int> GetMaxGridDims() const;
/*! \brief Return eigen device in the device context. */
Eigen::GpuDevice* eigen_device() const;
......@@ -138,8 +135,6 @@ class CUDADeviceContext : public DeviceContext {
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
std::tuple<int, int, int> grid_max_dims_;
int compute_capability;
int multi_process;
int max_threads_per_mp;
......
......@@ -48,54 +48,35 @@ __global__ static void ForRangeElemwiseOpGridIsOne(Function func) {
}
template <typename Function>
__global__ static void ForRangeElemwiseOp(Function func, size_t limit) {
__global__ static void ForRangeElemwiseOp(Function func, int limit) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
if (idx < limit) {
func(idx);
}
}
template <typename Function>
__global__ static void ForRangeElemwiseOpGridLarge(Function func, size_t limit,
int grid_dim) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
while (idx < limit) {
func(idx);
idx += grid_dim;
}
}
template <>
struct ForRange<CUDADeviceContext> {
ForRange(const CUDADeviceContext& dev_ctx, size_t limit)
: dev_ctx_(dev_ctx), limit_(limit) {}
: dev_ctx_(dev_ctx), limit_(static_cast<int>(limit)) {}
template <typename Function>
inline void operator()(Function func) const {
constexpr int num_threads = 1024;
int block_size = limit_ <= num_threads ? limit_ : num_threads;
size_t grid_size = (limit_ + num_threads - 1) / num_threads;
int max_grid_dim = std::get<0>(dev_ctx_.GetMaxGridDims());
if (grid_size < max_grid_dim) {
int grid_size_int = static_cast<int>(grid_size);
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOp<<<grid_size_int, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
int grid_size = (limit_ + num_threads - 1) / num_threads;
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOpGridLarge<<<max_grid_dim, block_size, 0,
dev_ctx_.stream()>>>(func, limit_,
max_grid_dim);
ForRangeElemwiseOp<<<grid_size, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
}
const CUDADeviceContext& dev_ctx_;
size_t limit_;
int limit_;
};
#endif
......
......@@ -152,22 +152,5 @@ void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream) {
PADDLE_ENFORCE(cudaMemsetAsync(dst, value, count, stream),
"cudaMemsetAsync failed in paddle::platform::GpuMemsetAsync");
}
std::tuple<int, int, int> GpuMaxGridDim(int id) {
std::tuple<int, int, int> result;
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<0>(result), cudaDevAttrMaxBlockDimX, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<1>(result), cudaDevAttrMaxBlockDimY, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
PADDLE_ENFORCE(
cudaDeviceGetAttribute(&std::get<2>(result), cudaDevAttrMaxBlockDimZ, id),
"cudaDeviceGetAttribute failed in "
"cudaDevAttrMaxBlockDim");
return result;
}
} // namespace platform
} // namespace paddle
......@@ -19,7 +19,6 @@ limitations under the License. */
#include <cuda_runtime.h>
#include <stddef.h>
#include <string>
#include <tuple>
namespace paddle {
namespace platform {
......@@ -73,8 +72,6 @@ void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
//! Set memory dst with value count size asynchronously
void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream);
std::tuple<int, int, int> GpuMaxGridDim(int id);
} // namespace platform
} // namespace paddle
......
......@@ -311,7 +311,6 @@ def _copy_reader_var_(block, var):
new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER)
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.desc.set_lod_levels(var.desc.lod_levels())
new_var.persistable = True
return new_var
......@@ -633,7 +632,6 @@ def py_reader(capacity,
})
startup_var.desc.set_dtypes(dtypes)
startup_var.desc.set_lod_levels(lod_levels)
startup_var.persistable = True
main_prog_var = _copy_reader_var_(default_main_program().current_block(),
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册