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未验证 提交 6b47507d 编写于 作者: Y YuanRisheng 提交者： GitHub
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move reduce func (#46248)

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paddle/phi/kernels/cpu/dirichlet_kernel.cc
浏览文件 @ 6b47507d
......@@ -16,9 +16,9 @@
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/elementwise.h"
#include "paddle/phi/kernels/cpu/reduce.h"
#include "paddle/phi/kernels/funcs/elementwise_functor.h"
#include "paddle/phi/kernels/funcs/for_range.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/funcs/reduce_functor.h"
#include "paddle/phi/kernels/impl/dirichlet_kernel_impl.h"
......@@ -83,7 +83,7 @@ struct DirichletSampler<CPUContext, T> {
gamma_sum.Resize(new_shape);
dev_ctx.template Alloc<T>(&gamma_sum);
ReduceKernelImpl<CPUContext, T, T, funcs::SumFunctor>(
funcs::ReduceKernelImpl<CPUContext, T, T, funcs::SumFunctor>(
dev_ctx,
gamma_samples,
&gamma_sum,
......
paddle/phi/kernels/cpu/reduce.h
浏览文件 @ 6b47507d
......@@ -16,181 +16,11 @@
#include <set>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/visit_type.h"
#include "paddle/phi/kernels/cast_kernel.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/math_function.h"
// See Note [ Why still include the fluid headers? ]
#include "paddle/fluid/operators/eigen/eigen_function.h"
namespace phi {
template <typename DeviceContext,
typename T,
size_t D,
size_t R_D,
typename Functor>
void ReduceFunctor(const DeviceContext& context,
const phi::DenseTensor& input,
phi::DenseTensor* output,
const std::vector<int64_t>& dims,
bool keep_dim) {
auto x = EigenTensor<T, D>::From(input);
auto x_rank = static_cast<int>(x.dimensions().size());
auto reduce_dim = Eigen::array<int, R_D>();
std::vector<int64_t> dims_ref = dims;
for (size_t i = 0; i < dims_ref.size(); ++i) {
if (dims_ref[i] < 0) dims_ref[i] = x_rank + dims_ref[i];
reduce_dim[i] = dims_ref[i];
}
// construct the squeezed output tensor
DDim out_dims = output->dims();
if (keep_dim && x_rank > 1) {
const int kDelFlag = -2;
auto dims_vector = phi::vectorize(out_dims);
for (size_t i = 0; i < dims_ref.size(); ++i) {
dims_vector[dims_ref[i]] = kDelFlag;
}
dims_vector.erase(remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
dims_vector.end());
out_dims = phi::make_ddim(dims_vector);
}
auto& place = *context.eigen_device();
Functor functor;
if (D == 1) {
auto out = EigenScalar<T>::From(*output);
functor(place, &x, &out, reduce_dim);
} else {
auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);
functor(place, &x, &out, reduce_dim);
}
}
#define HANDLE_REDUCE_DIM(NDIM, RDIM) \
if (ndim == NDIM && rdim == RDIM) { \
ReduceFunctor<DeviceContext, OutT, NDIM, RDIM, Functor>( \
dev_ctx, input, output, dims, keep_dim); \
}
//////////////// HandleLargeDim
inline void GetShuffledDim(const DDim& src_dims,
DDim* dst_dims,
const std::vector<int64_t>& reduced_dims,
std::vector<int>* perm_axis) {
// check if it's a reduced dim
std::vector<bool> src_dims_check(src_dims.size(), false);
size_t src_size = src_dims.size();
size_t reduce_size = reduced_dims.size();
std::vector<int64_t> regular_reduced_dims = reduced_dims;
for (size_t i = 0; i < regular_reduced_dims.size(); i++) {
if (regular_reduced_dims[i] < 0) {
regular_reduced_dims[i] = src_size + regular_reduced_dims[i];
}
}
for (size_t i = 0; i < reduce_size; ++i) {
dst_dims->at(src_size - reduce_size + i) =
src_dims[regular_reduced_dims[i]];
(*perm_axis)[src_size - reduce_size + i] = regular_reduced_dims[i];
src_dims_check[regular_reduced_dims[i]] = true;
}
size_t offset = 0;
for (size_t i = 0; i < src_dims_check.size(); ++i) {
bool is_reduced = src_dims_check[i];
if (!is_reduced) {
(*perm_axis)[offset] = i;
dst_dims->at(offset++) = src_dims[i];
}
}
}
template <typename DeviceContext, typename OutT>
void GetShuffledInput(const DeviceContext& dev_ctx,
const phi::DenseTensor& input,
phi::DenseTensor* shuffled_input,
const std::vector<int64_t>& dims) {
DDim shuffled_dims(input.dims());
std::vector<int> perm_axis(input.dims().size());
GetShuffledDim(input.dims(), &shuffled_dims, dims, &perm_axis);
#include "paddle/phi/kernels/funcs/reduce_function.h"
shuffled_input->Resize(shuffled_dims);
dev_ctx.template Alloc<OutT>(shuffled_input);
phi::funcs::TransposeNormal<DeviceContext, OutT> trans;
trans(dev_ctx, input, shuffled_input, perm_axis);
}
template <typename DeviceContext, typename OutT, typename Functor>
void HandleLargeDim(const DeviceContext& dev_ctx,
const phi::DenseTensor& input,
phi::DenseTensor* output,
const std::vector<int64_t>& dims,
bool keep_dim) {
// shuffle the reduced dim to the end
phi::DenseTensor shuffled_input;
GetShuffledInput<DeviceContext, OutT>(dev_ctx, input, &shuffled_input, dims);
// transpose to 2D tensor whose shape is {unreduced, reduced}.
const int64_t unreduced = output->numel();
const int64_t reduced = shuffled_input.numel() / unreduced;
shuffled_input.ResizeAndAllocate({unreduced, reduced});
DDim output_dim = output->dims();
output->ResizeAndAllocate({unreduced});
ReduceFunctor<DeviceContext, OutT, 2, 1, Functor>(
dev_ctx, shuffled_input, output, {1}, keep_dim);
output->ResizeAndAllocate(output_dim);
}
////////////// ReduceKernel
template <typename DeviceContext, typename T, typename OutT, typename Functor>
void ReduceKernelImpl(const DeviceContext& dev_ctx,
const phi::DenseTensor& input,
phi::DenseTensor* output,
const std::vector<int64_t>& dims,
bool keep_dim,
bool reduce_all) {
dev_ctx.template Alloc<OutT>(output);
if (reduce_all) {
// Flatten and reduce 1-D tensor
auto x = EigenVector<OutT>::Flatten(input);
auto out = EigenScalar<OutT>::From(*output);
auto& dev = *dev_ctx.eigen_device();
auto reduce_dim = Eigen::array<int, 1>({{0}});
Functor functor;
functor(dev, &x, &out, reduce_dim);
} else {
int ndim = input.dims().size();
int rdim = dims.size();
if (ndim > 6) {
HandleLargeDim<DeviceContext, OutT, Functor>(
dev_ctx, input, output, dims, keep_dim);
} else {
HANDLE_REDUCE_DIM(6, 5);
HANDLE_REDUCE_DIM(6, 4);
HANDLE_REDUCE_DIM(6, 3);
HANDLE_REDUCE_DIM(6, 2);
HANDLE_REDUCE_DIM(6, 1);
HANDLE_REDUCE_DIM(5, 4);
HANDLE_REDUCE_DIM(5, 3);
HANDLE_REDUCE_DIM(5, 2);
HANDLE_REDUCE_DIM(5, 1);
HANDLE_REDUCE_DIM(4, 3);
HANDLE_REDUCE_DIM(4, 2);
HANDLE_REDUCE_DIM(4, 1);
HANDLE_REDUCE_DIM(3, 2);
HANDLE_REDUCE_DIM(3, 1);
HANDLE_REDUCE_DIM(2, 1);
HANDLE_REDUCE_DIM(1, 1);
}
}
}
namespace phi {
template <typename DeviceContext, typename T, typename Functor>
void Reduce(const DeviceContext& dev_ctx,
......@@ -218,7 +48,7 @@ void Reduce(const DeviceContext& dev_ctx,
// do reduce sum
PD_VISIT_ALL_TYPES(
x.dtype(), "ReduceKernelImpl", ([&] {
phi::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
phi::funcs::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
dev_ctx, x, out, dims, keep_dim, reduce_all);
}));
} else {
......@@ -228,7 +58,7 @@ void Reduce(const DeviceContext& dev_ctx,
// do reduce sum
PD_VISIT_ALL_TYPES(
out_dtype, "ReduceKernelImpl", ([&] {
phi::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
phi::funcs::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
dev_ctx, tmp_tensor, out, dims, keep_dim, reduce_all);
}));
}
......@@ -255,7 +85,7 @@ void BoolReduceKernel(const DeviceContext& dev_ctx,
}
reduce_all = (reduce_all || full_dim);
ReduceKernelImpl<DeviceContext, bool, OutT, Functor>(
funcs::ReduceKernelImpl<DeviceContext, bool, OutT, Functor>(
dev_ctx, input, output, dims, keep_dim, reduce_all);
}
......
paddle/phi/kernels/funcs/matrix_reduce.cc
浏览文件 @ 6b47507d
......@@ -14,7 +14,7 @@
#include "paddle/phi/kernels/funcs/matrix_reduce.h"
#include "paddle/phi/kernels/cpu/reduce.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/funcs/reduce_functor.h"
namespace phi {
......@@ -47,7 +47,7 @@ class MatrixReduceSumFunctor<T, CPUContext> {
out_reduce_dims.push_back(idx);
}
}
phi::ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
dev_ctx, in, out, out_reduce_dims, true, false);
}
};
......
paddle/phi/kernels/funcs/reduce_function.h
浏览文件 @ 6b47507d
......@@ -15,8 +15,7 @@
#pragma once
// CUDA, XPU and HIP use same api
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) || \
defined(PADDLE_WITH_XPU_KP)
#if defined(__NVCC__) || defined(__HIPCC__) || defined(__xpu__)
#include <algorithm>
#include <cmath>
......@@ -40,10 +39,6 @@ namespace cub = hipcub;
#include "paddle/phi/backends/gpu/gpu_info.h"
#endif
#include "paddle/phi/api/ext/dispatch.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/utils/array.h"
#include "paddle/phi/kernels/cast_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
......@@ -58,9 +53,19 @@ namespace kps = phi::kps;
#ifdef PADDLE_WITH_XPU_KP
using dim3 = phi::kps::dim3;
#endif
#endif
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/utils/array.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
namespace funcs {
#if defined(__NVCC__) || defined(__HIPCC__) || defined(__xpu__)
namespace details {
static inline int GetLastPow2(int n) {
......@@ -1190,8 +1195,174 @@ void ReduceKernel(const KPDevice& dev_ctx,
is_mean);
}
#endif
template <typename DeviceContext,
typename T,
size_t D,
size_t R_D,
typename Functor>
void ReduceFunctor(const DeviceContext& context,
const phi::DenseTensor& input,
phi::DenseTensor* output,
const std::vector<int64_t>& dims,
bool keep_dim) {
auto x = EigenTensor<T, D>::From(input);
auto x_rank = static_cast<int>(x.dimensions().size());
auto reduce_dim = Eigen::array<int, R_D>();
std::vector<int64_t> dims_ref = dims;
for (size_t i = 0; i < dims_ref.size(); ++i) {
if (dims_ref[i] < 0) dims_ref[i] = x_rank + dims_ref[i];
reduce_dim[i] = dims_ref[i];
}
// construct the squeezed output tensor
DDim out_dims = output->dims();
if (keep_dim && x_rank > 1) {
const int kDelFlag = -2;
auto dims_vector = phi::vectorize(out_dims);
for (size_t i = 0; i < dims_ref.size(); ++i) {
dims_vector[dims_ref[i]] = kDelFlag;
}
dims_vector.erase(remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
dims_vector.end());
out_dims = phi::make_ddim(dims_vector);
}
auto& place = *context.eigen_device();
Functor functor;
if (D == 1) {
auto out = EigenScalar<T>::From(*output);
functor(place, &x, &out, reduce_dim);
} else {
auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);
functor(place, &x, &out, reduce_dim);
}
}
#define HANDLE_REDUCE_DIM(NDIM, RDIM) \
if (ndim == NDIM && rdim == RDIM) { \
ReduceFunctor<DeviceContext, OutT, NDIM, RDIM, Functor>( \
dev_ctx, input, output, dims, keep_dim); \
}
//////////////// HandleLargeDim
inline void GetShuffledDim(const DDim& src_dims,
DDim* dst_dims,
const std::vector<int64_t>& reduced_dims,
std::vector<int>* perm_axis) {
// check if it's a reduced dim
std::vector<bool> src_dims_check(src_dims.size(), false);
size_t src_size = src_dims.size();
size_t reduce_size = reduced_dims.size();
std::vector<int64_t> regular_reduced_dims = reduced_dims;
for (size_t i = 0; i < regular_reduced_dims.size(); i++) {
if (regular_reduced_dims[i] < 0) {
regular_reduced_dims[i] = src_size + regular_reduced_dims[i];
}
}
for (size_t i = 0; i < reduce_size; ++i) {
dst_dims->at(src_size - reduce_size + i) =
src_dims[regular_reduced_dims[i]];
(*perm_axis)[src_size - reduce_size + i] = regular_reduced_dims[i];
src_dims_check[regular_reduced_dims[i]] = true;
}
size_t offset = 0;
for (size_t i = 0; i < src_dims_check.size(); ++i) {
bool is_reduced = src_dims_check[i];
if (!is_reduced) {
(*perm_axis)[offset] = i;
dst_dims->at(offset++) = src_dims[i];
}
}
}
template <typename DeviceContext, typename OutT>
void GetShuffledInput(const DeviceContext& dev_ctx,
const phi::DenseTensor& input,
phi::DenseTensor* shuffled_input,
const std::vector<int64_t>& dims) {
DDim shuffled_dims(input.dims());
std::vector<int> perm_axis(input.dims().size());
GetShuffledDim(input.dims(), &shuffled_dims, dims, &perm_axis);
shuffled_input->Resize(shuffled_dims);
dev_ctx.template Alloc<OutT>(shuffled_input);
phi::funcs::TransposeNormal<DeviceContext, OutT> trans;
trans(dev_ctx, input, shuffled_input, perm_axis);
}
template <typename DeviceContext, typename OutT, typename Functor>
void HandleLargeDim(const DeviceContext& dev_ctx,
const phi::DenseTensor& input,
phi::DenseTensor* output,
const std::vector<int64_t>& dims,
bool keep_dim) {
// shuffle the reduced dim to the end
phi::DenseTensor shuffled_input;
GetShuffledInput<DeviceContext, OutT>(dev_ctx, input, &shuffled_input, dims);
// transpose to 2D tensor whose shape is {unreduced, reduced}.
const int64_t unreduced = output->numel();
const int64_t reduced = shuffled_input.numel() / unreduced;
shuffled_input.ResizeAndAllocate({unreduced, reduced});
DDim output_dim = output->dims();
output->ResizeAndAllocate({unreduced});
ReduceFunctor<DeviceContext, OutT, 2, 1, Functor>(
dev_ctx, shuffled_input, output, {1}, keep_dim);
output->ResizeAndAllocate(output_dim);
}
////////////// ReduceKernel
template <typename DeviceContext, typename T, typename OutT, typename Functor>
void ReduceKernelImpl(const DeviceContext& dev_ctx,
const phi::DenseTensor& input,
phi::DenseTensor* output,
const std::vector<int64_t>& dims,
bool keep_dim,
bool reduce_all) {
dev_ctx.template Alloc<OutT>(output);
if (reduce_all) {
// Flatten and reduce 1-D tensor
auto x = EigenVector<OutT>::Flatten(input);
auto out = EigenScalar<OutT>::From(*output);
auto& dev = *dev_ctx.eigen_device();
auto reduce_dim = Eigen::array<int, 1>({{0}});
Functor functor;
functor(dev, &x, &out, reduce_dim);
} else {
int ndim = input.dims().size();
int rdim = dims.size();
if (ndim > 6) {
HandleLargeDim<DeviceContext, OutT, Functor>(
dev_ctx, input, output, dims, keep_dim);
} else {
HANDLE_REDUCE_DIM(6, 5);
HANDLE_REDUCE_DIM(6, 4);
HANDLE_REDUCE_DIM(6, 3);
HANDLE_REDUCE_DIM(6, 2);
HANDLE_REDUCE_DIM(6, 1);
HANDLE_REDUCE_DIM(5, 4);
HANDLE_REDUCE_DIM(5, 3);
HANDLE_REDUCE_DIM(5, 2);
HANDLE_REDUCE_DIM(5, 1);
HANDLE_REDUCE_DIM(4, 3);
HANDLE_REDUCE_DIM(4, 2);
HANDLE_REDUCE_DIM(4, 1);
HANDLE_REDUCE_DIM(3, 2);
HANDLE_REDUCE_DIM(3, 1);
HANDLE_REDUCE_DIM(2, 1);
HANDLE_REDUCE_DIM(1, 1);
}
}
}
} // namespace funcs
} // namespace phi
#endif
paddle/phi/kernels/gpu/dirichlet_kernel.cu
浏览文件 @ 6b47507d
......@@ -16,10 +16,10 @@
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/reduce.h"
#include "paddle/phi/kernels/funcs/broadcast_function.h"
#include "paddle/phi/kernels/funcs/elementwise_functor.h"
#include "paddle/phi/kernels/funcs/for_range.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/funcs/reduce_functor.h"
#include "paddle/phi/kernels/impl/dirichlet_kernel_impl.h"
......@@ -99,7 +99,7 @@ struct DirichletSampler<GPUContext, T> {
gamma_sum.Resize(new_shape);
dev_ctx.template Alloc<T>(&gamma_sum);
ReduceKernelImpl<GPUContext, T, T, funcs::SumFunctor>(
funcs::ReduceKernelImpl<GPUContext, T, T, funcs::SumFunctor>(
dev_ctx,
gamma_samples,
&gamma_sum,
......
paddle/phi/kernels/impl/logsumexp_kernel_impl.h
浏览文件 @ 6b47507d
......@@ -16,16 +16,16 @@
#include <type_traits>
#include <vector>
#include "paddle/phi/kernels/cpu/reduce.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/logsumexp_kernel.h"
namespace phi {
#define HANDLE_DIM(NDIM, RDIM) \
if (ndim == NDIM && rdim == RDIM) { \
ReduceFunctor<Context, T, NDIM, RDIM, LogsumexpFunctor>( \
funcs::ReduceFunctor<Context, T, NDIM, RDIM, LogsumexpFunctor>( \
dev_ctx, x, out, axis, keepdim); \
}
......
paddle/phi/kernels/impl/matmul_grad_kernel_impl.h
浏览文件 @ 6b47507d
......@@ -17,8 +17,8 @@ limitations under the License. */
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/kernels/complex_kernel.h"
#include "paddle/phi/kernels/cpu/reduce.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/funcs/reduce_functor.h"
#include "paddle/phi/kernels/impl/dot_grad_kernel_impl.h"
#include "paddle/phi/kernels/impl/matmul_kernel_impl.h"
......@@ -45,7 +45,7 @@ struct ReduceSumForMatmulGrad<CPUContext, T> {
const std::vector<int>& reduce_dims) {
std::vector<int64_t> reduce_dims_tmp(reduce_dims.begin(),
reduce_dims.end());
ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
funcs::ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
dev_ctx, input, output, reduce_dims_tmp, true, false);
}
};
......
paddle/phi/kernels/impl/solve_grad_kernel_impl.h
浏览文件 @ 6b47507d
......@@ -16,11 +16,11 @@ limitations under the License. */
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/kernels/cpu/reduce.h"
#include "paddle/phi/kernels/expand_as_kernel.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/matrix_solve.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/funcs/reduce_functor.h"
#include "paddle/phi/kernels/impl/solve_kernel_impl.h"
#include "paddle/phi/kernels/squeeze_kernel.h"
......@@ -50,7 +50,7 @@ struct ReduceSumForSolvelGrad<CPUContext, T> {
bool keep_dims) {
std::vector<int64_t> reduce_dims_tmp(reduce_dims.begin(),
reduce_dims.end());
phi::ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
funcs::ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
dev_ctx, input, output, reduce_dims_tmp, keep_dims, false);
}
};
......
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