wehub-resource-sync
411 lines
13 KiB
Plaintext
411 lines
13 KiB
Plaintext
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/phi/kernels/reduce_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_nansum_grad_kernel.h"
|
|
|
|
#include "paddle/phi/kernels/funcs/for_range.h"
|
|
#include "paddle/phi/kernels/gpu/reduce.h"
|
|
#include "paddle/phi/kernels/gpu/reduce_amin_amax_common.h"
|
|
#include "paddle/phi/kernels/reduce_amin_grad_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_max_grad_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_mean_grad_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_min_grad_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_sum_grad_kernel.h"
|
|
|
|
#include "paddle/phi/backends/gpu/gpu_context.h"
|
|
#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
|
|
#include "paddle/phi/kernels/funcs/broadcast_function.h"
|
|
#include "paddle/phi/kernels/funcs/compare_functors.h"
|
|
#include "paddle/phi/kernels/funcs/elementwise_functor.h"
|
|
#include "paddle/phi/kernels/funcs/reduce_function.h"
|
|
#include "paddle/phi/kernels/gpu/reduce_grad.h"
|
|
|
|
#include "paddle/phi/backends/all_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
|
|
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
|
|
#include "paddle/phi/core/distributed/nccl_comm_context.h"
|
|
#endif
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceSumGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out_grad,
|
|
const IntArray& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
reduce_all = recompute_reduce_all(x, dims, reduce_all);
|
|
if (x_grad && x_grad->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
return;
|
|
}
|
|
// get reduce_dim for reduce_mean_grad
|
|
int dim_size = x.dims().size();
|
|
std::vector<int> reduce_dims =
|
|
funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
|
|
|
|
auto update_dims = vectorize(x.dims());
|
|
for (auto i : reduce_dims) {
|
|
update_dims[i] = 1;
|
|
}
|
|
|
|
// make new tensor
|
|
DenseTensor new_out_grad(out_grad.dtype());
|
|
new_out_grad.ShareDataWith(out_grad);
|
|
new_out_grad.Resize(update_dims);
|
|
|
|
// call ReduceGrad
|
|
dev_ctx.Alloc(x_grad, x.dtype());
|
|
using MT = typename MPTypeTrait<T>::Type;
|
|
ReduceGrad<kps::IdentityFunctor<T, MT>>(
|
|
dev_ctx, &new_out_grad, x_grad, x.dtype(), kps::IdentityFunctor<T, MT>());
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceMeanGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out_grad,
|
|
const IntArray& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
if (x_grad && x_grad->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
return;
|
|
}
|
|
|
|
reduce_all = recompute_reduce_all(x, dims, reduce_all);
|
|
// get reduce_dim and reduce_num for reduce_mean_grad
|
|
int dim_size = x.dims().size();
|
|
std::vector<int> reduce_dims =
|
|
funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
|
|
|
|
auto update_dims = vectorize(x.dims());
|
|
int64_t reduce_num = 1;
|
|
for (auto i : reduce_dims) {
|
|
reduce_num *= (x.dims())[i];
|
|
update_dims[i] = 1;
|
|
}
|
|
|
|
// make new tensor
|
|
DenseTensor new_out_grad(out_grad.dtype());
|
|
new_out_grad.ShareDataWith(out_grad);
|
|
new_out_grad.Resize(update_dims);
|
|
|
|
// call BroadcastKernel
|
|
dev_ctx.Alloc(x_grad, x.dtype());
|
|
std::vector<const DenseTensor*> inputs = {&new_out_grad};
|
|
std::vector<DenseTensor*> outputs = {x_grad};
|
|
|
|
using MT = typename MPTypeTrait<T>::Type;
|
|
funcs::BroadcastKernel<T>(dev_ctx,
|
|
inputs,
|
|
&outputs,
|
|
kps::MPTypeDivideFunctor<T, MT>(reduce_num),
|
|
0);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceAMinGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out,
|
|
const DenseTensor& out_grad,
|
|
const std::vector<int64_t>& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
reduce_all = recompute_reduce_all(x, dims, reduce_all);
|
|
ReduceCudaAMaxAMinGrad<T, Context>(
|
|
dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceMinGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out,
|
|
const DenseTensor& out_grad,
|
|
const IntArray& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
ReduceAMinGradKernel<T, Context>(
|
|
dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceAMaxGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out,
|
|
const DenseTensor& out_grad,
|
|
const std::vector<int64_t>& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
reduce_all = recompute_reduce_all(x, dims, reduce_all);
|
|
ReduceCudaAMaxAMinGrad<T, Context>(
|
|
dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceMaxGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out,
|
|
const DenseTensor& out_grad,
|
|
const IntArray& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
ReduceAMaxGradKernel<T, Context>(
|
|
dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void ReduceKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
int root,
|
|
int reduce_type,
|
|
DenseTensor* out) {
|
|
PADDLE_ENFORCE_GT(x.numel(),
|
|
0,
|
|
common::errors::InvalidArgument(
|
|
"Tensor need be reduced must not empty."));
|
|
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
|
|
out->Resize(x.dims());
|
|
dev_ctx.template Alloc<T>(out);
|
|
|
|
auto comm_ctx =
|
|
static_cast<distributed::NCCLCommContext*>(dev_ctx.GetCommContext());
|
|
PADDLE_ENFORCE_NE(
|
|
comm_ctx,
|
|
nullptr,
|
|
errors::Unavailable("NCCLCommContext is nullptr, collective op should "
|
|
"has ring_id attr."));
|
|
gpuStream_t stream = dev_ctx.stream();
|
|
PADDLE_ENFORCE_NOT_NULL(stream,
|
|
errors::NotFound("Should initialize NCCL firstly."));
|
|
|
|
ncclRedOp_t red_type = ncclSum;
|
|
switch (static_cast<ReduceType>(reduce_type)) {
|
|
case ReduceType::kRedSum:
|
|
red_type = ncclSum;
|
|
break;
|
|
case ReduceType::kRedMax:
|
|
red_type = ncclMax;
|
|
break;
|
|
case ReduceType::kRedMin:
|
|
red_type = ncclMin;
|
|
break;
|
|
case ReduceType::kRedProd:
|
|
red_type = ncclProd;
|
|
break;
|
|
#if NCCL_VERSION_CODE >= 21000
|
|
case ReduceType::kRedAvg:
|
|
red_type = ncclAvg;
|
|
break;
|
|
#endif
|
|
}
|
|
comm_ctx->Reduce(out, x, red_type, root, stream);
|
|
#else
|
|
PADDLE_THROW(
|
|
errors::PreconditionNotMet("PaddlePaddle should compile with GPU."));
|
|
#endif
|
|
}
|
|
|
|
template <typename T>
|
|
struct NanMaskFunctor {
|
|
const T* x_data;
|
|
T* x_grad_data;
|
|
|
|
NanMaskFunctor(const T* x_data, T* x_grad_data)
|
|
: x_data(x_data), x_grad_data(x_grad_data) {}
|
|
|
|
HOSTDEVICE void operator()(size_t idx) const {
|
|
// NaN != NaN for floating-point; always false for integral types
|
|
if (x_data[idx] != x_data[idx]) {
|
|
x_grad_data[idx] = static_cast<T>(0);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T, typename Context>
|
|
void NansumGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& out_grad,
|
|
const IntArray& dims,
|
|
bool keep_dim,
|
|
bool reduce_all,
|
|
DenseTensor* x_grad) {
|
|
reduce_all = recompute_reduce_all(x, dims, reduce_all);
|
|
if (x_grad && x_grad->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
return;
|
|
}
|
|
|
|
// Step 1: broadcast out_grad to x_grad shape (same as sum_grad)
|
|
int dim_size = x.dims().size();
|
|
std::vector<int> reduce_dims =
|
|
funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
|
|
|
|
auto update_dims = vectorize(x.dims());
|
|
for (auto i : reduce_dims) {
|
|
update_dims[i] = 1;
|
|
}
|
|
|
|
DenseTensor new_out_grad(out_grad.dtype());
|
|
new_out_grad.ShareDataWith(out_grad);
|
|
new_out_grad.Resize(update_dims);
|
|
|
|
dev_ctx.Alloc(x_grad, x.dtype());
|
|
using MT = typename MPTypeTrait<T>::Type;
|
|
ReduceGrad<kps::IdentityFunctor<T, MT>>(
|
|
dev_ctx, &new_out_grad, x_grad, x.dtype(), kps::IdentityFunctor<T, MT>());
|
|
|
|
// Step 2: zero out gradient where x is NaN
|
|
const T* x_data = x.data<T>();
|
|
T* x_grad_data = x_grad->data<T>();
|
|
int64_t numel = x.numel();
|
|
funcs::ForRange<Context> for_range(dev_ctx, numel);
|
|
for_range(NanMaskFunctor<T>(x_data, x_grad_data));
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
#if NCCL_VERSION_CODE >= 21000
|
|
PD_REGISTER_KERNEL(reduce,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
bool,
|
|
int8_t,
|
|
uint8_t,
|
|
int64_t,
|
|
phi::bfloat16,
|
|
phi::float16) {}
|
|
#else
|
|
PD_REGISTER_KERNEL(reduce,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
bool,
|
|
int8_t,
|
|
uint8_t,
|
|
int64_t,
|
|
phi::float16) {}
|
|
#endif
|
|
|
|
PD_REGISTER_KERNEL(amax_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceAMaxGradKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|
|
|
|
PD_REGISTER_KERNEL(amin_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceAMinGradKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t) {}
|
|
|
|
PD_REGISTER_KERNEL(max_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceMaxGradKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|
|
|
|
PD_REGISTER_KERNEL(mean_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceMeanGradKernel,
|
|
bool,
|
|
float,
|
|
double,
|
|
phi::float8_e4m3fn,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
phi::complex64,
|
|
phi::complex128,
|
|
int,
|
|
int64_t) {}
|
|
|
|
PD_REGISTER_KERNEL(min_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceMinGradKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|
|
|
|
PD_REGISTER_KERNEL(sum_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::ReduceSumGradKernel,
|
|
bool,
|
|
float,
|
|
double,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
int8_t,
|
|
uint8_t,
|
|
int16_t,
|
|
int,
|
|
int64_t,
|
|
phi::complex64,
|
|
phi::complex128) {
|
|
kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
|
|
}
|
|
|
|
PD_REGISTER_KERNEL(nansum_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::NansumGradKernel,
|
|
bool,
|
|
float,
|
|
double,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
int8_t,
|
|
uint8_t,
|
|
int16_t,
|
|
int,
|
|
int64_t,
|
|
phi::complex64,
|
|
phi::complex128) {
|
|
kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
|
|
}
|