wehub-resource-sync
114 lines
3.6 KiB
Plaintext
114 lines
3.6 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/c_split_kernel.h"
|
|
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
|
|
namespace phi {
|
|
|
|
static constexpr int64_t kNumCUDAThreads = 512;
|
|
static constexpr int64_t kNumMaximumNumBlocks = 4096;
|
|
|
|
static inline int64_t NumBlocks(const int64_t N) {
|
|
return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
|
|
kNumMaximumNumBlocks);
|
|
}
|
|
|
|
template <typename T>
|
|
__global__ void SplitFromRank(const T* input,
|
|
T* output,
|
|
const int64_t rows,
|
|
const int64_t columns,
|
|
const int rank,
|
|
const int nranks,
|
|
const int64_t limit) {
|
|
CUDA_KERNEL_LOOP_TYPE(i, limit, int64_t) {
|
|
int64_t row = i / columns;
|
|
int64_t col = i % columns;
|
|
|
|
int64_t block = columns / nranks;
|
|
int64_t start = block * rank;
|
|
int64_t end = start + block;
|
|
|
|
if (col >= start && col < end) {
|
|
int64_t idx = block * row + col % block;
|
|
output[idx] = input[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void CSplitKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
int rank,
|
|
int nranks,
|
|
bool use_model_parallel,
|
|
DenseTensor* out) {
|
|
auto place = dev_ctx.GetPlace();
|
|
|
|
PADDLE_ENFORCE_GE(rank,
|
|
0,
|
|
common::errors::PreconditionNotMet(
|
|
"The value of rank (%d) for c_split must be "
|
|
"greater than or equal to 0.",
|
|
rank));
|
|
PADDLE_ENFORCE_GE(nranks,
|
|
2,
|
|
common::errors::PreconditionNotMet(
|
|
"The value of nranks (%d) for c_split must be "
|
|
"greater than or equal to 2.",
|
|
nranks));
|
|
PADDLE_ENFORCE_LT(rank,
|
|
nranks,
|
|
common::errors::PreconditionNotMet(
|
|
"The value of rank (%d) for c_split must be "
|
|
"less than that of nranks (%d).",
|
|
rank,
|
|
nranks));
|
|
|
|
auto dims = x.dims();
|
|
auto dims_size = dims.size();
|
|
// final dim
|
|
int64_t end_size = dims[dims_size - 1];
|
|
|
|
// remain dim
|
|
auto remain_ddim = slice_ddim(dims, 0, dims_size - 1);
|
|
int64_t remain_numel = common::product(remain_ddim);
|
|
|
|
int64_t limit = x.numel();
|
|
int64_t blocks = NumBlocks(limit);
|
|
int64_t threads = kNumCUDAThreads;
|
|
|
|
dims[dims_size - 1] /= nranks;
|
|
out->Resize(dims);
|
|
dev_ctx.template Alloc<T>(out);
|
|
|
|
SplitFromRank<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
|
|
x.data<T>(), out->data<T>(), remain_numel, end_size, rank, nranks, limit);
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(c_split,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::CSplitKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::bfloat16,
|
|
phi::float16) {}
|