wehub-resource-sync
106 lines
3.5 KiB
C++
106 lines
3.5 KiB
C++
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#ifdef PADDLE_WITH_CUDA
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#include <cuda_runtime.h> // NOLINT
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/core/enforce.h"
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namespace phi {
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namespace backends {
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namespace gpu {
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/*
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* Summary: Grid stride looping macro in CUDA kernel
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*
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* [ Why need this macro? ]
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*
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* The original looping in CUDA kernel is:
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*
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* `for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
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* i += blockDim.x * gridDim.x)`
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*
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* This for condition is risky. The value of `blockIdx.x * blockDim.x`
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* may be large, such as over 1GB, the first iteration is no problem here,
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* but when `i += blockDim.x * gridDim.x` is executed, the value of i
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* will greater than INT_MAX and overflow becomes negative value, at
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* this time, the cycle condition `i < (n)` is still satisfied, so it
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* will cause illegal access to cuda memory.
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*
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* Here is a real example in ERNIE, it will trigger above error.
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* The related data are:
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* - blockIdx.x = 2172938
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* - blockDim.x = 512
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* - blockIdx.x * blockDim.x = 1112543864
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* - INT_MAX = 2147483647
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*
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* So we polish the for condition as follow, the int64_t __index__ will
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* prevent overflow in the loop increment.
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*
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* Parameters:
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* - i: loop index
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* - num: total element numbers
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*
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* Examples:
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* template <typename T>
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* __global__ void Scale(T* logit_grad, const T* loss_grad, const int num,
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* const int d, const int remain) {
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* CUDA_KERNEL_LOOP(index, num) {
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* int idx_n = index / d;
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* int idx_remain = index % remain;
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* logit_grad[index] *= loss_grad[idx_n * remain + idx_remain];
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* }
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* }
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*
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*/
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#define CUDA_KERNEL_LOOP_TYPE(i, num, index_type) \
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int64_t __index__ = \
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static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x; \
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int64_t __stride__ = static_cast<int64_t>(blockDim.x) * gridDim.x; \
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for (index_type i = __index__; __index__ < (num); \
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__index__ += __stride__, i = __index__)
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template <typename T>
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cudaDataType_t ToCudaDataType() {
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if (std::is_same<T, float>::value) {
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return CUDA_R_32F;
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} else if (std::is_same<T, double>::value) {
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return CUDA_R_64F;
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} else if (std::is_same<T, phi::dtype::float16>::value) {
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return CUDA_R_16F;
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#if CUDA_VERSION >= 11000
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} else if (std::is_same<T, phi::dtype::bfloat16>::value) {
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return CUDA_R_16BF;
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#endif
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#if CUDA_VERSION >= 11060
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} else if (std::is_same<T, int8_t>::value) {
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return CUDA_R_8I;
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} else if (std::is_same<T, int32_t>::value) {
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return CUDA_R_32I;
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#endif
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} else {
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PADDLE_THROW(common::errors::InvalidArgument(
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"DataType %s is unsupported for CUDA.",
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DataTypeToString(phi::CppTypeToDataType<T>::Type())));
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}
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}
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} // namespace gpu
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} // namespace backends
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} // namespace phi
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#endif
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