wehub-resource-sync
108 lines
3.5 KiB
Plaintext
108 lines
3.5 KiB
Plaintext
// Copyright (c) 2022 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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#include "paddle/phi/kernels/bernoulli_kernel.h"
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#ifdef __NVCC__
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#include <curand_kernel.h>
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#endif
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#ifdef __HIPCC__
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#include <hiprand_kernel.h>
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#endif
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#include <algorithm>
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#include <vector>
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/distribution_helper.h"
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namespace phi {
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// 'curand_uniform4/hiprand_uniform4' generate 4 random number each time
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template <typename T>
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__global__ void bernoulli_cuda_kernel(
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size_t size, uint64_t seed, uint64_t offset, const T* x_data, T* out_data) {
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size_t thread_idx =
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static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
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#if defined(__NVCC__)
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curandStatePhilox4_32_10_t state;
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curand_init(seed, thread_idx, offset, &state);
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#else
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hiprandStatePhilox4_32_10_t state;
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hiprand_init(seed, thread_idx, offset, &state);
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#endif
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size_t total_thread =
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static_cast<size_t>(gridDim.x) * static_cast<size_t>(blockDim.x);
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for (size_t i = 4 * thread_idx; i < size; i += total_thread * 4) {
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funcs::uniform_distribution<float> dist;
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float4 rand = dist(&state);
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using MT = typename MPTypeTrait<T>::Type;
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#pragma unroll
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for (size_t j = 0; j < 4; j++) {
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size_t idx = i + j;
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if (idx < size) {
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MT p = static_cast<MT>(x_data[idx]);
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PADDLE_ENFORCE(p >= 0 && p <= 1,
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"The probability should be in [0, 1], but got %f",
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p);
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out_data[idx] = static_cast<T>((&rand.x)[j] <= static_cast<MT>(p));
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}
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}
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}
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}
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template <typename T, typename Context>
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void BernoulliKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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const T* x_data = x.data<T>();
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T* out_data = dev_ctx.template Alloc<T>(out);
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auto numel = x.numel();
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auto gen_cuda = dev_ctx.GetGenerator();
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auto seed_offset = gen_cuda->IncrementOffset(12);
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uint64_t seed = seed_offset.first;
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uint64_t offset = seed_offset.second;
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auto gpu_config = backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel, 4);
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size_t grid_size_64 = gpu_config.GetGridSize();
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size_t block_size_64 = gpu_config.GetBlockSize();
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PADDLE_ENFORCE_LE_UINT32_MAX(grid_size_64, "bernoulli grid.x");
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PADDLE_ENFORCE_LE_UINT32_MAX(block_size_64, "bernoulli block.x");
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uint32_t grid_size = static_cast<uint32_t>(grid_size_64);
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uint32_t block_size = static_cast<uint32_t>(block_size_64);
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bernoulli_cuda_kernel<<<grid_size, block_size, 0, dev_ctx.stream()>>>(
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numel, seed, offset, x_data, out_data);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(bernoulli,
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GPU,
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ALL_LAYOUT,
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phi::BernoulliKernel,
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phi::float16,
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phi::bfloat16,
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float,
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double) {}
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