wehub-resource-sync
126 lines
4.3 KiB
Plaintext
126 lines
4.3 KiB
Plaintext
// Copyright (c) 2023 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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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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#ifndef _MSC_VER
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#include <thrust/device_ptr.h>
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#include <thrust/iterator/counting_iterator.h>
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#include <thrust/random.h>
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#include <thrust/shuffle.h>
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#endif
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#include "paddle/common/errors.h"
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#include "paddle/phi/common/memory_utils.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/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/for_range.h"
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#include "paddle/phi/kernels/gpu/shuffle_batch_utils.h"
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#include "paddle/phi/kernels/shuffle_batch_kernel.h"
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#ifdef PADDLE_WITH_CUDA
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#include "paddle/phi/kernels/funcs/shuffle_batch.cu.h"
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#endif
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namespace phi {
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template <typename T, typename Context>
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void ShuffleBatchKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& seed,
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int startup_seed,
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DenseTensor* out,
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DenseTensor* shuffleidx,
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DenseTensor* seed_out) {
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#ifdef _MSC_VER
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PADDLE_THROW(common::errors::Unimplemented(
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"GPU shuffle_batch is not supported on Windows yet"));
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#else
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int64_t x_embed_size = x.dims()[x.dims().size() - 1];
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int64_t elem_size = 1;
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for (int i = 0; i < x.dims().size() - 1; i++) {
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elem_size *= x.dims()[i];
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}
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shuffleidx->Resize({elem_size});
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int64_t seed_int = 0;
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if (seed.initialized()) {
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const auto& seed_place = seed.place().GetType();
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bool is_gpu_place = seed_place == AllocationType::GPU ||
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seed_place == AllocationType::CUSTOM;
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if (is_gpu_place) {
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// NOTE: We have overwritten GetKernelTypeForVar, so seed_place would
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// not be CUDAPlace in practice. This case would only happen in Python
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// op_test framework.
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DenseTensor tmp_tensor;
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Copy(dev_ctx, seed, CPUPlace(), false, &tmp_tensor);
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seed_int = *(tmp_tensor.data<int64_t>());
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} else {
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seed_int = *(seed.data<int64_t>());
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}
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} else {
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seed_int = startup_seed;
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}
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auto* shuffleidx_data = dev_ctx.template Alloc<int64_t>(shuffleidx);
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#ifdef PADDLE_WITH_CUDA
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// CacheAllocator allocator(dev_ctx.GetPlace());
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memory_utils::ThrustAllocator<cudaStream_t> allocator(dev_ctx.GetPlace(),
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dev_ctx.stream());
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const auto& exec_policy = thrust::cuda::par(allocator).on(dev_ctx.stream());
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#else
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const auto& exec_policy = thrust::hip::par.on(dev_ctx.stream());
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#endif
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thrust::random::default_random_engine engine(seed_int);
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thrust::counting_iterator<int64_t> cnt_iter(0);
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#ifdef PADDLE_WITH_CUDA
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funcs::shuffle_copy_fixed(
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thrust::detail::derived_cast(thrust::detail::strip_const(exec_policy)),
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#else
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thrust::shuffle_copy(exec_policy,
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#endif
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cnt_iter,
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cnt_iter + elem_size,
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thrust::device_pointer_cast(shuffleidx_data),
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engine);
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// TODO(zengjinle): for small data, direct cudaMemcpy may be better
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auto* x_data = x.data<T>();
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auto* out_data = dev_ctx.template Alloc<T>(out);
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ReorderFunctor<T, true> functor(
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x_data, shuffleidx_data, out_data, x_embed_size);
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funcs::ForRange<GPUContext> for_range(dev_ctx, elem_size * x_embed_size);
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for_range(functor);
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seed_out->Resize({1});
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auto* seed_out_data = dev_ctx.template HostAlloc<int64_t>(seed_out);
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*seed_out_data = engine();
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#endif
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}
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} // namespace phi
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PD_REGISTER_KERNEL(shuffle_batch,
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GPU,
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ALL_LAYOUT,
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phi::ShuffleBatchKernel,
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float,
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double,
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int32_t,
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int64_t) {
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kernel->OutputAt(1).SetDataType(phi::DataType::INT64);
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kernel->OutputAt(2).SetDataType(phi::DataType::INT64);
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}
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#endif
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