wehub-resource-sync
128 lines
4.1 KiB
Plaintext
128 lines
4.1 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/masked_select_grad_kernel.h"
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#include <thrust/device_ptr.h>
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#include <thrust/device_vector.h>
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#include <thrust/reverse.h>
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#include <thrust/scan.h>
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/expand_grad_kernel.h"
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#include "paddle/phi/kernels/expand_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/common_shape.h"
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#include "paddle/phi/kernels/funcs/reduce_function.h"
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#include "paddle/phi/kernels/funcs/select_impl.cu.h"
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namespace phi {
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template <typename MT, typename InT, typename OutT>
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struct MaskedSelectGradFunctor {
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HOSTDEVICE MaskedSelectGradFunctor() = default;
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HOSTDEVICE inline void operator()(OutT* out,
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const MT* mask,
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const InT* value,
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int num) {
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int read_fix = 0;
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for (int idx = 0; idx < num; idx++) {
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if (mask[idx]) {
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out[idx] = value[read_fix++];
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} else {
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out[idx] = 0;
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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 MaskedSelectGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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const DenseTensor& out_grad,
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DenseTensor* x_grad) {
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if (out_grad.numel() == 0 && x_grad) {
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// x = [1, 2], mask = [False, False], out = [], x_grad = [0, 0]
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Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
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return;
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}
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// x_grad.size() == x.size()
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// x.size() == mask.size(), no broadcast, expand_mask = false, expand_x =
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// false x.size() < mask.size(), x broadcast to mask, expand_mask = false,
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// expand_x = true x.size() > mask.size(), mask broadcast to x, expand_mask =
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// true, expand_x = false
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DenseTensor mask_expand;
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DenseTensor x_grad_expand;
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bool expand_x = false;
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auto expanded_size = funcs::MatrixGetBroadcastBatchPortion(
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vectorize(x_grad->dims()), vectorize(mask.dims()));
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auto expanded_dims = make_ddim(expanded_size);
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if (mask.dims() != expanded_dims) {
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ExpandKernel<bool, Context>(
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dev_ctx, mask, IntArray(expanded_size), &mask_expand);
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} else {
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mask_expand = mask;
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}
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if (x_grad->dims() != expanded_dims) {
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x_grad_expand = Empty<T, Context>(dev_ctx, IntArray(expanded_size));
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expand_x = true;
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} else {
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expand_x = false;
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}
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dev_ctx.template Alloc<T>(x_grad);
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auto mask_size = mask_expand.numel();
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if (mask_size <= 0) return;
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using Functor = MaskedSelectGradFunctor<bool, T, T>;
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DenseTensor* x_grad_tmp = x_grad;
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if (expand_x) {
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x_grad_tmp = &x_grad_expand;
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}
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funcs::SelectKernel<bool, T, T, 2, Functor>(
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dev_ctx, mask_expand, out_grad, x_grad_tmp, Functor());
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if (expand_x) {
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ExpandGradKernel<T, Context>(
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dev_ctx, x, x_grad_expand, IntArray(expanded_size), x_grad);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(masked_select_grad,
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GPU,
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ALL_LAYOUT,
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phi::MaskedSelectGradKernel,
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bool,
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float,
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double,
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int,
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int8_t,
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int64_t,
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int16_t,
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uint8_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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