wehub-resource-sync
115 lines
3.6 KiB
C++
115 lines
3.6 KiB
C++
// 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 "paddle/phi/kernels/expand_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.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/funcs/common_shape.h"
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namespace phi {
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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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// 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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auto* out_data = dev_ctx.template Alloc<T>(x_grad);
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if (expand_x) {
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out_data = x_grad_expand.data<T>();
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}
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auto* mask_data = mask_expand.data<bool>();
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auto* input_data = out_grad.data<T>();
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int mask_size = static_cast<int>(mask_expand.numel());
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int index = 0;
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for (int i = 0; i < mask_size; i++) {
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if (mask_data[i]) {
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out_data[i] = input_data[index];
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index++;
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} else {
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out_data[i] = 0;
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}
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}
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auto out_grad_numel = out_grad.numel();
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PADDLE_ENFORCE_EQ(
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index,
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out_grad_numel,
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common::errors::InvalidArgument(
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"The dim size of input and x_grad in OP(masked_selected_grad) "
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"must be equal, but got mask with ones:(%ld), out_grad numel: "
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"(%ld). Please check input "
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"value.",
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index,
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out_grad_numel));
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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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CPU,
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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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