wehub-resource-sync
76 lines
2.6 KiB
C++
76 lines
2.6 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/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.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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using XPUType = typename XPUTypeTrait<T>::Type;
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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// If out_grad is empty (e.g. mask all false), x_grad should be all zeros.
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if (out_grad.numel() == 0 && x_grad) {
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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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auto* mask_data = mask.data<bool>();
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auto* input_data = reinterpret_cast<const XPUType*>(out_grad.data<T>());
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auto* out_data = reinterpret_cast<XPUType*>(x_grad->data<T>());
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auto mask_shape = vectorize<int64_t>(mask.dims());
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auto xshape = vectorize<int64_t>(x_grad->dims());
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if (mask.dims().size() == 0) {
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mask_shape = std::vector<int64_t>({1});
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}
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if (x_grad->dims().size() == 0) {
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xshape = std::vector<int64_t>({1});
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}
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const int64_t out_size = out_grad.numel();
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int r = xpu::masked_select_grad(dev_ctx.x_context(),
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input_data,
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mask_data,
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out_data,
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xshape,
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mask_shape,
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out_size);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "masked_select_grad");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(masked_select_grad,
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XPU,
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ALL_LAYOUT,
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phi::MaskedSelectGradKernel,
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float,
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phi::float16,
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phi::bfloat16,
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int,
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bool,
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int64_t) {}
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