wehub-resource-sync
118 lines
3.3 KiB
C++
118 lines
3.3 KiB
C++
// Copyright (c) 2025 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_fill_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.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/expand_kernel.h"
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#include "paddle/phi/kernels/funcs/common_infer_shape_functions.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 MaskedFillKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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const DenseTensor& value,
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DenseTensor* out) {
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if (x.numel() == 0 || mask.numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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auto x_dims = x.dims();
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auto mask_dims = mask.dims();
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auto expanded_size =
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vectorize(funcs::BroadcastTwoDims(x_dims, mask_dims, -1));
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DenseTensor mask_expand;
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DenseTensor x_expand;
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DenseTensor value_expand;
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DDim expand_dims = make_ddim(expanded_size);
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if (mask.dims() != expand_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.dims() != expand_dims) {
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ExpandKernel<T, Context>(dev_ctx, x, IntArray(expanded_size), &x_expand);
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} else {
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x_expand = x;
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}
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if (value.dims() != expand_dims && value.numel() != 1) {
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ExpandKernel<T, Context>(
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dev_ctx, value, IntArray(expanded_size), &value_expand);
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} else {
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value_expand = value;
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}
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auto input_data = x_expand.data<T>();
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auto mask_data = mask_expand.data<bool>();
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auto value_data = value_expand.data<T>();
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auto x_size = x_expand.numel();
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out->Resize(expand_dims);
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auto out_data = dev_ctx.template HostAlloc<T>(out);
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if (value.numel() == 1) {
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#pragma unroll
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for (int i = 0; i < x_size; i++) {
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if (mask_data[i]) {
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out_data[i] = value_data[0];
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} else {
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out_data[i] = input_data[i];
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}
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}
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} else {
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#pragma unroll
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for (int i = 0; i < x_size; i++) {
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if (mask_data[i]) {
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out_data[i] = value_data[i];
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} else {
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out_data[i] = input_data[i];
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}
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(masked_fill,
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CPU,
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ALL_LAYOUT,
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phi::MaskedFillKernel,
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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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kernel->InputAt(1).SetDataType(phi::DataType::BOOL);
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}
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