wehub-resource-sync
102 lines
3.2 KiB
C++
102 lines
3.2 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/xpu/enforce_xpu.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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using XPUType = typename XPUTypeTrait<T>::Type;
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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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const auto& x_dims = x.dims();
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const auto& mask_dims = mask.dims();
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DDim x_dims_ex = x_dims;
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DDim mask_dims_ex = mask_dims;
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if (x_dims.size() == 0 && mask_dims.size() == 0) {
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x_dims_ex = make_ddim({1});
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mask_dims_ex = make_ddim({1});
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} else {
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int rank = std::max(x_dims.size(), mask_dims.size());
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x_dims_ex = funcs::ExtendDims2Rank(x_dims, rank);
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mask_dims_ex = funcs::ExtendDims2Rank(mask_dims, rank);
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}
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auto out_dims = funcs::BroadcastTwoDims(x_dims_ex, mask_dims_ex, -1);
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out->Resize(out_dims);
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T* out_data = dev_ctx.template Alloc<T>(out);
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if (out && out->numel() == 0) {
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return;
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}
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const bool* cond_data = mask.data<bool>();
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const XPUType* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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XPUType* out_xpu = reinterpret_cast<XPUType*>(out_data);
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auto cond_vec = vectorize<int64_t>(mask_dims_ex);
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auto x_vec = vectorize<int64_t>(x_dims_ex);
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auto* ctx = dev_ctx.x_context();
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int r = xpu::SUCCESS;
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DenseTensor value_expand;
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const DenseTensor* value_tensor = &value;
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if (value.dims() != x_dims) {
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auto target = vectorize(x_dims);
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phi::ExpandKernel<T, Context>(
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dev_ctx, value, IntArray(target), &value_expand);
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value_tensor = &value_expand;
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}
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const XPUType* y_data =
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reinterpret_cast<const XPUType*>(value_tensor->data<T>());
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r = xpu::masked_fill(
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ctx, cond_data, x_data, y_data, out_xpu, cond_vec, x_vec);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "masked_fill_tensor");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(masked_fill,
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XPU,
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ALL_LAYOUT,
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phi::MaskedFillKernel,
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float,
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int,
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int8_t,
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int64_t,
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uint8_t,
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phi::float16,
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phi::bfloat16) {
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kernel->InputAt(1).SetDataType(phi::DataType::BOOL);
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}
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