130 lines
4.5 KiB
C++
130 lines
4.5 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_kernel.h"
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#include "glog/logging.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/common/memory_utils.h"
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#include "paddle/phi/kernels/expand_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 MaskedSelectKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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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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out->Resize({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 expanded_size = funcs::MatrixGetBroadcastBatchPortion(
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vectorize(x.dims()), vectorize(mask.dims()));
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DDim expand_dims = make_ddim(expanded_size);
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DenseTensor mask_expand;
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DenseTensor x_expand;
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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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auto* mask_data = mask_expand.data<bool>();
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auto* input_data = reinterpret_cast<const XPUType*>(x_expand.data<T>());
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auto input_dim = x_expand.dims();
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auto mask_dim = mask_expand.dims();
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PADDLE_ENFORCE_EQ(input_dim,
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mask_dim,
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common::errors::InvalidArgument(
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"The dim size of input and mask in OP(masked_selected) "
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"must be equal, but got input dim:(%ld), mask dim: "
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"(%ld). Please check input "
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"value.",
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input_dim,
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mask_dim));
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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int64_t* out_size = RAII_GUARD.alloc_l3_or_gm<int64_t>(1);
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int64_t out_size_cpu;
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PADDLE_ENFORCE_XDNN_SUCCESS(
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xpu::nonzero_count(
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dev_ctx.x_context(), mask_data, out_size, mask.numel()),
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"nonzero_count ");
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memory_utils::Copy(CPUPlace(),
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static_cast<void*>(&out_size_cpu),
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mask.place(),
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static_cast<void*>(out_size),
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sizeof(int64_t));
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if (std::getenv("XPUSIM_SKIP_RUN") &&
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std::strcmp(std::getenv("XPUSIM_SKIP_RUN"), "1") == 0) {
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VLOG(3) << "WARNING: In the simulator mode, the variable out_size_cpu "
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"stores an uninitialized value. To avoid allocating a memory of "
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"random size, we assign numel to out_size_cpu";
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out_size_cpu = mask.numel();
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}
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DDim out_dim{out_size_cpu};
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out->Resize(out_dim);
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auto out_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
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auto input_shape = vectorize<int64_t>(input_dim);
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auto mask_shape = vectorize<int64_t>(mask_dim);
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if (input_dim.size() == 0) {
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input_shape = std::vector<int64_t>({1});
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}
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if (mask_dim.size() == 0) {
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mask_shape = std::vector<int64_t>({1});
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}
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if (out_size_cpu > 0) {
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PADDLE_ENFORCE_XDNN_SUCCESS(xpu::masked_select(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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input_shape,
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mask_shape,
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out_size_cpu),
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"masked_select");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(masked_select,
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XPU,
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ALL_LAYOUT,
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phi::MaskedSelectKernel,
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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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int64_t) {
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kernel->InputAt(1).SetDataType(phi::DataType::BOOL);
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}
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