wehub-resource-sync
93 lines
3.6 KiB
C++
93 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/unfold_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/funcs/unfold_functor.h"
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namespace phi {
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template <typename T, typename Context>
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void UnfoldKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int>& kernel_sizes_,
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const std::vector<int>& strides_,
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const std::vector<int>& paddings_,
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const std::vector<int>& dilations_,
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DenseTensor* out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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dev_ctx.template Alloc<T>(out);
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if (out->numel() == 0) {
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return;
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}
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const std::string data_format = DataLayoutToString(x.layout());
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bool is_nchw = data_format == "NCHW";
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PADDLE_ENFORCE_EQ(is_nchw,
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true,
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common::errors::PreconditionNotMet(
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"Unfold op only supports datalayout == NCHW"));
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auto x_dims = x.dims();
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int64_t n = x_dims[0];
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int64_t c = x_dims[1];
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int64_t h = x_dims[2];
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int64_t w = x_dims[3];
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std::vector<int64_t> kernel_sizes(kernel_sizes_.begin(), kernel_sizes_.end());
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std::vector<int64_t> strides(strides_.begin(), strides_.end());
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std::vector<int64_t> paddings(paddings_.begin(), paddings_.end());
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std::vector<int64_t> dilations(dilations_.begin(), dilations_.end());
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int64_t out_height = funcs::CalcOutputSize(x_dims[2],
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kernel_sizes[0],
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dilations[0],
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paddings[0],
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paddings[2],
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strides[0]);
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int64_t out_width = funcs::CalcOutputSize(x_dims[3],
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kernel_sizes[1],
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dilations[1],
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paddings[1],
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paddings[3],
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strides[1]);
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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XPUType* out_pre_trans = RAII_GUARD.alloc_l3_or_gm<XPUType>(out->numel());
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int r = xpu::im2col(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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out_pre_trans,
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n,
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c,
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h,
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w,
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kernel_sizes,
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strides,
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paddings,
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dilations,
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is_nchw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "im2col");
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r = xpu::transpose(
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dev_ctx.x_context(),
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out_pre_trans,
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reinterpret_cast<XPUType*>(out->data<T>()),
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{n, out_height, out_width, c, kernel_sizes[0], kernel_sizes[1]},
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{0, 3, 4, 5, 1, 2});
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "transpose");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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unfold, XPU, ALL_LAYOUT, phi::UnfoldKernel, float, phi::float16) {}
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