wehub-resource-sync
107 lines
4.0 KiB
C++
107 lines
4.0 KiB
C++
// Copyright (c) 2023 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/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cpu/conv_util.h"
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#include "paddle/phi/kernels/xpu/xpu_api_wrapper.h"
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namespace phi {
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namespace fusion {
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template <typename T, typename Context>
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void Conv2dTransposeXPUKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& x_max,
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const DenseTensor& filter,
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const DenseTensor& filter_max,
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const optional<DenseTensor>& bias,
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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>& output_padding,
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const IntArray& output_size,
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const std::string& padding_algorithm,
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int groups,
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const std::vector<int>& dilations_,
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const std::string& data_format,
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bool has_bias,
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bool with_act,
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const std::string& act_type,
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DenseTensor* out,
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DenseTensor* out_max) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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dev_ctx.template Alloc<T>(out);
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dev_ctx.template Alloc<float>(out_max);
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bool is_nchw;
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is_nchw = (data_format == "NHWC") ? false : true;
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DDim in_data_dims = slice_ddim(x.dims(), 2, x.dims().size()); // hw
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DDim filter_data_dims = slice_ddim(filter.dims(), 2, filter.dims().size());
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std::vector<int64_t> ksize = vectorize<int64_t>(filter_data_dims);
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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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UpdatePaddingAndDilation(
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&paddings, &dilations, padding_algorithm, in_data_dims, strides, ksize);
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const int64_t batch_size = x.dims()[0];
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const int64_t img_yc = x.dims()[1];
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const int64_t img_xc = out->dims()[1];
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const int64_t img_xh = out->dims()[2];
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const int64_t img_xw = out->dims()[3];
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auto act = xpu::Activation_t::LINEAR;
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if (with_act) {
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if (act_type == "relu") {
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act = xpu::Activation_t::RELU;
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}
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}
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auto bias_data =
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bias.get_ptr() == nullptr ? nullptr : bias.get_ptr()->data<float>();
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auto x_max_data =
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x_max.get_ptr() == nullptr ? nullptr : x_max.get_ptr()->data<float>();
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auto filter_max_data = filter_max.data<float>();
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int r = xpu::conv2d_transpose_fusion_v2<XPUType, int16_t, XPUType, int16_t>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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filter.data<int16_t>(),
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reinterpret_cast<XPUType*>(out->data<T>()),
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batch_size,
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img_yc,
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img_xh,
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img_xw,
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img_xc,
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ksize,
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strides,
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paddings,
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dilations,
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groups,
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x_max_data,
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filter_max_data,
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out_max->data<float>(),
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bias_data,
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act,
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is_nchw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "conv2d_transpose_fusion_v2");
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}
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} // namespace fusion
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} // namespace phi
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PD_REGISTER_KERNEL(conv2d_transpose_xpu,
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XPU,
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ALL_LAYOUT,
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phi::fusion::Conv2dTransposeXPUKernel,
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float,
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phi::float16) {}
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