wehub-resource-sync
112 lines
4.4 KiB
C++
112 lines
4.4 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 "glog/logging.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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namespace phi {
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namespace fusion {
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template <typename T, typename Context>
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void Conv1dXPUKernel(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 optional<DenseTensor>& branch,
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const optional<DenseTensor>& branch_max,
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const std::vector<int>& paddings,
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const std::string& padding_algorithm,
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int dilations,
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int strides,
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int groups,
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int act_type,
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float act_param,
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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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auto input_dims = x.dims();
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auto filter_dims = filter.dims();
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int batch = static_cast<int>(input_dims[0]);
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int in_c = static_cast<int>(input_dims[1]);
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int in_xw = static_cast<int>(input_dims[2]);
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int out_c = static_cast<int>(filter_dims[0]);
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int ksize_w = static_cast<int>(filter_dims[2]);
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std::vector<int64_t> paddings_vec(std::begin(paddings), std::end(paddings));
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auto* input_data = reinterpret_cast<const XPUType*>(x.data<T>());
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const float* input_max_data =
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x_max.get_ptr() == nullptr ? nullptr : x_max.get_ptr()->data<float>();
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auto* filter_data = filter.data<int16_t>();
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auto* filter_max_data = filter_max.data<float>();
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auto* branch_data =
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branch.get_ptr() == nullptr
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? nullptr
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: reinterpret_cast<const XPUType*>(branch.get_ptr()->data<T>());
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const float* branch_max_data = branch_max.get_ptr() == nullptr
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? nullptr
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: branch_max.get_ptr()->data<float>();
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const float* bias_data =
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bias.get_ptr() == nullptr ? nullptr : bias.get_ptr()->data<float>();
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auto* out_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
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auto* out_max_data = dev_ctx.template Alloc<float>(out_max);
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xpu::Activation_t act(static_cast<xpu::Activation_t::act_enum>(act_type));
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if (act_type == xpu::Activation_t::LEAKY_RELU) {
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act.leaky_alpha = act_param;
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} else if (act_type == xpu::Activation_t::HARD_SIGMOID) {
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act.hard_sigmoid_slope = act_param;
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}
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int r =
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xpu::conv1d_fusion<XPUType, int16_t, XPUType, int16_t>( // TX/TW/TY/TGEMM
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/* baidu::xpu::api::Context* ctx */ dev_ctx.x_context(),
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/* const TX* x */ input_data,
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/* const TW* weight */ filter_data,
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/* TY* y */ out_data,
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/* int64_t n */ batch,
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/* int64_t c */ in_c,
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/* int64_t xw */ in_xw,
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/* int64_t f */ out_c,
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/* int64_t ksize_w */ ksize_w,
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/* int64_t stride_w */ strides,
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/* const std::vector<int64_t>& pad */ paddings_vec,
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/* int64_t dilation_w */ dilations,
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/* int64_t group */ groups,
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/* const float* x_maxptr */ input_max_data,
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/* const float* w_maxptr */ filter_max_data,
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/* float* y_maxptr */ out_max_data,
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/* bool is_nchw */ true,
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/* const float* bias */ bias_data,
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/* const TY* branch */ branch_data,
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/* const baidu::xpu::api::Activation_t& act */ act,
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/* const float* branch_maxptr */ branch_max_data);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "conv1d_xpu");
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}
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} // namespace fusion
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} // namespace phi
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PD_REGISTER_KERNEL(conv1d_xpu,
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XPU,
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ALL_LAYOUT,
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phi::fusion::Conv1dXPUKernel,
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float,
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phi::float16) {}
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