wehub-resource-sync
104 lines
4.1 KiB
C++
104 lines
4.1 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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namespace phi {
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namespace fusion {
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template <typename T, typename Context>
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void YoloBoxXPUKernel(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& grid,
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const DenseTensor& stride,
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const DenseTensor& anchor_grid,
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float offset,
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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* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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auto* out_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
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// float* x_max
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float* x_max_data = nullptr;
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const float* grid_data;
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const float* stride_data;
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const float* anchor_grid_data;
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// fix precision of fp16 model
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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if (std::is_same<T, phi::float16>::value) {
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float* grid_data_temp = RAII_GUARD.alloc_l3_or_gm<float>(grid.numel());
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int r = xpu::cast<XPUType, float>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(grid.data<T>()),
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grid_data_temp,
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grid.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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float* stride_data_temp = RAII_GUARD.alloc_l3_or_gm<float>(stride.numel());
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r = xpu::cast<XPUType, float>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(stride.data<T>()),
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stride_data_temp,
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stride.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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float* anchor_grid_data_temp =
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RAII_GUARD.alloc_l3_or_gm<float>(anchor_grid.numel());
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r = xpu::cast<XPUType, float>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(anchor_grid.data<T>()),
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anchor_grid_data_temp,
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anchor_grid.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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grid_data = grid_data_temp;
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stride_data = stride_data_temp;
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anchor_grid_data = anchor_grid_data_temp;
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} else {
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grid_data = grid.data<float>();
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stride_data = stride.data<float>();
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anchor_grid_data = anchor_grid.data<float>();
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}
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std::vector<int64_t> x_shape = vectorize(x.dims());
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std::vector<int64_t> grid_shape = vectorize(grid.dims());
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std::vector<int64_t> stride_shape = vectorize(stride.dims());
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std::vector<int64_t> anchor_grid_shape = vectorize(anchor_grid.dims());
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// yolo_box_coord only support fp32&&fp16 precision
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int r = xpu::yolo_box_coord<XPUType>(
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/* baidu::xpu::api::Context* ctx */ dev_ctx.x_context(),
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/* const T* x */ x_data,
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/* T* y */ out_data,
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/* const std::vector<int64_t>& x_shape */ x_shape,
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/* const float* grid */ grid_data,
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/* const float* stride */ stride_data,
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/* const float* anchor_grid */ anchor_grid_data,
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/* const std::vector<int64_t>& grid_shape */ grid_shape,
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/* const std::vector<int64_t>& stride_shape */ stride_shape,
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/* const std::vector<int64_t>& anchor_grid */ anchor_grid_shape,
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/* float offset */ offset,
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/* float* x_max */ x_max_data,
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/* float* y_max */ dev_ctx.template Alloc<float>(out_max));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "yolo_box_xpu");
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}
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} // namespace fusion
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} // namespace phi
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PD_REGISTER_KERNEL(yolo_box_xpu,
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XPU,
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ALL_LAYOUT,
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phi::fusion::YoloBoxXPUKernel,
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float,
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phi::float16) {}
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