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287 lines
12 KiB
287 lines
12 KiB
4 years ago
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#ifdef PADDLE_WITH_XPU
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#include <algorithm>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/platform/xpu_header.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class DeformableConvXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* input = ctx.Input<Tensor>("Input");
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auto* offset = ctx.Input<Tensor>("Offset");
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auto* mask = ctx.Input<Tensor>("Mask");
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Tensor filter = *ctx.Input<Tensor>("Filter");
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Tensor* output = ctx.Output<Tensor>("Output");
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output->mutable_data<T>(ctx.GetPlace());
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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const int groups = ctx.Attr<int>("groups");
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const int deformable_groups = ctx.Attr<int>("deformable_groups");
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const int im2col_step = ctx.Attr<int>("im2col_step");
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const std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
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const std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
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const std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
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PADDLE_ENFORCE_EQ(
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deformable_groups == 1, true,
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platform::errors::InvalidArgument((
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"XPU only support deformable_groups == 1 in deformable_conv op.")));
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PADDLE_ENFORCE_EQ(
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groups == 1, true,
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platform::errors::InvalidArgument(
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("XPU only support groups == 1 in deformable_conv op.")));
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PADDLE_ENFORCE_EQ(filter.dims()[2] <= 8 && filter.dims()[3] <= 8, true,
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platform::errors::InvalidArgument(
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"Filter high and weight should less than 8 on xpu "
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"in deformable_conv op."));
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const int batch_size = static_cast<int>(input->dims()[0]);
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std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
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const T* input_ptr = input->data<T>();
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const T* filter_ptr = filter.data<T>();
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const float* offset_ptr = offset->data<T>();
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const float* mask_ptr = mask->data<T>();
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T* output_prt = output->data<T>();
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// set zeros for d_table_data
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const int zero = 0;
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int r = xpu::constant<T>(dev_ctx.x_context(), output_prt, output->numel(),
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zero);
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PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
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platform::errors::External(
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"XPU API return wrong value[%d], please check where "
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"Baidu Kunlun Card is properly installed.",
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r));
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int input_dim = input->numel() / input->dims()[0];
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int input_offset_dim = offset->numel() / offset->dims()[0];
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int input_mask_dim = mask->numel() / mask->dims()[0];
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int output_dim =
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output_shape_vec[1] * output_shape_vec[2] * output_shape_vec[3];
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std::vector<int> ksize{static_cast<int>(filter.dims()[2]),
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static_cast<int>(filter.dims()[3])};
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int n = im2col_step;
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int c = input->dims()[1];
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int h = input->dims()[2];
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int w = input->dims()[3];
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int f = filter.dims()[0];
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for (int i = 0; i < batch_size / im2col_step; ++i) {
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int r = xpu::deformable_conv<float, float, float, int>(
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dev_ctx.x_context(), input_ptr + i * im2col_step * input_dim,
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filter_ptr, offset_ptr + i * im2col_step * input_offset_dim,
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mask_ptr + i * im2col_step * input_mask_dim,
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output_prt + i * im2col_step * output_dim, n, c, h, w, f, ksize,
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strides, paddings, dilations, groups, deformable_groups, nullptr,
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nullptr, nullptr, true);
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PADDLE_ENFORCE_EQ(
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r, XPU_SUCCESS,
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platform::errors::External(
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"XPU deformable_conv kernel return wrong value[%d].", r));
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}
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}
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};
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template <typename DeviceContext, typename T>
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class DeformableConvGradXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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const Tensor* output_grad =
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ctx.Input<Tensor>(framework::GradVarName("Output"));
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Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
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Tensor* filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
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Tensor* offset_grad = ctx.Output<Tensor>(framework::GradVarName("Offset"));
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Tensor* mask_grad = ctx.Output<Tensor>(framework::GradVarName("Mask"));
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T* dx_data = nullptr;
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T* dw_data = nullptr;
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T* dmask_data = nullptr;
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T* doffset_data = nullptr;
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if (input_grad != nullptr) {
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input_grad->mutable_data<T>(ctx.GetPlace());
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dx_data = input_grad->data<T>();
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}
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if (filter_grad != nullptr) {
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filter_grad->mutable_data<T>(ctx.GetPlace());
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dw_data = filter_grad->data<T>();
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}
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if (offset_grad != nullptr) {
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offset_grad->mutable_data<T>(ctx.GetPlace());
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doffset_data = offset_grad->data<T>();
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}
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if (mask_grad != nullptr) {
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mask_grad->mutable_data<T>(ctx.GetPlace());
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dmask_data = mask_grad->data<T>();
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}
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const Tensor* input = ctx.Input<Tensor>("Input");
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Tensor offset = *ctx.Input<Tensor>("Offset");
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Tensor mask = *ctx.Input<Tensor>("Mask");
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Tensor filter = *ctx.Input<Tensor>("Filter");
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int groups = ctx.Attr<int>("groups");
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int deformable_groups = ctx.Attr<int>("deformable_groups");
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int im2col_step = ctx.Attr<int>("im2col_step");
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std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
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std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
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std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
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PADDLE_ENFORCE_EQ(
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deformable_groups == 1, true,
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platform::errors::InvalidArgument((
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"XPU only support deformable_groups == 1 in deformable_conv op.")));
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PADDLE_ENFORCE_EQ(
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groups == 1, true,
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platform::errors::InvalidArgument(
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("XPU only support groups == 1 in deformable_conv op.")));
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PADDLE_ENFORCE_EQ(filter.dims()[2] <= 8 && filter.dims()[3] <= 8, true,
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platform::errors::InvalidArgument(
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"Filter high and weight should less than 8 on xpu "
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"in deformable_conv op."));
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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const int batch_size = static_cast<int>(input->dims()[0]);
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std::vector<int64_t> output_shape_vec(
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framework::vectorize(output_grad->dims()));
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const T* output_grad_ptr = output_grad->data<T>();
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const T* input_ptr = input->data<T>();
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const T* filter_ptr = filter.data<T>();
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const float* offset_ptr = offset.data<float>();
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const float* mask_ptr = mask.data<float>();
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if (dx_data == nullptr) {
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PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&dx_data),
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input->numel() * sizeof(T)),
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XPU_SUCCESS, platform::errors::ResourceExhausted(
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"XPU has no enough memory"));
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}
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if (dw_data == nullptr) {
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PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&dw_data),
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filter.numel() * sizeof(T)),
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XPU_SUCCESS, platform::errors::ResourceExhausted(
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"XPU has no enough memory"));
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}
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if (doffset_data == nullptr) {
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PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&doffset_data),
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offset.numel() * sizeof(T)),
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XPU_SUCCESS, platform::errors::ResourceExhausted(
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"XPU has no enough memory"));
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}
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if (dmask_data == nullptr) {
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PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&dmask_data),
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mask.numel() * sizeof(T)),
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XPU_SUCCESS, platform::errors::ResourceExhausted(
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"XPU has no enough memory"));
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}
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int input_dim = input->numel() / input->dims()[0];
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int input_offset_dim = offset.numel() / offset.dims()[0];
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int input_mask_dim = mask.numel() / mask.dims()[0];
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int output_dim =
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output_shape_vec[1] * output_shape_vec[2] * output_shape_vec[3];
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std::vector<int> ksize{static_cast<int>(filter.dims()[2]),
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static_cast<int>(filter.dims()[3])};
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int n = im2col_step;
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int c = input->dims()[1];
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int h = input->dims()[2];
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int w = input->dims()[3];
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int f = filter.dims()[0];
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T* filter_grad_tmp = nullptr;
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PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&filter_grad_tmp),
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filter_grad->numel() * sizeof(T)),
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XPU_SUCCESS, platform::errors::ResourceExhausted(
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"XPU has no enough memory"));
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// set zeros for d_table_data
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const int zero = 0;
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int r_dx =
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xpu::constant<T>(dev_ctx.x_context(), dx_data, input->numel(), zero);
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int r_dw =
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xpu::constant<T>(dev_ctx.x_context(), dw_data, filter.numel(), zero);
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int r_doffset = xpu::constant<T>(dev_ctx.x_context(), doffset_data,
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offset.numel(), zero);
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int r_dmask =
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xpu::constant<T>(dev_ctx.x_context(), dmask_data, mask.numel(), zero);
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int r_filter = xpu::constant<T>(dev_ctx.x_context(), filter_grad_tmp,
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filter.numel(), zero);
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auto ret = (r_dx == xpu::Error_t::SUCCESS) && (r_dx == r_dw) &&
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(r_dx == r_doffset) && (r_dx == r_dmask) && (r_dx == r_filter);
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PADDLE_ENFORCE_EQ(ret, true,
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platform::errors::External(
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"XPU API return wrong value, please check where "
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"Baidu Kunlun Card is properly installed."));
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for (int i = 0; i < batch_size / im2col_step; ++i) {
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int r = xpu::deformable_conv_grad<float, float, float, int>(
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dev_ctx.x_context(), input_ptr + i * im2col_step * input_dim,
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filter_ptr, offset_ptr + i * im2col_step * input_offset_dim,
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mask_ptr + i * im2col_step * input_mask_dim,
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output_grad_ptr + i * im2col_step * output_dim,
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dx_data + i * im2col_step * input_dim, filter_grad_tmp,
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doffset_data + i * im2col_step * input_offset_dim,
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dmask_data + i * im2col_step * input_mask_dim, n, c, h, w, f, ksize,
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strides, paddings, dilations, groups, deformable_groups, nullptr,
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nullptr, nullptr, nullptr, nullptr, true);
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PADDLE_ENFORCE_EQ(
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r, XPU_SUCCESS,
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platform::errors::External(
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"XPU deformable_conv_grad kernel return wrong value[%d].", r));
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r = baidu::xpu::api::add<T>(dev_ctx.x_context(), filter_grad_tmp, dw_data,
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dw_data, filter.numel());
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PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
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platform::errors::External(
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"XPU add kernel return wrong value[%d].", r));
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}
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dev_ctx.Wait();
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xpu_free(filter_grad_tmp);
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if (input_grad == nullptr) {
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xpu_free(dx_data);
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}
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if (filter_grad == nullptr) {
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xpu_free(dw_data);
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}
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if (offset_grad == nullptr) {
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xpu_free(doffset_data);
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}
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if (mask_grad == nullptr) {
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xpu_free(dmask_data);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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using XPUDeviceContext = paddle::platform::XPUDeviceContext;
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REGISTER_OP_XPU_KERNEL(deformable_conv,
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ops::DeformableConvXPUKernel<XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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deformable_conv_grad,
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ops::DeformableConvGradXPUKernel<XPUDeviceContext, float>);
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#endif
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