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180 lines
7.5 KiB
180 lines
7.5 KiB
/* Copyright (c) 2016 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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#include "paddle/fluid/operators/pool_op.h"
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#include <unordered_map>
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#ifdef PADDLE_WITH_XPU
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namespace paddle {
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namespace operators {
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xpu::Pooling_t XPUPoolingType(const std::string& pooltype, bool exclusive,
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bool is_test) {
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if (pooltype == "max") {
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return xpu::Pooling_t::MAX_WITHOUT_INDEX;
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} else if (pooltype == "avg") {
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if (exclusive) {
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return xpu::Pooling_t::AVG_WITHOUT_PAD;
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} else {
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return xpu::Pooling_t::AVG_WITH_PAD;
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}
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} else {
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PADDLE_THROW(platform::errors::InvalidArgument(
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"Pool op only supports 2D and 3D input."));
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}
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}
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template <typename DeviceContext, typename T>
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class PoolXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* in_x = context.Input<Tensor>("X");
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Tensor* out = context.Output<Tensor>("Out");
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std::string pooling_type = context.Attr<std::string>("pooling_type");
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std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
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std::vector<int> strides = context.Attr<std::vector<int>>("strides");
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std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
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bool exclusive = context.Attr<bool>("exclusive");
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bool is_test = context.Attr<bool>("is_test");
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bool adaptive = context.Attr<bool>("adaptive");
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PADDLE_ENFORCE_EQ(
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!adaptive, true,
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platform::errors::InvalidArgument(
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"The Pool2d XPU OP does not support adaptive == true!"));
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PADDLE_ENFORCE_EQ(
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ksize.size(), 2,
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platform::errors::InvalidArgument(
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"The Pool2d XPU OP only support 2 dimension pooling!"));
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int* index_data = nullptr;
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if (context.Attr<bool>("global_pooling")) {
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
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}
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}
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const int c = in_x->dims()[0] * in_x->dims()[1];
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const int in_h = in_x->dims()[2];
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const int in_w = in_x->dims()[3];
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const int out_h = out->dims()[2];
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const int out_w = out->dims()[3];
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const int win_h = ksize[0];
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const int win_w = ksize[1];
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const int stride_h = strides[0];
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const int stride_w = strides[1];
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const int pad_up = paddings[0];
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const int pad_down = paddings[0];
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const int pad_left = paddings[1];
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const int pad_right = paddings[1];
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const float* input = in_x->data<float>();
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out->mutable_data<T>(context.GetPlace());
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float* output = out->data<float>();
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xpu::Pooling_t pool_type = XPUPoolingType(pooling_type, exclusive, is_test);
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = xpu::pooling_forward<float, float>(
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dev_ctx.x_context(), input, output, index_data, pool_type, c, in_h,
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in_w, pad_left, pad_right, pad_up, pad_down, win_h, win_w, stride_h,
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stride_w, out_h, out_w);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The pool2d XPU API return wrong value[%d], please check "
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"where Baidu Kunlun Card is properly installed.",
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r));
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}
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};
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template <typename DeviceContext, typename T>
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class PoolGradXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* in_x = context.Input<Tensor>("X");
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const Tensor* out = context.Input<Tensor>("Out");
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const Tensor* out_grad =
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context.Input<Tensor>(framework::GradVarName("Out"));
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Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
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std::string pooling_type = context.Attr<std::string>("pooling_type");
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std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
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std::vector<int> strides = context.Attr<std::vector<int>>("strides");
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std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
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bool exclusive = context.Attr<bool>("exclusive");
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bool adaptive = context.Attr<bool>("adaptive");
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const int* index_data = nullptr;
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PADDLE_ENFORCE_EQ(
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!adaptive, true,
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platform::errors::InvalidArgument(
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"The Pool2d XPU OP does not support adaptive == true!"));
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PADDLE_ENFORCE_EQ(ksize.size(), 2, platform::errors::InvalidArgument(
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"The Pool2d XPU OP only support 2 "
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"dimension pooling!, but received "
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"%d-dimension pool kernel size",
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ksize.size()));
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if (context.Attr<bool>("global_pooling")) {
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
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}
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}
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if (!in_x_grad) {
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return;
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}
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const int c = in_x->dims()[0] * in_x->dims()[1];
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const int in_h = in_x->dims()[2];
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const int in_w = in_x->dims()[3];
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const int out_h = out->dims()[2];
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const int out_w = out->dims()[3];
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const int win_h = ksize[0];
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const int win_w = ksize[1];
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const int stride_h = strides[0];
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const int stride_w = strides[1];
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const int pad_up = paddings[0];
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const int pad_down = paddings[0];
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const int pad_left = paddings[1];
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const int pad_right = paddings[1];
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const float* input = in_x->data<float>();
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const float* output = out->data<float>();
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const float* output_grad = out_grad->data<float>();
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in_x_grad->mutable_data<T>(context.GetPlace());
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float* input_grad = in_x_grad->data<float>();
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xpu::Pooling_t pool_type = XPUPoolingType(pooling_type, exclusive, false);
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auto& dev_ctx = context.template device_context<DeviceContext>();
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// Need to init memory in the first place
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const int zero = 0;
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int r =
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xpu::memset(dev_ctx.x_context(), reinterpret_cast<void**>(input_grad),
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zero, in_x_grad->numel() * sizeof(float));
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The Pool2d XPU OP return wrong value[%d], please check "
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"where Baidu Kunlun Card is properly installed.",
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r));
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r = xpu::pooling_backward(dev_ctx.x_context(), input, output, index_data,
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output_grad, input_grad, pool_type, c, in_h, in_w,
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pad_left, pad_right, pad_up, pad_down, win_h,
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win_w, stride_h, stride_w, out_h, out_w);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The Pool2d XPU OP return wrong value[%d], please check "
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"where Baidu Kunlun Card is properly installed.",
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r));
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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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REGISTER_OP_XPU_KERNEL(
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pool2d, ops::PoolXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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pool2d_grad,
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ops::PoolGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif
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