Add reduce sum and reduce mean xpu op (#27939)
* add reduce xpu op test=develop;test=kunlun * add reduce xpu op test=develop;test=kunlun * add reduce xpu op test=develop;test=kunlun * add reduce xpu op test=develop;test=kunlun * add reduce xpu op test=develop;test=kunlunswt-req
Qinghe JING
4 years ago
committed by
GitHub
parent
bf412f4665
commit
4a4f773658
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// Copyright (c) 2020 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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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/reduce_ops/reduce_mean_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class ReduceMeanXPUKernel : 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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PADDLE_ENFORCE_EQ(
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platform::is_xpu_place(context.GetPlace()), true,
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platform::errors::Unavailable("This kernel only runs on XPU."));
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// bool reduce_all = context.Attr<bool>("reduce_all");
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auto* input = context.Input<Tensor>("X");
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auto* output = context.Output<Tensor>("Out");
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output->mutable_data<T>(context.GetPlace());
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int ndim = input->dims().size();
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std::vector<int> idims;
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for (int i = 0; i < input->dims().size(); i++) {
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idims.push_back(input->dims()[i]);
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}
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auto dims = context.Attr<std::vector<int>>("dim");
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int rdim = dims.size();
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int r =
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xpu::reduce(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
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idims.data(), ndim, dims.data(), rdim, xpu::REDUCE_MEAN);
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PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
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platform::errors::External("XPU kernel error!"));
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_XPU_KERNEL(
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reduce_mean,
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ops::ReduceMeanXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif
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// Copyright (c) 2018 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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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
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#include <memory>
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#include <string>
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class ReduceSumXPUKernel : 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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PADDLE_ENFORCE_EQ(
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platform::is_xpu_place(context.GetPlace()), true,
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platform::errors::Unavailable("This kernel only runs on XPU."));
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bool reduce_all = context.Attr<bool>("reduce_all");
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auto* input = context.Input<Tensor>("X");
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auto* output = context.Output<Tensor>("Out");
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output->mutable_data<T>(context.GetPlace());
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auto& dev_ctx = context.template device_context<DeviceContext>();
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if (reduce_all) {
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int input_len = input->numel();
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int r = xpu::sum(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
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input_len);
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PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
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platform::errors::External("XPU kernel error!"));
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} else {
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int ndim = input->dims().size();
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std::vector<int> idims;
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for (int i = 0; i < input->dims().size(); i++) {
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idims.push_back(input->dims()[i]);
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}
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auto dims = context.Attr<std::vector<int>>("dim");
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int rdim = dims.size();
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int r =
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xpu::reduce(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
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idims.data(), ndim, dims.data(), rdim, xpu::REDUCE_SUM);
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PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
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platform::errors::External("XPU kernel error!"));
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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 ReduceSumGradXPUKernel : 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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auto dims = context.Attr<std::vector<int>>("dim");
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bool reduce_all = context.Attr<bool>("reduce_all");
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auto* input0 = context.Input<Tensor>("X");
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auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* output = context.Output<Tensor>(framework::GradVarName("X"));
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output->mutable_data<T>(context.GetPlace());
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const auto* input2_d = input2->data<T>();
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auto* output_d = output->data<T>();
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = 0;
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std::vector<int> idims;
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int reduce_dim = 0;
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if (reduce_all) {
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idims.push_back(input0->numel());
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idims.push_back(1);
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idims.push_back(1);
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r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
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idims.data(), idims.size(), &reduce_dim, 1,
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xpu::REDUCE_SUM);
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PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
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platform::errors::External("XPU kernel error!"));
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} else if (dims.size() == 1) {
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// handle reduce by one dimension
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int reduce_dim_index = dims[0];
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if (reduce_dim_index < 0) {
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reduce_dim_index += input0->dims().size();
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}
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auto& input_dim = input0->dims();
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int before_dim = 1;
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for (int i = 0; i < reduce_dim_index; ++i) {
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before_dim *= input_dim[i];
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}
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int reduce_dim = input_dim[reduce_dim_index];
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int after_dim = 1;
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for (int i = reduce_dim_index + 1; i < input_dim.size(); ++i) {
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after_dim *= input_dim[i];
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}
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idims.push_back(before_dim);
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idims.push_back(input_dim[reduce_dim_index]);
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idims.push_back(after_dim);
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reduce_dim = 1;
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r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
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idims.data(), idims.size(), &reduce_dim, 1,
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xpu::REDUCE_SUM);
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PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
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platform::errors::External("XPU kernel error!"));
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} else {
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PADDLE_THROW(
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platform::errors::Unimplemented("unsupport reduce sum grad"));
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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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REGISTER_OP_XPU_KERNEL(
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reduce_sum,
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ops::ReduceSumXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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reduce_sum_grad,
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ops::ReduceSumGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif
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# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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import numpy as np
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import sys
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sys.path.append("..")
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from op_test import OpTest, skip_check_grad_ci
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import paddle
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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from paddle.fluid.framework import convert_np_dtype_to_dtype_
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class TestMeanOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def check_grad_(self):
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self.check_grad(['X'], 'Out')
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class TestMeanOp5D(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {
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'X': np.random.random((1, 2, 5, 6, 10)).astype("float64")
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}
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self.attrs = {'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestMeanOp6D(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {
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'X': np.random.random((1, 1, 2, 5, 6, 10)).astype("float64")
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}
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self.attrs = {'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestMeanOp8D(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {
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'X': np.random.random((1, 3, 1, 2, 1, 4, 3, 10)).astype("float64")
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}
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self.attrs = {'dim': (0, 3), 'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].mean(axis=(0, 3))}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class Test1DReduce(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random(120).astype("float64")}
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self.attrs = {'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class Test2DReduce0(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.attrs = {'dim': [0], 'use_xpu': True}
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self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
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class Test2DReduce1(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.attrs = {'dim': [1], 'use_xpu': True}
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self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce0(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.attrs = {'dim': [1], 'use_xpu': True}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce1(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.attrs = {'dim': [2], 'use_xpu': True}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce2(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.attrs = {'dim': [-2], 'use_xpu': True}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce3(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.attrs = {'dim': [1, 2], 'use_xpu': True}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
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}
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class TestKeepDimReduce(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [1], 'keep_dim': True, 'use_xpu': True}
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self.outputs = {
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'Out': self.inputs['X'].mean(
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axis=tuple(self.attrs['dim']), keepdims=self.attrs['keep_dim'])
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}
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class TestKeepDim8DReduce(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {
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'X': np.random.random((2, 5, 3, 2, 2, 3, 4, 2)).astype("float64")
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}
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self.attrs = {'dim': (3, 4, 5), 'keep_dim': True, 'use_xpu': True}
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self.outputs = {
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'Out': self.inputs['X'].mean(
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axis=tuple(self.attrs['dim']), keepdims=self.attrs['keep_dim'])
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}
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class TestReduceAll(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
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self.attrs = {'reduce_all': True, 'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].mean()}
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,206 @@
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# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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import numpy as np
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import sys
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sys.path.append("..")
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from op_test import OpTest, skip_check_grad_ci
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import paddle
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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from paddle.fluid.framework import convert_np_dtype_to_dtype_
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class TestSumOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'use_xpu': True}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def check_grad_(self):
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self.check_grad(['X'], 'Out')
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class TestSumOp5D(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {
|
||||
'X': np.random.random((1, 2, 5, 6, 10)).astype("float64")
|
||||
}
|
||||
self.attrs = {'use_xpu': True}
|
||||
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
|
||||
|
||||
def test_check_output(self):
|
||||
if paddle.is_compiled_with_xpu():
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(['X'], 'Out')
|
||||
|
||||
|
||||
class TestSumOp6D(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {
|
||||
'X': np.random.random((1, 1, 2, 5, 6, 10)).astype("float64")
|
||||
}
|
||||
self.attrs = {'use_xpu': True}
|
||||
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
|
||||
|
||||
def test_check_output(self):
|
||||
if paddle.is_compiled_with_xpu():
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(['X'], 'Out')
|
||||
|
||||
|
||||
class TestSumOp8D(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {
|
||||
'X': np.random.random((1, 3, 1, 2, 1, 4, 3, 10)).astype("float64")
|
||||
}
|
||||
self.attrs = {'dim': (0, 3), 'use_xpu': True}
|
||||
self.outputs = {'Out': self.inputs['X'].sum(axis=(0, 3))}
|
||||
|
||||
def test_check_output(self):
|
||||
if paddle.is_compiled_with_xpu():
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(['X'], 'Out')
|
||||
|
||||
|
||||
class Test1DReduce(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {'X': np.random.random(120).astype("float64")}
|
||||
self.attrs = {'use_xpu': True}
|
||||
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
|
||||
|
||||
def test_check_output(self):
|
||||
if paddle.is_compiled_with_xpu():
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(['X'], 'Out')
|
||||
|
||||
|
||||
class Test2DReduce0(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.attrs = {'dim': [0], 'use_xpu': True}
|
||||
self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
|
||||
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
|
||||
|
||||
|
||||
class Test2DReduce1(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.attrs = {'dim': [1], 'use_xpu': True}
|
||||
self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
|
||||
}
|
||||
|
||||
|
||||
class Test3DReduce0(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.attrs = {'dim': [1], 'use_xpu': True}
|
||||
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
|
||||
}
|
||||
|
||||
|
||||
class Test3DReduce1(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.attrs = {'dim': [2], 'use_xpu': True}
|
||||
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
|
||||
}
|
||||
|
||||
|
||||
class Test3DReduce2(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.attrs = {'dim': [-2], 'use_xpu': True}
|
||||
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
|
||||
}
|
||||
|
||||
|
||||
class Test3DReduce3(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.attrs = {'dim': [1, 2], 'use_xpu': True}
|
||||
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
|
||||
}
|
||||
|
||||
|
||||
class TestKeepDimReduce(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
|
||||
self.attrs = {'dim': [1], 'keep_dim': True, 'use_xpu': True}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
|
||||
keepdims=self.attrs['keep_dim'])
|
||||
}
|
||||
|
||||
|
||||
class TestKeepDim8DReduce(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {
|
||||
'X': np.random.random((2, 5, 3, 2, 2, 3, 4, 2)).astype("float64")
|
||||
}
|
||||
self.attrs = {'dim': (3, 4, 5), 'keep_dim': True, 'use_xpu': True}
|
||||
self.outputs = {
|
||||
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
|
||||
keepdims=self.attrs['keep_dim'])
|
||||
}
|
||||
|
||||
|
||||
class TestReduceAll(Test1DReduce):
|
||||
def setUp(self):
|
||||
self.op_type = "reduce_sum"
|
||||
self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
|
||||
self.attrs = {'reduce_all': True, 'use_xpu': True}
|
||||
self.outputs = {'Out': self.inputs['X'].sum()}
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue