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=kunlun

paddle/fluid/operators/reduce_ops/reduce_mean_op_xpu.cc

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifdef PADDLE_WITH_XPU
+
+#include "paddle/fluid/operators/reduce_ops/reduce_mean_op.h"
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+template <typename DeviceContext, typename T>
+class ReduceMeanXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_xpu_place(context.GetPlace()), true,
+        platform::errors::Unavailable("This kernel only runs on XPU."));
+    // bool reduce_all = context.Attr<bool>("reduce_all");
+    auto* input = context.Input<Tensor>("X");
+    auto* output = context.Output<Tensor>("Out");
+    output->mutable_data<T>(context.GetPlace());
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    int ndim = input->dims().size();
+    std::vector<int> idims;
+    for (int i = 0; i < input->dims().size(); i++) {
+      idims.push_back(input->dims()[i]);
+    }
+    auto dims = context.Attr<std::vector<int>>("dim");
+    int rdim = dims.size();
+    int r =
+        xpu::reduce(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
+                    idims.data(), ndim, dims.data(), rdim, xpu::REDUCE_MEAN);
+    PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
+                      platform::errors::External("XPU kernel error!"));
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_XPU_KERNEL(
+    reduce_mean,
+    ops::ReduceMeanXPUKernel<paddle::platform::XPUDeviceContext, float>);
+
+#endif

paddle/fluid/operators/reduce_ops/reduce_sum_op_xpu.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifdef PADDLE_WITH_XPU
+#include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
+#include <memory>
+#include <string>
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class ReduceSumXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_xpu_place(context.GetPlace()), true,
+        platform::errors::Unavailable("This kernel only runs on XPU."));
+    bool reduce_all = context.Attr<bool>("reduce_all");
+    auto* input = context.Input<Tensor>("X");
+    auto* output = context.Output<Tensor>("Out");
+    output->mutable_data<T>(context.GetPlace());
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    if (reduce_all) {
+      int input_len = input->numel();
+      int r = xpu::sum(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
+                       input_len);
+      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
+                        platform::errors::External("XPU kernel error!"));
+    } else {
+      int ndim = input->dims().size();
+      std::vector<int> idims;
+      for (int i = 0; i < input->dims().size(); i++) {
+        idims.push_back(input->dims()[i]);
+      }
+      auto dims = context.Attr<std::vector<int>>("dim");
+      int rdim = dims.size();
+      int r =
+          xpu::reduce(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
+                      idims.data(), ndim, dims.data(), rdim, xpu::REDUCE_SUM);
+      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
+                        platform::errors::External("XPU kernel error!"));
+    }
+  }
+};
+template <typename DeviceContext, typename T>
+class ReduceSumGradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto dims = context.Attr<std::vector<int>>("dim");
+    bool reduce_all = context.Attr<bool>("reduce_all");
+    auto* input0 = context.Input<Tensor>("X");
+    auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* output = context.Output<Tensor>(framework::GradVarName("X"));
+    output->mutable_data<T>(context.GetPlace());
+    const auto* input2_d = input2->data<T>();
+    auto* output_d = output->data<T>();
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    int r = 0;
+    std::vector<int> idims;
+    int reduce_dim = 0;
+    if (reduce_all) {
+      idims.push_back(input0->numel());
+      idims.push_back(1);
+      idims.push_back(1);
+      r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
+                           idims.data(), idims.size(), &reduce_dim, 1,
+                           xpu::REDUCE_SUM);
+      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
+                        platform::errors::External("XPU kernel error!"));
+    } else if (dims.size() == 1) {
+      // handle reduce by one dimension
+      int reduce_dim_index = dims[0];
+      if (reduce_dim_index < 0) {
+        reduce_dim_index += input0->dims().size();
+      }
+      auto& input_dim = input0->dims();
+      int before_dim = 1;
+      for (int i = 0; i < reduce_dim_index; ++i) {
+        before_dim *= input_dim[i];
+      }
+      int reduce_dim = input_dim[reduce_dim_index];
+      int after_dim = 1;
+      for (int i = reduce_dim_index + 1; i < input_dim.size(); ++i) {
+        after_dim *= input_dim[i];
+      }
+      idims.push_back(before_dim);
+      idims.push_back(input_dim[reduce_dim_index]);
+      idims.push_back(after_dim);
+      reduce_dim = 1;
+      r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
+                           idims.data(), idims.size(), &reduce_dim, 1,
+                           xpu::REDUCE_SUM);
+      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
+                        platform::errors::External("XPU kernel error!"));
+    } else {
+      PADDLE_THROW(
+          platform::errors::Unimplemented("unsupport reduce sum grad"));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_XPU_KERNEL(
+    reduce_sum,
+    ops::ReduceSumXPUKernel<paddle::platform::XPUDeviceContext, float>);
+REGISTER_OP_XPU_KERNEL(
+    reduce_sum_grad,
+    ops::ReduceSumGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
+
+#endif

python/paddle/fluid/tests/unittests/xpu/test_reduce_mean_op_xpu.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+from paddle.fluid.framework import convert_np_dtype_to_dtype_
+
+
+class TestMeanOp(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
+        self.attrs = {'use_xpu': True}
+        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def check_grad_(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestMeanOp5D(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.inputs = {
+            'X': np.random.random((1, 2, 5, 6, 10)).astype("float64")
+        }
+        self.attrs = {'use_xpu': True}
+        self.outputs = {'Out': self.inputs['X'].mean(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 TestMeanOp6D(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        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'].mean(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 TestMeanOp8D(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        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'].mean(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_mean"
+        self.inputs = {'X': np.random.random(120).astype("float64")}
+        self.attrs = {'use_xpu': True}
+        self.outputs = {'Out': self.inputs['X'].mean(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_mean"
+        self.attrs = {'dim': [0], 'use_xpu': True}
+        self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
+        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}
+
+
+class Test2DReduce1(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.attrs = {'dim': [1], 'use_xpu': True}
+        self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
+        }
+
+
+class Test3DReduce0(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.attrs = {'dim': [1], 'use_xpu': True}
+        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
+        }
+
+
+class Test3DReduce1(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.attrs = {'dim': [2], 'use_xpu': True}
+        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
+        }
+
+
+class Test3DReduce2(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.attrs = {'dim': [-2], 'use_xpu': True}
+        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
+        }
+
+
+class Test3DReduce3(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        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'].mean(axis=tuple(self.attrs['dim']))
+        }
+
+
+class TestKeepDimReduce(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        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'].mean(
+                axis=tuple(self.attrs['dim']), keepdims=self.attrs['keep_dim'])
+        }
+
+
+class TestKeepDim8DReduce(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        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'].mean(
+                axis=tuple(self.attrs['dim']), keepdims=self.attrs['keep_dim'])
+        }
+
+
+class TestReduceAll(Test1DReduce):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        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'].mean()}
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_reduce_sum_op_xpu.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+from paddle.fluid.framework import convert_np_dtype_to_dtype_
+
+
+class TestSumOp(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((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 check_grad_(self):
+        self.check_grad(['X'], 'Out')
+
+
+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()