add elementwise floordiv, mod; test=develop

paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc

+/* Copyright (c) 2016 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. */
+
+#include "paddle/fluid/operators/elementwise/elementwise_floordiv_op.h"
+#include <string>
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseFloorDivOpMaker : public ElementwiseOpMaker {
+ protected:
+  std::string GetName() const override { return "FloorDiv"; }
+  std::string GetEquation() const override { return "Out = X % Y"; }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,
+                             ops::ElementwiseFloorDivOpMaker);
+
+REGISTER_OP_CPU_KERNEL(
+    elementwise_floordiv,
+    ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext,
+                                   int64_t>);

paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu

+/* Copyright (c) 2016 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. */
+#include "paddle/fluid/operators/elementwise/elementwise_floordiv_op.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_floordiv,
+    ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int64_t>);

paddle/fluid/operators/elementwise/elementwise_floordiv_op.h

+/* Copyright (c) 2016 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. */
+
+#pragma once
+
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/operators/math/blas.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct FloorDivFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a / b; }
+};
+
+template <typename DeviceContext, typename T>
+void elementwise_floor_div(const framework::ExecutionContext &ctx,
+                           const framework::Tensor *x,
+                           const framework::Tensor *y, framework::Tensor *z) {
+  int axis = ctx.Attr<int>("axis");
+  ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
+      ctx, x, y, axis, FloorDivFunctor<T>(), z);
+}
+
+template <typename DeviceContext, typename T>
+class ElementwiseFloorDivKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *x = ctx.Input<framework::LoDTensor>("X");
+    auto *y = ctx.Input<framework::LoDTensor>("Y");
+    auto *z = ctx.Output<framework::LoDTensor>("Out");
+
+    z->mutable_data<T>(ctx.GetPlace());
+
+    // dtype of x and y is int64 or int32
+    elementwise_floor_div<DeviceContext, T>(ctx, x, y, z);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_mod_op.cc

+/* Copyright (c) 2016 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. */
+
+#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
+#include <string>
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseModOpMaker : public ElementwiseOpMaker {
+ protected:
+  std::string GetName() const override { return "Mod"; }
+  std::string GetEquation() const override { return "Out = X % Y"; }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(elementwise_mod, ops::ElementwiseOp,
+                             ops::ElementwiseModOpMaker);
+
+REGISTER_OP_CPU_KERNEL(
+    elementwise_mod,
+    ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/elementwise/elementwise_mod_op.cu

+/* Copyright (c) 2016 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. */
+#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_mod, ops::ElementwiseModKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseModKernel<plat::CUDADeviceContext, int64_t>);

paddle/fluid/operators/elementwise/elementwise_mod_op.h

+/* Copyright (c) 2016 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. */
+
+#pragma once
+
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/operators/math/blas.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct ModFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a % b; }
+};
+
+template <typename DeviceContext, typename T>
+void elementwise_mod(const framework::ExecutionContext &ctx,
+                     const framework::Tensor *x, const framework::Tensor *y,
+                     framework::Tensor *z) {
+  int axis = ctx.Attr<int>("axis");
+  ElementwiseComputeEx<ModFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                        ModFunctor<T>(), z);
+}
+
+template <typename DeviceContext, typename T>
+class ElementwiseModKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *x = ctx.Input<framework::LoDTensor>("X");
+    auto *y = ctx.Input<framework::LoDTensor>("Y");
+    auto *z = ctx.Output<framework::LoDTensor>("Out");
+
+    z->mutable_data<T>(ctx.GetPlace());
+
+    // dtype of x and y is int64 or int32
+    elementwise_mod<DeviceContext, T>(ctx, x, y, z);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/math_op_patch.py

@@ -174,6 +174,8 @@ def monkey_patch_variable():
         ("__rtruediv__", "elementwise_div", True),
         ("__pow__", "elementwise_pow", False),
         ("__rpow__", "elementwise_pow", True),
+        ("__floordiv__", "elementwise_floordiv", False),
+        ("__mod__", "elementwise_mod", False),
             # for logical compare
         ("__eq__", "equal", False),
         ("__ne__", "not_equal", False),

python/paddle/fluid/layers/nn.py

@@ -8887,9 +8887,24 @@ def elementwise_pow(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
 
 
+def elementwise_mod(x, y, axis=-1, act=None, name=None):
+    return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
+
+
+def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
+    return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))
+
+
 for func in [
-        elementwise_add, elementwise_div, elementwise_sub, elementwise_mul,
-        elementwise_max, elementwise_min, elementwise_pow
+        elementwise_add,
+        elementwise_div,
+        elementwise_sub,
+        elementwise_mul,
+        elementwise_max,
+        elementwise_min,
+        elementwise_pow,
+        elementwise_mod,
+        elementwise_floordiv,
 ]:
     op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
     func.__doc__ = _generate_doc_string_(

python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py

+#  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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+from op_test import OpTest
+
+import random
+
+
+class TestElementwiseModOp(OpTest):
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
+    def setUp(self):
+        self.op_type = "elementwise_floordiv"
+        self.dtype = np.int32
+        self.axis = -1
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+        self.outputs = {'Out': self.out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
+        self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
+        self.out = np.floor_divide(self.x, self.y)
+
+    def init_dtype(self):
+        pass
+
+    def init_axis(self):
+        pass
+
+
+class TestElementwiseModOp_scalar(TestElementwiseModOp):
+    def init_input_output(self):
+        scale_x = random.randint(0, 100000000)
+        scale_y = random.randint(1, 100000000)
+        self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
+        self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
+        self.out = np.floor_divide(self.x, self.y)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py

+#  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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+from op_test import OpTest
+
+import random
+
+
+class TestElementwiseModOp(OpTest):
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
+    def setUp(self):
+        self.op_type = "elementwise_mod"
+        self.dtype = np.int32
+        self.axis = -1
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+        self.outputs = {'Out': self.out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
+        self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
+        self.out = np.mod(self.x, self.y)
+
+    def init_dtype(self):
+        pass
+
+    def init_axis(self):
+        pass
+
+
+class TestElementwiseModOp_scalar(TestElementwiseModOp):
+    def init_input_output(self):
+        scale_x = random.randint(0, 100000000)
+        scale_y = random.randint(1, 100000000)
+        self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
+        self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
+        self.out = np.mod(self.x, self.y)
+
+
+if __name__ == '__main__':
+    unittest.main()