Add element-wise multiplication operator. (#3787)

Add element-wise multiplication operator

paddle/operators/elementwise_mul_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/elementwise_mul_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class ElementWiseMulOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+    auto x_dim = ctx.Input<Tensor>("X")->dims();
+    auto y_dim = ctx.Input<Tensor>("Y")->dims();
+    PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
+                      "Rank of first input must >= rank of second input.")
+    ctx.Output<Tensor>("Out")->Resize(x_dim);
+  }
+};
+
+class ElementWiseMulOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ElementWiseMulOpMaker(framework::OpProto *proto,
+                        framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The first input of elementwise mul op");
+    AddInput("Y", "The second input of elementwise mul op");
+    AddAttr<int>("axis",
+                 R"DOC(
+When shape(Y) does not equal shape(X),Y will be broadcasted 
+to match the shape of X and axis should be dimension index Y in X
+        )DOC")
+        .SetDefault(-1)
+        .EqualGreaterThan(-1);
+
+    AddOutput("Out", "The output of elementwise mul op");
+    AddComment(R"DOC(
+Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
+1. The shape of Y should be same with X or
+2. Y's shape is a subset of X. 
+   Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
+   example:
+      shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+      shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+      shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+      shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+      shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+)DOC");
+  }
+};
+
+class ElementWiseMulOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+
+    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+                      "Rank of first input must >= rank of second input.")
+
+    if (x_grad) {
+      x_grad->Resize(x_dims);
+    }
+
+    if (y_grad) {
+      y_grad->Resize(y_dims);
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_mul, ops::ElementWiseMulOp, ops::ElementWiseMulOpMaker,
+            elementwise_mul_grad, ops::ElementWiseMulOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_mul,
+    ops::ElementWiseMulKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_mul_grad,
+    ops::ElementWiseMulGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/elementwise_mul_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_mul_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    elementwise_mul,
+    ops::ElementWiseMulKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    elementwise_mul_grad,
+    ops::ElementWiseMulGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/elementwise_mul_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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 <iostream>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+/*
+ * Out = X ⊙ Y
+ * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+ *    pre=2, n=3*4, post=5
+ * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
+ *    pre=2*3, n=4*5, post=1
+ */
+
+inline void get_mid_dims(const framework::DDim& x_dims,
+                         const framework::DDim& y_dims, const int axis,
+                         int& pre, int& n, int& post) {
+  pre = 1;
+  n = 1;
+  post = 1;
+  for (int i = 0; i < axis; ++i) {
+    pre *= x_dims[i];
+  }
+
+  for (int i = 0; i < y_dims.size(); ++i) {
+    PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
+                      "Broadcast dimension mismatch.");
+    n *= y_dims[i];
+  }
+
+  for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
+    post *= x_dims[i];
+  }
+}
+
+template <typename Place, typename T>
+class ElementWiseMulKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto z_e = framework::EigenVector<T>::Flatten(*z);
+
+    auto x_dims = x->dims();
+    auto y_dims = y->dims();
+    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+                      "Rank of first input must >= rank of second input.")
+
+    if (x_dims == y_dims || product(y_dims) == 1) {
+      z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_e;
+      return;
+    }
+
+    int axis = ctx.Attr<int>("axis");
+    axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+    PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+                   "Axis should be in range [0, x_dims)");
+
+    int pre, n, post;
+    get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+    if (post == 1) {
+      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
+                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+      z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_bcast;
+      return;
+    } else {
+      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
+                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+      z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_bcast;
+      return;
+    }
+  }
+};
+
+template <typename Place, typename T>
+class ElementWiseMulGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dout_e = framework::EigenVector<T>::Flatten(*dout);
+
+    auto x_dims = x->dims();
+    auto y_dims = y->dims();
+
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    if (dx) {
+      dx->mutable_data<T>(ctx.GetPlace());
+    }
+    if (dy) {
+      dy->mutable_data<T>(ctx.GetPlace());
+    }
+
+    if (x_dims == y_dims || product(y_dims) == 1) {
+      if (dx) {
+        auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+        dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e;
+      }
+
+      if (dy) {
+        auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+        dy_e.device(ctx.GetEigenDevice<Place>()) = x_e * dout_e;
+      }
+      return;
+    }
+
+    int axis = ctx.Attr<int>("axis");
+    axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+
+    int pre, n, post;
+    get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+
+    // TODO(gongweibao): wrap reshape to a function.
+    if (post == 1) {
+      auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
+                           .broadcast(Eigen::DSizes<int, 2>(pre, 1))
+                           .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+      if (dx) {
+        auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+        dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e_bcast;
+      }
+
+      if (dy) {
+        auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+        dy_e.device(ctx.GetEigenDevice<Place>()) =
+            (x_e * dout_e)
+                .reshape(Eigen::DSizes<int, 2>(pre, n))
+                .sum(Eigen::array<int, 1>{{0}});
+      }
+      return;
+    } else {
+      auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
+                           .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
+                           .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+      if (dx) {
+        auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+        dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e_bcast;
+      }
+
+      if (dy) {
+        auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+        dy_e.device(ctx.GetEigenDevice<Place>()) =
+            (x_e * dout_e)
+                .reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                .sum(Eigen::array<int, 2>{{0, 2}});
+      }
+      return;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/pybind/pybind.cc

@@ -35,6 +35,7 @@ USE_OP(add);
 USE_OP(onehot_cross_entropy);
 USE_OP(sgd);
 USE_OP(mul);
+USE_OP(elementwise_mul);
 USE_OP(mean);
 USE_OP(sigmoid);
 USE_OP(softmax);

python/paddle/v2/framework/tests/test_elementwise_mul_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseMulOp_Matrix(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        """ Warning
+        CPU gradient check error!
+        'X': np.random.random((32,84)).astype("float32"),
+        'Y': np.random.random((32,84)).astype("float32")
+        """
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        }
+        self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_Vector(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        self.inputs = {
+            'X': np.random.random((32, )).astype("float32"),
+            'Y': np.random.random((32, )).astype("float32")
+        }
+        self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_0(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(2).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_1(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(3).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 1)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_2(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype(np.float32),
+            'Y': np.random.rand(4).astype(np.float32)
+        }
+
+        self.outputs = {
+            'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 4)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_3(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_mul"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
+            'Y': np.random.rand(3, 4).astype(np.float32)
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 4, 1)
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()