提交 29ae4107 编写于 作者: Y Yu Yang

Merge branch 'develop' of github.com:baidu/Paddle into feature/change_grad_reg_mechanism

......@@ -29,7 +29,7 @@ cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator proto_desc)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry sum_op)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
......
......@@ -3,7 +3,7 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(add);
USE_OP(sum);
namespace paddle {
namespace framework {
......@@ -41,17 +41,24 @@ namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
"add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
"sum", {{"X", {"x", "y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
EXPECT_EQ(grad_add_op->Input("X"), "x");
EXPECT_EQ(grad_add_op->Input("Y"), "y");
EXPECT_EQ(grad_add_op->Input("Out"), "out");
EXPECT_EQ(grad_add_op->Inputs().size(), 1UL);
EXPECT_EQ(grad_add_op->Outputs().size(), 1UL);
EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
auto &outputs = grad_add_op->Outputs(f::GradVarName("X"));
EXPECT_EQ(2UL, outputs.size());
auto in_output = [&outputs](const std::string &name) {
for (auto &output_name : outputs) {
if (output_name == name) return true;
}
return false;
};
EXPECT_TRUE(in_output(f::GradVarName("x")));
EXPECT_TRUE(in_output(f::GradVarName("y")));
}
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
......
/* 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/add_op.h"
namespace paddle {
namespace operators {
class AddOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of AddOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of AddOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of AddOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Two input of Add Op's dimension must be same.");
ctx->SetOutputDim("Out", x_dims);
}
};
class AddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AddOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of add op");
AddInput("Y", "The second input of add op");
AddOutput("Out", "The output of add op");
AddComment(R"DOC(
Two Element Add Operator.
The equation is: Out = X + Y
)DOC");
}
};
class AddOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad);
REGISTER_OP_CPU_KERNEL(add, ops::AddKernel<paddle::platform::CPUPlace, float>);
/* 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/add_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(add, ops::AddKernel<paddle::platform::GPUPlace, float>);
/* 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class AddKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input0 = context.Input<Tensor>("X");
auto* input1 = context.Input<Tensor>("Y");
auto* output = context.Output<Tensor>("Out");
output->mutable_data<T>(context.GetPlace());
auto X = EigenVector<T>::Flatten(*input0);
auto Y = EigenVector<T>::Flatten(*input1);
auto Z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
Z.device(place) = X + Y;
}
};
} // namespace operators
} // namespace paddle
......@@ -43,8 +43,10 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input tensors of sum operator.").AsDuplicable();
AddOutput("Out", "the output tensor of sum operator.");
AddInput("X", "the input tensors of sum operator.")
.AsDuplicable()
.NotInGradient();
AddOutput("Out", "the output tensor of sum operator.").NotInGradient();
AddComment(R"DOC(
Sum the input tensors.
......
import unittest
import numpy as np
from op_test import OpTest
class TestAddOp(OpTest):
def setUp(self):
self.op_type = "add"
self.inputs = {
'X': np.random.random((102, 105)).astype("float32"),
'Y': np.random.random((102, 105)).astype("float32")
}
self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
......@@ -15,7 +15,7 @@ class PySimpleCond(object):
for i in range(1, 10, 2):
array[i] = 0
self.cond = np.array(array)
self.x = np.ones(shape=(10, 1))
self.x = np.ones(shape=(10, 1)).astype("float32")
def forward(self):
self.index_t = np.where(self.cond == 1)
......
import unittest
import numpy as np
import paddle.v2.framework.core as core
from op_test import get_numeric_gradient
from op_test import create_op
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
x = np.random.random((10, 1)).astype("float32")
y = np.random.random((10, 1)).astype("float32")
z = x + y
scope = core.Scope()
add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict())
arr = get_numeric_gradient(scope, add_op, {'X': x,
'Y': y}, 'X', ['Out'])
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
def test_softmax_op(self):
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx = x - np.max(x)
exps = np.exp(shiftx)
return exps / np.sum(exps)
def label_softmax_grad(Y, dY):
dX = Y * 0.0
for i in range(Y.shape[0]):
d = np.dot(Y[i, :], dY[i, :])
dX[i, :] = Y[i, :] * (dY[i, :] - d)
return dX
X = np.random.random((2, 2)).astype("float32")
Y = np.apply_along_axis(stable_softmax, 1, X)
dY = np.ones(Y.shape)
dX = label_softmax_grad(Y, dY)
scope = core.Scope()
softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict())
arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y")
np.testing.assert_almost_equal(arr, dX, decimal=1e-2)
if __name__ == "__main__":
unittest.main()
......@@ -15,7 +15,7 @@ def fc(X, W, Y):
class TestNet(unittest.TestCase):
def test_net_all(self):
net = core.Net.create()
op1 = Operator("add", X="X", Y="Y", Out="Out")
op1 = Operator("sum", X=["X", "Y"], Out="Out")
net.append_op(op1)
net2 = core.Net.create()
......@@ -26,7 +26,7 @@ class TestNet(unittest.TestCase):
expected = '''
Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
Op(sum), inputs:{X[X, Y]}, outputs:{Out[Out]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
......
......@@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase):
class TestOpCreations(unittest.TestCase):
def test_all(self):
add_op = op.Operator("add", X="a", Y="b", Out="z")
add_op = op.Operator("sum", X=["a", "b"], Out="z")
self.assertIsNotNone(add_op)
# Invoke C++ DebugString()
self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
self.assertEqual('Op(sum), inputs:{X[a, b]}, outputs:{Out[z]}.',
str(add_op))
......
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