提交 d6b1f228 编写于 作者: Q qijun

Merge branch 'fix_elementwis_add_bug' into test_book_1

......@@ -3,17 +3,17 @@
## The Problem Posed
Currently, for each C++ operator class definition, there registers a *gradient operator creator* function, which takes a C++ operator instance and returns the corresponding gradient operator instance.
Currently, for each C++ operator class definition, a *gradient operator creator* function is registered, which takes as input a C++ operator instance and returns the corresponding gradient operator instance.
However, we noticed two problems with the current deisgn:
However, we noticed two problems with the current design:
1. As we decided to separate the *compilation* and *execution* phases, we need to change the creator to take an `OpDesc` protobuf message in a `ProgramDesc` and inserts corresponding `OpDesc` messages into the `ProgramDesc` message.
1. As we decided to separate the *compilation* and the *execution* phases, we need to change the creator to take an `OpDesc` protobuf message in a `ProgramDesc` and inserts corresponding `OpDesc` messages into the `ProgramDesc` message.
1. Some operator's gradient computation requires more than one gradient operators. For example, the gradient of *minus* consists of two operators -- an identity operaotr and a scale operator. So we need to make the registration mechanism to support the mapping from an operator to a set of operators for gradient computation.
1. For some operators, the gradient computation can be written in terms of existing operators. For example, the gradient of *minus* operator consists of two operators -- an *identity* operator followed by a *scale* operator. Hence the registration mechanism needs to support mapping from an operator to a set of operators for the gradient computation.
## The Current Implementation
The C++ class `OpInfos` store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
Instances of the C++ class `OpInfo` are stored an associative map whose key is the operator type. The `grad_op_type` indicates the associated gradient operator type. An operator can create the gradient operator by invoking `OpInfo::creator_` of the gradient operator. The pseudo code is as follows
```cpp
struct OpInfo {
......@@ -31,16 +31,16 @@ OperatorBase* CreateGradientOperator(const OperatorBase& op) {
## Proposed Solution
The mapping relationship between an operator and its gradient operators is a function. The interface of that function is:
The mapping relationship between an operator and its gradient operators is a function. The interface of this function is:
```cpp
// (OpDesc) --> vector<OpDesc>
std::function<std::vector<OpDescBind>(const OpDescBind&)>;
```
The function takes an `OpDescBind` of the forward operator and returns one or many gradient operator descriptions. `OpDescBind` is a C++ wrapper for protobuf message `OpDesc` to manipulate `OpDesc` fast.
The function takes an `OpDescBind` of the forward operator and returns one or many gradient operator descriptions. `OpDescBind` is a C++ wrapper for the protobuf message `OpDesc` for rapid manipulation of `OpDesc`.
The `GradOpDescMaker` will be registered in `OpInfo`, to replace `grad_op_type_` field. The `OpInfo` should be
The `GradOpDescMaker` will be registered in `OpInfo` and will replace the `grad_op_type_` field. The `OpInfo` should look like
```cpp
struct OpInfo {
......@@ -49,7 +49,7 @@ struct OpInfo {
};
```
The `grad_op_maker_ ` is `nullptr` if the operator does not have associated gradient operators.
The `grad_op_maker_ ` is a `nullptr` if the operator does not have any associated gradient operators.
We propose a base class called `GradOpDescMakerBase` to let operator developers generate `Gradient Operators` easily. The public interface of that class is
......@@ -74,7 +74,7 @@ func = [] (const OpDescBind& fwd_op) {
We can write many helper functions since the `GradOpDescMakerBase` is a class now. The basic helper functions get the variables of `Input`, `Output`, `InputGradient` and `OutputGradient` in the forwarding operator.
We should chagne register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`.
We should change register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`.
The user interface should be
......
......@@ -108,7 +108,7 @@ void ElementwiseCompute(const framework::ExecutionContext& ctx) {
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) {
if (x_dims == y_dims) {
functor f;
f.template Run<Place, T>(x, y, z, ctx);
return;
......@@ -174,12 +174,6 @@ void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
return;
}
if (product(y_dims) == 1) {
functor1 f;
f(place, x, y, out, dx, dy, dout);
return;
}
int axis = ctx.Attr<int>("axis");
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
......
......@@ -92,5 +92,33 @@ class TestElementwiseAddOp_broadcast_3(TestElementwiseOp):
}
class TestElementwiseAddOp_rowwise_add_0(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).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)
}
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 1).astype(np.float32),
'Y': np.random.rand(1).astype(np.float32)
}
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 1)
}
if __name__ == '__main__':
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册