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

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tonyyang-svail-feed-op-desgin
Yu Yang 7 years ago
parent 337b7ebe77 9fbf94b61a
commit a8c6ce9b4d
15 changed files with 290 additions and 73 deletions
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2
paddle/framework/CMakeLists.txt
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@ -26,7 +26,7 @@ cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
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)

39
paddle/framework/grad_op_builder.cc
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@ -54,5 +54,44 @@ OperatorBase* BuildGradOp(const OperatorBase* op) {
return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs());
}
static void TransOpDescArg(const OpDescBind* src_op, const OpArgType& src_type,
bool is_grad, OpDescBind* dst_op,
const OpArgType& dst_type) {
PADDLE_ENFORCE(dst_op != nullptr,
"Protobuf desc of gradient op must be initialized first.");
const auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
if (arg.not_in_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::vector<std::string> vars = src_type == OpArgType::IN
? src_op->Input(src_name)
: src_op->Output(src_name);
if (is_grad) {
for (std::string& var : vars) {
var = GradVarName(var);
}
}
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_type == OpArgType::IN ? dst_op->SetInput(dst_name, vars)
: dst_op->SetOutput(dst_name, vars);
}
}
void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op) {
auto& info = OpInfoMap::Instance().Get(forw_op->Type());
PADDLE_ENFORCE(info.HasGradientOp());
grad_op->SetType(info.grad_op_type_);
TransOpDescArg(forw_op, OpArgType::IN, false, grad_op, OpArgType::IN);
TransOpDescArg(forw_op, OpArgType::OUT, false, grad_op, OpArgType::IN);
TransOpDescArg(forw_op, OpArgType::OUT, true, grad_op, OpArgType::IN);
TransOpDescArg(forw_op, OpArgType::IN, true, grad_op, OpArgType::OUT);
grad_op->SetAttrMap(forw_op->GetAttrMap());
}
} // namespace framework
} // namespace paddle

3
paddle/framework/grad_op_builder.h
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@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/op_desc.h"
#include "paddle/framework/operator.h"
namespace paddle {
@ -21,5 +22,7 @@ namespace framework {
OperatorBase* BuildGradOp(const OperatorBase* op);
void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op);
} // namespace framework
} // namespace paddle

79
paddle/framework/grad_op_builder_test.cc
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@ -120,3 +120,82 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
std::vector<std::string>(
{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
}
TEST(GradOpDescBuilder, MutiInOut) {
f::OpDescBind *forw_op = new f::OpDescBind();
forw_op->SetType("mult_io");
forw_op->SetInput("In1", {"in1"});
forw_op->SetInput("In2_mult", {"in2_1", "in2_2", "in2_3"});
forw_op->SetInput("In3", {"in3"});
forw_op->SetOutput("Out1", {"out1"});
forw_op->SetOutput("Out2_mult", {"out2_1", "out2_2"});
f::OpDescBind *grad_op = new f::OpDescBind();
f::CompleteGradOpDesc(forw_op, grad_op);
EXPECT_EQ(grad_op->Type(), "mult_io_grad");
ASSERT_EQ(grad_op->InputNames().size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
EXPECT_EQ(grad_op->Input("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
EXPECT_EQ(grad_op->Input("In3"), std::vector<std::string>({"in3"}));
EXPECT_EQ(grad_op->Input("Out1"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op->Input("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out1")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out2_mult")),
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
std::vector<std::string>({f::GradVarName("in1")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
f::GradVarName("in2_2"),
f::GradVarName("in2_3")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In3")),
std::vector<std::string>({f::GradVarName("in3")}));
delete forw_op;
delete grad_op;
}
TEST(GradOpDescBuilder, IOIgnoredInGradient) {
f::OpDescBind *forw_op = new f::OpDescBind();
forw_op->SetType("io_ignored");
forw_op->SetInput("In1", {"in1"});
forw_op->SetInput("In2_mult", {"in2_1", "in2_2"});
forw_op->SetInput("In3_mult", {"in3_1", "in3_2"});
forw_op->SetOutput("Out1_mult", {"out1_1", "out1_2"});
forw_op->SetOutput("Out2", {"out2"});
f::OpDescBind *grad_op = new f::OpDescBind();
f::CompleteGradOpDesc(forw_op, grad_op);
EXPECT_EQ(grad_op->Type(), "io_ignored_grad");
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_op->InputNames().size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_op->Input("In1"), std::vector<std::string>({"in1"}));
EXPECT_EQ(grad_op->Input("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_op->Input("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out1_mult")),
std::vector<std::string>(
{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
EXPECT_EQ(grad_op->Input(f::GradVarName("Out2")),
std::vector<std::string>({f::GradVarName("out2")}));
ASSERT_EQ(grad_op->OutputNames().size(), 3UL);
EXPECT_EQ(grad_op->Output(f::GradVarName("In1")),
std::vector<std::string>({f::GradVarName("in1")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")),
std::vector<std::string>(
{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("In3_mult")),
std::vector<std::string>(
{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
delete forw_op;
delete grad_op;
}

11
paddle/framework/op_desc.cc
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@ -89,6 +89,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
need_update_ = true;
}
void OpDescBind::SetAttrMap(
const std::unordered_map<std::string, Attribute> &attr_map) {
attrs_ = attr_map;
need_update_ = true;
}
Attribute OpDescBind::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
@ -101,6 +107,11 @@ int OpDescBind::GetBlockAttr(const std::string &name) const {
return boost::get<BlockDesc *>(it->second)->idx();
}
const std::unordered_map<std::string, Attribute> &OpDescBind::GetAttrMap()
const {
return attrs_;
}
void OpDescBind::Sync() {
if (need_update_) {
this->op_desc_.mutable_inputs()->Clear();

6
paddle/framework/op_desc.h
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@ -60,10 +60,16 @@ class OpDescBind {
void SetBlockAttr(const std::string &name, BlockDescBind &block);
// Only be used in C++
void SetAttrMap(const std::unordered_map<std::string, Attribute> &attr_map);
Attribute GetAttr(const std::string &name) const;
int GetBlockAttr(const std::string &name) const;
// Only be used in C++
const std::unordered_map<std::string, Attribute> &GetAttrMap() const;
private:
struct SetAttrDescVisitor : public boost::static_visitor<void> {
explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}

14
paddle/operators/activation_op.cc
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@ -132,6 +132,17 @@ class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
}
};
class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftsignOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softsign operator");
AddOutput("Y", "Output of Softsign operator");
AddComment("Softsign activation operator, softsign(x) = x / (1 + |x|)");
}
};
template <typename AttrType>
class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
@ -223,6 +234,9 @@ REGISTER_OP(log, ops::ActivationOp, ops::LogOpMaker, log_grad,
REGISTER_OP(square, ops::ActivationOp, ops::SquareOpMaker, square_grad,
ops::ActivationOpGrad);
REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad,
ops::ActivationOpGrad);
REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker<float>, brelu_grad,
ops::ActivationOpGrad);

23
paddle/operators/activation_op.h
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@ -262,6 +262,26 @@ struct BReluGradFunctor : public BaseActivationFunctor<T> {
}
};
// softsign(x) = x / (1 + |x|)
template <typename T>
struct SoftsignFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) {
y.device(d) = x / (static_cast<T>(1) + x.abs());
}
};
// d(softsign(x))/dx = 1 / (1 + |x|)^2
// Taken from https://en.wikipedia.org/wiki/Activation_function
template <typename T>
struct SoftsignGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) {
dx.device(d) =
dy * (static_cast<T>(1) / (static_cast<T>(1) + x.abs()).square());
}
};
template <typename T>
struct SoftReluFunctor : public BaseActivationFunctor<T> {
float threshold;
@ -358,4 +378,5 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
__macro(brelu, BReluFunctor, BReluGradFunctor); \
__macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor); \
__macro(pow, PowFunctor, PowGradFunctor); \
__macro(stanh, STanhFunctor, STanhGradFunctor)
__macro(stanh, STanhFunctor, STanhGradFunctor); \
__macro(softsign, SoftsignFunctor, SoftsignGradFunctor)

16
paddle/pybind/pybind.cc
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@ -77,20 +77,18 @@ PYBIND11_PLUGIN(core) {
})
.def("set", PyCPUTensorSetFromArray<float>)
.def("set", PyCPUTensorSetFromArray<int>)
.def("set", PyCPUTensorSetFromArray<double>)
#ifndef PADDLE_ONLY_CPU
.def("set", PyCUDATensorSetFromArray<float>)
.def("set", PyCUDATensorSetFromArray<int>)
.def("set", PyCUDATensorSetFromArray<double>)
#endif
.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
.def("set_float_element",
[](Tensor &self, size_t offset, float f) {
// TODO(yuyang18): Only support GPU now.
self.data<float>()[offset] = f;
})
.def("get_float_element", [](Tensor &self, size_t offset) -> float {
// TODO(yuyang18): Only support GPU now.
return self.data<float>()[offset];
});
.def("set_float_element", TensorSetElement<float>)
.def("get_float_element", TensorGetElement<float>)
.def("set_double_element", TensorSetElement<double>)
.def("get_double_element", TensorGetElement<double>)
.def("dtype", [](Tensor &self) { return ToDataType(self.type()); });
py::class_<LoDTensor, Tensor>(m, "LoDTensor")
.def_buffer(

15
paddle/pybind/tensor_py.h
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@ -73,10 +73,23 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
};
} // namespace details
inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
auto buffer_info = details::CastToPyBufferImpl<true, 0, float, int>()(tensor);
auto buffer_info =
details::CastToPyBufferImpl<true, 0, float, int, double>()(tensor);
return buffer_info;
}
template <typename T>
T TensorGetElement(framework::Tensor &self, size_t offset) {
PADDLE_ENFORCE(platform::is_cpu_place(self.place()));
return self.data<T>()[offset];
}
template <typename T>
void TensorSetElement(framework::Tensor &self, size_t offset, T elem) {
PADDLE_ENFORCE(platform::is_cpu_place(self.place()));
self.data<T>()[offset] = elem;
}
template <typename T>
void PyCPUTensorSetFromArray(
framework::Tensor &self,

102
python/paddle/v2/framework/tests/op_test.py
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@ -12,17 +12,19 @@ def grad_var_name(var_name):
def create_op(scope, op_type, inputs, outputs, attrs):
kwargs = dict()
def __create_var__(name, var_name):
scope.new_var(var_name)
kwargs[name].append(var_name)
for in_name, in_dup in Operator.get_op_inputs(op_type):
if in_name in inputs:
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
for sub_in_name, _ in sub_in:
var = scope.new_var(sub_in_name)
kwargs[in_name].append(sub_in_name)
__create_var__(in_name, sub_in_name)
else:
var = scope.new_var(in_name)
kwargs[in_name].append(in_name)
__create_var__(in_name, in_name)
for out_name, out_dup in Operator.get_op_outputs(op_type):
if out_name in outputs:
@ -30,11 +32,9 @@ def create_op(scope, op_type, inputs, outputs, attrs):
if out_dup:
sub_out = outputs[out_name]
for sub_out_name, _ in sub_out:
var = scope.new_var(sub_out_name)
kwargs[out_name].append(sub_out_name)
__create_var__(out_name, sub_out_name)
else:
var = scope.new_var(out_name)
kwargs[out_name].append(out_name)
__create_var__(out_name, out_name)
for attr_name in Operator.get_op_attr_names(op_type):
if attr_name in attrs:
@ -44,49 +44,46 @@ def create_op(scope, op_type, inputs, outputs, attrs):
def set_input(scope, op, inputs, place):
def __set_input__(var_name, var):
tensor = scope.find_var(var_name).get_tensor()
if isinstance(var, tuple):
tensor.set_lod(var[1])
var = var[0]
tensor.set_dims(var.shape)
tensor.set(var, place)
for in_name, in_dup in Operator.get_op_inputs(op.type()):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
for sub_in_name, sub_in_val in sub_in:
var = scope.find_var(sub_in_name)
tensor = var.get_tensor()
sub_in_array = sub_in_val[0] \
if isinstance(sub_in_val, tuple) else sub_in_val
tensor.set_dims(sub_in_array.shape)
tensor.set(sub_in_array, place)
if isinstance(sub_in_val, tuple):
tensor.set_lod(sub_in_val[1])
__set_input__(sub_in_name, sub_in_val)
else:
var = scope.find_var(in_name)
tensor = var.get_tensor()
in_val = inputs[in_name]
in_array = in_val[0] if isinstance(in_val, tuple) else in_val
tensor.set_dims(in_array.shape)
tensor.set(in_array, place)
if isinstance(in_val, tuple):
tensor.set_lod(in_val[1])
__set_input__(in_name, inputs[in_name])
def set_output_grad(scope, op, outputs, place):
def __set_tensor__(name):
out_tensor = scope.find_var(name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
out_dtype = out_tensor.dtype()
if out_dtype == core.DataType.FP64:
data = np.ones(out_tensor.shape(), dtype=np.float64)
elif out_dtype == core.DataType.FP32:
data = np.ones(out_tensor.shape(), dtype=np.float32)
else:
raise ValueError("Not supported data type " + str(out_dtype))
grad_tensor.set(data, place)
for out_name, out_dup in Operator.get_op_outputs(op.type()):
if out_name in outputs:
if out_dup:
sub_out = outputs[out_name]
for sub_out_name, _ in sub_out:
out_tensor = scope.find_var(sub_out_name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(
sub_out_name)).get_tensor()
grad_tensor.set_dims(out_tensor.shape())
data = np.ones(out_tensor.shape(), dtype=np.float32)
grad_tensor.set(data, place)
__set_tensor__(sub_out_name)
else:
out_tensor = scope.find_var(out_name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(out_name)).get_tensor(
)
grad_tensor.set_dims(out_tensor.shape())
data = np.ones(out_tensor.shape(), dtype=np.float32)
grad_tensor.set(data, place)
__set_tensor__(out_name)
def get_numeric_gradient(scope,
@ -96,7 +93,6 @@ def get_numeric_gradient(scope,
output_names,
delta=0.005,
in_place=False):
set_input(scope, op, inputs, core.CPUPlace())
tensor_to_check = scope.find_var(input_to_check).get_tensor()
@ -115,7 +111,29 @@ def get_numeric_gradient(scope,
tensor_to_check = scope.find_var(input_to_check).get_tensor()
tensor_size = product(tensor_to_check.get_dims())
gradient_flat = np.zeros(shape=(tensor_size, ), dtype='float32')
tensor_to_check_dtype = tensor_to_check.dtype()
if tensor_to_check_dtype == core.DataType.FP32:
tensor_to_check_dtype = np.float32
elif tensor_to_check_dtype == core.DataType.FP64:
tensor_to_check_dtype = np.float64
else:
raise ValueError("Not supported data type " + str(
tensor_to_check_dtype))
gradient_flat = np.zeros(shape=(tensor_size, ), dtype=tensor_to_check_dtype)
def __get_elem__(tensor, i):
if tensor_to_check_dtype == np.float32:
return tensor.get_float_element(i)
else:
return tensor.get_double_element(i)
def __set_elem__(tensor, i, e):
if tensor_to_check_dtype == np.float32:
tensor.set_float_element(i, e)
else:
tensor.set_double_element(i, e)
# we only compute gradient of one element each time.
# we use a for loop to compute the gradient of every element.
for i in xrange(tensor_size):
@ -123,20 +141,20 @@ def get_numeric_gradient(scope,
set_input(scope, op, inputs, core.CPUPlace())
# get one input element throw it's index i.
origin = tensor_to_check.get_float_element(i)
origin = __get_elem__(tensor_to_check, i)
# add delta to it, run op and then get the sum of the result tensor.
x_pos = origin + delta
tensor_to_check.set_float_element(i, x_pos)
__set_elem__(tensor_to_check, i, x_pos)
y_pos = get_output()
if in_place:
set_input(scope, op, inputs, core.CPUPlace())
x_neg = origin - delta
tensor_to_check.set_float_element(i, x_neg)
__set_elem__(tensor_to_check, i, x_neg)
y_neg = get_output()
tensor_to_check.set_float_element(i, origin)
__set_elem__(tensor_to_check, i, origin)
gradient_flat[i] = (y_pos - y_neg) / delta / 2
return gradient_flat.reshape(tensor_to_check.get_dims())

17
python/paddle/v2/framework/tests/test_activation_op.py
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@ -219,5 +219,22 @@ class TestSTanh(OpTest):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
class TestSoftsign(OpTest):
def setUp(self):
self.op_type = "softsign"
self.inputs = {
'X': np.random.uniform(-1, 1, [11, 17]).astype("float32")
}
self.outputs = {
'Y': np.divide(self.inputs['X'], 1 + np.abs(self.inputs['X']))
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
if __name__ == "__main__":
unittest.main()

2
python/paddle/v2/framework/tests/test_cross_entropy_op.py
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@ -80,7 +80,7 @@ class TestCrossEntropyOp3(OpTest):
cross_entropy2 = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
self.inputs = {"X": X, "Label": label}
self.inputs = {"X": X, "Label": label.astype(np.float32)}
self.outputs = {"Y": cross_entropy}
self.attrs = {"softLabel": True}

32
python/paddle/v2/framework/tests/test_elementwise_mul_op.py
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@ -7,8 +7,8 @@ class ElementwiseMulOp(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
'X': np.random.uniform(0.1, 1, [13, 17]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float64")
}
self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
@ -16,23 +16,21 @@ class ElementwiseMulOp(OpTest):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
self.check_grad(['X', 'Y'], 'Out')
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
self.check_grad(['Y'], 'Out', 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'))
self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
class TestElementwiseMulOp_Vector(ElementwiseMulOp):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.random((32, )).astype("float32"),
'Y': np.random.random((32, )).astype("float32")
'X': np.random.random((32, )).astype("float64"),
'Y': np.random.random((32, )).astype("float64")
}
self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
@ -41,8 +39,8 @@ class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp):
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)
'X': np.random.rand(2, 3, 4).astype(np.float64),
'Y': np.random.rand(2).astype(np.float64)
}
self.attrs = {'axis': 0}
@ -55,8 +53,8 @@ class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp):
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)
'X': np.random.rand(2, 3, 4).astype(np.float64),
'Y': np.random.rand(3).astype(np.float64)
}
self.attrs = {'axis': 1}
@ -69,8 +67,8 @@ class TestElementwiseMulOp_broadcast_2(ElementwiseMulOp):
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)
'X': np.random.rand(2, 3, 4).astype(np.float64),
'Y': np.random.rand(4).astype(np.float64)
}
self.outputs = {
@ -82,8 +80,8 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
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)
'X': np.random.rand(2, 3, 4, 5).astype(np.float64),
'Y': np.random.rand(3, 4).astype(np.float64)
}
self.attrs = {'axis': 1}

2
python/paddle/v2/framework/tests/test_prelu_op.py
Unescape View File

@ -17,7 +17,7 @@ class PReluTest(OpTest):
x_np_sign = np.sign(x_np)
x_np = x_np_sign * np.maximum(x_np, .005)
alpha_np = np.array([.1])
alpha_np = np.array([.1], dtype="float32")
self.inputs = {'X': x_np, 'Alpha': alpha_np}
out_np = np.maximum(self.inputs['X'], 0.)
out_np = out_np + np.minimum(self.inputs['X'],

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