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P
Paddle
提交 a07cef33 编写于 作者: Y Yi Wang
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Merge branch 'develop' of https://github.com/paddlepaddle/paddle into cpplint_errors

上级 7aac1218 2fef13e3
隐藏空白更改
内联 并排
Showing with 286 addition and 291 deletion
cmake/cpplint.cmake
浏览文件 @ a07cef33
......@@ -56,11 +56,14 @@ macro(add_style_check_target TARGET_NAME)
# cpplint code style
get_filename_component(base_filename ${filename} NAME)
set(CUR_GEN ${CMAKE_CURRENT_BINARY_DIR}/${base_filename}.cpplint)
add_custom_command(TARGET ${TARGET_NAME} PRE_BUILD
add_custom_command(OUTPUT ${CUR_GEN} PRE_BUILD
COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
"--filter=${STYLE_FILTER}"
"--write-success=${CUR_GEN}" ${filename}
DEPENDS ${filename} ${PROJ_ROOT}/paddle/scripts/cpplint.py
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
add_custom_target(${base_filename}.cpplint DEPENDS ${CUR_GEN})
add_dependencies(${TARGET_NAME} ${base_filename}.cpplint)
endif()
endforeach()
endif()
......
cmake/util.cmake
浏览文件 @ a07cef33
......@@ -118,7 +118,6 @@ endfunction()
macro(add_unittest_without_exec TARGET_NAME)
add_executable(${TARGET_NAME} ${ARGN})
link_paddle_test(${TARGET_NAME})
add_style_check_target(${TARGET_NAME} ${ARGN})
endmacro()
# add_unittest
......
paddle/framework/CMakeLists.txt
浏览文件 @ a07cef33
......@@ -12,13 +12,15 @@ cc_test(variable_test SRCS variable_test.cc)
cc_library(scope SRCS scope.cc)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
proto_library(attr_type SRCS attr_type.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attr_type)
proto_library(op_desc SRCS op_desc.proto DEPS attr_type)
proto_library(attribute_proto SRCS attribute.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attribute_proto)
proto_library(op_desc SRCS op_desc.proto DEPS attribute_proto)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope)
cc_library(attribute SRCS attribute.cc DEPS op_desc op_proto)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope attribute)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)
......@@ -26,7 +28,7 @@ cc_library(op_registry SRCS op_registry.cc DEPS op_desc grad_op_builder)
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)
py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.proto)
py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
......
paddle/framework/attribute.cc 0 → 100644
浏览文件 @ a07cef33
/* 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/framework/attribute.h"
#include <vector>
namespace paddle {
namespace framework {
template <>
AttrType AttrTypeID<int>() {
return INT;
}
template <>
AttrType AttrTypeID<float>() {
return FLOAT;
}
template <>
AttrType AttrTypeID<std::string>() {
return STRING;
}
template <>
AttrType AttrTypeID<std::vector<int>>() {
return INTS;
}
template <>
AttrType AttrTypeID<std::vector<float>>() {
return FLOATS;
}
template <>
AttrType AttrTypeID<std::vector<std::string>>() {
return STRINGS;
}
Attribute GetAttrValue(const AttrDesc& attr_desc) {
switch (attr_desc.type()) {
case paddle::framework::AttrType::INT: {
return attr_desc.i();
}
case paddle::framework::AttrType::FLOAT: {
return attr_desc.f();
}
case paddle::framework::AttrType::STRING: {
return attr_desc.s();
}
case paddle::framework::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
return val;
}
case paddle::framework::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
return val;
}
case paddle::framework::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
return val;
}
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
}
} // namespace framework
} // namespace paddle
paddle/framework/attr_checker.h → paddle/framework/attribute.h
浏览文件 @ a07cef33
......@@ -6,6 +6,9 @@
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/framework/attribute.pb.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/platform/enforce.h"
namespace paddle {
......@@ -14,8 +17,14 @@ namespace framework {
typedef boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>>
Attribute;
typedef std::unordered_map<std::string, Attribute> AttributeMap;
template <typename T>
AttrType AttrTypeID();
Attribute GetAttrValue(const AttrDesc& attr_desc);
// check whether a value(attribute) fit a certain limit
template <typename T>
class LargerThanChecker {
......
paddle/framework/backward.cc
浏览文件 @ a07cef33
......@@ -59,19 +59,17 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
// If all input gradients of forwarding operator do not need to calculate,
// just return an NOP. Not return null ptr because NOP does not take
// too much time for calculation, but it is useful for simplifying logic.
if (AllInSet(forwardOp.inputs_, OperatorBase::GRAD_VAR_SUFFIX(),
no_grad_names)) {
if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
return NOP();
}
// All output gradients of forwarding operator do not need to calculate.
// Then all input gradients cannot be computed at all, and we put them into
// `no_grad_names` set. Return an NOP.
if (AllInSet(forwardOp.outputs_, OperatorBase::GRAD_VAR_SUFFIX(),
no_grad_names)) {
if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
for (auto& name : forwardOp.inputs_) {
// Mark all input is not need
no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
no_grad_names.insert(name + kGradVarSuffix);
}
return NOP();
}
......@@ -134,9 +132,9 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
for (std::string& grad_input : grad_op->inputs_) {
if (no_grad_names.count(grad_input)) {
std::string prefix = grad_input.substr(
0, grad_input.size() - OperatorBase::GRAD_VAR_SUFFIX().size());
grad_input = prefix + OperatorBase::ZERO_VAR_SUFFIX();
std::string prefix =
grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
grad_input = prefix + kZeroVarSuffix;
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
......@@ -147,7 +145,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
for (std::string& grad_output : grad_op->outputs_) {
if (no_grad_names.count(grad_output)) {
grad_output = OperatorBase::EMPTY_VAR_NAME();
grad_output = kEmptyVarName;
}
}
......@@ -168,14 +166,14 @@ std::shared_ptr<OperatorBase> Backward(
std::unordered_set<std::string> no_grad_names;
no_grad_names.reserve(no_grad_vars.size());
no_grad_names.insert(OperatorBase::EMPTY_VAR_NAME() +
OperatorBase::GRAD_VAR_SUFFIX());
no_grad_names.insert(kEmptyVarName + kGradVarSuffix);
for (auto& name : no_grad_vars) {
no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
no_grad_names.insert(name + kGradVarSuffix);
}
size_t uid = 0;
return BackwardRecursive(forwardOp, no_grad_names, uid);
}
} // namespace framework
} // namespace paddle
paddle/framework/backward_test.cc
浏览文件 @ a07cef33
......@@ -78,14 +78,14 @@ class FcOp : public ops::NetOp {
{Output("mul_result")}, {}));
auto b_name = Input("b");
std::string before_act = "mul_result";
if (b_name != EMPTY_VAR_NAME()) {
if (b_name != kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
{Output("add_result")}, {}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
if (out_varname != EMPTY_VAR_NAME()) {
this->Rename(out_varname, EMPTY_VAR_NAME());
if (out_varname != kEmptyVarName) {
this->Rename(out_varname, kEmptyVarName);
}
}
......@@ -163,13 +163,12 @@ TEST(Backward, simple_op_grad) {
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(4UL, gop->inputs_.size());
ASSERT_EQ(f::OperatorBase::EMPTY_VAR_NAME(), gop->inputs_[0]);
ASSERT_EQ(f::kEmptyVarName, gop->inputs_[0]);
ASSERT_EQ("rowwise_add_grad", gop->type_);
ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->outputs_[0]);
ASSERT_EQ("b" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->outputs_[1]);
ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(),
gop->Output("X" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
}
TEST(Backward, simple_op_not_need_grad) {
......@@ -177,7 +176,7 @@ TEST(Backward, simple_op_not_need_grad) {
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"X"});
ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
"X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"X" + f::kGradVarSuffix),
gop->outputs_.end());
auto no_input_gop = f::Backward(*fwd, {"X", "b"});
......@@ -210,9 +209,9 @@ TEST(Backward, net_fc_backward_normal) {
}
TEST(Backward, net_fc_backward_not_have_b) {
std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
"fc", {"X", "w", f::OperatorBase::EMPTY_VAR_NAME()},
{"mul_result", "add_result", "tmp"}, {});
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
{"mul_result", "add_result", "tmp"}, {});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
......@@ -242,24 +241,21 @@ TEST(Backward, net_input_of_network_not_need_grad) {
std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
bwd_net->outputs_.begin(), bwd_net->outputs_.end());
all_output.erase(f::OperatorBase::EMPTY_VAR_NAME());
all_output.erase(f::kEmptyVarName);
for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
ASSERT_NE(all_output.find(out + f::OperatorBase::GRAD_VAR_SUFFIX()),
all_output.end());
ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
}
// Not Generated X
ASSERT_EQ(all_output.find("X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
all_output.end());
ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.end());
ASSERT_EQ(2UL, bwd_net->ops_.size());
ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
ASSERT_EQ(3UL, first_fc_grad->ops_.size());
ASSERT_EQ(
f::OperatorBase::EMPTY_VAR_NAME(),
first_fc_grad->ops_[2]->Output("A" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ(f::kEmptyVarName,
first_fc_grad->ops_[2]->Output("A" + f::kGradVarSuffix));
}
TEST(Backward, net_shared_weight) {
......@@ -311,17 +307,15 @@ TEST(Backward, op_part_of_output_are_not_need) {
ASSERT_EQ(1UL, fill_zero.inputs_.size());
ASSERT_EQ("Z", fill_zero.inputs_[0]);
ASSERT_EQ(1UL, fill_zero.outputs_.size());
ASSERT_EQ("Z" + f::OperatorBase::ZERO_VAR_SUFFIX(), fill_zero.outputs_[0]);
ASSERT_EQ("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.type_);
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size()); // I/O/OG
ASSERT_EQ("Z" + f::OperatorBase::ZERO_VAR_SUFFIX(),
d_many_out.Input("z" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("Y" + f::OperatorBase::GRAD_VAR_SUFFIX(),
d_many_out.Input("y" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(),
d_many_out.Output("x" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("Z" + f::kZeroVarSuffix, d_many_out.Input("z" + f::kGradVarSuffix));
ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" + f::kGradVarSuffix));
ASSERT_EQ("X" + f::kGradVarSuffix,
d_many_out.Output("x" + f::kGradVarSuffix));
}
TEST(Backward, op_part_of_input_are_not_need) {
......@@ -331,12 +325,10 @@ TEST(Backward, op_part_of_input_are_not_need) {
ASSERT_EQ(grad_mul.type_, "mul_grad");
ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
ASSERT_EQ(grad_mul.Output("A" + f::OperatorBase::GRAD_VAR_SUFFIX()),
f::OperatorBase::EMPTY_VAR_NAME());
ASSERT_EQ(grad_mul.Output("B" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"b" + f::OperatorBase::GRAD_VAR_SUFFIX());
ASSERT_EQ(grad_mul.Input("Out" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out" + f::OperatorBase::GRAD_VAR_SUFFIX());
ASSERT_EQ(grad_mul.Output("A" + f::kGradVarSuffix), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
"out" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("A"), "a");
ASSERT_EQ(grad_mul.Input("B"), "b");
ASSERT_EQ(grad_mul.Input("Out"), "out");
......@@ -368,23 +360,4 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
/*
EXPECT_EQ(grad_fc.Output("X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
f::OperatorBase::EMPTY_VAR_NAME());
EXPECT_EQ(grad_fc.Output("W" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"w3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Output("b" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"b3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Output("mul_result" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"mul_out3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Input("Out" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Input("X"), "out2");
EXPECT_EQ(grad_fc.Input("W"), "w3");
EXPECT_EQ(grad_fc.Input("mul_result"), "mul_out3");
EXPECT_EQ(grad_fc.Input("add_result"), "tmp_out3");
EXPECT_EQ(grad_fc.Input("Out"), "out3");
*/
}
paddle/framework/grad_op_builder.cc
浏览文件 @ a07cef33
......@@ -56,8 +56,7 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
for (const auto& arg : src_arg_list) {
std::string src_name = arg.name();
std::string dst_name =
is_grad ? src_name + OperatorBase::GRAD_VAR_SUFFIX() : src_name;
std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
(*dst_op->in_out_idxs_)[dst_name] = idx++;
int src_arg_idx = src_op->in_out_idxs_->at(src_name);
int src_begin =
......@@ -65,10 +64,9 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
int src_end = src_format == nullptr ? src_arg_idx + 1
: src_format->at(src_arg_idx + 1);
for (int i = src_begin; i < src_end; ++i) {
std::string s = is_grad ? src_inout[i] + OperatorBase::GRAD_VAR_SUFFIX()
: arg.ignore_gradient()
? OperatorBase::EMPTY_VAR_NAME()
: src_inout[i];
std::string s =
is_grad ? src_inout[i] + kGradVarSuffix
: (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
dst_inout.emplace_back(s);
}
if (dst_format != nullptr) {
......
paddle/framework/grad_op_builder_test.cc
浏览文件 @ a07cef33
......@@ -83,24 +83,21 @@ TEST(GradOpBuilder, MutiInOut) {
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input("Out1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Inputs("Out2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>(
{"out2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"out2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Input("Out1" + f::kGradVarSuffix),
"out1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Inputs("Out2_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"out2_1" + f::kGradVarSuffix, "out2_2" + f::kGradVarSuffix}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_3" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Output("In3" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
"in1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
std::vector<std::string>({"in2_1" + f::kGradVarSuffix,
"in2_2" + f::kGradVarSuffix,
"in2_3" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Output("In3" + f::kGradVarSuffix),
"in3" + f::kGradVarSuffix);
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
......@@ -116,30 +113,25 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({f::OperatorBase::EMPTY_VAR_NAME(),
f::OperatorBase::EMPTY_VAR_NAME()}));
std::vector<std::string>({f::kEmptyVarName, f::kEmptyVarName}));
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Input("Out2"), f::OperatorBase::EMPTY_VAR_NAME());
EXPECT_EQ(
grad_test_op->Inputs("Out1_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>(
{"out1_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"out1_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out2" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_test_op->Input("Out2"), f::kEmptyVarName);
EXPECT_EQ(grad_test_op->Inputs("Out1_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"out1_1" + f::kGradVarSuffix, "out1_2" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::kGradVarSuffix),
"out2" + f::kGradVarSuffix);
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(
grad_test_op->Outputs("In3_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in3_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in3_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
"in1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"in2_1" + f::kGradVarSuffix, "in2_2" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Outputs("In3_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"in3_1" + f::kGradVarSuffix, "in3_2" + f::kGradVarSuffix}));
}
paddle/framework/op_desc.proto
浏览文件 @ a07cef33
......@@ -15,7 +15,7 @@ limitations under the License. */
syntax="proto2";
package paddle.framework;
import "attr_type.proto";
import "attribute.proto";
// AttrDesc is used to describe Attributes of an Operator. It contain's
// name, type, and value of Attribute.
......
paddle/framework/op_proto.proto
浏览文件 @ a07cef33
......@@ -21,7 +21,7 @@ limitations under the License. */
syntax="proto2";
package paddle.framework;
import "attr_type.proto";
import "attribute.proto";
// Attribute protocol message for 3rd-party language binding.
// It will store the Op support what attribute and what type.
......
paddle/framework/op_registry.cc
浏览文件 @ a07cef33
......@@ -14,37 +14,8 @@ limitations under the License. */
#include <paddle/framework/op_registry.h>
namespace paddle {
namespace framework {
template <>
void AttrTypeHelper::SetAttrType<int>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INT);
}
template <>
void AttrTypeHelper::SetAttrType<float>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOAT);
}
template <>
void AttrTypeHelper::SetAttrType<std::string>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRING);
}
#include <vector>
template <>
void AttrTypeHelper::SetAttrType<std::vector<int>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INTS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<float>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOATS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<std::string>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRINGS);
}
} // namespace framework
namespace paddle {
namespace framework {} // namespace framework
} // namespace paddle
paddle/framework/op_registry.h
浏览文件 @ a07cef33
......@@ -19,7 +19,7 @@ limitations under the License. */
#include <type_traits>
#include <unordered_map>
#include <unordered_set>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/scope.h"
......@@ -27,49 +27,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
// helper class to set attribute type
struct AttrTypeHelper {
template <typename T>
static void SetAttrType(AttrProto* attr);
static Attribute GetAttrValue(const AttrDesc& attr_desc) {
switch (attr_desc.type()) {
case paddle::framework::AttrType::INT: {
return attr_desc.i();
}
case paddle::framework::AttrType::FLOAT: {
return attr_desc.f();
}
case paddle::framework::AttrType::STRING: {
return attr_desc.s();
}
case paddle::framework::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
return val;
}
case paddle::framework::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
return val;
}
case paddle::framework::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
return val;
}
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
}
};
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {
public:
......@@ -136,7 +93,7 @@ class OpProtoAndCheckerMaker {
*attr->mutable_name() = name;
*attr->mutable_comment() = comment;
attr->set_generated(generated);
AttrTypeHelper::SetAttrType<T>(attr);
attr->set_type(AttrTypeID<T>());
return op_checker_->AddAttrChecker<T>(name);
}
......@@ -297,7 +254,7 @@ class OpRegistry {
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
attrs[attr.name()] = GetAttrValue(attr);
}
return CreateOp(op_desc.type(), inputs, outputs, attrs);
......@@ -341,7 +298,7 @@ class OpRegistry {
static void GenerateTempVariableName(OperatorBase* op) {
static std::atomic<size_t> gUniqId(0UL);
for (auto& outname : op->outputs_) {
if (outname == OperatorBase::TMP_VAR_NAME()) {
if (outname == kTempVarName) {
outname += op->type_;
outname += "@";
outname += std::to_string(gUniqId.fetch_add(1));
......
paddle/framework/operator.h
浏览文件 @ a07cef33
......@@ -20,7 +20,7 @@ limitations under the License. */
#include <unordered_map>
#include <vector>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/scope.h"
......@@ -32,9 +32,29 @@ limitations under the License. */
namespace paddle {
namespace framework {
/// If a variable is a empty variable, that name will be used.
const std::string kEmptyVarName = "@EMPTY@";
/// If a variable is a temporary variable, that name will be set in Python,
/// but it will be convert to a unique name in scope after OpCreator.
const std::string kTempVarName = "@TEMP@";
/// If a variable's name has a certain suffix, it means that the
/// variable is the gradient of another varibale.
/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
const std::string kGradVarSuffix = "@GRAD";
/// Variables with this suffix are supposed to be filled up with zeros.
const std::string kZeroVarSuffix = "@ZERO";
inline std::string GradVarName(const std::string& var_name) {
return var_name + kGradVarSuffix;
}
class OperatorBase;
class InferShapeContext;
class ExecutionContext;
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -43,25 +63,6 @@ class ExecutionContext;
*/
class OperatorBase {
public:
/// If a variable is a empty variable, that name will be used.
static std::string EMPTY_VAR_NAME() { return "@EMPTY@"; }
/// If a variable is a temporary variable, that name will be set in Python,
/// but it will be convert to a unique name in scope after OpCreator.
static std::string TMP_VAR_NAME() { return "@TEMP@"; }
/// If a variable's name has a certain suffix, it means that the
/// variable is the gradient of another varibale.
/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
static std::string GRAD_VAR_SUFFIX() { return "@GRAD"; }
static std::string GRAD_VAR_NAME(const std::string& name) {
return name + GRAD_VAR_SUFFIX();
}
/// Variables with this suffix are supposed to be filled up with zeros.
static std::string ZERO_VAR_SUFFIX() { return "@ZERO"; }
virtual ~OperatorBase() {}
template <typename T>
......
paddle/framework/pybind.cc
浏览文件 @ a07cef33
......@@ -163,8 +163,8 @@ All parameter, weight, gradient are variables in Paddle.
m.def_submodule(
"var_names",
"The module will return special predefined variable name in Paddle")
.def("empty", OperatorBase::EMPTY_VAR_NAME)
.def("temp", OperatorBase::TMP_VAR_NAME);
.def("empty", []() { return kEmptyVarName; })
.def("temp", []() { return kTempVarName; });
// clang-format off
py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
.def_static("create",
......
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ a07cef33
# gserver pacakge unittests
file(GLOB_RECURSE GSERVER_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.h")
file(GLOB_RECURSE GSERVER_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cpp")
add_style_check_target(paddle_gserver ${GSERVER_SOURCES})
add_style_check_target(paddle_gserver ${GSERVER_HEADER})
################### test_ProtoDataProvider ############
add_unittest_without_exec(test_ProtoDataProvider
test_ProtoDataProvider.cpp)
......@@ -50,7 +55,7 @@ add_unittest_without_exec(test_DetectionOutput
test_DetectionOutput.cpp
LayerGradUtil.cpp)
add_test(NAME test_DetectionOutput
add_test(NAME test_DetectionOutput
COMMAND test_DetectionOutput)
################# test_ConvUnify #######################
add_unittest_without_exec(test_ConvUnify
......
paddle/math/MathUtils.cpp
浏览文件 @ a07cef33
......@@ -25,7 +25,7 @@ namespace paddle {
*/
void sparseRand(
int* major, int* minor, int nnz, int majorLen, int minorMax, bool useGpu) {
CHECK(size_t(nnz) > size_t(1));
CHECK(size_t(nnz) >= size_t(1));
int* cpuMajor;
int* cpuMinor;
CpuIVector cpuMinorVec(nnz);
......
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @ a07cef33
......@@ -79,8 +79,8 @@ void testMatrixMaxSequence(int batchSize, int inputDim) {
}
TEST(Matrix, maxSequence) {
for (auto batchSize : {1, 10, 128, 1000, 6000}) {
for (auto inputDim : {1, 32, 100, 512}) {
for (auto batchSize : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
for (auto inputDim : {1, 7, 131}) { // prime numbers close to 1, 8, 128
VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
testMatrixMaxSequence(batchSize, inputDim);
}
......@@ -240,14 +240,10 @@ TEST(Matrix, unary) {
// inverse matrix
testMatrixInverse(height);
#else
LOG(WARNING) << "Cannot run Matrix Inverse Unit Test.\n"
<< "Failed to find lapack library in current system.\n"
<< "To address this issue, Please adopt one of the following "
"approaches: \n"
<< "1. Simply issue `sudo apt-get install liblapacke-dev` to "
"avoid re-build source code. \n"
<< "2. Install MKL/Openblas/ATLAS and re-build PaddlePaddle "
"source code.";
LOG(WARNING) << "This version of PaddlePaddle was not built with LAPACK"
<< "support so we cannot test matrix inverse. To test "
<< "matrix inverse, please install LAPACKE "
<< "and MKL/Openblas/ATLAS, and re-build PaddlePaddle.";
#endif
}
}
......@@ -341,8 +337,8 @@ void testMatrixSoftmaxBp(int height, int width) {
}
TEST(Matrix, softmax) {
for (auto height : {1, 11, 73, 128, 200}) {
for (auto width : {1, 32, 100, 512, 1000}) {
for (auto height : {1, 3, 131}) { // prime numbers close to 1, 4, 127
for (auto width : {1, 17, 251}) { // prime numbers close to 1, 16, 256
VLOG(3) << " height=" << height << " width=" << width;
testMatrixSoftmax(height, width);
......@@ -527,7 +523,7 @@ void testVectorRowFunc(int size) {
}
TEST(Vector, rowFunc) {
for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
for (auto size : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
VLOG(3) << " size=" << size;
testVectorRowFunc(size);
}
......@@ -604,7 +600,7 @@ void testVectorIsEqual(int size) {
}
TEST(Vector, Equal) {
for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
for (auto size : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
VLOG(3) << " size=" << size;
testVectorReset<int>(size);
testVectorReset<real>(size);
......@@ -635,9 +631,8 @@ void testMatrixTopK(int samples, int dim, int beamSize) {
}
TEST(Matrix, topK) {
for (auto samples : {1, 5, 31, 90, 150, 500}) {
for (auto dim :
{1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
for (auto samples : {1, 17, 131}) { // prime numbers close to 1, 16, 127
for (auto dim : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
for (auto beamSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
if (beamSize > dim) continue;
VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
......@@ -650,6 +645,7 @@ TEST(Matrix, topK) {
void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
int nnz = samples * dim * ratio;
if (nnz < 1) nnz = 1; // Because sparseRand in MathUtil.cpp requires this.
MatrixPtr cpuSrc = std::make_shared<CpuSparseMatrix>(samples, dim, nnz);
MatrixPtr gpuSrc = std::make_shared<GpuSparseMatrix>(samples, dim, nnz);
MatrixPtr cpuVal = std::make_shared<CpuMatrix>(samples, beamSize);
......@@ -683,9 +679,9 @@ void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
}
TEST(SMatrix, topK) {
for (auto samples : {1, 5, 100}) {
for (auto dim : {10000, 10000, 50000}) {
for (auto beamSize : {1, 5, 40, 100, 500}) {
for (auto samples : {1, 3, 61}) {
for (auto dim : {1, 3, 61}) {
for (auto beamSize : {1, 3, 61}) {
for (auto ratio : {0.01, 0.001}) {
if (beamSize > dim) continue;
VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
......@@ -806,10 +802,9 @@ void testClassificationError(int numSamples, int dim, int topkSize) {
}
TEST(Matrix, classificationError) {
for (auto numSamples : {1, 5, 31, 90, 150, 300}) {
for (auto dim :
{1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
for (auto topkSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
for (auto numSamples : {1, 3, 31}) {
for (auto dim : {1, 3, 31}) {
for (auto topkSize : {1, 3, (int)rand() % dim + 1}) {
if (topkSize > dim) continue;
VLOG(3) << " sample= " << numSamples << " topkSize= " << topkSize
<< " dim= " << dim;
......@@ -1016,13 +1011,15 @@ void testAvgPoolFwdBwd(int numSamples,
TensorCheckErr(*inputGrad, *inputGpuGrad);
}
// TODO(yi): I noticed many such blindly combinatorial tests in this
// file. They are no help to locate defects at all.
TEST(Matrix, PoolFwdBwd) {
for (auto numSamples : {5, 32}) {
for (auto channels : {1, 9, 32}) {
for (auto imgSizeH : {14, 28}) {
for (auto imgSizeW : {16, 30}) {
for (auto sizeX : {2, 5}) {
for (auto sizeY : {2, 5}) {
for (auto numSamples : {1, 3}) {
for (auto channels : {1, 3}) {
for (auto imgSizeH : {13, 17}) {
for (auto imgSizeW : {17, 19}) {
for (auto sizeX : {2, 3}) {
for (auto sizeY : {2, 3}) {
for (auto sH : {1, 2}) {
for (auto sW : {1, 2}) {
for (auto pH : {0, (sizeY - 1) / 2}) {
......@@ -1128,8 +1125,8 @@ TEST(Matrix, MaxOutFwdBwd) {
}
TEST(CpuMatrix, copyFrom) {
const size_t height = 1000;
const size_t width = 1000;
const size_t height = 31;
const size_t width = 53;
CpuMatrix cpu(height, width);
GpuMatrix gpu(height, width);
CpuMatrix copy(height, width);
......@@ -1149,6 +1146,10 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
IVectorPtr cpuSequence;
generateSequenceStartPositions(batchSize, cpuSequence);
for (int i = 0; i < cpuSequence->getSize(); ++i) {
(cpuSequence->getData())[i] += 1; // so no way that maxSeqLen is 0;
}
IVectorPtr gpuSequence = IVector::create(cpuSequence->getSize(), true);
gpuSequence->copyFrom(*cpuSequence);
......@@ -1156,45 +1157,46 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
size_t maxSeqLen = *std::max_element(cpuSequence->getData(),
cpuSequence->getData() + numSeq);
printf("numSeq = %ld, maxSeqLen = %ld\n", numSeq, maxSeqLen);
MatrixPtr cBatch = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
MatrixPtr gBatch = std::make_shared<GpuMatrix>(numSeq * maxSeqLen, inputDim);
MatrixPtr cCheck = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
hl_sequence2batch_copy_padding(gBatch->getData(),
gpuInput->getData(),
cpuSequence->getData(),
inputDim,
maxSeqLen,
numSeq,
false,
true);
cCheck->copyFrom(*gBatch);
int* seqStart = cpuSequence->getData();
float* batchData = cBatch->getData();
float* seqData = cpuInput->getData();
for (size_t i = 0; i < maxSeqLen; i++) {
for (size_t j = 0; j < numSeq; j++) {
size_t sequenceStart = seqStart[j];
size_t sequenceLength = seqStart[j + 1] - seqStart[j];
if (i < sequenceLength) {
memcpy(batchData + (i * numSeq + j) * inputDim,
seqData + (sequenceStart + i) * inputDim,
inputDim * sizeof(real));
} else {
memset(batchData + (i * numSeq + j) * inputDim,
0,
inputDim * sizeof(real));
}
}
}
TensorCheckErr(*cBatch, *cCheck);
// hl_sequence2batch_copy_padding(gBatch->getData(),
// gpuInput->getData(),
// cpuSequence->getData(),
// inputDim,
// maxSeqLen,
// numSeq,
// false,
// true);
// cCheck->copyFrom(*gBatch);
// int* seqStart = cpuSequence->getData();
// float* batchData = cBatch->getData();
// float* seqData = cpuInput->getData();
// for (size_t i = 0; i < maxSeqLen; i++) {
// for (size_t j = 0; j < numSeq; j++) {
// size_t sequenceStart = seqStart[j];
// size_t sequenceLength = seqStart[j + 1] - seqStart[j];
// if (i < sequenceLength) {
// memcpy(batchData + (i * numSeq + j) * inputDim,
// seqData + (sequenceStart + i) * inputDim,
// inputDim * sizeof(real));
// } else {
// memset(batchData + (i * numSeq + j) * inputDim,
// 0,
// inputDim * sizeof(real));
// }
// }
// }
// TensorCheckErr(*cBatch, *cCheck);
}
TEST(Matrix, warpCTC) {
for (auto batchSize : {51, 526, 2884}) {
for (auto inputDim : {32, 512, 2026}) {
for (auto batchSize : {1, 3, 17}) {
for (auto inputDim : {1, 3, 31}) {
VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
testBatch2seqPadding(batchSize, inputDim);
}
......
paddle/operators/fc_op.cc
浏览文件 @ a07cef33
......@@ -27,7 +27,7 @@ public:
{Output("before_act")},
{}));
auto b = Input("b");
if (b != EMPTY_VAR_NAME()) {
if (b != framework::kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add",
{Output("before_act"), Input("b")},
{Output("before_act")},
......
paddle/operators/mean_op.cc
浏览文件 @ a07cef33
......@@ -41,7 +41,7 @@ public:
class MeanGradOp : public OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
ctx.Output<Tensor>("X" + GRAD_VAR_SUFFIX())
ctx.Output<Tensor>("X" + framework::kGradVarSuffix)
->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......
paddle/operators/mean_op.h
浏览文件 @ a07cef33
......@@ -39,10 +39,10 @@ template <typename Place, typename T>
class MeanGradKernel : public OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
auto OG = context.Input<Tensor>("Out" + OperatorBase::GRAD_VAR_SUFFIX());
auto OG = context.Input<Tensor>("Out" + framework::kGradVarSuffix);
PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
"Mean Gradient should be scalar");
auto IG = context.Output<Tensor>("X" + OperatorBase::GRAD_VAR_SUFFIX());
auto IG = context.Output<Tensor>("X" + framework::kGradVarSuffix);
IG->mutable_data<T>(context.GetPlace());
T ig_size = (T)framework::product(IG->dims());
......
paddle/operators/softmax_op.cc
浏览文件 @ a07cef33
......@@ -48,12 +48,12 @@ protected:
PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
"Output of SoftmaxOpGrad should be 1");
PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
PADDLE_ENFORCE(ctx.InputVar(GRAD_VAR_NAME("Y")) != nullptr,
PADDLE_ENFORCE(ctx.InputVar(framework::GradVarName("Y")) != nullptr,
"Input(Y@GRAD) should not be null");
PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
ctx.Input<Tensor>(GRAD_VAR_NAME("Y"))->dims(),
ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
"the shape of Input(0) and Input(1) should be the same");
ctx.Output<Tensor>(GRAD_VAR_NAME("X"))
ctx.Output<Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("Y")->dims());
}
};
......
paddle/operators/softmax_op.h
浏览文件 @ a07cef33
......@@ -68,8 +68,8 @@ public:
std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
auto Y = context.Input<Tensor>("Y");
auto dY = context.Input<Tensor>(OperatorBase::GRAD_VAR_NAME("Y"));
auto dX = context.Output<Tensor>(OperatorBase::GRAD_VAR_NAME("X"));
auto dY = context.Input<Tensor>(framework::GradVarName("Y"));
auto dX = context.Output<Tensor>(framework::GradVarName("X"));
dX->mutable_data<T>(context.GetPlace());
const int batch_size = Y->dims()[0];
......
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