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未验证 提交 a64edbf1 编写于 作者: Y Yang Yang(Tony) 提交者： GitHub
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delete backward.cc related code on the python side (#9854)

上级 b26f5050
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Showing with 3 addition and 628 deletion
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ a64edbf1
......@@ -79,13 +79,12 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
COMMENT "Copy generated python proto into directory paddle/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS operator)
cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
framework_proto backward glog lod_rank_table feed_fetch_method)
framework_proto glog lod_rank_table feed_fetch_method)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor)
......
paddle/fluid/framework/backward.cc 已删除 100644 → 0
浏览文件 @ b26f5050
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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/fluid/framework/backward.h"
#include <deque>
#include <list>
#include <memory>
#include <unordered_set>
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace framework {
static std::unordered_set<std::string>* g_ctrl_flow_ops_ = nullptr;
// Control Flow operators's backward is significantly different from
// computational operators. Hack Code here.
// We should design a better way to backward CtrlFlowOps.
static std::unordered_set<std::string>& CtrlFlowOps() {
if (g_ctrl_flow_ops_ == nullptr) {
g_ctrl_flow_ops_ = new std::unordered_set<std::string>{
"increment", "lod_rank_table", "less_than"};
}
return *g_ctrl_flow_ops_;
}
static inline std::unique_ptr<OperatorBase> CreateGradOp(
const OperatorBase& op, const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var) {
OpDesc op_desc;
op_desc.SetInputMap(op.Inputs());
op_desc.SetOutputMap(op.Outputs());
op_desc.SetType(op.Type());
op_desc.SetAttrMap(op.Attrs());
auto& info = OpInfoMap::Instance().Get(op.Type());
auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var, {});
std::vector<std::unique_ptr<OperatorBase>> grad_ops;
grad_ops.reserve(grad_descs.size());
std::transform(grad_descs.begin(), grad_descs.end(),
std::back_inserter(grad_ops),
[](const std::unique_ptr<OpDesc>& grad_desc) {
return OpRegistry::CreateOp(*grad_desc);
});
PADDLE_ENFORCE(!grad_ops.empty());
if (grad_ops.size() == 1) {
return std::move(grad_ops[0]);
} else {
PADDLE_THROW("Unexpected Branch");
}
}
template <typename Map, typename T>
static void ForEachVarName(const Map& names, T callback) {
for (auto& name : names) {
for (auto& n : name.second) {
if (callback(n)) return;
}
}
}
// return whether all the names + suffixes in the set
static bool AllInSet(
const std::map<std::string, std::vector<std::string>>& names,
const std::string& suffix, const std::unordered_set<std::string>& set) {
bool all_in_set = true;
ForEachVarName(names, [&all_in_set, &set, &suffix](const std::string& n) {
all_in_set = set.find(n + suffix) != set.end();
return !all_in_set;
});
return all_in_set;
}
static std::unique_ptr<OperatorBase> NOP() {
PADDLE_THROW("Unexpected Branch");
}
// Get backward operator from a forward operator, a recursive implementation.
//
// no_grad_names the gradient variable names without gradient calculating.
//
// uniq_id is a unique index used inside recursively calling
// BackwardRecursive. use `uid = uniq_id++;` to get the unique index, and
// pass `uniq_id` through recursive calling.
//
// returns The backward operator. In a simple situation, it may be a simple
// operator, in a complex situation, it maybe a NetOp.
//
// See Backward.h for details
static std::unique_ptr<OperatorBase> BackwardRecursive(
const OperatorBase& forwardOp,
std::unordered_set<std::string>& no_grad_names,
std::unordered_map<std::string, std::string>* grad_to_var,
size_t& uniq_id) {
// 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() /*names*/, kGradVarSuffix /*suffix*/,
no_grad_names /*set*/)) {
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() /*names*/, kGradVarSuffix /*suffix*/,
no_grad_names /*set*/)) {
ForEachVarName(forwardOp.Inputs(),
[&no_grad_names](const std::string& name) -> bool {
no_grad_names.insert(GradVarName(name));
return false;
});
return NOP();
}
// Returned gradient network
PADDLE_THROW("Unexpected Branch");
}
// See header for comments
std::unique_ptr<OperatorBase> Backward(
const OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string> no_grad_names;
no_grad_names.reserve(no_grad_vars.size() + 1);
no_grad_names.insert(std::string(kEmptyVarName) + kGradVarSuffix);
for (auto& name : no_grad_vars) {
no_grad_names.insert(name + kGradVarSuffix);
}
size_t uid = 0;
std::unordered_map<std::string, std::string> grad_to_var;
return BackwardRecursive(forwardOp, no_grad_names, &grad_to_var, uid);
}
// ==================================== //
static bool AllGradInSet(const std::vector<std::string>& names,
const std::unordered_set<std::string>& set) {
for (const std::string& name : names) {
if (!set.count(GradVarName(name))) {
return false;
}
}
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
sout << "All input {";
for (auto& name : names) {
sout << name << ",";
}
sout << "} is in {";
for (auto& name : set) {
sout << name << ",";
}
sout << "}";
VLOG(10) << sout.str();
}
return true;
}
static std::string FwdName(const std::string& grad_name) {
auto pos = grad_name.find("@GRAD");
if (pos == std::string::npos) {
return "";
} else {
return grad_name.substr(0, pos);
}
}
static void CreateGradVarInBlock(
size_t grad_op_start_index,
const std::unordered_map<std::string, std::string>& param_name_map,
BlockDesc* block_desc,
std::unordered_map<std::string, GradVarInfo>* grad_var_record) {
auto ops = block_desc->AllOps();
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
std::unordered_set<std::string> new_vars;
auto& ctrl_flow_ops = CtrlFlowOps();
ForEachVarName(ops[op_index]->Outputs(),
[&](const std::string& grad_var_name) {
if (ctrl_flow_ops.find(ops[op_index]->Type()) !=
ctrl_flow_ops.end()) {
if (block_desc->HasVarRecursive(grad_var_name)) {
return false;
}
} else {
if (block_desc->HasVar(grad_var_name)) {
return false;
}
}
if (grad_var_name == framework::kEmptyVarName) {
return false;
}
auto var = block_desc->Var(grad_var_name);
VLOG(10) << "Creating Variable " << grad_var_name;
new_vars.insert(var->Name());
auto it = param_name_map.find(grad_var_name);
if (it == param_name_map.end()) {
return false;
}
auto param_var_name = it->second;
auto& grad_record = (*grad_var_record)[param_var_name];
grad_record.name_ = grad_var_name;
grad_record.block_idx_ = block_desc->ID();
grad_record.op_idx_ = static_cast<int>(op_index);
return false; /* not break */
});
ops[op_index]->InferVarType(block_desc);
for (auto& arg : ops[op_index]->OutputArgumentNames()) {
if (new_vars.find(arg) == new_vars.end()) {
continue;
}
auto pname = FwdName(arg);
auto* param = block_desc->FindVarRecursive(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
grad->SetDataType(proto::VarType::FP32);
} else {
grad->SetDataType(param->GetDataType());
}
}
ops[op_index]->InferShape(*block_desc);
}
}
std::vector<std::unique_ptr<OpDesc>> MakeOpGrad(
const OpDesc* op_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDesc*>& grad_block = std::vector<BlockDesc*>()) {
std::vector<std::unique_ptr<OpDesc>> grad_op_descs;
// All input gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
if (AllGradInSet(inputs, *no_grad_vars)) {
VLOG(10) << "Drop operator " << op_desc->Type();
return grad_op_descs; // empty vector
}
// All output gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& outputs = op_desc->OutputArgumentNames();
if (AllGradInSet(outputs, *no_grad_vars)) {
VLOG(10) << "Drop operator " << op_desc->Type();
// FIXME: Hack code here
auto& ctrl_flow_ops = CtrlFlowOps();
if (ctrl_flow_ops.find(op_desc->Type()) == ctrl_flow_ops.end()) {
// Only computational op need drop input's gradient.
for (const std::string& name : inputs) {
no_grad_vars->insert(GradVarName(name));
VLOG(10) << " Also drop " << GradVarName(name);
}
}
return grad_op_descs; // empty vector
}
grad_op_descs =
OpInfoMap::Instance()
.Get(op_desc->Type())
.GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var, grad_block);
std::list<std::unique_ptr<OpDesc>> pending_fill_zeros_ops;
for (auto& desc : grad_op_descs) {
for (const std::string& in_name : desc->InputArgumentNames()) {
if (no_grad_vars->count(in_name)) {
std::string prefix = in_name.substr(
0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
std::string new_name = prefix + kZeroVarSuffix;
desc->Rename(in_name, new_name);
std::unique_ptr<OpDesc> fill_zeros_op(
new OpDesc("fill_zeros_like", {{"X", {prefix}}},
{{"Out", {new_name}}}, AttributeMap{}));
pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
}
}
}
for (auto& p : pending_fill_zeros_ops) {
grad_op_descs.insert(grad_op_descs.begin(), std::move(p));
}
return grad_op_descs;
}
static BlockDesc* CreateStepBlock(
ProgramDesc& program_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
int step_block_idx);
std::vector<std::unique_ptr<OpDesc>> MakeBlockBackward(
ProgramDesc& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
VLOG(5) << "MakeBlockBackward";
BlockDesc* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDesc*> op_descs = cur_block->AllOps();
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
size_t grad_desc_idx = 0;
std::vector<std::unique_ptr<OpDesc>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
VLOG(5) << "Making backward " << (*it)->Type() << " op";
std::vector<std::unique_ptr<OpDesc>> op_grads;
if ((*it)->Type() == "recurrent" || (*it)->Type() == "while" ||
(*it)->Type() == "parallel_do") {
int step_block_idx = (*it)->GetBlockAttr("sub_block");
BlockDesc* backward_block = CreateStepBlock(program_desc, no_grad_vars,
grad_to_var, step_block_idx);
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
} else if ((*it)->Type() == "conditional_block") {
BlockDesc* backward_block =
CreateStepBlock(program_desc, no_grad_vars, grad_to_var,
(*it)->GetBlockAttr("sub_block"));
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
} else {
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
}
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
sout << "Made ";
for (auto& op_grad : op_grads) {
sout << op_grad->Type() << " ";
}
VLOG(10) << sout.str();
}
for (const auto& desc : op_grads) {
for (const std::string& out_name : desc->OutputArgumentNames()) {
if (out_name.find("@GRAD") == std::string::npos) {
// Not all outputs of a backward operator is a gradient. Only gradient
// need to be sum. Skip variables are not gradient.
continue;
}
dup_out_ops[out_name].emplace_back(grad_desc_idx);
}
++grad_desc_idx;
}
std::transform(op_grads.begin(), op_grads.end(),
std::back_inserter(backward_descs),
[](std::unique_ptr<OpDesc>& ptr) { return std::move(ptr); });
}
VLOG(5) << "Appending Sums";
// Check whether some variables are written more than once
std::list<std::pair<size_t, std::unique_ptr<OpDesc>>> pending_sum_ops;
for (const auto& dup : dup_out_ops) {
const std::string& out_name = dup.first;
const std::vector<size_t> dup_op = dup.second;
if (out_name != kEmptyVarName && dup_op.size() > 1) {
std::vector<std::string> sum_op_inputs;
std::string next_g_name = out_name;
for (size_t i = 0; i < dup_op.size(); ++i) {
VLOG(10) << backward_descs[dup_op[i]]->Type() << " has " << out_name
<< " duplicated";
std::string new_name = out_name + "@RENAME@" + std::to_string(i);
backward_descs[dup_op[i]]->RenameOutput(out_name, new_name);
backward_descs[dup_op[i]]->RenameInput(out_name, next_g_name);
sum_op_inputs.emplace_back(new_name);
next_g_name = sum_op_inputs.back();
}
std::unique_ptr<OpDesc> sum_op(new OpDesc("sum", {{"X", sum_op_inputs}},
{{"Out", {out_name}}},
AttributeMap{}));
pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
}
}
pending_sum_ops.sort([](const std::pair<size_t, std::unique_ptr<OpDesc>>& a,
const std::pair<size_t, std::unique_ptr<OpDesc>>& b) {
return a.first > b.first;
});
for (auto& p : pending_sum_ops) {
backward_descs.insert(backward_descs.begin() + p.first + 1,
std::move(p.second));
}
VLOG(5) << "MakeBlockBackward Finished";
return backward_descs;
}
static BlockDesc* CreateStepBlock(
ProgramDesc& program_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
int step_block_idx) {
auto backward_block_op_descs = MakeBlockBackward(program_desc, step_block_idx,
no_grad_vars, grad_to_var);
BlockDesc* backward_block =
program_desc.AppendBlock(*program_desc.MutableBlock(step_block_idx));
for (auto& ptr : backward_block_op_descs) {
backward_block->AppendAllocatedOp(move(ptr));
}
return backward_block;
}
ParamGradInfoMap AppendBackward(
ProgramDesc& program_desc, const VarDesc& target,
const std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string> no_grad_var_names;
no_grad_var_names.reserve(no_grad_vars.size() + 1);
no_grad_var_names.insert(std::string(kEmptyVarName) + kGradVarSuffix);
for (auto& name : no_grad_vars) {
no_grad_var_names.insert(GradVarName(name));
}
const int root_block_idx = 0;
auto root_block = program_desc.MutableBlock(root_block_idx);
std::string fill_one_op_out = GradVarName(target.Name());
bool is_scalar = target.GetShape() == std::vector<int64_t>{1};
PADDLE_ENFORCE(is_scalar, "target should be scalar");
VLOG(3) << "backward from loss=" << target.Name()
<< " data_type=" << target.GetDataType();
std::unique_ptr<OpDesc> fill_one_op(
new OpDesc("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"dtype", target.GetDataType()}}));
// infer var type of fill_one_op
fill_one_op->InferVarType(root_block);
root_block->AppendAllocatedOp(std::move(fill_one_op));
size_t forward_op_num = root_block->OpSize();
size_t forward_block_num = program_desc.Size();
// Insert backward operators
std::unordered_map<std::string, std::string> grad_to_var;
auto backward_op_descs = MakeBlockBackward(program_desc, root_block_idx,
&no_grad_var_names, &grad_to_var);
for (auto& ptr : backward_op_descs) {
root_block->AppendAllocatedOp(std::move(ptr));
}
// Create Variable
// Create target gradient variable
std::unordered_map<std::string, GradVarInfo> retv;
auto var = root_block->Var(fill_one_op_out);
var->SetDataType(target.GetDataType());
var->SetShape(target.GetShape());
auto& target_grad = retv[target.Name()];
target_grad.name_ = fill_one_op_out;
target_grad.block_idx_ = root_block_idx;
target_grad.op_idx_ = static_cast<int>(forward_op_num);
// create grad_var for all blocks in this program
CreateGradVarInBlock(forward_op_num, grad_to_var, root_block, &retv);
for (size_t block_index = forward_block_num;
block_index < program_desc.Size(); ++block_index) {
CreateGradVarInBlock(0, grad_to_var, program_desc.MutableBlock(block_index),
&retv);
}
return retv;
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/backward.h 已删除 100644 → 0
浏览文件 @ b26f5050
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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 <string>
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
// Create the backward operator from a forward operator.
// TODO(yuyang18): Add more API reference comment.
extern std::unique_ptr<OperatorBase> Backward(
const OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars);
struct GradVarInfo {
GradVarInfo() {}
GradVarInfo(const std::string& name, int block_idx, int op_idx)
: name_(name), block_idx_(block_idx), op_idx_(op_idx) {}
bool operator==(const GradVarInfo& b) const {
return name_ == b.name_ && block_idx_ == b.block_idx_ &&
op_idx_ == b.op_idx_;
}
std::string name_;
int block_idx_;
int op_idx_;
};
using ParamGradInfoMap = std::unordered_map<std::string /*fwd_var_name*/,
GradVarInfo /*grad_var_info*/>;
ParamGradInfoMap AppendBackward(
ProgramDesc& program_desc, const VarDesc& target,
const std::unordered_set<std::string>& no_grad_vars);
} // namespace framework
} // namespace paddle
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ a64edbf1
......@@ -2,13 +2,13 @@ if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
DEPS pybind python backward proto_desc memory executor prune init profiler feed_fetch_method
DEPS pybind python proto_desc memory executor prune init profiler feed_fetch_method
parallel_executor
${GLOB_OP_LIB})
else()
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
DEPS pybind python backward proto_desc memory executor prune init profiler feed_fetch_method
DEPS pybind python proto_desc memory executor prune init profiler feed_fetch_method
parallel_executor
${GLOB_OP_LIB})
if(NOT APPLE AND NOT ANDROID)
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ a64edbf1
......@@ -18,7 +18,6 @@ limitations under the License. */
#include <string>
#include <tuple>
#include "paddle/fluid/framework/backward.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/program_desc.h"
......@@ -125,23 +124,6 @@ void BindProgramDesc(pybind11::module *m) {
})
.def("append_block", &pd::ProgramDesc::AppendBlock,
pybind11::return_value_policy::reference)
.def("append_backward",
[](pd::ProgramDesc &program_desc, const pd::VarDesc &target,
const std::unordered_set<std::string> &no_grad_vars) {
pd::ParamGradInfoMap param_grad_map =
AppendBackward(program_desc, target, no_grad_vars);
std::unordered_map<
std::string, std::tuple<std::string /* grad_var_name */,
int /* block_idx */, int /* op_idx */>>
retv;
for (auto it = param_grad_map.begin(); it != param_grad_map.end();
++it) {
const auto &grad_info = it->second;
retv[it->first] = std::make_tuple(
grad_info.name_, grad_info.block_idx_, grad_info.op_idx_);
}
return retv;
})
.def("block", &pd::ProgramDesc::MutableBlock,
pybind11::return_value_policy::reference)
.def("num_blocks", &pd::ProgramDesc::Size)
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ a64edbf1
......@@ -20,7 +20,6 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/backward.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
......@@ -381,11 +380,6 @@ All parameter, weight, gradient are variables in Paddle.
desc.InitializationErrorString());
return OpRegistry::CreateOp(desc);
})
.def("backward",
[](const OperatorBase &forwardOp,
const std::unordered_set<std::string> &no_grad_vars) {
return Backward(forwardOp, no_grad_vars).release();
})
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::CPUPlace &place) { self.Run(scope, place); })
......
python/paddle/fluid/framework.py
浏览文件 @ a64edbf1
......@@ -1119,24 +1119,6 @@ class Program(object):
def current_block(self):
return self.blocks[self.current_block_idx]
def append_backward(self, target, no_grad_set=None):
"""
return map(param_name -> (grad_name, block_index, op_index))
"""
assert isinstance(target, Variable)
if no_grad_set is None:
no_grad_set = set()
try:
param_to_grad_info = self.desc.append_backward(target.desc,
no_grad_set)
except Exception as e:
raise core.EnforceNotMet(
str(e) + "\nCurrent protobuf is\n{0}".format(
self.to_string(False)))
self.sync_with_cpp()
return param_to_grad_info
def create_block(self, parent_idx=None):
new_block_idx = len(self.blocks)
parent = self.current_block() if parent_idx is None else self.block(
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ a64edbf1
......@@ -32,7 +32,6 @@ class TestBook(unittest.TestCase):
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
self.assertIsNotNone(avg_cost)
program.append_backward(avg_cost)
print(str(program))
......@@ -94,8 +93,6 @@ class TestBook(unittest.TestCase):
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(cost)
program.append_backward(avg_cost)
print(str(program))
def test_word_embedding(self):
......
python/paddle/fluid/tests/unittests/test_program.py
浏览文件 @ a64edbf1
......@@ -87,57 +87,6 @@ class TestProgram(unittest.TestCase):
print(prog)
print(prog_restored)
def test_append_backward(self):
prog = Program()
block = prog.global_block()
mul_x = block.create_var(
dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
mul_y = block.create_var(
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
mul_op = block.append_op(
type="mul",
inputs={"X": [mul_x],
"Y": mul_y},
outputs={"Out": [mul_out]},
attrs={"x_num_col_dims": 1})
add_y = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="add.y")
add_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="add.out")
add_op = block.append_op(
type="elementwise_add",
inputs={"X": mul_out,
"Y": add_y},
outputs={"Out": add_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": add_out}, outputs={"Out": mean_out})
self.assertEqual(mul_op.idx, 0)
self.assertEqual(add_op.idx, 1)
param_to_grad = prog.append_backward(mean_out, set())
for var_name in ("mul.x", "mul.y", "mul.out", "add.y", "add.out",
"mean.out"):
self.assertEqual(param_to_grad[var_name][0],
grad_var_name(var_name))
self.assertEqual(param_to_grad[var_name][1], 0)
expect_ops = [
"mul", "elementwise_add", "mean", "fill_constant", "mean_grad",
"elementwise_add_grad", "mul_grad"
]
actual_ops = []
for op in block.ops:
actual_ops.append(op.type)
self.assertEqual(actual_ops, expect_ops)
def test_program_clone_with_parameter(self):
main_program = Program()
startup_program = Program()
......
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