未验证 提交 e0d8c6ac 编写于 作者: C chengduo 提交者: GitHub

Add find_no_grad_vars in backward.py (#17942)

* add not_been_used_vars to no_grad_set
test=develop
上级 449c7a9f
......@@ -15,7 +15,6 @@ fusion_seqexpand_concat_fc
fusion_seqpool_concat
fusion_squared_mat_sub
gru
hierarchical_sigmoid
lrn
lstm_unit
max_pool2d_with_index
......
......@@ -86,6 +86,10 @@ class HierarchicalSigmoidOp : public framework::OperatorWithKernel {
}
};
/*
* Inputs: X, W, Label, PathTable, PathCode, Bias
* Outputs: Out, PreOut, W_out
*/
template <typename AttrType>
class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
......@@ -162,6 +166,37 @@ Hierarchical Probabilistic Neural Network Language Model."
}
};
/*
* Inputs: X, W, Label, PathTable, PathCode, PreOut, Out@GRAD
* Outputs: X@GRAD, W@GRAD, Bias@GRAD
*/
class HierarchicalSigmoidGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType(this->ForwardOpType() + "_grad");
// Inputs: X, W, Label, PathTable, PathCode, PreOut, Out@GRAD
op->SetInput("X", Input("X"));
op->SetInput("W", Input("W"));
op->SetInput("Bias", Input("Bias"));
op->SetInput("Label", Input("Label"));
op->SetInput("PathTable", Input("PathTable"));
op->SetInput("PathCode", Input("PathCode"));
op->SetInput("PreOut", Output("PreOut"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
// Outputs: X@GRAD, W@GRAD, Bias@GRAD
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("W"), InputGrad("W"));
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(op);
}
};
class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -209,17 +244,17 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
auto attr = ctx->GetAttr("is_sparse");
bool is_sparse = boost::get<bool>(attr);
if (is_sparse) {
VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to SelectedRows";
VLOG(3) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to SelectedRows";
ctx->SetType(w_grad_var_name, framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
VLOG(3) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
ctx->SetType(w_grad_var_name, framework::proto::VarType::LOD_TENSOR);
}
if (hasBias) {
VLOG(30) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to LoDTensor";
VLOG(3) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to LoDTensor";
ctx->SetType(bias_grad_var_name, framework::proto::VarType::LOD_TENSOR);
}
ctx->SetDataType(w_grad_var_name, ctx->GetDataType(ctx->Input("W")[0]));
......@@ -232,7 +267,7 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
namespace ops = paddle::operators;
REGISTER_OPERATOR(hierarchical_sigmoid, ops::HierarchicalSigmoidOp,
ops::HierarchicalSigmoidOpMaker<int>,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::HierarchicalSigmoidGradMaker);
REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp,
ops::HierarchicalSigmoidGradOpGradVarTypeInference);
REGISTER_OP_CPU_KERNEL(
......
......@@ -552,7 +552,9 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0]))
op_path = _find_op_path_(root_block, [loss], [], block_no_grad_set)
no_grad_vars = _find_no_grad_vars(root_block, op_path, [loss],
block_no_grad_set)
block_no_grad_set.update(no_grad_vars)
no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set)))
input_grad_names_set = None
......@@ -630,6 +632,26 @@ def _as_list(x):
return list(x) if isinstance(x, collections.Sequence) else [x]
def _find_no_grad_vars(block, op_path, targets, no_grad_set):
"""
Find the vars which is not used in the program, and
those var belong to no_grad_var.
"""
output_names = set([out.name for out in targets])
no_grad_var = []
for i, op in reversed(list(enumerate(op_path))):
# If the op has sub_block, it is too complicated to find the correct no_grad_var.
if not op.has_attr("sub_block"):
for out_var in op.desc.output_arg_names():
if out_var not in output_names and out_var not in op.desc.input_arg_names(
) and not block.vars[out_var].stop_gradient:
no_grad_var.append(out_var)
for name in op.desc.input_arg_names():
if name not in no_grad_set:
output_names.add(name)
return set(no_grad_var)
def _find_op_path_(block, outputs, inputs, no_grad_set):
"""
no_grad_set will also be changed
......
# Copyright (c) 2019 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
from simple_nets import init_data
def simple_net1():
x = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feature = fluid.layers.fc(input=x, size=20, act=None)
part1, part2 = fluid.layers.split(feature, num_or_sections=[10, 10], dim=1)
# Note that: part2 is not used.
loss = fluid.layers.cross_entropy(input=part1, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestBackward(unittest.TestCase):
def check_backward(self, model):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
main = fluid.Program()
startup = fluid.Program()
batch_size = 2
with fluid.program_guard(main, startup):
loss = model()
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
optimizer.minimize(loss)
exe.run(fluid.default_startup_program())
img, label = init_data(batch_size, img_shape=[784], label_range=9)
exe.run(feed={'image': img, 'label': label})
def test_backward(self):
self.check_backward(simple_net1)
if __name__ == '__main__':
unittest.main()
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