107 lines
3.5 KiB
107 lines
3.5 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import six
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from collections import defaultdict
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from paddle.fluid import core
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from paddle.fluid import framework
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__all__ = ['Tracer']
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def release_op(op):
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del framework._dygraph_tracer()._ops[op._trace_id].inputs
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del framework._dygraph_tracer()._ops[op._trace_id].outputs
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del framework._dygraph_tracer()._ops[op._trace_id].backward_refs
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class Tracer(core.Tracer):
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"""
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Python wrapper of dygraph tracer
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"""
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def __init__(self, block):
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super(Tracer, self).__init__(block)
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self._ops = defaultdict()
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self._vars = defaultdict()
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self._trace_id = 0
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self._train_mode = True
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def trace_var(self, name, var):
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self._vars[name] = var
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def all_parameters(self):
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return list((item for name, item in six.iteritems(self._vars)
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if isinstance(item, framework.Parameter)))
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def _clear_ops(self):
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self._ops = defaultdict()
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self._trace_id = 0
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def trace_op(self, op, inputs, outputs, stop_gradient=False):
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# TODO(hy): previous version will cause memory failed
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op.inputs = inputs
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inps = defaultdict(list)
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for k, vars in six.iteritems(inputs):
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if isinstance(vars, framework.Variable):
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inps[k].append(vars._ivar)
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elif isinstance(vars, list) or isinstance(vars, tuple):
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for var in vars:
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inps[k].append(var._ivar)
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op.outputs = outputs
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outs = defaultdict(list)
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for k, vars in six.iteritems(outputs):
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if isinstance(vars, framework.Variable):
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outs[k].append(vars._ivar)
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elif isinstance(vars, list) or isinstance(vars, tuple):
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for var in vars:
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outs[k].append(var._ivar)
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# record op's trace id
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op.iop._trace_id = self._trace_id
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backward_refs = self.trace(op.iop, inps, outs, op.attrs,
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framework._current_expected_place(),
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stop_gradient)
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if not stop_gradient and self._train_mode:
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self._trace_id += 1
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self._ops[op.iop._trace_id] = op
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# register backward hooks and variables if needed
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if len(backward_refs) > 0:
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op.iop.register_backward_hooks(release_op)
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# TODO(minqiyang): remove all inputs and outputs after separate
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# var and grad
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op.backward_refs = defaultdict(list)
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for k, v in six.iteritems(inputs):
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if k in backward_refs:
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op.backward_refs[k] = inputs[k]
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for k, v in six.iteritems(outputs):
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if k in backward_refs:
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op.backward_refs[k] = outputs[k]
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def train_mode(self):
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self._train_mode = True
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def eval_mode(self):
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self._train_mode = False
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