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137 lines
4.9 KiB
137 lines
4.9 KiB
# Copyright (c) 2019 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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__all__ = ['trace']
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from .base import program_desc_tracing_guard
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from .layers import Layer
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from paddle.fluid.framework import Program, Block, Variable, _dygraph_tracer, dygraph_only, _dygraph_guard
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def create_program_from_desc(program_desc):
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program = Program()
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program.desc = program_desc
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program.blocks = [Block(program, 0)]
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program._sync_with_cpp()
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return program
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def _extract_vars(inputs, result_list):
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if isinstance(inputs, Variable):
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result_list.append(inputs._ivar)
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if isinstance(inputs, (list, tuple)):
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for var in inputs:
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_extract_vars(var, result_list)
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def extract_vars(inputs):
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result_list = []
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_extract_vars(inputs, result_list)
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return result_list
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@dygraph_only
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def trace(layer, inputs, feed_names=None, fetch_names=None):
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"""
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Trace dygraph network into a :code:`Program`. The returned :code:`Program`
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can be run in static graph mode. This method would simply record all
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operators in the network with :code:`inputs` . Users should guarantee that
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the traced dygraph network is independent with input data, input shapes,
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and would not be changed between different batches. Otherwise, the traced
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result may be different.
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Parameters:
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layer(Layer): the layer to be traced.
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inputs(list): the input arguments of :code:`layer.forward()` method.
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feed_names(list(str), optional): the input variable names in the
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traced :code:`Program` corresponding to :code:`inputs` . If it
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is None, the variable name of :code:`inputs` would be used.
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It is suggested that users should set :code:`feed_names`
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manually. Otherwise, the input variable names would be
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different between different batches. Default None.
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fetch_names(list(str), optional): the output variable names in the
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traced :code:`Program` corresponding to the output variables
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of :code:`layer.forward()` method. If it is None, the variable
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name of the outputs of :code:`layer.forward()` would be used.
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It is suggested that users should set :code:`fetch_names`
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manually. Otherwise, the output variable names would be
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different between different batches. Default None.
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Returns:
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A tuple of 2 items, whose first item is the outputs of
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:code:`layer.forward()` method, and second item is the traced
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:code:`Program` .
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Examples:
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.. code-blocks: python:
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import paddle.fluid as fluid
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from paddle.fluid.dygraph import FC, to_variable
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import paddle.fluid.dygraph.jit as jit
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import numpy as np
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class ExampleLayer(fluid.dygraph.Layer):
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def __init__(self, name_scope):
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super(ExampleLayer, self).__init__(name_scope)
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self._fc = FC(self.full_name(), 10)
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def forward(self, input):
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return self._fc(input)
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with fluid.dygraph.guard():
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layer = ExampleLayer("example_layer")
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in_np = np.random.random([2, 3]).astype('float32')
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in_var = to_variable(in_np)
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out, program = jit.trace(layer, inputs=[in_var],
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feed_names=['input'],
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fetch_names=['fc_out'])
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"""
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assert isinstance(layer, Layer)
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if not isinstance(inputs, (list, tuple)):
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inputs = [inputs]
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if feed_names is None:
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feed_names = []
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if fetch_names is None:
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fetch_names = []
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tracer = _dygraph_tracer()._get_program_desc_tracer()
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var_list = extract_vars(inputs)
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tracer.set_feed_vars(var_list, feed_names)
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with program_desc_tracing_guard(True):
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original_outputs = layer(*inputs)
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if not isinstance(original_outputs, (list, tuple)):
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outputs = [original_outputs]
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else:
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outputs = original_outputs
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out_vars = [var._ivar for var in outputs]
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tracer.set_fetch_vars(out_vars, fetch_names)
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tracer.set_name_prefix('t_')
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program_desc = tracer.create_program_desc()
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tracer.reset()
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with _dygraph_guard(None):
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program = create_program_from_desc(program_desc)
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return original_outputs, program
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