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Expose fluid.dygraph.TracedLayer API (#21518)

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* expost fluid.dygraph.TracedLayer apis, test=develop

* polish doc, test=develop

* follow comments, test=develop, test=document_fix

* follow comments, test=develop

* remove save_inference_model return value, test=develop
release/1.7
Zeng Jinle 5 years ago committed by GitHub
parent 911eef4319
commit 86c40e207d
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 66 additions and 53 deletions
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4
python/paddle/fluid/dygraph/__init__.py
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@ -41,6 +41,9 @@ from .learning_rate_scheduler import *
from . import backward_strategy
from .backward_strategy import *
from . import jit
from .jit import *
__all__ = []
__all__ += layers.__all__
__all__ += base.__all__
@ -51,3 +54,4 @@ __all__ += parallel.__all__
__all__ += checkpoint.__all__
__all__ += learning_rate_scheduler.__all__
__all__ += backward_strategy.__all__
__all__ += jit.__all__

87
python/paddle/fluid/dygraph/jit.py
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@ -20,7 +20,6 @@ from paddle.fluid import core
from paddle.fluid.framework import Program, Block, Variable, _dygraph_tracer, dygraph_only, _dygraph_guard, _current_expected_place, in_dygraph_mode
from paddle.fluid.executor import Executor, scope_guard
from paddle.fluid.compiler import CompiledProgram
import paddle.fluid.io as fluid_io
def create_program_from_desc(program_desc):
@ -81,11 +80,15 @@ def _trace(layer,
class TracedLayer(object):
"""
TracedLayer is a callable object which is converted from dygraph model.
Inside TracedLayer, the dygraph model is converted into a static graph
model, and it would run the static graph model using
:code:`Executor` and :code:`CompiledProgram` . The static graph model
would share parameters with the dygraph model.
TracedLayer is used to convert a forward dygraph model to a static
graph model. This is mainly used to save the dygraph model for online
inference using C++. Besides, users can also do inference in Python
using the converted static graph model, which usually has better
performance than the original dygraph model.
TracedLayer would run the static graph model using :code:`Executor`
and :code:`CompiledProgram` . The static graph model would share
parameters with the dygraph model.
All TracedLayer objects should not be created by constructor and should
be created by static method :code:`TracedLayer.trace(layer, inputs)` .
@ -133,37 +136,43 @@ class TracedLayer(object):
model and convert it into a static graph model.
Args:
layer (paddle.fluid.dygraph.Layer): the layer object to be traced.
layer (dygraph.Layer): the layer object to be traced.
inputs (list(Variable)): the input variables of the layer object.
Returns:
A tuple of 2 items, whose the first item is the output of
tuple: A tuple of 2 items, whose the first item is the output of
:code:`layer(*inputs)` , and the second item is the created
TracedLayer object.
Examples:
.. code-block:: python:
import paddle.fluid as fluid
from paddle.fluid.dygraph import FC, to_variable, TracedLayer
import paddle.fluid.dygraph.jit as jit
from paddle.fluid.dygraph import Linear, to_variable, TracedLayer
import numpy as np
class ExampleLayer(fluid.dygraph.Layer):
def __init__(self, name_scope):
super(ExampleLayer, self).__init__(name_scope)
self._fc = FC(self.full_name(), 10)
def __init__(self):
super(ExampleLayer, self).__init__()
self._fc = Linear(3, 10)
def forward(self, input):
return self._fc(input)
with fluid.dygraph.guard():
layer = ExampleLayer("example_layer")
layer = ExampleLayer()
in_np = np.random.random([2, 3]).astype('float32')
in_var = to_variable(in_np)
out_dygraph, static_layer = TracedLayer.trace(layer, inputs=[in_var])
# run the static graph model using Executor inside
out_static_graph = static_layer([in_var])
print(len(out_static_graph)) # 1
print(out_static_graph[0].shape) # (2, 10)
# save the static graph model for inference
static_layer.save_inference_model(dirname='./saved_infer_model')
"""
outs, prog, feed, fetch = _trace(layer, inputs)
traced = TracedLayer(prog, layer.parameters(), feed, fetch)
@ -183,24 +192,22 @@ class TracedLayer(object):
None
Examples:
.. code-block:: python:
import paddle.fluid as fluid
from paddle.fluid.dygraph import FC, to_variable, TracedLayer
import paddle.fluid.dygraph.jit as jit
from paddle.fluid.dygraph import Linear, to_variable, TracedLayer
import numpy as np
class ExampleLayer(fluid.dygraph.Layer):
def __init__(self, name_scope):
super(ExampleLayer, self).__init__(name_scope)
self._fc = FC(self.full_name(), 10)
def __init__(self):
super(ExampleLayer, self).__init__()
self._fc = Linear(3, 10)
def forward(self, input):
return self._fc(input)
with fluid.dygraph.guard():
layer = ExampleLayer("example_layer")
layer = ExampleLayer()
in_np = np.random.random([2, 3]).astype('float32')
in_var = to_variable(in_np)
@ -257,7 +264,7 @@ class TracedLayer(object):
@switch_to_static_graph
def save_inference_model(self, dirname, feed=None, fetch=None):
"""
Save the TracedLayer to an model for inference. The saved
Save the TracedLayer to a model for inference. The saved
inference model can be loaded by C++ inference APIs.
Args:
@ -272,35 +279,41 @@ class TracedLayer(object):
model. Default None.
Returns:
The fetch variables' name list
Return Type:
list(str)
None
Examples:
.. code-block:: python:
import paddle.fluid as fluid
from paddle.fluid.dygraph import FC, to_variable, TracedLayer
import paddle.fluid.dygraph.jit as jit
from paddle.fluid.dygraph import Linear, to_variable, TracedLayer
import numpy as np
class ExampleLayer(fluid.dygraph.Layer):
def __init__(self, name_scope):
super(ExampleLayer, self).__init__(name_scope)
self._fc = FC(self.full_name(), 10)
def __init__(self):
super(ExampleLayer, self).__init__()
self._fc = Linear(3, 10)
def forward(self, input):
return self._fc(input)
with fluid.dygraph.guard():
layer = ExampleLayer("example_layer")
save_dirname = './saved_infer_model'
in_np = np.random.random([2, 3]).astype('float32')
with fluid.dygraph.guard():
layer = ExampleLayer()
in_var = to_variable(in_np)
out_dygraph, static_layer = TracedLayer.trace(layer, inputs=[in_var])
static_layer.save_inference_model('./saved_infer_model')
static_layer.save_inference_model(save_dirname, feed=[0], fetch=[0])
place = fluid.CPUPlace()
exe = fluid.Executor(place)
program, feed_vars, fetch_vars = fluid.io.load_inference_model(save_dirname,
exe)
fetch, = exe.run(program, feed={feed_vars[0]: in_np}, fetch_list=fetch_vars)
print(fetch.shape) # (2, 10)
"""
from paddle.fluid.io import save_inference_model
def get_feed_fetch(all_vars, partial_vars):
if partial_vars is None:
@ -317,7 +330,7 @@ class TracedLayer(object):
assert target_var is not None, "{} cannot be found".format(name)
target_vars.append(target_var)
return fluid_io.save_inference_model(
save_inference_model(
dirname=dirname,
feeded_var_names=feeded_var_names,
target_vars=target_vars,

2
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
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@ -27,7 +27,7 @@ from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
from utils import DyGraphProgramDescTracerTestHelper, is_equal_program
from paddle.fluid.dygraph.jit import TracedLayer
from paddle.fluid.dygraph import TracedLayer
class SimpleImgConvPool(fluid.dygraph.Layer):

2
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
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@ -21,7 +21,7 @@ from paddle.fluid.dygraph.nn import Embedding
import paddle.fluid.framework as framework
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph.jit import TracedLayer
from paddle.fluid.dygraph import TracedLayer
from test_imperative_base import new_program_scope
import numpy as np
import six

2
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
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@ -25,7 +25,7 @@ from paddle.fluid import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
from utils import DyGraphProgramDescTracerTestHelper, is_equal_program
from paddle.fluid.dygraph.jit import TracedLayer
from paddle.fluid.dygraph import TracedLayer
batch_size = 8
train_parameters = {

4
python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py
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@ -24,8 +24,6 @@ from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
import numpy as np
import six
from utils import DyGraphProgramDescTracerTestHelper, is_equal_program
from paddle.fluid.dygraph.jit import TracedLayer
class SimpleNet(fluid.Layer):
@ -121,8 +119,6 @@ class TestDygraphSimpleNet(unittest.TestCase):
dy_param_init = dict()
dy_loss = None
helper = DyGraphProgramDescTracerTestHelper(self)
program = None
backward_strategy = fluid.dygraph.BackwardStrategy()
backward_strategy.sort_sum_gradient = is_sort_sum_gradient

2
python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py
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@ -18,7 +18,7 @@ import unittest
import paddle.fluid as fluid
from paddle.fluid import Embedding, LayerNorm, Linear, Layer
from paddle.fluid.dygraph import to_variable, guard
from paddle.fluid.dygraph.jit import TracedLayer
from paddle.fluid.dygraph import TracedLayer
from test_imperative_base import new_program_scope
from paddle.fluid import core
import numpy as np

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