You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
463 lines
16 KiB
463 lines
16 KiB
# 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
|
|
|
|
__all__ = ['TracedLayer', 'declarative', 'dygraph_to_static_func']
|
|
|
|
import warnings
|
|
from paddle.fluid import core
|
|
from paddle.fluid.compiler import CompiledProgram
|
|
from paddle.fluid.dygraph.base import program_desc_tracing_guard, switch_to_static_graph
|
|
from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator
|
|
from paddle.fluid.dygraph.layers import Layer
|
|
from paddle.fluid.executor import Executor, scope_guard
|
|
from paddle.fluid.framework import Program, Block, Variable, _dygraph_tracer, dygraph_only, _dygraph_guard, _current_expected_place, in_dygraph_mode
|
|
from paddle.fluid.wrapped_decorator import wrap_decorator
|
|
|
|
|
|
def create_program_from_desc(program_desc):
|
|
program = Program()
|
|
program.desc = program_desc
|
|
program.blocks = [Block(program, 0)]
|
|
program._sync_with_cpp()
|
|
return program
|
|
|
|
|
|
def _extract_vars(inputs, result_list):
|
|
if isinstance(inputs, Variable):
|
|
result_list.append(inputs)
|
|
|
|
if isinstance(inputs, (list, tuple)):
|
|
for var in inputs:
|
|
_extract_vars(var, result_list)
|
|
|
|
|
|
def extract_vars(inputs):
|
|
result_list = []
|
|
_extract_vars(inputs, result_list)
|
|
return result_list
|
|
|
|
|
|
def _dygraph_to_static_func_(dygraph_func):
|
|
"""
|
|
Converts imperative dygraph APIs into declarative function APIs. Decorator
|
|
@dygraph_to_static_func only converts imperative dygraph APIs into
|
|
declarative net-building APIs, which means it doesn't return immediate
|
|
digital result as imperative mode. Users should handle Program and Executor
|
|
by themselves.
|
|
|
|
Note:
|
|
This decorator is NOT our recommended way to transform imperative function
|
|
to declarative function. We will remove this decorator after we finalize
|
|
cleaning up code.
|
|
|
|
Args:
|
|
dygraph_func (callable): callable imperative function.
|
|
|
|
Returns:
|
|
Callable: converting imperative dygraph APIs into declarative
|
|
net-building APIs.
|
|
|
|
Examples:
|
|
.. code-block:: python
|
|
|
|
import paddle.fluid as fluid
|
|
import numpy as np
|
|
from paddle.fluid.dygraph.jit import dygraph_to_static_func
|
|
|
|
@dygraph_to_static_func
|
|
def func(x):
|
|
if fluid.layers.mean(x) < 0:
|
|
x_v = x - 1
|
|
else:
|
|
x_v = x + 1
|
|
|
|
return x_v
|
|
|
|
x = fluid.layers.fill_constant(shape=[3, 3], value=0, dtype='float64')
|
|
|
|
x_v = func(x)
|
|
exe = fluid.Executor(fluid.CPUPlace())
|
|
out = exe.run(fetch_list=[x_v])
|
|
print(out[0])
|
|
# [[1. 1. 1.]
|
|
# [1. 1. 1.]
|
|
# [1. 1. 1.]]
|
|
|
|
"""
|
|
|
|
# TODO: remove this decorator after we finalize training API
|
|
def __impl__(*args, **kwargs):
|
|
program_translator = ProgramTranslator()
|
|
if in_dygraph_mode() or not program_translator.enable_declarative:
|
|
warnings.warn(
|
|
"The decorator 'dygraph_to_static_func' doesn't work in "
|
|
"dygraph mode or set ProgramTranslator.enable to False. "
|
|
"We will just return dygraph output.")
|
|
return dygraph_func(*args, **kwargs)
|
|
static_func = program_translator.get_func(dygraph_func)
|
|
return static_func(*args, **kwargs)
|
|
|
|
return __impl__
|
|
|
|
|
|
dygraph_to_static_func = wrap_decorator(_dygraph_to_static_func_)
|
|
|
|
|
|
def _declarative_(dygraph_func):
|
|
"""
|
|
Converts imperative dygraph APIs into declarative function APIs. Decorator
|
|
@declarative handles the Program and Executor of static mode and returns
|
|
the result as a dygraph VarBase.
|
|
|
|
Args:
|
|
dygraph_func (callable): callable imperative function.
|
|
|
|
Returns:
|
|
VarBase: containing the numerical result.
|
|
|
|
Examples:
|
|
.. code-block:: python
|
|
|
|
import paddle.fluid as fluid
|
|
import numpy as np
|
|
from paddle.fluid.dygraph.jit import declarative
|
|
|
|
|
|
@declarative
|
|
def func(x):
|
|
x = fluid.dygraph.to_variable(x)
|
|
if fluid.layers.mean(x) < 0:
|
|
x_v = x - 1
|
|
else:
|
|
x_v = x + 1
|
|
return x_v
|
|
|
|
x = np.ones([1, 2])
|
|
x_v = func(x)
|
|
print(x_v.numpy()) # [[2. 2.]]
|
|
|
|
"""
|
|
|
|
def __impl__(*args, **kwargs):
|
|
program_translator = ProgramTranslator()
|
|
if not program_translator.enable_declarative:
|
|
warnings.warn(
|
|
"The decorator 'declarative' doesn't work when setting ProgramTranslator.enable=False. "
|
|
"We will just return dygraph output.")
|
|
return dygraph_func(*args, **kwargs)
|
|
return program_translator.get_output(dygraph_func, *args, **kwargs)
|
|
|
|
return __impl__
|
|
|
|
|
|
declarative = wrap_decorator(_declarative_)
|
|
|
|
|
|
@dygraph_only
|
|
def _trace(layer,
|
|
inputs,
|
|
feed_prefix='feed_',
|
|
fetch_prefix='fetch_',
|
|
tmp_prefix='t_'):
|
|
assert isinstance(layer, Layer)
|
|
|
|
if not isinstance(inputs, (list, tuple)):
|
|
inputs = [inputs]
|
|
|
|
tracer = _dygraph_tracer()._get_program_desc_tracer()
|
|
|
|
var_list = extract_vars(inputs)
|
|
|
|
with program_desc_tracing_guard(True):
|
|
original_outputs = layer(*inputs)
|
|
if not isinstance(original_outputs, (list, tuple)):
|
|
outputs = [original_outputs]
|
|
else:
|
|
outputs = original_outputs
|
|
out_vars = [var for var in outputs]
|
|
|
|
program_desc, feed_names, fetch_names, parameters = tracer.create_program_desc(
|
|
var_list, feed_prefix, out_vars, fetch_prefix, tmp_prefix)
|
|
tracer.reset()
|
|
|
|
with _dygraph_guard(None):
|
|
program = create_program_from_desc(program_desc)
|
|
|
|
return original_outputs, program, feed_names, fetch_names, parameters
|
|
|
|
|
|
class TracedLayer(object):
|
|
"""
|
|
:api_attr: imperative
|
|
|
|
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)` .
|
|
|
|
The TracedLayer can only be used to convert the data-independent dygraph
|
|
model into the static graph model, which means the dygraph model should
|
|
be independent with the tensor data and shape.
|
|
"""
|
|
|
|
def __init__(self, program, parameters, feed_names, fetch_names):
|
|
self._program = program
|
|
self._feed_names = feed_names
|
|
self._fetch_names = fetch_names
|
|
self._params = parameters
|
|
|
|
self._place = _current_expected_place()
|
|
|
|
self._scope = core.Scope()
|
|
for p in parameters:
|
|
src_tensor = p.value().get_tensor()
|
|
dst_tensor = self._scope.var(p.name).get_tensor()
|
|
dst_tensor._share_data_with(src_tensor)
|
|
|
|
self._exe = Executor(self._place)
|
|
self._compiled_program = None
|
|
self._build_strategy = None
|
|
self._exec_strategy = None
|
|
|
|
@property
|
|
def program(self):
|
|
return self._program
|
|
|
|
def _switch(self, is_test=True):
|
|
for block_id in range(self._program.num_blocks):
|
|
block = self._program.block(block_id)
|
|
for op in block.ops:
|
|
if op.has_attr("is_test"):
|
|
op._set_attr("is_test", is_test)
|
|
|
|
@staticmethod
|
|
@dygraph_only
|
|
def trace(layer, inputs):
|
|
"""
|
|
This method is the only allowed method to create TracedLayer object.
|
|
It would call the :code:`layer(*inputs)` method to run the dygraph
|
|
model and convert it into a static graph model.
|
|
|
|
Args:
|
|
layer (dygraph.Layer): the layer object to be traced.
|
|
inputs (list(Variable)): the input variables of the layer object.
|
|
|
|
Returns:
|
|
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 Linear, to_variable, TracedLayer
|
|
import numpy as np
|
|
|
|
class ExampleLayer(fluid.dygraph.Layer):
|
|
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()
|
|
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, parameters = _trace(layer, inputs)
|
|
traced = TracedLayer(prog, parameters, feed, fetch)
|
|
return outs, traced
|
|
|
|
def set_strategy(self, build_strategy=None, exec_strategy=None):
|
|
"""
|
|
Set the strategies when running static graph model.
|
|
|
|
Args:
|
|
build_strategy (BuildStrategy, optional): build strategy of
|
|
:code:`CompiledProgram` inside TracedLayer. Default None.
|
|
exec_strategy (ExecutionStrategy, optional): execution strategy of
|
|
:code:`CompiledProgram` inside TracedLayer. Default None.
|
|
|
|
Returns:
|
|
None
|
|
|
|
Examples:
|
|
.. code-block:: python:
|
|
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.dygraph import Linear, to_variable, TracedLayer
|
|
import numpy as np
|
|
|
|
class ExampleLayer(fluid.dygraph.Layer):
|
|
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()
|
|
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])
|
|
|
|
build_strategy = fluid.BuildStrategy()
|
|
build_strategy.enable_inplace = True
|
|
|
|
exec_strategy = fluid.ExecutionStrategy()
|
|
exec_strategy.num_threads = 2
|
|
|
|
static_layer.set_strategy(build_strategy=build_strategy, exec_strategy=exec_strategy)
|
|
out_static_graph = static_layer([in_var])
|
|
"""
|
|
assert self._compiled_program is None, "Cannot set strategy after run"
|
|
self._build_strategy = build_strategy
|
|
self._exec_strategy = exec_strategy
|
|
|
|
@switch_to_static_graph
|
|
def _compile(self):
|
|
self._compiled_program = CompiledProgram(
|
|
self._program).with_data_parallel(
|
|
build_strategy=self._build_strategy,
|
|
exec_strategy=self._exec_strategy,
|
|
places=self._place)
|
|
|
|
def _build_feed(self, inputs):
|
|
assert isinstance(inputs, (list, tuple)), \
|
|
"Inputs should be a list or tuple of variables"
|
|
assert len(inputs) == len(self._feed_names)
|
|
feed_dict = {}
|
|
if in_dygraph_mode():
|
|
for x, name in zip(inputs, self._feed_names):
|
|
feed_dict[name] = x.value().get_tensor()
|
|
else:
|
|
for x, name in zip(inputs, self._feed_names):
|
|
feed_dict[name] = x
|
|
|
|
return feed_dict
|
|
|
|
@switch_to_static_graph
|
|
def _run(self, feed):
|
|
return self._exe.run(self._compiled_program,
|
|
feed=feed,
|
|
fetch_list=self._fetch_names)
|
|
|
|
def __call__(self, inputs):
|
|
with scope_guard(self._scope):
|
|
if self._compiled_program is None:
|
|
self._compile()
|
|
|
|
return self._run(self._build_feed(inputs))
|
|
|
|
@switch_to_static_graph
|
|
def save_inference_model(self, dirname, feed=None, fetch=None):
|
|
"""
|
|
Save the TracedLayer to a model for inference. The saved
|
|
inference model can be loaded by C++ inference APIs.
|
|
|
|
Args:
|
|
dirname (str): the directory to save the inference model.
|
|
feed (list[int], optional): the input variable indices of the saved
|
|
inference model. If None, all input variables of the
|
|
TracedLayer object would be the inputs of the saved inference
|
|
model. Default None.
|
|
fetch (list[int], optional): the output variable indices of the
|
|
saved inference model. If None, all output variables of the
|
|
TracedLayer object would be the outputs of the saved inference
|
|
model. Default None.
|
|
|
|
Returns:
|
|
None
|
|
|
|
Examples:
|
|
.. code-block:: python:
|
|
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.dygraph import Linear, to_variable, TracedLayer
|
|
import numpy as np
|
|
|
|
class ExampleLayer(fluid.dygraph.Layer):
|
|
def __init__(self):
|
|
super(ExampleLayer, self).__init__()
|
|
self._fc = Linear(3, 10)
|
|
|
|
def forward(self, input):
|
|
return self._fc(input)
|
|
|
|
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(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:
|
|
return all_vars
|
|
|
|
return [all_vars[idx] for idx in partial_vars]
|
|
|
|
with scope_guard(self._scope):
|
|
feeded_var_names = get_feed_fetch(self._feed_names, feed)
|
|
target_var_names = get_feed_fetch(self._fetch_names, fetch)
|
|
target_vars = []
|
|
for name in target_var_names:
|
|
target_var = self._program.global_block().vars.get(name, None)
|
|
assert target_var is not None, "{} cannot be found".format(name)
|
|
target_vars.append(target_var)
|
|
|
|
save_inference_model(
|
|
dirname=dirname,
|
|
feeded_var_names=feeded_var_names,
|
|
target_vars=target_vars,
|
|
executor=self._exe,
|
|
main_program=self._program.clone())
|