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Paddle/python/paddle/fluid/dygraph/io.py

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# Copyright (c) 2020 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 os
import six
import pickle
import numpy as np
import paddle
from paddle import compat as cpt
from paddle.fluid import core
from paddle.fluid import framework
from paddle.fluid import backward
from paddle.fluid import unique_name
from paddle.fluid.dygraph import layers
from paddle.fluid.layers import nn
from paddle.fluid.dygraph.base import switch_to_static_graph
__all__ = ['TranslatedLayer']
VARIABLE_FILENAME = "__variables__"
EXTRA_VAR_INFO_FILENAME = "__variables.info__"
LOADED_VAR_SUFFIX = "load"
PARAMETER_NAME_PREFIX = "param"
BUFFER_NAME_PREFIX = "buffer"
def _load_program_desc(model_file_path):
# 1. parse program desc
with open(model_file_path, "rb") as f:
program_desc_str = f.read()
program_desc = core.ProgramDesc(program_desc_str)
if not core._is_program_version_supported(program_desc._version()):
raise ValueError("Unsupported program version: %d\n" %
program_desc._version())
return program_desc
def _is_persistable(var_desc):
if var_desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var_desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var_desc.type() == core.VarDesc.VarType.READER or \
var_desc.type() == core.VarDesc.VarType.RAW:
return False
return var_desc.persistable()
def _is_parameter(persistable_var_desc, program_desc):
# 1. firstly, param should be input of op
input_ops = [] # op can be repeated
for block_idx in six.moves.range(program_desc.num_blocks()):
block = program_desc.block(block_idx)
for op_idx in six.moves.range(block.op_size()):
op = block.op(op_idx)
# NOTE: parameter is the input of a certain op
if persistable_var_desc.name() in op.input_arg_names():
input_ops.append(op)
# 2. secondly, param should not be output of op or be same op's output
for block_idx in six.moves.range(program_desc.num_blocks()):
block = program_desc.block(block_idx)
for op_idx in six.moves.range(block.op_size()):
op = block.op(op_idx)
if persistable_var_desc.name() in op.output_arg_names():
# such as batch_norm_op
if op in input_ops:
continue
else:
return False
return True
def _get_persistable_vars(program_desc):
persistable_vars = []
for i in six.moves.range(program_desc.num_blocks()):
block = program_desc.block(i)
persistable_vars.extend(list(filter(_is_persistable, block.all_vars())))
return persistable_vars
def _get_persistable_var_names(program_desc):
"""
Get all persistable variable names in ProgramDesc.
"""
var_names = []
persistable_vars = _get_persistable_vars(program_desc)
for var in persistable_vars:
var_names.append(var.name())
return var_names
def _get_all_var_names(program_desc):
all_var_names = set()
for i in six.moves.range(program_desc.num_blocks()):
block = program_desc.block(i)
for var in block.all_vars():
all_var_names.add(var.name())
return all_var_names
@switch_to_static_graph
def _append_loaded_suffix(name):
"""
Append loaded suffix to the given variable name
e.g. x ==> x.load_0, x.load_0 ==> x.load_0.load_0
"""
suffix = LOADED_VAR_SUFFIX
name = cpt.to_text(name)
new_name = unique_name.generate_with_ignorable_key('.'.join((name, suffix)))
return new_name
@switch_to_static_graph
def _generate_unique_var_name(prefix):
return unique_name.generate_with_ignorable_key(prefix)
def _append_loaded_suffix_to_var(program_desc):
suffix_varname_dict = dict()
persistable_vars = _get_persistable_vars(program_desc)
for var_desc in persistable_vars:
old_name = var_desc.name()
new_name = _append_loaded_suffix(var_desc.name())
suffix_varname_dict[new_name] = old_name
var_desc.set_name(new_name)
for block_idx in six.moves.range(program_desc.num_blocks()):
block = program_desc.block(block_idx)
for op_idx in six.moves.range(block.op_size()):
op = block.op(op_idx)
op._rename_input(old_name, new_name)
op._rename_output(old_name, new_name)
return suffix_varname_dict
@switch_to_static_graph
def _build_program_by_desc(program_desc):
prog = framework.Program()
prog.desc = program_desc
prog.blocks = [
framework.Block(prog, i)
for i in six.moves.range(prog.desc.num_blocks())
]
prog._sync_with_cpp()
return prog
def _change_is_test_status(program_desc, is_test):
# change all `is_test` attributes
for i in six.moves.range(program_desc.num_blocks()):
block = program_desc.block(i)
for j in six.moves.range(block.op_size()):
op = block.op(j)
if op.has_attr('is_test'):
op._set_attr('is_test', is_test)
class _ProgramHolder(object):
"""
Holds the execution information of a Program.
_ProgramHolder is the execution unit of TranslatedLayer,
if TranslatedLayer contains multiple _ProgramHolder,
it can execute multiple methods
_ProgramHolder is an internal concept.
"""
def __init__(self, program_desc):
super(_ProgramHolder, self).__init__()
# input, output, persistable var info
self._input_descs = []
self._output_descs = []
self._persistable_names = []
# execution scope
self._inner_scope = core.Scope()
# append suffix var name dict
self._suffix_varname_dict = None
# forward program
self._infer_program_desc = self._preprocess(program_desc)
# forward + backward program
self._train_program_desc = self._append_backward_desc(
self._infer_program_desc)
@property
def infer_program(self):
return self._infer_program_desc
@property
def train_program(self):
return self._train_program_desc
@property
def input_descs(self):
return self._input_descs
@property
def output_descs(self):
return self._output_descs
@property
def persistable_names(self):
return self._persistable_names
@property
def scope(self):
return self._inner_scope
def _preprocess(self, program_desc):
# 1. Prune original program
# remove feed, fetch and scale-1 op, remove op_callstack attr
ops_to_remove = []
root_block = program_desc.block(0)
for i in six.moves.range(root_block.op_size()):
op = root_block.op(i)
if op.type() == 'feed':
ops_to_remove.append(i)
feed_var_name = cpt.to_bytes(op.input('X')[0])
root_block._remove_var(feed_var_name)
self._input_descs.append(
root_block.find_var(cpt.to_bytes(op.output('Out')[0])))
elif op.type() == 'scale' and op.output('Out')[0].startswith(
'save_infer_model/scale_'):
ops_to_remove.append(i)
out_var_name = cpt.to_bytes(op.output('Out')[0])
root_block._remove_var(out_var_name)
self._output_descs.append(
root_block.find_var(cpt.to_bytes(op.input('X')[0])))
elif op.type() == 'fetch':
ops_to_remove.append(i)
fetch_var_name = cpt.to_bytes(op.output('Out')[0])
root_block._remove_var(fetch_var_name)
# NOTE: some old pre-train models have no extra scale_op
if not op.input('X')[0].startswith('save_infer_model/scale_'):
self._output_descs.append(
root_block.find_var(cpt.to_bytes(op.input('X')[0])))
else:
if op.has_attr("op_callstack"):
op.remove_attr("op_callstack")
for op_idx in reversed(ops_to_remove):
root_block._remove_op(op_idx, op_idx + 1)
# 2. Input processing, reverse feed vars
self._input_descs.reverse()
# 3. Output processing, add scale for outputs
tmp_program = _build_program_by_desc(program_desc)
# NOTE: [why need append scale for outputs]
# When dealing with some more complex pre-training models, there
# will be situations where the pre-training model has multiple
# fetch outputs. In the scenario of multiple fetch outputs,
# there is a special case where multiple outputs of the model
# may be on the same branch. According to the user's subsequent
# use, multiple outputs may be associated with multiple branches.
# These subsequent operations are added in TranslatedLayer is
# agnostic during initialization, which results in subsequent
# gradient accumulation operations that are required on the
# output node in the middle of the branch will not be performed,
# resulting in error, details see pull request:
# [https://github.com/PaddlePaddle/Paddle/pull/24627]
self._append_scale_to_output(tmp_program)
# 4. Persistable vars processing
# - append loaded suffix to persistable vars
# NOTE: [why need to append suffix to persistable vars]
# Dygraph and static graph mode use the same naming mechanism.
# If users want to load the model fine-tune, it is possible
# to add the existing Layer in the loaded model to enhance
# the network. For example, the original saved model has linear,
# and later after loading, a new linear is added. At this time,
# there will be a problem of duplicate names, so here is unified
# to add the LOADED suffix to the parameters of the model loaded
self._suffix_varname_dict = _append_loaded_suffix_to_var(program_desc)
# - get persistable var
self._persistable_names = _get_persistable_var_names(program_desc)
return program_desc
@switch_to_static_graph
def _append_scale_to_output(self, program):
# 1. append scale & save var
scale_output_vars = []
with framework.program_guard(program):
for i, out in enumerate(self._output_descs):
var = program.global_block().var(out.name())
var = nn.scale(
var, 1., name="translated_layer/scale_{}".format(i))
scale_output_vars.append(var)
# 2. update output names & descs
for i, var in enumerate(scale_output_vars):
self._output_descs[i] = var.desc
@switch_to_static_graph
def _append_backward_desc(self, infer_program_desc):
program_desc_copy = core.ProgramDesc(infer_program_desc)
# 1. set all `is_test` attributes to False
_change_is_test_status(program_desc_copy, False)
# 2. prepare program and related var
# NOTE: To reuse backward interfaces, build Program firstly.
# Originally, there is no need to build a program, but need to almost
# rewrite a series of methods for append_backward for program_desc.
# Therefore, in order to reuse the method of backward.py, build the program here.
program = _build_program_by_desc(program_desc_copy)
targets = []
for out in self._output_descs:
targets.append(program.global_block().var(out.name()))
# 3. append backward
backward.gradients(targets=targets, inputs=[])
return program.desc
# [ TranslatedLayer : Run program in imperative mode ]
#
# DESIGN IDEA: using an special operator `RunProgram`, execute program inside operator.
#
# Op's Inputs:
# - the input variable of the user feed
# - the necessary parameters of the network
# Op's Outputs:
# - the output variable of fetch
#
# This op receives a complete program desc, internally creates scope
# and executor, executes this program. Key points:
#
# 1. Data Sharing:
# The varBase of the dynamic graph is not in the scope, so before the op
# executes the program internally, create persistent variables with the
# same name as feed, parameters, and fetch in the scope, and share the
# LoDTensor of the op input.
#
# 2. Forward and Backward Separation:
# Because the dynamic graph op performs the forward and backward separately,
# in the forward op RunProgram, we only execute the forward part of whole program,
# and in the backward op RunProgramGrad, we execute the backward part of program.
# We can not separate the program into forward and backward part, which will
# make some control flow execution logic wrong.
# NOTE: [compatible] deal with model saved by save_inference_model,
# which need get var info from program desc
def _load_persistable_vars_by_program(model_path,
program_holder,
params_filename=None):
# make sure the path has been checked
persistable_vars = _get_persistable_vars(program_holder.infer_program)
load_var_dict = {}
for each_var in persistable_vars:
orig_each_name = program_holder._suffix_varname_dict[each_var.name()]
if _is_parameter(each_var, program_holder.infer_program):
# create output varbase
new_var = framework.ParamBase(
shape=each_var.shape(),
dtype=each_var.dtype(),
name=each_var.name(),
type=each_var.type(),
persistable=True)
else:
new_var = framework._varbase_creator(
type=each_var.type(),
name=each_var.name(),
shape=each_var.shape(),
dtype=each_var.dtype(),
persistable=True)
if params_filename is None:
framework._dygraph_tracer().trace_op(
type='load',
inputs={},
outputs={'Out': new_var},
attrs={'file_path': os.path.join(model_path, orig_each_name)})
new_var.stop_gradient = False
load_var_dict[each_var.name()] = new_var
if params_filename is not None:
load_var_list = []
for name in sorted(load_var_dict.keys()):
load_var_list.append(load_var_dict[name])
framework._dygraph_tracer().trace_op(
type='load_combine',
inputs={},
outputs={'Out': load_var_list},
attrs={'file_path': os.path.join(model_path, params_filename)})
for each_var in persistable_vars:
if not _is_parameter(each_var, program_holder.infer_program):
continue
param = load_var_dict[each_var.name()]
param.stop_gradient = False
# NOTE: [Recovery stop gradient information based on the program]
# After loading the model, the stop_gradient information
# of the original variable is lost, but if a parameter does not
# have a corresponding @GRAD variable in the backward program,
# it can be said that it is also stop_gradient
all_var_names = _get_all_var_names(program_holder.train_program)
for var_name in load_var_dict:
grad_var_name = var_name + core.grad_var_suffix()
if grad_var_name not in all_var_names:
load_var_dict[var_name].stop_gradient = True
return load_var_dict
def _load_persistable_vars(model_path,
var_info_path,
program_holder,
separate_params=False,
params_filename=None):
# 1. load extra var info
with open(var_info_path, 'rb') as f:
extra_var_info = pickle.load(f)
# 2. construct var dict
load_var_dict = dict()
load_var_list = []
inv_suffix_varname_dict = {
value: key
for key, value in program_holder._suffix_varname_dict.items()
}
# NOTE(chenweihang): we need load persistable vars based the program,
# because the program may be pruned when `save_inference_model`, some
# var in `extra_var_info` may have been pruned
for name in sorted(inv_suffix_varname_dict):
if name not in extra_var_info:
raise RuntimeError(
"The model to be loaded is not complete."
"The variable `%s` of program cannot be found in loaded model.",
name)
# get suffix var name, see [why need to append suffix to persistable vars]
new_name = inv_suffix_varname_dict[name]
# create output varbase
if extra_var_info[name].get('trainable', None) is not None:
# use default shape and dtype
new_var = framework.ParamBase(
shape=[1], # only to pass check, this shape is not meaningful
dtype=core.VarDesc.VarType.FP32,
name=new_name,
persistable=True)
else:
new_var = framework._varbase_creator(
name=new_name, persistable=True)
# load separate vars
if separate_params is True:
framework._dygraph_tracer().trace_op(
type='load',
inputs={},
outputs={'Out': new_var},
attrs={'file_path': os.path.join(model_path, name)})
new_var.stop_gradient = extra_var_info[name]['stop_gradient']
load_var_dict[new_name] = new_var
load_var_list.append(new_var)
# 3. load all vars
if separate_params is False:
if params_filename is not None:
var_file_path = os.path.join(model_path, params_filename)
else:
var_file_path = os.path.join(model_path, VARIABLE_FILENAME)
if not os.path.exists(var_file_path):
if len(extra_var_info) != 0:
raise ValueError("The model to be loaded is incomplete.")
else:
framework._dygraph_tracer().trace_op(
type='load_combine',
inputs={},
outputs={'Out': load_var_list},
attrs={'file_path': var_file_path})
return load_var_dict
# NOTE(chenweihang): to adapt paddle.load to get state_dict
def _remove_varname_suffix(var_dict, program_holder):
no_suffix_var_dict = dict()
for var_name in var_dict:
no_suffix_name = program_holder._suffix_varname_dict[var_name]
no_suffix_var_dict[no_suffix_name] = var_dict[var_name]
return no_suffix_var_dict
def _construct_program_holders(model_path, model_filename=None):
# make sure the path has been checked
program_holder_dict = dict()
if model_filename is not None:
# [compatible] if assign model_filename, only can load one program as Layer.forward
model_filename = os.path.basename(model_filename)
model_file_path = os.path.join(model_path, model_filename)
program_holder_dict['forward'] = _ProgramHolder(
_load_program_desc(model_file_path))
else:
for _, _, file_names in os.walk(model_path):
for name in file_names:
if 'model' in name:
model_file_path = os.path.join(model_path, name)
method_name = name.strip('_')
if method_name == 'model':
method_name = 'forward'
else:
method_name.replace('model', '')
program_holder_dict[method_name] = _ProgramHolder(
_load_program_desc(model_file_path))
return program_holder_dict
def _construct_params_and_buffers(model_path,
programs,
separate_params=False,
params_filename=None,
append_suffix=True):
var_info_path = os.path.join(model_path, EXTRA_VAR_INFO_FILENAME)
if os.path.exists(var_info_path):
var_dict = _load_persistable_vars(model_path, var_info_path,
programs['forward'], separate_params,
params_filename)
else:
var_dict = _load_persistable_vars_by_program(
model_path, programs['forward'], params_filename)
if not append_suffix:
var_dict = _remove_varname_suffix(var_dict, programs['forward'])
return var_dict
class TranslatedLayer(layers.Layer):
"""
TranslatedLayer is a imperative Layer for holding the model loaded by
:ref:`api_imperative_jit_load` . It can be used like a general Layer
object in eval or train mode.
.. note:
The TranslatedLayer objects should not be created by constructor, it only can be loaded and constructed by :ref:`api_imperative_jit_load` .
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn as nn
import paddle.optimizer as opt
BATCH_SIZE = 16
BATCH_NUM = 4
EPOCH_NUM = 4
IMAGE_SIZE = 784
CLASS_NUM = 10
# define a random dataset
class RandomDataset(paddle.io.Dataset):
def __init__(self, num_samples):
self.num_samples = num_samples
def __getitem__(self, idx):
image = np.random.random([IMAGE_SIZE]).astype('float32')
label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
return image, label
def __len__(self):
return self.num_samples
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
@paddle.jit.to_static
def forward(self, x):
return self._linear(x)
def train(layer, loader, loss_fn, opt):
for epoch_id in range(EPOCH_NUM):
for batch_id, (image, label) in enumerate(loader()):
out = layer(image)
loss = loss_fn(out, label)
loss.backward()
opt.step()
opt.clear_grad()
print("Epoch {} batch {}: loss = {}".format(
epoch_id, batch_id, np.mean(loss.numpy())))
# enable dygraph mode
place = paddle.CPUPlace()
paddle.disable_static(place)
# 1. train & save model.
# create network
layer = LinearNet()
loss_fn = nn.CrossEntropyLoss()
adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters())
# create data loader
dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
loader = paddle.io.DataLoader(dataset,
places=place,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=2)
# train
train(layer, loader, loss_fn, adam)
# save
model_path = "linear.example.model"
paddle.jit.save(layer, model_path)
# 2. load model as TranslatedLayer
# load
translated_layer = paddle.jit.load(model_path)
# inference
translated_layer.eval()
x = paddle.randn([1, IMAGE_SIZE], 'float32')
pred = translated_layer(x)
# fine-tune
translated_layer.train()
adam = opt.Adam(learning_rate=0.001, parameters=translated_layer.parameters())
train(translated_layer, loader, loss_fn, adam)
"""
def __init__(self, programs, persistable_vars):
super(TranslatedLayer, self).__init__()
if not isinstance(programs, dict):
raise TypeError(
"TranslatedLayer need to use _ProgramHolder's dict for initialization."
)
if not isinstance(persistable_vars, dict):
raise TypeError(
"TranslatedLayer need to use persistable variable dict for initialization."
)
self._program_holder_dict = programs
# NOTE(chenweihang): [ why not use var name directly? ]
# When add parameter or buffer to Layer by follow apis,
# the variable name can't contain `.`, beccause which may cause
# AttributeError when access the newly added parameter or buffer
# in the form of `self.**.**``, but the ParamBase or BarBase
# name contains `.` originally, such as `linear_0.w_0`, so here
# need to generate new var name for each var
self._persistable_var_name_dict = dict()
# the TranslatedLayer object holded var names count started from 0
with unique_name.guard():
for name, var in persistable_vars.items():
if isinstance(var, framework.ParamBase):
dy_name = _generate_unique_var_name(PARAMETER_NAME_PREFIX)
self._persistable_var_name_dict[name] = dy_name
self.add_parameter(dy_name, var)
elif isinstance(var, core.VarBase):
dy_name = _generate_unique_var_name(BUFFER_NAME_PREFIX)
self._persistable_var_name_dict[name] = dy_name
self.register_buffer(dy_name, var)
else:
raise TypeError(
"Adding persistent variable which to layer is not supported now"
)
self._is_test = True
@staticmethod
@framework.dygraph_only
def _construct(model_path, configs=None):
# 0. dir and filename check
model_path = os.path.normpath(model_path)
if not os.path.isdir(model_path):
raise ValueError("There is no directory named '%s'" % model_path)
model_filename = None
params_filename = None
separate_params = False
if configs is not None:
model_filename = configs.model_filename
params_filename = configs.params_filename
separate_params = configs.separate_params
# 1. load program desc & construct _ProgramHolder
programs = _construct_program_holders(model_path, model_filename)
# 2. load layer parameters & buffers
persistable_vars = _construct_params_and_buffers(
model_path, programs, separate_params, params_filename)
# 3. construct TranslatedLayer object
translated_layer = TranslatedLayer(programs, persistable_vars)
# 4. create TranslatedLayer's execution method
for method_name, program_holder in programs.items():
setattr(TranslatedLayer, method_name,
TranslatedLayer._execution_method_creator(method_name,
program_holder))
# 5. set TranslatedLayer's default mode to eval
translated_layer.eval()
return translated_layer
@staticmethod
def _execution_method_creator(method_name, program_holder):
def __impl__(self, *input):
# 1. prepare inputs, outputs, attrs
input_vars = []
for i, value in enumerate(input):
if not isinstance(value, (np.ndarray, core.VarBase)):
raise TypeError(
"The type of input in TranslatedLayer must be numpy array or Variable(VarBase), but received %s."
% type(value))
# NOTE: In order to unify the API, firstly convert the input to VarBase
if isinstance(value, np.ndarray):
var = core.VarBase(
value=value,
name=program_holder.input_descs[i].name(),
persistable=False,
place=framework._current_expected_place(),
zero_copy=True)
else:
var = value
# NOTE: we changed var name here,
# but it may be an important name set by user
var.name = program_holder.input_descs[i].name()
input_vars.append(var)
persistable_vars = []
for var_name in program_holder.persistable_names:
dy_var_name = self._persistable_var_name_dict[var_name]
if dy_var_name in self._parameters:
persistable_vars.append(self._parameters[dy_var_name])
elif dy_var_name in self._buffers:
persistable_vars.append(self._buffers[dy_var_name])
else:
raise ValueError(
"The persistable variable %s is not exists in current TranslatedLayer."
% var_name)
output_vars = []
for var_desc in program_holder.output_descs:
var = core.VarBase(var_desc.dtype(),
var_desc.shape(),
var_desc.name(), var_desc.type(), False)
output_vars.append(var)
# hold forward variables
tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [],
"program_out_scope",
core.VarDesc.VarType.STEP_SCOPES, True)
tmp_scope_vec.value().set_scope(program_holder.scope)
# 2. run program by op
trace_program = program_holder.infer_program if self._is_test else program_holder.train_program
end_op_index = program_holder.infer_program.block(0).op_size()
framework._dygraph_tracer().trace_op(
type='run_program',
inputs={'X': input_vars,
'Params': persistable_vars},
outputs={'Out': output_vars,
'OutScope': tmp_scope_vec},
attrs={
'global_block': trace_program.block(0),
'start_op_index': 0,
'end_op_index': end_op_index,
'is_test': self._is_test
})
# NOTE: [ why need set param's gradient type here ]
# if user set sparse gradient mode, the param's gradient
# will be SelectedRows, not LoDTensor. But tracer will just
# set param grad VarBase by forward VarBase(LoDTensor)
# If we don't change grad_var type here, RunProgramOp need
# transform SelectedRows to LoDTensor forcibly, it may not
# be user wanted result.
for persistable_var in persistable_vars:
grad_var_name = var.name + core.grad_var_suffix()
grad_var = trace_program.block(0).find_var(
cpt.to_bytes(grad_var_name))
# NOTE: cannot find var desc maybe not problem,
# such as in batch_norm
if grad_var is None:
continue
persistable_var._set_grad_type(grad_var.type())
# 3. prepare output, keep same form with inputs
outs = output_vars
if len(output_vars) == 1:
outs = output_vars[0]
return outs
__impl__.__name__ = method_name
return __impl__
def train(self):
self._is_test = False
def eval(self):
self._is_test = True
def program(self, method_name='forward'):
"""
Gets translated program of specified method.
Args:
- method_name (string): mehtod name corresponding to the program
to be obtained. Default: 'forward'.
Returns:
Program
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn as nn
import paddle.optimizer as opt
BATCH_SIZE = 16
BATCH_NUM = 4
EPOCH_NUM = 4
IMAGE_SIZE = 784
CLASS_NUM = 10
# define a random dataset
class RandomDataset(paddle.io.Dataset):
def __init__(self, num_samples):
self.num_samples = num_samples
def __getitem__(self, idx):
image = np.random.random([IMAGE_SIZE]).astype('float32')
label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
return image, label
def __len__(self):
return self.num_samples
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
@paddle.jit.to_static
def forward(self, x):
return self._linear(x)
def train(layer, loader, loss_fn, opt):
for epoch_id in range(EPOCH_NUM):
for batch_id, (image, label) in enumerate(loader()):
out = layer(image)
loss = loss_fn(out, label)
loss.backward()
opt.step()
opt.clear_grad()
print("Epoch {} batch {}: loss = {}".format(
epoch_id, batch_id, np.mean(loss.numpy())))
# enable dygraph mode
place = paddle.CPUPlace()
paddle.disable_static(place)
# create network
layer = LinearNet()
loss_fn = nn.CrossEntropyLoss()
adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters())
# create data loader
dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
loader = paddle.io.DataLoader(dataset,
places=place,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=2)
# train
train(layer, loader, loss_fn, adam)
# save
model_path = "linear.example.model"
paddle.jit.save(layer, model_path)
# load
translated_layer = paddle.jit.load(model_path)
# get program
program = translated_layer.program()
"""
# 1. get program holder
program_holder = self._get_program_holder(method_name)
# 2. get inference program desc
program_desc = program_holder.infer_program
# 3. construct program
program = _build_program_by_desc(program_desc)
return program
def _get_program_holder(self, method_name='forward'):
program_holder = self._program_holder_dict.get(method_name, None)
if program_holder is None:
raise ValueError(
"The method `%s` does not exist in loaded TranslatedLayer." %
method_name)
return program_holder
def _input_spec(self, method_name='forward'):
# 1. get program holder
program_holder = self._get_program_holder(method_name)
# 2. build input spec by input desc
input_spec = []
for var_desc in program_holder.input_descs:
spec = paddle.static.InputSpec(
shape=var_desc.shape(),
dtype=var_desc.dtype(),
name=var_desc.name())
input_spec.append(spec)
return input_spec
def _output_spec(self, method_name='forward'):
# 1. get program holder
program_holder = self._get_program_holder(method_name)
# 2. build output spec by output desc
output_spec = []
for var_desc in program_holder.output_descs:
# NOTE(chenweihang): InputSpec describes a tensor, not just input.
# Maybe the name is not good enough. Here we use InputSpec to
# construct the description of Output tensor
spec = paddle.static.InputSpec(
shape=var_desc.shape(),
dtype=var_desc.dtype(),
name=var_desc.name())
output_spec.append(spec)
return output_spec
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