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未验证 提交 bfc64801 编写于 作者: D Difer 提交者： GitHub
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repacle fluid.io.load_inference_model, fluid.io.save_inference_model in fluid... 

repacle fluid.io.load_inference_model, fluid.io.save_inference_model in fluid with 2.0 version  (#55345)

* repacle fluid.io.load_inference_model

* replace fluid.io.save_inference_model

* fix some bug

* fix some bugs of load & save model

* fix some bug

* fix test_inference_model_io bug

* fix word2vec_inference_model bug

* fix some bug

* fix valueError bug

* fix some bug

* fix a warning error

* for debug

* for debug

* fix io error

* fix test_wordvec_book error

* remove debug print

* fix load_var bug

* for debug cinn test

* revert cinn & fix inference_pass_test in windows

* fix some bugs

* revert cinn & fix inference_pass_test in windows

* for debug vars

* for debug

* fix quant_dequant_test

* fix some path errors

* remove fluid save/load

* fix incubate-fleet save

* move some from fluid.io to static.io
上级 eafc9889
隐藏空白更改
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Showing with 362 addition and 671 deletion
python/paddle/distributed/fleet/runtime/parameter_server_runtime.py
浏览文件 @ bfc64801
......@@ -714,7 +714,7 @@ class ParameterServerRuntime(RuntimeBase):
self,
executor,
dirname,
feeded_var_names,
feeded_vars,
target_vars,
main_program=None,
export_for_deployment=True,
......@@ -735,28 +735,21 @@ class ParameterServerRuntime(RuntimeBase):
raise TypeError(
"in fleet.save_inference_model() function, main_program must be as Program type, CompiledProgram is not allowed"
)
paddle.fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
dirname,
feeded_var_names,
feeded_vars,
target_vars,
executor,
main_program,
None,
None,
export_for_deployment,
program=main_program,
legacy_format=legacy_format,
)
else:
paddle.fluid.io.save_inference_model(
paddle.static.save_inference_model(
dirname,
feeded_var_names,
feeded_vars,
target_vars,
executor,
self.origin_main_program,
None,
None,
export_for_deployment,
True,
program=self.origin_main_program,
legacy_format=legacy_format,
)
......
python/paddle/fluid/io.py
浏览文件 @ bfc64801
......@@ -59,448 +59,9 @@ from . import core
from paddle.utils import deprecated
from paddle.fluid.framework import static_only
__all__ = [
'save_inference_model',
'load_inference_model',
] + reader.__all__
__all__ = reader.__all__
_logger = get_logger(
__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s'
)
def prepend_feed_ops(
inference_program, feed_target_names, feed_holder_name='feed'
):
if len(feed_target_names) == 0:
return
global_block = inference_program.global_block()
feed_var = global_block.create_var(
name=feed_holder_name,
type=core.VarDesc.VarType.FEED_MINIBATCH,
persistable=True,
)
for i, name in enumerate(feed_target_names):
if not global_block.has_var(name):
raise ValueError(
"The feeded_var_names[{i}]: '{name}' doesn't exist in pruned inference program. "
"Please check whether '{name}' is a valid feed_var name, or remove it from feeded_var_names "
"if '{name}' is not involved in the target_vars calculation.".format(
i=i, name=name
)
)
out = global_block.var(name)
global_block._prepend_op(
type='feed',
inputs={'X': [feed_var]},
outputs={'Out': [out]},
attrs={'col': i},
)
def append_fetch_ops(
inference_program, fetch_target_names, fetch_holder_name='fetch'
):
global_block = inference_program.global_block()
fetch_var = global_block.create_var(
name=fetch_holder_name,
type=core.VarDesc.VarType.FETCH_LIST,
persistable=True,
)
for i, name in enumerate(fetch_target_names):
global_block.append_op(
type='fetch',
inputs={'X': [name]},
outputs={'Out': [fetch_var]},
attrs={'col': i},
)
@static_only
@deprecated(since="2.0.0", update_to="paddle.static.save_inference_model")
def save_inference_model(
dirname,
feeded_var_names,
target_vars,
executor,
main_program=None,
model_filename=None,
params_filename=None,
export_for_deployment=True,
program_only=False,
clip_extra=True,
legacy_format=False,
):
"""
Prune the given `main_program` to build a new program especially for inference,
and then save it and all related parameters to given `dirname` .
If you just want to save parameters of your trained model, please use the
:ref:`api_fluid_io_save_params` . You can refer to :ref:`api_guide_model_save_reader_en`
for more details.
Note:
The :code:`dirname` is used to specify the folder where inference model
structure and parameters are going to be saved. If you would like to save params of
Program in separate files, set `params_filename` None; if you would like to save all
params of Program in a single file, use `params_filename` to specify the file name.
Args:
dirname(str): The directory path to save the inference model.
feeded_var_names(list[str]): list of string. Names of variables that need to be fed
data during inference.
target_vars(list[Variable]): list of Variable. Variables from which we can get
inference results.
executor(Executor): The executor that saves the inference model. You can refer
to :ref:`api_guide_executor_en` for more details.
main_program(Program, optional): The original program, which will be pruned to
build the inference model. If is set None,
the global default :code:`_main_program_` will be used.
Default: None.
model_filename(str, optional): The name of file to save the inference program
itself. If is set None, a default filename
:code:`__model__` will be used.
params_filename(str, optional): The name of file to save all related parameters.
If it is set None, parameters will be saved
in separate files .
export_for_deployment(bool, optional): If True, programs are modified to only support
direct inference deployment. Otherwise,
more information will be stored for flexible
optimization and re-training. Currently, only
True is supported.
Default: True.
program_only(bool, optional): If True, It will save inference program only, and do not
save params of Program.
Default: False.
legacy_format(bool, optional): Whether to save program in legacy format.
Default: False.
Returns:
list, The fetch variables' name list.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
path = "./infer_model"
# User defined network, here a softmax regession example
image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace())
predict = paddle.static.nn.fc(x=image, size=10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(
input=predict, label=label,
reduction='none', use_softmax=False
)
avg_loss = paddle.mean(loss)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
# Feed data and train process
# Save inference model. Note we don't save label and loss in this example
fluid.io.save_inference_model(dirname=path,
feeded_var_names=['img'],
target_vars=[predict],
executor=exe)
# In this example, the save_inference_mode inference will prune the default
# main program according to the network's input node (img) and output node(predict).
# The pruned inference program is going to be saved in the "./infer_model/__model__"
# and parameters are going to be saved in separate files under folder
# "./infer_model".
"""
if isinstance(feeded_var_names, str):
feeded_var_names = [feeded_var_names]
elif export_for_deployment:
if len(feeded_var_names) > 0:
# TODO(paddle-dev): polish these code blocks
if not (
bool(feeded_var_names)
and all(isinstance(name, str) for name in feeded_var_names)
):
raise ValueError("'feed_var_names' should be a list of str.")
if isinstance(target_vars, Variable):
target_vars = [target_vars]
elif export_for_deployment:
if not (
bool(target_vars)
and all(isinstance(var, Variable) for var in target_vars)
):
raise ValueError("'target_vars' should be a list of Variable.")
main_program = paddle.static.io._get_valid_program(main_program)
# remind user to set auc_states to zeros if the program contains auc op
all_ops = main_program.global_block().ops
for op in all_ops:
# clear device of Op
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
op._set_attr(device_attr_name, "")
if op.type == 'auc':
warnings.warn(
"please ensure that you have set the auc states to zeros before saving inference model"
)
break
with program_guard(main_program):
uniq_target_vars = []
for i, var in enumerate(target_vars):
uniq_target_vars.append(var)
target_vars = uniq_target_vars
target_var_name_list = [var.name for var in target_vars]
# when a pserver and a trainer running on the same machine, mkdir may conflict
save_dirname = dirname
try:
save_dirname = os.path.normpath(dirname)
os.makedirs(save_dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
if model_filename is not None:
model_basename = os.path.basename(model_filename)
else:
model_basename = "__model__"
model_basename = os.path.join(save_dirname, model_basename)
# When export_for_deployment is true, we modify the program online so that
# it can only be loaded for inference directly. If it's false, the whole
# original program and related meta are saved so that future usage can be
# more flexible.
origin_program = main_program.clone()
if export_for_deployment:
main_program = main_program.clone()
global_block = main_program.global_block()
need_to_remove_op_index = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed" or op.type == "fetch":
need_to_remove_op_index.append(i)
for index in need_to_remove_op_index[::-1]:
global_block._remove_op(index)
main_program.desc.flush()
main_program = main_program._prune_with_input(
feeded_var_names=feeded_var_names, targets=target_vars
)
main_program = main_program._inference_optimize(prune_read_op=True)
fetch_var_names = [v.name for v in target_vars]
for target_v in target_vars:
if not main_program.global_block().has_var(target_v.name):
main_program.global_block().create_var(
name=target_v.name,
shape=target_v.shape,
dtype=target_v.dtype,
persistable=target_v.persistable,
)
prepend_feed_ops(main_program, feeded_var_names)
append_fetch_ops(main_program, fetch_var_names)
with open(model_basename, "wb") as f:
f.write(
main_program._remove_training_info(
clip_extra=clip_extra
).desc.serialize_to_string()
)
else:
# TODO(panyx0718): Save more information so that it can also be used
# for training and more flexible post-processing.
with open(model_basename + ".main_program", "wb") as f:
f.write(
main_program._remove_training_info(
clip_extra=clip_extra
).desc.serialize_to_string()
)
if program_only:
warnings.warn(
"save_inference_model specified the param `program_only` to True, It will not save params of Program."
)
return target_var_name_list
main_program._copy_dist_param_info_from(origin_program)
if params_filename is not None:
params_filename = os.path.basename(params_filename)
paddle.distributed.io.save_persistables(
executor, save_dirname, main_program, params_filename
)
return target_var_name_list
@static_only
@deprecated(since="2.0.0", update_to="paddle.static.load_inference_model")
def load_inference_model(
dirname,
executor,
model_filename=None,
params_filename=None,
pserver_endpoints=None,
):
"""
Load the inference model from a given directory. By this API, you can get the model
structure(Inference Program) and model parameters. If you just want to load
parameters of the pre-trained model, please use the :ref:`api_fluid_io_load_params` API.
You can refer to :ref:`api_guide_model_save_reader_en` for more details.
Args:
dirname(str): One of the following:
- The given directory path.
- Set to None when reading the model from memory.
executor(Executor): The executor to run for loading inference model.
See :ref:`api_guide_executor_en` for more details about it.
model_filename(str, optional): One of the following:
- The name of file to load the inference program.
- If it is None, the default filename ``__model__`` will be used.
- When ``dirname`` is ``None``, it must be set to a string containing model.
Default: ``None``.
params_filename(str, optional): It is only used for the case that all
parameters were saved in a single binary file. One of the following:
- The name of file to load all parameters.
- When ``dirname`` is ``None``, it must be set to a string containing all the parameters.
- If parameters were saved in separate files, set it as ``None``.
Default: ``None``.
pserver_endpoints(list, optional): It is only needed by the distributed inference.
If using a distributed look up table during the training,
this table is also needed by the inference process. Its value is
a list of pserver endpoints.
Returns:
list: The return of this API is a list with three elements:
(program, feed_target_names, fetch_targets). The `program` is a
``Program`` (refer to :ref:`api_guide_Program_en`), which is used for inference.
The `feed_target_names` is a list of ``str``, which contains names of variables
that need to feed data in the inference program. The `fetch_targets` is a list of
``Variable`` (refer to :ref:`api_guide_Program_en`). It contains variables from which
we can get inference results.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
paddle.enable_static()
# Build the model
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
data = paddle.static.data(name="img", shape=[-1, 64, 784])
w = paddle.create_parameter(shape=[784, 200], dtype='float32')
b = paddle.create_parameter(shape=[200], dtype='float32')
hidden_w = paddle.matmul(x=data, y=w)
hidden_b = paddle.add(hidden_w, b)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
# Save the inference model
path = "./infer_model"
fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'],
target_vars=[hidden_b], executor=exe, main_program=main_prog)
# Demo one. Not need to set the distributed look up table, because the
# training doesn't use a distributed look up table.
[inference_program, feed_target_names, fetch_targets] = (
fluid.io.load_inference_model(dirname=path, executor=exe))
tensor_img = np.array(np.random.random((1, 64, 784)), dtype=np.float32)
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
# Demo two. If the training uses a distributed look up table, the pserver
# endpoints list should be supported when loading the inference model.
# The below is just an example.
endpoints = ["127.0.0.1:2023","127.0.0.1:2024"]
[dist_inference_program, dist_feed_target_names, dist_fetch_targets] = (
fluid.io.load_inference_model(dirname=path,
executor=exe,
pserver_endpoints=endpoints))
# In this example, the inference program was saved in the file
# "./infer_model/__model__" and parameters were saved in
# separate files under the directory "./infer_model".
# By the inference program, feed_target_names and
# fetch_targets, we can use an executor to run the inference
# program for getting the inference result.
"""
load_from_memory = False
if dirname is not None:
load_dirname = os.path.normpath(dirname)
if not os.path.isdir(load_dirname):
raise ValueError("There is no directory named '%s'" % dirname)
if model_filename is None:
model_filename = '__model__'
model_filename = os.path.join(
load_dirname, os.path.basename(model_filename)
)
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_filename, "rb") as f:
program_desc_str = f.read()
else:
load_from_memory = True
if params_filename is None:
raise ValueError(
"The path of params cannot be None when the directory path is None."
)
load_dirname = dirname
program_desc_str = model_filename
params_filename = params_filename
program = Program.parse_from_string(program_desc_str)
if not core._is_program_version_supported(program._version()):
raise ValueError(
"Unsupported program version: %d\n" % program._version()
)
# Binary data also need versioning.
paddle.distributed.io.load_persistables(
executor, load_dirname, program, params_filename
)
if pserver_endpoints:
program = _endpoints_replacement(program, pserver_endpoints)
feed_target_names = program.desc.get_feed_target_names()
fetch_target_names = program.desc.get_fetch_target_names()
fetch_targets = [
program.global_block().var(name) for name in fetch_target_names
]
return [program, feed_target_names, fetch_targets]
def _endpoints_replacement(program, endpoints):
ENDPOINT_MAP = "epmap"
for op in program.global_block().ops:
if op.has_attr(ENDPOINT_MAP):
op.set_attr(ENDPOINT_MAP, endpoints)
program._sync_with_cpp()
return program
python/paddle/hapi/model.py
浏览文件 @ bfc64801
......@@ -2268,17 +2268,15 @@ class Model:
infer_prog = prog.clone(for_test=True)
input_names = [v.name for v in self._adapter._input_vars['test']]
inputs = list(self._adapter._input_vars['test'])
endpoints = self._adapter._endpoints['test']['output']
fluid.io.save_inference_model(
paddle.static.save_inference_model(
model_path,
input_names,
inputs,
endpoints,
self._adapter._executor,
main_program=infer_prog,
model_filename=model_filename,
params_filename=params_filename,
program=infer_prog,
)
def _run_one_epoch(
......
python/paddle/incubate/distributed/fleet/collective.py
浏览文件 @ bfc64801
......@@ -18,7 +18,6 @@ import paddle
import paddle.distributed.transpiler.distribute_transpiler as dist_transpiler
from paddle import fluid
from paddle.distributed.fleet.meta_optimizers import RawProgramOptimizer
from paddle.fluid import io
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program
......@@ -31,6 +30,7 @@ from paddle.incubate.distributed.fleet.base import (
Fleet,
Mode,
)
from paddle.static import io
class Collective(Fleet):
......@@ -77,11 +77,10 @@ class Collective(Fleet):
def save_inference_model(
self,
executor,
dirname,
feeded_var_names=None,
target_vars=None,
main_program=None,
export_for_deployment=True,
path_prefix,
feeded_vas=None,
fetch_vars=None,
program=None,
legacy_format=False,
):
"""
......@@ -94,22 +93,19 @@ class Collective(Fleet):
" Executor type."
)
if main_program is None:
main_program = self._origin_program
assert isinstance(main_program, Program), (
if program is None:
program = self._origin_program
assert isinstance(program, Program), (
"In fleet.save_inference_model() function, main_program "
"must be as Program type."
)
io.save_inference_model(
dirname,
feeded_var_names,
target_vars,
path_prefix,
feeded_vas,
fetch_vars,
executor,
main_program,
None,
None,
export_for_deployment,
program=program,
legacy_format=legacy_format,
)
......
python/paddle/incubate/distributed/fleet/fleet_util.py
浏览文件 @ bfc64801
......@@ -965,28 +965,26 @@ class FleetUtil:
"""
day = str(day)
pass_id = str(pass_id)
feeded_var_names = [i.name for i in feeded_vars]
model_name = "inference_model"
# pull dense before save
self.pull_all_dense_params(scope, program)
if fleet.worker_index() == 0:
with fluid.scope_guard(scope):
if save_combine:
fluid.io.save_inference_model(
dirname=model_name,
feeded_var_names=feeded_var_names,
target_vars=target_vars,
executor=executor,
main_program=program.clone(),
params_filename="params",
paddle.static.io.save_inference_model(
model_name,
feeded_vars,
target_vars,
executor,
program=program.clone(),
)
else:
fluid.io.save_inference_model(
dirname=model_name,
feeded_var_names=feeded_var_names,
target_vars=target_vars,
executor=executor,
main_program=program.clone(),
paddle.static.io.save_inference_model(
model_name,
feeded_vars,
target_vars,
executor,
program=program.clone(),
)
configs = {
......
python/paddle/incubate/distributed/fleet/utils.py
浏览文件 @ bfc64801
......@@ -238,11 +238,12 @@ def try_load_model_vars(
dump_prog_fn = program_type_trans(
dump_dir, dump_prog_fn, is_text_dump_program
)
(
[
inference_program,
feed_target_names,
fetch_targets,
) = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
dump_dir,
exe,
model_filename=dump_prog_fn,
......
python/paddle/jit/api.py
浏览文件 @ bfc64801
......@@ -74,7 +74,7 @@ from paddle.fluid.framework import (
)
from paddle.fluid.framework import dygraph_only
from paddle.fluid.wrapped_decorator import wrap_decorator
from paddle.fluid.io import save_inference_model
from paddle.static.io import save_inference_model
from paddle.framework import in_dynamic_mode
......@@ -1222,23 +1222,25 @@ def save(layer, path, input_spec=None, **configs):
if 'forward' == attr_func or not isinstance(layer, Layer):
model_filename = file_prefix + INFER_MODEL_SUFFIX
params_filename = file_prefix + INFER_PARAMS_SUFFIX
path_prefix = file_prefix
else:
model_filename = file_prefix + '.' + attr_func + INFER_MODEL_SUFFIX
params_filename = (
file_prefix + '.' + attr_func + INFER_PARAMS_SUFFIX
)
file_prefix = file_prefix + '.' + attr_func
file_prefix = os.path.join(model_path, file_prefix)
with scope_guard(scope):
input_vars = []
for var in concrete_program.main_program.clone().list_vars():
if var.name in input_var_names:
input_vars.append(var)
save_inference_model(
dirname=model_path,
feeded_var_names=input_var_names,
target_vars=output_vars,
path_prefix=file_prefix,
feed_vars=input_vars,
fetch_vars=output_vars,
executor=Executor(_current_expected_place()),
main_program=concrete_program.main_program.clone(),
model_filename=model_filename,
params_filename=params_filename,
export_for_deployment=configs._export_for_deployment,
program_only=configs._program_only,
program=concrete_program.main_program.clone(),
clip_extra=configs.clip_extra,
)
......@@ -1893,24 +1895,24 @@ class TracedLayer:
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)
feed_vars = []
for name in feeded_var_names:
feed_var = self._program.global_block().vars.get(name, None)
assert feed_var is not None, f"{name} cannot be found"
feed_vars.append(feed_var)
target_vars = []
for name in target_var_names:
target_var = self._program.global_block().vars.get(name, None)
assert target_var is not None, f"{name} cannot be found"
target_vars.append(target_var)
model_filename = file_prefix + INFER_MODEL_SUFFIX
params_filename = file_prefix + INFER_PARAMS_SUFFIX
legacy_format = kwargs.get('legacy_format', False)
file_prefix = os.path.join(dirname, file_prefix)
save_inference_model(
dirname=dirname,
feeded_var_names=feeded_var_names,
target_vars=target_vars,
path_prefix=file_prefix,
feed_vars=feed_vars,
fetch_vars=target_vars,
executor=self._exe,
main_program=self._program.clone(),
model_filename=model_filename,
params_filename=params_filename,
program=self._program.clone(),
clip_extra=clip_extra,
legacy_format=legacy_format,
)
......
python/paddle/static/io.py
浏览文件 @ bfc64801
......@@ -34,7 +34,6 @@ from paddle.fluid import (
)
from paddle.fluid.executor import Executor, global_scope
from paddle.fluid.framework import Parameter, dygraph_not_support, static_only
from paddle.fluid.io import append_fetch_ops, prepend_feed_ops
from paddle.fluid.log_helper import get_logger
from paddle.framework.io_utils import (
_clone_var_in_block_,
......@@ -138,6 +137,56 @@ def _clone_var_in_block(block, var):
)
def prepend_feed_ops(
inference_program, feed_target_names, feed_holder_name='feed'
):
if len(feed_target_names) == 0:
return
global_block = inference_program.global_block()
feed_var = global_block.create_var(
name=feed_holder_name,
type=core.VarDesc.VarType.FEED_MINIBATCH,
persistable=True,
)
for i, name in enumerate(feed_target_names):
if not global_block.has_var(name):
raise ValueError(
"The feeded_var_names[{i}]: '{name}' doesn't exist in pruned inference program. "
"Please check whether '{name}' is a valid feed_var name, or remove it from feeded_var_names "
"if '{name}' is not involved in the target_vars calculation.".format(
i=i, name=name
)
)
out = global_block.var(name)
global_block._prepend_op(
type='feed',
inputs={'X': [feed_var]},
outputs={'Out': [out]},
attrs={'col': i},
)
def append_fetch_ops(
inference_program, fetch_target_names, fetch_holder_name='fetch'
):
global_block = inference_program.global_block()
fetch_var = global_block.create_var(
name=fetch_holder_name,
type=core.VarDesc.VarType.FETCH_LIST,
persistable=True,
)
for i, name in enumerate(fetch_target_names):
global_block.append_op(
type='fetch',
inputs={'X': [name]},
outputs={'Out': [fetch_var]},
attrs={'col': i},
)
def normalize_program(program, feed_vars, fetch_vars):
"""
......@@ -200,8 +249,7 @@ def normalize_program(program, feed_vars, fetch_vars):
op._set_attr(device_attr_name, "")
if op.type == 'auc':
warnings.warn(
"Be sure that you have set auc states to 0 "
"before saving inference model."
"Be sure that you have set auc states to 0 before saving inference model."
)
break
......@@ -521,15 +569,23 @@ def save_inference_model(
program = _get_valid_program(kwargs.get('program', None))
clip_extra = kwargs.get('clip_extra', True)
program = normalize_program(program, feed_vars, fetch_vars)
# serialize and save program
legacy_format = kwargs.get('legacy_format', False)
program_bytes = _serialize_program(
program._remove_training_info(clip_extra=clip_extra),
legacy_format=legacy_format,
)
save_to_file(model_path, program_bytes)
vars = list(filter(is_persistable, program.list_vars()))
if len(list(vars)) == 0:
warnings.warn(
"no variable in your model, please ensure there are any variables in your model to save"
)
if len(vars) > 0:
save_dirname = os.path.dirname(params_path)
params_filename = os.path.basename(params_path)
......@@ -832,7 +888,9 @@ def load_inference_model(path_prefix, executor, **kwargs):
else:
# check and norm path_prefix
path_prefix = _normalize_path_prefix(path_prefix)
dir_path = os.path.dirname(path_prefix)
if not os.path.isdir(dir_path):
raise ValueError(f"There is no directory named {dir_path}")
# set model_path and params_path in new way,
# path_prefix represents a file path without suffix in this case.
if not kwargs:
......@@ -867,6 +925,7 @@ def load_inference_model(path_prefix, executor, **kwargs):
model_path, params_path
)
)
program_bytes = load_from_file(model_path)
# deserialize bytes to program
......@@ -876,6 +935,7 @@ def load_inference_model(path_prefix, executor, **kwargs):
if len(vars) > 0:
load_dirname = os.path.dirname(params_path)
params_filename = os.path.basename(params_path)
load_vars(
executor,
dirname=load_dirname,
......@@ -1139,6 +1199,9 @@ def load_vars(
else:
vars_from_memory = True
if filename == '':
filename = None
if vars is None:
if main_program is None:
main_program = default_main_program()
......
test/book/notest_understand_sentiment.py
浏览文件 @ bfc64801
......@@ -110,8 +110,8 @@ def train(
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 0.4 and acc_val > 0.8:
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname, ["words"], prediction, exe
paddle.static.io.save_inference_model(
save_dirname, data, prediction, exe
)
return
if math.isnan(float(cost_val)):
......@@ -153,7 +153,7 @@ def infer(word_dict, use_cuda, save_dirname=None):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# Use paddle.static.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
......@@ -161,7 +161,7 @@ def infer(word_dict, use_cuda, save_dirname=None):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(save_dirname, exe)
] = paddle.static.io.load_inference_model(save_dirname, exe)
word_dict_len = len(word_dict)
......
test/book/test_image_classification.py
浏览文件 @ bfc64801
......@@ -188,8 +188,8 @@ def train(net_type, use_cuda, save_dirname, is_local):
)
if acc_value > 0.01: # Low threshold for speeding up CI
fluid.io.save_inference_model(
save_dirname, ["pixel"], [predict], exe
paddle.static.io.save_inference_model(
save_dirname, images, [predict], exe
)
return
......@@ -228,7 +228,7 @@ def infer(use_cuda, save_dirname=None):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# Use paddle.static.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
......@@ -236,7 +236,7 @@ def infer(use_cuda, save_dirname=None):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(save_dirname, exe)
] = paddle.static.io.load_inference_model(save_dirname, exe)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [0, 1.0].
......@@ -252,13 +252,16 @@ def infer(use_cuda, save_dirname=None):
)
print("infer results: ", results[0])
fluid.io.save_inference_model(
feeded_vars = [
inference_program.global_block().var(name)
for name in feed_target_names
]
paddle.static.io.save_inference_model(
save_dirname,
feed_target_names,
feeded_vars,
fetch_targets,
exe,
inference_program,
program=inference_program,
)
......@@ -269,7 +272,7 @@ def main(net_type, use_cuda, is_local=True):
# Directory for saving the trained model
temp_dir = tempfile.TemporaryDirectory()
save_dirname = os.path.join(
temp_dir.name, "image_classification_" + net_type + ".inference.model"
temp_dir.name, "image_classification_" + net_type + "_inference_model"
)
train(net_type, use_cuda, save_dirname, is_local)
......
test/book/test_recognize_digits.py
浏览文件 @ bfc64801
......@@ -137,23 +137,18 @@ def train(
if float(acc_val) > 0.2 or pass_id == (PASS_NUM - 1):
# Smaller value to increase CI speed
if save_dirname is not None:
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
save_dirname,
["img"],
img,
[prediction],
exe,
model_filename=model_filename,
params_filename=params_filename,
)
if save_full_dirname is not None:
fluid.io.save_inference_model(
paddle.static.save_inference_model(
save_full_dirname,
[],
[],
exe,
model_filename=model_filename,
params_filename=params_filename,
export_for_deployment=False,
)
return
else:
......@@ -206,7 +201,7 @@ def infer(
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# Use paddle.static.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
......@@ -214,8 +209,9 @@ def infer(
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
save_dirname, exe, model_filename, params_filename
] = paddle.static.io.load_inference_model(
save_dirname,
exe,
)
# The input's dimension of conv should be 4-D or 5-D.
......@@ -241,11 +237,13 @@ def main(use_cuda, parallel, nn_type, combine):
model_filename = None
params_filename = None
if not use_cuda and not parallel:
save_dirname = "recognize_digits_" + nn_type + ".inference.model"
save_full_dirname = "recognize_digits_" + nn_type + ".train.model"
save_dirname = "recognize_digits_" + nn_type + "_inference_model"
save_full_dirname = "recognize_digits_" + nn_type + "_train_model"
if combine:
model_filename = "__model_combined__"
params_filename = "__params_combined__"
save_dirname = save_dirname + model_filename
save_full_dirname = params_filename + params_filename
# call train() with is_local argument to run distributed train
train(
......
test/book/test_recommender_system.py
浏览文件 @ bfc64801
......@@ -213,6 +213,15 @@ def train(use_cuda, save_dirname, is_local=True):
'movie_title',
'score',
]
feed_infer_order = [
'user_id',
'gender_id',
'age_id',
'job_id',
'movie_id',
'category_id',
'movie_title',
]
def train_loop(main_program):
exe.run(framework.default_startup_program())
......@@ -220,6 +229,10 @@ def train(use_cuda, save_dirname, is_local=True):
feed_list = [
main_program.global_block().var(var_name) for var_name in feed_order
]
feed_infer_list = [
main_program.global_block().var(var_name)
for var_name in feed_infer_order
]
feeder = fluid.DataFeeder(feed_list, place)
PASS_NUM = 100
......@@ -248,17 +261,9 @@ def train(use_cuda, save_dirname, is_local=True):
if test_avg_cost < 6.0:
# if avg_cost less than 6.0, we think our code is good.
if save_dirname is not None:
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
save_dirname,
[
"user_id",
"gender_id",
"age_id",
"job_id",
"movie_id",
"category_id",
"movie_title",
],
feed_infer_list,
[scale_infer],
exe,
)
......@@ -302,7 +307,7 @@ def infer(use_cuda, save_dirname=None):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# Use paddle.static.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
......@@ -310,7 +315,7 @@ def infer(use_cuda, save_dirname=None):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(save_dirname, exe)
] = paddle.static.io.load_inference_model(save_dirname, exe)
# Use the first data from paddle.dataset.movielens.test() as input
assert feed_target_names[0] == "user_id"
......
test/book/test_word2vec_book.py
浏览文件 @ bfc64801
......@@ -159,9 +159,9 @@ def train(
)
if avg_cost_np[0] < 5.0:
if save_dirname is not None and not pure_bf16:
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
save_dirname,
['firstw', 'secondw', 'thirdw', 'forthw'],
[first_word, second_word, third_word, forth_word],
[predict_word],
exe,
)
......@@ -205,15 +205,16 @@ def infer(target, save_dirname=None):
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# Use paddle.static.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(save_dirname, exe)
] = paddle.static.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
......@@ -272,7 +273,8 @@ def infer(target, save_dirname=None):
infer_inputs = [to_infer_tensor(t) for t in infer_inputs]
infer_config = fluid.core.NativeConfig()
infer_config.model_dir = save_dirname
infer_config.prog_file = save_dirname + ".pdmodel"
infer_config.param_file = save_dirname + ".pdiparams"
if target == "cuda":
infer_config.use_gpu = True
infer_config.device = 0
......@@ -300,7 +302,7 @@ def main(target, is_sparse, is_parallel, use_bf16, pure_bf16):
temp_dir = tempfile.TemporaryDirectory()
if not is_parallel:
save_dirname = os.path.join(temp_dir.name, "word2vec.inference.model")
save_dirname = os.path.join(temp_dir.name, "word2vec_inference_model")
else:
save_dirname = None
......
test/cinn/fake_model/resnet_model.py
浏览文件 @ bfc64801
......@@ -14,7 +14,7 @@
import paddle
from paddle import fluid, static
from paddle import static
paddle.enable_static()
......@@ -48,7 +48,5 @@ exe = static.Executor(cpu)
exe.run(static.default_startup_program())
static.io.save_inference_model("./resnet_model", [resnet_input], [temp7], exe)
fluid.io.save_inference_model(
"./resnet_model_1", [resnet_input.name], [temp7], exe
)
static.io.save_inference_model("./resnet_model_1", [resnet_input], [temp7], exe)
print('res', temp7.name)
test/cinn/test_paddle_model_convertor.py
浏览文件 @ bfc64801
......@@ -81,7 +81,7 @@ paddle.enable_static()
# exe.run(paddle.static.default_startup_program())
# prog = paddle.static.default_main_program()
# paddle.fluid.io.save_inference_model("./stack", [x.name, y.name], [prediction], exe, prog)
# paddle.static.io.save_inference_model("./stack", [x.name, y.name], [prediction], exe, prog)
# ```
# Second load and run model like:
# ```
......@@ -150,11 +150,9 @@ class TestPaddleModel(OpMapperTest):
self.inference_program,
self.feed_names,
self.fetch_targets,
] = paddle.fluid.io.load_inference_model(
dirname=self.model_dir,
] = paddle.static.io.load_inference_model(
path_prefix=self.model_dir,
executor=self.exe,
model_filename=self.model_filename,
params_filename=self.params_filename,
)
self.param_vars = paddle.load(
......
test/contrib/test_image_classification_fp16.py
浏览文件 @ bfc64801
......@@ -217,12 +217,12 @@ def train(net_type, use_cuda, save_dirname, is_local):
)
if acc_value > 0.08: # Low threshold for speeding up CI
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
save_dirname,
["pixel"],
images,
[predict],
exe,
main_program=train_program,
program=train_program,
clip_extra=True,
)
return
......@@ -262,7 +262,7 @@ def infer(use_cuda, save_dirname=None):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# Use paddle.static.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
......@@ -270,7 +270,7 @@ def infer(use_cuda, save_dirname=None):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(save_dirname, exe)
] = paddle.static.io.load_inference_model(save_dirname, exe)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [0, 1.0].
......@@ -287,12 +287,12 @@ def infer(use_cuda, save_dirname=None):
print("infer results: ", results[0])
fluid.io.save_inference_model(
paddle.static.save_inference_model(
save_dirname,
feed_target_names,
fetch_targets,
exe,
inference_program,
parogram=inference_program,
clip_extra=True,
)
......
test/dygraph_to_static/test_bert.py
浏览文件 @ bfc64801
......@@ -181,7 +181,7 @@ class TestBert(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
self.model_save_dir,
executor=exe,
model_filename=self.model_filename,
......
test/dygraph_to_static/test_bmn.py
浏览文件 @ bfc64801
......@@ -855,7 +855,7 @@ class TestTrain(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
self.model_save_dir,
executor=exe,
model_filename=self.model_filename,
......
test/dygraph_to_static/test_lac.py
浏览文件 @ bfc64801
......@@ -667,7 +667,7 @@ class TestLACModel(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
self.model_save_dir,
executor=exe,
model_filename=self.model_filename,
......
test/dygraph_to_static/test_mnist.py
浏览文件 @ bfc64801
......@@ -292,8 +292,8 @@ class TestMNISTWithToStatic(TestMNIST):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
dirname=model_path,
] = paddle.static.io.load_inference_model(
path_prefix=model_path,
executor=exe,
model_filename=model_filename,
params_filename=params_filename,
......
test/dygraph_to_static/test_mobile_net.py
浏览文件 @ bfc64801
......@@ -608,7 +608,7 @@ def predict_static(args, data):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
args.model_save_dir,
executor=exe,
model_filename=args.model_filename,
......
test/dygraph_to_static/test_resnet.py
浏览文件 @ bfc64801
......@@ -355,7 +355,7 @@ class ResNetHelper:
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
self.model_save_dir,
executor=exe,
model_filename=self.model_filename,
......
test/dygraph_to_static/test_save_inference_model.py
浏览文件 @ bfc64801
......@@ -130,8 +130,8 @@ class TestDyToStaticSaveInferenceModel(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
dirname=model_path,
] = paddle.static.io.load_inference_model(
path_prefix=model_path,
executor=exe,
model_filename=model_filename,
params_filename=params_filename,
......
test/dygraph_to_static/test_se_resnet.py
浏览文件 @ bfc64801
......@@ -493,7 +493,7 @@ class TestSeResnet(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
] = paddle.static.io.load_inference_model(
self.model_save_dir,
executor=exe,
model_filename=self.model_filename,
......
test/ir/inference/inference_pass_test.py
浏览文件 @ bfc64801
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import errno
import os
import random
import tempfile
......@@ -57,13 +58,36 @@ class InferencePassTest(unittest.TestCase):
self, dirname, feeded_var_names, target_vars, executor, program, scope
):
with fluid.scope_guard(scope):
# save models as combined to ensure that
# there won't be too many useless files
# after finishing a couple of tests.
fluid.io.save_inference_model(
dirname, feeded_var_names, target_vars, executor, program
# save models as combined but sometimes params is null
# To adapt to this situation, the path needs to be adjusted to the old version format.
feeded_vars = []
for var in program.list_vars():
if var.name in feeded_var_names:
feeded_vars.append(var)
paddle.static.io.save_inference_model(
dirname,
feeded_vars,
target_vars,
executor,
program=program,
)
# if the param save is null
# replace model_path to old version
param_file = dirname + ".pdiparams"
if not os.path.exists(param_file):
model_path = dirname + ".pdmodel"
try:
save_dirname = os.path.normpath(dirname)
os.makedirs(save_dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
model_path_old = os.path.join(save_dirname, "__model__")
if not os.path.exists(model_path_old):
os.rename(model_path, model_path_old)
def _get_paddle_outs(self, executor, program, scope):
'''
Return PaddlePaddle outputs.
......@@ -109,7 +133,14 @@ class InferencePassTest(unittest.TestCase):
'''
Return a new object of AnalysisConfig.
'''
config = AnalysisConfig(self.path)
# To adapt to save_inference_model
param_file = self.path + ".pdiparams"
if not os.path.exists(param_file):
config = AnalysisConfig(self.path)
else:
config = AnalysisConfig(
self.path + ".pdmodel", self.path + ".pdiparams"
)
config.disable_gpu()
config.switch_specify_input_names(True)
config.switch_ir_optim(True)
......
test/ir/inference/program_config.py
浏览文件 @ bfc64801
......@@ -432,7 +432,7 @@ def create_quant_model(
inference_program,
feed_target_names,
fetch_targets,
] = paddle.static.load_inference_model(
] = paddle.static.io.load_inference_model(
path_prefix=None,
executor=exe,
model_filename=model,
......@@ -596,18 +596,19 @@ def create_quant_model(
tensor = scope.var(var_name).get_tensor()
tensor.set(np.ones(tensor.shape(), dtype=np.float32), place)
feed_vars = [
main_program.global_block().var(name) for name in feed_target_names
]
if save:
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
'test_inference_model',
feed_target_names,
feed_vars,
fetch_targets,
exe,
main_program=main_program,
program=main_program,
)
feed_vars = [
main_program.global_block().var(name) for name in feed_target_names
]
serialized_program = paddle.static.serialize_program(
feed_vars, fetch_targets, program=main_program
)
......
test/ir/inference/quant_dequant_test.py
浏览文件 @ bfc64801
......@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import errno
import os
import random
import unittest
import warnings
......@@ -23,7 +25,7 @@ from paddle import fluid
from paddle.fluid import Program, Variable, core
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor
from paddle.fluid.framework import IrGraph
from paddle.fluid.io import append_fetch_ops, prepend_feed_ops
from paddle.static.io import append_fetch_ops, prepend_feed_ops
from paddle.static.quantization import (
AddQuantDequantPass,
OutScaleForInferencePass,
......@@ -51,7 +53,7 @@ class QuantDequantTest(unittest.TestCase):
self.dynamic_shape_params = None
self.enable_lite = False
self.lite_parameters = None
self.path = "./inference_pass/" + self.__class__.__name__ + "/"
self.path = "./inference_pass/" + self.__class__.__name__
self.data = None
self.label = None
self.result = None
......@@ -118,15 +120,36 @@ class QuantDequantTest(unittest.TestCase):
def _save_models(
self, dirname, feeded_var_names, target_vars, executor, program, scope
):
# save models as combined but sometimes params is null
# To adapt to this situation, the path needs to be adjusted to the old version format.
feeded_vars = []
for var in program.list_vars():
if var.name in feeded_var_names:
feeded_vars.append(var)
with fluid.scope_guard(scope):
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
dirname,
feeded_var_names,
feeded_vars,
target_vars,
executor,
program,
program=program,
clip_extra=True,
)
# if the param save is null
# replace model_path to old version
param_file = dirname + ".pdiparams"
if not os.path.exists(param_file):
model_path = dirname + ".pdmodel"
try:
save_dirname = os.path.normpath(dirname)
os.makedirs(save_dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
model_path_old = os.path.join(save_dirname, "__model__")
if not os.path.exists(model_path_old):
os.rename(model_path, model_path_old)
def _get_paddle_outs(self, feed, fetch_list, executor, program, scope):
'''
......@@ -172,7 +195,14 @@ class QuantDequantTest(unittest.TestCase):
'''
Return a new object of AnalysisConfig.
'''
config = AnalysisConfig(self.path)
# To adapt to save_inference_model
param_file = self.path + ".pdiparams"
if not os.path.exists(param_file):
config = AnalysisConfig(self.path)
else:
config = AnalysisConfig(
self.path + ".pdmodel", self.path + ".pdiparams"
)
config.disable_gpu()
config.switch_specify_input_names(True)
config.switch_ir_optim(True)
......
test/legacy_test/test_dist_base.py
浏览文件 @ bfc64801
......@@ -435,10 +435,10 @@ class TestDistRunnerBase:
model_save_dir, "fleet_persistables"
)
infer_save_dir_fluid = os.path.join(
model_save_dir, "fluid_infer"
model_save_dir, "fluid_infer/infer"
)
infer_save_dir_fleet = os.path.join(
model_save_dir, "fleet_infer"
model_save_dir, "fleet_infer/infer"
)
else:
model_save_dir_fluid = os.path.join(
......@@ -448,25 +448,24 @@ class TestDistRunnerBase:
model_save_dir, "fleet_persistables_2"
)
infer_save_dir_fluid = os.path.join(
model_save_dir, "fluid_infer_2"
model_save_dir, "fluid_infer_2/infer_2"
)
infer_save_dir_fleet = os.path.join(
model_save_dir, "fleet_infer_2"
model_save_dir, "fleet_infer_2/infer_2"
)
paddle.distributed.io.save_persistables(
exe, model_save_dir_fluid, fleet._origin_program
)
fleet.save_persistables(executor=exe, dirname=model_save_dir_fleet)
feeded_var_names = [var.name for var in feed_var_list]
fluid.io.save_inference_model(
infer_save_dir_fluid,
feeded_var_names,
[avg_cost],
exe,
fleet._origin_program,
paddle.static.io.save_inference_model(
path_prefix=infer_save_dir_fluid,
feed_vars=feed_var_list,
fetch_vars=[avg_cost],
executor=exe,
program=fleet._origin_program,
)
fleet.save_inference_model(
exe, infer_save_dir_fleet, feeded_var_names, [avg_cost]
exe, infer_save_dir_fleet, feed_var_list, [avg_cost]
)
def run_trainer(self, args):
......
test/legacy_test/test_dist_mnist_fleet_save.py
浏览文件 @ bfc64801
......@@ -68,7 +68,6 @@ class TestDistMnistFleetSave(TestDistBase):
if fluid_persistables[i] != fleet_persistables[i]:
self._rm_temp_files(dirname)
raise ValueError("Test Failed.")
if len(fluid_infer_files) != len(fleet_infer_files):
self._rm_temp_files(dirname)
raise ValueError("Test Failed.")
......
test/legacy_test/test_eager_deletion_padding_rnn.py
浏览文件 @ bfc64801
......@@ -314,7 +314,7 @@ def lm_model(
paddle.assign(last_cell, output=init_cell)
paddle.assign(last_hidden, output=init_hidden)
feeding_list = ['x', 'y', 'init_hidden', 'init_cell']
feeding_list = [x, y, init_hidden, init_cell]
return loss, last_hidden, last_cell, feeding_list
......@@ -365,7 +365,7 @@ class PaddingRNNTestBase(unittest.TestCase):
self.loss,
self.last_hidden,
self.last_cell,
self.feed_order,
self.feed_list,
) = res_vars
paddle.nn.clip.set_gradient_clip(
......
test/legacy_test/test_executor_and_use_program_cache.py
浏览文件 @ bfc64801
......@@ -88,13 +88,12 @@ class ExecutorPaddingRNNTest(PaddingRNNTestBase):
config = RNNConfig("test", rnn_model)
with fluid.scope_guard(fluid.Scope()):
self.train(config, use_program_cache)
fluid.io.save_inference_model(
main_program=self.main_program,
feeded_var_names=self.feed_order,
target_vars=[self.loss, self.last_hidden, self.last_cell],
paddle.static.io.save_inference_model(
path_prefix="padding_rnn." + rnn_model + ".inference_model",
feed_vars=self.feed_list,
fetch_vars=[self.loss, self.last_hidden, self.last_cell],
executor=self.exe,
dirname="padding_rnn." + rnn_model + ".inference_model",
params_filename="__params__",
program=self.main_program,
)
def test_inference_output(self):
......@@ -134,8 +133,8 @@ class ExecutorPaddingRNNTest(PaddingRNNTestBase):
inference_program,
feed_target_names,
fetch_targets,
] = fluid.io.load_inference_model(
save_dirname, self.exe, params_filename="__params__"
] = paddle.static.io.load_inference_model(
save_dirname, self.exe
)
results = self.exe.run(
......
test/legacy_test/test_inference_model_io.py
浏览文件 @ bfc64801
......@@ -29,7 +29,7 @@ from paddle.distributed.io import (
from paddle.fluid import core, executor
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.io import load_inference_model, save_inference_model
from paddle.static.io import load_inference_model, save_inference_model
paddle.enable_static()
......@@ -82,15 +82,15 @@ class TestBook(unittest.TestCase):
)
# Separated model and unified model
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
save_inference_model(
MODEL_DIR, [x, y], [avg_cost], exe, program=program
)
save_inference_model(
UNI_MODEL_DIR,
["x", "y"],
[x, y],
[avg_cost],
exe,
program,
'model',
'params',
program=program,
)
main_program = program.clone()._prune_with_input(
feeded_var_names=["x", "y"], targets=[avg_cost]
......@@ -104,12 +104,22 @@ class TestBook(unittest.TestCase):
importlib.reload(executor) # reload to build a new scope
model_0 = InferModel(load_inference_model(MODEL_DIR, exe))
with open(os.path.join(UNI_MODEL_DIR, 'model'), "rb") as f:
with open((UNI_MODEL_DIR + '.pdmodel'), "rb") as f:
model_str = f.read()
model_1 = InferModel(
load_inference_model(None, exe, model_str, params_str)
model_1 = InferModel(load_inference_model(UNI_MODEL_DIR, exe))
# To be compatible with load_inference_model_distributed function
tmp_model_filename = MODEL_DIR + '.pdmodel'
tmp_params_filename = MODEL_DIR + '.pdiparams'
model_2 = InferModel(
load_inference_model_distributed(
root_path.name,
exe,
model_filename=tmp_model_filename,
params_filename=tmp_params_filename,
)
)
model_2 = InferModel(load_inference_model_distributed(MODEL_DIR, exe))
model_3 = InferModel(
load_inference_model_distributed(None, exe, model_str, params_str)
)
......@@ -134,11 +144,11 @@ class TestBook(unittest.TestCase):
self.assertRaises(
ValueError,
fluid.io.load_inference_model,
paddle.static.io.load_inference_model,
None,
exe,
model_str,
None,
model_filename=model_str,
params_filename=None,
)
self.assertRaises(
ValueError,
......@@ -173,7 +183,9 @@ class TestSaveInferenceModel(unittest.TestCase):
exe = executor.Executor(place)
exe.run(init_program, feed={}, fetch_list=[])
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
save_inference_model(
MODEL_DIR, [x, y], [avg_cost], exe, program=program
)
root_path.cleanup()
def test_save_inference_model_with_auc(self):
......@@ -202,10 +214,10 @@ class TestSaveInferenceModel(unittest.TestCase):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
save_inference_model(
MODEL_DIR, ["x", "y"], [avg_cost], exe, program
MODEL_DIR, [x, y], [avg_cost], exe, program=program
)
root_path.cleanup()
expected_warn = "please ensure that you have set the auc states to zeros before saving inference model"
expected_warn = "Be sure that you have set auc states to 0 before saving inference model."
self.assertTrue(len(w) > 0)
self.assertTrue(expected_warn == str(w[0].message))
......@@ -237,11 +249,13 @@ class TestInstance(unittest.TestCase):
cp_prog = CompiledProgram(program)
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, cp_prog)
save_inference_model(
MODEL_DIR, [x, y], [avg_cost], exe, program=cp_prog
)
self.assertRaises(
TypeError,
save_inference_model,
[MODEL_DIR, ["x", "y"], [avg_cost], [], cp_prog],
[MODEL_DIR, [x, y], [avg_cost], [], cp_prog],
)
root_path.cleanup()
......@@ -535,7 +549,7 @@ class TestLoadInferenceModelError(unittest.TestCase):
place = core.CPUPlace()
exe = executor.Executor(place)
self.assertRaises(
ValueError, load_inference_model, './test_not_exist_dir', exe
ValueError, load_inference_model, './test_not_exist_dir/model', exe
)
self.assertRaises(
ValueError,
......
test/legacy_test/test_io_save_load.py
浏览文件 @ bfc64801
......@@ -79,12 +79,12 @@ class TestSaveInferenceModelAPIError(unittest.TestCase):
with self.assertRaisesRegex(
ValueError, "not involved in the target_vars calculation"
):
fluid.io.save_inference_model(
dirname=os.path.join(self.temp_dir.name, 'model'),
feeded_var_names=['x', 'y'],
target_vars=[z],
paddle.static.io.save_inference_model(
path_prefix=os.path.join(self.temp_dir.name, 'model'),
feed_vars=[x, y],
fetch_vars=[z],
executor=exe,
main_program=main_prog,
program=main_prog,
)
......
test/legacy_test/test_load_state_dict_from_old_format.py
浏览文件 @ bfc64801
......@@ -125,13 +125,11 @@ class TestLoadStateDictFromSaveInferenceModel(unittest.TestCase):
param.name
)
fluid.io.save_inference_model(
paddle.static.io.save_inference_model(
self.save_dirname,
["img"],
[img],
[prediction],
exe,
model_filename=self.model_filename,
params_filename=self.params_filename,
)
return static_param_dict
......
test/legacy_test/test_save_model_without_var.py
浏览文件 @ bfc64801
......@@ -35,13 +35,11 @@ class TestSaveModelWithoutVar(unittest.TestCase):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
fluid.io.save_inference_model(
dirname='test',
feeded_var_names=['data'],
target_vars=[data_plus],
executor=exe,
model_filename='model',
params_filename='params',
paddle.static.io.save_inference_model(
'test',
data,
[data_plus],
exe,
)
expected_warn = "no variable in your model, please ensure there are any variables in your model to save"
self.assertTrue(len(w) > 0)
......
test/quantization/convert_model2dot.py
浏览文件 @ bfc64801
......@@ -56,7 +56,9 @@ def generate_dot_for_model(model_path, save_graph_dir, save_graph_name):
inference_program,
feed_target_names,
fetch_targets,
] = paddle.fluid.io.load_inference_model(model_path, exe)
] = paddle.static.io.load_inference_model(
model_path, exe, model_filename='__model__'
)
else:
[
inference_program,
......
test/quantization/quant2_int8_image_classification_comparison.py
浏览文件 @ bfc64801
......@@ -195,7 +195,9 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = paddle.fluid.io.load_inference_model(model_path, exe)
] = paddle.static.io.load_inference_model(
model_path, exe, model_filename=None, params_filename=None
)
else:
[
inference_program,
......
test/quantization/quant_int8_image_classification_comparison.py
浏览文件 @ bfc64801
......@@ -173,7 +173,9 @@ class QuantInt8ImageClassificationComparisonTest(unittest.TestCase):
inference_program,
feed_target_names,
fetch_targets,
] = paddle.fluid.io.load_inference_model(model_path, exe)
] = paddle.static.io.load_inference_model(
model_path, exe, model_filename=None, params_filename=None
)
else:
[
inference_program,
......
tools/cinn/paddle_benchmark/paddle_save_model.py
浏览文件 @ bfc64801
......@@ -13,7 +13,7 @@
# limitations under the License.
import paddle
from paddle import fluid, static
from paddle import static
# For paddlepaddle version >=2.0rc, we need to set paddle.enable_static()
paddle.enable_static()
......@@ -30,7 +30,7 @@ loss = exe = static.Executor(cpu)
exe.run(static.default_startup_program())
fluid.io.save_inference_model(
"./elementwise_add_model", [a.name, b.name], [a1], exe
paddle.static.io.save_inference_model(
"./elementwise_add_model", [a, b], [a1], exe
)
print('input and output names are: ', a.name, b.name, a1.name)
tools/cinn/paddle_benchmark/test_paddle_ops.py
浏览文件 @ bfc64801
......@@ -17,7 +17,7 @@ import time
import numpy as np
import paddle
from paddle import fluid, static
from paddle import static
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor
......@@ -84,7 +84,7 @@ def create_model(input_names, input_shapes, input_dtypes, fn, attrs=None):
model_name += "_" + str(input_shapes[0][i])
print("save model:", model_name)
fluid.io.save_inference_model(model_name, input_args_names, [res], exe)
paddle.static.io.save_inference_model(model_name, input_args, [res], exe)
print('output name is: ', res.name)
......
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