chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,617 @@
|
||||
# Copyright (c) 2023 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 annotations
|
||||
|
||||
import numpy as np
|
||||
|
||||
import paddle
|
||||
from paddle.base.core import Place, VarDesc
|
||||
from paddle.base.libpaddle import DataType
|
||||
from paddle.base.libpaddle.pir import (
|
||||
Program,
|
||||
get_current_insertion_point,
|
||||
reset_insertion_point_to_start,
|
||||
set_insertion_point,
|
||||
set_insertion_point_to_block_end,
|
||||
)
|
||||
|
||||
from .._pir_ops import data, parameter, set_parameter, set_persistable_value
|
||||
from ..base import unique_name
|
||||
from ..base.core import set_static_op_arg_pre_cast_hook
|
||||
from ..base.wrapped_decorator import signature_safe_contextmanager
|
||||
|
||||
vartype_to_datatype = {
|
||||
VarDesc.VarType.FP32: DataType.FLOAT32,
|
||||
VarDesc.VarType.FP64: DataType.FLOAT64,
|
||||
VarDesc.VarType.FP16: DataType.FLOAT16,
|
||||
VarDesc.VarType.BF16: DataType.BFLOAT16,
|
||||
VarDesc.VarType.INT32: DataType.INT32,
|
||||
VarDesc.VarType.INT16: DataType.INT16,
|
||||
VarDesc.VarType.INT64: DataType.INT64,
|
||||
VarDesc.VarType.BOOL: DataType.BOOL,
|
||||
VarDesc.VarType.UINT8: DataType.UINT8,
|
||||
VarDesc.VarType.UINT16: DataType.UINT16,
|
||||
VarDesc.VarType.UINT32: DataType.UINT32,
|
||||
VarDesc.VarType.UINT64: DataType.UINT64,
|
||||
VarDesc.VarType.INT8: DataType.INT8,
|
||||
VarDesc.VarType.COMPLEX64: DataType.COMPLEX64,
|
||||
VarDesc.VarType.COMPLEX128: DataType.COMPLEX128,
|
||||
VarDesc.VarType.FP8_E4M3FN: DataType.FLOAT8_E4M3FN,
|
||||
VarDesc.VarType.FP8_E5M2: DataType.FLOAT8_E5M2,
|
||||
VarDesc.VarType.STRING: DataType.PSTRING,
|
||||
VarDesc.VarType.RAW: DataType.ALL_DTYPE,
|
||||
}
|
||||
|
||||
datatype_to_vartype = {v: k for k, v in vartype_to_datatype.items()}
|
||||
|
||||
nptype_to_datatype = {
|
||||
np.dtype("float32"): DataType.FLOAT32,
|
||||
np.dtype("float64"): DataType.FLOAT64,
|
||||
np.dtype("float16"): DataType.FLOAT16,
|
||||
np.dtype("int32"): DataType.INT32,
|
||||
np.dtype("int16"): DataType.INT16,
|
||||
np.dtype("int64"): DataType.INT64,
|
||||
np.dtype("bool_"): DataType.BOOL,
|
||||
np.dtype("uint16"): DataType.BFLOAT16,
|
||||
np.dtype("uint8"): DataType.UINT8,
|
||||
np.dtype("uint32"): DataType.UINT32,
|
||||
np.dtype("uint64"): DataType.UINT64,
|
||||
np.dtype("int8"): DataType.INT8,
|
||||
np.dtype("complex64"): DataType.COMPLEX64,
|
||||
np.dtype("complex128"): DataType.COMPLEX128,
|
||||
np.float16: DataType.FLOAT16,
|
||||
np.float32: DataType.FLOAT32,
|
||||
np.float64: DataType.FLOAT64,
|
||||
np.int32: DataType.INT32,
|
||||
np.int16: DataType.INT16,
|
||||
np.int64: DataType.INT64,
|
||||
np.bool_: DataType.BOOL,
|
||||
np.uint16: DataType.BFLOAT16,
|
||||
np.uint8: DataType.UINT8,
|
||||
np.uint32: DataType.UINT32,
|
||||
np.uint64: DataType.UINT64,
|
||||
np.int8: DataType.INT8,
|
||||
np.complex64: DataType.COMPLEX64,
|
||||
np.complex128: DataType.COMPLEX128,
|
||||
}
|
||||
|
||||
datatype_to_str = {
|
||||
DataType.BOOL: 'bool',
|
||||
DataType.FLOAT16: 'float16',
|
||||
DataType.BFLOAT16: 'uint16',
|
||||
DataType.FLOAT32: 'float32',
|
||||
DataType.FLOAT64: 'float64',
|
||||
DataType.INT8: 'int8',
|
||||
DataType.INT16: 'int16',
|
||||
DataType.INT32: 'int32',
|
||||
DataType.INT64: 'int64',
|
||||
DataType.UINT8: 'uint8',
|
||||
DataType.UINT16: 'uint16',
|
||||
DataType.UINT32: 'uint32',
|
||||
DataType.UINT64: 'uint64',
|
||||
DataType.COMPLEX64: 'complex64',
|
||||
DataType.COMPLEX128: 'complex128',
|
||||
DataType.FLOAT8_E4M3FN: 'float8_e4m3fn',
|
||||
DataType.FLOAT8_E5M2: 'float8_e5m2',
|
||||
}
|
||||
|
||||
|
||||
str_to_datatype = {
|
||||
"float32": DataType.FLOAT32,
|
||||
"float64": DataType.FLOAT64,
|
||||
"float16": DataType.FLOAT16,
|
||||
"int32": DataType.INT32,
|
||||
"int16": DataType.INT16,
|
||||
"int64": DataType.INT64,
|
||||
"bool": DataType.BOOL,
|
||||
"bool_": DataType.BOOL,
|
||||
"uint16": DataType.BFLOAT16,
|
||||
"uint8": DataType.UINT8,
|
||||
"uint32": DataType.UINT32,
|
||||
"uint64": DataType.UINT64,
|
||||
"int8": DataType.INT8,
|
||||
"complex64": DataType.COMPLEX64,
|
||||
"complex128": DataType.COMPLEX128,
|
||||
"bfloat16": DataType.BFLOAT16,
|
||||
"float8_e4m3fn": DataType.FLOAT8_E4M3FN,
|
||||
"float8_e5m2": DataType.FLOAT8_E5M2,
|
||||
}
|
||||
|
||||
|
||||
def convert_nptype_to_datatype(np_dtype) -> DataType:
|
||||
"""
|
||||
Convert a NumPy or string dtype to Paddle PIR DataType.
|
||||
|
||||
Args:
|
||||
np_dtype (np.dtype|str): The data type in numpy or valid data type
|
||||
string.
|
||||
|
||||
Returns:
|
||||
DataType : The data type in Paddle.
|
||||
|
||||
"""
|
||||
# Convert the data type string to numpy data type.
|
||||
if isinstance(np_dtype, str):
|
||||
key = np_dtype.lower().strip()
|
||||
if key in str_to_datatype:
|
||||
return str_to_datatype[key]
|
||||
dtype = np.dtype(np_dtype)
|
||||
if dtype in nptype_to_datatype:
|
||||
return nptype_to_datatype[dtype]
|
||||
else:
|
||||
raise ValueError(f"Not supported numpy dtype {dtype}")
|
||||
|
||||
|
||||
# program is a global instance.
|
||||
_main_program_ = Program()
|
||||
# set the global program for c++ and this program will be used to build ops in c++
|
||||
set_insertion_point_to_block_end(_main_program_.global_block())
|
||||
|
||||
_startup_program_ = Program()
|
||||
|
||||
|
||||
def default_startup_program():
|
||||
"""
|
||||
Get default/global startup program.
|
||||
|
||||
The :code:`paddle.nn` function will append the initialization operators into startup program.
|
||||
The :code:`startup_program` will initialize the parameters by the OPs.
|
||||
|
||||
This method will return the default or the current startup program. Users can use
|
||||
:ref:`api_paddle_ir_core_program_guard` to switch :ref:`api_paddle_ir_Program` .
|
||||
|
||||
Returns:
|
||||
Program: current default startup program.
|
||||
|
||||
Returns type:
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
|
||||
>>> paddle.enable_static()
|
||||
>>> x = paddle.static.data(name="x", shape=[-1, 784], dtype='float32')
|
||||
>>> out = paddle.static.nn.fc(name="fc", x=x, size=10, activation="relu")
|
||||
>>> print("main program is: {}".format(paddle.static.default_main_program()))
|
||||
>>> print("start up program is: {}".format(paddle.static.default_startup_program()))
|
||||
"""
|
||||
return _startup_program_
|
||||
|
||||
|
||||
def default_main_program():
|
||||
"""
|
||||
This API can be used to get ``default main program`` which store the
|
||||
descriptions of Ops and tensors.
|
||||
|
||||
For example ``z = paddle.add(x, y)`` will create a new ``add``
|
||||
Op and a new ``z`` tensor, and they will be recorded in ``default main program`` .
|
||||
|
||||
The ``default main program`` is the default value for ``Program`` parameter in
|
||||
a lot of APIs. For example, the :code:`Executor.run()` will execute the
|
||||
:code:`default_main_program` when the program is not specified.
|
||||
|
||||
If you want to switch the ``default main program``, you can use :ref:`api_paddle_ir_core_program_guard` .
|
||||
|
||||
Returns:
|
||||
Program: A ``Program`` which holding the descriptions of OPs and tensors in the network.
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
|
||||
>>> paddle.enable_static()
|
||||
>>> # Sample Network:
|
||||
>>> x = paddle.static.data(name='x', shape=[100, 100], dtype='float32')
|
||||
>>> y = paddle.static.data(name='y', shape=[100, 100], dtype='float32')
|
||||
>>> out = paddle.add(x, y)
|
||||
|
||||
>>> # print the number of blocks in the program, 1 in this case
|
||||
>>> print(paddle.static.default_main_program().num_blocks) # 1
|
||||
>>> # print the default_main_program
|
||||
>>> print(paddle.static.default_main_program())
|
||||
"""
|
||||
return _main_program_
|
||||
|
||||
|
||||
def switch_main_program(program, insertion_point=None):
|
||||
"""
|
||||
Switch the main program to a new program.
|
||||
|
||||
Args:
|
||||
program(Program): The new main program
|
||||
|
||||
Returns:
|
||||
Program: The previous main program
|
||||
"""
|
||||
global _main_program_
|
||||
prev_program = _main_program_
|
||||
prev_insertion_point = get_current_insertion_point()
|
||||
_main_program_ = program
|
||||
if program == prev_program and insertion_point is None:
|
||||
insertion_point = prev_insertion_point
|
||||
if insertion_point is None:
|
||||
set_insertion_point_to_block_end(_main_program_.global_block())
|
||||
else:
|
||||
set_insertion_point(insertion_point)
|
||||
return prev_program, prev_insertion_point
|
||||
|
||||
|
||||
def switch_startup_program(program):
|
||||
"""
|
||||
Switch the startup program to a new program
|
||||
Args:
|
||||
program(Program): The new startup program
|
||||
|
||||
Returns:
|
||||
Program: The previous startup program
|
||||
"""
|
||||
global _startup_program_
|
||||
prev_program = _startup_program_
|
||||
_startup_program_ = program
|
||||
return prev_program
|
||||
|
||||
|
||||
@signature_safe_contextmanager
|
||||
def program_guard(main_program, startup_program=None):
|
||||
"""
|
||||
:api_attr: Static Graph
|
||||
|
||||
Change the global main program and startup program with ``with`` statement.
|
||||
Layer functions in the Python ``with`` block will append operators and
|
||||
Tensors to the new main programs.
|
||||
|
||||
Args:
|
||||
main_program(Program): New main program inside ``with`` statement.
|
||||
startup_program(Program, optional): New startup program inside ``with``
|
||||
statement. :code:`None` means not changing startup program,
|
||||
default_startup_program is still used.
|
||||
Default: None.
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
:name: code-example-1
|
||||
|
||||
>>> import paddle
|
||||
|
||||
>>> paddle.enable_static()
|
||||
>>> main_program = paddle.static.Program()
|
||||
>>> startup_program = paddle.static.Program()
|
||||
>>> with paddle.static.program_guard(main_program, startup_program):
|
||||
... data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
|
||||
... hidden = paddle.static.nn.fc(x=data, size=10, activation='relu')
|
||||
|
||||
Notes: The temporary :code:`Program` can be used if the user does not need
|
||||
to construct either of startup program or main program.
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
:name: code-example-2
|
||||
|
||||
>>> import paddle
|
||||
|
||||
>>> paddle.enable_static()
|
||||
>>> main_program = paddle.static.Program()
|
||||
>>> # does not care about startup program. Just pass a temporary value.
|
||||
>>> with paddle.static.program_guard(main_program, paddle.static.Program()):
|
||||
... data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
|
||||
"""
|
||||
from ..base.data_feeder import check_type
|
||||
|
||||
check_type(
|
||||
main_program, 'main_program', Program, 'paddle.static.program_guard'
|
||||
)
|
||||
main_program, prev_insertion_point = switch_main_program(main_program)
|
||||
if startup_program is not None:
|
||||
check_type(
|
||||
startup_program,
|
||||
'startup_program',
|
||||
Program,
|
||||
'paddle.static.program_guard',
|
||||
)
|
||||
startup_program = switch_startup_program(startup_program)
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
switch_main_program(main_program, prev_insertion_point)
|
||||
if startup_program is not None:
|
||||
switch_startup_program(startup_program)
|
||||
|
||||
|
||||
class ParameterMeta:
|
||||
def __init__(self, shape, dtype):
|
||||
self.shape = shape
|
||||
self.dtype = dtype
|
||||
|
||||
|
||||
def create_parameter(
|
||||
dtype,
|
||||
shape,
|
||||
name=None,
|
||||
**kwargs,
|
||||
):
|
||||
if 'initializer' not in kwargs:
|
||||
raise ValueError(
|
||||
"initializer is None, if you want to create parameter, please pass its initializer."
|
||||
)
|
||||
if dtype is not None:
|
||||
if not isinstance(dtype, DataType):
|
||||
dtype = convert_nptype_to_datatype(dtype)
|
||||
value_name = name
|
||||
if not value_name:
|
||||
value_name = unique_name.generate('parameter')
|
||||
startup_program = default_startup_program()
|
||||
main_program = default_main_program()
|
||||
parameter_meta = ParameterMeta(shape, dtype)
|
||||
|
||||
is_dist = False
|
||||
if (
|
||||
'placements' in kwargs
|
||||
and kwargs['placements']
|
||||
and 'process_mesh' in kwargs
|
||||
and kwargs['process_mesh']
|
||||
):
|
||||
is_dist = True
|
||||
|
||||
def to_dist(value):
|
||||
import paddle
|
||||
import paddle.distributed as dist
|
||||
|
||||
process_mesh = kwargs['process_mesh']
|
||||
dim_map, partial_status = dist.auto_parallel.placement_type.to_dim_map(
|
||||
kwargs['placements'], len(shape)
|
||||
)
|
||||
dist_attr = paddle.base.libpaddle.pir.create_tensor_dist_attribute(
|
||||
process_mesh, dim_map, partial_status
|
||||
)
|
||||
dist_type = paddle.base.libpaddle.pir.cvt_to_dist_type(
|
||||
value.type(), dist_attr
|
||||
)
|
||||
value.set_type(dist_type)
|
||||
op_dist_attr = paddle.base.libpaddle.pir.create_op_dist_attribute(
|
||||
process_mesh, [], [dist_attr]
|
||||
)
|
||||
value.get_defining_op().dist_attr = op_dist_attr
|
||||
|
||||
with program_guard(startup_program):
|
||||
initializer = kwargs['initializer']
|
||||
init_result = initializer(
|
||||
parameter_meta, startup_program.global_block()
|
||||
)
|
||||
init_result.persistable = True
|
||||
if is_dist:
|
||||
to_dist(init_result)
|
||||
|
||||
set_parameter(init_result, value_name)
|
||||
|
||||
main_program.set_parameters_from(startup_program)
|
||||
with program_guard(default_main_program()):
|
||||
reset_insertion_point_to_start()
|
||||
param = parameter(value_name)
|
||||
param.persistable = True
|
||||
|
||||
if is_dist:
|
||||
to_dist(param)
|
||||
|
||||
param.trainable = kwargs.get('trainable', True)
|
||||
param.stop_gradient = not param.trainable
|
||||
param.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
|
||||
param.regularizer = kwargs.get('regularizer', None)
|
||||
param.do_model_average = kwargs.get('do_model_average', None)
|
||||
param.need_clip = kwargs.get('need_clip', True)
|
||||
param.is_distributed = False
|
||||
param.is_parameter = True
|
||||
return param
|
||||
|
||||
|
||||
def create_persistable_value(dtype, shape, name=None, **kwargs):
|
||||
"""
|
||||
Create Value that is persistable in startup program and main program. The Value is initialized in startup program and
|
||||
used in main program.
|
||||
|
||||
Returns:
|
||||
Value: The created Value from main program
|
||||
"""
|
||||
if 'initializer' not in kwargs:
|
||||
raise ValueError(
|
||||
"initializer is None, if you want to create parameter, please pass its initializer."
|
||||
)
|
||||
if dtype is not None:
|
||||
if not isinstance(dtype, DataType):
|
||||
dtype = convert_nptype_to_datatype(dtype)
|
||||
value_name = name
|
||||
if not value_name:
|
||||
value_name = unique_name.generate('persistable_value')
|
||||
|
||||
is_dist = 'dist_attr' in kwargs and kwargs['dist_attr']
|
||||
|
||||
def to_dist(value):
|
||||
import paddle
|
||||
|
||||
dist_attr = kwargs['dist_attr']
|
||||
dist_type = paddle.base.libpaddle.pir.cvt_to_dist_type(
|
||||
value.type(), dist_attr
|
||||
)
|
||||
value.set_type(dist_type)
|
||||
op_dist_attr = paddle.base.libpaddle.pir.create_op_dist_attribute(
|
||||
dist_attr.process_mesh, [], [dist_attr]
|
||||
)
|
||||
define_op = value.get_defining_op()
|
||||
define_op.dist_attr = op_dist_attr
|
||||
if define_op.has_attr("shape"):
|
||||
define_op.set_int_array_attr("shape", value._local_shape)
|
||||
|
||||
startup_program = default_startup_program()
|
||||
main_program = default_main_program()
|
||||
|
||||
with program_guard(startup_program):
|
||||
initializer = kwargs['initializer']
|
||||
parameter_meta = ParameterMeta(shape, dtype)
|
||||
init_result = initializer(
|
||||
parameter_meta, startup_program.global_block()
|
||||
)
|
||||
init_result.persistable = True
|
||||
if is_dist:
|
||||
to_dist(init_result)
|
||||
set_persistable_value(init_result, value_name)
|
||||
|
||||
with program_guard(default_main_program()):
|
||||
reset_insertion_point_to_start()
|
||||
persist_value = data(value_name, shape, dtype, Place())
|
||||
persist_value.persistable = True
|
||||
if is_dist:
|
||||
to_dist(persist_value)
|
||||
return persist_value
|
||||
|
||||
|
||||
def _get_persistable_value(target_program, value_info):
|
||||
"""
|
||||
Get a persistable value from a target program by using value that is in other program.
|
||||
"""
|
||||
with program_guard(target_program):
|
||||
target_value = data(
|
||||
value_info.name, value_info.shape, value_info.dtype, Place()
|
||||
)
|
||||
target_value.persistable = True
|
||||
return target_value
|
||||
|
||||
|
||||
def _get_parameter(target_program, param_info):
|
||||
"""
|
||||
Get a parameter from a target program by using parameter that is in other program.
|
||||
"""
|
||||
target_program.set_parameters_from(default_startup_program())
|
||||
with program_guard(target_program):
|
||||
target_param = parameter(param_info.name)
|
||||
target_param.persistable = True
|
||||
target_param.stop_gradient = param_info.stop_gradient
|
||||
|
||||
if hasattr(param_info, 'regularizer'):
|
||||
target_param.regularizer = param_info.regularizer
|
||||
if hasattr(param_info, 'need_clip'):
|
||||
target_param.need_clip = param_info.need_clip
|
||||
return target_param
|
||||
|
||||
|
||||
def _convert_into_value(tensor):
|
||||
"""
|
||||
Convert Tensor into Value.
|
||||
"""
|
||||
import paddle
|
||||
from paddle.jit.dy2static.parameter_recorder import (
|
||||
_global_parameter_recorder,
|
||||
)
|
||||
|
||||
if isinstance(tensor, paddle.Tensor):
|
||||
value = _global_parameter_recorder.get(
|
||||
paddle.pir.core.default_main_program(), tensor
|
||||
)
|
||||
NON_PERSISTABLE_VAR_NAME_SUFFIX = "__non_persistable"
|
||||
# NOTE(SigureMo): Why do not use `tensor.name.endswith(NON_PERSISTABLE_VAR_NAME_SUFFIX)`?
|
||||
# Because the tensor maybe copied, the name of the tensor will be appended with a new suffix.
|
||||
# Such as `lstm_0.dropout_state__non_persistable_deepcopy_204`
|
||||
if NON_PERSISTABLE_VAR_NAME_SUFFIX in tensor.name:
|
||||
value.persistable = False
|
||||
return value
|
||||
|
||||
return tensor
|
||||
|
||||
|
||||
@signature_safe_contextmanager
|
||||
def static_op_arg_cast_guard(hook):
|
||||
"""
|
||||
Set a hook function to cast the arguments of static op.
|
||||
"""
|
||||
|
||||
original_callback = set_static_op_arg_pre_cast_hook(hook)
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
set_static_op_arg_pre_cast_hook(original_callback)
|
||||
|
||||
|
||||
def set_state_dict(program, state_dict, scope=None):
|
||||
"""
|
||||
Set parameters and persistable buffers in state_dict to program.
|
||||
An exception will throw if shape or dtype of the parameters is not match.
|
||||
|
||||
.. note::
|
||||
This function MUST called after run start_up_program
|
||||
|
||||
Args:
|
||||
state_dict(dict): the dict store parameters and persistable buffers.
|
||||
The key is the name of the parameter or the name of the buffer.
|
||||
The value is the tensor of this variable in the given scope.
|
||||
scope(Scope, optional) : If scope is None, state_dict will be set to global scope
|
||||
obtained through 'paddle.static.global_scope()'. Otherwise, value will be set to scope.
|
||||
Default: None
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
>>> import paddle.static as static
|
||||
|
||||
>>> paddle.enable_static()
|
||||
|
||||
>>> x = static.data(name="x", shape=[10, 10], dtype='float32')
|
||||
>>> y = static.nn.fc(x, 10)
|
||||
>>> z = static.nn.fc(y, 10)
|
||||
|
||||
>>> place = paddle.CPUPlace()
|
||||
>>> exe = static.Executor(place)
|
||||
>>> exe.run(static.default_startup_program())
|
||||
>>> prog = static.default_main_program()
|
||||
|
||||
>>> path = "./temp/model.pdparams"
|
||||
>>> paddle.save(prog.state_dict(), path)
|
||||
>>> state_dict_load = paddle.load(path)
|
||||
>>> prog.set_state_dict(state_dict_load)
|
||||
"""
|
||||
if not isinstance(state_dict, dict):
|
||||
raise TypeError(
|
||||
f"Type of `state_dict` should be dict, but received {type(state_dict)}."
|
||||
)
|
||||
|
||||
condition = True if "StructuredToParameterName@@" in state_dict else False
|
||||
if condition:
|
||||
clear_state_dict = {}
|
||||
for name, value in state_dict.items():
|
||||
if name == "StructuredToParameterName@@":
|
||||
continue
|
||||
if name in state_dict["StructuredToParameterName@@"]:
|
||||
name = state_dict["StructuredToParameterName@@"][name]
|
||||
clear_state_dict[name] = value
|
||||
else:
|
||||
clear_state_dict[name] = value
|
||||
else:
|
||||
clear_state_dict = state_dict
|
||||
|
||||
for name, value in clear_state_dict.items():
|
||||
if isinstance(value, paddle.base.libpaddle.DenseTensor):
|
||||
continue
|
||||
elif isinstance(value, np.ndarray):
|
||||
clear_state_dict[name] = paddle.to_tensor(value)
|
||||
else:
|
||||
raise TypeError(
|
||||
f"The type of `{name}` should be Tensor, ndarray, but received {type(value)}."
|
||||
)
|
||||
if scope is None:
|
||||
scope = paddle.static.global_scope()
|
||||
program.set_state_dict(clear_state_dict, scope)
|
||||
Reference in New Issue
Block a user