wehub-resource-sync
618 lines
20 KiB
Python
618 lines
20 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import numpy as np
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import paddle
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from paddle.base.core import Place, VarDesc
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from paddle.base.libpaddle import DataType
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from paddle.base.libpaddle.pir import (
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Program,
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get_current_insertion_point,
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reset_insertion_point_to_start,
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set_insertion_point,
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set_insertion_point_to_block_end,
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)
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from .._pir_ops import data, parameter, set_parameter, set_persistable_value
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from ..base import unique_name
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from ..base.core import set_static_op_arg_pre_cast_hook
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from ..base.wrapped_decorator import signature_safe_contextmanager
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vartype_to_datatype = {
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VarDesc.VarType.FP32: DataType.FLOAT32,
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VarDesc.VarType.FP64: DataType.FLOAT64,
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VarDesc.VarType.FP16: DataType.FLOAT16,
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VarDesc.VarType.BF16: DataType.BFLOAT16,
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VarDesc.VarType.INT32: DataType.INT32,
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VarDesc.VarType.INT16: DataType.INT16,
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VarDesc.VarType.INT64: DataType.INT64,
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VarDesc.VarType.BOOL: DataType.BOOL,
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VarDesc.VarType.UINT8: DataType.UINT8,
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VarDesc.VarType.UINT16: DataType.UINT16,
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VarDesc.VarType.UINT32: DataType.UINT32,
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VarDesc.VarType.UINT64: DataType.UINT64,
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VarDesc.VarType.INT8: DataType.INT8,
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VarDesc.VarType.COMPLEX64: DataType.COMPLEX64,
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VarDesc.VarType.COMPLEX128: DataType.COMPLEX128,
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VarDesc.VarType.FP8_E4M3FN: DataType.FLOAT8_E4M3FN,
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VarDesc.VarType.FP8_E5M2: DataType.FLOAT8_E5M2,
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VarDesc.VarType.STRING: DataType.PSTRING,
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VarDesc.VarType.RAW: DataType.ALL_DTYPE,
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}
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datatype_to_vartype = {v: k for k, v in vartype_to_datatype.items()}
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nptype_to_datatype = {
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np.dtype("float32"): DataType.FLOAT32,
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np.dtype("float64"): DataType.FLOAT64,
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np.dtype("float16"): DataType.FLOAT16,
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np.dtype("int32"): DataType.INT32,
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np.dtype("int16"): DataType.INT16,
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np.dtype("int64"): DataType.INT64,
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np.dtype("bool_"): DataType.BOOL,
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np.dtype("uint16"): DataType.BFLOAT16,
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np.dtype("uint8"): DataType.UINT8,
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np.dtype("uint32"): DataType.UINT32,
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np.dtype("uint64"): DataType.UINT64,
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np.dtype("int8"): DataType.INT8,
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np.dtype("complex64"): DataType.COMPLEX64,
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np.dtype("complex128"): DataType.COMPLEX128,
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np.float16: DataType.FLOAT16,
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np.float32: DataType.FLOAT32,
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np.float64: DataType.FLOAT64,
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np.int32: DataType.INT32,
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np.int16: DataType.INT16,
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np.int64: DataType.INT64,
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np.bool_: DataType.BOOL,
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np.uint16: DataType.BFLOAT16,
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np.uint8: DataType.UINT8,
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np.uint32: DataType.UINT32,
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np.uint64: DataType.UINT64,
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np.int8: DataType.INT8,
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np.complex64: DataType.COMPLEX64,
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np.complex128: DataType.COMPLEX128,
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}
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datatype_to_str = {
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DataType.BOOL: 'bool',
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DataType.FLOAT16: 'float16',
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DataType.BFLOAT16: 'uint16',
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DataType.FLOAT32: 'float32',
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DataType.FLOAT64: 'float64',
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DataType.INT8: 'int8',
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DataType.INT16: 'int16',
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DataType.INT32: 'int32',
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DataType.INT64: 'int64',
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DataType.UINT8: 'uint8',
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DataType.UINT16: 'uint16',
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DataType.UINT32: 'uint32',
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DataType.UINT64: 'uint64',
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DataType.COMPLEX64: 'complex64',
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DataType.COMPLEX128: 'complex128',
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DataType.FLOAT8_E4M3FN: 'float8_e4m3fn',
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DataType.FLOAT8_E5M2: 'float8_e5m2',
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}
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str_to_datatype = {
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"float32": DataType.FLOAT32,
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"float64": DataType.FLOAT64,
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"float16": DataType.FLOAT16,
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"int32": DataType.INT32,
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"int16": DataType.INT16,
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"int64": DataType.INT64,
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"bool": DataType.BOOL,
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"bool_": DataType.BOOL,
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"uint16": DataType.BFLOAT16,
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"uint8": DataType.UINT8,
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"uint32": DataType.UINT32,
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"uint64": DataType.UINT64,
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"int8": DataType.INT8,
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"complex64": DataType.COMPLEX64,
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"complex128": DataType.COMPLEX128,
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"bfloat16": DataType.BFLOAT16,
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"float8_e4m3fn": DataType.FLOAT8_E4M3FN,
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"float8_e5m2": DataType.FLOAT8_E5M2,
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}
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def convert_nptype_to_datatype(np_dtype) -> DataType:
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"""
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Convert a NumPy or string dtype to Paddle PIR DataType.
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Args:
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np_dtype (np.dtype|str): The data type in numpy or valid data type
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string.
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Returns:
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DataType : The data type in Paddle.
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"""
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# Convert the data type string to numpy data type.
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if isinstance(np_dtype, str):
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key = np_dtype.lower().strip()
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if key in str_to_datatype:
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return str_to_datatype[key]
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dtype = np.dtype(np_dtype)
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if dtype in nptype_to_datatype:
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return nptype_to_datatype[dtype]
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else:
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raise ValueError(f"Not supported numpy dtype {dtype}")
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# program is a global instance.
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_main_program_ = Program()
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# set the global program for c++ and this program will be used to build ops in c++
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set_insertion_point_to_block_end(_main_program_.global_block())
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_startup_program_ = Program()
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def default_startup_program():
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"""
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Get default/global startup program.
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The :code:`paddle.nn` function will append the initialization operators into startup program.
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The :code:`startup_program` will initialize the parameters by the OPs.
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This method will return the default or the current startup program. Users can use
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:ref:`api_paddle_ir_core_program_guard` to switch :ref:`api_paddle_ir_Program` .
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Returns:
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Program: current default startup program.
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Returns type:
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.enable_static()
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>>> x = paddle.static.data(name="x", shape=[-1, 784], dtype='float32')
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>>> out = paddle.static.nn.fc(name="fc", x=x, size=10, activation="relu")
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>>> print("main program is: {}".format(paddle.static.default_main_program()))
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>>> print("start up program is: {}".format(paddle.static.default_startup_program()))
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"""
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return _startup_program_
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def default_main_program():
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"""
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This API can be used to get ``default main program`` which store the
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descriptions of Ops and tensors.
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For example ``z = paddle.add(x, y)`` will create a new ``add``
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Op and a new ``z`` tensor, and they will be recorded in ``default main program`` .
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The ``default main program`` is the default value for ``Program`` parameter in
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a lot of APIs. For example, the :code:`Executor.run()` will execute the
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:code:`default_main_program` when the program is not specified.
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If you want to switch the ``default main program``, you can use :ref:`api_paddle_ir_core_program_guard` .
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Returns:
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Program: A ``Program`` which holding the descriptions of OPs and tensors in the network.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.enable_static()
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>>> # Sample Network:
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>>> x = paddle.static.data(name='x', shape=[100, 100], dtype='float32')
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>>> y = paddle.static.data(name='y', shape=[100, 100], dtype='float32')
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>>> out = paddle.add(x, y)
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>>> # print the number of blocks in the program, 1 in this case
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>>> print(paddle.static.default_main_program().num_blocks) # 1
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>>> # print the default_main_program
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>>> print(paddle.static.default_main_program())
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"""
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return _main_program_
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def switch_main_program(program, insertion_point=None):
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"""
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Switch the main program to a new program.
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Args:
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program(Program): The new main program
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Returns:
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Program: The previous main program
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"""
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global _main_program_
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prev_program = _main_program_
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prev_insertion_point = get_current_insertion_point()
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_main_program_ = program
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if program == prev_program and insertion_point is None:
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insertion_point = prev_insertion_point
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if insertion_point is None:
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set_insertion_point_to_block_end(_main_program_.global_block())
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else:
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set_insertion_point(insertion_point)
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return prev_program, prev_insertion_point
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def switch_startup_program(program):
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"""
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Switch the startup program to a new program
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Args:
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program(Program): The new startup program
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Returns:
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Program: The previous startup program
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"""
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global _startup_program_
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prev_program = _startup_program_
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_startup_program_ = program
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return prev_program
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@signature_safe_contextmanager
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def program_guard(main_program, startup_program=None):
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"""
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:api_attr: Static Graph
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Change the global main program and startup program with ``with`` statement.
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Layer functions in the Python ``with`` block will append operators and
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Tensors to the new main programs.
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Args:
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main_program(Program): New main program inside ``with`` statement.
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startup_program(Program, optional): New startup program inside ``with``
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statement. :code:`None` means not changing startup program,
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default_startup_program is still used.
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Default: None.
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Examples:
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.. code-block:: pycon
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:name: code-example-1
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>>> import paddle
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>>> paddle.enable_static()
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>>> main_program = paddle.static.Program()
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>>> startup_program = paddle.static.Program()
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>>> with paddle.static.program_guard(main_program, startup_program):
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... data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
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... hidden = paddle.static.nn.fc(x=data, size=10, activation='relu')
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Notes: The temporary :code:`Program` can be used if the user does not need
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to construct either of startup program or main program.
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Examples:
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.. code-block:: pycon
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:name: code-example-2
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>>> import paddle
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>>> paddle.enable_static()
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>>> main_program = paddle.static.Program()
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>>> # does not care about startup program. Just pass a temporary value.
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>>> with paddle.static.program_guard(main_program, paddle.static.Program()):
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... data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
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"""
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from ..base.data_feeder import check_type
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check_type(
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main_program, 'main_program', Program, 'paddle.static.program_guard'
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)
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main_program, prev_insertion_point = switch_main_program(main_program)
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if startup_program is not None:
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check_type(
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startup_program,
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'startup_program',
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Program,
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'paddle.static.program_guard',
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)
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startup_program = switch_startup_program(startup_program)
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try:
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yield
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finally:
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switch_main_program(main_program, prev_insertion_point)
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if startup_program is not None:
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switch_startup_program(startup_program)
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class ParameterMeta:
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def __init__(self, shape, dtype):
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self.shape = shape
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self.dtype = dtype
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def create_parameter(
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dtype,
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shape,
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name=None,
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**kwargs,
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):
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if 'initializer' not in kwargs:
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raise ValueError(
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"initializer is None, if you want to create parameter, please pass its initializer."
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)
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if dtype is not None:
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if not isinstance(dtype, DataType):
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dtype = convert_nptype_to_datatype(dtype)
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value_name = name
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if not value_name:
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value_name = unique_name.generate('parameter')
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startup_program = default_startup_program()
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main_program = default_main_program()
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parameter_meta = ParameterMeta(shape, dtype)
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is_dist = False
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if (
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'placements' in kwargs
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and kwargs['placements']
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and 'process_mesh' in kwargs
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and kwargs['process_mesh']
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):
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is_dist = True
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def to_dist(value):
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import paddle
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import paddle.distributed as dist
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process_mesh = kwargs['process_mesh']
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dim_map, partial_status = dist.auto_parallel.placement_type.to_dim_map(
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kwargs['placements'], len(shape)
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)
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dist_attr = paddle.base.libpaddle.pir.create_tensor_dist_attribute(
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process_mesh, dim_map, partial_status
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)
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dist_type = paddle.base.libpaddle.pir.cvt_to_dist_type(
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value.type(), dist_attr
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)
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value.set_type(dist_type)
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op_dist_attr = paddle.base.libpaddle.pir.create_op_dist_attribute(
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process_mesh, [], [dist_attr]
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)
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value.get_defining_op().dist_attr = op_dist_attr
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with program_guard(startup_program):
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initializer = kwargs['initializer']
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init_result = initializer(
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parameter_meta, startup_program.global_block()
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)
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init_result.persistable = True
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if is_dist:
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to_dist(init_result)
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set_parameter(init_result, value_name)
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main_program.set_parameters_from(startup_program)
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with program_guard(default_main_program()):
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reset_insertion_point_to_start()
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param = parameter(value_name)
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param.persistable = True
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if is_dist:
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to_dist(param)
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param.trainable = kwargs.get('trainable', True)
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param.stop_gradient = not param.trainable
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param.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
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param.regularizer = kwargs.get('regularizer', None)
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param.do_model_average = kwargs.get('do_model_average', None)
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param.need_clip = kwargs.get('need_clip', True)
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param.is_distributed = False
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param.is_parameter = True
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return param
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def create_persistable_value(dtype, shape, name=None, **kwargs):
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"""
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Create Value that is persistable in startup program and main program. The Value is initialized in startup program and
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used in main program.
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Returns:
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Value: The created Value from main program
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"""
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if 'initializer' not in kwargs:
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raise ValueError(
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"initializer is None, if you want to create parameter, please pass its initializer."
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)
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if dtype is not None:
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if not isinstance(dtype, DataType):
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dtype = convert_nptype_to_datatype(dtype)
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value_name = name
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if not value_name:
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value_name = unique_name.generate('persistable_value')
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is_dist = 'dist_attr' in kwargs and kwargs['dist_attr']
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def to_dist(value):
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import paddle
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dist_attr = kwargs['dist_attr']
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dist_type = paddle.base.libpaddle.pir.cvt_to_dist_type(
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value.type(), dist_attr
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)
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value.set_type(dist_type)
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op_dist_attr = paddle.base.libpaddle.pir.create_op_dist_attribute(
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dist_attr.process_mesh, [], [dist_attr]
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)
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define_op = value.get_defining_op()
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define_op.dist_attr = op_dist_attr
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if define_op.has_attr("shape"):
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define_op.set_int_array_attr("shape", value._local_shape)
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startup_program = default_startup_program()
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main_program = default_main_program()
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with program_guard(startup_program):
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initializer = kwargs['initializer']
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parameter_meta = ParameterMeta(shape, dtype)
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init_result = initializer(
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parameter_meta, startup_program.global_block()
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)
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init_result.persistable = True
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if is_dist:
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to_dist(init_result)
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set_persistable_value(init_result, value_name)
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with program_guard(default_main_program()):
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reset_insertion_point_to_start()
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persist_value = data(value_name, shape, dtype, Place())
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persist_value.persistable = True
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if is_dist:
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to_dist(persist_value)
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return persist_value
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def _get_persistable_value(target_program, value_info):
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"""
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Get a persistable value from a target program by using value that is in other program.
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"""
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with program_guard(target_program):
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target_value = data(
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value_info.name, value_info.shape, value_info.dtype, Place()
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)
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target_value.persistable = True
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return target_value
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def _get_parameter(target_program, param_info):
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"""
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Get a parameter from a target program by using parameter that is in other program.
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"""
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target_program.set_parameters_from(default_startup_program())
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with program_guard(target_program):
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target_param = parameter(param_info.name)
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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)
|