# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Auto mixed precision."""
from __future__ import absolute_import
import mindspore as ms
from mindspore import nn
from mindspore import _checkparam as validator
from mindspore.common import dtype as mstype
from mindspore.nn.wrap.cell_wrapper import _TrainPipelineAccuStepCell
from mindspore.nn.wrap.loss_scale import _TrainPipelineWithLossScaleCell
from mindspore.ops import functional as F
from mindspore.parallel._utils import _get_pipeline_stages
from mindspore.train.loss_scale_manager import DynamicLossScaleManager, LossScaleManager
from mindspore import boost, context
from mindspore.ops import operations as P
from mindspore.ops import Primitive
from mindspore import log as logger
STREE = None
AMP_WHITE_LIST = [
nn.Conv1d,
nn.Conv2d,
nn.Conv3d,
nn.Conv1dTranspose,
nn.Conv2dTranspose,
nn.Conv3dTranspose,
nn.Dense,
nn.LSTMCell,
nn.RNNCell,
nn.GRUCell,
P.Conv2D,
P.Conv3D,
P.Conv2DTranspose,
P.Conv3DTranspose,
P.Conv2DBackpropInput,
P.MatMul,
P.BatchMatMul,
P.PReLU,
P.ReLU,
P.Ger
]
AMP_BLACK_LIST = [
nn.BatchNorm1d,
nn.BatchNorm2d,
nn.BatchNorm3d,
nn.LayerNorm
]
class _OutputTo16(nn.Cell):
"""Wrap cell for amp. Cast network output back to float16."""
def __init__(self, backbone):
super(_OutputTo16, self).__init__(auto_prefix=False)
self._backbone = backbone
if isinstance(backbone, nn.Cell) and backbone.jit_config_dict:
self._jit_config_dict = backbone.jit_config_dict
def construct(self, x):
return F.cast(self._backbone(x), mstype.float16)
class _OutputTo32(nn.Cell):
"""Wrap loss for amp. Cast network output back to float32."""
def __init__(self, backbone):
super(_OutputTo32, self).__init__(auto_prefix=False)
self._backbone = backbone
if isinstance(backbone, nn.Cell) and backbone.jit_config_dict:
self._jit_config_dict = backbone.jit_config_dict
def construct(self, *inputs):
out = self._backbone(*inputs)
return F.mixed_precision_cast(mstype.float32, out)
def _allow_mix_precision(node, allowed_list) -> bool:
"""
Check whether current node need do mix precision. Follow conditions need to be satisfied:
1) Type of node is one of (Primitive, nn.Cell)
2) Node is not P.Cast()
3) to_float(mindspore.float16) is not set in Cell
"""
if node.get_instance() in allowed_list:
return True
if not issubclass(node.get_instance_type(), (Primitive, nn.Cell)):
return False
if isinstance(node.get_instance(), P.Cast):
return False
if issubclass(node.get_instance_type(), nn.Cell):
# if cell is already in allowed_list, it means to_float(mindspore.float16) is set by amp.
# if cell is not in allowed_list, but has to_float(mindspore.float16),
# it means to_float(mindspore.float16) is set by user.
if hasattr(node.get_instance(), "to_float_fp16") and node.get_instance().to_float_fp16:
return False
allowed_list.append(node.get_instance())
return True
def _insert_cast_operator_process(node, stree):
"""insert cast for operators in white_list."""
new_cast_node = None
# insert cast float16 before the primitive operators
if issubclass(node.get_instance_type(), Primitive):
for idx in range(len(node.get_inputs())):
position = stree.before(node)
new_node = P.Cast()
arg = ms.rewrite.ScopedValue.create_name_values([node.get_inputs()[idx].get_targets()[0].value,
"mindspore.float16"])
new_cast_node = ms.rewrite.Node.create_call_cell(new_node,
targets=['x_cast_{}'.format(node.get_name())],
args=arg,
name='incast_{}{}'.format(node.get_name(), idx))
stree.insert(position, new_cast_node)
node.set_arg_by_node(idx, new_cast_node)
# insert cast float16 before the Cell operators
elif issubclass(node.get_instance_type(), nn.Cell):
node.get_instance().to_float(mstype.float16)
# ignore if subclass is not one of (Primitive, nn.Cell)
else:
return
# insert cast float32 after the operators
position = stree.after(node)
new_node = P.Cast()
arg = ms.rewrite.ScopedValue.create_name_values([node.get_targets()[0].value,
"mindspore.float32"])
new_cast_node = ms.rewrite.Node.create_call_cell(new_node,
targets=['x_cast_{}'.format(node.get_name())],
args=arg,
name='outcast_{}'.format(node.get_name()))
# insert node & unique names
stree.insert(position, new_cast_node)
# update argument names
for user in node.get_users():
if user.get_name() == new_cast_node.get_name():
continue
for idx, arg in enumerate(user.get_args()):
if arg == node.get_targets()[0]:
user.set_arg_by_node(idx, new_cast_node)
def _insert_cast_operator_white_list(stree, white_list):
"""insert cast for operators in white_list."""
allowed_list = []
# Ignore if net called ".to_float(mindspore.float16)"
net = stree.get_handler().get_origin_network()
if isinstance(net, nn.Cell) and hasattr(net, "to_float_fp16") and net.to_float_fp16:
return
for node in stree.nodes():
if node.get_targets() is None:
continue
if node.get_node_type() == ms.rewrite.NodeType.CellContainer:
for n in node.get_handler().node_list:
if n.get_node_type() == ms.rewrite.NodeType.Tree:
_insert_cast_operator_white_list(ms.rewrite.TreeNodeHelper.get_sub_tree(ms.rewrite.Node(n)),
white_list)
elif node.get_node_type() == ms.rewrite.NodeType.Tree:
substree = ms.rewrite.TreeNodeHelper.get_sub_tree(node)
_insert_cast_operator_white_list(substree, white_list)
elif node.get_instance_type() in white_list and _allow_mix_precision(node, allowed_list):
_insert_cast_operator_process(node, stree)
def _need_removed_cast_pair(node):
"""check whether the cast pairs should be removed."""
cast_dtypes = ms.rewrite.ScopedValue.create_name_values(["mindspore.float16", "mindspore.float32"])
cast_dtype_f16 = cast_dtypes[0]
cast_dtype_f32 = cast_dtypes[1]
# current node should be P.Cast()(x, mindspore.float32)
if node.get_instance_type() != P.Cast:
return False
node_cast_type = node.get_args()[1]
if node_cast_type != cast_dtype_f32:
return False
# all user nodes should be P.Cast()(x, mindspore.float16) or Cell with to_float(mindspore.float16)
if not node.get_users():
return False
for user in node.get_users():
if isinstance(user.get_instance(), nn.Cell):
if not hasattr(user.get_instance(), "to_float_fp16"):
return False
if not user.get_instance().to_float_fp16:
return False
elif user.get_instance_type() == P.Cast:
user_cast_type = user.get_args()[1]
if user_cast_type != cast_dtype_f16:
return False
else:
return False
return True
def _removed_cast_pair_process(stree, cast_f32_node):
"""remove the duplicated cast operators."""
for user_node in cast_f32_node.get_users():
# remove cast f16 nodes
if user_node.get_instance_type() == P.Cast:
cast_f16_node = user_node
# modify arguments using cast_f16's target[0] to cast_f32's args[0], which is f16 type
for cast_f16_user in cast_f16_node.get_users():
for idx, arg in enumerate(cast_f16_user.get_args()):
if arg == cast_f16_node.get_targets()[0]:
cast_f16_user.set_arg(idx, cast_f32_node.get_args()[0])
stree.erase_node(cast_f16_node)
# update args of cell f16 nodes
elif isinstance(user_node.get_instance(), nn.Cell):
cell_f16_node = user_node
for idx, arg in enumerate(cell_f16_node.get_args()):
if arg == cast_f32_node.get_targets()[0]:
cell_f16_node.set_arg(idx, cast_f32_node.get_args()[0])
# remove the cast f32 node
stree.erase_node(cast_f32_node)
def _remove_duplicated_cast(stree):
"""remove the duplicated cast operators."""
for node in stree.nodes():
if node.get_targets() is None:
continue
if node.get_node_type() == ms.rewrite.NodeType.CellContainer:
for n in node.get_handler().node_list:
if n.get_node_type() == ms.rewrite.NodeType.Tree:
_remove_duplicated_cast(ms.rewrite.TreeNodeHelper.get_sub_tree(ms.rewrite.Node(n)))
elif node.get_node_type() == ms.rewrite.NodeType.Tree:
substree = ms.rewrite.TreeNodeHelper.get_sub_tree(node)
_remove_duplicated_cast(substree)
elif _need_removed_cast_pair(node):
_removed_cast_pair_process(stree, node)
def _auto_white_list(network, white_list):
"""process the white list of network."""
global STREE
STREE = ms.rewrite.SymbolTree.create(network)
_insert_cast_operator_white_list(STREE, white_list)
_remove_duplicated_cast(STREE)
return STREE.get_network()
def _auto_black_list(network, black_list):
"""process the black list of network."""
network.to_float(mstype.float16)
cells = network.name_cells()
change = False
for name in cells:
subcell = cells[name]
if subcell == network:
continue
if isinstance(subcell, tuple(black_list)):
network._cells[name] = _OutputTo16(subcell.to_float(mstype.float32))
change = True
else:
_auto_black_list(subcell, black_list)
if isinstance(network, nn.SequentialCell) and change:
network.cell_list = list(network.cells())
[文档]def auto_mixed_precision(network, amp_level="O0"):
"""
Returns a network processed with auto mixed precision.
This interface will automatically perform mixed-precision processing on the input network, and the cells
and operators in the processed network will add precision conversion operations to calculate with float16 accuracy.
Inputs and parameters of cells and operators are converted to float16 type, and calculation results are converted
back to float32 type.
The framework has a set of built-in blacklists and whitelists, and the `amp_level` determines which cells and
operators are specifically converted:
- When `amp_level="O0"` , no precision conversion is performed.
- When `amp_level="O1"` , only the cells and operators in the whitelist will be converted.
- When `amp_level="O2"` , all cells and operators except those in the blacklist will be converted.
- When `amp_level="O3"` , all cells and operators in the network are converted.
The current built-in whitelist contents are:
[:class:`mindspore.nn.Conv1d`, :class:`mindspore.nn.Conv2d`, :class:`mindspore.nn.Conv3d`,
:class:`mindspore.nn.Conv1dTranspose`, :class:`mindspore.nn.Conv2dTranspose`,
:class:`mindspore.nn.Conv3dTranspose`, :class:`mindspore.nn.Dense`, :class:`mindspore.nn.LSTMCell`,
:class:`mindspore.nn.RNNCell`, :class:`mindspore.nn.GRUCell`, :class:`mindspore.ops.Conv2D`,
:class:`mindspore.ops.Conv3D`, :class:`mindspore.ops.Conv2DTranspose`,
:class:`mindspore.ops.Conv3DTranspose`, :class:`mindspore.ops.MatMul`, :class:`mindspore.ops.BatchMatMul`,
:class:`mindspore.ops.PReLU`, :class:`mindspore.ops.ReLU`, :class:`mindspore.ops.Ger`]
The current built-in blacklist contents are:
[:class:`mindspore.nn.BatchNorm1d`, :class:`mindspore.nn.BatchNorm2d`, :class:`mindspore.nn.BatchNorm3d`,
:class:`mindspore.nn.LayerNorm`]
For details on automatic mixed precision, refer to
`Automatic Mix Precision <https://www.mindspore.cn/tutorials/en/r2.0/advanced/mixed_precision.html>`_ .
Args:
network (Cell): Definition of the network.
amp_level (str): Supports ["O0", "O1", "O2", "O3"]. Default: "O0".
- "O0": Do not change.
- "O1": Convert cells and operators in whitelist to float16 precision operations, and keep float32
precision operations for the rest.
- "O2": Keep float32 precision operations for cells and operators in blacklist, and convert the rest
to float16 precision operations.
- "O3": Cast network to float16.
Raises:
ValueError: If amp level is not supported.
Examples:
>>> from mindspore import amp, nn
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/lenet.py
>>> network = LeNet5()
>>> amp_level = "O1"
>>> net = amp.auto_mixed_precision(network, amp_level)
"""
if not isinstance(network, nn.Cell):
raise TypeError("The network type should be Cell.")
if amp_level == "O0":
pass
elif amp_level == "O1":
return _auto_white_list(network, AMP_WHITE_LIST)
elif amp_level == "O2":
_auto_black_list(network, AMP_BLACK_LIST)
elif amp_level == "O3":
network.to_float(mstype.float16)
else:
raise ValueError("The amp level {} is not supported".format(amp_level))
if amp_level in ("O2", "O3"):
network = _OutputTo32(network)
return network
def _do_keep_batchnorm_fp32(network):
"""Do keep batchnorm fp32."""
cells = network.name_cells()
change = False
for name in cells:
subcell = cells[name]
if subcell == network:
continue
elif isinstance(subcell, nn.Cell) and isinstance(subcell, tuple(AMP_BLACK_LIST)):
network._cells[name] = _OutputTo16(subcell.to_float(mstype.float32))
change = True
else:
_do_keep_batchnorm_fp32(subcell)
if isinstance(network, nn.SequentialCell) and change:
network.cell_list = list(network.cells())
_config_level = {
"O0": {
"keep_batchnorm_fp32": False,
"cast_model_type": mstype.float32,
"loss_scale_manager": None},
"O1": {
"keep_batchnorm_fp32": False,
"cast_model_type": mstype.float32,
"loss_scale_manager": None},
"O2": {
"keep_batchnorm_fp32": True,
"cast_model_type": mstype.float16,
"loss_scale_manager": DynamicLossScaleManager()},
"O3": {
"keep_batchnorm_fp32": False,
"cast_model_type": mstype.float16,
"loss_scale_manager": None}}
def _check_kwargs(key_words):
"""Check kwargs."""
for arg in key_words:
if arg not in ['cast_model_type', 'keep_batchnorm_fp32', 'loss_scale_manager']:
raise ValueError(f"Unsupported arg '{arg}'")
if 'cast_model_type' in key_words:
validator.check_type_name('cast_model_type', key_words['cast_model_type'],
[mstype.float16, mstype.float32], None)
if 'keep_batchnorm_fp32' in key_words:
validator.check_value_type('keep_batchnorm_fp32', key_words['keep_batchnorm_fp32'], bool)
if 'loss_scale_manager' in key_words:
loss_scale_manager = key_words['loss_scale_manager']
if loss_scale_manager:
validator.check_value_type('loss_scale_manager', loss_scale_manager,
[LossScaleManager, boost.GroupLossScaleManager])
def _check_level(level, boost_level):
"""Check level."""
if not isinstance(level, str):
raise TypeError("The argument `level` must be a string in ['O0', 'O1', 'O2', 'O3', 'auto'], \
but got type {}.".format(type(level)))
validator.check('level', level, "", ['O0', 'O1', 'O2', 'O3', 'auto'], validator.IN)
validator.check('boost_level', boost_level, "", ['O0', 'O1', 'O2'], validator.IN)
if level == "auto":
device_target = context.get_context('device_target')
if device_target == "GPU":
level = "O2"
elif device_target == "Ascend":
level = "O3"
else:
raise ValueError("Level `auto` only support when `device_target` is GPU or Ascend.")
enable_boost = False
if boost_level in ["O1", "O2"]:
enable_boost = True
return level, enable_boost
def _add_loss_network(network, loss_fn, cast_model_type):
"""Add loss network."""
class WithLossCell(nn.Cell):
"""Wrap loss for amp. Cast network output back to float32."""
def __init__(self, backbone, loss_fn):
super(WithLossCell, self).__init__(auto_prefix=False)
self._backbone = backbone
self._loss_fn = loss_fn
if isinstance(backbone, nn.Cell) and backbone.jit_config_dict:
self._jit_config_dict = backbone.jit_config_dict
def construct(self, data, label):
out = self._backbone(data)
label = F.mixed_precision_cast(mstype.float32, label)
return self._loss_fn(F.mixed_precision_cast(mstype.float32, out), label)
validator.check_value_type('loss_fn', loss_fn, nn.Cell)
if cast_model_type == mstype.float16:
network = WithLossCell(network, loss_fn)
else:
network = nn.WithLossCell(network, loss_fn)
return network
[文档]def build_train_network(network, optimizer, loss_fn=None, level='O0', boost_level='O0', **kwargs):
"""
Build the mixed precision training cell automatically.
Args:
network (Cell): Definition of the network.
optimizer (Optimizer): Define the optimizer to update the Parameter.
loss_fn (Union[None, Cell]): Define the loss function. If None, the `network` should have the loss inside.
Default: None.
level (str): Supports ['O0', 'O1', 'O2', 'O3', 'auto']. Default: 'O0'.
- 'O0': Do not change.
- 'O1': Cast the operators in white_list to float16, the remaining operators are kept in float32.
The operators in the whitelist: [Conv1d, Conv2d, Conv3d, Conv1dTranspose, Conv2dTranspose,
Conv3dTranspose, Dense, LSTMCell, RNNCell, GRUCell, MatMul, BatchMatMul, PReLU, ReLU, Ger].
- 'O2': Cast network to float16, keep batchnorm and `loss_fn` (if set) run in float32,
using dynamic loss scale.
- 'O3': Cast network to float16, with additional property `keep_batchnorm_fp32=False` .
- 'auto': Set to level to recommended level in different devices. Set level to 'O2' on GPU, Set
level to 'O3' Ascend. The recommended level is chosen by the export experience, not applicable to all
scenarios. User should specify the level for special network.
'O2' is recommended on GPU, "O3" is recommended on Ascend. Property of `keep_batchnorm_fp32`,
`cast_model_type` and `loss_scale_manager` determined by `level` setting may be overwritten by settings in
`kwargs`.
boost_level (str): Option for argument `level` in `mindspore.boost` , level for boost mode
training. Supports ['O0', 'O1', 'O2']. Default: 'O0'.
- 'O0': Do not change.
- 'O1': Enable the boost mode, the performance is improved by about 20%, and
the accuracy is the same as the original accuracy.
- 'O2': Enable the boost mode, the performance is improved by about 30%, and
the accuracy is reduced by less than 3%.
If 'O1' or 'O2' mode is set, the boost related library will take effect automatically.
cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32` . If set, the
network will be casted to `cast_model_type` ( `mstype.float16` or `mstype.float32` ), but not to be casted
to the type determined by `level` setting.
keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32` when the network is set to cast to `float16` . If
set, the `level` setting will take no effect on this property.
loss_scale_manager (Union[None, LossScaleManager]): If not None, must be subclass of
:class:`mindspore.amp.LossScaleManager` for scaling the loss. If set, the `level` setting will
take no effect on this property.
Raises:
ValueError: If device is CPU, property `loss_scale_manager` is not `None` or `FixedLossScaleManager`
(with property `drop_overflow_update=False` ).
Examples:
>>> from mindspore import amp, nn
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/lenet.py
>>> network = LeNet5()
>>> net_loss = nn.SoftmaxCrossEntropyWithLogits(reduction="mean")
>>> net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)
>>> amp_level="O3"
>>> net = amp.build_train_network(network, net_opt, net_loss, amp_level)
"""
validator.check_value_type('optimizer', optimizer, (nn.Optimizer, boost.FreezeOpt,
nn.AdaSumByGradWrapCell, nn.AdaSumByDeltaWeightWrapCell))
level, enable_boost = _check_level(level, boost_level)
_check_kwargs(kwargs)
config = dict(_config_level.get(level), **kwargs)
if config["cast_model_type"] == mstype.float16:
network.to_float(mstype.float16)
if config["keep_batchnorm_fp32"]:
_do_keep_batchnorm_fp32(network)
elif not config["keep_batchnorm_fp32"] and level == "O2":
network.to_float(mstype.float16)
elif config["cast_model_type"] == mstype.float32 and level in ("O2", "O3"):
pass
else:
network = auto_mixed_precision(network, level)
if loss_fn:
network = _add_loss_network(network, loss_fn, config["cast_model_type"])
loss_scale = 1.0
if config["loss_scale_manager"] is not None:
loss_scale_manager = config["loss_scale_manager"]
loss_scale = loss_scale_manager.get_loss_scale()
update_cell = loss_scale_manager.get_update_cell()
if update_cell is not None:
# only cpu not support `TrainOneStepWithLossScaleCell` for control flow.
if not context.get_context("enable_ge") and context.get_context("device_target") == "CPU":
raise ValueError("Only `loss_scale_manager=None` or "
"`loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False)`"
"are supported on device `CPU`. ")
if _get_pipeline_stages() > 1:
network = _TrainPipelineWithLossScaleCell(network, optimizer,
scale_sense=update_cell).set_train()
elif enable_boost:
network = boost.BoostTrainOneStepWithLossScaleCell(network, optimizer,
scale_sense=update_cell).set_train()
else:
network = nn.TrainOneStepWithLossScaleCell(network, optimizer,
scale_sense=update_cell).set_train()
return network
if _get_pipeline_stages() > 1:
network = _TrainPipelineAccuStepCell(network, optimizer).set_train()
elif enable_boost:
network = boost.BoostTrainOneStepCell(network, optimizer, loss_scale).set_train()
else:
network = nn.TrainOneStepCell(network, optimizer, loss_scale).set_train()
return network
[文档]def get_white_list():
"""
Provide a copy of internal white list used by auto mixed precision.
.. warning::
This is an experimental API that is subject to change or deletion.
Returns:
list, A copy of internal white list.
"""
white_list = AMP_WHITE_LIST.copy()
return white_list
[文档]def get_black_list():
"""
Provide a copy of internal black list used by auto mixed precision.
.. warning::
This is an experimental API that is subject to change or deletion.
Returns:
list, A copy of internal black list.
"""
black_list = AMP_BLACK_LIST.copy()
return black_list
[文档]def custom_mixed_precision(network, *, white_list=None, black_list=None):
"""
Custom mixed precision by setting whitelist or blacklist.
When the `white_list` is provided, primitives and cells in `white_list` will perform the precision conversion.
When the `black_list` is provided, primitives and cells that are not in `black_list` will perform the pereision
conversion.
Only one of `white_list` and `black_list` should be provided.
.. warning::
This is an experimental API that is subject to change or deletion.
Note:
- `custom_mixed_precision` should not be used at the same time as `auto_mixed_precision` . When both
`build_train_network` and `custom_mixed_precision` are used, `build_train_network` need to be called with
`level='O0'` before call `custom_mixed_precision` .
- Primitives for blacklist is not support yet.
Args:
network (Cell): Definition of the network.
white_list (list[Primitive, Cell], optional): White list of custom mixed precision. Defaults: None, means
white list is not used.
black_list (list[Primitive, Cell], optional): Black list of custom mixed precision. Defaults: None, means
black list is not used.
Returns:
network (Cell), A network supporting mixed precision.
Raises:
TypeError: The network type is not Cell.
ValueError: Neither `white_list` nor `black_list` is provided.
ValueError: Both `white_list` and `black_list` are provided.
Examples:
>>> from mindspore import amp
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> custom_white_list = amp.get_white_list()
>>> custom_white_list.append(nn.Flatten)
>>> net = amp.custom_mixed_precision(net, white_list=custom_white_list)
"""
if not isinstance(network, nn.Cell):
raise TypeError("The network type should be Cell.")
if white_list is None and black_list is None:
raise ValueError("For custom_mixed_precision, one of white_list and black_list must be provided.")
if white_list is not None and black_list is not None:
raise ValueError("For custom_mixed_precision, the white_list or black_list cannot be provided "
"at the same time, please provide one or the other.")
if white_list is not None:
_list_check(white_list, "white_list")
return _auto_white_list(network, white_list)
_list_check(black_list, "black_list")
_auto_black_list(network, black_list)
network = _OutputTo32(network)
return network
def _list_check(custom_list: list, list_name: str):
"""
check whether custom list is valid
Raises:
TypeError: The type of custom_list is not list.
TypeError: The element in custom_list is not a class.
TypeError: The subclass of element in custom_list is not one of ['Cell', 'Primitive'].
"""
if not isinstance(custom_list, list):
raise TypeError(f"The type of {list_name} should be list, but got {type(custom_list)}")
for elem in custom_list:
if not isinstance(elem, type):
raise TypeError(f"The element in {list_name} should be a class, but got {elem}")
if not issubclass(elem, nn.Cell) and not issubclass(elem, Primitive):
raise TypeError(f"The subclass of element in {list_name} should be one of 'Cell' and 'Primitive', "
f"but got {elem}")
if list_name == 'black_list':
for elem in AMP_BLACK_LIST:
if elem not in custom_list:
logger.warning(f"{elem} is removed from internal black list.")