# 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 inspect
import types
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 _TrainGradAccuStepCell
from mindspore.nn.wrap.loss_scale import _TrainGradAccuWithLossScaleCell
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
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
]
# Primitives in inner amp black list will not be converted in O2/O3
_INNER_AMP_BLACK_LIST = []
MS_AMP_BY_REWRITE = False
def amp_cast(value, dtype):
"""This function is used to insert cast operators for tensors during auto mixed precision."""
if isinstance(value, ms.Tensor) and value.dtype in mstype.float_type:
return P.Cast()(value, dtype)
return value
_amp_cast_op = amp_cast
class _OutputTo16(nn.Cell):
"""Wrap cell for amp. Cast network output back to float16."""
def __init__(self, backbone, dtype=mstype.float16):
super(_OutputTo16, self).__init__(auto_prefix=False)
self._backbone = backbone
self.dtype = dtype
self._get_attr_from_cell(backbone)
def construct(self, *args, **kwargs):
return F.cast(self._backbone(*args, **kwargs), self.dtype)
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
self._get_attr_from_cell(backbone)
def construct(self, *args, **kwargs):
out = self._backbone(*args, **kwargs)
return F.mixed_precision_cast(mstype.float32, out)
def _operator_need_cast(node, force_cast: bool, white_list=None, black_list=None) -> bool:
"""
Check whether current node is a operator that need to be casted. Follow conditions need to be satisfied:
1) Type of node is CallPrimitive and type of instance is Primitive
2) Type of instance is not P.Cast
3) force_cast is True, which means one of upper layer cells is under casting
4) white_list exist and type of node is in white_list
5) black_list exist and type of node is in not black_list
"""
if node.get_node_type() != ms.rewrite.NodeType.CallPrimitive:
return False
if not inspect.isclass(node.get_instance_type()):
return False
if not issubclass(node.get_instance_type(), Primitive):
return False
if issubclass(node.get_instance_type(), P.Cast):
return False
if node.get_instance_type() in _INNER_AMP_BLACK_LIST:
return False
if force_cast:
return True
if white_list is not None and node.get_instance_type() in white_list:
return True
if black_list is not None and node.get_instance_type() not in black_list:
return True
return False
def _precision_set_by_user(cell_inst: nn.Cell) -> bool:
"""Check whether cell precision is set by user."""
for flag in ["fp32", "fp16", "bf16"]:
if hasattr(cell_inst, flag) and getattr(cell_inst, flag):
return True
return False
def _net_need_cast(node, force_cast: bool, white_list=None, black_list=None) -> bool:
"""
Check whether current node is type of tree whose network needs to be casted. Follow conditions need to
be satisfied:
1) Type of node is Tree and type of instance is Cell
2) Cell.to_float(xxx) is not set by user
3) force_cast is True, which means one of upper layer networks is under casting
4) white_list exist and type of node is in white_list
5) black_list exist and type of node is in not black_list
"""
if node.get_node_type() != ms.rewrite.NodeType.Tree:
return False
if not inspect.isclass(node.get_instance_type()):
return False
if not issubclass(node.get_instance_type(), nn.Cell):
return False
if node.get_instance_type() in _INNER_AMP_BLACK_LIST:
return False
if _precision_set_by_user(node.get_instance()):
return False
if force_cast:
return True
if white_list is not None and node.get_instance_type() in white_list:
return True
if black_list is not None and node.get_instance_type() not in black_list:
return True
return False
def _insert_cast_for_operator(node, dtype):
"""insert cast pair for node."""
dtype_str = "bfloat16" if dtype == mstype.bfloat16 else "float16"
stree = node.get_symbol_tree()
# insert cast fp16/bf16 for inputs of node
for idx, arg in enumerate(node.get_args()):
if arg.type != ms.rewrite.ValueType.NamingValue:
continue
incast_args = ms.rewrite.ScopedValue.create_name_values([arg.value, dtype_str], [arg.scope, "mindspore"])
arg_providers = node.get_arg_providers()
if not arg_providers or idx not in arg_providers or \
len(arg_providers[idx][0].get_target_users(arg_providers[idx][1])) > 1:
# create new target names when argument is used by other node
incast_targets = [stree.unique_name(f"{arg.value}_var")]
else:
incast_targets = ms.rewrite.ScopedValue.create_name_values([arg.value], [arg.scope])
incast_node = ms.rewrite.Node.create_call_function(_amp_cast_op, targets=incast_targets, args=incast_args)
stree.insert(stree.before(node), incast_node)
node.set_arg_by_node(idx, incast_node)
# insert cast fp32 for outputs of node
for _, target in enumerate(node.get_targets()):
if target.type != ms.rewrite.ValueType.NamingValue:
continue
outcast_args = ms.rewrite.ScopedValue.create_name_values([target.value, "float32"],
[target.scope, "mindspore"])
outcast_targets = ms.rewrite.ScopedValue.create_name_values([target.value], [target.scope])
outcast_node = ms.rewrite.Node.create_call_function(_amp_cast_op, targets=outcast_targets, args=outcast_args)
stree.insert(stree.after(node), outcast_node)
def _insert_cast_for_operators(stree, dtype, force_cast, *, white_list=None, black_list=None):
"""insert cast for operators not in black_list."""
# get all nodes of stree exclude nodes in subtree.
all_nodes = stree.all_nodes(False)
for node in all_nodes:
if not node.get_targets():
continue
if _operator_need_cast(node, force_cast, white_list, black_list):
_insert_cast_for_operator(node, dtype)
elif node.get_node_type() == ms.rewrite.NodeType.Tree:
force_cast_ = force_cast or _net_need_cast(node, force_cast, white_list, black_list)
if not _precision_set_by_user(node.get_instance()):
subtree = node.get_sub_tree()
_insert_cast_for_operators(subtree, dtype, force_cast_, white_list=white_list, black_list=black_list)
def _need_removed_cast_pair(node, dtype):
"""check whether the cast pairs should be removed."""
dtype_str = "bfloat16" if dtype == mstype.bfloat16 else "float16"
cast_dtypes = ms.rewrite.ScopedValue.create_name_values([dtype_str, "float32"], ["mindspore", "mindspore"])
cast_dtype_f16 = cast_dtypes[0]
cast_dtype_f32 = cast_dtypes[1]
# current node should be cast fp32
if node.get_instance_type() != _amp_cast_op:
return False
node_cast_type = node.get_args()[1]
if node_cast_type != cast_dtype_f32:
return False
# all user nodes should be cast fp16/bf16
if not node.get_users():
return False
all_nodes = [ms.rewrite.Node(n) for n in node.get_handler().get_node_manager().nodes()]
for user in node.get_users():
# If ControlFlow node(e.g. if, for, while) exists between current node and user node,
# cast pair should not be removed.
middle_nodes = all_nodes[all_nodes.index(node): all_nodes.index(user)]
if any([n.get_node_type() == ms.rewrite.NodeType.ControlFlow for n in middle_nodes]):
return False
if user.get_instance_type() != _amp_cast_op:
return False
user_cast_type = user.get_args()[1]
if user_cast_type != cast_dtype_f16:
return False
# cast pair detected, check next user
continue
return True
def _remove_duplicated_cast(stree, dtype):
"""remove the duplicated cast operators."""
all_nodes = list(stree.nodes(all_nodes=True))
for node in all_nodes:
if _need_removed_cast_pair(node, dtype):
incast_nodes = node.get_users()
# remove cast fp16/bf16 nodes
for incast_node in incast_nodes:
# get_target_users() return {target0: [(user0, arg_idx), ...], ...}
target_users = list(incast_node.get_target_users().values())
if not target_users or not target_users[0]:
continue
for user_node, arg_idx in target_users[0]:
user_node.set_arg(arg_idx, incast_node.get_args()[0])
stree.erase(incast_node)
# remove the cast fp32 node
stree.erase(node)
def _auto_mixed_precision_rewrite(network, dtype, *, white_list=None, black_list=None):
"""Implement auto mixed precision by rewrite"""
if (white_list is None and black_list is None) or (white_list is not None and black_list is not None):
raise ValueError("For _auto_mixed_precision_rewrite, one of white_list and black_list must be provided.")
# enable rewrite configs for amp
ms.rewrite.common.namespace._ms_cells_to_subtree = True
ms.rewrite.parsers.assign_parser.AssignParser._share_one_implementation = True
# insert casts by rewrite
stree = ms.rewrite.SymbolTree.create(network)
_insert_cast_for_operators(stree, dtype, False, white_list=white_list, black_list=black_list)
_remove_duplicated_cast(stree, dtype)
new_net = stree.get_network()
# disable rewrite configs
ms.rewrite.parsers.assign_parser.AssignParser._share_one_implementation = False
ms.rewrite.common.namespace._ms_cells_to_subtree = False
ms.rewrite.common.config.clear_caches()
return new_net
def _auto_black_list(network, black_list, dtype):
"""process the black list of network."""
network.to_float(dtype)
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), dtype)
change = True
else:
_auto_black_list(subcell, black_list, dtype)
if isinstance(network, nn.SequentialCell) and change:
network.cell_list = list(network.cells())
return network
[文档]def auto_mixed_precision(network, amp_level="O0", dtype=mstype.float16):
"""
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 lower
precision: ``mstype.float16`` or ``mstype.bfloat16`` . Inputs and parameters of cells and operators are
converted to lower precision float, and calculation results are converted back to full precision float,
i.e. ``mstype.float32`` .
The framework has a set of built-in blacklists and whitelists, and the `amp_level` determines which cells and
operators are specifically 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/master/advanced/mixed_precision.html>`_ .
Note:
- Repeatedly calling mixed-precision interfaces, such as `custom_mixed_precision` and `auto_mixed_precision`,
can result in a larger network hierarchy and slower performance.
- If interfaces like `Model` and `build_train_network` is used to train the network which is converted by
mixed-precision interfaces such as `custom_mixed_precision` and `auto_mixed_precision`, `amp_level`
need to be configured to ``O0`` to avoid the duplicated accuracy conversion.
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 lower precision operations, and keep full
precision operations for the rest.
- "O2": Keep full precision operations for cells and operators in blacklist, and convert the rest
to lower precision operations.
- "O3": Cast network to lower precision.
dtype (Type): The type used in lower precision calculations, can be ``mstype.float16`` or ``mstype.bfloat16`` ,
default: ``mstype.float16`` .
Raises:
TypeError: If `network` is not a Cell.
ValueError: If `dtype` is not one of ``mstype.float16`` , ``mstype.bfloat16`` .
ValueError: If `amp_level` is not within the supported range.
Examples:
>>> from mindspore import amp
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/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 dtype not in (mstype.float16, mstype.bfloat16):
raise ValueError(f"The dtype should be one of (mstype.float16, mstype.bfloat16), but got {dtype}.")
if amp_level == "O0":
return network
# Return network if the same amp level has already been configurated
if getattr(network, "_amp_level") in ("O1", "O2", "O3"):
logger.warning(f"The network's auto mixed-precision level is adjusted from {getattr(network, '_amp_level')} "
f"to {amp_level}, and repeated calls to mixed-precision interfaces can cause performance "
f"degradation.")
if amp_level == "O1":
network = _auto_mixed_precision_rewrite(network, dtype, white_list=AMP_WHITE_LIST)
elif amp_level == "O2":
if MS_AMP_BY_REWRITE:
network = _auto_mixed_precision_rewrite(network, dtype, black_list=AMP_BLACK_LIST)
else:
network = _auto_black_list(network, AMP_BLACK_LIST, dtype)
network = _OutputTo32(network)
elif amp_level == "O3":
if MS_AMP_BY_REWRITE:
network = _auto_mixed_precision_rewrite(network, dtype, black_list=[])
else:
network.to_float(dtype)
network = _OutputTo32(network)
else:
raise ValueError("The amp level {} is not supported".format(amp_level))
setattr(network, "_amp_level", amp_level)
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
self._get_attr_from_cell(backbone)
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
def _is_grad_accumulation(mcell):
if mcell.cls_name == "GradAccumulationCell":
return True
for cell in mcell.cells():
if _is_grad_accumulation(cell):
return True
return False
def _auto_mixed_precision_process(network, config, level):
"""Auto mixed precision process."""
if MS_AMP_BY_REWRITE:
if config["cast_model_type"] == mstype.float16 or level == "O2":
level = "O2" if config["keep_batchnorm_fp32"] else "O3"
elif config["cast_model_type"] == mstype.float32 and level in ("O2", "O3"):
# cast_model_type set by kwargs
level = "O0"
network = auto_mixed_precision(network, level)
else:
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)
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.
Note:
- After using `custom_mixed_precision` or `auto_mixed_precision` for precision conversion, it is not supported
to perform the precision conversion again. If `build_train_network` is used to train a converted network,
`level` need to be configured to ``O0`` to avoid the duplicated accuracy conversion.
Args:
network (Cell): Definition of the network.
optimizer (:class:`mindspore.nn.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 `mindspore.nn.BatchNorm` series interface,
:class:`mindspore.nn.LayerNorm` 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
:class:`mindspore.amp.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/master/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)
network = _auto_mixed_precision_process(network, config, level)
if loss_fn:
network = _add_loss_network(network, loss_fn, config["cast_model_type"])
loss_scale = None
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 or _is_grad_accumulation(network):
network = _TrainGradAccuWithLossScaleCell(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 or _is_grad_accumulation(network):
network = _TrainGradAccuStepCell(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.
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`]
Returns:
list, A copy of internal white list.
Examples:
>>> from mindspore import amp
>>> white_list = amp.get_white_list()
>>> print(white_list)
[<class 'mindspore.nn.layer.conv.Conv1d'>, <class 'mindspore.nn.layer.conv.Conv2d'>,
<class 'mindspore.nn.layer.conv.Conv3d'>, <class 'mindspore.nn.layer.conv.Conv1dTranspose'>,
<class 'mindspore.nn.layer.conv.Conv2dTranspose'>, <class 'mindspore.nn.layer.conv.Conv3dTranspose'>,
<class 'mindspore.nn.layer.basic.Dense'>, <class 'mindspore.nn.layer.rnn_cells.LSTMCell'>,
<class 'mindspore.nn.layer.rnn_cells.RNNCell'>, <class 'mindspore.nn.layer.rnn_cells.GRUCell'>,
<class 'mindspore.ops.operations.nn_ops.Conv2D'>, <class 'mindspore.ops.operations.nn_ops.Conv3D'>,
<class 'mindspore.ops.operations.nn_ops.Conv2DTranspose'>,
<class 'mindspore.ops.operations.nn_ops.Conv3DTranspose'>,
<class 'mindspore.ops.operations.nn_ops.Conv2DBackpropInput'>,
<class 'mindspore.ops.operations.math_ops.MatMul'>, <class 'mindspore.ops.operations.math_ops.BatchMatMul'>,
<class 'mindspore.ops.operations.nn_ops.PReLU'>, <class 'mindspore.ops.operations.nn_ops.ReLU'>,
<class 'mindspore.ops.operations.math_ops.Ger'>]
"""
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.
The current built-in blacklist contents are:
[:class:`mindspore.nn.BatchNorm1d`, :class:`mindspore.nn.BatchNorm2d`, :class:`mindspore.nn.BatchNorm3d`,
:class:`mindspore.nn.LayerNorm`]
Returns:
list, A copy of internal black list.
Examples:
>>> from mindspore import amp
>>> black_list = amp.get_black_list()
>>> print(black_list)
[<class 'mindspore.nn.layer.normalization.BatchNorm1d'>, <class 'mindspore.nn.layer.normalization.BatchNorm2d'>,
<class 'mindspore.nn.layer.normalization.BatchNorm3d'>, <class 'mindspore.nn.layer.normalization.LayerNorm'>]
"""
black_list = AMP_BLACK_LIST.copy()
return black_list
[文档]def custom_mixed_precision(network, *, white_list=None, black_list=None, dtype=mstype.float16):
"""
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, cells that are not in `black_list` will perform the pereision conversion.
Only one of `white_list` and `black_list` should be provided.
Note:
- Repeatedly calling mixed-precision interfaces, such as `custom_mixed_precision` and `auto_mixed_precision`,
can result in a larger network hierarchy and slower performance.
- If interfaces like `Model` and `build_train_network` is used to train the network which is converted by
mixed-precision interfaces such as `custom_mixed_precision` and `auto_mixed_precision`, `amp_level`
need to be configured to ``O0`` to avoid the duplicated accuracy conversion.
- 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[Cell], optional): Black list of custom mixed precision. Defaults: ``None`` , means
black list is not used.
dtype (Type): The type used in lower precision calculations, can be ``mstype.float16`` or ``mstype.bfloat16`` ,
default: ``mstype.float16`` .
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: If `dtype` is not one of ``mstype.float16`` , ``mstype.bfloat16`` .
ValueError: Both `white_list` and `black_list` are provided.
Examples:
>>> from mindspore import amp, nn
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/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 dtype not in (mstype.float16, mstype.bfloat16):
raise ValueError(f"The dtype should be one of (mstype.float16, mstype.bfloat16), but got {dtype}.")
if white_list is not None:
_list_check(white_list, "white_list")
network = _auto_mixed_precision_rewrite(network, dtype, white_list=white_list)
else:
_list_check(black_list, "black_list")
if MS_AMP_BY_REWRITE:
network = _auto_mixed_precision_rewrite(network, dtype, black_list=black_list)
else:
network = _auto_black_list(network, black_list, dtype)
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 list_name == "white_list" and 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" and not issubclass(elem, nn.Cell):
raise TypeError(f"The subclass of element in {list_name} should be one of 'Cell', 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.")
def _config_amp(*, enable_rewrite: bool = None, cast_op: types.FunctionType = None): # pylint: disable=unused-variable
"""Configure auto mixed precision."""
global MS_AMP_BY_REWRITE
global _amp_cast_op
if enable_rewrite is not None:
MS_AMP_BY_REWRITE = enable_rewrite
if cast_op is not None:
_amp_cast_op = cast_op