Source code for mindspore.train.amp

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Auto mixed precision."""
from easydict import EasyDict as edict

from .. import nn
from .._checkparam import Validator as validator
from .._checkparam import Rel
from ..common import dtype as mstype
from ..nn.wrap.cell_wrapper import _VirtualDatasetCell
from ..ops import functional as F
from ..parallel._utils import _get_parallel_mode
from .loss_scale_manager import DynamicLossScaleManager, LossScaleManager
from ..context import ParallelMode
from .. import context

class OutputTo16(nn.Cell):
    "Wrap cell for amp. Cast network output back to float16"

    def __init__(self, op):
        super(OutputTo16, self).__init__(auto_prefix=False)
        self._op = op

    def construct(self, x):
        return F.cast(self._op(x), mstype.float16)

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:
        elif isinstance(subcell, (nn.BatchNorm2d, nn.BatchNorm1d)):
            network._cells[name] = OutputTo16(subcell.to_float(mstype.float32))
            change = True
    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},
    "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)

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

        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)
        network = nn.WithLossCell(network, loss_fn)
    return network

[docs]def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs): """ Build the mixed precision training cell automatically. Args: network (Cell): Definition of the network. loss_fn (Union[None, Cell]): Definition of the loss_fn. If None, the `network` should have the loss inside. Default: None. optimizer (Optimizer): Optimizer to update the Parameter. level (str): Supports ["O0", "O2", "O3", "auto"]. Default: "O0". - O0: Do not change. - 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 choose by the export experience, cannot always generalize. User should specify the level for special network. O2 is recommended on GPU, O3 is recommended on Ascend. cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32`. If set to `mstype.float16`, use `float16` mode to train. If set, overwrite the level setting. keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32`. If set, overwrite the level setting. Only `cast_model_type` is `float16`, `keep_batchnorm_fp32` will take effect. loss_scale_manager (Union[None, LossScaleManager]): If None, not scale the loss, or else scale the loss by `LossScaleManager`. If set, overwrite the level setting. """ validator.check_value_type('network', network, nn.Cell) validator.check_value_type('optimizer', optimizer, nn.Optimizer) validator.check('level', level, "", ['O0', 'O2', 'O3', "auto"], Rel.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.") _check_kwargs(kwargs) config = dict(_config_level[level], **kwargs) config = edict(config) if config.cast_model_type == mstype.float16: network.to_float(mstype.float16) if config.keep_batchnorm_fp32: _do_keep_batchnorm_fp32(network) if loss_fn: network = _add_loss_network(network, loss_fn, config.cast_model_type) if _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL): network = _VirtualDatasetCell(network) 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` and " "`loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False)`" "are supported in current version. If you use `O2` option, please" "use `loss_scale_manager=None` or `FixedLossScaleManager`") network = nn.TrainOneStepWithLossScaleCell(network, optimizer, scale_sense=update_cell).set_train() return network network = nn.TrainOneStepCell(network, optimizer, loss_scale).set_train() return network