mirror of https://github.com/hpcaitech/ColossalAI
142 lines
5.2 KiB
Python
142 lines
5.2 KiB
Python
import inspect
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import math
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from typing import Callable
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from torch import dtype, nn
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from colossalai.utils import get_current_device
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from ... import init as init
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from ..parallel_1d import *
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from ..parallel_2d import *
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from ..parallel_2p5d import *
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from ..parallel_3d import *
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from ..utils import get_tensor_parallel_mode
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from ..vanilla import *
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from ._utils import ColossalaiModule
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_parallel_linear = {None: VanillaLinear, '1d': Linear1D, '2d': Linear2D, '2.5d': Linear2p5D, '3d': Linear3D}
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_parallel_classifier = {
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None: VanillaClassifier,
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'1d': Classifier1D,
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'2d': Classifier2D,
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'2.5d': Classifier2p5D,
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'3d': Classifier3D
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}
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_vocab_parallel_classifier = {
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'1d': VocabParallelClassifier1D,
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'2d': VocabParallelClassifier2D,
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'2.5d': VocabParallelClassifier2p5D,
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'3d': VocabParallelClassifier3D
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}
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class Linear(ColossalaiModule):
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"""Linear layer of colossalai.
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Args:
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in_features (int): size of each input sample.
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out_features (int): size of each output sample.
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bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
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dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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weight_initializer (:class:`typing.Callable`, optional):
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The initializer of weight, defaults to kaiming uniform initializer.
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bias_initializer (:class:`typing.Callable`, optional):
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The initializer of bias, defaults to xavier uniform initializer.
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Note: ``kwargs`` would contain different parameters when you use different parallelisms.
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The ``kwargs`` should contain parameters below:
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::
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Linear1D:
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gather_output: bool (optional, default to be false)
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skip_bias_add: bool (optional, default to be false)
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Linear2D:
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skip_bias_add: bool (optional, default to be false)
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Linear2p5D:
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skip_bias_add: bool (optional, default to be false)
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Linear3D:
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None
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More details about ``initializer`` please refer to
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`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
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"""
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def __init__(self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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dtype: dtype = None,
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weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
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bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
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**kwargs) -> None:
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tensor_parallel = get_tensor_parallel_mode()
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linear_cls = _parallel_linear[tensor_parallel]
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gather_output = kwargs.pop('gather_output', None)
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if 'gather_output' in inspect.signature(linear_cls.__init__).parameters.keys(): # gather_out arg is available
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kwargs['gather_output'] = gather_output
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layer = linear_cls(
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in_features,
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out_features,
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bias=bias,
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dtype=dtype,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer,
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**kwargs,
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)
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super().__init__(layer)
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class Classifier(ColossalaiModule):
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"""Classifier layer of colossalai.
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Args:
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in_features (int): size of each input sample.
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num_classes (int): number of classes.
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weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
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bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
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dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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weight_initializer (:class:`typing.Callable`, optional):
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The initializer of weight, defaults to kaiming uniform initializer.
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bias_initializer (:class:`typing.Callable`, optional):
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The initializer of bias, defaults to xavier uniform initializer.
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More details about ``initializer`` please refer to
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`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
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"""
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def __init__(self,
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in_features: int,
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num_classes: int,
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weight: nn.Parameter = None,
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bias: bool = True,
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dtype: dtype = None,
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weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
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bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
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vocab_parallel_limit: int = 2048) -> None:
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tensor_parallel = get_tensor_parallel_mode()
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if num_classes <= vocab_parallel_limit or tensor_parallel is None:
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layer = _parallel_classifier[tensor_parallel](
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in_features,
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num_classes,
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weight=weight,
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bias=bias,
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dtype=dtype,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer,
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)
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else:
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layer = _vocab_parallel_classifier[tensor_parallel](
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in_features,
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num_classes,
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weight=weight,
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bias=bias,
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dtype=dtype,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer,
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)
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super().__init__(layer)
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