mirror of https://github.com/hpcaitech/ColossalAI
470 lines
19 KiB
Python
470 lines
19 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import math
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import numbers
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from contextlib import nullcontext
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from typing import Callable, Tuple
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import torch
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import torch.nn.functional as F
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from colossalai.communication import broadcast
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from colossalai.context import ParallelMode, seed
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from colossalai.core import global_context as gpc
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from colossalai.nn import init as init
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from colossalai.registry import LAYERS
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from colossalai.utils import get_current_device
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from torch import Tensor, dtype
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from torch.nn.parameter import Parameter
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from ..base_layer import ParallelLayer
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from ..utils import divide, set_tensor_parallel_attribute_by_partition
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from ._operation import FusedLayerNormAffineFunction1D
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from ._utils import (gather_forward_split_backward, get_parallel_input, reduce_grad, reduce_input, set_parallel_input,
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split_forward_gather_backward)
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@LAYERS.register_module
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class Linear1D(torch.nn.Module):
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"""
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Linear layer for 1D parallelism
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:param in_features: size of each input sample
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:type in_features: int
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:param out_features: size of each output sample
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:type out_features: int
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:param bias: If set to ``False``, the layer will not learn an additive bias, defaults to True
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:type bias: bool, optional
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:param dtype: The dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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:param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False
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:type skip_bias_add: bool, optional
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:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
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:type weight_initializer: typing.Callable, optional
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:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
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:type bias_initializer: typing.Callable, optional
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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: torch.dtype = None,
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gather_output: bool = False,
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skip_bias_add: bool = False,
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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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super().__init__()
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parallel_input = get_parallel_input()
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if not parallel_input:
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self.layer = Linear1D_Col(in_features,
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out_features,
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bias=bias,
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dtype=dtype,
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gather_output=gather_output,
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skip_bias_add=skip_bias_add,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer)
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else:
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self.layer = Linear1D_Row(in_features,
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out_features,
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bias=bias,
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dtype=dtype,
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parallel_input=parallel_input,
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skip_bias_add=skip_bias_add,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer)
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@property
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def weight(self):
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return self.layer.weight
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@property
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def bias(self):
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return self.layer.bias
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def forward(self, input_: Tensor) -> Tensor:
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return self.layer(input_)
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@LAYERS.register_module
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class Classifier1D(ParallelLayer):
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"""RowLinear with given weight
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Classifier of 1D parallelism
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:param in_features: size of input features
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:type in_features: int
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:param num_classes: number of classes in the dataset
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:type num_classes: int
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:param weight: weight of the classifier, defaults to True
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:type weight: torch.nn.Parameter, optional
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:param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
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:type bias: bool, optional
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:param dtype: The dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
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:type weight_initializer: typing.Callable, optional
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:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
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:type bias_initializer: typing.Callable, optional
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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: 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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super().__init__()
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self.in_features = in_features
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self.num_classes = num_classes
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self.parallel_input = get_parallel_input()
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# Divide the weight matrix along the last dimension.
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self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size)
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# Parameters.
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# Initialize weight.
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factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
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if weight is not None:
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self.weight = weight
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self.has_weight = False
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else:
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self.weight = Parameter(torch.empty(self.num_classes, self.input_size_per_partition, **factory_kwargs))
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self.has_weight = True
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if bias:
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self.bias = Parameter(torch.empty(self.num_classes, **factory_kwargs))
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else:
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self.bias = None
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with seed(ParallelMode.TENSOR):
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self.reset_parameters(weight_initializer, bias_initializer)
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self._set_tensor_parallel_attributes()
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set_parallel_input(False)
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def reset_parameters(self, weight_initializer, bias_initializer) -> None:
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fan_in, fan_out = self.in_features, self.num_classes
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if self.has_weight:
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weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
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if self.bias is not None:
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bias_initializer(self.bias, fan_in=fan_in)
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broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
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def _set_tensor_parallel_attributes(self):
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if self.has_weight:
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num_partition = gpc.get_world_size(ParallelMode.TENSOR)
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set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
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def forward(self, input_: Tensor) -> Tensor:
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# Set up backprop all-reduce.
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if self.parallel_input:
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input_ = input_
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else:
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input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1)
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output_parallel = F.linear(input_, self.weight)
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output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
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if self.bias is not None:
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output = output + self.bias
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return output
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@LAYERS.register_module
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class Linear1D_Col(ParallelLayer):
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"""Linear layer with column parallelism.
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The linear layer is defined as :math:`Y = XA + b`. A is parallelized along
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its second dimension as :math:`A = [A_1, ..., A_p]`.
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:param in_features: first dimension of matrix A.
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:type in_features: int
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:param output_size: second dimension of matrix A.
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:type output_size: int
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:param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
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:type bias: bool, optional
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:param dtype: The dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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:param gather_output: If true, call all-gether on output and make Y avaiable
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to all GPUs, otherwise, every GPU will have its output
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which is :math:`Y_i = XA_i`, defaults to False
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:type gather_output: bool, optional
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:param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False
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:type skip_bias_add: bool, optional
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:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
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:type weight_initializer: typing.Callable, optional
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:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
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:type bias_initializer: typing.Callable, optional
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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: torch.dtype = None,
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gather_output: bool = False,
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skip_bias_add: bool = False,
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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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super().__init__()
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# Keep input parameters
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self.in_features = in_features
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self.out_features = out_features
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self.gather_output = gather_output
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self.skip_bias_add = skip_bias_add
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if skip_bias_add and not bias:
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raise ValueError('cannot skip bias addition if bias is None')
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self.out_features_per_partition = divide(out_features, gpc.tensor_parallel_size)
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# Parameters.
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# Initialize weight.
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factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
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self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs))
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if bias:
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self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs))
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else:
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self.bias = None
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with seed(ParallelMode.TENSOR):
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self.reset_parameters(weight_initializer, bias_initializer)
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self._set_tensor_parallel_attributes()
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set_parallel_input(True)
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def reset_parameters(self, weight_initializer, bias_initializer) -> None:
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fan_in, fan_out = self.in_features, self.out_features
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weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
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if self.bias is not None:
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bias_initializer(self.bias, fan_in=fan_in)
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def _set_tensor_parallel_attributes(self):
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num_partition = gpc.get_world_size(ParallelMode.TENSOR)
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set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
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if self.bias is not None:
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set_tensor_parallel_attribute_by_partition(self.bias, num_partition)
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def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
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# Set up backprop all-reduce.
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input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D)
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# Matrix multiply.
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bias = self.bias if not self.skip_bias_add else None
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output_parallel = F.linear(input_parallel, self.weight, bias)
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if self.gather_output:
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# All-gather across the partitions.
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output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
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else:
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output = output_parallel
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if self.skip_bias_add:
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return output, self.bias
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else:
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return output
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@LAYERS.register_module
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class Linear1D_Row(ParallelLayer):
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""" Linear layer with row parallelism
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:param in_features: size of each input sample
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:type in_features: int
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:param out_features: size of each output sample
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:type out_features: int
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:param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
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:type bias: bool, optional
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:param dtype: The dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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:param parallel_input: If set to ``True``, it's assumed that the input is splitted, defaults to False
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:type parallel_input: bool, optional
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:param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False
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:type skip_bias_add: bool, optional
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:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
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:type weight_initializer: typing.Callable, optional
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:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
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:type bias_initializer: typing.Callable, optional
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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: torch.dtype = None,
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parallel_input: bool = True,
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skip_bias_add: bool = False,
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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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super().__init__()
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# Keep input parameters
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self.in_features = in_features
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self.out_features = out_features
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self.parallel_input = parallel_input
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self.skip_bias_add = skip_bias_add
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if skip_bias_add and not bias:
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raise ValueError('cannot skip bias addition if bias is None')
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# Divide the weight matrix along the last dimension.
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self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size)
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# Parameters.
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# Initialize weight.
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factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
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self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs))
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if bias:
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self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
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else:
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self.bias = None
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with seed(ParallelMode.TENSOR):
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self.reset_parameters(weight_initializer, bias_initializer)
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self._set_tensor_parallel_attributes()
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set_parallel_input(False)
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def reset_parameters(self, weight_initializer, bias_initializer) -> None:
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fan_in, fan_out = self.in_features, self.out_features
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weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
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if self.bias is not None:
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bias_initializer(self.bias, fan_in=fan_in)
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broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
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def _set_tensor_parallel_attributes(self):
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num_partition = gpc.get_world_size(ParallelMode.TENSOR)
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set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
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def forward(self, input_: Tensor) -> Tensor:
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# Set up backprop all-reduce.
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if self.parallel_input:
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input_ = input_
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else:
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input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1)
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output_parallel = F.linear(input_, self.weight)
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output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
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if not self.skip_bias_add:
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output = output + self.bias
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return output
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else:
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return output, self.bias
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@LAYERS.register_module
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class MixedFusedLayerNorm1D(torch.nn.Module):
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r"""
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Layer Normalization for 1D parallelism
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:param normalized_shape: input shape from an expected input
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of size. :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
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If a single integer is used, it is treated as a singleton list, and this module will
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normalize over the last dimension which is expected to be of that specific size.
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:type normalized_shape: int
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:param eps: a value added to the denominator for numerical stability, defaults to 1e-05
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:type eps: float, optional
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"""
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def __init__(self, normalized_shape, eps=1e-5):
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super(MixedFusedLayerNorm1D, self).__init__()
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if isinstance(normalized_shape, numbers.Integral):
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normalized_shape = (normalized_shape, )
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self.normalized_shape = torch.Size(normalized_shape)
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self.eps = eps
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self.weight = Parameter(torch.Tensor(*normalized_shape))
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self.bias = Parameter(torch.Tensor(*normalized_shape))
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self.reset_parameters()
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def reset_parameters(self):
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init.ones_(self.weight)
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init.zeros_(self.bias)
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def forward(self, input):
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return FusedLayerNormAffineFunction1D.apply(input, self.weight, self.bias, self.normalized_shape, self.eps)
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@LAYERS.register_module
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class Embedding1D(ParallelLayer):
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"""
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Embedding for 1D parallelism
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:param num_embeddings: number of embeddings
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:type num_embeddings: int
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:param embedding_dim: dimension of embedding
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:type embedding_dim: int
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:param padding_idx: index of padding, defaults to None
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:type padding_idx: int, optional
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:param dtype: The dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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:param weight_initializer: The intializer of weight, defaults to normal initializer
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:type weight_initializer: typing.Callable, optional
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:param args: Args used in F.embedding
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:param kwargs: Kwargs used in F.embedding
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"""
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def __init__(self,
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num_embeddings: int,
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embedding_dim: int,
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padding_idx: int = None,
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dtype: dtype = None,
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weight_initializer: Callable = init.normal_(),
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*args,
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**kwargs):
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super().__init__()
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self.num_embeddings = num_embeddings
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self.embed_dim = embedding_dim
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embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size)
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self.padding_idx = padding_idx
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self.embed_args = args
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self.embed_kwargs = kwargs
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self.weight = Parameter(
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torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
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self.reset_parameters(weight_initializer)
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self._set_tensor_parallel_attributes()
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set_parallel_input(False)
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def _set_tensor_parallel_attributes(self):
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set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size)
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def reset_parameters(self, weight_initializer) -> None:
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with seed(ParallelMode.TENSOR):
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fan_in, fan_out = self.num_embeddings, self.embed_dim
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weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
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self._fill_padding_idx_with_zero()
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def _fill_padding_idx_with_zero(self) -> None:
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if self.padding_idx is not None:
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with torch.no_grad():
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self.weight[self.padding_idx].fill_(0)
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def forward(self, input_: Tensor) -> Tensor:
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output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
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output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
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return output
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@LAYERS.register_module
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class Dropout1D(ParallelLayer):
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"""
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Dropout layer of 1D parallelism
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:param p: dropout rate, defaults to 0.5
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:type p: float, optional
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:param inplace: If set to ``True``, will do this operation in-place, defaults tp ``False``
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:type inplace: bool, optional
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"""
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def __init__(self, p: float = 0.5, inplace: bool = False):
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super().__init__()
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self.parallel_input = get_parallel_input()
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self.p = p
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self.inplace = inplace
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|
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def forward(self, input_: Tensor) -> Tensor:
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cm = nullcontext() if not self.parallel_input else seed(ParallelMode.TENSOR)
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with cm:
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output = F.dropout(input_, self.p, self.training, self.inplace)
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return output
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