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1211 lines
49 KiB
1211 lines
49 KiB
import math
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from collections import OrderedDict
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from typing import Callable
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from torch.nn import Parameter
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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.global_variables import tensor_parallel_env as env
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from colossalai.legacy.registry import LAYERS
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from colossalai.nn import init as init
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from colossalai.utils.checkpointing import gather_tensor_parallel_state_dict, partition_tensor_parallel_state_dict
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from colossalai.utils.cuda import get_current_device
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from ..base_layer import ParallelLayer
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from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
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from ._operation import (
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Matmul_AB_2D,
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Matmul_ABT_2D,
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add_bias_2d,
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all_gather_tensor_2d,
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classifier_2d,
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layernorm_2d,
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reduce_scatter_tensor_2d,
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split_batch_2d,
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)
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from ._utils import assert_summa_initialization, get_summa_dim_from_env
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@LAYERS.register_module
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class Linear2D(ParallelLayer):
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r"""Linear layer for 2D parallelism
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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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skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
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which is preserved for kernel fusion, defaults to False.
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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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out_features: int,
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bias: bool = True,
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dtype: torch.dtype = None,
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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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self.in_features = in_features
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self.out_features = out_features
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self.skip_bias_add = skip_bias_add
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# parallel settings
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assert_summa_initialization()
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self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
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self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
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self.summa_dim = get_summa_dim_from_env()
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# partitioning dimension
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self.input_size_per_partition = divide(self.in_features, self.summa_dim)
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self.hidden_size_per_partition = divide(self.out_features, self.summa_dim)
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# create weight, shape: [k/q, h/q]
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factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
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self.weight = Parameter(
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torch.empty(self.input_size_per_partition, self.hidden_size_per_partition, **factory_kwargs))
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# create bias, shape: [h/q]
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if bias:
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self.bias = Parameter(torch.empty(divide(self.out_features, self.summa_dim**2), **factory_kwargs))
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else:
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self.register_parameter('bias', None)
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# initialize parameters
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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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def _set_tensor_parallel_attributes(self):
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set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
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if self.bias is not None:
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set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
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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 _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
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local_state = OrderedDict()
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weight_key = prefix + 'weight'
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bias_key = prefix + 'bias'
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if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
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# weight
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weight = state_dict.pop(weight_key, None)
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if weight is not None:
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local_state[weight_key] = weight.transpose(0, 1)
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# bias
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if self.bias is not None:
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bias = state_dict.pop(bias_key, None)
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if bias is not None:
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local_state[bias_key] = bias
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# partition in row groups
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if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
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local_state = partition_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_ROW,
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dims={
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weight_key: -1,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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)
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# partition in column groups
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local_state = partition_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_COL,
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dims={
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weight_key: 0,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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)
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super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
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def _save_to_global_state_dict(self, destination, prefix, keep_vars):
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weight_key = prefix + 'weight'
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bias_key = prefix + 'bias'
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local_state = OrderedDict({weight_key: self.weight})
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if self.bias is not None:
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local_state[bias_key] = self.bias
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# gather in column groups
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local_state = gather_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_COL,
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dims={
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weight_key: 0,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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keep_vars=keep_vars,
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)
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# gather in row groups
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if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
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local_state = gather_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_ROW,
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dims={
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weight_key: -1,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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keep_vars=keep_vars,
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)
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if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
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local_state[weight_key] = local_state[weight_key].transpose(0, 1)
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destination.update(local_state)
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def forward(self, x: Tensor) -> Tensor:
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# input: [m/q, n/q, k/q]
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# output: [m/q, n/q, h/q]
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out_shape = x.shape[:-1] + (self.hidden_size_per_partition,)
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output = Matmul_AB_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
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self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
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if self.bias is not None:
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if self.skip_bias_add:
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bias = add_bias_2d(None, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
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self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
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self.tensor_parallel_size)
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return output, bias
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else:
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output = add_bias_2d(output, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, False,
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self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
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self.tensor_parallel_size)
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return output
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else:
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return output
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@LAYERS.register_module
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class LayerNorm2D(ParallelLayer):
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r"""Layer Normalization for 2D parallelism.
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Args:
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normalized_shape (int): input shape from an expected input of size.
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:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
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\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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eps (float, optional): a value added to the denominator for numerical stability, defaults to 1e-05.
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bias (bool, optional): Whether to add a bias, defaults to ``True``.
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dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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"""
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def __init__(self, normalized_shape: int, eps: float = 1e-05, bias=True, dtype=None):
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super().__init__()
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# layer norm config
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self.normalized_shape = normalized_shape
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self.variance_epsilon = eps
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# parallel setting
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assert_summa_initialization()
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self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
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self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
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self.summa_dim = get_summa_dim_from_env()
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# partitioning dimension
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self.partitioned_partition = divide(normalized_shape, self.summa_dim**2)
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# create parameters
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factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
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self.weight = Parameter(torch.ones(self.partitioned_partition, **factory_kwargs))
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if bias:
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self.bias = Parameter(torch.zeros(self.partitioned_partition, **factory_kwargs))
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else:
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self.bias = None
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self._set_tensor_parallel_attributes()
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def _set_tensor_parallel_attributes(self):
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set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
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if self.bias is not None:
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set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
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def _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
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local_state = OrderedDict()
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weight_key = prefix + 'weight'
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bias_key = prefix + 'bias'
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if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
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# weight
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weight = state_dict.pop(weight_key, None)
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if weight is not None:
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local_state[weight_key] = weight
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# bias
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bias = state_dict.pop(bias_key, None)
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if bias is not None:
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local_state[bias_key] = bias
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# partition in row groups
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if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
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local_state = partition_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_ROW,
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dims={
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weight_key: 0,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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)
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# partition in column groups
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local_state = partition_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_COL,
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dims={
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weight_key: 0,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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)
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super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
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def _save_to_global_state_dict(self, destination, prefix, keep_vars):
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weight_key = prefix + 'weight'
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bias_key = prefix + 'bias'
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local_state = OrderedDict({weight_key: self.weight})
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if self.bias is not None:
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local_state[bias_key] = self.bias
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# gather in column groups
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local_state = gather_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_COL,
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dims={
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weight_key: 0,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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keep_vars=keep_vars,
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)
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# gather in row groups
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if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
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local_state = gather_tensor_parallel_state_dict(
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local_state,
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ParallelMode.PARALLEL_2D_ROW,
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dims={
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weight_key: 0,
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bias_key: 0
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},
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partition_states={
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weight_key: True,
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bias_key: True
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},
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keep_vars=keep_vars,
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)
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if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
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destination.update(local_state)
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def forward(self, x: Tensor) -> Tensor:
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with torch.no_grad():
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E_x = torch.sum(x, dim=-1, keepdim=True) # [b/q, s, 1]
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torch.distributed.all_reduce(E_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
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E_x /= self.normalized_shape
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# Var_x in the block below is the sum of input^2
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Var_x = torch.sum(x * x, dim=-1, keepdim=True) # [b/q, s, 1]
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torch.distributed.all_reduce(Var_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
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Var_x /= self.normalized_shape
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Var_x = Var_x - E_x * E_x # variance of x [b/q, s, 1]
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# this time 1/sqrt(Var_x + epsilon)
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Var_x = 1.0 / torch.sqrt(Var_x + self.variance_epsilon)
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output = layernorm_2d(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2D_ROW,
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ParallelMode.PARALLEL_2D_COL)
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scale = add_bias_2d(None, self.weight, self.partitioned_partition, self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True, self.data_parallel_rank,
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self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
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if self.bias is not None:
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bias = add_bias_2d(None, self.bias, self.partitioned_partition, self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
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self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
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self.tensor_parallel_size)
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output = torch.addcmul(bias, scale, output)
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else:
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output = torch.mul(scale, output)
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return output
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@LAYERS.register_module
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class PatchEmbedding2D(ParallelLayer):
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r"""2D Image to Patch Embedding.
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Args:
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img_size (int): image size.
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patch_size (int): patch size.
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in_chans (int): number of channels of input image.
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embed_size (int): size of embedding.
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dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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flatten (bool, optional): whether to flatten output tensor, defaults to True.
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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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position_embed_initializer (:class:`typing.Callable`, optional):
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The initializer of position embedding, defaults to zeros initializer.
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|
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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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img_size: int,
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patch_size: int,
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in_chans: int,
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embed_size: int,
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flatten: bool = True,
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dtype: torch.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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position_embed_initializer: Callable = init.zeros_()):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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assert_summa_initialization()
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self.summa_dim = get_summa_dim_from_env()
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self.img_size = img_size
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self.patch_size = patch_size
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self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.flatten = flatten
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self.embed_size = embed_size
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self.embed_size_per_partition = embed_size // (self.summa_dim**2)
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with seed(ParallelMode.TENSOR):
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self.weight = Parameter(
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torch.empty((self.embed_size_per_partition, in_chans, *self.patch_size),
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device=get_current_device(),
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dtype=dtype))
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self.bias = Parameter(torch.empty(self.embed_size_per_partition, device=get_current_device(), dtype=dtype))
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self.cls_token = Parameter(
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torch.zeros((1, 1, self.embed_size_per_partition), device=get_current_device(), dtype=dtype))
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self.pos_embed = Parameter(
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torch.zeros((1, self.num_patches + 1, self.embed_size_per_partition),
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device=get_current_device(),
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dtype=dtype))
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self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
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self._set_tensor_parallel_attribute()
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def _set_tensor_parallel_attribute(self):
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set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
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set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
|
|
set_tensor_parallel_attribute_by_partition(self.cls_token, self.summa_dim**2)
|
|
set_tensor_parallel_attribute_by_partition(self.pos_embed, self.summa_dim**2)
|
|
|
|
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer):
|
|
with seed(ParallelMode.TENSOR):
|
|
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
|
|
fan_out = self.embed_size
|
|
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
|
|
bias_initializer(self.bias, fan_in=fan_in)
|
|
position_embed_initializer(self.pos_embed)
|
|
|
|
def _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
|
|
local_state = OrderedDict()
|
|
weight_key = prefix + 'weight'
|
|
bias_key = prefix + 'bias'
|
|
cls_token_key = prefix + 'cls_token'
|
|
pos_embed_key = prefix + 'pos_embed'
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
# weight
|
|
weight = state_dict.pop(weight_key, None)
|
|
if weight is not None:
|
|
local_state[weight_key] = weight
|
|
# bias
|
|
bias = state_dict.pop(bias_key, None)
|
|
if bias is not None:
|
|
local_state[bias_key] = bias
|
|
# cls token
|
|
cls_token = state_dict.pop(cls_token_key, None)
|
|
if cls_token is not None:
|
|
local_state[cls_token_key] = cls_token
|
|
# pos embed
|
|
pos_embed = state_dict.pop(pos_embed_key, None)
|
|
if pos_embed is not None:
|
|
local_state[pos_embed_key] = pos_embed
|
|
|
|
# partition in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={
|
|
weight_key: 0,
|
|
bias_key: 0,
|
|
cls_token_key: -1,
|
|
pos_embed_key: -1
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True,
|
|
cls_token_key: True,
|
|
pos_embed_key: True
|
|
},
|
|
)
|
|
# partition in column groups
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={
|
|
weight_key: 0,
|
|
bias_key: 0,
|
|
cls_token_key: -1,
|
|
pos_embed_key: -1
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True,
|
|
cls_token_key: True,
|
|
pos_embed_key: True
|
|
},
|
|
)
|
|
|
|
super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
|
|
|
|
def _save_to_global_state_dict(self, destination, prefix, keep_vars):
|
|
weight_key = prefix + 'weight'
|
|
bias_key = prefix + 'bias'
|
|
cls_token_key = prefix + 'cls_token'
|
|
pos_embed_key = prefix + 'pos_embed'
|
|
local_state = OrderedDict({
|
|
weight_key: self.weight,
|
|
bias_key: self.bias,
|
|
cls_token_key: self.cls_token,
|
|
pos_embed_key: self.pos_embed
|
|
})
|
|
|
|
# gather in column groups
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={
|
|
weight_key: 0,
|
|
bias_key: 0,
|
|
cls_token_key: -1,
|
|
pos_embed_key: -1
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True,
|
|
cls_token_key: True,
|
|
pos_embed_key: True
|
|
},
|
|
keep_vars=keep_vars,
|
|
)
|
|
# gather in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={
|
|
weight_key: 0,
|
|
bias_key: 0,
|
|
cls_token_key: -1,
|
|
pos_embed_key: -1
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True,
|
|
cls_token_key: True,
|
|
pos_embed_key: True
|
|
},
|
|
keep_vars=keep_vars,
|
|
)
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
destination.update(local_state)
|
|
|
|
def forward(self, input_: Tensor) -> Tensor:
|
|
input_ = split_batch_2d(input_)
|
|
|
|
B, C, H, W = input_.shape
|
|
assert H == self.img_size[0] and W == self.img_size[1], \
|
|
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
|
|
|
|
weight = all_gather_tensor_2d(self.weight, 0, ParallelMode.PARALLEL_2D_COL)
|
|
bias = all_gather_tensor_2d(self.bias, 0, ParallelMode.PARALLEL_2D_COL)
|
|
|
|
output = F.conv2d(input_, weight, bias, stride=self.patch_size)
|
|
if self.flatten:
|
|
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
|
|
|
|
cls_token = all_gather_tensor_2d(self.cls_token, -1, ParallelMode.PARALLEL_2D_COL)
|
|
pos_embed = all_gather_tensor_2d(self.pos_embed, -1, ParallelMode.PARALLEL_2D_COL)
|
|
cls_token = cls_token.expand(output.shape[0], -1, -1)
|
|
output = torch.cat((cls_token, output), dim=1)
|
|
output = output + pos_embed
|
|
|
|
return output
|
|
|
|
|
|
@LAYERS.register_module
|
|
class Embedding2D(ParallelLayer):
|
|
r"""Embedding for 2D parallelism.
|
|
|
|
Args:
|
|
num_embeddings (int): number of embeddings.
|
|
embedding_dim (int): dimension of embedding.
|
|
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
|
|
therefore, the embedding vector at padding_idx is not updated during training,
|
|
i.e. it remains as a fixed “pad”, defaults to None.
|
|
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
|
|
weight_initializer (:class:`typing.Callable`, optional):
|
|
he initializer of weight, defaults to normal initializer.
|
|
|
|
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
|
|
::
|
|
|
|
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
|
|
renormalized to have norm max_norm. Note: this will modify weight in-place.
|
|
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
|
|
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
|
|
of frequency of the words in the mini-batch. Default False.
|
|
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
|
|
|
|
More details about ``args`` and ``kwargs`` could be found in
|
|
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
|
|
|
|
More details about initializer please refer to
|
|
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
|
|
"""
|
|
|
|
def __init__(self,
|
|
num_embeddings: int,
|
|
embedding_dim: int,
|
|
padding_idx: int = None,
|
|
dtype: torch.dtype = None,
|
|
weight_initializer: Callable = init.normal_(),
|
|
*args,
|
|
**kwargs):
|
|
super().__init__()
|
|
|
|
assert_summa_initialization()
|
|
self.summa_dim = get_summa_dim_from_env()
|
|
self.num_embeddings = num_embeddings
|
|
self.embed_dim = embedding_dim
|
|
embed_dim_per_partition = divide(embedding_dim, self.summa_dim**2)
|
|
|
|
self.padding_idx = padding_idx
|
|
self.embed_args = args
|
|
self.embed_kwargs = kwargs
|
|
|
|
self.weight = Parameter(
|
|
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
|
|
|
|
self.reset_parameters(weight_initializer)
|
|
self._set_tensor_parallel_attributes()
|
|
|
|
def _set_tensor_parallel_attributes(self):
|
|
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
|
|
|
|
def reset_parameters(self, weight_initializer) -> None:
|
|
with seed(ParallelMode.TENSOR):
|
|
fan_in, fan_out = self.num_embeddings, self.embed_dim
|
|
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
|
|
self._fill_padding_idx_with_zero()
|
|
|
|
def _fill_padding_idx_with_zero(self) -> None:
|
|
if self.padding_idx is not None:
|
|
with torch.no_grad():
|
|
self.weight[self.padding_idx].fill_(0)
|
|
|
|
def _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
|
|
local_state = OrderedDict()
|
|
weight_key = prefix + 'weight'
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
# weight
|
|
weight = state_dict.pop(weight_key, None)
|
|
if weight is not None:
|
|
local_state[weight_key] = weight
|
|
|
|
# partition in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={weight_key: -1},
|
|
partition_states={weight_key: True},
|
|
)
|
|
# partition in column groups
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={weight_key: -1},
|
|
partition_states={weight_key: True},
|
|
)
|
|
|
|
super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
|
|
|
|
def _save_to_global_state_dict(self, destination, prefix, keep_vars):
|
|
weight_key = prefix + 'weight'
|
|
local_state = OrderedDict({weight_key: self.weight})
|
|
|
|
# gather in column groups
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={weight_key: -1},
|
|
partition_states={weight_key: True},
|
|
keep_vars=keep_vars,
|
|
)
|
|
# gather in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={weight_key: -1},
|
|
partition_states={weight_key: True},
|
|
keep_vars=keep_vars,
|
|
)
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
destination.update(local_state)
|
|
|
|
def forward(self, input_: Tensor) -> Tensor:
|
|
input_ = split_batch_2d(input_)
|
|
|
|
weight = all_gather_tensor_2d(self.weight, -1, ParallelMode.PARALLEL_2D_COL)
|
|
output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
|
|
|
|
return output
|
|
|
|
|
|
@LAYERS.register_module
|
|
class VocabParallelEmbedding2D(ParallelLayer):
|
|
r"""Embedding parallelized in the vocabulary dimension.
|
|
|
|
Args:
|
|
num_embeddings (int): number of embeddings.
|
|
embedding_dim (int): dimension of embedding.
|
|
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
|
|
therefore, the embedding vector at padding_idx is not updated during training,
|
|
i.e. it remains as a fixed “pad”, defaults to None.
|
|
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
|
|
weight_initializer (:class:`typing.Callable`, optional):
|
|
he initializer of weight, defaults to normal initializer.
|
|
|
|
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
|
|
::
|
|
|
|
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
|
|
renormalized to have norm max_norm. Note: this will modify weight in-place.
|
|
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
|
|
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
|
|
of frequency of the words in the mini-batch. Default False.
|
|
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
|
|
|
|
More details about ``args`` and ``kwargs`` could be found in
|
|
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
|
|
|
|
More details about initializer please refer to
|
|
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
|
|
"""
|
|
|
|
def __init__(self,
|
|
num_embeddings: int,
|
|
embedding_dim: int,
|
|
padding_idx: int = None,
|
|
dtype: torch.dtype = None,
|
|
weight_initializer: Callable = init.normal_(),
|
|
*args,
|
|
**kwargs):
|
|
super().__init__()
|
|
self.num_embeddings = num_embeddings
|
|
self.embed_dim = embedding_dim
|
|
self.padding_idx = padding_idx
|
|
self.embed_args = args
|
|
self.embed_kwargs = kwargs
|
|
|
|
assert_summa_initialization()
|
|
self.summa_dim = get_summa_dim_from_env()
|
|
self.num_embeddings_per_partition = divide(self.num_embeddings, self.summa_dim)
|
|
self.embed_dim_per_partition = divide(self.embed_dim, self.summa_dim)
|
|
tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
|
|
self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
|
|
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
|
|
|
|
self.weight = Parameter(
|
|
torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
|
|
device=get_current_device(),
|
|
dtype=dtype))
|
|
|
|
self.reset_parameters(weight_initializer)
|
|
self._set_tensor_parallel_attributes()
|
|
env.vocab_parallel = True
|
|
|
|
def _set_tensor_parallel_attributes(self):
|
|
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
|
|
|
|
def reset_parameters(self, weight_initializer) -> None:
|
|
with seed(ParallelMode.TENSOR):
|
|
fan_in, fan_out = self.num_embeddings, self.embed_dim
|
|
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
|
|
self._fill_padding_idx_with_zero()
|
|
|
|
def _fill_padding_idx_with_zero(self) -> None:
|
|
if self.padding_idx is not None and \
|
|
self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
|
|
with torch.no_grad():
|
|
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
|
|
|
|
def _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
|
|
local_state = OrderedDict()
|
|
weight_key = prefix + 'weight'
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
# weight
|
|
weight = state_dict.pop(weight_key, None)
|
|
if weight is not None:
|
|
local_state[weight_key] = weight
|
|
|
|
# partition in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={weight_key: -1},
|
|
partition_states={weight_key: True},
|
|
)
|
|
# partition in column groups
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={weight_key: 0},
|
|
partition_states={weight_key: True},
|
|
)
|
|
|
|
super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
|
|
|
|
def _save_to_global_state_dict(self, destination, prefix, keep_vars):
|
|
weight_key = prefix + 'weight'
|
|
local_state = OrderedDict({weight_key: self.weight})
|
|
|
|
# gather in column groups
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={weight_key: 0},
|
|
partition_states={weight_key: True},
|
|
keep_vars=keep_vars,
|
|
)
|
|
# gather in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={weight_key: -1},
|
|
partition_states={weight_key: True},
|
|
keep_vars=keep_vars,
|
|
)
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
destination.update(local_state)
|
|
|
|
def forward(self, input_: Tensor) -> Tensor:
|
|
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
|
|
masked_input = input_.clone() - self.vocab_start_index
|
|
masked_input[input_mask] = 0
|
|
|
|
output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
|
|
**self.embed_kwargs)
|
|
|
|
output_parallel[input_mask, :] = 0.
|
|
output = reduce_scatter_tensor_2d(output_parallel, 0, ParallelMode.PARALLEL_2D_COL)
|
|
return output
|
|
|
|
|
|
@LAYERS.register_module
|
|
class Classifier2D(ParallelLayer):
|
|
r"""Classifier for 2D parallelism.
|
|
|
|
Args:
|
|
in_features (int): size of each input sample.
|
|
num_classes (int): number of classes.
|
|
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
|
|
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
|
|
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
|
|
weight_initializer (:class:`typing.Callable`, optional):
|
|
The initializer of weight, defaults to kaiming uniform initializer.
|
|
bias_initializer (:class:`typing.Callable`, optional):
|
|
The initializer of bias, defaults to xavier uniform initializer.
|
|
|
|
More details about ``initializer`` please refer to
|
|
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_features: int,
|
|
num_classes: int,
|
|
weight: Parameter = None,
|
|
bias: bool = True,
|
|
dtype: torch.dtype = None,
|
|
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
|
|
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
|
|
super().__init__()
|
|
self.in_features = in_features
|
|
self.num_classes = num_classes
|
|
assert_summa_initialization()
|
|
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
|
|
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
|
|
self.summa_dim = get_summa_dim_from_env()
|
|
|
|
# partitioning dimension
|
|
self.input_size_per_partition = divide(self.in_features, self.summa_dim**2)
|
|
|
|
if weight is not None:
|
|
self.weight = weight
|
|
self.has_weight = False
|
|
else:
|
|
self.weight = Parameter(
|
|
torch.empty(self.num_classes, self.input_size_per_partition, device=get_current_device(), dtype=dtype))
|
|
self.has_weight = True
|
|
if bias:
|
|
self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
|
|
else:
|
|
self.bias = None
|
|
|
|
self.reset_parameters(weight_initializer, bias_initializer)
|
|
self._set_tensor_parallel_attributes()
|
|
|
|
def _set_tensor_parallel_attributes(self):
|
|
if self.has_weight:
|
|
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
|
|
|
|
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
|
|
with seed(ParallelMode.TENSOR):
|
|
fan_in, fan_out = self.in_features, self.num_classes
|
|
col_src_rank = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2D_COL)[0]
|
|
row_src_rank = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2D_ROW)[0]
|
|
|
|
if self.has_weight:
|
|
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
|
|
|
|
if self.bias is not None:
|
|
bias_initializer(self.bias, fan_in=fan_in)
|
|
broadcast(self.bias, col_src_rank, ParallelMode.PARALLEL_2D_COL)
|
|
broadcast(self.bias, row_src_rank, ParallelMode.PARALLEL_2D_ROW)
|
|
|
|
def _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
|
|
local_state = OrderedDict()
|
|
weight_key = prefix + 'weight'
|
|
bias_key = prefix + 'bias'
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
# weight
|
|
if self.has_weight:
|
|
weight = state_dict.pop(weight_key, None)
|
|
if weight is not None:
|
|
local_state[weight_key] = weight
|
|
# bias
|
|
if self.bias is not None:
|
|
bias = state_dict.pop(bias_key, None)
|
|
if bias is not None:
|
|
local_state[bias_key] = bias
|
|
|
|
# partition in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={
|
|
weight_key: -1,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: False
|
|
},
|
|
)
|
|
# partition in column groups
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={
|
|
weight_key: -1,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: False
|
|
},
|
|
)
|
|
|
|
super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
|
|
|
|
def _save_to_global_state_dict(self, destination, prefix, keep_vars):
|
|
weight_key = prefix + 'weight'
|
|
bias_key = prefix + 'bias'
|
|
local_state = OrderedDict()
|
|
if self.has_weight:
|
|
local_state[weight_key] = self.weight
|
|
if self.bias is not None:
|
|
local_state[bias_key] = self.bias
|
|
|
|
# gather in column groups
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={
|
|
weight_key: -1,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: False
|
|
},
|
|
keep_vars=keep_vars,
|
|
)
|
|
# gather in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={
|
|
weight_key: -1,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: False
|
|
},
|
|
keep_vars=keep_vars,
|
|
)
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
destination.update(local_state)
|
|
|
|
def forward(self, input_: Tensor) -> Tensor:
|
|
out_shape = input_.shape[:-1] + (self.num_classes,)
|
|
|
|
return classifier_2d(input_, self.weight, self.bias, self.summa_dim, out_shape, self.row_rank, self.col_rank,
|
|
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
|
|
self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
|
|
|
|
|
|
@LAYERS.register_module
|
|
class VocabParallelClassifier2D(ParallelLayer):
|
|
r"""Vocab parallel classifier layer for 2D parallelism.
|
|
|
|
Args:
|
|
in_features (int): size of each input sample.
|
|
num_classes (int): number of classes.
|
|
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
|
|
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
|
|
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
|
|
weight_initializer (:class:`typing.Callable`, optional):
|
|
The initializer of weight, defaults to kaiming uniform initializer.
|
|
bias_initializer (:class:`typing.Callable`, optional):
|
|
The initializer of bias, defaults to xavier uniform initializer.
|
|
|
|
More details about ``initializer`` please refer to
|
|
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_features: int,
|
|
num_classes: int,
|
|
weight: Parameter = None,
|
|
bias: bool = True,
|
|
dtype: torch.dtype = None,
|
|
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
|
|
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
|
|
super().__init__()
|
|
|
|
self.in_features = in_features
|
|
self.num_classes = num_classes
|
|
|
|
# parallel setting
|
|
assert_summa_initialization()
|
|
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
|
|
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
|
|
self.summa_dim = get_summa_dim_from_env()
|
|
|
|
# partitioning dimension
|
|
self.input_size_per_partition = divide(in_features, self.summa_dim)
|
|
self.output_size_per_partition = divide(num_classes, self.summa_dim)
|
|
|
|
# create weight, shape: [k/q, h/q]
|
|
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
|
|
if weight is not None:
|
|
self.weight = weight
|
|
self.has_weight = False
|
|
else:
|
|
self.weight = Parameter(
|
|
torch.empty(self.output_size_per_partition, self.input_size_per_partition, **factory_kwargs))
|
|
self.has_weight = True
|
|
# create bias, shape: [h/q]
|
|
if bias:
|
|
self.bias = Parameter(torch.empty(divide(self.num_classes, self.summa_dim**2), **factory_kwargs))
|
|
else:
|
|
self.bias = None
|
|
|
|
# initialize parameters
|
|
with seed(ParallelMode.TENSOR):
|
|
self.reset_parameters(weight_initializer, bias_initializer)
|
|
self._set_tensor_parallel_attributes()
|
|
env.vocab_parallel = True
|
|
|
|
def _set_tensor_parallel_attributes(self):
|
|
if self.has_weight:
|
|
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
|
|
if self.bias is not None:
|
|
set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
|
|
|
|
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
|
|
fan_in, fan_out = self.in_features, self.num_classes
|
|
if self.has_weight:
|
|
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
|
|
if self.bias is not None:
|
|
bias_initializer(self.bias, fan_in=fan_in)
|
|
|
|
def _load_from_global_state_dict(self, state_dict, prefix, *args, **kwargs):
|
|
local_state = OrderedDict()
|
|
weight_key = prefix + 'weight'
|
|
bias_key = prefix + 'bias'
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
# weight
|
|
if self.has_weight:
|
|
weight = state_dict.pop(weight_key, None)
|
|
if weight is not None:
|
|
local_state[weight_key] = weight
|
|
# bias
|
|
if self.bias is not None:
|
|
bias = state_dict.pop(bias_key, None)
|
|
if bias is not None:
|
|
local_state[bias_key] = bias
|
|
|
|
# partition in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={
|
|
weight_key: -1,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True
|
|
},
|
|
)
|
|
# partition in column groups
|
|
local_state = partition_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={
|
|
weight_key: 0,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True
|
|
},
|
|
)
|
|
|
|
super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
|
|
|
|
def _save_to_global_state_dict(self, destination, prefix, keep_vars):
|
|
weight_key = prefix + 'weight'
|
|
bias_key = prefix + 'bias'
|
|
local_state = OrderedDict()
|
|
if self.has_weight:
|
|
local_state[weight_key] = self.weight
|
|
if self.bias is not None:
|
|
local_state[bias_key] = self.bias
|
|
|
|
# gather in column groups
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_COL,
|
|
dims={
|
|
weight_key: 0,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True
|
|
},
|
|
keep_vars=keep_vars,
|
|
)
|
|
# gather in row groups
|
|
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
|
|
local_state = gather_tensor_parallel_state_dict(
|
|
local_state,
|
|
ParallelMode.PARALLEL_2D_ROW,
|
|
dims={
|
|
weight_key: -1,
|
|
bias_key: 0
|
|
},
|
|
partition_states={
|
|
weight_key: True,
|
|
bias_key: True
|
|
},
|
|
keep_vars=keep_vars,
|
|
)
|
|
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
|
|
local_state[weight_key] = local_state[weight_key].transpose(0, 1)
|
|
destination.update(local_state)
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
# input: [m/q, n/q, k/q]
|
|
# output: [m/q, n/q, h/q]
|
|
out_shape = x.shape[:-1] + (self.output_size_per_partition,)
|
|
|
|
output = Matmul_ABT_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
|
|
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
|
|
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
|
|
self.tensor_parallel_size)
|
|
|
|
if self.bias is not None:
|
|
output = add_bias_2d(output, self.bias, self.output_size_per_partition, self.row_rank, self.col_rank,
|
|
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, False,
|
|
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
|
|
self.tensor_parallel_size)
|
|
return output
|