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259 lines
9.3 KiB
259 lines
9.3 KiB
3 years ago
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import math
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import torch
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import torch.distributed as dist
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from torch import Tensor
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from torch.nn import Parameter, init as init
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from colossalai.context import seed, ParallelMode
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from colossalai.core import global_context as gpc
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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 ._operation import Matmul_AB_2D, Add_Bias_2D, _LayerNorm_2D
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from ._utils import get_summa_dim_from_env, assert_summa_initialization
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from .._common_utils import divide, set_tensor_parallel_attribute
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from ..base_layer import ParallelLayer
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@LAYERS.register_module
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class Linear2D(ParallelLayer):
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""" Linear layer for 2D 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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"""
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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=None,
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skip_bias_add: bool = False
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):
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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(
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self.in_features, self.summa_dim)
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self.hidden_size_per_partition = divide(
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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(torch.empty(
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self.input_size_per_partition,
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self.hidden_size_per_partition,
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**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(
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self.hidden_size_per_partition,
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**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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self.reset_parameters()
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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(self.weight)
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if self.bias is not None:
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set_tensor_parallel_attribute(self.bias)
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def reset_parameters(self) -> None:
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# setting
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fan_in = self.in_features
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a = math.sqrt(5)
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nonlinearity = 'leaky_relu'
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# init weight
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std = init.calculate_gain(nonlinearity, a) / math.sqrt(fan_in)
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bound = math.sqrt(3.0) * std
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with seed(ParallelMode.TENSOR):
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init.uniform_(self.weight, -bound, bound)
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# init bias
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if self.bias is not None:
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bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
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with seed(ParallelMode.TENSOR):
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init.uniform_(self.bias, -bound, bound)
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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(
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x,
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self.weight,
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self.summa_dim,
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out_shape,
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self.row_rank,
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self.col_rank,
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ParallelMode.PARALLEL_2D_ROW,
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ParallelMode.PARALLEL_2D_COL,
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self.data_parallel_rank,
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self.pipeline_parallel_rank,
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self.pipeline_parallel_size,
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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.apply(
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None,
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self.bias,
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self.hidden_size_per_partition,
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self.row_rank,
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self.col_rank,
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ParallelMode.PARALLEL_2D_ROW,
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ParallelMode.PARALLEL_2D_COL,
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True,
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self.data_parallel_rank,
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self.pipeline_parallel_rank,
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self.pipeline_parallel_size,
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self.tensor_parallel_size
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)
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return output, bias
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else:
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output = Add_Bias_2D.apply(
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output,
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self.bias,
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self.hidden_size_per_partition,
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self.row_rank,
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self.col_rank,
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ParallelMode.PARALLEL_2D_ROW,
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ParallelMode.PARALLEL_2D_COL,
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False,
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self.data_parallel_rank,
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self.pipeline_parallel_rank,
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self.pipeline_parallel_size,
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self.tensor_parallel_size
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)
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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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: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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:param dtype: The dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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"""
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def __init__(self,
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normalized_shape: int,
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eps: float = 1e-05,
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dtype=None
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):
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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)
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# create parameters
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factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
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if self.row_rank == 0:
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self.gamma = Parameter(torch.ones(
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self.partitioned_partition,
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**factory_kwargs))
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self.beta = Parameter(torch.zeros(
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self.partitioned_partition,
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**factory_kwargs))
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else:
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self.gamma = Parameter(torch.tensor(
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1.0,
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requires_grad=True,
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**factory_kwargs))
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self.beta = Parameter(torch.tensor(
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1.0,
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requires_grad=True,
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**factory_kwargs))
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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(self.gamma)
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set_tensor_parallel_attribute(self.beta)
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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(
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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(
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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.apply(x, E_x, Var_x, self.normalized_shape,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL)
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bias = Add_Bias_2D.apply(
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None, self.beta, self.partitioned_partition,
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self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
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True,
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self.data_parallel_rank,
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self.pipeline_parallel_rank,
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self.pipeline_parallel_size,
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self.tensor_parallel_size
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)
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scale = Add_Bias_2D.apply(
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None, self.gamma, self.partitioned_partition,
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self.row_rank, self.col_rank,
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ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
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True,
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self.data_parallel_rank,
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self.pipeline_parallel_rank,
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self.pipeline_parallel_size,
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self.tensor_parallel_size
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)
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output = torch.addcmul(bias, scale, output)
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return output
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