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https://git.datalinker.icu/vllm-project/vllm.git
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710 lines
30 KiB
Python
710 lines
30 KiB
Python
from abc import abstractmethod
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from typing import List, Optional
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import torch
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import torch.nn.functional as F
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from torch.nn.parameter import Parameter
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from vllm.distributed import (divide, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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split_tensor_along_last_dim,
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tensor_model_parallel_all_gather,
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tensor_model_parallel_all_reduce)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.utils import set_weight_attrs
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logger = init_logger(__name__)
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def adjust_marlin_shard(param, shard_size, shard_offset):
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marlin_tile_size = getattr(param, "marlin_tile_size", None)
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if marlin_tile_size is None:
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return shard_size, shard_offset
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return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
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class LinearMethodBase(QuantizeMethodBase):
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"""Base class for different (maybe quantized) linear methods."""
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@abstractmethod
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def create_weights(self, layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int], input_size: int,
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output_size: int, params_dtype: torch.dtype,
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**extra_weight_attrs):
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"""Create weights for a linear layer.
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The weights will be set as attributes of the layer.
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Args:
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layer: The layer that is using the LinearMethodBase factory.
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input_size_per_partition: Size of the weight input dim on rank X.
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output_partition_sizes: Sizes of the output dim of each logical
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weight on rank X. E.g., output_partition_sizes for QKVLinear
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is a list contains the width of Wq, Wk, Wv on rank X.
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input_size: Size of the input dim of the weight across all ranks.
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output_size: Size of the output dim of the weight across all ranks.
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params_dtype: Datatype of the parameters.
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"""
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raise NotImplementedError
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@abstractmethod
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def apply(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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"""Apply the weights in layer to the input tensor.
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Expects create_weights to have been called before on the layer."""
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raise NotImplementedError
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class UnquantizedLinearMethod(LinearMethodBase):
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"""Linear method without quantization.
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Args:
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separate_bias_add: If true, add bias separately after matrix
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multiplication.
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"""
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def __init__(self, separate_bias_add: bool = False):
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self.separate_bias_add = separate_bias_add
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def create_weights(self, layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int], input_size: int,
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output_size: int, params_dtype: torch.dtype,
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**extra_weight_attrs):
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output_size_per_partition = sum(output_partition_sizes)
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weight = Parameter(torch.empty(output_size_per_partition,
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input_size_per_partition,
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dtype=params_dtype),
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requires_grad=False)
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set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
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layer.register_parameter("weight", weight)
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set_weight_attrs(weight, extra_weight_attrs)
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def apply(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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weight = layer.weight
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if self.separate_bias_add:
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if bias is not None:
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return F.linear(x, weight) + bias
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return F.linear(x, weight)
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return F.linear(x, weight, bias)
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class LinearBase(torch.nn.Module):
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"""Base linear layer.
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Args:
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input_size: input dimension of the linear layer.
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output_size: output dimension of the linear layer.
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bias: If true, add bias.
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skip_bias_add: If true, skip adding bias but instead return it.
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params_dtype: Data type for the parameters.
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quant_config: Quantization configure.
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"""
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def __init__(
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self,
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input_size: int,
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output_size: int,
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skip_bias_add: bool = False,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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# Keep input parameters
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self.input_size = input_size
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self.output_size = output_size
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self.skip_bias_add = skip_bias_add
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if params_dtype is None:
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params_dtype = torch.get_default_dtype()
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self.params_dtype = params_dtype
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if quant_config is None:
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self.quant_method: Optional[
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QuantizeMethodBase] = UnquantizedLinearMethod()
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else:
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self.quant_method = quant_config.get_quant_method(self)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError
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class ReplicatedLinear(LinearBase):
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"""Replicated linear layer.
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Args:
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input_size: input dimension of the linear layer.
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output_size: output dimension of the linear layer.
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bias: If true, add bias.
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skip_bias_add: If true, skip adding bias but instead return it.
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params_dtype: Data type for the parameters.
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quant_config: Quantization configure.
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"""
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def __init__(
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self,
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input_size: int,
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output_size: int,
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bias: bool = True,
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skip_bias_add: bool = False,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__(input_size, output_size, skip_bias_add, params_dtype,
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quant_config)
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# All the linear layer supports quant method.
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assert self.quant_method is not None
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self.quant_method.create_weights(self, self.input_size,
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[self.output_size], self.input_size,
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self.output_size, self.params_dtype)
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if bias:
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self.bias = Parameter(
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torch.empty(self.output_size, dtype=self.params_dtype))
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set_weight_attrs(self.bias, {"output_dim": 0})
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else:
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self.register_parameter("bias", None)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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bias = self.bias if not self.skip_bias_add else None
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assert self.quant_method is not None
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output = self.quant_method.apply(self, x, bias)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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def extra_repr(self) -> str:
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s = f"in_features={self.input_size}"
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s += f", output_features={self.output_size}"
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s += f", bias={self.bias is not None}"
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return s
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class ColumnParallelLinear(LinearBase):
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"""Linear layer with column parallelism.
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The linear layer is defined as Y = XA + b. A is parallelized along
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its second dimension as A = [A_1, ..., A_p].
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Args:
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input_size: first dimension of matrix A.
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output_size: second dimension of matrix A.
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bias: If true, add bias.
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gather_output: If true, call all-gather on output and make Y available
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to all GPUs, otherwise, every GPU will have its output
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which is Y_i = XA_i
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skip_bias_add: This was added to enable performance optimizations where
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bias can be fused with other element-wise operations. we
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skip adding bias but instead return it.
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params_dtype: Data type for the parameters.
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quant_config: Quantization configure.
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output_sizes: list of output sizes packed into one output, like for QKV
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the list would be size 3.
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"""
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def __init__(
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self,
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input_size: int,
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output_size: int,
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bias: bool = True,
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gather_output: bool = False,
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skip_bias_add: bool = False,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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output_sizes: Optional[List[int]] = None,
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):
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super().__init__(input_size, output_size, skip_bias_add, params_dtype,
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quant_config)
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self.gather_output = gather_output
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# Divide the weight matrix along the last dimension.
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tp_size = get_tensor_model_parallel_world_size()
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self.output_size_per_partition = divide(output_size, tp_size)
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if output_sizes is None:
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output_sizes = [output_size]
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# All the linear layer supports quant method.
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assert self.quant_method is not None
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self.quant_method.create_weights(self,
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self.input_size,
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[x // tp_size for x in output_sizes],
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self.input_size,
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self.output_size,
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self.params_dtype,
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weight_loader=self.weight_loader)
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if bias:
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self.bias = Parameter(
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torch.empty(self.output_size_per_partition,
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dtype=params_dtype))
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set_weight_attrs(self.bias, {
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"output_dim": 0,
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"weight_loader": self.weight_loader,
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})
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else:
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self.register_parameter("bias", None)
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def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
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# Special case for Fp8 scales.
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fp8_scales_shard_indexer = getattr(param, "fp8_scales_shard_indexer",
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None)
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tp_rank = get_tensor_model_parallel_rank()
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output_dim = getattr(param, "output_dim", None)
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param_data = param.data
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if output_dim is not None:
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shard_size = param_data.shape[output_dim]
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start_idx = tp_rank * shard_size
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loaded_weight = loaded_weight.narrow(output_dim, start_idx,
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shard_size)
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# Special case for Fp8 scales.
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elif fp8_scales_shard_indexer is not None:
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param_data, loaded_weight = fp8_scales_shard_indexer(param_data,
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loaded_weight,
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shard_id=0)
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assert param_data.shape == loaded_weight.shape
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param_data.copy_(loaded_weight)
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def forward(self, input_):
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bias = self.bias if not self.skip_bias_add else None
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# Matrix multiply.
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assert self.quant_method is not None
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output_parallel = self.quant_method.apply(self, input_, bias)
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if self.gather_output:
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# All-gather across the partitions.
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output = tensor_model_parallel_all_gather(output_parallel)
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else:
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output = output_parallel
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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def extra_repr(self) -> str:
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s = f"in_features={self.input_size}"
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s += f", output_features={self.output_size_per_partition}"
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s += f", bias={self.bias is not None}"
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s += f", tp_size={get_tensor_model_parallel_world_size()}"
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s += f", gather_output={self.gather_output}"
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return s
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class MergedColumnParallelLinear(ColumnParallelLinear):
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"""Packed linear layers with column parallelism.
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Similar to ColumnParallelLinear, but the weight matrix is concatenated
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along the output dimension. When the weight matrix is loaded, the
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different partitions are sharded separately.
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Args:
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input_size: input dimension of the linear layer.
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output_sizes: list of output dimensions of the linear layer.
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bias: If true, add bias.
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gather_output: If true, call all-gather on output and make the output
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available to all GPUs, otherwise, every GPU will have
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its own output.
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skip_bias_add: This was added to enable performance optimizations where
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bias can be fused with other element-wise operations. we
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skip adding bias but instead return it.
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params_dtype: Data type for the parameters.
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quant_config: Quantization configure.
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"""
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def __init__(
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self,
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input_size: int,
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output_sizes: List[int],
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bias: bool = True,
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gather_output: bool = False,
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skip_bias_add: bool = False,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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self.output_sizes = output_sizes
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tp_size = get_tensor_model_parallel_world_size()
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assert all(output_size % tp_size == 0 for output_size in output_sizes)
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super().__init__(input_size, sum(output_sizes), bias, gather_output,
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skip_bias_add, params_dtype, quant_config,
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self.output_sizes)
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def weight_loader(self,
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param: Parameter,
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loaded_weight: torch.Tensor,
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loaded_shard_id: Optional[int] = None):
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param_data = param.data
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output_dim = getattr(param, "output_dim", None)
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# Special case for AQLM codebooks.
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is_metadata = getattr(param, "is_metadata", False)
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# Special case for Fp8 scales.
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fp8_scales_shard_indexer = getattr(param, "fp8_scales_shard_indexer",
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None)
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if loaded_shard_id is None:
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# Loaded weight is already packed.
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if output_dim is None:
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assert param_data.shape == loaded_weight.shape
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param_data.copy_(loaded_weight)
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return
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current_shard_offset = 0
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shard_offsets = []
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for i, output_size in enumerate(self.output_sizes):
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shard_offsets.append((i, current_shard_offset, output_size))
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current_shard_offset += output_size
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packed_dim = getattr(param, "packed_dim", None)
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for shard_id, shard_offset, shard_size in shard_offsets:
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# Special case for Quantization.
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# If quantized, we need to adjust the offset and size to account
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# for the packing.
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if packed_dim == output_dim:
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shard_size = shard_size // param.pack_factor
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shard_offset = shard_offset // param.pack_factor
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# Special case for Marlin.
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shard_size, shard_offset = adjust_marlin_shard(
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param, shard_size, shard_offset)
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loaded_weight_shard = loaded_weight.narrow(
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output_dim, shard_offset, shard_size)
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self.weight_loader(param, loaded_weight_shard, shard_id)
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return
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assert loaded_shard_id < len(self.output_sizes)
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tp_rank = get_tensor_model_parallel_rank()
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tp_size = get_tensor_model_parallel_world_size()
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if output_dim is not None:
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shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
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shard_size = self.output_sizes[loaded_shard_id] // tp_size
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# Special case for quantization.
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# If quantized, we need to adjust the offset and size to account
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# for the packing.
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packed_dim = getattr(param, "packed_dim", None)
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if packed_dim == output_dim:
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shard_size = shard_size // param.pack_factor
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shard_offset = shard_offset // param.pack_factor
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# Special case for Marlin.
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shard_size, shard_offset = adjust_marlin_shard(
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param, shard_size, shard_offset)
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param_data = param_data.narrow(output_dim, shard_offset,
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shard_size)
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start_idx = tp_rank * shard_size
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loaded_weight = loaded_weight.narrow(output_dim, start_idx,
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shard_size)
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# Special case for AQLM codebooks.
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elif is_metadata:
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# metadata indicates fixed size concatenated along dim 0
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shard_size = loaded_weight.shape[0]
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shard_offset = loaded_shard_id * shard_size
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param_data = param_data.narrow(0, shard_offset, shard_size)
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# Special case for Fp8 scales.
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elif fp8_scales_shard_indexer is not None:
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param_data, loaded_weight = fp8_scales_shard_indexer(
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param_data, loaded_weight, loaded_shard_id)
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else:
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ignore_warning = getattr(param, "ignore_warning", False)
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if not ignore_warning:
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logger.warning(
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"Loading a weight without `output_dim` attribute in "
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"MergedColumnParallelLinear, assume the weight is "
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"the same for all partitions.")
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assert param_data.shape == loaded_weight.shape
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param_data.copy_(loaded_weight)
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class QKVParallelLinear(ColumnParallelLinear):
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"""Linear layers for the attention's QKV transformation.
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Linear layers for the linear transformation of the query, key, and value
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vectors in the attention layer. The weight matrix is concatenated along
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the output dimension. The layer is parallelized along the head dimension.
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When the number of key/value heads is smaller than the number of query
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heads (e.g., multi-query/grouped-query attention), the key/value head may
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be replicated while the query heads are partitioned.
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Args:
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hidden_size: input hidden state size of the transformer.
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head_size: size of each attention head.
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total_num_heads: total number of attention query heads.
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total_num_kv_heads: total number of attention key/value heads. If
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None, assume total_num_kv_heads = total_num_heads.
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bias: If true, add bias.
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skip_bias_add: This was added to enable performance optimizations where
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bias can be fused with other element-wise operations. we
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skip adding bias but instead return it.
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params_dtype: Data type for the parameters.
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quant_config: Quantization configure.
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"""
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def __init__(
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self,
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hidden_size: int,
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head_size: int,
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total_num_heads: int,
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total_num_kv_heads: Optional[int] = None,
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bias: bool = True,
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skip_bias_add: bool = False,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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self.hidden_size = hidden_size
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self.head_size = head_size
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self.total_num_heads = total_num_heads
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if total_num_kv_heads is None:
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total_num_kv_heads = total_num_heads
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self.total_num_kv_heads = total_num_kv_heads
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# Divide the weight matrix along the last dimension.
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tp_size = get_tensor_model_parallel_world_size()
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self.num_heads = divide(self.total_num_heads, tp_size)
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if tp_size >= self.total_num_kv_heads:
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self.num_kv_heads = 1
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self.num_kv_head_replicas = divide(tp_size,
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self.total_num_kv_heads)
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else:
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self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
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self.num_kv_head_replicas = 1
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input_size = self.hidden_size
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output_size = (self.num_heads +
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2 * self.num_kv_heads) * tp_size * self.head_size
|
|
output_sizes = [
|
|
self.num_heads * tp_size * self.head_size,
|
|
self.num_kv_heads * tp_size * self.head_size,
|
|
self.num_kv_heads * tp_size * self.head_size
|
|
]
|
|
|
|
super().__init__(input_size, output_size, bias, False, skip_bias_add,
|
|
params_dtype, quant_config, output_sizes)
|
|
|
|
def weight_loader(self,
|
|
param: Parameter,
|
|
loaded_weight: torch.Tensor,
|
|
loaded_shard_id: Optional[str] = None):
|
|
param_data = param.data
|
|
output_dim = getattr(param, "output_dim", None)
|
|
# Special case for AQLM codebooks.
|
|
is_metadata = getattr(param, "is_metadata", False)
|
|
# Special case for Fp8 scales.
|
|
fp8_scales_shard_indexer = getattr(param, "fp8_scales_shard_indexer",
|
|
None)
|
|
|
|
if loaded_shard_id is None:
|
|
# Loaded weight is already packed.
|
|
if output_dim is None:
|
|
assert param_data.shape == loaded_weight.shape
|
|
param_data.copy_(loaded_weight)
|
|
return
|
|
shard_offsets = [
|
|
# (shard_id, shard_offset, shard_size)
|
|
("q", 0, self.total_num_heads * self.head_size),
|
|
("k", self.total_num_heads * self.head_size,
|
|
self.total_num_kv_heads * self.head_size),
|
|
("v", (self.total_num_heads + self.total_num_kv_heads) *
|
|
self.head_size, self.total_num_kv_heads * self.head_size),
|
|
]
|
|
packed_dim = getattr(param, "packed_dim", None)
|
|
for shard_id, shard_offset, shard_size in shard_offsets:
|
|
# Special case for Quantized Weights.
|
|
# If quantized, we need to adjust the offset and size to account
|
|
# for the packing.
|
|
if packed_dim == output_dim:
|
|
shard_size = shard_size // param.pack_factor
|
|
shard_offset = shard_offset // param.pack_factor
|
|
|
|
# Special case for Marlin.
|
|
shard_size, shard_offset = adjust_marlin_shard(
|
|
param, shard_size, shard_offset)
|
|
|
|
loaded_weight_shard = loaded_weight.narrow(
|
|
output_dim, shard_offset, shard_size)
|
|
self.weight_loader(param, loaded_weight_shard, shard_id)
|
|
return
|
|
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
assert loaded_shard_id in ["q", "k", "v"]
|
|
if output_dim is not None:
|
|
if loaded_shard_id == "q":
|
|
shard_offset = 0
|
|
shard_size = self.num_heads * self.head_size
|
|
elif loaded_shard_id == "k":
|
|
shard_offset = self.num_heads * self.head_size
|
|
shard_size = self.num_kv_heads * self.head_size
|
|
elif loaded_shard_id == "v":
|
|
shard_offset = (self.num_heads +
|
|
self.num_kv_heads) * self.head_size
|
|
shard_size = self.num_kv_heads * self.head_size
|
|
# Special case for Quantized Weights.
|
|
# If quantized, we need to adjust the offset and size to account
|
|
# for the packing.
|
|
packed_dim = getattr(param, "packed_dim", None)
|
|
if packed_dim == output_dim:
|
|
shard_size = shard_size // param.pack_factor
|
|
shard_offset = shard_offset // param.pack_factor
|
|
|
|
# Special case for Marlin.
|
|
shard_size, shard_offset = adjust_marlin_shard(
|
|
param, shard_size, shard_offset)
|
|
|
|
param_data = param_data.narrow(output_dim, shard_offset,
|
|
shard_size)
|
|
if loaded_shard_id == "q":
|
|
shard_id = tp_rank
|
|
else:
|
|
shard_id = tp_rank // self.num_kv_head_replicas
|
|
start_idx = shard_id * shard_size
|
|
loaded_weight = loaded_weight.narrow(output_dim, start_idx,
|
|
shard_size)
|
|
# Special case for for AQLM codebooks.
|
|
elif is_metadata:
|
|
# metadata indicates fixed size concatenated along dim 0
|
|
shard_size = loaded_weight.shape[0]
|
|
shard_index = ["q", "k", "v"].index(loaded_shard_id)
|
|
param_data = param_data.narrow(0, shard_index * shard_size,
|
|
shard_size)
|
|
# Special case for Fp8 scales.
|
|
elif fp8_scales_shard_indexer is not None:
|
|
param_data, loaded_weight = fp8_scales_shard_indexer(
|
|
param_data, loaded_weight, loaded_shard_id)
|
|
else:
|
|
ignore_warning = getattr(param, "ignore_warning", False)
|
|
if not ignore_warning:
|
|
logger.warning(
|
|
"Loading a weight without `output_dim` attribute in "
|
|
"QKVParallelLinear, assume the weight is the same "
|
|
"for all partitions.")
|
|
assert param_data.shape == loaded_weight.shape
|
|
param_data.copy_(loaded_weight)
|
|
|
|
|
|
class RowParallelLinear(LinearBase):
|
|
"""Linear layer with row parallelism.
|
|
|
|
The linear layer is defined as Y = XA + b. A is parallelized along
|
|
its first dimension and X along its second dimension as:
|
|
- -
|
|
| A_1 |
|
|
| . |
|
|
A = | . | X = [X_1, ..., X_p]
|
|
| . |
|
|
| A_p |
|
|
- -
|
|
Arguments:
|
|
input_size: first dimension of matrix A.
|
|
output_size: second dimension of matrix A.
|
|
bias: If true, add bias. Note that bias is not parallelized.
|
|
input_is_parallel: If true, we assume that the input is already
|
|
split across the GPUs and we do not split
|
|
again.
|
|
skip_bias_add: This was added to enable performance optimization where
|
|
bias can be fused with other element-wise operations.
|
|
We skip adding bias but instead return it.
|
|
params_dtype: Data type for the parameters.
|
|
quant_config: Quantization configure.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
input_size: int,
|
|
output_size: int,
|
|
bias: bool = True,
|
|
input_is_parallel: bool = True,
|
|
skip_bias_add: bool = False,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
reduce_results: bool = True,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__(input_size, output_size, skip_bias_add, params_dtype,
|
|
quant_config)
|
|
|
|
self.input_is_parallel = input_is_parallel
|
|
self.reduce_results = reduce_results
|
|
|
|
# Divide the weight matrix along the last dimension.
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.input_size_per_partition = divide(input_size, self.tp_size)
|
|
# All the linear layer supports quant method.
|
|
assert self.quant_method is not None
|
|
self.quant_method.create_weights(self,
|
|
self.input_size_per_partition,
|
|
[self.output_size],
|
|
self.input_size,
|
|
self.output_size,
|
|
self.params_dtype,
|
|
weight_loader=self.weight_loader)
|
|
|
|
if not reduce_results and (bias and not skip_bias_add):
|
|
raise ValueError("When not reduce the results, adding bias to the "
|
|
"results can lead to incorrect results")
|
|
|
|
if bias:
|
|
self.bias = Parameter(
|
|
torch.empty(self.output_size, dtype=params_dtype))
|
|
set_weight_attrs(self.bias, {
|
|
"output_dim": 0,
|
|
"weight_loader": self.weight_loader,
|
|
})
|
|
else:
|
|
self.register_parameter("bias", None)
|
|
|
|
def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
|
|
# Special case for Fp8 scales.
|
|
fp8_scales_shard_indexer = getattr(param, "fp8_scales_shard_indexer",
|
|
None)
|
|
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
input_dim = getattr(param, "input_dim", None)
|
|
param_data = param.data
|
|
if input_dim is not None:
|
|
shard_size = param_data.shape[input_dim]
|
|
start_idx = tp_rank * shard_size
|
|
loaded_weight = loaded_weight.narrow(input_dim, start_idx,
|
|
shard_size)
|
|
# Special case for Fp8 scales.
|
|
elif fp8_scales_shard_indexer is not None:
|
|
param_data, loaded_weight = fp8_scales_shard_indexer(param_data,
|
|
loaded_weight,
|
|
shard_id=0)
|
|
|
|
assert param_data.shape == loaded_weight.shape
|
|
param_data.copy_(loaded_weight)
|
|
|
|
def forward(self, input_):
|
|
# Set up backprop all-reduce.
|
|
if self.input_is_parallel:
|
|
input_parallel = input_
|
|
else:
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
splitted_input = split_tensor_along_last_dim(
|
|
input_, num_partitions=self.tp_size)
|
|
input_parallel = splitted_input[tp_rank].contiguous()
|
|
|
|
# Matrix multiply.
|
|
assert self.quant_method is not None
|
|
output_parallel = self.quant_method.apply(self, input_parallel)
|
|
if self.reduce_results and self.tp_size > 1:
|
|
output_ = tensor_model_parallel_all_reduce(output_parallel)
|
|
else:
|
|
output_ = output_parallel
|
|
|
|
if not self.skip_bias_add:
|
|
output = output_ + self.bias if self.bias is not None else output_
|
|
output_bias = None
|
|
else:
|
|
output = output_
|
|
output_bias = self.bias
|
|
return output, output_bias
|
|
|
|
def extra_repr(self) -> str:
|
|
s = f"input_features={self.input_size_per_partition}"
|
|
s += f", output_features={self.output_size}"
|
|
s += f", bias={self.bias is not None}"
|
|
s += f", tp_size={self.tp_size}"
|
|
s += f", reduce_results={self.reduce_results}"
|
|
return s
|