114 lines
3.3 KiB
Python
114 lines
3.3 KiB
Python
from typing import Any, Dict, List, Optional
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import torch
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from torch.nn.parameter import Parameter
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from vllm import _custom_ops as ops
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from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.parameter import (GroupQuantScaleParameter,
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PackedvLLMParameter)
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from vllm.model_executor.utils import set_weight_attrs
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class W8a16Config(QuantizationConfig):
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"""Config class for W8a16.
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"""
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def __init__(
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self,
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) -> None:
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pass
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def __repr__(self) -> str:
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return ("W8a16Config")
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def get_name(self) -> str:
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return "w8a16"
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def get_supported_act_dtypes(self) -> List[torch.dtype]:
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return [torch.half, torch.bfloat16]
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def get_min_capability(self) -> int:
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return 75
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@staticmethod
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def get_config_filenames():
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "W8a16Config":
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return cls()
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["W8a16LinearMethod"]:
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if isinstance(layer, LinearBase):
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return W8a16LinearMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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return []
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class W8a16LinearMethod(LinearMethodBase):
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"""Linear method for w8a16.
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"""
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def __init__(self, quant_config: W8a16Config):
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self.quant_config = quant_config
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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(
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torch.empty(
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output_size_per_partition,
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input_size_per_partition,
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dtype=torch.int8,
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),
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requires_grad=False,
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)
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set_weight_attrs(
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weight, {
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"input_dim": 1,
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"output_dim": 0,
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})
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scales = Parameter(
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torch.empty(
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1,
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output_size_per_partition,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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set_weight_attrs(scales, {
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"input_dim": None,
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"output_dim": 1,
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})
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layer.register_parameter("weight", weight)
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set_weight_attrs(weight, extra_weight_attrs)
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layer.register_parameter("scales", scales)
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set_weight_attrs(scales, 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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qweight = layer.weight
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scales = layer.scales
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out_shape = (x.shape[:-1] + (qweight.shape[-2],))
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reshaped_x = x.reshape(-1, x.shape[-1])
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out = ops.linear_w8a16(reshaped_x, qweight, scales, format="TN")
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if bias is not None:
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out = out + bias
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return out.reshape(out_shape) |