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129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, 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.model_executor.layers.linear import LinearBase, LinearMethodBase
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from vllm.model_executor.layers.quantization import QuantizationMethods
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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.utils import set_weight_attrs
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class TorchAOConfig(QuantizationConfig):
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"""Config class for torchao."""
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def __init__(self, torchao_config) -> None:
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self.torchao_config = torchao_config
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def __repr__(self) -> str:
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return f"TorchAOConfig({self.torchao_config})"
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def get_name(self) -> QuantizationMethods:
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return "torchao"
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def get_supported_act_dtypes(self) -> list[torch.dtype]:
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return [torch.float32, torch.float16, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 75
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@staticmethod
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def get_config_filenames() -> list[str]:
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return ["config.json"]
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig":
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"""Create the quant config from an hf model config"""
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try:
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from torchao.core.config import config_from_dict
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except ImportError as err:
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raise ImportError(
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"Please install torchao>=0.10.0 via "
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"`pip install torchao>=0.10.0` to use torchao quantization."
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) from err
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hf_config = cls.get_from_keys_or(config, ["quant_type"], None)
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assert hf_config is not None, "quant_type must be specified"
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assert (len(hf_config) == 1 and "default" in hf_config
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), "Expected only one key 'default' in quant_type dictionary"
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quant_type = hf_config["default"]
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ao_config = config_from_dict(quant_type)
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return cls(ao_config)
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["TorchAOLinearMethod"]:
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if isinstance(layer, LinearBase):
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return TorchAOLinearMethod(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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def torchao_quantize_param_data(param: torch.Tensor,
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torchao_config: Any) -> torch.nn.Parameter:
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"""Quantize a Tensor with torchao quantization specified by torchao_config
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Args:
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`param`: weight parameter of the linear module
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`torchao_config`: type of quantization and their arguments we want to
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use to quantize the Tensor
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"""
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from torchao.core.config import AOBaseConfig
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from torchao.quantization import quantize_
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assert isinstance(torchao_config, AOBaseConfig)
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dummy_linear = torch.nn.Linear(param.shape[1], param.shape[0], bias=False)
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dummy_linear.weight = param
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quantize_(dummy_linear, torchao_config)
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return dummy_linear.weight
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class TorchAOLinearMethod(LinearMethodBase):
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"""Linear method for torchao.
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Args:
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torchao_config: The torchao quantization config, a string
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that encodes the type of quantization and all relevant arguments.
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"""
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def __init__(self, quant_config: TorchAOConfig):
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: list[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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weight = Parameter(
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torch.empty(
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sum(output_partition_sizes),
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input_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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weight = torchao_quantize_param_data(weight,
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self.quant_config.torchao_config)
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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(
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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,
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) -> torch.Tensor:
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return F.linear(x, layer.weight, bias)
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