76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
from typing import Any, Dict, List
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import torch
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class QuantizationConfig:
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@classmethod
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def get_name(cls) -> str:
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"""Name of the quantization method."""
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raise NotImplementedError
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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"""List of supported activation dtypes."""
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raise NotImplementedError
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@classmethod
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def get_min_capability(cls) -> int:
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"""Minimum GPU capability to support the quantization method.
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E.g., 70 for Volta, 75 for Turing, 80 for Ampere.
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This requirement is due to the custom CUDA kernels used by the
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quantization method.
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"""
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raise NotImplementedError
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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"""List of filenames to search for in the model directory."""
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raise NotImplementedError
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "QuantizationConfig":
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"""Create a config class from the model's quantization config."""
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raise NotImplementedError
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@staticmethod
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def get_from_keys(config: Dict[str, Any], keys: List[str]) -> Any:
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"""Get a value from the model's quantization config."""
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for key in keys:
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if key in config:
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return config[key]
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raise ValueError(f"Cannot find any of {keys} in the model's "
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"quantization config.")
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@classmethod
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def get_packed_tensor_names(cls) -> List[str]:
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raise NotImplementedError
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@classmethod
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def is_packed(cls, tensor_name: str) -> bool:
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"""Returns True if a tensor is packed.
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A tensor is considered packed if each element in the tensor is a
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packed representation of multiple elements in the original tensor.
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For example, an INT32 element in the tensor may represent 8 INT4
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elements in the original tensor.
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"""
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return any(tag in tensor_name for tag in cls.get_packed_tensor_names())
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@classmethod
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def get_transposed_tensor_names(cls) -> List[str]:
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raise NotImplementedError
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@classmethod
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def is_transposed(cls, tensor_name: str) -> bool:
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"""Returns True if a tensor is transposed relative to nn.Linear.weight.
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"""
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return any(tag in tensor_name
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for tag in cls.get_transposed_tensor_names())
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@classmethod
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def get_tp_tensor_names(cls) -> List[str]:
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raise NotImplementedError
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