381 lines
13 KiB
Python
381 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import importlib
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import json
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import types
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from importlib.util import find_spec
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from typing import Any, Optional
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import regex as re
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import torch
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import torch.nn.functional as F
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from packaging import version
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from torch.nn.parameter import Parameter
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from vllm.logger import init_logger
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from vllm.model_executor.layers.linear import (
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LinearBase,
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LinearMethodBase,
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UnquantizedLinearMethod,
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)
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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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QuantizeMethodBase,
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)
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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 _bond_method_to_cls(func, obj):
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if hasattr(func, "__self__") or not callable(func):
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# If the function is already bound to an instance, return it as is
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return func
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else:
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return types.MethodType(func, obj)
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def _get_weight_attrs(param):
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# record attributes attached to the weight, so we can
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# recover later
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recorded_weight_attr = {}
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for key in param.__dict__:
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if hasattr(param, key):
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attr = getattr(param, key)
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if not callable(attr):
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recorded_weight_attr[key] = attr
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elif hasattr(attr, "__self__") and param is attr.__self__:
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# if attr is a bonded method for an instance, and
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# attr.__self__ points to the instance (param)
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# we'll record the underlying function object
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recorded_weight_attr[key] = attr.__func__
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else:
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recorded_weight_attr[key] = attr
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return recorded_weight_attr
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def _restore_weight_attrs(param, recorded_weight_attr):
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for attr_name, attr in recorded_weight_attr.items():
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if not hasattr(param, attr_name):
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setattr(param, attr_name, _bond_method_to_cls(attr, param))
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def torchao_version_at_least(torchao_version: str) -> bool:
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if find_spec("torchao"):
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try:
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if version.parse(importlib.metadata.version("torchao")) >= version.parse(
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torchao_version
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):
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return True
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except (ImportError, version.InvalidVersion):
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return False
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return False
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def should_skip(prefix: str, skip_modules: list[str]) -> bool:
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"""
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Robust skipping logic:
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should_skip("model.model.layers.1.q_proj",
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["model.model.layers.1.q_proj"]) # True
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should_skip("model.model.layers.10.o_proj", ["o_proj"]) -> True
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should_skip("visual.model.layers.1.q_proj", ["visual"]) -> True
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should_skip("model.model.layers.1.q_proj", ["layers.1"]) -> True
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should_skip("model.model.layers.11.q_proj", ["layers.1"]) -> False
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"""
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for s in skip_modules:
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if prefix == s:
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return True
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if f".{s}." in f".{prefix}.":
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return True
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return False
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if torchao_version_at_least("0.15.0"):
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from torchao.prototype.tensor_conversion.api import (
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convert_to_packed_tensor_based_on_current_hardware,
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)
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else:
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convert_to_packed_tensor_based_on_current_hardware = lambda t: t
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class TorchAOConfig(QuantizationConfig):
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"""Config class for torchao."""
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def __init__(
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self,
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torchao_config,
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skip_modules: list[str] | None = None,
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is_checkpoint_torchao_serialized: bool = False,
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) -> None:
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"""
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# TorchAO quantization relies on tensor subclasses. In order,
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# to enable proper caching this needs standalone compile
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if is_torch_equal_or_newer("2.8.0.dev"):
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os.environ["VLLM_TEST_STANDALONE_COMPILE"] = "1"
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logger.info(
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"Using TorchAO: Setting VLLM_TEST_STANDALONE_COMPILE=1")
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# TODO: remove after the torch dependency is updated to 2.8
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if is_torch_equal_or_newer(
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"2.7.0") and not is_torch_equal_or_newer("2.8.0.dev"):
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os.environ["VLLM_DISABLE_COMPILE_CACHE"] = "1"
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logger.info("Using TorchAO: Setting VLLM_DISABLE_COMPILE_CACHE=1")
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"""
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super().__init__()
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self.torchao_config = torchao_config
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self.skip_modules = skip_modules or []
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self.is_checkpoint_torchao_serialized = is_checkpoint_torchao_serialized
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def __repr__(self) -> str:
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return (
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f"TorchAOConfig({self.torchao_config=}, {self.skip_modules=}, "
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f"{self.is_checkpoint_torchao_serialized=})"
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)
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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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"""torchao doesn't require additional config files, we use
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`config.json` from huggingface: `model_config.hf_config`
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"""
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return []
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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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quant_method = cls.get_from_keys_or(config, ["quant_method"], None)
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is_checkpoint_torchao_serialized = (
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quant_method is not None and "torchao" in quant_method
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)
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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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)
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quant_type = hf_config["default"]
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ao_config = config_from_dict(quant_type)
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# Adds skipped modules defined in "modules_to_not_convert"
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skip_modules = config.get("modules_to_not_convert", []) or []
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# Adds skipped modules defined in "module_fqn_to_config"
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_data = quant_type.get("_data", {})
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if not isinstance(_data, dict):
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_data = {}
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module_fqn = _data.get("module_fqn_to_config", {})
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if not isinstance(module_fqn, dict):
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module_fqn = {}
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for layer, layer_cfg in module_fqn.items():
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if layer_cfg is None:
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skip_modules.append(layer)
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return cls(ao_config, skip_modules, is_checkpoint_torchao_serialized)
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@classmethod
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def from_config_file(cls, config_file: str) -> "TorchAOConfig":
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"""Initialize class from a config file. Example:
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```
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config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
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fn = "torchao_config.json"
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with open(fn, "w") as f:
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f.write(json.dumps(config_to_dict(config)))
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```
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"""
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with open(config_file) as f:
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f.seek(0)
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f_read = f.read()
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config_dict = json.loads(f_read)
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hf_config = {"quant_type": {"default": config_dict}}
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return cls.from_config(hf_config)
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@classmethod
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def from_config_dict_json(cls, config_dict_json: str) -> "TorchAOConfig":
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"""Iniitalize class from a config_dict json string, got from
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torchao_config_object = some AOBaseConfig object
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json.dumps(config_to_dict(torchao_config_object))
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"""
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config_dict = json.loads(config_dict_json)
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hf_config = {"quant_type": {"default": config_dict}}
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return cls.from_config(hf_config)
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["QuantizeMethodBase"]:
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if not isinstance(layer, LinearBase):
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return None
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from torchao.quantization import ModuleFqnToConfig
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if should_skip(prefix, self.skip_modules):
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return UnquantizedLinearMethod()
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module_fqn = prefix
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if isinstance(self.torchao_config, ModuleFqnToConfig):
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module_fqn_to_config = self.torchao_config.module_fqn_to_config
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c = None
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if module_fqn in module_fqn_to_config:
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assert not module_fqn.startswith("re:"), (
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"module fqn should not start with"
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"`re:`, which is used for specifying regex"
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)
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c = module_fqn_to_config[module_fqn]
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else:
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for maybe_module_fqn_pattern in module_fqn_to_config:
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if not maybe_module_fqn_pattern.startswith("re:"):
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continue
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elif re.fullmatch(maybe_module_fqn_pattern[3:], module_fqn):
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# we'll apply the config for first fully matched pattern
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c = module_fqn_to_config[maybe_module_fqn_pattern]
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break
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else:
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# fallback to use default if no module specific
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# config is provided
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c = module_fqn_to_config.get("_default", None)
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if c is not None:
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current_torchao_config = TorchAOConfig(
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c, self.skip_modules, self.is_checkpoint_torchao_serialized
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)
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return TorchAOLinearMethod(current_torchao_config)
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else:
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return UnquantizedLinearMethod()
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return TorchAOLinearMethod(self)
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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(
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param: torch.Tensor, torchao_config: Any
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) -> 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), f"{torchao_config}"
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"""
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Avoid real weight allocation for faster load, since we will
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end up setting it to param.
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"""
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with torch.device("meta"):
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# linear can't be top level module since quantize_ is inplace
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# while some of our configs need to do module swap, and only non-top
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# level modules support module swap
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dummy_linear = torch.nn.Sequential(
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torch.nn.Linear(param.shape[1], param.shape[0], bias=False)
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)
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dummy_linear[0].weight = param
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quantize_(dummy_linear, torchao_config)
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return dummy_linear[0].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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quant_config: The torchao quantization config, a string that encodes
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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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if self.quant_config.is_checkpoint_torchao_serialized:
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weight = torchao_quantize_param_data(
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weight, self.quant_config.torchao_config
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)
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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: torch.Tensor | None = None,
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) -> torch.Tensor:
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return F.linear(x, layer.weight, bias)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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if self.quant_config.is_checkpoint_torchao_serialized:
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if not hasattr(layer, "weight"):
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return
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# record attributes attached to the weight, so we can
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# recover later
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recorded_weight_attr = _get_weight_attrs(layer.weight)
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layer.weight = Parameter(
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convert_to_packed_tensor_based_on_current_hardware(layer.weight),
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requires_grad=layer.weight.requires_grad,
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)
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_restore_weight_attrs(layer.weight, recorded_weight_attr)
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return
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# online quantize the weight if the checkpoint is not already
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# quantized by torchao
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recorded_weight_attr = _get_weight_attrs(layer.weight)
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weight = torchao_quantize_param_data(
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layer.weight, self.quant_config.torchao_config
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)
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weight = torch.nn.Parameter(
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convert_to_packed_tensor_based_on_current_hardware(weight),
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weight.requires_grad,
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)
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_restore_weight_attrs(weight, recorded_weight_attr)
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layer.register_parameter("weight", weight)
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