text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
value = Placeholder
elif name in self._class_to_module.keys():
module = self._get_module(self._class_to_module[name])
value = getattr(module, name)
elif name in self._modules:
value = self._get_module(name)
else:
raise AttributeError(f"module {self... | 2,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
class Placeholder(metaclass=DummyObject):
_backends = missing_backends
def __init__(self, *args, **kwargs):
requires_backends(self, missing_backends) | 2,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
class OptionalDependencyNotAvailable(BaseException):
"""Internally used error class for signalling an optional dependency was not found.""" | 2,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
class QuantizationMethod(str, Enum):
BITS_AND_BYTES = "bitsandbytes"
GPTQ = "gptq"
AWQ = "awq"
AQLM = "aqlm"
VPTQ = "vptq"
QUANTO = "quanto"
EETQ = "eetq"
HIGGS = "higgs"
HQQ = "hqq"
COMPRESSED_TENSORS = "compressed-tensors"
FBGEMM_FP8 = "fbgemm_fp8"
TORCHAO = "torchao"
... | 2,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class AWQLinearVersion(str, Enum):
GEMM = "gemm"
GEMV = "gemv"
EXLLAMA = "exllama"
IPEX = "ipex"
@staticmethod
def from_str(version: str):
version = version.lower()
if version == "gemm":
return AWQLinearVersion.GEMM
elif version == "gemv":
return ... | 2,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class AwqBackendPackingMethod(str, Enum):
AUTOAWQ = "autoawq"
LLMAWQ = "llm-awq" | 2,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class QuantizationConfigMixin:
"""
Mixin class for quantization config
"""
quant_method: QuantizationMethod
@classmethod
def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
"""
Instantiates a [`QuantizationConfigMixin`] from a Python dictionary of parameters.... | 2,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
setattr(config, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
if return_unused_kwargs:
return config, kwargs
else... | 2,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the defau... | 2,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __iter__(self):
"""allows `dict(obj)` for situations where obj may be a dict or QuantizationConfigMixin"""
for attr, value in copy.deepcopy(self.__dict__).items():
yield attr, value
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
def to_j... | 2,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def update(self, **kwargs):
"""
Updates attributes of this class instance with attributes from `kwargs` if they match existing attributes,
returning all the unused kwargs.
Args:
kwargs (`Dict[str, Any]`):
Dictionary of attributes to tentatively update this cl... | 2,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class HqqConfig(QuantizationConfigMixin):
"""
This is wrapper around hqq's BaseQuantizeConfig. | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Args:
nbits (`int`, *optional*, defaults to 4):
Number of bits. Supported values are (8, 4, 3, 2, 1).
group_size (`int`, *optional*, defaults to 64):
Group-size value. Supported values are any value that is divisble by weight.shape[axis]).
view_as_float (`bool`, *optional... | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Additional parameters from which to initialize the configuration object.
""" | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
nbits: int = 4,
group_size: int = 64,
view_as_float: bool = False,
axis: Optional[int] = None,
dynamic_config: Optional[dict] = None,
skip_modules: List[str] = ["lm_head"],
**kwargs,
):
if is_hqq_available():
fro... | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if axis is None:
axis = 1
logger.info("Setting axis=1 as faster backends such as TorchAO or BitBlas are only compatible with it.")
if axis not in [0, 1]:
raise ValueError("Invalid axis value. Only 0 and 1 are allowed.")
if dynamic_config is not None:
sel... | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
"""
pass
@classmethod
def from_dict(cls, config: Dict[str, Any]):
"""
Override from_dict, used in AutoQuantizationConfig.from_di... | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __repr__(self):
config_dict = self.to_dict()
return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default config attributes for better re... | 2,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class BitsAndBytesConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `bitsandbytes`.
This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.
Currently... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Args:
load_in_8bit (`bool`, *optional*, defaults to `False`):
This flag is used to enable 8-bit quantization with LLM.int8().
load_in_4bit (`bool`, *optional*, defaults to `False`):
This flag is used to enable 4-bit quantization by replacing the Linear layers with FP4/NF4 layers ... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of
magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6,
but a lower threshold might be needed for more unstable models (small models, fine-t... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
your model in different parts and run some parts in int8 on GPU and some parts in fp32 on CPU, you can use
this flag. This is useful for offloading large models such as `google/flan-t5-xxl`. Note that the int8
operations will not be run on CPU.
llm_int8_has_fp16_weight (`bool`, *optional... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
which are specified by `fp4` or `nf4`.
bnb_4bit_use_double_quant (`bool`, *optional*, defaults to `False`):
This flag is used for nested quantization where the quantization constants from the first quantization are
quantized again.
bnb_4bit_quant_storage (`torch.dtype` or str, *o... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
load_in_8bit=False,
load_in_4bit=False,
llm_int8_threshold=6.0,
llm_int8_skip_modules=None,
llm_int8_enable_fp32_cpu_offload=False,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=None,
bnb_4bit_quant_type="fp4",
... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
self._load_in_8bit = load_in_8bit
self._load_in_4bit = load_in_4bit
self.llm_int8_threshold = llm_int8_threshold
self.llm_int8_skip_modules = llm_int8_skip_modules
self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload
self.llm_int8_has_fp16_weight = llm_int8_ha... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if bnb_4bit_quant_storage is None:
self.bnb_4bit_quant_storage = torch.uint8
elif isinstance(bnb_4bit_quant_storage, str):
if bnb_4bit_quant_storage not in ["float16", "float32", "int8", "uint8", "float64", "bfloat16"]:
raise ValueError(
"`bnb_4bit_qua... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
@load_in_4bit.setter
def load_in_4bit(self, value: bool):
if not isinstance(value, bool):
raise TypeError("load_in_4bit must be a boolean")
if self.load_in_8bit and value:
raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same tim... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
"""
if not isinstance(self.load_in_4bit, bool):
raise TypeError("load_in_4bit must be a boolean")
if not isinstance(self.load_in_8bi... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.bnb_4bit_compute_dtype is not None and not isinstance(self.bnb_4bit_compute_dtype, torch.dtype):
raise TypeError("bnb_4bit_compute_dtype must be torch.dtype")
if not isinstance(self.bnb_4bit_quant_type, str):
raise TypeError("bnb_4bit_quant_type must be a string")
if no... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def quantization_method(self):
r"""
This method returns the quantization method used for the model. If the model is not quantizable, it returns
`None`.
"""
if self.load_in_8bit:
return "llm_int8"
elif self.load_in_4bit and self.bnb_4bit_quant_type == "fp4":
... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary. Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
output["bnb_4bit_compute_dtype"] =... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
"""
config_dict = self.to_dict()
# get the default config dict
default_config_dict = BitsAndBytesConfig().to_dict()
serializable_config_dict = {}
# only se... | 2,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class ExllamaVersion(int, Enum):
ONE = 1
TWO = 2 | 2,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class GPTQConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `optimum` api for gptq quantization relying on auto_gptq backend. | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Args:
bits (`int`):
The number of bits to quantize to, supported numbers are (2, 3, 4, 8).
tokenizer (`str` or `PreTrainedTokenizerBase`, *optional*):
The tokenizer used to process the dataset. You can pass either:
- A custom tokenizer object.
- A ... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
The group size to use for quantization. Recommended value is 128 and -1 uses per-column quantization.
damp_percent (`float`, *optional*, defaults to 0.1):
The percent of the average Hessian diagonal to use for dampening. Recommended value is 0.1.
desc_act (`bool`, *optional*, defaults to `Fa... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
quantization using inputs that have passed through the previously quantized layers.
checkpoint_format (`str`, *optional*, defaults to `"gptq"`):
GPTQ weight format. `gptq`(v1) is supported by both gptqmodel and auto-gptq. `gptq_v2` is gptqmodel only.
meta (`Dict[str, any]`, *optional*):
... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Whether or not to use optimized cuda kernel for fp16 model. Need to have model in fp16. Auto-gptq only.
model_seqlen (`int`, *optional*):
The maximum sequence length that the model can take.
block_name_to_quantize (`str`, *optional*):
The transformers block name to quantize. If N... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
The maximum input length. This is needed to initialize a buffer that depends on the maximum expected input
length. It is specific to the exllama backend with act-order.
exllama_config (`Dict[str, Any]`, *optional*):
The exllama config. You can specify the version of the exllama kernel th... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Example: `modules_in_block_to_quantize =[["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"], ["self_attn.o_proj"]]`.
In this example, we will first quantize the q,k,v layers simultaneously since they are independent.
Then, we will quantize `self_attn.o_proj` layer with the q,k,v layers... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
bits: int,
tokenizer: Any = None,
dataset: Optional[Union[List[str], str]] = None,
group_size: int = 128,
damp_percent: float = 0.1,
desc_act: bool = False,
sym: bool = True,
true_sequential: bool = True,
checkpoint_form... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
self.quant_method = QuantizationMethod.GPTQ
self.bits = bits
self.tokenizer = tokenizer
self.dataset = dataset
self.group_size = group_size
self.damp_percent = damp_percent
self.desc_act = desc_act
self.sym = sym
self.true_sequential = true_sequential
... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
self.cache_block_outputs = cache_block_outputs
self.modules_in_block_to_quantize = modules_in_block_to_quantize
self.post_init() | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def get_loading_attributes(self):
attibutes_dict = copy.deepcopy(self.__dict__)
loading_attibutes = [
"disable_exllama",
"use_exllama",
"exllama_config",
"use_cuda_fp16",
"max_input_length",
"backend",
]
loading_atti... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct
"""
if self.bits not in [2, 3, 4, 8]:
raise ValueError(f"Only support quantization to [2,3,4,8] bits but found {self.bits}")
if self.group_size != -1 and self.group_size <= 0:
raise ValueE... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
f"""You have entered a string value for dataset. You can only choose between
['wikitext2','c4','c4-new'], but we found {self.dataset}"""
)
elif not isinstance(self.dataset, list):
raise ValueError(
f"""dataset needs to be either... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
# make sure backend is back/forward compatible with both gptqmodel (full) and auto-gptq (partial)
if is_gptqmodel_available():
# convert auto-gptq control into gptqmodel backend
if self.backend is None:
self.backend = "auto_trainable" if self.use_exllama is not None and n... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
# auto-gptq specific kernel control logic
if self.disable_exllama is None and self.use_exllama is None:
# New default behaviour
self.use_exllama = True
elif self.disable_exllama is not None and self.use_exllama is None:
# Follow pattern of old config
logge... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
raise ValueError("Cannot specify both `disable_exllama` and `use_exllama`. Please use just `use_exllama`") | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.exllama_config is None:
self.exllama_config = {"version": ExllamaVersion.ONE}
else:
if "version" not in self.exllama_config:
raise ValueError("`exllama_config` needs to have a `version` key.")
elif self.exllama_config["version"] not in [ExllamaVersion.... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.bits == 4 and self.use_exllama:
if self.exllama_config["version"] == ExllamaVersion.ONE:
logger.info(
"You have activated exllama backend. Note that you can get better inference "
"speed using exllamav2 kernel by setting `exllama_config`."
... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
)
if self.modules_in_block_to_quantize is not None:
optimum_version = version.parse(importlib.metadata.version("optimum"))
if optimum_version < version.parse("1.15.0"):
raise ValueError(
"You current version of `optimum` does not support `modules_in_bl... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def to_dict(self):
config_dict = super().to_dict()
config_dict.pop("disable_exllama", None)
return config_dict
def to_dict_optimum(self):
"""
Get compatible dict for optimum gptq config
"""
quant_dict = self.to_dict()
# make it compatible with optimum... | 2,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class AwqConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `auto-awq` library awq quantization relying on auto_awq backend. | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Args:
bits (`int`, *optional*, defaults to 4):
The number of bits to quantize to.
group_size (`int`, *optional*, defaults to 128):
The group size to use for quantization. Recommended value is 128 and -1 uses per-column quantization.
zero_point (`bool`, *optional*, default... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
do_fuse (`bool`, *optional*, defaults to `False`):
Whether to fuse attention and mlp layers together for faster inference
fuse_max_seq_len (`int`, *optional*):
The Maximum sequence length to generate when using fusing.
modules_to_fuse (`dict`, *optional*, default to `None`):
... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
length through the `max_input_len` key, and the maximum batch size through the `max_batch_size` key.
Defaults to `{"version": 2, "max_input_len": 2048, "max_batch_size": 8}` if unset.
""" | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
bits: int = 4,
group_size: int = 128,
zero_point: bool = True,
version: AWQLinearVersion = AWQLinearVersion.GEMM,
backend: AwqBackendPackingMethod = AwqBackendPackingMethod.AUTOAWQ,
do_fuse: Optional[bool] = None,
fuse_max_seq_len: Opti... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
self.modules_to_fuse = modules_to_fuse
if do_fuse is None:
self.do_fuse = modules_to_fuse is not None and len(modules_to_fuse) > 0
else:
self.do_fuse = do_fuse
self.fuse_max_seq_len = fuse_max_seq_len
self.post_init()
def post_init(self):
r"""
... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
self.version = AWQLinearVersion.from_str(self.version)
if self.version not in [
AWQLinearVersion.GEMM,
AWQLinearVersion.GEMV,
AWQLinearVersion.EXLLAMA,
AWQLinearVersion.IPEX,
]:
raise ValueError(
f"Only supported versions are in... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.do_fuse and self.fuse_max_seq_len is None:
raise ValueError(
"You cannot enable fused modules without specifying a `fuse_max_seq_len`, make sure to pass a valid `fuse_max_seq_len` for your usecase"
)
if self.do_fuse:
awq_version_supports_fusing = Fals... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.modules_to_not_convert is not None:
awq_version_supports_non_conversion = False
MIN_AWQ_VERSION = "0.1.8"
if is_auto_awq_available():
awq_version_supports_non_conversion = version.parse(
importlib.metadata.version("autoawq")
... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.do_fuse and self.modules_to_fuse is not None:
required_keys = [
"hidden_size",
"num_attention_heads",
"num_key_value_heads",
"mlp",
"attention",
"layernorm",
"use_alibi",
]
... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if not awq_version_supports_exllama:
raise ValueError(
f"You current version of `autoawq` does not support exllama backend, "
f"please upgrade `autoawq` package to at least {MIN_AWQ_VERSION}."
)
if self.exllama_config is None:
... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def get_loading_attributes(self):
attibutes_dict = copy.deepcopy(self.__dict__)
loading_attibutes = ["version", "do_fuse", "modules_to_fuse", "fuse_max_seq_len", "exllama_config"]
loading_attibutes_dict = {i: j for i, j in attibutes_dict.items() if i in loading_attibutes}
return loading_... | 2,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class AqlmConfig(QuantizationConfigMixin):
"""
This is a wrapper class about `aqlm` parameters.
Args:
in_group_size (`int`, *optional*, defaults to 8):
The group size along the input dimension.
out_group_size (`int`, *optional*, defaults to 1):
The group size along t... | 2,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
in_group_size: int = 8,
out_group_size: int = 1,
num_codebooks: int = 1,
nbits_per_codebook: int = 16,
linear_weights_not_to_quantize: Optional[List[str]] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.AQLM
self... | 2,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
"""
if not isinstance(self.in_group_size, int):
raise TypeError("in_group_size must be a float")
if not isinstance(self.out_group_siz... | 2,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class VptqLayerConfig(QuantizationConfigMixin):
"""
This is used to explain vptq config params for each layer
Args:
enable_norm (`bool`, *optional*, defaults to `True`): to control if we have scale/bias for fp-weight
enable_perm (`bool`, *optional*, defaults to `True`): to perm input_channel... | 2,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
vector_lens (`list`, *optional*, defaults to `[-1, -1]`): centroid vector length in quantization
""" | 2,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
enable_norm: bool = True,
enable_perm: bool = True,
group_num: int = 1,
group_size: int = -1,
in_features: int = -1,
indices_as_float: bool = False,
is_indice_packed: bool = True,
num_centroids: tuple = [-1, -1],
num_res... | 2,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct
"""
if self.is_indice_packed is False:
raise ValueError("is_indice_packed should always be True") | 2,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class VptqConfig(QuantizationConfigMixin):
"""
This is a wrapper class about `vptq` parameters.
Args:
enable_proxy_error (`bool`, *optional*, defaults to `False`): calculate proxy error for each layer
config_for_layers (`Dict`, *optional*, defaults to `{}`): quantization params for each lay... | 2,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
enable_proxy_error: bool = False,
config_for_layers: Dict[str, Any] = {},
shared_layer_config: Dict[str, Any] = {},
modules_to_not_convert: Optional[List] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.VPTQ
self.enable_... | 2,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class QuantoConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `quanto`.
Args:
weights (`str`, *optional*, defaults to `"int8"`):
The target dtype for the weights after qua... | 2,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
weights="int8",
activations=None,
modules_to_not_convert: Optional[List] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.QUANTO
self.weights = weights
self.activations = activations
self.modules_to_not_convert = ... | 2,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class EetqConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `eetq`.
Args:
weights (`str`, *optional*, defaults to `"int8"`):
The target dtype for the weights. Supported va... | 2,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct
"""
accepted_weights = ["int8"]
if self.weights not in accepted_weights:
raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights}") | 2,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class CompressedTensorsConfig(QuantizationConfigMixin):
"""
This is a wrapper class that handles compressed-tensors quantization config options.
It is a wrapper around `compressed_tensors.QuantizationConfig`
Args:
config_groups (`typing.Dict[str, typing.Union[ForwardRef('QuantizationScheme'), ty... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
global_compression_ratio (`typing.Union[float, NoneType]`, *optional*):
0-1 float percentage of model compression
ignore (`typing.Union[typing.List[str], NoneType]`, *optional*):
layer names or types to not quantize, supports regex prefixed by 're:'
sparsity_config (`typing.Dict[... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
config_groups: Dict[str, Union["QuantizationScheme", List[str]]] = None, # noqa: F821
format: str = "dense",
quantization_status: "QuantizationStatus" = "initialized", # noqa: F821
kv_cache_scheme: Optional["QuantizationArgs"] = None, # noqa: F821
g... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
# parse from dict to load nested QuantizationScheme objects
if config_groups or kv_cache_scheme:
self.quantization_config = QuantizationConfig.parse_obj(
{
"config_groups": config_groups,
"quant_method": quant_method,
"forma... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
@classmethod
def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
"""
Instantiates a [`CompressedTensorsConfig`] from a Python dictionary of parameters.
Optionally unwraps any args from the nested quantization_config
Args:
config_dict (`Dict[str, Any]`)... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if "quantization_config" in config_dict:
config_dict = dict(
sparsity_config=config_dict.get("sparsity_config"),
**config_dict["quantization_config"],
)
return super().from_dict(config_dict, return_unused_kwargs=return_unused_kwargs, **kwargs)
def to... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
if self.sparsity_config is not None:
quantization_config["sparsity_config"] = self.sparsity_config.dict()
else:
quantization_config["sparsity_config"] = {}
return quantization_config
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from c... | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
return serializable_config_dict
def get_loading_attributes(self):
return {"run_compressed": self.run_compressed} | 2,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class FbgemmFp8Config(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using fbgemm fp8 quantization.
Args:
activation_scale_ub (`float`, *optional*, defaults to 1200.0):
The activati... | 2,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def get_loading_attributes(self):
attibutes_dict = copy.deepcopy(self.__dict__)
loading_attibutes = ["activation_scale_ub"]
loading_attibutes_dict = {i: j for i, j in attibutes_dict.items() if i in loading_attibutes}
return loading_attibutes_dict | 2,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class HiggsConfig(QuantizationConfigMixin):
"""
HiggsConfig is a configuration class for quantization using the HIGGS method. | 2,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
Args:
bits (int, *optional*, defaults to 4):
Number of bits to use for quantization. Can be 2, 3 or 4. Default is 4.
p (int, *optional*, defaults to 2):
Quantization grid dimension. 1 and 2 are supported. 2 is always better in practice. Default is 2.
modules_to_not_conver... | 2,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def __init__(
self,
bits: int = 4,
p: int = 2,
modules_to_not_convert: Optional[List[str]] = None,
hadamard_size: int = 512,
group_size: int = 256,
**kwargs,
):
if modules_to_not_convert is None:
modules_to_not_convert = ["lm_head"]
... | 2,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
"""
if self.bits not in [2, 3, 4]:
raise ValueError("bits must be 2, 3, or 4")
if self.p not in [1, 2]:
raise ValueError(... | 2,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class TorchAoConfig(QuantizationConfigMixin):
"""This is a config class for torchao quantization/sparsity techniques.
Args:
quant_type (`str`):
The type of quantization we want to use, currently supporting: `int4_weight_only`, `int8_weight_only` and `int8_dynamic_activation_int8_weight`.
... | 2,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
```python
quantization_config = TorchAoConfig("int4_weight_only", group_size=32)
# int4_weight_only quant is only working with *torch.bfloat16* dtype right now
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
`... | 2,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
def post_init(self):
r"""
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
"""
if is_torchao_available():
if not version.parse(importlib.metadata.version("torchao")) >= version.parse("0.4.0"):
raise V... | 2,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
method = _STR_TO_METHOD[self.quant_type]
sig = signature(method)
all_kwargs = [
param.name
for param in sig.parameters.values()
if param.kind in [Parameter.KEYWORD_ONLY, Parameter.POSITIONAL_OR_KEYWORD]
]
for k in self.quant_type_kwargs:
if... | 2,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
return {
"int4_weight_only": int4_weight_only,
"int8_weight_only": int8_weight_only,
"int8_dynamic_activation_int8_weight": int8_dynamic_activation_int8_weight,
}
else:
raise ValueError(
"TorchAoConfig requires torchao to be... | 2,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class BitNetConfig(QuantizationConfigMixin):
def __init__(
self,
modules_to_not_convert: Optional[List] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.BITNET
self.modules_to_not_convert = modules_to_not_convert
self.post_init()
def post_init(self... | 2,618 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py |
class TimmWrapperImageProcessor(metaclass=DummyObject):
_backends = ["timm", "torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["timm", "torchvision"]) | 2,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_timm_and_torchvision_objects.py |
class FlaxForcedBOSTokenLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | 2,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py |
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