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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"
BITNET = "bitnet"
|
class_definition
| 1,188 | 1,528 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,600 |
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 AWQLinearVersion.GEMV
elif version == "exllama":
return AWQLinearVersion.EXLLAMA
elif version == "ipex":
return AWQLinearVersion.IPEX
else:
raise ValueError(f"Unknown AWQLinearVersion {version}")
|
class_definition
| 1,531 | 2,106 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,601 |
class AwqBackendPackingMethod(str, Enum):
AUTOAWQ = "autoawq"
LLMAWQ = "llm-awq"
|
class_definition
| 2,109 | 2,197 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,602 |
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.
Args:
config_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object.
return_unused_kwargs (`bool`,*optional*, defaults to `False`):
Whether or not to return a list of unused keyword arguments. Used for `from_pretrained` method in
`PreTrainedModel`.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`QuantizationConfigMixin`]: The configuration object instantiated from those parameters.
"""
config = cls(**config_dict)
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:
return config
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
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 default
`QuantizationConfig()` is serialized to JSON file.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
config_dict = self.to_dict()
json_string = json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
writer.write(json_string)
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.
"""
return copy.deepcopy(self.__dict__)
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_json_string(self, use_diff: bool = True) -> str:
"""
Serializes this instance to a JSON string.
Args:
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`
is serialized to JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
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 class.
Returns:
`Dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
"""
to_remove = []
for key, value in kwargs.items():
if hasattr(self, key):
setattr(self, key, value)
to_remove.append(key)
# Remove all the attributes that were updated, without modifying the input dict
unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
return unused_kwargs
|
class_definition
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/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,603 |
class HqqConfig(QuantizationConfigMixin):
"""
This is wrapper around hqq's BaseQuantizeConfig.
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*, defaults to `False`):
View the quantized weight as float (used in distributed training) if set to `True`.
axis (`Optional[int]`, *optional*):
Axis along which grouping is performed. Supported values are 0 or 1.
dynamic_config (dict, *optional*):
Parameters for dynamic configuration. The key is the name tag of the layer and the value is a quantization config.
If set, each layer specified by its id will use its dedicated quantization configuration.
skip_modules (`List[str]`, *optional*, defaults to `['lm_head']`):
List of `nn.Linear` layers to skip.
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object.
"""
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():
from hqq.core.quantize import BaseQuantizeConfig as HQQBaseQuantizeConfig
else:
raise ImportError(
"A valid HQQ version (>=0.2.1) is not available. Please follow the instructions to install it: `https://github.com/mobiusml/hqq/`."
)
for deprecated_key in ["quant_zero", "quant_scale", "offload_meta"]:
if deprecated_key in kwargs:
logger.info(
deprecated_key + " is deprecated. This parameter will be ignored in quantization settings."
)
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:
self.quant_config = {}
for key in dynamic_config:
self.quant_config[key] = HQQBaseQuantizeConfig(**dynamic_config[key])
else:
self.quant_config = HQQBaseQuantizeConfig(
**{
"nbits": nbits,
"group_size": group_size,
"view_as_float": view_as_float,
"axis": axis,
}
)
self.quant_method = QuantizationMethod.HQQ
self.skip_modules = skip_modules
self.post_init()
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_dict in quantizers/auto.py
"""
instance = cls()
instance.quant_config = config["quant_config"]
instance.skip_modules = config["skip_modules"]
return instance
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.
"""
return {
"quant_config": self.quant_config,
"quant_method": self.quant_method,
"skip_modules": self.skip_modules,
}
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 readability and
serializes to a Python dictionary.
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 = HqqConfig().to_dict()
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
serializable_config_dict[key] = value
return serializable_config_dict
|
class_definition
| 6,510 | 11,351 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,604 |
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 only supports `LLM.int8()`, `FP4`, and `NF4` quantization. If more methods are added to `bitsandbytes`,
then more arguments will be added to this class.
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 from
`bitsandbytes`.
llm_int8_threshold (`float`, *optional*, defaults to 6.0):
This corresponds to the outlier threshold for outlier detection as described in `LLM.int8() : 8-bit Matrix
Multiplication for Transformers at Scale` paper: https://arxiv.org/abs/2208.07339 Any hidden states value
that is above this threshold will be considered an outlier and the operation on those values will be done
in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but
there are some exceptional systematic outliers that are very differently distributed for large models.
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-tuning).
llm_int8_skip_modules (`List[str]`, *optional*):
An explicit list of the modules that we do not want to convert in 8-bit. This is useful for models such as
Jukebox that has several heads in different places and not necessarily at the last position. For example
for `CausalLM` models, the last `lm_head` is kept in its original `dtype`.
llm_int8_enable_fp32_cpu_offload (`bool`, *optional*, defaults to `False`):
This flag is used for advanced use cases and users that are aware of this feature. If you want to split
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*, defaults to `False`):
This flag runs LLM.int8() with 16-bit main weights. This is useful for fine-tuning as the weights do not
have to be converted back and forth for the backward pass.
bnb_4bit_compute_dtype (`torch.dtype` or str, *optional*, defaults to `torch.float32`):
This sets the computational type which might be different than the input type. For example, inputs might be
fp32, but computation can be set to bf16 for speedups.
bnb_4bit_quant_type (`str`, *optional*, defaults to `"fp4"`):
This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types
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, *optional*, defaults to `torch.uint8`):
This sets the storage type to pack the quanitzed 4-bit prarams.
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object.
"""
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",
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_storage=None,
**kwargs,
):
self.quant_method = QuantizationMethod.BITS_AND_BYTES
if load_in_4bit and load_in_8bit:
raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
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_has_fp16_weight
self.bnb_4bit_quant_type = bnb_4bit_quant_type
self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant
if bnb_4bit_compute_dtype is None:
self.bnb_4bit_compute_dtype = torch.float32
elif isinstance(bnb_4bit_compute_dtype, str):
self.bnb_4bit_compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
elif isinstance(bnb_4bit_compute_dtype, torch.dtype):
self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype")
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_quant_storage` must be a valid string (one of 'float16', 'float32', 'int8', 'uint8', 'float64', 'bfloat16') "
)
self.bnb_4bit_quant_storage = getattr(torch, bnb_4bit_quant_storage)
elif isinstance(bnb_4bit_quant_storage, torch.dtype):
self.bnb_4bit_quant_storage = bnb_4bit_quant_storage
else:
raise ValueError("bnb_4bit_quant_storage must be a string or a torch.dtype")
if kwargs:
logger.warning(f"Unused kwargs: {list(kwargs.keys())}. These kwargs are not used in {self.__class__}.")
self.post_init()
@property
def load_in_4bit(self):
return self._load_in_4bit
@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 time")
self._load_in_4bit = value
@property
def load_in_8bit(self):
return self._load_in_8bit
@load_in_8bit.setter
def load_in_8bit(self, value: bool):
if not isinstance(value, bool):
raise TypeError("load_in_8bit must be a boolean")
if self.load_in_4bit and value:
raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
self._load_in_8bit = value
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_8bit, bool):
raise TypeError("load_in_8bit must be a boolean")
if not isinstance(self.llm_int8_threshold, float):
raise TypeError("llm_int8_threshold must be a float")
if self.llm_int8_skip_modules is not None and not isinstance(self.llm_int8_skip_modules, list):
raise TypeError("llm_int8_skip_modules must be a list of strings")
if not isinstance(self.llm_int8_enable_fp32_cpu_offload, bool):
raise TypeError("llm_int8_enable_fp32_cpu_offload must be a boolean")
if not isinstance(self.llm_int8_has_fp16_weight, bool):
raise TypeError("llm_int8_has_fp16_weight must be a boolean")
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 not isinstance(self.bnb_4bit_use_double_quant, bool):
raise TypeError("bnb_4bit_use_double_quant must be a boolean")
if self.load_in_4bit and not version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse(
"0.39.0"
):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"
)
def is_quantizable(self):
r"""
Returns `True` if the model is quantizable, `False` otherwise.
"""
return self.load_in_8bit or self.load_in_4bit
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":
return "fp4"
elif self.load_in_4bit and self.bnb_4bit_quant_type == "nf4":
return "nf4"
else:
return None
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"] = str(output["bnb_4bit_compute_dtype"]).split(".")[1]
output["bnb_4bit_quant_storage"] = str(output["bnb_4bit_quant_storage"]).split(".")[1]
output["load_in_4bit"] = self.load_in_4bit
output["load_in_8bit"] = self.load_in_8bit
return output
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 readability and
serializes to a Python dictionary.
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 serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
serializable_config_dict[key] = value
return serializable_config_dict
|
class_definition
| 11,365 | 22,729 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,605 |
class ExllamaVersion(int, Enum):
ONE = 1
TWO = 2
|
class_definition
| 22,732 | 22,788 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,606 |
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.
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 string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co.
- A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved
using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
dataset (`Union[List[str]]`, *optional*):
The dataset used for quantization. You can provide your own dataset in a list of string or just use the
original datasets used in GPTQ paper ['wikitext2','c4','c4-new']
group_size (`int`, *optional*, defaults to 128):
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 `False`):
Whether to quantize columns in order of decreasing activation size. Setting it to False can significantly
speed up inference but the perplexity may become slightly worse. Also known as act-order.
sym (`bool`, *optional*, defaults to `True`):
Whether to use symetric quantization.
true_sequential (`bool`, *optional*, defaults to `True`):
Whether to perform sequential quantization even within a single Transformer block. Instead of quantizing
the entire block at once, we perform layer-wise quantization. As a result, each layer undergoes
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*):
Properties, such as tooling:version, that do not directly contributes to quantization or quant inference are stored in meta.
i.e. `meta.quantizer`: ["optimum:_version_", "gptqmodel:_version_"]
backend (`str`, *optional*):
Controls which gptq kernel to be used. Valid values for gptqmodel are `auto`, `auto_trainable` and more. For auto-gptq, only
valid value is None and `auto_trainable`. Ref gptqmodel backends: https://github.com/ModelCloud/GPTQModel/blob/main/gptqmodel/utils/backend.py
use_cuda_fp16 (`bool`, *optional*, defaults to `False`):
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 None, we will infer the block name using common patterns (e.g. model.layers)
module_name_preceding_first_block (`List[str]`, *optional*):
The layers that are preceding the first Transformer block.
batch_size (`int`, *optional*, defaults to 1):
The batch size used when processing the dataset
pad_token_id (`int`, *optional*):
The pad token id. Needed to prepare the dataset when `batch_size` > 1.
use_exllama (`bool`, *optional*):
Whether to use exllama backend. Defaults to `True` if unset. Only works with `bits` = 4.
max_input_length (`int`, *optional*):
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 through the `version` key. Defaults
to `{"version": 1}` if unset.
cache_block_outputs (`bool`, *optional*, defaults to `True`):
Whether to cache block outputs to reuse as inputs for the succeeding block.
modules_in_block_to_quantize (`List[List[str]]`, *optional*):
List of list of module names to quantize in the specified block. This argument is useful to exclude certain linear modules from being quantized.
The block to quantize can be specified by setting `block_name_to_quantize`. We will quantize each list sequentially. If not set, we will quantize all linear layers.
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 quantized. This way, we will get
better results since it reflects the real input `self_attn.o_proj` will get when the model is quantized.
"""
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_format: str = "gptq",
meta: Optional[Dict[str, any]] = None,
backend: Optional[str] = None,
use_cuda_fp16: bool = False,
model_seqlen: Optional[int] = None,
block_name_to_quantize: Optional[str] = None,
module_name_preceding_first_block: Optional[List[str]] = None,
batch_size: int = 1,
pad_token_id: Optional[int] = None,
use_exllama: Optional[bool] = None,
max_input_length: Optional[int] = None,
exllama_config: Optional[Dict[str, Any]] = None,
cache_block_outputs: bool = True,
modules_in_block_to_quantize: Optional[List[List[str]]] = None,
**kwargs,
):
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
self.checkpoint_format = checkpoint_format.lower()
self.meta = meta
self.backend = backend.lower() if isinstance(backend, str) else backend
self.use_cuda_fp16 = use_cuda_fp16
self.model_seqlen = model_seqlen
self.block_name_to_quantize = block_name_to_quantize
self.module_name_preceding_first_block = module_name_preceding_first_block
self.batch_size = batch_size
self.pad_token_id = pad_token_id
self.use_exllama = use_exllama
self.max_input_length = max_input_length
self.exllama_config = exllama_config
self.disable_exllama = kwargs.pop("disable_exllama", None)
self.cache_block_outputs = cache_block_outputs
self.modules_in_block_to_quantize = modules_in_block_to_quantize
self.post_init()
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_attibutes_dict = {i: j for i, j in attibutes_dict.items() if i in loading_attibutes}
return loading_attibutes_dict
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 ValueError("group_size must be greater than 0 or equal to -1")
if not (0 < self.damp_percent < 1):
raise ValueError("damp_percent must between 0 and 1.")
if self.dataset is not None:
if isinstance(self.dataset, str):
if self.dataset in ["ptb", "ptb-new"]:
raise ValueError(
f"""{self.dataset} dataset was deprecated. You can only choose between
['wikitext2','c4','c4-new']"""
)
if self.dataset not in ["wikitext2", "c4", "c4-new"]:
raise ValueError(
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 a list of string or a value in
['wikitext2','c4','c4-new'], but we found {self.dataset}"""
)
# 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 not self.use_exllama else "auto"
else:
# convert gptqmodel backend `auto_trainable` into auto-gptq control
if self.backend == "auto_trainable":
self.use_exllama = False
# 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
logger.warning(
"Using `disable_exllama` is deprecated and will be removed in version 4.37. Use `use_exllama` instead and specify the version with `exllama_config`."
"The value of `use_exllama` will be overwritten by `disable_exllama` passed in `GPTQConfig` or stored in your config file."
)
self.use_exllama = not self.disable_exllama
self.disable_exllama = None
elif self.disable_exllama is not None and self.use_exllama is not None:
# Only happens if user explicitly passes in both arguments
raise ValueError("Cannot specify both `disable_exllama` and `use_exllama`. Please use just `use_exllama`")
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.ONE, ExllamaVersion.TWO]:
exllama_version = self.exllama_config["version"]
raise ValueError(
f"Only supported versions are in [ExllamaVersion.ONE, ExllamaVersion.TWO] - not recognized version {exllama_version}"
)
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`."
)
elif self.exllama_config["version"] == ExllamaVersion.TWO:
if is_auto_gptq_available():
optimum_version = version.parse(importlib.metadata.version("optimum"))
autogptq_version = version.parse(importlib.metadata.version("auto_gptq"))
if optimum_version <= version.parse("1.13.2") or autogptq_version <= version.parse("0.4.2"):
raise ValueError(
f"You need optimum > 1.13.2 and auto-gptq > 0.4.2 . Make sure to have that version installed - detected version : optimum {optimum_version} and autogptq {autogptq_version}"
)
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_block_to_quantize` quantization argument, please upgrade `optimum` package to a version superior than 1.15.0 ."
)
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 config
quant_dict["disable_exllama"] = not self.use_exllama
return quant_dict
@classmethod
def from_dict_optimum(cls, config_dict):
"""
Get compatible class with optimum gptq config dict
"""
if "disable_exllama" in config_dict:
config_dict["use_exllama"] = not config_dict["disable_exllama"]
# switch to None to not trigger the warning
config_dict["disable_exllama"] = None
config = cls(**config_dict)
return config
|
class_definition
| 22,802 | 36,844 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,607 |
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.
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*, defaults to `True`):
Whether to use zero point quantization.
version (`AWQLinearVersion`, *optional*, defaults to `AWQLinearVersion.GEMM`):
The version of the quantization algorithm to use. GEMM is better for big batch_size (e.g. >= 8) otherwise,
GEMV is better (e.g. < 8 ). GEMM models are compatible with Exllama kernels.
backend (`AwqBackendPackingMethod`, *optional*, defaults to `AwqBackendPackingMethod.AUTOAWQ`):
The quantization backend. Some models might be quantized using `llm-awq` backend. This is useful for users
that quantize their own models using `llm-awq` library.
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`):
Overwrite the natively supported fusing scheme with the one specified by the users.
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
Note you cannot quantize directly with transformers, please refer to `AutoAWQ` documentation for quantizing HF models.
exllama_config (`Dict[str, Any]`, *optional*):
You can specify the version of the exllama kernel through the `version` key, the maximum sequence
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.
"""
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: Optional[int] = None,
modules_to_fuse: Optional[dict] = None,
modules_to_not_convert: Optional[List] = None,
exllama_config: Optional[Dict[str, int]] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.AWQ
self.bits = bits
self.group_size = group_size
self.zero_point = zero_point
self.version = version
self.backend = backend
self.fuse_max_seq_len = fuse_max_seq_len
self.modules_to_not_convert = modules_to_not_convert
self.exllama_config = exllama_config
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"""
Safety checker that arguments are correct
"""
if self.backend not in [AwqBackendPackingMethod.AUTOAWQ, AwqBackendPackingMethod.LLMAWQ]:
raise ValueError(
f"Only supported quantization backends in {AwqBackendPackingMethod.AUTOAWQ} and {AwqBackendPackingMethod.LLMAWQ} - not recognized backend {self.backend}"
)
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 [AWQLinearVersion.GEMM, AWQLinearVersion.GEMV, AWQLinearVersion.EXLLAMA, AWQLinearVersion.IPEX] - not recognized version {self.version}"
)
if self.backend == AwqBackendPackingMethod.LLMAWQ:
compute_capability = torch.cuda.get_device_capability()
major, minor = compute_capability
if major < 8:
raise ValueError("LLM-AWQ backend is only supported on GPUs with compute capability >= 8.0")
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 = False
MIN_AWQ_VERSION = "0.1.7"
if is_auto_awq_available():
awq_version_supports_fusing = version.parse(importlib.metadata.version("autoawq")) >= version.parse(
MIN_AWQ_VERSION
)
if not awq_version_supports_fusing:
raise ValueError(
f"You current version of `autoawq` does not support module fusing, please upgrade `autoawq` package to at least {MIN_AWQ_VERSION}."
)
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")
) >= version.parse(MIN_AWQ_VERSION)
if not awq_version_supports_non_conversion:
raise ValueError(
f"You current version of `autoawq` does not support module quantization skipping, please upgrade `autoawq` package to at least {MIN_AWQ_VERSION}."
)
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",
]
if not all(key in self.modules_to_fuse for key in required_keys):
raise ValueError(
f"Required fields are missing in the fusing mapping, required fields are {required_keys}"
)
if self.version == AWQLinearVersion.EXLLAMA:
awq_version_supports_exllama = False
MIN_AWQ_VERSION = "0.2.0"
if is_auto_awq_available():
awq_version_supports_exllama = version.parse(importlib.metadata.version("autoawq")) >= version.parse(
MIN_AWQ_VERSION
)
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:
self.exllama_config = {"version": ExllamaVersion.TWO, "max_input_len": 2048, "max_batch_size": 8}
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.ONE, ExllamaVersion.TWO]:
exllama_version = self.exllama_config["version"]
raise ValueError(
f"Only supported versions are in [ExllamaVersion.ONE, ExllamaVersion.TWO] - not recognized version {exllama_version}"
)
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_attibutes_dict
|
class_definition
| 36,858 | 45,405 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,608 |
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 the output dimension. It's recommended to always use 1.
num_codebooks (`int`, *optional*, defaults to 1):
Number of codebooks for the Additive Quantization procedure.
nbits_per_codebook (`int`, *optional*, defaults to 16):
Number of bits encoding a single codebook vector. Codebooks size is 2**nbits_per_codebook.
linear_weights_not_to_quantize (`Optional[List[str]]`, *optional*):
List of full paths of `nn.Linear` weight parameters that shall not be quantized.
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object.
"""
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.in_group_size = in_group_size
self.out_group_size = out_group_size
self.num_codebooks = num_codebooks
self.nbits_per_codebook = nbits_per_codebook
self.linear_weights_not_to_quantize = linear_weights_not_to_quantize
self.post_init()
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_size, int):
raise TypeError("out_group_size must be a float")
if not isinstance(self.num_codebooks, int):
raise TypeError("num_codebooks must be a float")
if not isinstance(self.nbits_per_codebook, int):
raise TypeError("nbits_per_codebook must be a float")
if self.linear_weights_not_to_quantize is not None and not isinstance(
self.linear_weights_not_to_quantize, list
):
raise ValueError("linear_weights_not_to_quantize must be a list of strings")
if self.linear_weights_not_to_quantize is None:
self.linear_weights_not_to_quantize = []
|
class_definition
| 45,419 | 47,976 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,609 |
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 or not
group_num (`int`, *optional*, defaults to `1`): how many single groups for vector-quantization
group_size (`int`, *optional*, defaults to `-1`): depends on out-features
indices_as_float (`bool`, *optional*, defaults to `False`): for Finetuning
is_indice_packed (`bool`, *optional*, defaults to `True`): should always be True
num_centroids (`list`, *optional*, defaults to `[-1, -1]`): centriod numbers of clusters
num_res_centroids (`list`, *optional*, defaults to `[-1, -1]`): ditto for residual
outlier_size (`int`, *optional*, defaults to `1`): outliers
vector_lens (`list`, *optional*, defaults to `[-1, -1]`): centroid vector length in quantization
"""
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_centroids: tuple = [-1, -1],
out_features: int = -1,
outlier_size: int = 0,
vector_lens: tuple = [-1, -1],
**kwargs,
):
self.enable_norm = enable_norm
self.enable_perm = enable_perm
self.group_num = group_num
self.group_size = group_size
self.in_features = in_features
self.indices_as_float = indices_as_float
self.is_indice_packed = is_indice_packed
self.num_centroids = num_centroids
self.num_res_centroids = num_res_centroids
self.out_features = out_features
self.outlier_size = outlier_size
self.vector_lens = vector_lens
self.post_init()
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")
|
class_definition
| 47,990 | 50,267 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,610 |
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 layer
shared_layer_config (`Dict`, *optional*, defaults to `{}`): shared quantization params among layers
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object.
"""
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_proxy_error = enable_proxy_error
self.config_for_layers: Dict[str, Any] = config_for_layers
self.shared_layer_config: Dict[str, Any] = shared_layer_config
self.modules_to_not_convert = modules_to_not_convert
self.post_init()
def post_init(self):
r"""
Safety checker that arguments are correct
"""
for layer_name, layer_param in self.config_for_layers.items():
VptqLayerConfig(**layer_param)
if self.enable_proxy_error is True:
raise ValueError("enable_proxy_error should always be False until we support training")
|
class_definition
| 50,281 | 52,092 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,611 |
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 quantization. Supported values are ("float8","int8","int4","int2")
activations (`str`, *optional*):
The target dtype for the activations after quantization. Supported values are (None,"int8","float8")
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
"""
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 = modules_to_not_convert
self.post_init()
def post_init(self):
r"""
Safety checker that arguments are correct
"""
accepted_weights = ["float8", "int8", "int4", "int2"]
accepted_activations = [None, "int8", "float8"]
if self.weights not in accepted_weights:
raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights}")
if self.activations not in accepted_activations:
raise ValueError(f"Only support weights in {accepted_activations} but found {self.activations}")
|
class_definition
| 52,106 | 53,859 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,612 |
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 value is only "int8"
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision.
"""
def __init__(
self,
weights: str = "int8",
modules_to_not_convert: Optional[List] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.EETQ
self.weights = weights
self.modules_to_not_convert = modules_to_not_convert
self.post_init()
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}")
|
class_definition
| 53,873 | 55,060 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,613 |
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'), typing.List[str]]]`, *optional*):
dictionary mapping group name to a quantization scheme definition
format (`str`, *optional*, defaults to `"dense"`):
format the model is represented as. Set `run_compressed` True to execute model as the
compressed format if not `dense`
quantization_status (`QuantizationStatus`, *optional*, defaults to `"initialized"`):
status of model in the quantization lifecycle, ie 'initialized', 'calibration', 'frozen'
kv_cache_scheme (`typing.Union[QuantizationArgs, NoneType]`, *optional*):
specifies quantization of the kv cache. If None, kv cache is not quantized.
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[str, typing.Any]`, *optional*):
configuration for sparsity compression
quant_method (`str`, *optional*, defaults to `"compressed-tensors"`):
do not override, should be compressed-tensors
run_compressed (`bool`, *optional*, defaults to `True`): alter submodules (usually linear) in order to
emulate compressed model execution if True, otherwise use default submodule
"""
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
global_compression_ratio: Optional[float] = None,
ignore: Optional[List[str]] = None,
sparsity_config: Dict[str, Any] = None,
quant_method: str = "compressed-tensors",
run_compressed: bool = True,
**kwargs,
):
from compressed_tensors.config import SparsityCompressionConfig
from compressed_tensors.quantization import QuantizationConfig
self.quantization_config = None
self.sparsity_config = None
self.run_compressed = run_compressed
# 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,
"format": format,
"quantization_status": quantization_status,
"kv_cache_scheme": kv_cache_scheme,
"global_compression_ratio": global_compression_ratio,
"ignore": ignore,
"run_compressed": run_compressed,
**kwargs,
}
)
if sparsity_config:
self.sparsity_config = SparsityCompressionConfig.load_from_registry(
sparsity_config.get("format"), **sparsity_config
)
super().__init__(quant_method=QuantizationMethod.COMPRESSED_TENSORS)
@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]`):
Dictionary that will be used to instantiate the configuration object.
return_unused_kwargs (`bool`,*optional*, defaults to `False`):
Whether or not to return a list of unused keyword arguments. Used for `from_pretrained` method in
`PreTrainedModel`.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`QuantizationConfigMixin`]: The configuration object instantiated from those parameters.
"""
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_dict(self) -> Dict[str, Any]:
"""
Quantization config to be added to config.json
Serializes this instance to a Python dictionary. Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
quantization_config = {}
if self.quantization_config is not None:
quantization_config = self.quantization_config.dict()
else:
quantization_config["quant_method"] = QuantizationMethod.COMPRESSED_TENSORS
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 config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
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 = CompressedTensorsConfig().to_dict()
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
serializable_config_dict[key] = value
return serializable_config_dict
def get_loading_attributes(self):
return {"run_compressed": self.run_compressed}
|
class_definition
| 55,063 | 61,496 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,614 |
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 activation scale upper bound. This is used when quantizing the input activation.
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision.
"""
def __init__(
self,
activation_scale_ub: float = 1200.0,
modules_to_not_convert: Optional[List] = None,
**kwargs,
):
self.quant_method = QuantizationMethod.FBGEMM_FP8
self.activation_scale_ub = activation_scale_ub
self.modules_to_not_convert = modules_to_not_convert
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
|
class_definition
| 61,510 | 62,767 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,615 |
class HiggsConfig(QuantizationConfigMixin):
"""
HiggsConfig is a configuration class for quantization using the HIGGS method.
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_convert (`list`, *optional*, default to ["lm_head"]):
List of linear layers that should not be quantized.
hadamard_size (int, *optional*, defaults to 512):
Hadamard size for the HIGGS method. Default is 512. Input dimension of matrices is padded to this value. Decreasing this below 512 will reduce the quality of the quantization.
group_size (int, *optional*, defaults to 256):
Group size for the HIGGS method. Can be 64, 128 or 256. Decreasing it barely affects the performance. Default is 256. Must be a divisor of hadamard_size.
"""
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"]
self.quant_method = QuantizationMethod.HIGGS
self.bits = bits
self.p = p
self.modules_to_not_convert = modules_to_not_convert
self.hadamard_size = hadamard_size
self.group_size = group_size
self.post_init()
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("p must be 1 or 2. 2 is always better in practice")
if self.group_size not in [64, 128, 256]:
raise ValueError("group_size must be 64, 128, or 256")
if self.hadamard_size % self.group_size != 0:
raise ValueError("hadamard_size must be divisible by group_size")
|
class_definition
| 62,781 | 65,034 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,616 |
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`.
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision.
kwargs (`Dict[str, Any]`, *optional*):
The keyword arguments for the chosen type of quantization, for example, int4_weight_only quantization supports two keyword arguments
`group_size` and `inner_k_tiles` currently. More API examples and documentation of arguments can be found in
https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques
Example:
```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)
```
"""
def __init__(self, quant_type: str, modules_to_not_convert: Optional[List] = None, **kwargs):
self.quant_method = QuantizationMethod.TORCHAO
self.quant_type = quant_type
self.modules_to_not_convert = modules_to_not_convert
# when we load from serailized config, "quant_type_kwargs" will be the key
if "quant_type_kwargs" in kwargs:
self.quant_type_kwargs = kwargs["quant_type_kwargs"]
else:
self.quant_type_kwargs = kwargs
self.post_init()
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 ValueError("Requires torchao 0.4.0 version and above")
else:
raise ValueError(
"TorchAoConfig requires torchao to be installed, please install with `pip install torchao`"
)
_STR_TO_METHOD = self._get_torchao_quant_type_to_method()
if self.quant_type not in _STR_TO_METHOD.keys():
raise ValueError(
f"Requested quantization type: {self.quant_type} is not supported yet, please add support in TorchAoConfig and TorchAoHfQuantizer."
)
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 k not in all_kwargs:
raise ValueError(
f"Unexpected keyword arg: {k} for API: {method}, accepted keyword args are: {all_kwargs}"
)
def _get_torchao_quant_type_to_method(self):
if is_torchao_available():
from torchao.quantization import (
int4_weight_only,
int8_dynamic_activation_int8_weight,
int8_weight_only,
)
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 installed, please install with `pip install torchao`"
)
def get_apply_tensor_subclass(self):
_STR_TO_METHOD = self._get_torchao_quant_type_to_method()
return _STR_TO_METHOD[self.quant_type](**self.quant_type_kwargs)
def __repr__(self):
config_dict = self.to_dict()
return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"
|
class_definition
| 65,048 | 69,292 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,617 |
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):
r"""
Safety checker that arguments are correct
"""
pass
|
class_definition
| 69,306 | 69,716 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/quantization_config.py
| null | 2,618 |
class TimmWrapperImageProcessor(metaclass=DummyObject):
_backends = ["timm", "torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["timm", "torchvision"])
|
class_definition
| 129 | 323 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_timm_and_torchvision_objects.py
| null | 2,619 |
class FlaxForcedBOSTokenLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 129 | 301 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,620 |
class FlaxForcedEOSTokenLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 304 | 476 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,621 |
class FlaxForceTokensLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 479 | 648 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,622 |
class FlaxGenerationMixin(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 651 | 809 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,623 |
class FlaxLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 812 | 970 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,624 |
class FlaxLogitsProcessorList(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 973 | 1,135 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,625 |
class FlaxLogitsWarper(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 1,138 | 1,293 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,626 |
class FlaxMinLengthLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 1,296 | 1,463 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,627 |
class FlaxSuppressTokensAtBeginLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 1,466 | 1,645 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,628 |
class FlaxSuppressTokensLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 1,648 | 1,820 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,629 |
class FlaxTemperatureLogitsWarper(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 1,823 | 1,989 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,630 |
class FlaxTopKLogitsWarper(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 1,992 | 2,151 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,631 |
class FlaxTopPLogitsWarper(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 2,154 | 2,313 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,632 |
class FlaxWhisperTimeStampLogitsProcessor(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 2,316 | 2,490 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,633 |
class FlaxPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 2,493 | 2,651 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,634 |
class FlaxAlbertForMaskedLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 2,654 | 2,814 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,635 |
class FlaxAlbertForMultipleChoice(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 2,817 | 2,983 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,636 |
class FlaxAlbertForPreTraining(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 2,986 | 3,149 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,637 |
class FlaxAlbertForQuestionAnswering(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 3,152 | 3,321 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,638 |
class FlaxAlbertForSequenceClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 3,324 | 3,498 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,639 |
class FlaxAlbertForTokenClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 3,501 | 3,672 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,640 |
class FlaxAlbertModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 3,675 | 3,829 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,641 |
class FlaxAlbertPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 3,832 | 3,996 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,642 |
class FlaxAutoModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 4,673 | 4,825 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,643 |
class FlaxAutoModelForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 4,828 | 4,991 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,644 |
class FlaxAutoModelForImageClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 4,994 | 5,168 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,645 |
class FlaxAutoModelForMaskedLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 5,171 | 5,334 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,646 |
class FlaxAutoModelForMultipleChoice(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 5,337 | 5,506 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,647 |
class FlaxAutoModelForNextSentencePrediction(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 5,509 | 5,686 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,648 |
class FlaxAutoModelForPreTraining(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 5,689 | 5,855 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,649 |
class FlaxAutoModelForQuestionAnswering(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 5,858 | 6,030 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,650 |
class FlaxAutoModelForSeq2SeqLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 6,033 | 6,197 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,651 |
class FlaxAutoModelForSequenceClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 6,200 | 6,377 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,652 |
class FlaxAutoModelForSpeechSeq2Seq(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 6,380 | 6,548 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,653 |
class FlaxAutoModelForTokenClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 6,551 | 6,725 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,654 |
class FlaxAutoModelForVision2Seq(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 6,728 | 6,893 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,655 |
class FlaxBartDecoderPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 6,896 | 7,065 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,656 |
class FlaxBartForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 7,068 | 7,226 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,657 |
class FlaxBartForConditionalGeneration(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 7,229 | 7,400 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,658 |
class FlaxBartForQuestionAnswering(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 7,403 | 7,570 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,659 |
class FlaxBartForSequenceClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 7,573 | 7,745 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,660 |
class FlaxBartModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 7,748 | 7,900 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,661 |
class FlaxBartPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 7,903 | 8,065 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,662 |
class FlaxBeitForImageClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 8,068 | 8,237 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,663 |
class FlaxBeitForMaskedImageModeling(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 8,240 | 8,409 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,664 |
class FlaxBeitModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 8,412 | 8,564 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,665 |
class FlaxBeitPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 8,567 | 8,729 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,666 |
class FlaxBertForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 8,732 | 8,890 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,667 |
class FlaxBertForMaskedLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 8,893 | 9,051 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,668 |
class FlaxBertForMultipleChoice(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 9,054 | 9,218 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,669 |
class FlaxBertForNextSentencePrediction(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 9,221 | 9,393 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,670 |
class FlaxBertForPreTraining(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 9,396 | 9,557 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,671 |
class FlaxBertForQuestionAnswering(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 9,560 | 9,727 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,672 |
class FlaxBertForSequenceClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 9,730 | 9,902 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,673 |
class FlaxBertForTokenClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 9,905 | 10,074 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,674 |
class FlaxBertModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 10,077 | 10,229 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,675 |
class FlaxBertPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 10,232 | 10,394 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,676 |
class FlaxBigBirdForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 10,397 | 10,558 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,677 |
class FlaxBigBirdForMaskedLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 10,561 | 10,722 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,678 |
class FlaxBigBirdForMultipleChoice(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 10,725 | 10,892 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,679 |
class FlaxBigBirdForPreTraining(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 10,895 | 11,059 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,680 |
class FlaxBigBirdForQuestionAnswering(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 11,062 | 11,232 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,681 |
class FlaxBigBirdForSequenceClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 11,235 | 11,410 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,682 |
class FlaxBigBirdForTokenClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 11,413 | 11,585 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,683 |
class FlaxBigBirdModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 11,588 | 11,743 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,684 |
class FlaxBigBirdPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 11,746 | 11,911 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,685 |
class FlaxBlenderbotForConditionalGeneration(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 11,914 | 12,091 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,686 |
class FlaxBlenderbotModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 12,094 | 12,252 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,687 |
class FlaxBlenderbotPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 12,255 | 12,423 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,688 |
class FlaxBlenderbotSmallForConditionalGeneration(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 12,426 | 12,608 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,689 |
class FlaxBlenderbotSmallModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 12,611 | 12,774 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,690 |
class FlaxBlenderbotSmallPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 12,777 | 12,950 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,691 |
class FlaxBloomForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 12,953 | 13,112 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,692 |
class FlaxBloomModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 13,115 | 13,268 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,693 |
class FlaxBloomPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 13,271 | 13,434 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,694 |
class FlaxCLIPModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 13,437 | 13,589 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,695 |
class FlaxCLIPPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 13,592 | 13,754 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,696 |
class FlaxCLIPTextModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 13,757 | 13,913 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,697 |
class FlaxCLIPTextModelWithProjection(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 13,916 | 14,086 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,698 |
class FlaxCLIPTextPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
|
class_definition
| 14,089 | 14,255 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py
| null | 2,699 |
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