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def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
param_device = kwargs.pop("param_device", None)
# check if the param_name is not in self.modules_t... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: List[str],
):
"""
Each nn.Linear layer that needs to be quanti... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
if self.pre_quantized:
module._parameters[tensor_name] = torch.nn.Parameter(param_value.to(device=target_device))
if isinstance(module, nn.Linear):
module.extra_repr = types.MethodType(_linear_extra_repr, module)
else:
module._parameters[tensor_name] = torch.n... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
def is_serializable(self, safe_serialization=None):
if safe_serialization:
logger.warning(
"torchao quantized model does not support safe serialization, "
"please set `safe_serialization` to False"
)
return False
_is_torchao_serializabl... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
return False
return _is_torchao_serializable | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
@property
def is_trainable(self):
supported_quant_types_for_training = [
"int8_weight_only",
"int8_dynamic_activation_int8_weight",
]
return self.quantization_config.quant_type in supported_quant_types_for_training | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
class GptqHfQuantizer(HfQuantizer):
"""
Quantizer of the GPTQ method - for GPTQ the quantizer support calibration of the model through
`auto_gptq` or `gptqmodel` package. Quantization is done under the hood for users if they load a non-prequantized model.
"""
requires_calibration = False
requir... | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
def validate_environment(self, *args, **kwargs):
if not is_optimum_available():
raise ImportError("Loading a GPTQ quantized model requires optimum (`pip install optimum`)")
if is_auto_gptq_available() and is_gptqmodel_available():
logger.warning("Detected gptqmodel and auto-gptq,... | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
gptq_supports_cpu = (
is_auto_gptq_available()
and version.parse(importlib.metadata.version("auto-gptq")) > version.parse("0.4.2")
) or is_gptqmodel_available()
if not gptq_supports_cpu and not torch.cuda.is_available():
raise RuntimeError("GPU is required to quantize... | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
version.parse(importlib.metadata.version("gptqmodel")) < version.parse("1.4.3")
or version.parse(importlib.metadata.version("optimum")) < version.parse("1.23.99")
):
raise ImportError("The gptqmodel version should be >= 1.4.3, optimum version should >= 1.24.0") | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = torch.float16
logger.info("Loading the model in `torch.float16`. To overwrite it, set `torch_dtype` manually.")
elif torch_dtype != torch.float16:
logger.i... | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
if model.__class__.main_input_name != "input_ids":
raise RuntimeError("We can only quantize pure text model.")
if self.pre_quantized:
model = self.optimum_quantizer.convert_model(model, **kwargs)... | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
def is_serializable(self, safe_serialization=None):
return True | 346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py |
class QuantoHfQuantizer(HfQuantizer):
"""
Quantizer for the quanto library
"""
required_packages = ["quanto", "accelerate"]
requires_parameters_quantization = True
requires_calibration = False
def __init__(self, quantization_config: QuantoConfig, **kwargs):
super().__init__(quantiz... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
def validate_environment(self, *args, **kwargs):
if not is_optimum_quanto_available():
raise ImportError(
"Loading an optimum-quanto quantized model requires optimum-quanto library (`pip install optimum-quanto`)"
)
if not is_accelerate_available():
rai... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
logger.info("You did not specify `torch_dtype` in `from_pretrained`. Setting it to `torch.float32`.")
torch_dtype = torch.float32
return torch_dtype
def update_missing_keys(sel... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, QModuleMixin):
for missing in missing_keys:
if (
(name in missing or name in f"{prefix}.{missing}")
and not missing.endswith(".we... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
device_map = kwargs.get("device_map", None)
param_device = kwargs.get("param_device", None)
# we don't quantize the model if the module is going to be offloaded to the cpu
if device_map is not None and param_device is not None:
device_map_values = set(device_map.values())
... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
max_memory = {key: val * 0.90 for key, val in max_memory.items()}
return max_memory
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if version.parse(importlib.metadata.version("accelerate")) > version.parse("0.27.0"):
from accelerate.utils import CustomDtype
mapping = {
"int8": torch.int8,
"float8": CustomDty... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
def _process_model_before_weight_loading(
self, model: "PreTrainedModel", keep_in_fp32_modules: List[str] = [], **kwargs
):
from ..integrations import get_keys_to_not_convert, replace_with_quanto_layers
# We keep some modules such as the lm_head in their original dtype for numerical stabili... | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
def _process_model_after_weight_loading(self, model, **kwargs):
return model
@property
def is_trainable(self, model: Optional["PreTrainedModel"] = None):
return True
def is_serializable(self, safe_serialization=None):
return False | 347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py |
class EetqHfQuantizer(HfQuantizer):
"""
8-bit quantization from EETQ quantization method:
before loading: converts transformer layers into W8A16Linear during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear8bitLt into 8bit at first .cuda() cal... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
try:
import eetq # noqa: F401
except ImportError as exc:
if "shard_checkpoint" in str(exc):
# EETQ 1.0.0 is currently broken with the latest transformers because it tries to import the removed
# shard_checkpoint function, see https://github.com/NetEase-Fu... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
raise ValueError(
"Converting into 8-bit weights from tf/flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
)
if not torch.cuda.is_available()... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
device_map = kwargs.get("device_map", None)
if device_map is None:
logger.warning_once(
"You have loaded an EETQ model on CPU and have a CUDA device available, make sure to set "
"your model on a GPU device in order to run your model."
)
elif devic... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = torch.float16
logger.info(
"Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
"requirements of `eetq` to enable model loading... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, EetqLinear):
if self.pre_quantized or tensor_name == "bias":
if tensor_name == "weight" and param_value.dtype != torch.int8:
raise ValueError("Expect quantized weights but got an ... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
module, tensor_name = get_module_from_name(model, param_name)
new_value, weight_scale = quantize_and_preprocess_weights(param_value)
module._buffers[tensor_name] = new_value.to(target_device)
module.register("weight_scales", weight_scale.to(target_device))
def _process_model_after_weight_l... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
model = replace_with_eetq_linear(
model,
modules_to_not_convert=self.modules_to_not_convert,
quantization_config=self.quantization_config,
pre_quantized=self.pre_quantized,
)
model.config.quantization_config = self.quantization_config
def is_serializ... | 348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py |
class BitNetHfQuantizer(HfQuantizer):
"""
1.58-bit quantization from BitNet quantization method:
Before loading: it converts the linear layers into BitLinear layers during loading.
Checkout the paper introducing this method : https://arxiv.org/pdf/2402.17764
"""
requires_parameters_quantizatio... | 349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bitnet.py |
if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
raise ValueError(
"Loading ternary weights from tf/flax is currently not supported, please make"
" sure the weights are in PyTorch format."
)
if not torch.cuda.is_available():
... | 349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bitnet.py |
device_map = kwargs.get("device_map", None)
if device_map is None:
logger.warning_once(
"You have loaded a BitNet model on CPU and have a CUDA device available, make sure to set "
"your model on a GPU device in order to run your model."
)
elif devi... | 349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bitnet.py |
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
from ..integrations import get_keys_to_not_convert, replace_with_bitnet_linear
self.modules_to_not_convert = get_keys_to_n... | 349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bitnet.py |
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
target_dtype = torch.int8
return target_dtype
def is_serializable(self, safe_serialization=None):
return True
@property
def is_trainable(self) -> bool:
return False | 349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bitnet.py |
class AqlmHfQuantizer(HfQuantizer):
"""
Quantizer of the AQLM method. Enables the loading of prequantized models.
"""
requires_calibration = True
required_packages = ["aqlm"]
optimum_quantizer = None
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super(... | 350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_aqlm.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
if torch.cuda.is_available():
torch_dtype = torch.float16
logger.info(
"CUDA available. Assuming AQLM inference on GPU and loading the model in `torc... | 350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_aqlm.py |
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
**kwargs,
):
replace_with_aqlm_linear(
model,
quantization_config=self.quantization_config,
linear_weights_not_to_quantize=self.quantization_config.linear_weights_not_to_quan... | 350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_aqlm.py |
@property
def is_trainable(self, model: Optional["PreTrainedModel"] = None):
aqlm_supports_training = version.parse(importlib.metadata.version("aqlm")) >= version.parse("1.0.2")
if aqlm_supports_training:
return True
else:
logger.warning(
f"Currently i... | 350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_aqlm.py |
class HqqHfQuantizer(HfQuantizer):
"""
HQQ quantizer base HF class.
nn.Linear modules are first tagged with quant_config in _process_model_before_weight_loading().
The actual quantization and offloading to the GPU is done in check_quantized_param().
"""
use_keep_in_fp32_modules = False
requ... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
raise ValueError(
"Converting weights from tf/flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
)
if not torch.cuda.is_available():
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
device_map = kwargs.get("device_map", None)
if isinstance(device_map, dict):
if "cpu" in device_map.values() or "disk" in device_map.values():
raise ValueError(
"You are attempting to use an HQQ model with a device_map that contains a CPU or disk device."
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
# Adds missing keys for HQQLinear modules that are loaded but the model with initialized with torch.nn.Linear
def update_expected_keys(
self, model: "PreTrainedModel", expected_keys: List[str], loaded_keys: List[str]
) -> List[str]:
if not self.pre_quantized:
return expected_keys
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
# valid modules are Linear layers that have HQQLinear state_dict. We ignore skip_modules and any layers with Linear state_dict() params
_valid_modules = set()
_find_hqq_quantizable_layers(model, _valid_modules)
_valid_modules -= set(model.config.quantization_config["skip_modules"])
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
# Re-populate Linear/HQQLinear
for _module in _valid_modules:
if _module + ".weight" in loaded_keys:
new_keys.add(_module + ".weight")
else:
new_keys.update({_module + "." + _ref_key for _ref_key in _ref_keys})
if _modul... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
if self.pre_quantized:
return (
(isinstance(module, torch.nn.Linear) or isinstance(module, HQQLinear))
and tensor_name != "weight"
and tensor_name != "bias"
)
else:
return isinstance(module, torch.nn.Linear) and tensor_name == "... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
module, tensor_name = get_module_from_name(model, param_name)
layer_name = ".".join(param_name.split(".")[:-1])
parent_module = find_parent(model, layer_name)
node = layer_name.split(".")[-1]
# set module state_dict
module_state_dict = {}
for k, v in state_dict.items():
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
if self.using_multi_gpu:
hqq_layer = self._patch_layer_for_multigpu(hqq_layer)
setattr(parent_module, node, hqq_layer)
# ... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
# Step 2: Replace module with either HQQLinear or move it to device. We do this via setattr on the parent as doing on it on the module
# directly doesn't work.
if hasattr(module, "quant_config"):
hqq_layer = HQQLinear(
module,
module.quant_config,
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
# Remove accelerate hook and uses a simpler forward pass. Otherwise, this breaks with multi-gpu
def _patch_layer_for_multigpu(self, hqq_layer):
hqq_layer = remove_hook_from_module(hqq_layer)
def forward_with_device(self, x):
out = torch.matmul(x.to(self.device), self.dequantize().t())
... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
# Add the corresponding quant_config to each valid module. This allows us to do the actual nn.Linear -> HQQLinear conversion in create_quantized_param().
# prepare_for_hqq_linear() also sets the right quantization config inside the model (model.config.quantization_config) and the layers (hqq_layer.quant_config)... | 351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py |
class VptqHfQuantizer(HfQuantizer):
"""
Quantizer of the VPTQ method. Enables the loading of prequantized models.
"""
requires_calibration = True
required_packages = ["vptq"]
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super().__init__(quantization_confi... | 352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_vptq.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
if torch.cuda.is_available():
torch_dtype = torch.float16
logger.info(
"CUDA available. Assuming VPTQ inference on GPU and loading the model in `torc... | 352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_vptq.py |
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
**kwargs,
):
"""
we don't have param like modules_to_not_convert to indicate which layers should not be quantized
because `quantization_config` include the layers that should be quantized
... | 352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_vptq.py |
def is_serializable(self, safe_serialization=None):
return True | 352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_vptq.py |
class Bnb8BitHfQuantizer(HfQuantizer):
"""
8-bit quantization from bitsandbytes quantization method:
before loading: converts transformer layers into Linear8bitLt during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear8bitLt into 8bit at fitst... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
def validate_environment(self, *args, **kwargs):
if not is_accelerate_available():
raise ImportError(
f"Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
)
if not is_bitsandbytes_available():
... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
raise ValueError(
"Converting into 4-bit or 8-bit weights from tf/flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
) | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
device_map = kwargs.get("device_map", None)
if (
device_map is not None
and isinstance(device_map, dict)
and not self.quantization_config.llm_int8_enable_fp32_cpu_offload
):
device_map_without_lm_head = {
key: device_map[key] for key in dev... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
"`from_pretrained`. Check "
"https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu "
"for more details. "
) | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.2"):
raise ValueError(
"You have a version of `bitsandbytes` that is not compatible with 8bit inference and training"
" make sure you have the latest version of `bitsandbytes` installed"
... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
# We force the `dtype` to be float16, this is a requirement from `bitsandbytes`
logger.info(
"Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
def update_device_map(self, device_map):
if device_map is None:
if torch.cuda.is_available():
device_map = {"": torch.cuda.current_device()}
elif is_torch_xpu_available():
device_map = {"": f"xpu:{torch.xpu.current_device()}"}
else:
... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
):
import bitsandbytes as bnb
module, tensor_name = get_module_from_name(model, param_name)
if isins... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: Optional[List[str]] = None,
):
"""
combines logic from _load_s... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
module, tensor_name = get_module_from_name(model, param_name)
if tensor_name not in module._parameters:
raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
old_value = getattr(module, tensor_name)
if not isinstance(module._parameters[tensor_name... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
new_value = param_value.to("cpu")
if self.pre_quantized and not self.is_serializable():
raise ValueError(
"Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
"Make sure to download the latest `bitsandbytes` version. `pip... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
module._parameters[tensor_name] = new_value
if fp16_statistics is not None:
setattr(module.weight, "SCB", fp16_statistics.to(target_device))
if unexpected_keys is not None:
unexpected_keys.remove(fp16_statistics_key)
# We just need to pop the `weight_format` keys... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
from ..integrations import get_keys_to_not_convert, replace_with_bnb_linear
llm_int8_enable_fp32_cpu_offload = self.quanti... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
# Extend `self.modules_to_not_convert` to keys that are supposed to be offloaded to `cpu` or `disk`
if isinstance(device_map, dict) and len(device_map.keys()) > 1:
keys_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]]
if len(keys_on_cpu) > 0 and not llm_in... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
model.config.quantization_config = self.quantization_config
def is_serializable(self, safe_serialization=None):
_bnb_supports_8bit_serialization = version.parse(importlib.metadata.version("bitsandbytes")) > version.parse(
"0.37.2"
)
if not _bnb_supports_8bit_serialization:
... | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
model = dequantize_and_replace(
model, self.modules_to_not_convert, quantization_config=self.quantization_config
)
return model | 353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py |
class HfQuantizer(ABC):
"""
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF transformers models for inference and/or quantization.
This class is used only for transformers.PreTrainedModel.from_pretrained and cannot be easily used outside the scope of that method
yet. | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
Attributes
quantization_config (`transformers.utils.quantization_config.QuantizationConfigMixin`):
The quantization config that defines the quantization parameters of your model that you want to quantize.
modules_to_not_convert (`List[str]`, *optional*):
The list of module names ... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
self.quantization_config = quantization_config
# -- Handle extra kwargs below --
self.modules_to_not_convert = kwargs.pop("modules_to_not_convert", [])
self.pre_quantized = kwargs.pop("pre_quantized", True)
... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
"""
Some quantization methods require to explicitly set the dtype of the model to a
target dtype. You need to override this method in case you want to make sure that behavior is
preserved
Args:
... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def adjust_target_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
"""
Override this method if you want to adjust the `target_dtype` variable used in `from_pretrained`
to compute the device_map in case the device_map is a `str`. E.g. for bitsandbytes we force-set `target_dtype`
... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def update_expected_keys(self, model, expected_keys: List[str], loaded_keys: List[str]) -> List[str]:
"""
Override this method if you want to adjust the `update_expected_keys`.
Args:
expected_keys (`List[str]`, *optional*):
The list of the expected keys in the initia... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
Args:
model (`~transformers.PreTrainedModel`):
The model to quantize
torch_dtype (`torch.dtype`):
The dtype passed in `from_pretrained` method.
"""
return {
name: torch_dtype
for name, _ in model.named_parameters()
... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
"""
checks if a loaded state_dict component is part of quantized param + some validation; only def... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def validate_environment(self, *args, **kwargs):
"""
This method is used to potentially check for potential conflicts with arguments that are
passed in `from_pretrained`. You need to define it for all future quantizers that are integrated with transformers.
If no explicit check are neede... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
Args:
model (`~transformers.PreTrainedModel`):
The model to quantize
kwargs (`dict`, *optional*):
The keyword arguments that are passed along `_process_model_before_weight_loading`.
"""
model.is_quantized = True
model.quantization_method = ... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
def dequantize(self, model):
"""
Potentially dequantize the model to retrive the original model, with some loss in accuracy / performance.
Note not all quantization schemes support this.
"""
model = self._dequantize(model)
# Delete quantizer and quantization config
... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
@abstractmethod
def _process_model_after_weight_loading(self, model, **kwargs): ...
@abstractmethod
def is_serializable(self, safe_serialization=None): ...
@property
@abstractmethod
def is_trainable(self): ... | 354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py |
class HiggsHfQuantizer(HfQuantizer):
"""
Quantizer of the HIGGS method. Enables the loading of prequantized models and in-flight quantization of full-precision models.
"""
requires_calibration = False
requires_parameters_quantization = True
required_packages = ["flute-kernel", "fast_hadamard_tr... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
if not is_flute_available():
raise ImportError("Using `higgs` quantization requires FLUTE: `pip install flute-kernel>=0.3.0`")
if not is_hadamard_available():
raise ImportError(
"Using `higgs` quantization requires fast_hadamard_transform: `pip install fast_hadamard_tran... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
logger.info("`torch_dtype` is None. Setting `torch_dtype=torch.float16` for FLUTE compatibility.")
torch_dtype = torch.float16
elif torch_dtype != torch.float16 and torch_dtype != t... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
"""
Quantizes weights into weight and weight_scale
"""
flute_dict = quantize_with_higgs(
param_value.to(target_device),
self.quantization_config.bits,
self.quantization_config.p,
self.quantization_config.group_size,
self.quantization_co... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
module.num_sms_packed = torch.nn.Parameter(
torch.tensor(get_num_sms_from_device(target_device), device=target_device, dtype=torch.int32),
requires_grad=False,
)
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
**kwargs,
):
... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
flute_workspaces = {}
for name, module in model.named_modules():
if isinstance(module, HiggsLinear):
# Every HiggsLinear needs a "workspace": a buffer for the unpacking operation.
# This buffer needs to be on the same device as the weights, but can be reused across mo... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
# FLUTE weights are packed in a way that is optimized for a specific number of SMs (GPU streaming multiprocessors).
# If the model is loaded on a different device than the one it was saved on, we need to repack the weights.
if module.num_sms_packed.item() != get_num_sms_from_device(modul... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
module.group_size,
)
module.num_sms_packed = torch.nn.Parameter(
torch.tensor(new_num_sms, device=new_device, dtype=torch.int32),
requires_grad=False,
) | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
def update_missing_keys(self, model, missing_keys: List[str], prefix: str) -> List[str]:
from ..integrations import HiggsLinear
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, HiggsLinear):
for missing in missing_keys:
... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
from ..integrations import HiggsLinear
module, tensor_name = get_module_from_name(model, param_na... | 355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py |
class AutoQuantizationConfig:
"""
The Auto-HF quantization config class that takes care of automatically dispatching to the correct
quantization config given a quantization config stored in a dictionary.
"""
@classmethod
def from_dict(cls, quantization_config_dict: Dict):
quant_method =... | 356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
if quant_method not in AUTO_QUANTIZATION_CONFIG_MAPPING.keys():
raise ValueError(
f"Unknown quantization type, got {quant_method} - supported types are:"
f" {list(AUTO_QUANTIZER_MAPPING.keys())}"
)
target_cls = AUTO_QUANTIZATION_CONFIG_MAPPING[quant_metho... | 356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
if getattr(model_config, "quantization_config", None) is None:
raise ValueError(
f"Did not found a `quantizat... | 356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
class AutoHfQuantizer:
"""
The Auto-HF quantizer class that takes care of automatically instantiating to the correct
`HfQuantizer` given the `QuantizationConfig`.
"""
@classmethod
def from_config(cls, quantization_config: Union[QuantizationConfigMixin, Dict], **kwargs):
# Convert it to... | 357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
if quant_method not in AUTO_QUANTIZER_MAPPING.keys():
raise ValueError(
f"Unknown quantization type, got {quant_method} - supported types are:"
f" {list(AUTO_QUANTIZER_MAPPING.keys())}"
)
target_cls = AUTO_QUANTIZER_MAPPING[quant_method]
return ta... | 357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
@classmethod
def merge_quantization_configs(
cls,
quantization_config: Union[dict, QuantizationConfigMixin],
quantization_config_from_args: Optional[QuantizationConfigMixin],
):
"""
handles situations where both quantization_config from args and quantization_config from m... | 357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
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