318 lines
14 KiB
Python
318 lines
14 KiB
Python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
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from packaging import version
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from .base import HfQuantizer
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from .quantizers_utils import get_module_from_name
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if TYPE_CHECKING:
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from ..modeling_utils import PreTrainedModel
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from ..utils import is_accelerate_available, is_bitsandbytes_available, is_torch_available, logging
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if is_torch_available():
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import torch
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from ..pytorch_utils import Conv1D
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logger = logging.get_logger(__name__)
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class Bnb4BitHfQuantizer(HfQuantizer):
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"""
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4-bit quantization from bitsandbytes.py quantization method:
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before loading: converts transformer layers into Linear4bit during loading: load 16bit weight and pass to the
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layer object after: quantizes individual weights in Linear4bit into 4bit at the first .cuda() call
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saving:
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from state dict, as usual; saves weights and `quant_state` components
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loading:
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need to locate `quant_state` components and pass to Param4bit constructor
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"""
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use_keep_in_fp32_modules = True
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requires_parameters_quantization = True
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requires_calibration = False
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required_packages = ["bitsandbytes", "accelerate"]
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def __init__(self, quantization_config, **kwargs):
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super().__init__(quantization_config, **kwargs)
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if self.quantization_config.llm_int8_skip_modules is not None:
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self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
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def validate_environment(self, *args, **kwargs):
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if not (is_accelerate_available() and is_bitsandbytes_available()):
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raise ImportError(
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"Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install accelerate` "
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"and the latest version of bitsandbytes: `pip install -i https://pypi.org/simple/ bitsandbytes`"
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)
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if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
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raise ValueError(
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"Converting into 4-bit or 8-bit weights from tf/flax weights is currently not supported, please make"
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" sure the weights are in PyTorch format."
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)
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if not torch.cuda.is_available():
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raise RuntimeError("No GPU found. A GPU is needed for quantization.")
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device_map = kwargs.get("device_map", None)
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if (
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device_map is not None
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and isinstance(device_map, dict)
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and not self.quantization_config.llm_int8_enable_fp32_cpu_offload
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):
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device_map_without_lm_head = {
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key: device_map[key] for key in device_map.keys() if key not in self.modules_to_not_convert
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}
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if "cpu" in device_map_without_lm_head.values() or "disk" in device_map_without_lm_head.values():
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raise ValueError(
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"""
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Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the
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quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules
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in 32-bit, you need to set `llm_int8_enable_fp32_cpu_offload=True` and pass a custom `device_map` to
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`from_pretrained`. Check
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https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu
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for more details.
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"""
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)
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if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.39.0"):
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raise ValueError(
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"You have a version of `bitsandbytes` that is not compatible with 4bit inference and training"
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" make sure you have the latest version of `bitsandbytes` installed"
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)
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def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
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if version.parse(importlib.metadata.version("accelerate")) > version.parse("0.19.0"):
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from accelerate.utils import CustomDtype
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if target_dtype != torch.int8:
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logger.info("target_dtype {target_dtype} is replaced by `CustomDtype.INT4` for 4-bit BnB quantization")
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return CustomDtype.INT4
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else:
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raise ValueError(
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"You are using `device_map='auto'` on a 4bit loaded version of the model. To automatically compute"
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" the appropriate device map, you should upgrade your `accelerate` library,"
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"`pip install --upgrade accelerate` or install it from source to support fp4 auto device map"
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"calculation. You may encounter unexpected behavior, or pass your own device map"
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)
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def check_quantized_param(
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self,
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model: "PreTrainedModel",
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param_value: "torch.Tensor",
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param_name: str,
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state_dict: Dict[str, Any],
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**kwargs,
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) -> bool:
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import bitsandbytes as bnb
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module, tensor_name = get_module_from_name(model, param_name)
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if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Params4bit):
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# Add here check for loaded components' dtypes once serialization is implemented
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return True
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elif isinstance(module, bnb.nn.Linear4bit) and tensor_name == "bias":
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# bias could be loaded by regular set_module_tensor_to_device() from accelerate,
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# but it would wrongly use uninitialized weight there.
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return True
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else:
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return False
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def create_quantized_param(
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self,
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model: "PreTrainedModel",
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param_value: "torch.Tensor",
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param_name: str,
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target_device: "torch.device",
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state_dict: Dict[str, Any],
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unexpected_keys: Optional[List[str]] = None,
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):
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"""
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combines logic from _load_state_dict_into_meta_model and .integrations.bitsandbytes.py::set_module_quantized_tensor_to_device()
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"""
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import bitsandbytes as bnb
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module, tensor_name = get_module_from_name(model, param_name)
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if tensor_name not in module._parameters:
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raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
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old_value = getattr(module, tensor_name)
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if tensor_name == "bias":
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if param_value is None:
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new_value = old_value.to(target_device)
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else:
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new_value = param_value.to(target_device)
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new_value = torch.nn.Parameter(new_value, requires_grad=old_value.requires_grad)
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module._parameters[tensor_name] = new_value
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return
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if not isinstance(module._parameters[tensor_name], bnb.nn.Params4bit):
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raise ValueError("this function only loads `Linear4bit components`")
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if (
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old_value.device == torch.device("meta")
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and target_device not in ["meta", torch.device("meta")]
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and param_value is None
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):
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raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {target_device}.")
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# construct `new_value` for the module._parameters[tensor_name]:
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if self.pre_quantized:
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# 4bit loading. Collecting components for restoring quantized weight
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# This can be expanded to make a universal call for any quantized weight loading
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if not self.is_serializable:
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raise ValueError(
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"Detected int4 weights but the version of bitsandbytes is not compatible with int4 serialization. "
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"Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
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)
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if (param_name + ".quant_state.bitsandbytes__fp4" not in state_dict) and (
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param_name + ".quant_state.bitsandbytes__nf4" not in state_dict
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):
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raise ValueError(
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f"Supplied state dict for {param_name} does not contain `bitsandbytes__*` and possibly other `quantized_stats` components."
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)
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quantized_stats = {}
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for k, v in state_dict.items():
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if param_name + "." in k:
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quantized_stats[k] = v
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if unexpected_keys is not None and k in unexpected_keys:
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unexpected_keys.remove(k)
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new_value = bnb.nn.Params4bit.from_prequantized(
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data=param_value,
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quantized_stats=quantized_stats,
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requires_grad=False,
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device=target_device,
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)
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else:
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new_value = param_value.to("cpu")
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# Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization.
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# Since weights are saved in the correct "orientation", we skip transposing when loading.
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if issubclass(module.source_cls, Conv1D):
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new_value = new_value.T
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kwargs = old_value.__dict__
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new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)
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module._parameters[tensor_name] = new_value
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# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer.adjust_max_memory
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def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
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# need more space for buffers that are created during quantization
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max_memory = {key: val * 0.90 for key, val in max_memory.items()}
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return max_memory
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# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer.update_torch_dtype
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def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
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if torch_dtype is None:
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# We force the `dtype` to be float16, this is a requirement from `bitsandbytes`
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logger.info(
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"Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
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"requirements of `bitsandbytes` to enable model loading in 8-bit or 4-bit. "
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"Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
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" torch_dtype=torch.float16 to remove this warning.",
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torch_dtype,
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)
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torch_dtype = torch.float16
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return torch_dtype
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# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer.update_device_map
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def update_device_map(self, device_map):
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if device_map is None:
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device_map = {"": torch.cuda.current_device()}
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logger.info(
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"The device_map was not initialized. "
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"Setting device_map to {'':torch.cuda.current_device()}. "
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"If you want to use the model for inference, please set device_map ='auto' "
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)
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return device_map
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# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer._process_model_before_weight_loading
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def _process_model_before_weight_loading(
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self,
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model: "PreTrainedModel",
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device_map,
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keep_in_fp32_modules: List[str] = [],
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**kwargs,
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):
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from ..integrations import get_keys_to_not_convert, replace_with_bnb_linear
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load_in_8bit_fp32_cpu_offload = self.quantization_config.llm_int8_enable_fp32_cpu_offload
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# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
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if self.quantization_config.llm_int8_skip_modules is None:
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self.modules_to_not_convert = get_keys_to_not_convert(model)
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else:
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self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
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if not isinstance(self.modules_to_not_convert, list):
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self.modules_to_not_convert = [self.modules_to_not_convert]
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self.modules_to_not_convert.extend(keep_in_fp32_modules)
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# Extend `self.modules_to_not_convert` to keys that are supposed to be offloaded to `cpu` or `disk`
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if isinstance(device_map, dict) and len(device_map.keys()) > 1:
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keys_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]]
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if len(keys_on_cpu) > 0 and not load_in_8bit_fp32_cpu_offload:
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raise ValueError(
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"If you want to offload some keys to `cpu` or `disk`, you need to set "
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"`llm_int8_enable_fp32_cpu_offload=True`. Note that these modules will not be "
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" converted to 8-bit but kept in 32-bit."
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)
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self.modules_to_not_convert.extend(keys_on_cpu)
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model = replace_with_bnb_linear(
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model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config
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)
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# TODO: consider bringing replace_with_bnb_linear() code from ..integrations/bitsandbyter.py to here
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model.config.quantization_config = self.quantization_config
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# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer._process_model_after_weight_loading with 8bit->4bit
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def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
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model.is_loaded_in_4bit = True
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model.is_4bit_serializable = self.is_serializable
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return model
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@property
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def is_serializable(self):
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_is_4bit_serializable = version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse("0.41.3")
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if not _is_4bit_serializable:
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logger.warning(
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"You are calling `save_pretrained` to a 4-bit converted model, but your `bitsandbytes` version doesn't support it. "
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"If you want to save 4-bit models, make sure to have `bitsandbytes>=0.41.3` installed."
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)
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return False
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return True
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@property
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def is_trainable(self) -> bool:
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return True
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