577 lines
26 KiB
Python
577 lines
26 KiB
Python
# Copyright 2025 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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"""
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Adapted from
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https://github.com/huggingface/transformers/blob/c409cd81777fb27aadc043ed3d8339dbc020fb3b/src/transformers/quantizers/quantizer_bnb_4bit.py
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"""
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
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from ...utils import get_module_from_name
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from ..base import DiffusersQuantizer
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if TYPE_CHECKING:
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from ...models.modeling_utils import ModelMixin
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from ...utils import (
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is_accelerate_available,
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is_accelerate_version,
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is_bitsandbytes_available,
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is_bitsandbytes_version,
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is_torch_available,
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logging,
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)
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if is_torch_available():
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import torch
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logger = logging.get_logger(__name__)
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class BnB4BitDiffusersQuantizer(DiffusersQuantizer):
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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 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_calibration = False
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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 (torch.cuda.is_available() or torch.xpu.is_available()):
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raise RuntimeError("No GPU found. A GPU is needed for quantization.")
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if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
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raise ImportError(
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"Using `bitsandbytes` 4-bit quantization requires Accelerate: `pip install 'accelerate>=0.26.0'`"
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)
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if not is_bitsandbytes_available() or is_bitsandbytes_version("<", "0.43.3"):
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raise ImportError(
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"Using `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
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)
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if kwargs.get("from_flax", False):
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raise ValueError(
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"Converting into 4-bit weights from 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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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_no_convert = {
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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_no_convert.values() or "disk" in device_map_without_no_convert.values():
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raise ValueError(
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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 `load_in_8bit_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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def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
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if target_dtype != torch.int8:
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from accelerate.utils import CustomDtype
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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(f"Wrong `target_dtype` ({target_dtype}) provided.")
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def check_if_quantized_param(
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self,
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model: "ModelMixin",
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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: "ModelMixin",
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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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**kwargs,
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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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# `startswith` to counter for edge cases where `param_name`
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# substring can be present in multiple places in the `state_dict`
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if param_name + "." in k and k.startswith(param_name):
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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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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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def check_quantized_param_shape(self, param_name, current_param, loaded_param):
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current_param_shape = current_param.shape
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loaded_param_shape = loaded_param.shape
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n = current_param_shape.numel()
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inferred_shape = (n,) if "bias" in param_name else ((n + 1) // 2, 1)
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if loaded_param_shape != inferred_shape:
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raise ValueError(
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f"Expected the flattened shape of the current param ({param_name}) to be {loaded_param_shape} but is {inferred_shape}."
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)
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else:
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return True
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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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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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def update_device_map(self, device_map):
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if device_map is None:
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if torch.xpu.is_available():
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current_device = f"xpu:{torch.xpu.current_device()}"
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else:
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current_device = f"cuda:{torch.cuda.current_device()}"
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device_map = {"": 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 {"
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": {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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def _process_model_before_weight_loading(
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self,
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model: "ModelMixin",
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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 .utils import 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 may keep some modules such as the `proj_out` in their original dtype for numerical stability reasons
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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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# Purge `None`.
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# Unlike `transformers`, we don't know if we should always keep certain modules in FP32
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# in case of diffusion transformer models. For language models and others alike, `lm_head`
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# and tied modules are usually kept in FP32.
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self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]
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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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model.config.quantization_config = self.quantization_config
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model.is_loaded_in_4bit = True
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def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
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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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# Because we're mandating `bitsandbytes` 0.43.3.
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return True
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@property
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def is_trainable(self) -> bool:
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# Because we're mandating `bitsandbytes` 0.43.3.
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return True
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def _dequantize(self, model):
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from .utils import dequantize_and_replace
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is_model_on_cpu = model.device.type == "cpu"
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if is_model_on_cpu:
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logger.info(
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"Model was found to be on CPU (could happen as a result of `enable_model_cpu_offload()`). So, moving it to GPU. After dequantization, will move the model back to CPU again to preserve the previous device."
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)
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if torch.xpu.is_available():
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model.to(torch.xpu.current_device())
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else:
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model.to(torch.cuda.current_device())
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model = dequantize_and_replace(
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model, self.modules_to_not_convert, quantization_config=self.quantization_config
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)
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if is_model_on_cpu:
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model.to("cpu")
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return model
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class BnB8BitDiffusersQuantizer(DiffusersQuantizer):
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"""
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8-bit quantization from bitsandbytes quantization method:
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before loading: converts transformer layers into Linear8bitLt during loading: load 16bit weight and pass to the
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layer object after: quantizes individual weights in Linear8bitLt into 8bit at fitst .cuda() call
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saving:
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from state dict, as usual; saves weights and 'SCB' component
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loading:
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need to locate SCB component and pass to the Linear8bitLt object
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"""
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use_keep_in_fp32_modules = True
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requires_calibration = False
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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 (torch.cuda.is_available() or torch.xpu.is_available()):
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raise RuntimeError("No GPU found. A GPU is needed for quantization.")
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if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
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raise ImportError(
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"Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install 'accelerate>=0.26.0'`"
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)
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if not is_bitsandbytes_available() or is_bitsandbytes_version("<", "0.43.3"):
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raise ImportError(
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"Using `bitsandbytes` 8-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
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)
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if kwargs.get("from_flax", False):
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raise ValueError(
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"Converting into 8-bit weights from 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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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_no_convert = {
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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_no_convert.values() or "disk" in device_map_without_no_convert.values():
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raise ValueError(
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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 `load_in_8bit_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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# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.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 diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.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 diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.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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if torch.xpu.is_available():
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current_device = f"xpu:{torch.xpu.current_device()}"
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else:
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current_device = f"cuda:{torch.cuda.current_device()}"
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device_map = {"": 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 {"
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": {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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def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
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if target_dtype != torch.int8:
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logger.info("target_dtype {target_dtype} is replaced by `torch.int8` for 8-bit BnB quantization")
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return torch.int8
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def check_if_quantized_param(
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self,
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model: "ModelMixin",
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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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):
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import bitsandbytes as bnb
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|
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module, tensor_name = get_module_from_name(model, param_name)
|
|
if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Int8Params):
|
|
if self.pre_quantized:
|
|
if param_name.replace("weight", "SCB") not in state_dict.keys():
|
|
raise ValueError("Missing quantization component `SCB`")
|
|
if param_value.dtype != torch.int8:
|
|
raise ValueError(
|
|
f"Incompatible dtype `{param_value.dtype}` when loading 8-bit prequantized weight. Expected `torch.int8`."
|
|
)
|
|
return True
|
|
return False
|
|
|
|
def create_quantized_param(
|
|
self,
|
|
model: "ModelMixin",
|
|
param_value: "torch.Tensor",
|
|
param_name: str,
|
|
target_device: "torch.device",
|
|
state_dict: Dict[str, Any],
|
|
unexpected_keys: Optional[List[str]] = None,
|
|
**kwargs,
|
|
):
|
|
import bitsandbytes as bnb
|
|
|
|
fp16_statistics_key = param_name.replace("weight", "SCB")
|
|
fp16_weights_format_key = param_name.replace("weight", "weight_format")
|
|
|
|
fp16_statistics = state_dict.get(fp16_statistics_key, None)
|
|
fp16_weights_format = state_dict.get(fp16_weights_format_key, None)
|
|
|
|
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], bnb.nn.Int8Params):
|
|
raise ValueError(f"Parameter `{tensor_name}` should only be a `bnb.nn.Int8Params` instance.")
|
|
if (
|
|
old_value.device == torch.device("meta")
|
|
and target_device not in ["meta", torch.device("meta")]
|
|
and param_value is None
|
|
):
|
|
raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {target_device}.")
|
|
|
|
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 install --upgrade bitsandbytes`."
|
|
)
|
|
|
|
kwargs = old_value.__dict__
|
|
new_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(target_device)
|
|
|
|
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 from the state dict to remove unneeded
|
|
# messages. The correct format is correctly retrieved during the first forward pass.
|
|
if fp16_weights_format is not None and unexpected_keys is not None:
|
|
unexpected_keys.remove(fp16_weights_format_key)
|
|
|
|
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer._process_model_after_weight_loading with 4bit->8bit
|
|
def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
|
|
model.is_8bit_serializable = self.is_serializable
|
|
return model
|
|
|
|
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer._process_model_before_weight_loading with 4bit->8bit
|
|
def _process_model_before_weight_loading(
|
|
self,
|
|
model: "ModelMixin",
|
|
device_map,
|
|
keep_in_fp32_modules: List[str] = [],
|
|
**kwargs,
|
|
):
|
|
from .utils import replace_with_bnb_linear
|
|
|
|
load_in_8bit_fp32_cpu_offload = self.quantization_config.llm_int8_enable_fp32_cpu_offload
|
|
|
|
# We may keep some modules such as the `proj_out` in their original dtype for numerical stability reasons
|
|
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
|
|
|
|
if not isinstance(self.modules_to_not_convert, list):
|
|
self.modules_to_not_convert = [self.modules_to_not_convert]
|
|
|
|
self.modules_to_not_convert.extend(keep_in_fp32_modules)
|
|
|
|
# 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 load_in_8bit_fp32_cpu_offload:
|
|
raise ValueError(
|
|
"If you want to offload some keys to `cpu` or `disk`, you need to set "
|
|
"`llm_int8_enable_fp32_cpu_offload=True`. Note that these modules will not be "
|
|
" converted to 8-bit but kept in 32-bit."
|
|
)
|
|
self.modules_to_not_convert.extend(keys_on_cpu)
|
|
|
|
# Purge `None`.
|
|
# Unlike `transformers`, we don't know if we should always keep certain modules in FP32
|
|
# in case of diffusion transformer models. For language models and others alike, `lm_head`
|
|
# and tied modules are usually kept in FP32.
|
|
self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]
|
|
|
|
model = replace_with_bnb_linear(
|
|
model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config
|
|
)
|
|
model.config.quantization_config = self.quantization_config
|
|
model.is_loaded_in_8bit = True
|
|
|
|
@property
|
|
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.is_serializable
|
|
def is_serializable(self):
|
|
# Because we're mandating `bitsandbytes` 0.43.3.
|
|
return True
|
|
|
|
@property
|
|
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.is_serializable
|
|
def is_trainable(self) -> bool:
|
|
# Because we're mandating `bitsandbytes` 0.43.3.
|
|
return True
|
|
|
|
@property
|
|
def is_compileable(self) -> bool:
|
|
return True
|
|
|
|
def _dequantize(self, model):
|
|
from .utils import dequantize_and_replace
|
|
|
|
model = dequantize_and_replace(
|
|
model, self.modules_to_not_convert, quantization_config=self.quantization_config
|
|
)
|
|
return model
|