# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Adapted from
https://github.com/huggingface/transformers/blob/c409cd81777fb27aadc043ed3d8339dbc020fb3b/src/transformers/quantizers/quantizer_bnb_4bit.py
"""
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from ...utils import get_module_from_name
from ..base import DiffusersQuantizer
if TYPE_CHECKING:
from ...models.modeling_utils import ModelMixin
from ...utils import (
is_accelerate_available,
is_accelerate_version,
is_bitsandbytes_available,
is_bitsandbytes_version,
is_torch_available,
logging,
)
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class BnB4BitDiffusersQuantizer(DiffusersQuantizer):
"""
4-bit quantization from bitsandbytes.py quantization method:
before loading: converts transformer layers into Linear4bit during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear4bit into 4bit at the first .cuda() call saving:
from state dict, as usual; saves weights and `quant_state` components
loading:
need to locate `quant_state` components and pass to Param4bit constructor
"""
use_keep_in_fp32_modules = True
requires_calibration = False
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
if self.quantization_config.llm_int8_skip_modules is not None:
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
def validate_environment(self, *args, **kwargs):
if not torch.cuda.is_available():
raise RuntimeError("No GPU found. A GPU is needed for quantization.")
if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
raise ImportError(
"Using `bitsandbytes` 4-bit quantization requires Accelerate: `pip install 'accelerate>=0.26.0'`"
)
if not is_bitsandbytes_available() or is_bitsandbytes_version("<", "0.43.3"):
raise ImportError(
"Using `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
)
if kwargs.get("from_flax", False):
raise ValueError(
"Converting into 4-bit weights from flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
)
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_no_convert = {
key: device_map[key] for key in device_map.keys() if key not in self.modules_to_not_convert
}
if "cpu" in device_map_without_no_convert.values() or "disk" in device_map_without_no_convert.values():
raise ValueError(
"Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the "
"quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules "
"in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to "
"`from_pretrained`. Check "
"https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu "
"for more details. "
)
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if target_dtype != torch.int8:
from accelerate.utils import CustomDtype
logger.info("target_dtype {target_dtype} is replaced by `CustomDtype.INT4` for 4-bit BnB quantization")
return CustomDtype.INT4
else:
raise ValueError(f"Wrong `target_dtype` ({target_dtype}) provided.")
def check_if_quantized_param(
self,
model: "ModelMixin",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
import bitsandbytes as bnb
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Params4bit):
# Add here check for loaded components' dtypes once serialization is implemented
return True
elif isinstance(module, bnb.nn.Linear4bit) and tensor_name == "bias":
# bias could be loaded by regular set_module_tensor_to_device() from accelerate,
# but it would wrongly use uninitialized weight there.
return True
else:
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,
):
import bitsandbytes as bnb
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 tensor_name == "bias":
if param_value is None:
new_value = old_value.to(target_device)
else:
new_value = param_value.to(target_device)
new_value = torch.nn.Parameter(new_value, requires_grad=old_value.requires_grad)
module._parameters[tensor_name] = new_value
return
if not isinstance(module._parameters[tensor_name], bnb.nn.Params4bit):
raise ValueError("this function only loads `Linear4bit components`")
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}.")
# construct `new_value` for the module._parameters[tensor_name]:
if self.pre_quantized:
# 4bit loading. Collecting components for restoring quantized weight
# This can be expanded to make a universal call for any quantized weight loading
if not self.is_serializable:
raise ValueError(
"Detected int4 weights but the version of bitsandbytes is not compatible with int4 serialization. "
"Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
)
if (param_name + ".quant_state.bitsandbytes__fp4" not in state_dict) and (
param_name + ".quant_state.bitsandbytes__nf4" not in state_dict
):
raise ValueError(
f"Supplied state dict for {param_name} does not contain `bitsandbytes__*` and possibly other `quantized_stats` components."
)
quantized_stats = {}
for k, v in state_dict.items():
# `startswith` to counter for edge cases where `param_name`
# substring can be present in multiple places in the `state_dict`
if param_name + "." in k and k.startswith(param_name):
quantized_stats[k] = v
if unexpected_keys is not None and k in unexpected_keys:
unexpected_keys.remove(k)
new_value = bnb.nn.Params4bit.from_prequantized(
data=param_value,
quantized_stats=quantized_stats,
requires_grad=False,
device=target_device,
)
else:
new_value = param_value.to("cpu")
kwargs = old_value.__dict__
new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)
module._parameters[tensor_name] = new_value
def check_quantized_param_shape(self, param_name, current_param_shape, loaded_param_shape):
n = current_param_shape.numel()
inferred_shape = (n,) if "bias" in param_name else ((n + 1) // 2, 1)
if loaded_param_shape != inferred_shape:
raise ValueError(
f"Expected the flattened shape of the current param ({param_name}) to be {loaded_param_shape} but is {inferred_shape}."
)
else:
return True
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
# need more space for buffers that are created during quantization
max_memory = {key: val * 0.90 for key, val in max_memory.items()}
return max_memory
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 "
"requirements of `bitsandbytes` to enable model loading in 8-bit or 4-bit. "
"Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
" torch_dtype=torch.float16 to remove this warning.",
torch_dtype,
)
torch_dtype = torch.float16
return torch_dtype
# (sayakpaul): I think it could be better to disable custom `device_map`s
# for the first phase of the integration in the interest of simplicity.
# Commenting this for discussions on the PR.
# def update_device_map(self, device_map):
# if device_map is None:
# device_map = {"": torch.cuda.current_device()}
# logger.info(
# "The device_map was not initialized. "
# "Setting device_map to {'':torch.cuda.current_device()}. "
# "If you want to use the model for inference, please set device_map ='auto' "
# )
# return device_map
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
def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
model.is_loaded_in_4bit = True
model.is_4bit_serializable = self.is_serializable
return model
@property
def is_serializable(self):
# Because we're mandating `bitsandbytes` 0.43.3.
return True
@property
def is_trainable(self) -> bool:
# Because we're mandating `bitsandbytes` 0.43.3.
return True
def _dequantize(self, model):
from .utils import dequantize_and_replace
is_model_on_cpu = model.device.type == "cpu"
if is_model_on_cpu:
logger.info(
"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."
)
model.to(torch.cuda.current_device())
model = dequantize_and_replace(
model, self.modules_to_not_convert, quantization_config=self.quantization_config
)
if is_model_on_cpu:
model.to("cpu")
return model
class BnB8BitDiffusersQuantizer(DiffusersQuantizer):
"""
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 .cuda() call
saving:
from state dict, as usual; saves weights and 'SCB' component
loading:
need to locate SCB component and pass to the Linear8bitLt object
"""
use_keep_in_fp32_modules = True
requires_calibration = False
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
if self.quantization_config.llm_int8_skip_modules is not None:
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
def validate_environment(self, *args, **kwargs):
if not torch.cuda.is_available():
raise RuntimeError("No GPU found. A GPU is needed for quantization.")
if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
raise ImportError(
"Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install 'accelerate>=0.26.0'`"
)
if not is_bitsandbytes_available() or is_bitsandbytes_version("<", "0.43.3"):
raise ImportError(
"Using `bitsandbytes` 8-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
)
if kwargs.get("from_flax", False):
raise ValueError(
"Converting into 8-bit weights from flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
)
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_no_convert = {
key: device_map[key] for key in device_map.keys() if key not in self.modules_to_not_convert
}
if "cpu" in device_map_without_no_convert.values() or "disk" in device_map_without_no_convert.values():
raise ValueError(
"Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the "
"quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules "
"in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to "
"`from_pretrained`. Check "
"https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu "
"for more details. "
)
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.adjust_max_memory
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
# need more space for buffers that are created during quantization
max_memory = {key: val * 0.90 for key, val in max_memory.items()}
return max_memory
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.update_torch_dtype
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 "
"requirements of `bitsandbytes` to enable model loading in 8-bit or 4-bit. "
"Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
" torch_dtype=torch.float16 to remove this warning.",
torch_dtype,
)
torch_dtype = torch.float16
return torch_dtype
# # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.update_device_map
# def update_device_map(self, device_map):
# if device_map is None:
# device_map = {"": torch.cuda.current_device()}
# logger.info(
# "The device_map was not initialized. "
# "Setting device_map to {'':torch.cuda.current_device()}. "
# "If you want to use the model for inference, please set device_map ='auto' "
# )
# return device_map
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if target_dtype != torch.int8:
logger.info("target_dtype {target_dtype} is replaced by `torch.int8` for 8-bit BnB quantization")
return torch.int8
def check_if_quantized_param(
self,
model: "ModelMixin",
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 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,
):
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_loaded_in_8bit = True
model.is_8bit_serializable = self.is_serializable
return model
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer._process_model_before_weight_loading
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
@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
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