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# 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.
import importlib
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from packaging import version
from .base import HfQuantizer
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import is_accelerate_available, is_fbgemm_gpu_available, is_torch_available, logging
from .quantizers_utils import get_module_from_name
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class FbgemmFp8HfQuantizer(HfQuantizer):
"""
FP8 quantization using fbgemm kernels
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["fbgemm-gpu", "accelerate"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
self.quantization_config = quantization_config
def validate_environment(self, *args, **kwargs):
if not is_torch_available() or version.parse(importlib.metadata.version("torch")) < version.parse("2.1.0"):
raise ImportError(
"Using fbgemm fp8 quantization requires torch > 2.1.0"
"Please install the latest version of torch ( pip install --upgrade torch )"
)
if not is_fbgemm_gpu_available():
raise ImportError(
"Using fbgemm fp8 quantization requires fbgemm-gpu library"
"Please install the latest version of fbgemm-gpu library by following : https://pytorch.org/FBGEMM/fbgemm_gpu-development/InstallationInstructions.html#fbgemm-gpu-install-libraries"
)
if not is_accelerate_available("0.32.2"):
raise ImportError(
"Loading an FP8 quantized model requires accelerate > 0.32.1 (`pip install --upgrade accelerate`)"
)
if not torch.cuda.is_available():
raise RuntimeError("Using FP8 quantized models with fbgemm kernels requires a GPU")
compute_capability = torch.cuda.get_device_capability()
major, minor = compute_capability
if major < 9:
raise ValueError(
"FP8 quantized models is only supported on GPUs with compute capability >= 9.0 (e.g H100)"
)
device_map = kwargs.get("device_map", None)
if device_map is None:
logger.warning_once(
"You have loaded an FP8 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. To remove this warning, pass device_map = 'cuda'. "
)
elif device_map is not None:
if (
not self.pre_quantized
and isinstance(device_map, dict)
and ("cpu" in device_map.values() or "disk" in device_map.values())
):
raise ValueError(
"You are attempting to load an FP8 model with a device_map that contains a CPU or disk device."
"This is not supported when the model is quantized on the fly. "
"Please use a quantized checkpoint or remove the CPU or disk device from the device_map."
)
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = torch.bfloat16
logger.info(
"Overriding torch_dtype=%s with `torch_dtype=torch.bloat16` due to "
"requirements of `fbgemm-gpu` to enable model loading in fp8. "
"Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
" torch_dtype=torch.bfloat16 to remove this warning.",
torch_dtype,
)
elif torch_dtype == torch.float16:
raise ValueError(
"You cannot use FP8 with torch_dtype=torch.float16."
"We recommend you passing torch_dtype=torch.bfloat16"
)
return torch_dtype
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
):
from ..integrations import FbgemmFp8Linear
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, FbgemmFp8Linear):
if self.pre_quantized or tensor_name == "bias":
if tensor_name == "weight" and param_value.dtype != torch.float8_e4m3fn:
raise ValueError("Expect quantized weights but got an unquantized weight")
return False
else:
if tensor_name == "weight_scale":
raise ValueError("Expect unquantized weights but got a quantized weight_scale")
return True
return False
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,
):
"""
Quantizes weights into weight and weight_scale
"""
new_value, weight_scale = torch.ops.fbgemm.quantize_fp8_per_row(param_value)
module, tensor_name = get_module_from_name(model, param_name)
module._buffers[tensor_name] = new_value.to(target_device)
# to have the right output shape -> (out_features, 1)
module._buffers["weight_scale"] = weight_scale.view(weight_scale.shape[0], 1).to(target_device)
if unexpected_keys is not None and param_name in unexpected_keys:
unexpected_keys.remove(param_name)
del param_name
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
return model
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_fbgemm_fp8_linear
self.modules_to_not_convert = get_keys_to_not_convert(model)
if self.quantization_config.modules_to_not_convert is not None:
self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
model = replace_with_fbgemm_fp8_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 update_missing_keys(self, model, missing_keys: List[str], prefix: str) -> List[str]:
from ..integrations import FbgemmFp8Linear
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, FbgemmFp8Linear):
for missing in missing_keys:
if (
(name in missing or name in f"{prefix}.{missing}")
and not missing.endswith(".weight")
and not missing.endswith(".bias")
):
not_missing_keys.append(missing)
return [k for k in missing_keys if k not in not_missing_keys]
@property
def is_serializable(self):
return True
@property
def is_trainable(self) -> bool:
return False
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