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# Copyright 2025 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.
from typing import TYPE_CHECKING, Optional
from .base import HfQuantizer
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import (
is_accelerate_available,
is_kernels_available,
is_torch_available,
is_triton_available,
logging,
)
from .quantizers_utils import get_module_from_name
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
triton_kernels_hub = None
class Mxfp4HfQuantizer(HfQuantizer):
"""
FP4 quantization using fbgemm kernels
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["accelerate"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
self.quantization_config = quantization_config
self.triton_kernels_hub = None
def _lazy_import_kernels(self):
"""Lazy import and initialize kernels only when needed"""
if self.triton_kernels_hub is None:
try:
from kernels import get_kernel
self.triton_kernels_hub = get_kernel("kernels-community/triton_kernels")
except ImportError:
raise ImportError("kernels package is required for MXFP4 quantization")
return self.triton_kernels_hub
def validate_environment(self, *args, **kwargs):
if not is_torch_available():
raise ImportError(
"Using mxfp4 quantization requires torch"
"Please install the latest version of torch ( pip install --upgrade torch )"
)
if self.quantization_config.dequantize:
return
if not (torch.cuda.is_available() or torch.xpu.is_available()):
if self.pre_quantized:
logger.warning_once(
"Using MXFP4 quantized models requires a GPU, we will default to dequantizing the model to bf16"
)
self.quantization_config.dequantize = True
return
else:
raise RuntimeError("Quantizing a model using MXFP4 requires a GPU")
if not is_accelerate_available():
raise ImportError("Using mxfp4 requires Accelerate: `pip install accelerate`")
if torch.xpu.is_available():
gpu_is_supported = True
kernels_available = is_triton_available("3.5.0") and is_kernels_available()
else:
compute_capability = torch.cuda.get_device_capability()
gpu_is_supported = compute_capability >= (7, 5)
kernels_available = is_triton_available("3.4.0") and is_kernels_available()
if self.pre_quantized:
# On unsupported GPUs or without kernels, we will dequantize the model to bf16
if not gpu_is_supported:
logger.warning_once(
"MXFP4 quantization is only supported on GPUs with compute capability >= 7.5 (e.g T4, A100, L4, H100, or B200) or XPUs (e.g Intel® Data Center GPU Max Series) "
"We will default to dequantizing the model to bf16."
)
self.quantization_config.dequantize = True
return
if not kernels_available:
logger.warning_once(
"MXFP4 quantization requires Triton and kernels installed: CUDA requires Triton >= 3.4.0, XPU requires Triton >= 3.5.0, we will default to dequantizing the model to bf16"
)
self.quantization_config.dequantize = True
return
elif not gpu_is_supported:
# we can't quantize the model in this case so we raise an error
raise ValueError(
"MXFP4 quantization is only supported on GPUs with compute capability >= 7.5 (e.g T4, A100, L4, H100, or B200) or XPUs (e.g Intel® Data Center GPU Max Series) "
)
elif not kernels_available:
# we can't quantize the model in this case so we raise an error
raise ValueError(
"MXFP4 quantization requires Triton and kernels installed: CUDA requires Triton >= 3.4.0, XPU requires Triton >= 3.5.0"
)
if not self.pre_quantized:
self._lazy_import_kernels()
device_map = kwargs.get("device_map")
if device_map is None:
logger.warning_once(
"You have loaded an FP4 model on CPU and have a CUDA/XPU device available, make sure to set "
"your model on a GPU/XPU device in order to run your model. To remove this warning, pass device_map = 'cuda' or device_map = 'xpu'. "
)
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 FP4 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_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
if dtype is None:
dtype = torch.bfloat16
logger.info(
"Overriding dtype=%s with `dtype=torch.bfloat16` due to "
"requirements of `fbgemm-gpu` to enable model loading in fp4. "
"Pass your own dtype to specify the dtype of the remaining non-linear layers or pass"
" dtype=torch.bfloat16 to remove this warning.",
dtype,
)
return dtype
def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
from ..integrations import Mxfp4GptOssExperts
from ..models.gpt_oss.modeling_gpt_oss import GptOssExperts
# if we are dequantizing, the model doesn't have scales, and blocks only params like gate_up_proj and down_proj so we need to handle this case differently
if self.quantization_config.dequantize and ("blocks" in param_name or "scales" in param_name):
module, tensor_name = get_module_from_name(model, param_name[: -len("_blocks")])
else:
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, Mxfp4GptOssExperts) or (
isinstance(module, GptOssExperts) and self.quantization_config.dequantize
):
if tensor_name in ["down_proj_bias", "gate_up_proj_bias"]:
return False
return True
return False
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
**kwargs,
):
from ..integrations import (
Mxfp4GptOssExperts,
dequantize,
load_and_swizzle_mxfp4,
quantize_to_mxfp4,
swizzle_mxfp4,
)
from ..models.gpt_oss.modeling_gpt_oss import GptOssExperts
if not self.pre_quantized:
triton_kernels_hub = self._lazy_import_kernels()
module, _ = get_module_from_name(model, param_name)
with torch.device(target_device):
if isinstance(module, Mxfp4GptOssExperts):
triton_weight_tensor, weight_scale = quantize_to_mxfp4(param_value, triton_kernels_hub)
PrecisionConfig, FlexCtx, InFlexData = (
triton_kernels_hub.matmul_ogs.PrecisionConfig,
triton_kernels_hub.matmul_ogs.FlexCtx,
triton_kernels_hub.matmul_ogs.InFlexData,
)
triton_weight_tensor, weight_scale = swizzle_mxfp4(
triton_weight_tensor, weight_scale, triton_kernels_hub
)
proj = "gate_up_proj" if "gate_up_proj" in param_name else "down_proj"
setattr(module, proj, triton_weight_tensor)
setattr(
module,
f"{proj}_precision_config",
PrecisionConfig(weight_scale=weight_scale, flex_ctx=FlexCtx(rhs_data=InFlexData())),
)
delattr(module, f"{proj}_blocks")
delattr(module, f"{proj}_scales")
# The params going here are either gate_up_proj_blocks, or down_proj_blocks, or gate_up_proj_scales, or down_proj_scales
else:
# This is when loading a quantized model (blocks and scales exist)
empty_param = kwargs.get("empty_param")
casting_dtype = kwargs.get("casting_dtype")
to_contiguous = kwargs.get("to_contiguous")
rank = kwargs.get("rank")
device_mesh = kwargs.get("device_mesh")
if ("blocks" in param_name or "scales" in param_name) and self.quantization_config.dequantize:
# blocks and scales have the same length that's why this works for both
module, _ = get_module_from_name(model, param_name[: -len("_blocks")])
else:
module, _ = get_module_from_name(model, param_name)
shard_kwargs = {
"empty_param": empty_param,
"casting_dtype": casting_dtype,
"to_contiguous": to_contiguous,
"rank": rank,
"device_mesh": device_mesh,
"model": model,
}
if isinstance(module, Mxfp4GptOssExperts) or (
isinstance(module, GptOssExperts) and self.quantization_config.dequantize
):
if self.quantization_config.dequantize:
# dq_param_name is the name of the parameter without the blocks or scales suffix, it's used in this case since we don't switch linears
# so we only have the original param name
dq_param_name = param_name[: -len("_blocks")]
dequantize(module, param_name, param_value, target_device, dq_param_name, **shard_kwargs)
else:
load_and_swizzle_mxfp4(
module,
param_name,
param_value,
target_device,
self._lazy_import_kernels(),
**shard_kwargs,
)
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
# we are not really dequantizing, we are just removing everything related to quantization here
if self.quantization_config.dequantize:
self.remove_quantization_config(model)
# clean cache due to triton ops
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
def update_expected_keys(self, model: "PreTrainedModel", expected_keys: list[str], checkpoint_keys: list[str]):
# Replace expected_keys for experts' gate_up_proj and down_proj with their _blocks and _scales variants
new_expected_keys = []
for key in expected_keys:
if key.endswith(".mlp.experts.gate_up_proj"):
base = key[: -len("gate_up_proj")]
new_expected_keys.append(base + "gate_up_proj_blocks")
new_expected_keys.append(base + "gate_up_proj_scales")
elif key.endswith(".mlp.experts.down_proj"):
base = key[: -len("down_proj")]
new_expected_keys.append(base + "down_proj_blocks")
new_expected_keys.append(base + "down_proj_scales")
elif not self.pre_quantized:
# in this case, we are quantizing the model so we need to update the keys as we changed the layers
if key.endswith(".mlp.experts.down_proj_blocks"):
base = key[: -len("down_proj_blocks")]
new_expected_keys.append(base + "down_proj")
elif key.endswith(".mlp.experts.gate_up_proj_blocks"):
base = key[: -len("gate_up_proj_blocks")]
new_expected_keys.append(base + "gate_up_proj")
elif key.endswith("scales"):
# we remove it the scales as the checkpoint don't contain them
continue
else:
new_expected_keys.append(key)
else:
new_expected_keys.append(key)
return new_expected_keys
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
keep_in_fp32_modules: Optional[list[str]] = None,
**kwargs,
):
from ..integrations import replace_with_mxfp4_linear
self.modules_to_not_convert = self.get_modules_to_not_convert(
model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules
)
use_kernels = kwargs.get("use_kernels", False)
# if we are using kernels, we can't use the quantized model, since the forward pass is different and needs special handling
if use_kernels:
logger.warning_once(
"You are using full precision kernels, we will dequantize the model to bf16. "
"To use the quantized model with quantization kernels, please set use_kernels=False"
)
self.quantization_config.dequantize = True
config = model.config
model = replace_with_mxfp4_linear(
model,
modules_to_not_convert=self.modules_to_not_convert,
quantization_config=self.quantization_config,
config=config,
)
model.config.quantization_config = self.quantization_config
def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]:
from ..integrations import Mxfp4GptOssExperts
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, Mxfp4GptOssExperts):
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]
def update_tp_plan(self, config):
if "GptOssConfig" in config.__class__.__name__:
if getattr(config, "base_model_tp_plan", None) is not None:
config.base_model_tp_plan.update(
{
"layers.*.mlp.experts.gate_up_proj_blocks": "grouped_gemm",
"layers.*.mlp.experts.gate_up_proj_scales": "grouped_gemm",
"layers.*.mlp.experts.down_proj_blocks": "grouped_gemm",
"layers.*.mlp.experts.down_proj_scales": "grouped_gemm",
}
)
return config
def update_ep_plan(self, config):
if "GptOssConfig" in config.__class__.__name__:
if getattr(config, "base_model_ep_plan", None) is not None:
config.base_model_ep_plan.update(
{
"layers.*.mlp.experts.gate_up_proj_blocks": "grouped_gemm",
"layers.*.mlp.experts.gate_up_proj_scales": "grouped_gemm",
"layers.*.mlp.experts.down_proj_blocks": "grouped_gemm",
"layers.*.mlp.experts.down_proj_scales": "grouped_gemm",
}
)
return config
def get_param_name(self, param_name: str) -> str:
if self.quantization_config.dequantize:
if "_blocks" in param_name:
return param_name.replace("_blocks", "")
elif "_scales" in param_name:
return param_name.replace("_scales", "")
elif not self.pre_quantized:
if param_name.endswith("gate_up_proj"):
return param_name.replace("gate_up_proj", "gate_up_proj_blocks")
if param_name.endswith("down_proj"):
return param_name.replace("down_proj", "down_proj_blocks")
return param_name
def get_state_dict_and_metadata(self, model, safe_serialization: bool = False):
from ..integrations import Mxfp4GptOssExperts
state_dict = model.state_dict()
for name, module in model.named_modules():
if (
isinstance(module, Mxfp4GptOssExperts)
and hasattr(module, "gate_up_proj")
and hasattr(module, "down_proj")
):
state_dict[f"{name}.gate_up_proj_blocks"] = (
module.gate_up_proj.storage.layout.unswizzle_data(module.gate_up_proj.storage.data)
.transpose(-1, -2)
.reshape(32, -1, 90, 16)
)
state_dict[f"{name}.gate_up_proj_scales"] = (
module.gate_up_proj_precision_config.weight_scale.storage.layout.unswizzle_data(
module.gate_up_proj_precision_config.weight_scale.storage.data
).transpose(-1, -2)
)
state_dict[f"{name}.down_proj_blocks"] = (
module.down_proj.storage.layout.unswizzle_data(module.down_proj.storage.data)
.transpose(-1, -2)
.reshape(32, 2880, 90, -1)
)
state_dict[f"{name}.down_proj_scales"] = (
module.down_proj_precision_config.weight_scale.storage.layout.unswizzle_data(
module.down_proj_precision_config.weight_scale.storage.data
).transpose(-1, -2)
)
metadata = {}
return state_dict, metadata
def is_serializable(self, safe_serialization=None):
return True
@property
def is_trainable(self) -> bool:
logger.warning_once(
"MXFP4 quantization don't support training, please consider dequantizing the model first by passing quantization_config=Mxfp4Config(dequantize=True) to .from_pretrained()"
)
return False
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