248 lines
8.5 KiB
Python
248 lines
8.5 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
|
|
|
|
|
|
import logging
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
import torch
|
|
from torch.nn.parameter import Parameter
|
|
|
|
from sglang.srt.layers.linear import LinearBase
|
|
from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
|
|
from sglang.srt.layers.quantization.base_config import (
|
|
LinearMethodBase,
|
|
QuantizationConfig,
|
|
QuantizeMethodBase,
|
|
)
|
|
from sglang.srt.layers.quantization.petit_utils import (
|
|
apply_petit_nvfp4_linear,
|
|
prepare_nvfp4_layer_for_petit,
|
|
verify_petit_nvfp4_supported,
|
|
)
|
|
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
|
|
from sglang.srt.layers.quantization.utils import is_layer_skipped
|
|
from sglang.srt.utils import is_hip
|
|
|
|
_is_hip = is_hip()
|
|
|
|
# Initialize logger for the module
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
# Configuration class to support the NVFP4 quantized model generated by the ModelOpt quantization tool
|
|
class PetitNvFp4Config(QuantizationConfig):
|
|
"""Config class for Petit NVFP4.
|
|
|
|
This config is intentionally NVFP4-only. MXFP4 is handled by
|
|
`petit_mxfp4` (see `petit_mxfp4.py`).
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
is_checkpoint_nvfp4_serialized: bool = False,
|
|
kv_cache_quant_algo: str = None,
|
|
group_size: int = None,
|
|
exclude_modules: List[str] = None,
|
|
) -> None:
|
|
self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
|
|
|
|
if is_checkpoint_nvfp4_serialized:
|
|
logger.warning(
|
|
"Detected nvfp4 checkpoint. Please note that the "
|
|
"format is experimental and subject to change."
|
|
)
|
|
self.group_size = group_size
|
|
self.kv_cache_quant_algo = kv_cache_quant_algo
|
|
self.exclude_modules = exclude_modules
|
|
|
|
@classmethod
|
|
def get_name(cls) -> str:
|
|
return "petit_nvfp4"
|
|
|
|
@classmethod
|
|
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
|
|
return [torch.bfloat16, torch.half]
|
|
|
|
@classmethod
|
|
def get_min_capability(cls) -> int:
|
|
# Petit supports the gfx90a and gfx942 GPUs
|
|
return 90
|
|
|
|
@classmethod
|
|
def get_config_filenames(cls) -> List[str]:
|
|
return ["hf_quant_config.json"]
|
|
|
|
@classmethod
|
|
def from_config(cls, config: Dict[str, Any]) -> "PetitNvFp4Config":
|
|
quant_config = cls.get_from_keys(config, ["quantization"])
|
|
quant_method = str(quant_config["quant_algo"])
|
|
quant_method_upper = quant_method.upper()
|
|
group_size = quant_config.get("group_size", None)
|
|
verify_petit_nvfp4_supported(quant_method, group_size)
|
|
|
|
is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method_upper
|
|
kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
|
|
if not kv_cache_quant_algo:
|
|
kv_cache_quant_algo = "auto"
|
|
exclude_modules = quant_config.get("exclude_modules", None)
|
|
if not (group_size and kv_cache_quant_algo and (exclude_modules is not None)):
|
|
logger.warning(
|
|
f"group_size: {group_size},"
|
|
f"kv_cache_quant_algo: {kv_cache_quant_algo},"
|
|
f"exclude_modules: {exclude_modules}"
|
|
)
|
|
raise ValueError(
|
|
"NVFP4 quantization requires group size and kv_cache_quant_algo "
|
|
"specified in hf_quant_config.json"
|
|
)
|
|
return cls(
|
|
is_checkpoint_nvfp4_serialized,
|
|
kv_cache_quant_algo,
|
|
group_size,
|
|
exclude_modules,
|
|
)
|
|
|
|
@classmethod
|
|
def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
|
|
can_convert = cls.is_petit_nvfp4_compatible(hf_quant_cfg)
|
|
if can_convert:
|
|
return cls.get_name()
|
|
return None
|
|
|
|
@classmethod
|
|
def is_petit_nvfp4_compatible(cls, quant_config: Dict[str, Any]) -> bool:
|
|
quant_method = quant_config.get("quant_method", "").lower()
|
|
return _is_hip and quant_method == "modelopt"
|
|
|
|
def get_quant_method(
|
|
self, layer: torch.nn.Module, prefix: str
|
|
) -> Optional["QuantizeMethodBase"]:
|
|
if isinstance(layer, LinearBase):
|
|
if is_layer_skipped(prefix, self.exclude_modules):
|
|
return UnquantizedLinearMethod()
|
|
return PetitNvFp4LinearMethod(self)
|
|
return None
|
|
|
|
def get_scaled_act_names(self) -> List[str]:
|
|
return []
|
|
|
|
|
|
class PetitNvFp4LinearMethod(LinearMethodBase):
|
|
"""Linear method for NVFP4.
|
|
|
|
For NVFP4:
|
|
|Tensor Name | datatype | shape |
|
|
|----------------------------------------------------|
|
|
|input_scale | torch.float32 | scalar |
|
|
|weight | NVFP4(SE2M1) | [1, X, y/2] |
|
|
|weight_scale | FP8-E4M3 | [X, Y] |
|
|
|weight_scale_2 | torch.float32 | scalar |
|
|
|
|
Args: quant_config: The Petit quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: PetitNvFp4Config):
|
|
self.quant_config = quant_config
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: List[int],
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
del input_size, output_size
|
|
if not self.quant_config.is_checkpoint_nvfp4_serialized:
|
|
raise ValueError(
|
|
"NVFP4 quantization was selected, "
|
|
" dynamic quantization is not supported."
|
|
)
|
|
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
|
|
layer.logical_widths = output_partition_sizes
|
|
|
|
layer.input_size_per_partition = input_size_per_partition
|
|
layer.output_size_per_partition = output_size_per_partition
|
|
if input_size_per_partition % 16 != 0:
|
|
raise ValueError(
|
|
"Unsupported model when in features size is " "not multiple of 16"
|
|
)
|
|
|
|
weight_dtype = (
|
|
torch.float8_e4m3fn
|
|
if self.quant_config.is_checkpoint_nvfp4_serialized
|
|
else params_dtype
|
|
)
|
|
|
|
weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
# 2 fp4 data is packed in one uint8 in the input dimension
|
|
output_size_per_partition,
|
|
input_size_per_partition // 2,
|
|
dtype=torch.uint8,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight", weight)
|
|
|
|
input_scale = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
|
|
layer.register_parameter("input_scale", input_scale)
|
|
|
|
weight_scale_2 = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight_scale_2", weight_scale_2)
|
|
|
|
weight_scale = ModelWeightParameter(
|
|
data=torch.empty(
|
|
output_size_per_partition,
|
|
input_size_per_partition // self.quant_config.group_size,
|
|
dtype=weight_dtype,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
|
|
layer.register_parameter("weight_scale", weight_scale)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
input_scale_2 = layer.input_scale.max().to(torch.float32)
|
|
weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
|
|
layer.input_scale = Parameter(input_scale_2, requires_grad=False)
|
|
layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
|
|
layer.alpha = Parameter(
|
|
layer.input_scale * layer.weight_scale_2, requires_grad=False
|
|
)
|
|
|
|
prepare_nvfp4_layer_for_petit(layer)
|
|
del layer.input_scale
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
return apply_petit_nvfp4_linear(
|
|
input=x,
|
|
weight=layer.weight,
|
|
weight_scale=layer.weight_scale,
|
|
weight_scale_2=layer.weight_scale_2,
|
|
size_n=layer.output_size_per_partition,
|
|
size_k=layer.input_size_per_partition,
|
|
bias=bias,
|
|
)
|