Support NVFP4 quantized dense models on AMD CDNA2/CDNA3 GPUs (#7302)
Co-authored-by: HAI <hixiao@gmail.com> Co-authored-by: Sai Enduri <saimanas.enduri@amd.com>
This commit is contained in:
Haohui Mai
@@ -79,6 +79,7 @@ blackwell = [
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srt_hip = [
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"sglang[runtime_common]",
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"torch",
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"petit_kernel",
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]
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# xpu is not enabled in public vllm and torch whl,
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@@ -391,6 +391,7 @@ class ModelConfig:
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"compressed-tensors",
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"fbgemm_fp8",
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"w8a8_fp8",
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"petit_nvfp4",
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]
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optimized_quantization_methods = [
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"fp8",
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@@ -408,9 +409,11 @@ class ModelConfig:
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"moe_wna16",
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"qoq",
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"w4afp8",
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"petit_nvfp4",
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]
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compatible_quantization_methods = {
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"modelopt_fp4": ["modelopt"],
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"petit_nvfp4": ["modelopt"],
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"w8a8_int8": ["compressed-tensors", "compressed_tensors"],
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"w8a8_fp8": ["compressed-tensors", "compressed_tensors"],
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}
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@@ -53,6 +53,7 @@ WEIGHT_LOADER_V2_SUPPORTED = [
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"ModelOptFp8LinearMethod",
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"ModelOptFp4LinearMethod",
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"IPEXAWQLinearMethod",
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"PetitNvFp4LinearMethod",
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]
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_is_cpu = is_cpu()
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@@ -58,6 +58,7 @@ from sglang.srt.layers.quantization.modelopt_quant import (
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ModelOptFp8Config,
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)
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from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config
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from sglang.srt.layers.quantization.petit import PetitNvFp4Config
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from sglang.srt.layers.quantization.qoq import QoQConfig
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from sglang.srt.layers.quantization.utils import get_linear_quant_method
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from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
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@@ -76,6 +77,7 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
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"compressed-tensors": CompressedTensorsConfig,
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"qoq": QoQConfig,
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"w4afp8": W4AFp8Config,
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"petit_nvfp4": PetitNvFp4Config,
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}
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# VLLM-dependent quantization methods
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249
python/sglang/srt/layers/quantization/petit.py
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249
python/sglang/srt/layers/quantization/petit.py
Normal file
@@ -0,0 +1,249 @@
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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
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import logging
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from typing import Any, Callable, Dict, List, Optional
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import regex as re
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import torch
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from torch.nn.parameter import Parameter
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from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
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from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
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from sglang.srt.layers.quantization.base_config import (
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LinearMethodBase,
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.petit_utils import (
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apply_petit_nvfp4_linear,
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prepare_nvfp4_layer_for_petit,
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verify_petit_nvfp4_supported,
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)
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from sglang.srt.layers.quantization.utils import is_layer_skipped
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# Initialize logger for the module
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logger = logging.getLogger(__name__)
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# Configuration class to support the NVFP4 quantized model generated by the ModelOpt quantization tool
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class PetitNvFp4Config(QuantizationConfig):
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"""Config class for Petit FP4."""
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def __init__(
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self,
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is_checkpoint_nvfp4_serialized: bool = False,
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kv_cache_quant_algo: str = None,
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group_size: int = None,
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exclude_modules: List[str] = None,
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) -> None:
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self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
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if is_checkpoint_nvfp4_serialized:
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logger.warning(
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"Detected nvfp4 checkpoint. Please note that the "
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"format is experimental and subject to change."
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)
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self.group_size = group_size
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self.kv_cache_quant_algo = kv_cache_quant_algo
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self.exclude_modules = exclude_modules
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@classmethod
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def get_name(cls) -> str:
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return "petit_nvfp4"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.bfloat16, torch.half]
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@classmethod
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def get_min_capability(cls) -> int:
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# Petit supports the gfx90a and gfx942 GPUs
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return 90
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return ["hf_quant_config.json"]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "PetitNvFp4Config":
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quant_config = cls.get_from_keys(config, ["quantization"])
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quant_method = quant_config["quant_algo"]
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group_size = quant_config.get("group_size", None)
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verify_petit_nvfp4_supported(quant_method, group_size)
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is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
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kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
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if not kv_cache_quant_algo:
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kv_cache_quant_algo = "auto"
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exclude_modules = quant_config.get("exclude_modules", None)
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if not (group_size and kv_cache_quant_algo and (exclude_modules is not None)):
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logger.warning(
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f"group_size: {group_size},"
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f"kv_cache_quant_algo: {kv_cache_quant_algo},"
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f"exclude_modules: {exclude_modules}"
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)
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raise ValueError(
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"NVFP4 quantization requires group size and "
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"kv_cache_quant_algo specified in "
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"hf_quant_config.json"
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)
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return cls(
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is_checkpoint_nvfp4_serialized,
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kv_cache_quant_algo,
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group_size,
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exclude_modules,
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)
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@classmethod
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def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
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can_convert = cls.is_petit_nvfp4_compatible(hf_quant_cfg)
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if can_convert:
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return cls.get_name()
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return None
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@classmethod
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def is_petit_nvfp4_compatible(cls, quant_config: Dict[str, Any]) -> bool:
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quant_method = quant_config.get("quant_method", "").lower()
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return quant_method == "modelopt"
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def is_layer_excluded(self, prefix: str, exclude_modules: list):
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for pattern in exclude_modules:
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regex_str = pattern.replace(".", r"\.").replace("*", r".*")
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if re.fullmatch(regex_str, prefix):
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return True
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return False
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["QuantizeMethodBase"]:
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if isinstance(layer, LinearBase):
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if is_layer_skipped(prefix, self.exclude_modules) or self.is_layer_excluded(
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prefix, self.exclude_modules
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):
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return UnquantizedLinearMethod()
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return PetitNvFp4LinearMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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return []
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class PetitNvFp4LinearMethod(LinearMethodBase):
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"""Linear method for NVFP4.
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Supports loading NVFP4 checkpoints with the following structure:
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|Tensor Name | datatype | shape |
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|----------------------------------------------------|
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|input_scale | torch.float32 | scalar |
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|weight | NVFP4(SE2M1) | [1, X, y/2] |
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|weight_scale | FP8-E4M3 | [X, Y] |
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|weight_scale_2 | torch.float32 | scalar |
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The weights are quantized per block of 16 elements.
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Args: quant_config: The ModelOpt quantization config.
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"""
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def __init__(self, quant_config: PetitNvFp4Config):
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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del input_size, output_size
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if not self.quant_config.is_checkpoint_nvfp4_serialized:
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raise ValueError(
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"NVFP4 quantization was selected, "
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" dynamic quantization is not supported."
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)
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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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layer.logical_widths = output_partition_sizes
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layer.input_size_per_partition = input_size_per_partition
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layer.output_size_per_partition = output_size_per_partition
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if input_size_per_partition % 16 != 0:
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raise ValueError(
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"Unsupported model when in features size is " "not multiple of 16"
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)
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weight_dtype = (
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torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_nvfp4_serialized
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else params_dtype
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)
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weight = ModelWeightParameter(
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data=torch.empty(
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# 2 fp4 data is packed in one uint8 in the input dimension
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output_size_per_partition,
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input_size_per_partition // 2,
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dtype=torch.uint8,
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight", weight)
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input_scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader,
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)
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layer.register_parameter("input_scale", input_scale)
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weight_scale_2 = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight_scale_2", weight_scale_2)
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weight_scale = ModelWeightParameter(
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data=torch.empty(
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output_size_per_partition,
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input_size_per_partition // self.quant_config.group_size,
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dtype=weight_dtype,
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight_scale", weight_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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input_scale_2 = layer.input_scale.max().to(torch.float32)
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weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
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layer.input_scale = Parameter(input_scale_2, requires_grad=False)
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layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
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layer.alpha = Parameter(
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layer.input_scale * layer.weight_scale_2, requires_grad=False
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)
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prepare_nvfp4_layer_for_petit(layer)
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del layer.input_scale
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return apply_petit_nvfp4_linear(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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weight_scale_2=layer.weight_scale_2,
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size_n=layer.output_size_per_partition,
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size_k=layer.input_size_per_partition,
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bias=bias,
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)
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104
python/sglang/srt/layers/quantization/petit_utils.py
Normal file
104
python/sglang/srt/layers/quantization/petit_utils.py
Normal file
@@ -0,0 +1,104 @@
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from typing import Optional
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import torch
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try:
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from petit_kernel import mul_nvfp4_a16, process_nvfp4_scales, repack_nvfp4
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except ImportError:
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def _check_petit_nvfp4_supported(
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quant_method: str, group_size: Optional[int]
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) -> tuple[bool, Optional[str]]:
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return (
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False,
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"Petit is not installed. Please install it with `pip install petit-kernel`.",
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)
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def prepare_nvfp4_layer_for_petit(layer: torch.nn.Module) -> None:
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raise ValueError(
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"Petit is not installed. Please install it with `pip install petit-kernel`."
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)
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def apply_petit_nvfp4_linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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weight_scale: torch.Tensor,
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weight_scale_2: torch.Tensor,
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size_n: int,
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size_k: int,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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raise ValueError(
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"Petit is not installed. Please install it with `pip install petit-kernel`."
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)
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def _check_petit_nvfp4_supported(
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quant_method: str, group_size: Optional[int]
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) -> tuple[bool, Optional[str]]:
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if quant_method != "NVFP4":
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return (
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False,
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"Petit currently only supports: NVFP4"
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" quantizations in sglang. Please check the "
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"`hf_quant_config.json` file for your model's "
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"quant configuration.",
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)
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if group_size is not None and group_size != 16:
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return (
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False,
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"Petit currently only supports: group_size=16" " quantizations.",
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)
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return (True, None)
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def verify_petit_nvfp4_supported(quant_method: str, group_size: Optional[int]) -> None:
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supported, error_msg = _check_petit_nvfp4_supported(quant_method, group_size)
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if not supported:
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raise ValueError(error_msg)
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def prepare_nvfp4_layer_for_petit(layer: torch.nn.Module) -> None:
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# Repack weights to petit format
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part_size_n = layer.output_size_per_partition
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part_size_k = layer.input_size_per_partition
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qweight = layer.weight.view(torch.int32).contiguous()
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petit_qweight = repack_nvfp4(qweight, size_n=part_size_n, size_k=part_size_k)
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layer.weight = torch.nn.Parameter(petit_qweight, requires_grad=False)
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# Permute scales
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weight_scale = process_nvfp4_scales(
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scales=layer.weight_scale, size_k=part_size_k, size_n=part_size_n
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)
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layer.weight_scale = torch.nn.Parameter(weight_scale, requires_grad=False)
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return
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def apply_petit_nvfp4_linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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weight_scale: torch.Tensor,
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weight_scale_2: torch.Tensor,
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size_n: int,
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size_k: int,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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reshaped_x = input.reshape(-1, input.shape[-1])
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out_shape = input.shape[:-1] + (size_n,)
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# TODO: Use auto-tuning to find the performant solution_id
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output = mul_nvfp4_a16(
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a=reshaped_x,
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b=weight,
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s=weight_scale,
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global_scale=weight_scale_2,
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size_m=reshaped_x.size(0),
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size_n=size_n,
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size_k=size_k,
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solution_id=-1,
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)
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if bias is not None:
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output.add_(bias) # In-place add
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return output.reshape(out_shape)
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@@ -766,6 +766,7 @@ class ServerArgs:
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"gguf",
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"modelopt",
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"modelopt_fp4",
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"petit_nvfp4",
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"w8a8_int8",
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"w8a8_fp8",
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"moe_wna16",
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