EngineX-Hygon/sglang
5
0
Fork 0
Code Issues Pull Requests Actions 7 Projects Releases Wiki Activity

FP4 weight loading and inference (2/2) (#3972)

Browse Source
This commit is contained in:
Trevor Morris
2025-04-08 17:26:21 -07:00
committed by GitHub
parent 5039d54772
commit 11d760d56a
6 changed files with 262 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
python/sglang/srt/configs/model_config.py
View File

@@ -279,6 +279,7 @@ class ModelConfig:
"moe_wna16",
]
compatible_quantization_methods = {
"modelopt_fp4": ["modelopt"],
"w8a8_int8": ["compressed-tensors", "compressed_tensors"],
"w8a8_fp8": ["compressed-tensors", "compressed_tensors"],
}

1
python/sglang/srt/layers/linear.py
View File

@@ -47,6 +47,7 @@ WEIGHT_LOADER_V2_SUPPORTED = [
"GPTQLinearMethod",
"FBGEMMFp8LinearMethod",
"ModelOptFp8LinearMethod",
"ModelOptFp4LinearMethod",
"IPEXAWQLinearMethod",
]

6
python/sglang/srt/layers/quantization/__init__.py
View File

@@ -59,7 +59,10 @@ from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import
)
from sglang.srt.layers.quantization.fp8 import Fp8Config
from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
from sglang.srt.layers.quantization.modelopt_quant import (
ModelOptFp4Config,
ModelOptFp8Config,
)
from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config
from sglang.srt.layers.quantization.w8a8_fp8 import W8A8Fp8Config
from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
@@ -69,6 +72,7 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
"fp8": Fp8Config,
"blockwise_int8": BlockInt8Config,
"modelopt": ModelOptFp8Config,
"modelopt_fp4": ModelOptFp4Config,
"w8a8_int8": W8A8Int8Config,
"w8a8_fp8": W8A8Fp8Config,
"moe_wna16": MoeWNA16Config,

246
python/sglang/srt/layers/quantization/modelopt_quant.py
View File

@@ -22,6 +22,10 @@ from sglang.srt.layers.quantization.utils import (
requantize_with_max_scale,
)
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.utils import is_cuda_available
if is_cuda_available():
from sgl_kernel import cutlass_scaled_fp4_mm, scaled_fp4_quant
# Initialize logger for the module
logger = logging.getLogger(__name__)
@@ -215,3 +219,245 @@ class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
def __init__(self, quant_config: ModelOptFp8Config):
super().__init__(quant_config)
class ModelOptFp4Config(QuantizationConfig):
"""Config class for FP4."""
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 "modelopt_fp4"
@classmethod
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
return [torch.bfloat16, torch.half, torch.float8_e4m3fn]
@classmethod
def get_min_capability(cls) -> int:
return 100
@classmethod
def get_config_filenames(cls) -> List[str]:
return ["hf_quant_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp4Config":
quant_config = cls.get_from_keys(config, ["quantization"])
quant_method = quant_config["quant_algo"]
if not quant_method in ["FP8", "NVFP4"]:
raise ValueError(
f"ModelOpt currently only supports: FP8, NVFP4"
" quantizations in sglang. Please check the "
"`hf_quant_config.json` file for your model's "
"quant configuration."
)
is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
group_size = quant_config["group_size"]
exclude_modules = quant_config["exclude_modules"]
if not (group_size and kv_cache_quant_algo and 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,
)
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
if self.exclude_modules and any(
module in prefix for module in self.exclude_modules
):
return None
if isinstance(layer, LinearBase):
return ModelOptFp4LinearMethod(self)
if self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
return ModelOptFp8KVCacheMethod(self)
return None
def get_scaled_act_names(self) -> List[str]:
return []
class ModelOptFp4LinearMethod(LinearMethodBase):
"""Linear method for NVFP4.
Supports loading NVFP4 checkpoints with the following structure:
|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 |
The weights are quantized per block of 16 elements.
Args: quant_config: The ModelOpt quantization config.
"""
def __init__(self, quant_config: ModelOptFp4Config):
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
)
# Pad and blockwise interleave weight_scale
scales = layer.weight_scale
scale_ndim = scales.ndim
if scale_ndim == 2:
scales = scales.unsqueeze(0)
assert scales.ndim == 3
B, M, K = scales.shape
round_up_multiple = lambda x, m: (x + m - 1) // m * m
M_padded = round_up_multiple(M, 128)
K_padded = round_up_multiple(K, 4)
padded_scales = torch.zeros((B, M_padded, K_padded), dtype=scales.dtype)
padded_scales[:B, :M, :K] = scales
batches, rows, cols = padded_scales.shape
assert rows % 128 == 0
assert cols % 4 == 0
padded_scales = padded_scales.reshape(batches, rows // 128, 4, 32, cols // 4, 4)
padded_scales = padded_scales.permute((0, 1, 4, 3, 2, 5))
padded_scales = padded_scales.contiguous().cuda()
padded_scales = (
padded_scales.reshape(M, K)
if scale_ndim == 2
else padded_scales.reshape(B, M, K)
)
layer.weight_scale_interleaved = Parameter(padded_scales, requires_grad=False)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
output_dtype = x.dtype
x_m, _ = x.shape
w_n, _ = layer.weight.shape
output_shape = [x_m, w_n]
# Quantize BF16 or FP16 to (FP4 and interleaved block scale)
x_fp4, x_scale_interleaved = scaled_fp4_quant(x, 1 / layer.input_scale)
assert x_fp4.dtype == torch.uint8
assert x_scale_interleaved.dtype == torch.float8_e4m3fn
assert layer.weight.dtype == torch.uint8
assert layer.weight_scale_interleaved.dtype == torch.float8_e4m3fn
assert layer.alpha.dtype == torch.float32
out = cutlass_scaled_fp4_mm(
x_fp4,
layer.weight,
x_scale_interleaved,
layer.weight_scale_interleaved,
layer.alpha,
output_dtype,
)
if bias is not None:
out = out + bias
return out.view(*output_shape)

1
python/sglang/srt/server_args.py
View File

@@ -495,6 +495,7 @@ class ServerArgs:
"bitsandbytes",
"gguf",
"modelopt",
"modelopt_fp4",
"w8a8_int8",
"w8a8_fp8",
"moe_wna16",

8
sgl-kernel/README.md
View File

@@ -156,6 +156,14 @@ unset CCACHE_READONLY
python -m uv build --wheel -Cbuild-dir=build --color=always .
```
##### Configuring CMake Build Options
Cmake options can be configuring by adding `-Ccmake.define.<option>=<value>` to the `uv build` flags.
For example, to enable building FP4 kernels, use:
```bash
python -m uv build --wheel -Cbuild-dir=build -Ccmake.define.SGL_KERNEL_ENABLE_FP4=1 --color=always .
```
See CMakeLists.txt for more options.
### Testing & Benchmarking
1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests), if you need to skip some test, please use `@pytest.mark.skipif`