464 lines
16 KiB
Python
464 lines
16 KiB
Python
# 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, Dict, List, Optional
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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, LinearMethodBase
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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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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.fp8_utils import (
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apply_fp8_linear,
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cutlass_fp8_supported,
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)
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from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
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from sglang.srt.layers.quantization.utils import (
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convert_to_channelwise,
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requantize_with_max_scale,
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)
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.utils import is_cuda_available
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if is_cuda_available():
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from sgl_kernel import cutlass_scaled_fp4_mm, scaled_fp4_quant
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# Initialize logger for the module
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logger = logging.getLogger(__name__)
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# Supported activation schemes for the current configuration
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ACTIVATION_SCHEMES = ["static"]
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class ModelOptFp8Config(QuantizationConfig):
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"""Configuration for ModelOpt FP8 quantization, including serialization and compatibility checks."""
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def __init__(
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self,
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is_checkpoint_fp8_serialized: bool = False,
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kv_cache_quant_method: Optional[str] = None,
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exclude_modules: Optional[List[str]] = None,
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) -> None:
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"""
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Args:
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is_checkpoint_fp8_serialized (bool): Indicates if the checkpoint uses serialized FP8 format.
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"""
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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self.kv_cache_quant_method = kv_cache_quant_method
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self.exclude_modules = exclude_modules
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if is_checkpoint_fp8_serialized:
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logger.warning(
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"Detected ModelOpt FP8 checkpoint. The format is experimental and subject to change."
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)
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@classmethod
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def get_name(cls) -> str:
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return "modelopt"
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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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return 89 # Minimum hardware capability (e.g., Hopper GPUs).
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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]) -> "ModelOptFp8Config":
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quant_method = cls.get_from_keys(config, ["quantization"]).get("quant_algo")
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kv_cache_quant_method = cls.get_from_keys(config, ["quantization"]).get(
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"kv_cache_quant_algo"
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)
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exclude_modules = cls.get_from_keys(config, ["quantization"]).get(
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"exclude_modules"
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)
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if "FP8" not in quant_method:
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raise ValueError(
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"ModelOpt only supports static FP8 quantization in SGLang. "
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"Check the `hf_quant_config.json` file for your model's configuration."
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)
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return cls(
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is_checkpoint_fp8_serialized=True,
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kv_cache_quant_method=kv_cache_quant_method,
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exclude_modules=exclude_modules,
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)
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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 self.exclude_modules and any(
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module in prefix for module in self.exclude_modules
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):
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return None
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if isinstance(layer, LinearBase):
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return ModelOptFp8LinearMethod(self)
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if self.kv_cache_quant_method and isinstance(layer, RadixAttention):
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return ModelOptFp8KVCacheMethod(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 ModelOptFp8LinearMethod(LinearMethodBase):
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"""Linear method for ModelOpt static FP8 quantization.
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Supports loading FP8 checkpoints with static weight and activation scales.
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Future support may include dynamic scales.
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**Limitations**:
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1. Only supports per-tensor quantization due to `torch._scaled_mm` limitations.
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2. Only supports the `float8_e4m3fn` data type.
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Args:
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quant_config (ModelOptFp8Config): The ModelOpt quantization configuration.
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"""
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def __init__(self, quant_config: ModelOptFp8Config):
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super().__init__()
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self.quant_config = quant_config
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self.cutlass_fp8_supported = cutlass_fp8_supported()
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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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params_dtype: torch.dtype,
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**extra_weight_attrs,
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) -> None:
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"""Creates and registers weights, weight scales, and input scales for FP8 quantization."""
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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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weight_dtype = (
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torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_serialized
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else params_dtype
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)
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# Set layer attributes
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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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# Register weight
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layer.register_parameter(
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"weight",
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ModelWeightParameter(
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data=torch.empty(
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output_size_per_partition,
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input_size_per_partition,
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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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)
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if self.quant_config.is_checkpoint_fp8_serialized:
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# Register weight and input scales
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for scale_name in ["weight_scale", "input_scale"]:
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layer.register_parameter(
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scale_name,
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PerTensorScaleParameter(
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data=torch.full(
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(len(output_partition_sizes),),
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torch.finfo(torch.float32).min,
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dtype=torch.float32,
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),
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weight_loader=weight_loader,
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),
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)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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"""Requantizes weights after loading using the maximum scale."""
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max_w_scale, quantized_weight = requantize_with_max_scale(
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layer.weight, layer.weight_scale, layer.logical_widths
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)
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layer.weight = Parameter(quantized_weight.t(), requires_grad=False)
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# cutlass sgl-kernel only supports per-channel scale
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if self.cutlass_fp8_supported:
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max_w_scale = convert_to_channelwise(max_w_scale, layer.logical_widths)
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layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
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layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
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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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"""Applies FP8 linear transformation."""
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return apply_fp8_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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input_scale=layer.input_scale,
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bias=bias,
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cutlass_fp8_supported=self.cutlass_fp8_supported,
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)
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class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
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"""
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Handles loading FP8 kv-cache scaling factors from modelopt quantized checkpoints.
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"""
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def __init__(self, quant_config: ModelOptFp8Config):
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super().__init__(quant_config)
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class ModelOptFp4Config(QuantizationConfig):
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"""Config class for 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 "modelopt_fp4"
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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, torch.float8_e4m3fn]
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@classmethod
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def get_min_capability(cls) -> int:
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return 100
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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]) -> "ModelOptFp4Config":
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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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if not quant_method in ["FP8", "NVFP4"]:
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raise ValueError(
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f"ModelOpt currently only supports: FP8, 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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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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group_size = quant_config["group_size"]
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exclude_modules = quant_config["exclude_modules"]
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if not (group_size and kv_cache_quant_algo and exclude_modules):
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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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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 self.exclude_modules and any(
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module in prefix for module in self.exclude_modules
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):
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return None
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if isinstance(layer, LinearBase):
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return ModelOptFp4LinearMethod(self)
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if self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
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return ModelOptFp8KVCacheMethod(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 ModelOptFp4LinearMethod(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: ModelOptFp4Config):
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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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# Pad and blockwise interleave weight_scale
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scales = layer.weight_scale
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scale_ndim = scales.ndim
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if scale_ndim == 2:
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scales = scales.unsqueeze(0)
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assert scales.ndim == 3
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B, M, K = scales.shape
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round_up_multiple = lambda x, m: (x + m - 1) // m * m
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M_padded = round_up_multiple(M, 128)
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K_padded = round_up_multiple(K, 4)
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padded_scales = torch.zeros((B, M_padded, K_padded), dtype=scales.dtype)
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padded_scales[:B, :M, :K] = scales
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batches, rows, cols = padded_scales.shape
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assert rows % 128 == 0
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assert cols % 4 == 0
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padded_scales = padded_scales.reshape(batches, rows // 128, 4, 32, cols // 4, 4)
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padded_scales = padded_scales.permute((0, 1, 4, 3, 2, 5))
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padded_scales = padded_scales.contiguous().cuda()
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padded_scales = (
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padded_scales.reshape(M, K)
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if scale_ndim == 2
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else padded_scales.reshape(B, M, K)
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)
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layer.weight_scale_interleaved = Parameter(padded_scales, requires_grad=False)
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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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output_dtype = x.dtype
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x_m, _ = x.shape
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w_n, _ = layer.weight.shape
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output_shape = [x_m, w_n]
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# Quantize BF16 or FP16 to (FP4 and interleaved block scale)
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x_fp4, x_scale_interleaved = scaled_fp4_quant(x, 1 / layer.input_scale)
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assert x_fp4.dtype == torch.uint8
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assert x_scale_interleaved.dtype == torch.float8_e4m3fn
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assert layer.weight.dtype == torch.uint8
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assert layer.weight_scale_interleaved.dtype == torch.float8_e4m3fn
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assert layer.alpha.dtype == torch.float32
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out = cutlass_scaled_fp4_mm(
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x_fp4,
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layer.weight,
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x_scale_interleaved,
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layer.weight_scale_interleaved,
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layer.alpha,
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output_dtype,
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)
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if bias is not None:
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out = out + bias
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return out.view(*output_shape)
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