411 lines
17 KiB
Python
411 lines
17 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Dict, List, Optional, Union
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import torch
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from torch.nn import Module
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from torch.nn.parameter import Parameter
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from vllm._custom_ops import (cutlass_scaled_fp4_mm,
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cutlass_scaled_mm_supports_fp4, scaled_fp4_quant)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
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UnquantizedLinearMethod)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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is_layer_skipped)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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Fp8LinearOp, requantize_with_max_scale)
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from vllm.model_executor.parameter import (ModelWeightParameter,
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PerTensorScaleParameter)
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from vllm.platforms import current_platform
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logger = init_logger(__name__)
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QUANT_ALGOS = ["FP8", "NVFP4"]
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KV_CACHE_QUANT_ALGOS = ["FP8"]
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class ModelOptFp8Config(QuantizationConfig):
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"""Config class for ModelOpt FP8."""
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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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) -> None:
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super().__init__()
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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if is_checkpoint_fp8_serialized:
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logger.warning("Detected ModelOpt fp8 checkpoint. Please note that"
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" the format is experimental and could change.")
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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
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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_config = cls.get_from_keys(config, ["quantization"])
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quant_method = quant_config["quant_algo"]
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if quant_method not in QUANT_ALGOS:
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raise ValueError(f"ModelOpt currently only supports: {QUANT_ALGOS}"
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" quantizations in vLLM. 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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is_checkpoint_fp8_serialized = ("FP8" in quant_method)
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return cls(is_checkpoint_fp8_serialized)
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QuantizeMethodBase"]:
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from vllm.attention.layer import Attention # Avoid circular import
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if isinstance(layer, LinearBase):
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return ModelOptFp8LinearMethod(self)
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elif isinstance(layer, Attention):
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return ModelOptFp8KVCacheMethod(self)
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return None
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class ModelOptFp8LinearMethod(LinearMethodBase):
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"""Linear method for Model Optimizer static quantization.
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Supports loading FP8 checkpoints with static weight scale and
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activation scale. Future support might be added for dynamic
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scales.
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Limitations:
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1. Only support per-tensor quantization due to torch._scaled_mm support.
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2. Only support float8_e4m3fn datatype
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Args: quant_config: The ModelOpt quantization config.
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"""
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def __init__(self, quant_config: ModelOptFp8Config):
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self.quant_config = quant_config
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self.fp8_linear = Fp8LinearOp()
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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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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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weight_dtype = (torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_serialized else
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params_dtype)
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weight = ModelWeightParameter(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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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader)
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layer.register_parameter("weight", weight)
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if self.quant_config.is_checkpoint_fp8_serialized:
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# WEIGHT SCALE
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weight_scale = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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weight_scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("weight_scale", weight_scale)
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# INPUT SCALE
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scale = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("input_scale", scale)
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def process_weights_after_loading(self, layer: Module) -> None:
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weight = layer.weight
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max_w_scale = layer.weight_scale.max()
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if not (layer.weight_scale == layer.weight_scale[0]).all():
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max_w_scale, weight = requantize_with_max_scale(
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layer.weight, layer.weight_scale, layer.logical_widths)
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layer.weight = Parameter(weight.t(), requires_grad=False)
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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(),
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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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return self.fp8_linear.apply(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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class ModelOptNvFp4Config(QuantizationConfig):
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"""Config class for ModelOpt FP4."""
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def __init__(
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self,
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is_checkpoint_nvfp4_serialized: bool,
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kv_cache_quant_algo: str,
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exclude_modules: List[str],
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group_size: int = 16,
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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 ModelOpt NVFP4 checkpoint. Please note that"
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" the format is experimental and could change in future.")
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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_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, 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]) -> "ModelOptNvFp4Config":
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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 quant_method not in QUANT_ALGOS:
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raise ValueError(f"ModelOpt currently only supports: {QUANT_ALGOS}"
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" quantizations in vLLM. 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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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("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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return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
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exclude_modules, group_size)
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QuantizeMethodBase"]:
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from vllm.attention.layer import Attention # Avoid circular import
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if isinstance(layer, LinearBase):
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if is_layer_skipped(prefix, self.exclude_modules):
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return UnquantizedLinearMethod()
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return ModelOptNvFp4LinearMethod(self)
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elif isinstance(layer, Attention):
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return ModelOptFp8KVCacheMethod(self)
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return None
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def cutlass_fp4_supported() -> bool:
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if not current_platform.is_cuda():
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return False
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capability_tuple = current_platform.get_device_capability()
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capability = -1 if capability_tuple is None else capability_tuple.to_int()
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return cutlass_scaled_mm_supports_fp4(capability)
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class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
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"""
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Supports loading kv-cache scaling factors from FP8 checkpoints.
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"""
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def __init__(self, quant_config: Union[ModelOptFp8Config,
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ModelOptNvFp4Config]):
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super().__init__(quant_config)
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class ModelOptNvFp4LinearMethod(LinearMethodBase):
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"""Linear method for Model Optimizer NVFP4.
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Supports loading NVFP4 checkpoints with the following structure:
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input_scale: torch.float32, scalar ,
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weight: NVFP4(represented as byte) Shape: [1, X, y/2]
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weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
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weight_scale_2: torch.float32, scalar,
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Args: quant_config: The ModelOpt quantization config.
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"""
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def __init__(self, quant_config: ModelOptNvFp4Config):
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self.quant_config = quant_config
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self.cutlass_nvfp4_supported = cutlass_fp4_supported()
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if not self.cutlass_nvfp4_supported:
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raise ValueError("Current platform does not support NVFP4"
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" quantization. Please use Blackwell and above.")
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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("NVFP4 quantization was selected, "
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" dynamic quantization is not supported.")
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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("Unsupported model when in features size is "
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"not multiple of 16")
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# The nvfp4 weight is still represented as
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weight_dtype = (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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# Weight
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weight = ModelWeightParameter(
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data=torch.empty(
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# 2 fp4 items are packed in the input dimension
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layer.output_size_per_partition,
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layer.input_size_per_partition // 2,
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dtype=torch.uint8),
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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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layer.register_parameter("weight", weight)
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# Input Weight Scale
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input_scale = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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layer.register_parameter("input_scale", input_scale)
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# Global Weight Scale
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weight_scale_2 = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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layer.register_parameter("weight_scale_2", weight_scale_2)
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# Per Block Weight Scale
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weight_scale = ModelWeightParameter(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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layer.register_parameter("weight_scale", weight_scale)
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def swizzle_blockscale(self, scale: torch.tensor):
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assert (scale.dtype == torch.float8_e4m3fn)
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# Pad and blockwise interleave weight_scale
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scale_ndim = scale.ndim
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if scale.ndim == 2:
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scale = scale.unsqueeze(0)
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assert scale.ndim == 3
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B, M, K = scale.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_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
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padded_scale[:B, :M, :K] = scale
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batches, rows, cols = padded_scale.shape
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assert rows % 128 == 0
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assert cols % 4 == 0
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padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
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cols // 4, 4)
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swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
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swizzled_scale = swizzled_scale.contiguous().cuda()
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return (swizzled_scale.reshape(M, K)
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if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))
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def process_weights_after_loading(self, layer: Module) -> None:
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# global scales:
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input_scale_2 = layer.input_scale.max().to(torch.float32)
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layer.input_scale = Parameter(input_scale_2, requires_grad=False)
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weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
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layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
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layer.alpha = Parameter(layer.input_scale * layer.weight_scale_2,
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requires_grad=False)
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# Swizzle the weight blockscale.
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# contracting dimension is input dimension
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# block_size = 16;
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assert (layer.weight_scale.shape[1] % 16 == 0), (
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"Expected weight_scale.dim(1) to be divisible by 16")
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assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
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"Weight Block scale must be represented as FP8-E4M3")
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swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)
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layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
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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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# for input only the contracting dimension has a constraint.
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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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s_quant = 1 / layer.input_scale
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x_fp4, x_blockscale = scaled_fp4_quant(x, s_quant)
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# validate dtypes of quantized input, input block scale,
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# weight and weight_blockscale
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assert (x_fp4.dtype == torch.uint8)
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assert (layer.weight.dtype == torch.uint8)
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assert (x_blockscale.dtype == torch.float8_e4m3fn)
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assert (layer.weight_scale_swizzled.dtype == torch.float8_e4m3fn)
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assert (layer.alpha.dtype == torch.float32)
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out = cutlass_scaled_fp4_mm(x_fp4, layer.weight, x_blockscale,
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layer.weight_scale_swizzled, layer.alpha,
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output_dtype)
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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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