170 lines
6.4 KiB
Python
170 lines
6.4 KiB
Python
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from typing import Any, Dict, List, Optional
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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.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.fp8 import cutlass_fp8_supported
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
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apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
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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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apply_fp8_linear, normalize_e4m3fn_to_e4m3fnuz)
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from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
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ModelWeightParameter)
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from vllm.platforms import current_platform
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from vllm.utils import is_hip
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logger = init_logger(__name__)
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class FBGEMMFp8Config(QuantizationConfig):
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"""Config class for FBGEMM Fp8."""
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def __init__(self, ignore_list: List[str], input_scale_ub: float):
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self.ignore_list = ignore_list if ignore_list else []
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self.input_scale_ub = input_scale_ub
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# For GPUs that lack FP8 hardware support, we can leverage the Marlin
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# kernel for fast weight-only FP8 quantization
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self.use_marlin = not current_platform.has_device_capability(89)
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@classmethod
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def get_name(cls) -> str:
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return "fbgemm_fp8"
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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.float16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 80
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "FBGEMMFp8Config":
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ignore_list = cls.get_from_keys(config, ["modules_to_not_convert"])
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input_scale_ub = cls.get_from_keys(config, ["activation_scale_ub"])
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return cls(ignore_list=ignore_list, input_scale_ub=input_scale_ub)
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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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if isinstance(layer, LinearBase):
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if is_layer_skipped(prefix, self.ignore_list):
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return UnquantizedLinearMethod()
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return FBGEMMFp8LinearMethod(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 FBGEMMFp8LinearMethod(LinearMethodBase):
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def __init__(self, quant_config: FBGEMMFp8Config):
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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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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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weight_loader = extra_weight_attrs.get("weight_loader")
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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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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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layer.orig_dtype = params_dtype
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# WEIGHT
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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=torch.float8_e4m3fn),
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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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# WEIGHT SCALE
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weight_scale = ChannelQuantScaleParameter(data=torch.empty(
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(sum(output_partition_sizes), 1), dtype=torch.float32),
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output_dim=0,
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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 UPPER BOUND
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input_scale_ub = torch.nn.Parameter(torch.tensor(
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(self.quant_config.input_scale_ub), dtype=torch.float32),
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requires_grad=False)
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layer.input_scale_ub = input_scale_ub
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def process_weights_after_loading(self, layer: Module) -> None:
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# required by torch.compile
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layer.weight_scale = Parameter(layer.weight_scale.data,
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requires_grad=False)
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layer.weight = Parameter(layer.weight.data, requires_grad=False)
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weight = layer.weight
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if is_hip():
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weight, weight_scale, input_scale = \
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normalize_e4m3fn_to_e4m3fnuz(
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weight=weight,
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weight_scale=layer.weight_scale,
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input_scale=None)
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if input_scale is not None:
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layer.input_scale = Parameter(input_scale, requires_grad=False)
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layer.weight_scale = Parameter(weight_scale, requires_grad=False)
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layer.weight = Parameter(weight.t(), requires_grad=False)
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if self.quant_config.use_marlin:
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prepare_fp8_layer_for_marlin(layer)
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# Activations not quantized for marlin.
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del layer.input_scale_ub
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def apply(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) -> torch.Tensor:
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if self.quant_config.use_marlin:
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return apply_fp8_marlin_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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workspace=layer.workspace,
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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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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=None,
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input_scale_ub=layer.input_scale_ub,
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bias=bias,
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cutlass_fp8_supported=self.cutlass_fp8_supported,
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use_per_token_if_dynamic=True)
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