127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
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 import _custom_ops as ops
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from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
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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.utils import set_weight_attrs
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class Fp8Config(QuantizationConfig):
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"""Config class for FP8."""
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def __init__(
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self,
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activation_scheme: str = "dynamic",
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) -> None:
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self.activation_scheme = activation_scheme
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@classmethod
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def get_name(cls) -> str:
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return "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.half]
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@classmethod
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def get_min_capability(cls) -> int:
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# TODO: PyTorch 2.3.0+ is required to run FP8 on
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# SM 89 (e.g. Ada) GPUs. Specifically, this PR has to
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# be included: https://github.com/pytorch/pytorch/pull/118881
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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 []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
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activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
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return cls(activation_scheme)
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def get_quant_method(
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self, layer: torch.nn.Module) -> Optional["QuantizeMethodBase"]:
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if isinstance(layer, LinearBase):
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return Fp8LinearMethod(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 Fp8LinearMethod(LinearMethodBase):
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"""Linear method for FP8.
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We now support common FP16/BF16 model checkpoints ONLY. The weight
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scaling factor will be initialized after the model weights are loaded.
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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 data type due to the limitation of
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torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
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Args:
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quant_config: The quantization config.
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"""
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def __init__(self, quant_config: Fp8Config):
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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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output_size_per_partition = sum(output_partition_sizes)
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weight = Parameter(torch.empty(output_size_per_partition,
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input_size_per_partition,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("weight", weight)
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set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
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set_weight_attrs(weight, extra_weight_attrs)
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w_scale = Parameter(
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torch.empty(1, dtype=torch.float32),
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requires_grad=False,
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)
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layer.register_parameter("weight_scaling_factor", w_scale)
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def process_weights_after_loading(self, layer: Module) -> None:
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# Although the quant_method is propagated to all layers,
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# only linear layers invoke "create_weights". So we check
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# whether "weight_scaling_facor" is registered to determine
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# whether the layer is a linear layer that requires quantization.
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if not hasattr(layer, "weight_scaling_factor"):
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return
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qweight, weight_scale = ops.scaled_fp8_quant(layer.weight)
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# torch._scaled_mm requires column-major in the second
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# input (weight), so we transpose the quantized weight.
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layer.weight = Parameter(qweight.t(), requires_grad=False)
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layer.weight_scaling_factor.data.copy_(weight_scale)
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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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qinput, x_scale = ops.scaled_fp8_quant(x)
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output, _ = torch._scaled_mm(
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qinput,
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layer.weight,
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out_dtype=x.dtype,
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scale_a=x_scale,
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scale_b=layer.weight_scaling_factor,
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bias=bias,
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)
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return output
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