140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
from typing import Any, Dict, List, Optional, Tuple
|
|
|
|
import torch
|
|
from torch.nn import Module
|
|
from torch.nn.parameter import Parameter
|
|
|
|
from vllm.model_executor.layers.linear import (LinearMethodBase,
|
|
set_weight_attrs)
|
|
from vllm.model_executor.layers.quantization.base_config import (
|
|
QuantizationConfig)
|
|
|
|
|
|
class FP8Config(QuantizationConfig):
|
|
"""Config class for FP8."""
|
|
|
|
@classmethod
|
|
def get_name(cls) -> str:
|
|
return "fp8"
|
|
|
|
@classmethod
|
|
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
|
|
return [torch.bfloat16, torch.half]
|
|
|
|
@classmethod
|
|
def get_min_capability(cls) -> int:
|
|
# TODO: PyTorch 2.3.0+ is required to run FP8 on
|
|
# SM 89 (e.g. Ada) GPUs. Specifically, this PR has to
|
|
# be included: https://github.com/pytorch/pytorch/pull/118881
|
|
return 90
|
|
|
|
@classmethod
|
|
def get_config_filenames(cls) -> List[str]:
|
|
return []
|
|
|
|
@classmethod
|
|
def from_config(cls, config: Dict[str, Any]) -> "FP8Config":
|
|
return cls()
|
|
|
|
def get_linear_method(self) -> "Fp8LinearMethod":
|
|
return Fp8LinearMethod(self)
|
|
|
|
def get_scaled_act_names(self) -> List[str]:
|
|
return []
|
|
|
|
|
|
class Fp8LinearMethod(LinearMethodBase):
|
|
"""Linear method for FP8.
|
|
We now support common FP16/BF16 model checkpoints ONLY. The weight
|
|
scaling factor will be initialized after the model weights are loaded.
|
|
|
|
Limitations:
|
|
1. Only support per-tensor quantization due to torch._scaled_mm support.
|
|
2. Only support float8_e4m3fn data type due to the limitation of
|
|
torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
|
|
|
|
Args:
|
|
quant_config: The quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: FP8Config):
|
|
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,
|
|
):
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
weight = Parameter(torch.empty(output_size_per_partition,
|
|
input_size_per_partition,
|
|
dtype=params_dtype),
|
|
requires_grad=False)
|
|
layer.register_parameter("weight", weight)
|
|
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
|
|
set_weight_attrs(weight, extra_weight_attrs)
|
|
|
|
w_scale = Parameter(
|
|
torch.empty(1, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("weight_scaling_factor", w_scale)
|
|
|
|
def process_weights_after_loading(self, layer: Module) -> None:
|
|
# Although the linear_method is propagated to all layers,
|
|
# only linear layers invoke "create_weights". So we check
|
|
# whether "weight_scaling_facor" is registered to determine
|
|
# whether the layer is a linear layer that requires quantization.
|
|
if not hasattr(layer, "weight_scaling_factor"):
|
|
return
|
|
|
|
qweight, weight_scale = per_tensor_quantize(layer.weight)
|
|
# torch._scaled_mm requires column-major in the second
|
|
# input (weight), so we transpose the quantized weight.
|
|
layer.weight = Parameter(qweight.t(), requires_grad=False)
|
|
layer.weight_scaling_factor.data.copy_(weight_scale)
|
|
|
|
def apply_weights(self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
qinput, x_scale = per_tensor_quantize(x)
|
|
output, _ = torch._scaled_mm(
|
|
qinput,
|
|
layer.weight,
|
|
out_dtype=x.dtype,
|
|
scale_a=x_scale,
|
|
scale_b=layer.weight_scaling_factor,
|
|
bias=bias,
|
|
)
|
|
return output
|
|
|
|
|
|
def per_tensor_quantize(tensor: torch.Tensor) -> Tuple[torch.Tensor, float]:
|
|
"""Quantize a tensor using per-tensor static scaling factor.
|
|
|
|
Args:
|
|
tensor: The input tensor.
|
|
"""
|
|
finfo = torch.finfo(torch.float8_e4m3fn)
|
|
# Calculate the scale as dtype max divided by absmax.
|
|
# Since .abs() creates a new tensor, we use aminmax to get
|
|
# the min and max first and then calculate the absmax.
|
|
min_val, max_val = tensor.aminmax()
|
|
amax = min_val.abs().max(max_val.abs())
|
|
scale = finfo.max / amax.clamp(min=1e-12)
|
|
# scale and clamp the tensor to bring it to
|
|
# the representative range of float8 data type
|
|
# (as default cast is unsaturated)
|
|
qweight = (tensor * scale).clamp(min=finfo.min, max=finfo.max)
|
|
# Return both float8 data and the inverse scale (as float),
|
|
# as both required as inputs to torch._scaled_mm
|
|
qweight = qweight.to(torch.float8_e4m3fn)
|
|
scale = scale.float().reciprocal()
|
|
return qweight, scale
|