from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from loguru import logger
from torch.nn import Module
from torch.nn.parameter import Parameter
from aphrodite import _custom_ops as ops
from aphrodite.modeling.layers.linear import LinearBase, LinearMethodBase
from aphrodite.quantization.base_config import (QuantizationConfig,
QuantizeMethodBase)
from aphrodite.modeling.utils import set_weight_attrs
from aphrodite.common.utils import print_warning_once
ACTIVATION_SCHEMES = ["static", "dynamic"]
def cutlass_fp8_supported() -> bool:
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
major, minor = torch.version.cuda.split(".")
version = int(major) * 10 + int(minor)
# CUTLASS FP8 kernels need at least
# CUDA 12.0 on SM90 systems (Hopper)
# CUDA 12.4 on SM89 systems (Lovelace)
gpu_is_supported = False
if capability >= 90:
gpu_is_supported = version > 120
elif capability >= 89:
gpu_is_supported = version > 124
return gpu_is_supported
class Fp8Config(QuantizationConfig):
"""Config class for FP8."""
def __init__(
self,
is_checkpoint_fp8_serialized: bool = False,
activation_scheme: str = "dynamic",
) -> None:
self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
if is_checkpoint_fp8_serialized:
logger.warning("Detected fp8 checkpoint. Please note that the "
"format is experimental and subject to change.")
if activation_scheme not in ACTIVATION_SCHEMES:
raise ValueError(
f"Unsupported activation scheme {activation_scheme}")
self.activation_scheme = activation_scheme
@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:
return 89
@classmethod
def get_config_filenames(cls) -> List[str]:
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
quant_method = cls.get_from_keys(config, ["quant_method"])
is_checkpoint_fp8_serialized = ("fp8" in quant_method)
activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
activation_scheme=activation_scheme)
def get_quant_method(
self, layer: torch.nn.Module) -> Optional["QuantizeMethodBase"]:
from aphrodite.attention.layer import Attention # Avoid circular import
if isinstance(layer, LinearBase):
return Fp8LinearMethod(self)
if isinstance(layer, Attention):
return Fp8KVCacheMethod(self)
return None
def get_scaled_act_names(self) -> List[str]:
return []
class Fp8LinearMethod(LinearMethodBase):
"""Linear method for FP8.
Supports loading FP8 checkpoints with static weight scale and
dynamic/static activation scale.
Also supports loading quantized FP16/BF16 model checkpoints with dynamic
activation scaling. 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
self.cutlass_fp8_supported = cutlass_fp8_supported()
def _create_scale_param(
self,
scale_name: str,
layer: torch.nn.Module,
output_partition_sizes: List[int],
**extra_weight_attrs,
) -> None:
scale = Parameter(torch.empty(len(output_partition_sizes),
dtype=torch.float32),
requires_grad=False)
layer.register_parameter(scale_name, scale)
set_weight_attrs(
scale, {
**extra_weight_attrs,
"fp8_scales_shard_indexer":
self.scales_shard_indexer,
})
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,
):
del input_size, output_size
output_size_per_partition = sum(output_partition_sizes)
layer.process_after_load = True
layer.logical_widths = output_partition_sizes
# WEIGHT
weight_dtype = (torch.float8_e4m3fn
if self.quant_config.is_checkpoint_fp8_serialized else
params_dtype)
weight = Parameter(torch.empty(output_size_per_partition,
input_size_per_partition,
dtype=weight_dtype),
requires_grad=False)
layer.register_parameter("weight", weight)
set_weight_attrs(weight, {
**extra_weight_attrs,
"input_dim": 1,
"output_dim": 0,
})
# If checkpoint is serialized fp8, load them.
# Otherwise, wait until process_weights_after_loading.
if self.quant_config.is_checkpoint_fp8_serialized:
# WEIGHT SCALE
self._create_scale_param(
scale_name="weight_scale",
layer=layer,
output_partition_sizes=output_partition_sizes,
**extra_weight_attrs)
# INPUT ACTIVATION SCALE
if self.quant_config.activation_scheme == "static":
self._create_scale_param(
scale_name="input_scale",
layer=layer,
output_partition_sizes=output_partition_sizes,
**extra_weight_attrs)
def scales_shard_indexer(
self, param: torch.Tensor, loaded_weight: torch.Tensor,
shard_id: Union[str, int]) -> Tuple[torch.Tensor, torch.Tensor]:
qkv_idxs = {"q": 0, "k": 1, "v": 2}
if isinstance(shard_id, int):
pass
elif isinstance(shard_id, str):
if shard_id not in qkv_idxs:
raise ValueError(f"Unknown shard_id: {shard_id}")
shard_id = qkv_idxs[shard_id]
else:
ValueError(f"Shard id must be int or str but got {type(shard_id)}")
return param[shard_id], loaded_weight
def process_weights_after_loading(self, layer: Module) -> None:
if (not hasattr(layer, "process_after_load")
or not layer.process_after_load):
return
# If checkpoint is fp/bf16 (not serialized fp8), quantize the weights.
if not self.quant_config.is_checkpoint_fp8_serialized:
qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
scale=None)
layer.weight = Parameter(qweight.t(), requires_grad=False)
layer.weight_scale = Parameter(weight_scale, requires_grad=False)
layer.logical_widths = None
layer.input_scale = None
return
# If checkpoint is fp8, requantize the separately quantized logical
# weights into a single fp8 weight with a single weight scale.
else:
# WEIGHT_SCALE / WEIGHT
# Loop over logical weights, requantizing with single scale.
max_w_scale = layer.weight_scale.max()
start = 0
for idx, logical_width in enumerate(layer.logical_widths):
end = start + logical_width
weight_dq = per_tensor_dequantize(layer.weight[start:end, :],
layer.weight_scale[idx])
layer.weight[start:end, :] = per_tensor_quantize(
weight_dq, layer.weight_scale.max())
start = end
layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
# WEIGHT
# Transpose weight for passing to torch._scaled_mm
weight = layer.weight
layer.weight = Parameter(weight.t(), requires_grad=False)
# INPUT ACTIVATION SCALE
# Dynamic: set to None (required input to ops.scaled_fp8_quant).
# Static: set to max of the input_scales (since they are equal).
if self.quant_config.activation_scheme == "dynamic":
layer.input_scale = None
elif self.quant_config.activation_scheme == "static":
if not all_close_1d(layer.input_scale):
raise ValueError(
"All the input_scales for the logical weights of a "
f"layer must be equal. But got {layer.input_scale}")
layer.input_scale = Parameter(layer.input_scale.max(),
requires_grad=False)
else:
raise ValueError(
f"Unknown scheme {self.quant_config.activation_scheme}")
def apply(self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
# ops.scaled_fp8_quant supports both dynamic and static quant.
# If dynamic, layer.input_scale is None and x_scale computed from x.
# If static, layer.input_scale is scalar and x_scale is input_scale.
if bias is None and self.cutlass_fp8_supported:
qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale)
# Fused GEMM_DQ
output = ops.cutlass_scaled_mm(
qinput,
layer.weight,
out_dtype=x.dtype,
scale_a=x_scale,
scale_b=layer.weight_scale,
)
else:
qinput, x_scale = ops.scaled_fp8_quant(x,
layer.input_scale,
batch_dim_padding=17)
# Fused GEMM_DQ -- note we padded the input above because
# torch._scaled_mm is more performant for matrices with
# batch dimension > 16. Note that this could change
# in the future.
output, _ = torch._scaled_mm(
qinput,
layer.weight,
out_dtype=x.dtype,
scale_a=x_scale,
scale_b=layer.weight_scale,
bias=bias,
)
return torch.narrow(output, 0, 0, x.shape[0])
class Fp8KVCacheMethod(QuantizeMethodBase):
"""Supports loading kv-cache scaling factors from FP8 checkpoints.
"""
def __init__(self, quant_config: Fp8Config):
self.quant_config = quant_config
def create_weights(self, layer: torch.nn.Module):
"""Create "weight" (aka kv_scale) for an attention layer.
Args:
layer: The layer that is using the QuantizeMethodBase factory.
"""
# Initialize the KV cache scale to 1.0 as the default value.
# If the kv_scale appears in the checkpoint, it will be
# overwritten when loading weights.
layer.kv_scale = Parameter(torch.tensor(1.0), requires_grad=False)
def apply(self, layer: torch.nn.Module) -> torch.Tensor:
raise RuntimeError("Fp8KVCacheMethod.apply should not be called.")
def process_weights_after_loading(self, layer: Module) -> None:
# If the kv-cache dtype is auto, we enforce the kv-scale to be 1.0
# regardless whether the kv-scale is available in the checkpoint.
if layer.kv_cache_dtype != "auto":
kv_scale = layer.kv_scale.to("cpu").tolist()
if not isinstance(kv_scale, float):
raise ValueError("Only support per-tensor scaling factor "
"for fp8 KV cache")
layer._kv_scale = kv_scale
if layer._kv_scale == 1.0 and "e5m2" not in layer.kv_cache_dtype:
print_warning_once(
"Using KV cache scaling factor 1.0 for fp8_e4m3. This may "
"cause accuracy issues. Please make sure kv-cache scaling "
"factor is available in the fp8 checkpoint.")
del layer.kv_scale
def all_close_1d(x: torch.Tensor) -> bool:
assert len(x.shape) == 1
return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))
def per_tensor_quantize(tensor: torch.Tensor,
inv_scale: Union[float, torch.Tensor]) -> torch.Tensor:
finfo = torch.finfo(torch.float8_e4m3fn)
qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max)
return qweight.to(torch.float8_e4m3fn)
def per_tensor_dequantize(
tensor: torch.Tensor, inv_scale: Union[float,
torch.Tensor]) -> torch.Tensor:
fake_qweight = tensor.to(torch.float16)
dq_weight = fake_qweight * inv_scale
return dq_weight