228 lines
7.5 KiB
Python
228 lines
7.5 KiB
Python
from typing import Any, Dict, List, Optional
|
|
|
|
import torch
|
|
from torch.nn.parameter import Parameter
|
|
|
|
from vllm import _custom_ops as ops
|
|
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
|
|
from vllm.model_executor.layers.quantization.base_config import (
|
|
QuantizationConfig)
|
|
from vllm.model_executor.utils import set_weight_attrs
|
|
|
|
|
|
class MarlinConfig(QuantizationConfig):
|
|
"""Config class for Marlin.
|
|
|
|
Reference: https://github.com/IST-DASLab/marlin/tree/master
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
group_size: int,
|
|
) -> None:
|
|
# Group size for the quantization.
|
|
self.group_size = group_size
|
|
if self.group_size != 128 and self.group_size != -1:
|
|
raise ValueError(
|
|
"Currently, only group size 128 and -1 (channelwise) "
|
|
"is supported for Marlin, but got group_size of "
|
|
f"{self.group_size}")
|
|
|
|
# 4 Bits packed into 32 bit datatype.
|
|
self.pack_factor = 32 // 4
|
|
|
|
# Tile size used by marlin kernels.
|
|
self.tile_size = 16
|
|
|
|
# Min out_features dim
|
|
self.min_n_threads = 64
|
|
|
|
# Min in_features dim
|
|
self.min_k_threads = 128
|
|
|
|
# Max parallel problems to solve at once (improves large
|
|
# batch performance)
|
|
self.max_parallel = 16
|
|
|
|
# Permutation length used by the marlin kernels.
|
|
self.perm_len = 1024
|
|
|
|
def __repr__(self) -> str:
|
|
return f"MarlinConfig(group_size={self.group_size})"
|
|
|
|
@classmethod
|
|
def get_name(cls) -> str:
|
|
return "marlin"
|
|
|
|
@classmethod
|
|
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
|
|
return [torch.half]
|
|
|
|
@classmethod
|
|
# Need to figure it out
|
|
def get_min_capability(cls) -> int:
|
|
return 80
|
|
|
|
@classmethod
|
|
def get_config_filenames(cls) -> List[str]:
|
|
return ["quantize_config.json"]
|
|
|
|
@classmethod
|
|
def from_config(cls, config: Dict[str, Any]) -> "MarlinConfig":
|
|
group_size = cls.get_from_keys(config, ["group_size"])
|
|
return cls(group_size)
|
|
|
|
def get_quant_method(
|
|
self, layer: torch.nn.Module) -> Optional["MarlinLinearMethod"]:
|
|
if isinstance(layer, LinearBase):
|
|
return MarlinLinearMethod(self)
|
|
return None
|
|
|
|
def get_scaled_act_names(self) -> List[str]:
|
|
return []
|
|
|
|
|
|
class MarlinLinearMethod(LinearMethodBase):
|
|
"""Linear method for Marlin.
|
|
|
|
Args:
|
|
quant_config: The Marlin quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: MarlinConfig):
|
|
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,
|
|
):
|
|
del output_size # Unused.
|
|
|
|
if params_dtype != torch.float16:
|
|
raise ValueError(
|
|
f"The params dtype must be float16, but got {params_dtype}")
|
|
|
|
# Validate output_size_per_partition
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
if output_size_per_partition % self.quant_config.min_n_threads != 0:
|
|
raise ValueError(
|
|
f"Weight output_size_per_partition = "
|
|
f"{output_size_per_partition} is not divisible by "
|
|
f"min_n_threads = {self.quant_config.min_n_threads}.")
|
|
if output_size_per_partition % self.quant_config.pack_factor != 0:
|
|
raise ValueError(
|
|
f"Weight output_size_per_partition = "
|
|
f"{output_size_per_partition} is not divisible by "
|
|
f"pack_factor = {self.quant_config.pack_factor}.")
|
|
|
|
# Validate input_size_per_partition
|
|
if input_size_per_partition % self.quant_config.min_k_threads != 0:
|
|
raise ValueError(
|
|
f"Weight input_size_per_partition = "
|
|
f"{input_size_per_partition} is not divisible by "
|
|
f"min_k_threads = {self.quant_config.min_k_threads}.")
|
|
if (self.quant_config.group_size != -1 and
|
|
input_size_per_partition % self.quant_config.group_size != 0):
|
|
raise ValueError(f"Weight input_size_per_partition = "
|
|
f"{input_size_per_partition} is not divisible by "
|
|
f"group_size = {self.quant_config.group_size}.")
|
|
|
|
# Check that we have at least 4 tiles horizontally in the shard
|
|
num_tiles_per_perm = self.quant_config.perm_len // (
|
|
self.quant_config.tile_size**2)
|
|
if output_size_per_partition % num_tiles_per_perm != 0:
|
|
raise ValueError(
|
|
"Each permutation group must reside on the same gpu")
|
|
|
|
# Quantized 4Bit weights packed into Int32.
|
|
qweight = Parameter(
|
|
torch.empty(
|
|
input_size_per_partition // self.quant_config.tile_size,
|
|
output_size_per_partition * self.quant_config.tile_size //
|
|
self.quant_config.pack_factor,
|
|
device="cuda",
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
set_weight_attrs(
|
|
qweight,
|
|
{
|
|
"input_dim": 0,
|
|
"output_dim": 1,
|
|
"packed_dim": 1,
|
|
"pack_factor": self.quant_config.pack_factor,
|
|
"marlin_tile_size": self.quant_config.tile_size,
|
|
},
|
|
)
|
|
|
|
# Determine if channelwise or not
|
|
input_groups = (1 if self.quant_config.group_size == -1 else
|
|
input_size_per_partition //
|
|
self.quant_config.group_size)
|
|
|
|
scales = Parameter(
|
|
torch.empty(
|
|
input_groups,
|
|
output_size_per_partition,
|
|
device="cuda",
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
set_weight_attrs(
|
|
scales,
|
|
{
|
|
"input_dim": None if input_groups == 1 else 0,
|
|
"output_dim": 1,
|
|
},
|
|
)
|
|
|
|
# Allocate workspace (Used for internal locking mechanism)
|
|
max_workspace_size = (
|
|
output_size_per_partition //
|
|
self.quant_config.min_n_threads) * self.quant_config.max_parallel
|
|
workspace = Parameter(torch.zeros(max_workspace_size,
|
|
device="cuda",
|
|
dtype=torch.int),
|
|
requires_grad=False)
|
|
|
|
layer.register_parameter("B", qweight)
|
|
set_weight_attrs(qweight, extra_weight_attrs)
|
|
layer.register_parameter("s", scales)
|
|
set_weight_attrs(scales, extra_weight_attrs)
|
|
layer.register_parameter("workspace", workspace)
|
|
set_weight_attrs(workspace, extra_weight_attrs)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
qweight = layer.B
|
|
scales = layer.s
|
|
workspace = layer.workspace
|
|
|
|
x_2d = x.view(-1, x.shape[-1])
|
|
|
|
size_m = x_2d.shape[0]
|
|
size_k = x_2d.shape[1]
|
|
size_n = scales.shape[1]
|
|
|
|
output_2d = ops.marlin_gemm(x_2d, qweight, scales, workspace, size_m,
|
|
size_n, size_k)
|
|
|
|
output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
|
|
|
|
if bias is not None:
|
|
output.add_(bias) # In-place add
|
|
|
|
return output
|