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[Kernel/Quant] Remove the original marlin format and qqq (#23204)

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Signed-off-by: mgoin <mgoin64@gmail.com>
This commit is contained in:
Michael Goin
2025-08-20 15:13:36 -04:00
committed by GitHub
parent ebe56a0064
commit 0cdbf5e61c
26 changed files with 92 additions and 3698 deletions
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6
vllm/model_executor/layers/quantization/__init__.py
View File

@@ -15,7 +15,6 @@ QuantizationMethods = Literal[
"fbgemm_fp8",
"modelopt",
"modelopt_fp4",
"marlin",
"bitblas",
"gguf",
"gptq_marlin_24",
@@ -25,7 +24,6 @@ QuantizationMethods = Literal[
"gptq",
"compressed-tensors",
"bitsandbytes",
"qqq",
"hqq",
"experts_int8",
"neuron_quant",
@@ -106,13 +104,11 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
from .hqq_marlin import HQQMarlinConfig
from .inc import INCConfig
from .ipex_quant import IPEXConfig
from .marlin import MarlinConfig
from .modelopt import ModelOptFp8Config, ModelOptNvFp4Config
from .moe_wna16 import MoeWNA16Config
from .mxfp4 import Mxfp4Config
from .neuron_quant import NeuronQuantConfig
from .ptpc_fp8 import PTPCFp8Config
from .qqq import QQQConfig
from .rtn import RTNConfig
from .torchao import TorchAOConfig
from .tpu_int8 import Int8TpuConfig
@@ -125,7 +121,6 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
"fbgemm_fp8": FBGEMMFp8Config,
"modelopt": ModelOptFp8Config,
"modelopt_fp4": ModelOptNvFp4Config,
"marlin": MarlinConfig,
"bitblas": BitBLASConfig,
"gguf": GGUFConfig,
"gptq_marlin_24": GPTQMarlin24Config,
@@ -136,7 +131,6 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
"compressed-tensors": CompressedTensorsConfig,
"bitsandbytes": BitsAndBytesConfig,
"ptpc_fp8": PTPCFp8Config,
"qqq": QQQConfig,
"hqq": HQQMarlinConfig,
"experts_int8": ExpertsInt8Config,
"neuron_quant": NeuronQuantConfig,

263
vllm/model_executor/layers/quantization/marlin.py
View File

@@ -1,263 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any, Optional
import torch
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.parameter import (BasevLLMParameter,
ChannelQuantScaleParameter,
GroupQuantScaleParameter,
PackedvLLMParameter)
logger = init_logger(__name__)
class MarlinConfig(QuantizationConfig):
"""Config class for Marlin.
Reference: https://github.com/IST-DASLab/marlin/tree/master
"""
def __init__(
self,
group_size: int,
lm_head_quantized: bool,
) -> None:
super().__init__()
# Group size for the quantization.
self.group_size = group_size
self.lm_head_quantized = lm_head_quantized
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}, "
f"lm_head_quantized={self.lm_head_quantized})")
@classmethod
def get_name(cls) -> QuantizationMethods:
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"])
lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
default=False)
return cls(group_size, lm_head_quantized)
@classmethod
def override_quantization_method(
cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
# compat: autogptq >=0.8.0 use checkpoint_format: str
# compat: autogptq <=0.7.1 is_marlin_format: bool
is_marlin_format = (hf_quant_cfg.get("checkpoint_format") == "marlin"
or hf_quant_cfg.get("is_marlin_format", False))
is_valid_user_quant = (user_quant is None or user_quant == "gptq"
or user_quant == "marlin")
if is_marlin_format and is_valid_user_quant:
msg = ("The model is serialized in {} format. Using {} kernel.".
format(cls.get_name(), cls.get_name()))
logger.info(msg)
return cls.get_name()
return None
def get_quant_method(self, layer: torch.nn.Module,
prefix: str) -> Optional["MarlinLinearMethod"]:
if (isinstance(layer, LinearBase) or
(isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
return MarlinLinearMethod(self)
return None
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.
weight_loader = extra_weight_attrs["weight_loader"]
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 = PackedvLLMParameter(
data=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,
),
input_dim=0,
output_dim=1,
packed_dim=1,
packed_factor=self.quant_config.pack_factor,
marlin_tile_size=self.quant_config.tile_size,
weight_loader=weight_loader)
# 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)
weight_scale_args = {
"data":
torch.empty(
input_groups,
output_size_per_partition,
device="cuda",
dtype=params_dtype,
),
"weight_loader":
weight_loader
}
if input_groups == 1:
scales = ChannelQuantScaleParameter(output_dim=1,
**weight_scale_args)
else:
scales = GroupQuantScaleParameter(output_dim=1,
input_dim=0,
**weight_scale_args)
# 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 = BasevLLMParameter(data=torch.zeros(max_workspace_size,
device="cuda",
dtype=torch.int),
weight_loader=weight_loader)
layer.register_parameter("B", qweight)
layer.register_parameter("s", scales)
layer.register_parameter("workspace", workspace)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
# required by torch.compile
layer.B = Parameter(layer.B.data, requires_grad=False)
layer.s = Parameter(layer.s.data, requires_grad=False)
layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
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

275
vllm/model_executor/layers/quantization/qqq.py
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@@ -1,275 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any, Optional
import torch
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.parameter import (BasevLLMParameter,
ChannelQuantScaleParameter,
GroupQuantScaleParameter,
PackedvLLMParameter)
logger = init_logger(__name__)
MARLIN_QQQ_TILE = 16
MARLIN_QQQ_MIN_THREAD_N = 64
MARLIN_QQQ_MIN_THREAD_K = 128
MARLIN_QQQ_MAX_PARALLEL = 16
MARLIN_QQQ_SUPPORTED_NUM_BITS = [4]
MARLIN_QQQ_SUPPORTED_GROUP_SIZES = [-1, 128]
MARLIN_QQQ_SUPPORTED_SYM = [True]
class QQQConfig(QuantizationConfig):
"""Config class for QQQ
Reference: https://arxiv.org/pdf/2406.09904
"""
def __init__(
self,
weight_bits: int,
group_size: int,
is_sym: bool = True,
) -> None:
super().__init__()
self.weight_bits = weight_bits
self.group_size = group_size
self.is_sym = is_sym
# Verify
if self.weight_bits not in MARLIN_QQQ_SUPPORTED_NUM_BITS:
raise ValueError(
f"QQQ does not support weight_bits = {self.weight_bits}. "
f"Only weight_bits = {MARLIN_QQQ_SUPPORTED_NUM_BITS} "
"are supported.")
if self.group_size not in MARLIN_QQQ_SUPPORTED_GROUP_SIZES:
raise ValueError(
f"QQQ does not support group_size = {self.group_size}. "
f"Only group_sizes = {MARLIN_QQQ_SUPPORTED_GROUP_SIZES} "
"are supported.")
if self.is_sym not in MARLIN_QQQ_SUPPORTED_SYM:
raise ValueError(
f"QQQ does not support is_sym = {self.is_sym}. "
f"Only sym = {MARLIN_QQQ_SUPPORTED_SYM} are supported.")
# 4 Bits packed into 32 bit datatype.
self.pack_factor = 32 // self.weight_bits
# Tile size used by QQQ kernels.
self.tile_size = MARLIN_QQQ_TILE
# Min out_features dim
self.min_n_threads = MARLIN_QQQ_MIN_THREAD_N
# Min in_features dim
self.min_k_threads = MARLIN_QQQ_MIN_THREAD_K
# Max parallel problems to solve at once (improves large
# batch performance)
self.max_parallel = MARLIN_QQQ_MAX_PARALLEL
# Permutation length used by the QQQ kernels.
self.perm_len = 1024
def __repr__(self) -> str:
return "QQQConfig(weight_bits={}, group_size={})".format(
self.weight_bits, self.group_size)
@classmethod
def get_name(cls) -> QuantizationMethods:
return "qqq"
@classmethod
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
return [torch.half]
@classmethod
def get_min_capability(cls) -> int:
return 80
@classmethod
def get_config_filenames(cls) -> list[str]:
"""List of filenames to search for in the model directory."""
return [
"quant_config.json",
"quantize_config.json",
]
@classmethod
def from_config(cls, config: dict[str, Any]) -> "QQQConfig":
weight_bits = cls.get_from_keys(config, ["wbits"])
group_size = cls.get_from_keys(config, ["group_size"])
return cls(weight_bits, group_size)
def get_quant_method(self, layer: torch.nn.Module,
prefix: str) -> Optional["QQQLinearMethod"]:
if isinstance(layer, LinearBase):
return QQQLinearMethod(self)
return None
class QQQLinearMethod(LinearMethodBase):
"""Linear method for QQQ.
Args:
quant_config: The QQQ quantization config.
"""
def __init__(self, quant_config: QQQConfig):
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,
):
weight_loader = extra_weight_attrs["weight_loader"]
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 = PackedvLLMParameter(
data=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,
),
input_dim=0,
output_dim=1,
packed_dim=1,
packed_factor=self.quant_config.pack_factor,
marlin_tile_size=self.quant_config.tile_size,
weight_loader=weight_loader)
s_channel = ChannelQuantScaleParameter(data=torch.empty(
1,
output_size_per_partition,
device="cuda",
dtype=torch.float,
),
weight_loader=weight_loader,
output_dim=1)
if self.quant_config.group_size == -1:
s_group_data = torch.tensor(
[],
device="cuda",
dtype=torch.half,
)
else:
s_group_data = torch.empty(
input_size_per_partition // self.quant_config.group_size,
output_size_per_partition,
device="cuda",
dtype=torch.half,
)
s_group_attr = {"data": s_group_data, "weight_loader": weight_loader}
if self.quant_config.group_size == -1:
s_group = BasevLLMParameter(**s_group_attr)
else:
s_group = GroupQuantScaleParameter(output_dim=1,
input_dim=0,
**s_group_attr)
# 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 = BasevLLMParameter(data=torch.zeros(max_workspace_size,
device="cuda",
dtype=torch.int),
weight_loader=weight_loader)
layer.register_parameter("B", qweight)
layer.register_parameter("s_channel", s_channel)
layer.register_parameter("s_group", s_group)
layer.register_parameter("workspace", workspace)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
# required by torch.compile
layer.B = Parameter(layer.B.data, requires_grad=False)
layer.s_channel = Parameter(layer.s_channel.data, requires_grad=False)
layer.s_group = Parameter(layer.s_group.data, requires_grad=False)
layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
qweight = layer.B
s_ch = layer.s_channel
s_group = layer.s_group
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 = s_ch.shape[1]
x_int8, s_tok, _ = ops.scaled_int8_quant(x_2d)
output_2d = ops.marlin_qqq_gemm(x_int8, qweight, s_tok, s_ch, s_group,
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

126
vllm/model_executor/layers/quantization/utils/marlin_utils_test_qqq.py
View File

@@ -1,126 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import numpy
import torch
from .marlin_utils_test import marlin_permute_weights
from .quant_utils import get_pack_factor, qqq_quantize_weights
def marlin_qqq_weights(q_w, size_k, size_n, num_bits, perm, group_size):
# Permute
q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
# Pack
pack_factor = get_pack_factor(num_bits)
orig_device = q_w.device
q_w = q_w.cpu().numpy().astype(numpy.uint32)
q_packed = numpy.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
dtype=numpy.uint32)
if group_size == size_k:
for i in range(pack_factor):
q_packed |= (q_w[:, i::pack_factor] & 0xF) << num_bits * i
else:
for i in range(pack_factor):
q_packed |= q_w[:, i::pack_factor] << num_bits * i
q_packed = torch.from_numpy(q_packed.astype(numpy.int32)).to(orig_device)
return q_packed
def get_qqq_scale_perms():
scale_perm: list[int] = []
for i in range(8):
scale_perm.extend([i + 8 * j for j in range(8)])
scale_perm_single: list[int] = []
for i in range(4):
scale_perm_single.extend(
[2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
return scale_perm, scale_perm_single
# NOTE(HandH1998): QQQ employs different perms for per-group and per-channel weight quantization. # noqa: E501
def get_qqq_weight_perm(num_bits: int, quant_type: str):
perm_list: list[int] = []
for i in range(32):
perm1: list[int] = []
col = i // 4
for block in [0, 1]:
for row in [
4 * (i % 4),
4 * (i % 4) + 1,
4 * (i % 4) + 2,
4 * (i % 4) + 3,
]:
perm1.append(16 * row + col + 8 * block)
for j in range(4):
perm_list.extend([p + 256 * j for p in perm1])
perm = numpy.array(perm_list)
assert quant_type in ["per-channel",
"per-group"], "not supported quantization type"
if num_bits == 4:
if quant_type == "per-channel":
interleave = numpy.array([4, 0, 5, 1, 6, 2, 7, 3])
else:
interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
else:
raise Exception("num_bits must be 4, got {}".format(num_bits))
perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
perm = torch.from_numpy(perm)
return perm
def marlin_qqq_permute_scales(s_group, s_channel, size_k, size_n, group_size):
scale_perm, scale_perm_single = get_qqq_scale_perms()
if group_size < size_k and group_size != -1:
s_group = s_group.reshape((-1, len(scale_perm)))[:, scale_perm]
s_channel = s_channel.reshape(
(-1, len(scale_perm_single)))[:, scale_perm_single]
s_group = s_group.reshape((-1, size_n)).contiguous()
else:
s_channel = s_channel.reshape(
(-1, len(scale_perm_single)))[:, scale_perm_single]
s_channel = s_channel.reshape((-1, size_n)).contiguous()
return s_group, s_channel
def marlin_qqq_quantize(
w: torch.Tensor,
num_bits: int,
group_size: int,
):
size_k, size_n = w.shape
# Normalize group_size
if group_size == -1:
group_size = size_k
assert group_size <= size_k
quant_type = "per-channel" if group_size == size_k else "per-group"
# Quantize
w_ref, q_w, s_group, s_channel = qqq_quantize_weights(
w, num_bits, group_size)
# Reformat to marlin_qqq
weight_perm = get_qqq_weight_perm(num_bits, quant_type)
marlin_qqq_q_w = marlin_qqq_weights(q_w, size_k, size_n, num_bits,
weight_perm, group_size)
marlin_qqq_s_group, marlin_qqq_s_channel = marlin_qqq_permute_scales(
s_group, s_channel, size_k, size_n, group_size)
# Create result
res_list = [
w_ref, marlin_qqq_q_w, marlin_qqq_s_group, marlin_qqq_s_channel
]
for i in range(len(res_list)):
res_list[i] = res_list[i].to(w.device)
return res_list

85
vllm/model_executor/layers/quantization/utils/quant_utils.py
View File

@@ -9,8 +9,6 @@ import numpy
import torch
from vllm._custom_ops import cutlass_scaled_mm_supports_fp4
from vllm.model_executor.layers.quantization.qqq import (
MARLIN_QQQ_SUPPORTED_NUM_BITS)
from vllm.platforms import current_platform
from vllm.scalar_type import ScalarType, scalar_types
@@ -386,89 +384,6 @@ def gptq_quantize_weights(w: torch.Tensor,
return w_ref, w_q, w_s, g_idx, rand_perm
# QQQ employs different quant schemes for per-group and
# per-channel quantization.
def qqq_quantize_weights(w: torch.Tensor, num_bits: int, group_size: int):
orig_device = w.device
size_k, size_n = w.shape
assert w.is_floating_point(), "w must be float"
assert num_bits in MARLIN_QQQ_SUPPORTED_NUM_BITS, \
f"Unsupported num_bits = {num_bits}"
assert group_size in SUPPORTED_GROUP_SIZES + [
size_k
], f"Unsupported groupsize = {group_size}"
if group_size == -1:
group_size = size_k
assert group_size <= size_k
if group_size < size_k:
# Reshape to [groupsize, -1]
w = w.reshape((-1, group_size, size_n))
w = w.permute(1, 0, 2)
w = w.reshape((group_size, -1))
max_q_val = 2**num_bits - 1
half_q_val = (max_q_val + 1) // 2
# Compute scale for each group
s_group = torch.max(torch.abs(w), 0, keepdim=True)[0]
s_group *= 2 / max_q_val # 2 => symmetric
# Quantize
q_w = torch.round(w / s_group).int()
q_w += half_q_val
q_w = torch.clamp(q_w, 0, max_q_val)
# Compute ref (dequantized)
w_ref = (q_w - half_q_val).half() * s_group
# Restore original shapes
def reshape_w(w):
w = w.reshape((group_size, -1, size_n))
w = w.permute(1, 0, 2)
w = w.reshape((size_k, size_n)).contiguous()
return w
q_w = reshape_w(q_w)
w_ref = reshape_w(w_ref)
# Compute int8 quantization scale for each channel
s_channel = torch.max(torch.abs(w_ref), 0, keepdim=True)[0]
s_channel /= 127.0
t_int8 = (w_ref / s_channel).round().clamp(-128, 127).to(torch.int8)
w_ref = t_int8.half() * s_channel
s_channel = s_channel.reshape(1, -1).to(dtype=torch.float)
# Fuse scales
s_group = (s_group.reshape(-1, size_n).contiguous() /
s_channel).to(dtype=torch.half)
else:
max_q_val = 2**(num_bits - 1) - 1
# Compute scale for each channel
s_channel = torch.max(torch.abs(w), 0, keepdim=True)[0]
s_channel /= max_q_val
# Quantize
q_w = torch.round(w / s_channel).int()
q_w = torch.clamp(q_w, -max_q_val, max_q_val)
# Compute ref (dequantized)
w_ref = q_w.half() * s_channel
s_group = torch.tensor([], dtype=torch.half)
# div 2 ** (8 - self.bits)) to offset right shift in unpacking
s_channel /= (2**(8 - num_bits))
s_channel = s_channel.reshape(-1, size_n).contiguous().to(torch.float)
return (
w_ref.to(device=orig_device),
q_w.to(device=orig_device),
s_group.to(device=orig_device),
s_channel.to(device=orig_device),
)
def sort_weights(q_w: torch.Tensor, g_idx: torch.Tensor):
orig_device = q_w.device