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Unverified Commit 5ad04465 authored by Robert Shaw's avatar Robert Shaw Committed by GitHub
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Revert "Consolidate AWQ quantization into single awq_marlin.py file" (#37768)

parent 8cc700dd
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vllm/model_executor/layers/quantization/__init__.py
View file @ 5ad04465
...@@ -107,7 +107,8 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]: ...@@ -107,7 +107,8 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
# lazy import to avoid triggering `torch.compile` too early # lazy import to avoid triggering `torch.compile` too early
from vllm.model_executor.layers.quantization.quark.quark import QuarkConfig from vllm.model_executor.layers.quantization.quark.quark import QuarkConfig
from .awq_marlin import AWQConfig, AWQMarlinConfig from .awq import AWQConfig
from .awq_marlin import AWQMarlinConfig
from .bitsandbytes import BitsAndBytesConfig from .bitsandbytes import BitsAndBytesConfig
from .compressed_tensors.compressed_tensors import ( from .compressed_tensors.compressed_tensors import (
CompressedTensorsConfig, CompressedTensorsConfig,
......
vllm/model_executor/layers/quantization/awq.py
View file @ 5ad04465
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Backward compatibility: AWQConfig and AWQLinearMethod have been consolidated
# into awq_marlin.py from typing import TYPE_CHECKING, Any, Union
from vllm.model_executor.layers.quantization.awq_marlin import ( # noqa: F401
AWQConfig, import torch
AWQLinearMethod, from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe.layer import FusedMoE
from vllm.model_executor.layers.linear import (
LinearBase,
LinearMethodBase,
UnquantizedLinearMethod,
) )
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig,
QuantizeMethodBase,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import is_layer_skipped
from vllm.model_executor.parameter import GroupQuantScaleParameter, PackedvLLMParameter
from vllm.transformers_utils.config import get_safetensors_params_metadata
if TYPE_CHECKING:
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.models.utils import WeightsMapper
logger = init_logger(__name__)
class AWQConfig(QuantizationConfig):
"""Config class for AWQ.
Reference: https://arxiv.org/abs/2306.00978
"""
def __init__(
self,
weight_bits: int,
group_size: int,
zero_point: bool,
modules_to_not_convert: list[str] | None = None,
) -> None:
super().__init__()
self.weight_bits = weight_bits
self.group_size = group_size
self.zero_point = zero_point
self.modules_to_not_convert = modules_to_not_convert or []
if self.weight_bits != 4:
raise ValueError(
"Currently, only 4-bit weight quantization is supported for "
f"AWQ, but got {self.weight_bits} bits."
)
self.pack_factor = 32 // self.weight_bits
def __repr__(self) -> str:
return (
f"AWQConfig(weight_bits={self.weight_bits}, "
f"group_size={self.group_size}, "
f"zero_point={self.zero_point}, "
f"modules_to_not_convert={self.modules_to_not_convert})"
)
def get_name(self) -> "QuantizationMethods":
return "awq"
def get_supported_act_dtypes(self) -> list[torch.dtype]:
return [torch.half]
@classmethod
def get_min_capability(cls) -> int:
# The AWQ kernel only supports Turing or newer GPUs.
return 75
@staticmethod
def get_config_filenames() -> list[str]:
return [
"quant_config.json", # E.g., casperhansen/vicuna-7b-v1.5-awq
# E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
"quantize_config.json",
]
@classmethod
def from_config(cls, config: dict[str, Any]) -> "AWQConfig":
weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
zero_point = cls.get_from_keys(config, ["zero_point"])
modules_to_not_convert = cls.get_from_keys_or(
config, ["modules_to_not_convert"], None
)
return cls(weight_bits, group_size, zero_point, modules_to_not_convert)
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Union["LinearMethodBase", "QuantizeMethodBase"] | None:
if isinstance(layer, LinearBase):
if is_layer_skipped(
prefix,
self.modules_to_not_convert,
self.packed_modules_mapping,
skip_with_substr=True,
):
return UnquantizedLinearMethod()
return AWQLinearMethod(self)
elif isinstance(layer, FusedMoE):
# Lazy import to avoid circular import.
from .awq_marlin import AWQMarlinConfig
from .moe_wna16 import MoeWNA16Config
from .utils.marlin_utils import check_moe_marlin_supports_layer
if not check_moe_marlin_supports_layer(layer, self.group_size):
logger.warning_once(
f"Layer '{prefix}' is not supported by AWQMoeMarlin. "
"Falling back to Moe WNA16 kernels."
)
config = {
"quant_method": "awq",
"bits": self.weight_bits,
"group_size": self.group_size,
"zero_point": self.zero_point,
"lm_head": False,
"modules_to_not_convert": self.modules_to_not_convert,
}
return MoeWNA16Config.from_config(config).get_quant_method(
layer, prefix
)
marlin_compatible_config_dict = {
"quant_method": "awq",
"bits": self.weight_bits,
"group_size": self.group_size,
"zero_point": self.zero_point,
"lm_head": False,
"modules_to_not_convert": self.modules_to_not_convert,
}
awq_marlin_config = AWQMarlinConfig.from_config(
marlin_compatible_config_dict
)
return awq_marlin_config.get_quant_method(layer, prefix)
return None
def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
if self.modules_to_not_convert:
self.modules_to_not_convert = hf_to_vllm_mapper.apply_list(
self.modules_to_not_convert
)
def maybe_update_config(self, model_name: str, revision: str | None = None):
if self.modules_to_not_convert:
return
unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
metadata = get_safetensors_params_metadata(model_name, revision=revision)
layers = {param_name.rsplit(".", 1)[0] for param_name in metadata}
quant_layers: set[str] = {
param_name.rsplit(".", 1)[0]
for param_name, info in metadata.items()
if (dtype := info.get("dtype", None))
and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
}
self.modules_to_not_convert = list(layers - quant_layers)
class AWQLinearMethod(LinearMethodBase):
"""Linear method for AWQ.
Args:
quant_config: The AWQ quantization config.
"""
def __init__(self, quant_config: AWQConfig):
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,
):
# Normalize group_size
if self.quant_config.group_size != -1:
group_size = self.quant_config.group_size
else:
group_size = input_size
if input_size_per_partition % group_size != 0:
raise ValueError(
"The input size is not aligned with the quantized "
"weight shape. This can be caused by too large "
"tensor parallel size."
)
output_size_per_partition = sum(output_partition_sizes)
if output_size_per_partition % self.quant_config.pack_factor != 0:
raise ValueError(
"The output size is not aligned with the quantized "
"weight shape. This can be caused by too large "
"tensor parallel size."
)
weight_loader = extra_weight_attrs.get("weight_loader")
qweight = PackedvLLMParameter(
data=torch.empty(
input_size_per_partition,
output_size_per_partition // self.quant_config.pack_factor,
dtype=torch.int32,
),
input_dim=0,
output_dim=1,
packed_dim=1,
packed_factor=self.quant_config.pack_factor,
weight_loader=weight_loader,
)
num_groups = input_size_per_partition // group_size
qzeros = PackedvLLMParameter(
data=torch.empty(
num_groups,
output_size_per_partition // self.quant_config.pack_factor,
dtype=torch.int32,
),
input_dim=0,
output_dim=1,
packed_dim=1,
packed_factor=self.quant_config.pack_factor,
weight_loader=weight_loader,
)
scales = GroupQuantScaleParameter(
data=torch.empty(
num_groups,
output_size_per_partition,
dtype=params_dtype,
),
input_dim=0,
output_dim=1,
weight_loader=weight_loader,
)
layer.register_parameter("qweight", qweight)
layer.register_parameter("qzeros", qzeros)
layer.register_parameter("scales", scales)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: torch.Tensor | None = None,
) -> torch.Tensor:
qweight = layer.qweight
scales = layer.scales
qzeros = layer.qzeros
pack_factor = self.quant_config.pack_factor
out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
reshaped_x = x.reshape(-1, x.shape[-1])
# num_tokens >= threshold
FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256
if FP16_MATMUL_HEURISTIC_CONDITION:
out = ops.awq_dequantize(qweight, scales, qzeros, 0, 0, 0)
out = torch.matmul(reshaped_x, out)
else:
out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros, pack_factor)
if bias is not None:
out.add_(bias)
return out.reshape(out_shape)
vllm/model_executor/layers/quantization/awq_marlin.py
View file @ 5ad04465
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import TYPE_CHECKING, Any, Union from typing import TYPE_CHECKING, Any
import torch import torch
from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
...@@ -27,6 +27,7 @@ from vllm.model_executor.layers.linear import ( ...@@ -27,6 +27,7 @@ from vllm.model_executor.layers.linear import (
UnquantizedLinearMethod, UnquantizedLinearMethod,
set_weight_attrs, set_weight_attrs,
) )
from vllm.model_executor.layers.quantization.awq import AWQConfig
from vllm.model_executor.layers.quantization.base_config import ( from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig, QuantizationConfig,
QuantizeMethodBase, QuantizeMethodBase,
...@@ -63,252 +64,6 @@ if TYPE_CHECKING: ...@@ -63,252 +64,6 @@ if TYPE_CHECKING:
logger = init_logger(__name__) logger = init_logger(__name__)
class AWQConfig(QuantizationConfig):
"""Config class for AWQ.
Reference: https://arxiv.org/abs/2306.00978
"""
def __init__(
self,
weight_bits: int,
group_size: int,
zero_point: bool,
modules_to_not_convert: list[str] | None = None,
) -> None:
super().__init__()
self.weight_bits = weight_bits
self.group_size = group_size
self.zero_point = zero_point
self.modules_to_not_convert = modules_to_not_convert or []
if self.weight_bits != 4:
raise ValueError(
"Currently, only 4-bit weight quantization is supported for "
f"AWQ, but got {self.weight_bits} bits."
)
self.pack_factor = 32 // self.weight_bits
def __repr__(self) -> str:
return (
f"AWQConfig(weight_bits={self.weight_bits}, "
f"group_size={self.group_size}, "
f"zero_point={self.zero_point}, "
f"modules_to_not_convert={self.modules_to_not_convert})"
)
def get_name(self) -> "QuantizationMethods":
return "awq"
def get_supported_act_dtypes(self) -> list[torch.dtype]:
return [torch.half]
@classmethod
def get_min_capability(cls) -> int:
# The AWQ kernel only supports Turing or newer GPUs.
return 75
@staticmethod
def get_config_filenames() -> list[str]:
return [
"quant_config.json", # E.g., casperhansen/vicuna-7b-v1.5-awq
# E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
"quantize_config.json",
]
@classmethod
def from_config(cls, config: dict[str, Any]) -> "AWQConfig":
weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
zero_point = cls.get_from_keys(config, ["zero_point"])
modules_to_not_convert = cls.get_from_keys_or(
config, ["modules_to_not_convert"], None
)
return cls(weight_bits, group_size, zero_point, modules_to_not_convert)
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Union["LinearMethodBase", "QuantizeMethodBase"] | None:
if isinstance(layer, LinearBase):
if is_layer_skipped(
prefix,
self.modules_to_not_convert,
self.packed_modules_mapping,
skip_with_substr=True,
):
return UnquantizedLinearMethod()
return AWQLinearMethod(self)
elif isinstance(layer, FusedMoE):
from .moe_wna16 import MoeWNA16Config
if not check_moe_marlin_supports_layer(layer, self.group_size):
logger.warning_once(
f"Layer '{prefix}' is not supported by AWQMoeMarlin. "
"Falling back to Moe WNA16 kernels."
)
config = {
"quant_method": "awq",
"bits": self.weight_bits,
"group_size": self.group_size,
"zero_point": self.zero_point,
"lm_head": False,
"modules_to_not_convert": self.modules_to_not_convert,
}
return MoeWNA16Config.from_config(config).get_quant_method(
layer, prefix
)
marlin_compatible_config_dict = {
"quant_method": "awq",
"bits": self.weight_bits,
"group_size": self.group_size,
"zero_point": self.zero_point,
"lm_head": False,
"modules_to_not_convert": self.modules_to_not_convert,
}
awq_marlin_config = AWQMarlinConfig.from_config(
marlin_compatible_config_dict
)
return awq_marlin_config.get_quant_method(layer, prefix)
return None
def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
if self.modules_to_not_convert:
self.modules_to_not_convert = hf_to_vllm_mapper.apply_list(
self.modules_to_not_convert
)
def maybe_update_config(self, model_name: str, revision: str | None = None):
if self.modules_to_not_convert:
return
unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
metadata = get_safetensors_params_metadata(model_name, revision=revision)
layers = {param_name.rsplit(".", 1)[0] for param_name in metadata}
quant_layers: set[str] = {
param_name.rsplit(".", 1)[0]
for param_name, info in metadata.items()
if (dtype := info.get("dtype", None))
and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
}
self.modules_to_not_convert = list(layers - quant_layers)
class AWQLinearMethod(LinearMethodBase):
"""Linear method for AWQ.
Args:
quant_config: The AWQ quantization config.
"""
def __init__(self, quant_config: AWQConfig):
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,
):
# Normalize group_size
if self.quant_config.group_size != -1:
group_size = self.quant_config.group_size
else:
group_size = input_size
if input_size_per_partition % group_size != 0:
raise ValueError(
"The input size is not aligned with the quantized "
"weight shape. This can be caused by too large "
"tensor parallel size."
)
output_size_per_partition = sum(output_partition_sizes)
if output_size_per_partition % self.quant_config.pack_factor != 0:
raise ValueError(
"The output size is not aligned with the quantized "
"weight shape. This can be caused by too large "
"tensor parallel size."
)
weight_loader = extra_weight_attrs.get("weight_loader")
qweight = PackedvLLMParameter(
data=torch.empty(
input_size_per_partition,
output_size_per_partition // self.quant_config.pack_factor,
dtype=torch.int32,
),
input_dim=0,
output_dim=1,
packed_dim=1,
packed_factor=self.quant_config.pack_factor,
weight_loader=weight_loader,
)
num_groups = input_size_per_partition // group_size
qzeros = PackedvLLMParameter(
data=torch.empty(
num_groups,
output_size_per_partition // self.quant_config.pack_factor,
dtype=torch.int32,
),
input_dim=0,
output_dim=1,
packed_dim=1,
packed_factor=self.quant_config.pack_factor,
weight_loader=weight_loader,
)
scales = GroupQuantScaleParameter(
data=torch.empty(
num_groups,
output_size_per_partition,
dtype=params_dtype,
),
input_dim=0,
output_dim=1,
weight_loader=weight_loader,
)
layer.register_parameter("qweight", qweight)
layer.register_parameter("qzeros", qzeros)
layer.register_parameter("scales", scales)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: torch.Tensor | None = None,
) -> torch.Tensor:
qweight = layer.qweight
scales = layer.scales
qzeros = layer.qzeros
pack_factor = self.quant_config.pack_factor
out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
reshaped_x = x.reshape(-1, x.shape[-1])
# num_tokens >= threshold
FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256
if FP16_MATMUL_HEURISTIC_CONDITION:
out = ops.awq_dequantize(qweight, scales, qzeros, 0, 0, 0)
out = torch.matmul(reshaped_x, out)
else:
out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros, pack_factor)
if bias is not None:
out.add_(bias)
return out.reshape(out_shape)
class AWQMarlinConfig(QuantizationConfig): class AWQMarlinConfig(QuantizationConfig):
"""Config class for AWQ Marlin""" """Config class for AWQ Marlin"""
......
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