[QUANT] Add GPTQModel Dynamic Quantization + lm_head Quantization (#3790)
Signed-off-by: ZX-ModelCloud <zx@modelcloud.ai> Co-authored-by: ZX-ModelCloud <zx@modelcloud.ai>
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commit
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@@ -1,5 +1,7 @@
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# Adapted from https://raw.githubusercontent.com/vllm-project/vllm/v0.5.5/vllm/model_executor/layers/quantization/__init__.py
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from typing import Callable, Dict, Optional, Type
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import re
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from copy import deepcopy
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from typing import Callable, Dict, Optional, Type, Union
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import torch
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from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
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@@ -16,8 +18,6 @@ from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfi
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from vllm.model_executor.layers.quantization.experts_int8 import ExpertsInt8Config
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from vllm.model_executor.layers.quantization.fbgemm_fp8 import FBGEMMFp8Config
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from vllm.model_executor.layers.quantization.gguf import GGUFConfig
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from vllm.model_executor.layers.quantization.gptq import GPTQConfig
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from vllm.model_executor.layers.quantization.gptq_marlin import GPTQMarlinConfig
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from vllm.model_executor.layers.quantization.gptq_marlin_24 import GPTQMarlin24Config
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from vllm.model_executor.layers.quantization.marlin import MarlinConfig
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from vllm.model_executor.layers.quantization.qqq import QQQConfig
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@@ -26,6 +26,7 @@ from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
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from sglang.srt.layers.quantization.fp8 import Fp8Config
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from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
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from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
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from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
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@@ -61,19 +62,119 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
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return QUANTIZATION_METHODS[quantization]
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# Match dynamic rules with module name (prefix) and override quantize
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# config if module (prefix) matches a rule
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def override_config(config: QuantizationConfig, prefix: str):
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weight_bits = get_dynamic_override(config, prefix, "bits", config.weight_bits)
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if isinstance(weight_bits, int):
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config.weight_bits = weight_bits
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group_size = get_dynamic_override(config, prefix, "group_size", config.group_size)
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if isinstance(group_size, int):
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config.group_size = group_size
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desc_act = get_dynamic_override(config, prefix, "desc_act", config.desc_act)
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if isinstance(desc_act, bool):
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config.desc_act = desc_act
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config.pack_factor = 32 // config.weight_bits # packed into int32
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if config.get_name() == "gptq_marlin":
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is_sym = get_dynamic_override(config, prefix, "sym", config.is_sym)
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if isinstance(is_sym, bool):
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config.is_sym = is_sym
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if (config.weight_bits, config.is_sym) not in config.TYPE_MAP:
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raise ValueError(
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"Unsupported quantization config: "
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f"bits={config.weight_bits}, sym={config.is_sym}"
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)
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config.quant_type = config.TYPE_MAP[(config.weight_bits, config.is_sym)]
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elif config.get_name() == "gptq":
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if config.weight_bits not in [2, 3, 4, 8]:
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raise ValueError(
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"Currently, only 2/3/4/8-bit weight quantization is "
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f"supported for GPTQ, but got {config.weight_bits} bits."
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)
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def get_dynamic_override(
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config: QuantizationConfig,
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layer_name: str,
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key: Optional[str] = None,
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default_value: Union[int, bool, None] = None,
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) -> Union[Dict, int, bool, None]:
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for pattern, pattern_dict in config.dynamic.items():
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# Negative match: matched modules are excluded from quantized init
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if pattern.startswith("-:"):
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if re.match(pattern.removeprefix("-:"), layer_name):
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return False
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# Positive match: matched modules have quant properties overrides
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# base quant config
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elif re.match(pattern.removeprefix("+:"), layer_name):
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if key is None:
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return pattern_dict
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else:
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return pattern_dict.get(key, default_value)
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return default_value
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def get_linear_quant_method(
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config: QuantizationConfig,
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layer: torch.nn.Module,
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prefix: str,
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linear_method_cls: type,
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):
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from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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UnquantizedEmbeddingMethod,
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)
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cloned_config = deepcopy(config)
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parallel_lm_head_quantized = (
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isinstance(layer, ParallelLMHead) and cloned_config.lm_head_quantized
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)
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if isinstance(layer, LinearBase) or parallel_lm_head_quantized:
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# False = skip module, None = no override, else = Positive match
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if (
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get_dynamic_override( # noqa: E712
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cloned_config, layer_name=prefix # noqa: E712
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)
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== False
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): # noqa: E712
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if parallel_lm_head_quantized:
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return UnquantizedEmbeddingMethod()
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return UnquantizedLinearMethod()
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if prefix:
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# Dynamic per module/layer rules may override base config
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override_config(cloned_config, prefix=prefix)
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return linear_method_cls(cloned_config)
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return None
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def gptq_get_quant_method(self, layer, prefix):
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from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
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from vllm.model_executor.layers.quantization.gptq_marlin import (
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GPTQMarlinLinearMethod,
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GPTQMarlinMoEMethod,
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)
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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if isinstance(layer, LinearBase):
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return GPTQMarlinLinearMethod(self)
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elif isinstance(layer, FusedMoE):
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if isinstance(layer, FusedMoE):
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return GPTQMarlinMoEMethod(self)
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if isinstance(self, GPTQConfig):
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return get_linear_quant_method(
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self, layer, prefix=prefix, linear_method_cls=GPTQLinearMethod
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)
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elif isinstance(self, GPTQMarlinConfig):
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return get_linear_quant_method(
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self, layer, prefix=prefix, linear_method_cls=GPTQMarlinLinearMethod
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)
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return None
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@@ -155,6 +256,7 @@ def apply_monkey_patches():
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from vllm.model_executor.layers.quantization.awq_marlin import AWQMoEMethod
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setattr(GPTQMarlinConfig, "get_quant_method", gptq_get_quant_method)
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setattr(GPTQConfig, "get_quant_method", gptq_get_quant_method)
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setattr(AWQMarlinConfig, "get_quant_method", awq_get_quant_method)
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setattr(AWQMoEMethod, "apply", awq_moe_method_apply)
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416
python/sglang/srt/layers/quantization/gptq.py
Normal file
416
python/sglang/srt/layers/quantization/gptq.py
Normal file
@@ -0,0 +1,416 @@
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import logging
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from fractions import Fraction
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from typing import Any, Dict, List, Optional, Union
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import torch
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from vllm.scalar_type import scalar_types
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
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logger = logging.getLogger(__name__)
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class GPTQConfig(QuantizationConfig):
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"""Config class for GPTQ.
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Reference: https://arxiv.org/abs/2210.17323
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"""
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def __init__(
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self,
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weight_bits: int,
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group_size: int,
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desc_act: bool,
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lm_head_quantized: bool,
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dynamic: Dict[str, Dict[str, Union[int, bool]]],
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) -> None:
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# GPTQModel use `dynamic` config property to allow per module
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# quantization config so each module can be individually optimized.
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# Format is Dict[str, Dict] where key is a regex string that can
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# perform both positive ("+:" prefixed) or negative ("-:" prefixed)
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# matching of a module.
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# Default to positive match, override base quant config mode, if no
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# prefix is used. Value is in dict format of field key and override
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# value.
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# Negative matching will skip quantization init for this module
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# entirely:
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# non-quantized inference. More details and quantization examples can be
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# found at: https://github.com/ModelCloud/GPTQModel
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# Example:
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# # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
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# # last 1/4 of the layers 16-21 has 8bit and group_size 64
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# dynamic = {
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# #`.*\.` matches the layers_node prefix
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# # positive match layer 10-15
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# r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
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# # positive match layer 16-21
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# r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
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# r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
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# }
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super().__init__()
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.desc_act = desc_act
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self.lm_head_quantized = lm_head_quantized
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self.pack_factor = Fraction(32, self.weight_bits)
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if self.weight_bits not in [2, 3, 4, 8]:
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raise ValueError(
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"Currently, only 2/3/4/8-bit weight quantization is "
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f"supported for GPTQ, but got {self.weight_bits} bits."
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)
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def __repr__(self) -> str:
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return (
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f"GPTQConfig(weight_bits={self.weight_bits}, "
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f"group_size={self.group_size}, "
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f"desc_act={self.desc_act}),"
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f"lm_head_quantized={self.lm_head_quantized}), "
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f"dynamic={self.dynamic}"
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)
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def get_scaled_act_names(self) -> List[str]:
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"""Returns the activation function names that should be post-scaled.
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For now, this is only used by AWQ.
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"""
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raise NotImplementedError
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@classmethod
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def get_name(cls) -> str:
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return "gptq"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.half]
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@classmethod
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# Need to figure it out
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def get_min_capability(cls) -> int:
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return 60
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "GPTQConfig":
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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dynamic = {} if dynamic is None else dynamic
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weight_bits = cls.get_from_keys(config, ["bits"])
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group_size = cls.get_from_keys(config, ["group_size"])
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desc_act = cls.get_from_keys(config, ["desc_act"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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return cls(weight_bits, group_size, desc_act, lm_head_quantized, dynamic)
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["GPTQLinearMethod"]:
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from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
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from sglang.srt.layers.quantization import get_linear_quant_method
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return get_linear_quant_method(self, layer, prefix, GPTQLinearMethod)
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class GPTQMarlinConfig(QuantizationConfig):
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"""Config class for GPTQ Marlin"""
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# (num_bits, is_sym) -> quant_type
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TYPE_MAP = {
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(4, True): scalar_types.uint4b8,
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(8, True): scalar_types.uint8b128,
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}
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def __init__(
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self,
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weight_bits: int,
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group_size: int,
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desc_act: bool,
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is_sym: bool,
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lm_head_quantized: bool,
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dynamic: Dict[str, Dict[str, Union[int, bool]]],
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full_config: Dict[str, Any],
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) -> None:
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super().__init__()
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if desc_act and group_size == -1:
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# In this case, act_order == True is the same as act_order == False
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# (since we have only one group per output channel)
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desc_act = False
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# GPTQModel use `dynamic` config property to allow per module
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# quantization config so each module can be individually optimized.
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# Format is Dict[str, Dict] where key is a regex string that can
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# perform both positive ("+:" prefixed) or negative ("-:" prefixed)
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# matching of a module.
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# Default to positive match, override base quant config mode, if no
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# prefix is used. Value is in dict format of field key and override
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# value.
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# Negative matching will skip quantization init for this module
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# entirely:
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# non-quantized inference. More details and quantization examples can be
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# found at: https://github.com/ModelCloud/GPTQModel
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# Example:
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# # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
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# # last 1/4 of the layers 16-21 has 8bit and group_size 64
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# dynamic = {
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# #`.*\.` matches the layers_node prefix
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# # positive match layer 10-15
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# r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
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# # positive match layer 16-21
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# r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
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# r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
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# }
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.is_sym = is_sym
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self.pack_factor = 32 // weight_bits # packed into int32
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self.group_size = group_size
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self.desc_act = desc_act
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self.lm_head_quantized = lm_head_quantized
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self.full_config = full_config
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if (weight_bits, is_sym) not in self.TYPE_MAP:
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raise ValueError(
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"Unsupported quantization config: " f"bits={weight_bits}, sym={is_sym}"
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)
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self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
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def __repr__(self) -> str:
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return (
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f"GPTQMarlinConfig(quant_type={self.quant_type}, "
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f"group_size={self.group_size}, "
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f"desc_act={self.desc_act}, "
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f"lm_head_quantized={self.lm_head_quantized}), "
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f"dynamic={self.dynamic}"
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)
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def get_scaled_act_names(self) -> List[str]:
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"""Returns the activation function names that should be post-scaled.
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For now, this is only used by AWQ.
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"""
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raise NotImplementedError
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@classmethod
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def get_name(cls) -> str:
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return "gptq_marlin"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.half, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 80
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlinConfig":
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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dynamic = {} if dynamic is None else dynamic
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weight_bits = cls.get_from_keys(config, ["bits"])
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group_size = cls.get_from_keys(config, ["group_size"])
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desc_act = cls.get_from_keys(config, ["desc_act"])
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is_sym = cls.get_from_keys(config, ["sym"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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return cls(
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weight_bits,
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group_size,
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desc_act,
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is_sym,
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lm_head_quantized,
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dynamic,
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config,
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)
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@classmethod
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def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
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can_convert = cls.is_gptq_marlin_compatible(hf_quant_cfg)
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is_valid_user_quant = (
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user_quant is None or user_quant == "marlin" or user_quant == "gptq_marlin"
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)
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if can_convert and is_valid_user_quant:
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msg = (
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"The model is convertible to {} during runtime."
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" Using {} kernel.".format(cls.get_name(), cls.get_name())
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)
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logger.info(msg)
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return cls.get_name()
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if can_convert and user_quant == "gptq":
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logger.info(
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"Detected that the model can run with gptq_marlin"
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", however you specified quantization=gptq explicitly,"
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" so forcing gptq. Use quantization=gptq_marlin for"
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" faster inference"
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)
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return None
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["QuantizeMethodBase"]:
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from vllm.model_executor.layers.quantization.gptq_marlin import (
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GPTQMarlinLinearMethod,
|
||||
GPTQMarlinMoEMethod,
|
||||
)
|
||||
|
||||
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
|
||||
from sglang.srt.layers.quantization import get_linear_quant_method
|
||||
|
||||
if isinstance(layer, FusedMoE):
|
||||
return GPTQMarlinMoEMethod(self)
|
||||
# TODO: re-enable after SGLang syncs with vllm >= 0.7.3
|
||||
# if layer.num_experts > 32:
|
||||
# # For MoEs with many experts the moe_wna16 kernel is faster
|
||||
# return MoeWNA16Config.from_config(self.full_config).get_quant_method(
|
||||
# layer, prefix
|
||||
# )
|
||||
# else:
|
||||
# return GPTQMarlinMoEMethod(self)
|
||||
return get_linear_quant_method(self, layer, prefix, GPTQMarlinLinearMethod)
|
||||
|
||||
@classmethod
|
||||
def is_gptq_marlin_compatible(cls, quant_config: Dict[str, Any]):
|
||||
quant_method = quant_config.get("quant_method", "").lower()
|
||||
num_bits = quant_config.get("bits")
|
||||
group_size = quant_config.get("group_size")
|
||||
sym = quant_config.get("sym")
|
||||
desc_act = quant_config.get("desc_act")
|
||||
|
||||
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
|
||||
check_marlin_supported,
|
||||
)
|
||||
from vllm.platforms import current_platform
|
||||
|
||||
if not current_platform.is_cuda():
|
||||
return False
|
||||
|
||||
if quant_method != "gptq":
|
||||
return False
|
||||
|
||||
# Marlin conversion is only valid if required properties are found
|
||||
if num_bits is None or group_size is None or sym is None or desc_act is None:
|
||||
return False
|
||||
|
||||
if (num_bits, sym) not in cls.TYPE_MAP:
|
||||
return False
|
||||
|
||||
return check_marlin_supported(
|
||||
quant_type=cls.TYPE_MAP[(num_bits, sym)], group_size=group_size
|
||||
)
|
||||
|
||||
|
||||
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:
|
||||
# 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) -> 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"])
|
||||
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[str]:
|
||||
# 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"]:
|
||||
from vllm.model_executor.layers.quantization.marlin import MarlinLinearMethod
|
||||
|
||||
if isinstance(layer, LinearBase) or (
|
||||
isinstance(layer, ParallelLMHead) and self.lm_head_quantized
|
||||
):
|
||||
return MarlinLinearMethod(self)
|
||||
return None
|
||||
Reference in New Issue
Block a user