8fb85b7bb6
Signed-off-by: Xin Yang <xyangx@amazon.com> Co-authored-by: Michael Goin <mgoin64@gmail.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
474 lines
21 KiB
Python
474 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any, Callable, Optional
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import torch
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from vllm.distributed import get_tensor_model_parallel_rank, get_tp_group
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, FusedMoEConfig, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
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from vllm.model_executor.layers.linear import (LinearBase,
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UnquantizedLinearMethod)
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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check_marlin_supports_layer)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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class MoeWNA16Config(QuantizationConfig):
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"""Config class for MOE WNA16 (W8A16/W4A16) quantization."""
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def __init__(self, linear_quant_method: str, weight_bits: int,
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group_size: int, has_zp: bool, lm_head_quantized: bool,
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modules_to_not_convert: Optional[list[str]],
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full_config: dict[str, Any]) -> None:
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super().__init__()
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.has_zp = has_zp
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self.bit8_pack_factor = 8 // self.weight_bits
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self.lm_head_quantized = lm_head_quantized
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self.linear_quant_method = linear_quant_method
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self.full_config = full_config
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self.use_marlin = False
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# Avoid circular import
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from vllm.model_executor.layers.quantization.awq import AWQConfig
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from vllm.model_executor.layers.quantization.awq_marlin import (
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AWQMarlinConfig)
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from vllm.model_executor.layers.quantization.gptq_marlin import (
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GPTQMarlinConfig)
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if self.linear_quant_method == "gptq":
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self.use_marlin = GPTQMarlinConfig.is_gptq_marlin_compatible(
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full_config)
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elif self.linear_quant_method == "awq":
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capability_tuple = current_platform.get_device_capability()
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device_capability = (-1 if capability_tuple is None else
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capability_tuple.to_int())
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awq_min_capability = AWQConfig.get_min_capability()
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if device_capability < awq_min_capability:
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raise ValueError(
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"The quantization method moe_wna16 + awq is not supported "
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"for the current GPU. "
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f"Minimum capability: {awq_min_capability}. "
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f"Current capability: {device_capability}.")
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self.use_marlin = AWQMarlinConfig.is_awq_marlin_compatible(
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full_config)
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else:
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raise ValueError("moe_wna16 only support gptq and awq.")
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if modules_to_not_convert is None:
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self.modules_to_not_convert = []
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else:
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self.modules_to_not_convert = modules_to_not_convert
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "moe_wna16"
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@classmethod
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def get_supported_act_dtypes(cls) -> list[torch.dtype]:
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return [torch.bfloat16, torch.half]
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@classmethod
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def get_min_capability(cls) -> int:
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return 70
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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]) -> "MoeWNA16Config":
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linear_quant_method = cls.get_from_keys(config, ["quant_method"])
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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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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
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default=False)
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if linear_quant_method == "gptq":
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has_zp = not cls.get_from_keys(config, ["sym"])
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modules_to_not_convert = []
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elif linear_quant_method == "awq":
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has_zp = cls.get_from_keys(config, ["zero_point"])
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modules_to_not_convert = cls.get_from_keys_or(
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config, ["modules_to_not_convert"], None)
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else:
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raise ValueError("moe_wna16 only support gptq and awq.")
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return cls(linear_quant_method, weight_bits, group_size, has_zp,
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lm_head_quantized, modules_to_not_convert, config)
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@classmethod
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def override_quantization_method(
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cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
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can_convert = cls.is_moe_wna16_compatible(hf_quant_cfg)
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if can_convert and user_quant == "moe_wna16":
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return cls.get_name()
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return None
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@classmethod
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def is_moe_wna16_compatible(cls, quant_config: dict[str, Any]):
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# Extract data from quant config.
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quant_method = quant_config.get("quant_method", "").lower()
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num_bits = quant_config.get("bits")
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desc_act = quant_config.get("desc_act")
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capability_tuple = current_platform.get_device_capability()
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device_capability = (-1 if capability_tuple is None else
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capability_tuple.to_int())
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# Avoid circular import
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from vllm.model_executor.layers.quantization.awq import AWQConfig
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awq_min_capability = AWQConfig.get_min_capability()
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gptq_compatible = quant_method == "gptq" and \
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not desc_act and num_bits in [4, 8]
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awq_compatible = quant_method == "awq" and num_bits == 4 and \
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device_capability >= awq_min_capability
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return gptq_compatible or awq_compatible
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QuantizeMethodBase"]:
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if is_layer_skipped_quant(prefix, self.modules_to_not_convert):
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return UnquantizedLinearMethod()
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elif isinstance(layer, LinearBase):
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# Avoid circular import
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from vllm.model_executor.layers.quantization.awq import AWQConfig
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from vllm.model_executor.layers.quantization.awq_marlin import (
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AWQMarlinConfig)
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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 (
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GPTQMarlinConfig)
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if self.linear_quant_method == "gptq":
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if self.use_marlin:
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return GPTQMarlinConfig.from_config(
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self.full_config).get_quant_method(layer, prefix)
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else:
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return GPTQConfig.from_config(
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self.full_config).get_quant_method(layer, prefix)
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elif self.linear_quant_method == "awq":
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if self.use_marlin and check_marlin_supports_layer(
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layer, self.group_size):
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return AWQMarlinConfig.from_config(
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self.full_config).get_quant_method(layer, prefix)
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else:
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return AWQConfig.from_config(
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self.full_config).get_quant_method(layer, prefix)
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else:
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raise ValueError("moe_wna16 only support gptq and awq.")
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elif isinstance(layer, FusedMoE):
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return MoeWNA16Method(self, layer.moe_config)
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return None
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def is_layer_skipped_quant(prefix: str, modules_to_not_convert: list[str]):
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return any(module_name in prefix for module_name in modules_to_not_convert)
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class MoeWNA16Method(FusedMoEMethodBase):
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"""Linear method for MOE WNA16 (W8A16/W4A16) quantization.
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Args:
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quant_config: The MOE WNA16 (W8A16/W4A16) quantization config.
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"""
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def __init__(self, quant_config: MoeWNA16Config,
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moe: "FusedMoEConfig") -> None:
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super().__init__(moe)
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self.quant_config = quant_config
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def create_weights(self, layer: torch.nn.Module, num_experts: int,
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hidden_size: int, intermediate_size_per_partition: int,
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params_dtype: torch.dtype, **extra_weight_attrs):
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self.moe = layer
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layer.quant_config = self.quant_config
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bit8_pack_factor = self.quant_config.bit8_pack_factor
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group_size = self.quant_config.group_size
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group_size_div_factor = 1
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# make intermediate_size and hidden_size diviable by group_size
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# we reduce the group size to ensure that
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# and we would repeat the loaded_weight later
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while intermediate_size_per_partition % group_size or \
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hidden_size % group_size:
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group_size = group_size // 2
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group_size_div_factor *= 2
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assert group_size >= 32
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layer.group_size = group_size
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layer.group_size_div_factor = group_size_div_factor
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strategy = FusedMoeWeightScaleSupported.GROUP.value
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extra_weight_attrs.update({
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"quant_method": strategy,
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"is_transposed": False
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})
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assert 'weight_loader' in extra_weight_attrs
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weight_loader = extra_weight_attrs['weight_loader']
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wrapped_weight_loader = MoeWNA16Method.get_weight_loader(
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layer, weight_loader)
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extra_weight_attrs['weight_loader'] = wrapped_weight_loader
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# Fused gate_up_proj (column parallel)
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w13_qweight = torch.nn.Parameter(torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size // bit8_pack_factor,
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dtype=torch.uint8),
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requires_grad=False)
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layer.register_parameter("w13_qweight", w13_qweight)
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set_weight_attrs(w13_qweight, extra_weight_attrs)
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# down_proj (row parallel)
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w2_qweight = torch.nn.Parameter(torch.empty(
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num_experts,
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hidden_size,
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intermediate_size_per_partition // bit8_pack_factor,
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dtype=torch.uint8),
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requires_grad=False)
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layer.register_parameter("w2_qweight", w2_qweight)
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set_weight_attrs(w2_qweight, extra_weight_attrs)
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w13_scales = torch.nn.Parameter(torch.zeros(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size // group_size,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w13_scales", w13_scales)
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set_weight_attrs(w13_scales, extra_weight_attrs)
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w2_scales = torch.nn.Parameter(torch.zeros(
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num_experts,
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hidden_size,
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intermediate_size_per_partition // group_size,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w2_scales", w2_scales)
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set_weight_attrs(w2_scales, extra_weight_attrs)
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if self.quant_config.has_zp:
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w13_qzeros = torch.nn.Parameter(torch.zeros(
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num_experts,
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2 * intermediate_size_per_partition // bit8_pack_factor,
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hidden_size // group_size,
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dtype=torch.uint8),
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requires_grad=False)
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layer.register_parameter("w13_qzeros", w13_qzeros)
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set_weight_attrs(w13_qzeros, extra_weight_attrs)
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w2_qzeros = torch.nn.Parameter(torch.zeros(
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num_experts,
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hidden_size // bit8_pack_factor,
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intermediate_size_per_partition // group_size,
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dtype=torch.uint8),
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requires_grad=False)
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layer.register_parameter("w2_qzeros", w2_qzeros)
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set_weight_attrs(w2_qzeros, extra_weight_attrs)
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if self.quant_config.linear_quant_method == "gptq":
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# some param are unused, but we need to init them in order to
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# load weights
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invalid_param_keys = ["w13_g_idx", "w2_g_idx"]
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if not self.quant_config.has_zp:
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invalid_param_keys += ["w13_qzeros", "w2_qzeros"]
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for key in invalid_param_keys:
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param = torch.nn.Parameter(torch.empty((0, ),
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dtype=torch.int32),
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requires_grad=False)
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layer.register_parameter(key, param)
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set_weight_attrs(param, extra_weight_attrs)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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enable_eplb: bool = False,
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expert_load_view: Optional[torch.Tensor] = None,
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logical_to_physical_map: Optional[torch.Tensor] = None,
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logical_replica_count: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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assert self.fused_experts is None
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if enable_eplb:
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raise NotImplementedError(
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"EPLB not supported for `MoeWNA16Method` yet.")
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from vllm.model_executor.layers.fused_moe import fused_experts
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assert activation == "silu", "Only SiLU activation is supported."
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topk_weights, topk_ids = FusedMoE.select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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routed_scaling_factor=routed_scaling_factor,
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e_score_correction_bias=e_score_correction_bias,
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indices_type=self.topk_indices_dtype)
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weight_bits = self.quant_config.weight_bits
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has_zp = self.quant_config.has_zp
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return fused_experts(
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x,
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layer.w13_qweight,
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layer.w2_qweight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=True,
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use_int4_w4a16=weight_bits == 4,
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use_int8_w8a16=weight_bits == 8,
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global_num_experts=global_num_experts,
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apply_router_weight_on_input=apply_router_weight_on_input,
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expert_map=expert_map,
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w1_scale=layer.w13_scales,
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w2_scale=layer.w2_scales,
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w1_zp=layer.w13_qzeros if has_zp else None,
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w2_zp=layer.w2_qzeros if has_zp else None,
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block_shape=[0, layer.group_size])
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@staticmethod
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def get_weight_loader(layer, weight_loader):
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def convert_awq_tensor(tensor, tensor_type):
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# convert awq qweight/qzeros to a standard format (assume int4)
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# qweight: (k, n // pack_factor_bit32) -> (n, k // pack_factor_bit8)
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# qzeros: (k // group_size, n // pack_factor_bit32) ->
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# (n // pack_factor_bit8, k // group_size)
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# pack_factor_bit32 = 32 // weight_bits
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# pack_factor_bit8 = 8 // weight_bits
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# 0. suppose origin shape (a, b), dtype int32
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# 1. convert to uint8, shape (a, b) -> (a, 4 * b)
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size0 = tensor.size(0)
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tensor = tensor.view(torch.uint8)
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# 2. unpack to uint4 (only when weight_bits == 4)
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# shape (a, 4 * b) -> (a, 4 * b, 2)
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shifter = torch.tensor([0, 4],
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dtype=torch.uint8,
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device=tensor.device)
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tensor = (tensor[:, :, None] >> shifter) & 0xF
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# 3. change order, see
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# https://github.com/casper-hansen/AutoAWQ/blob/v0.2.8/awq/utils/quant_utils.py
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# shape -> (a, 4 * b * pack_factor_bit8)
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reverse_awq_pack_order = [0, 4, 1, 5, 2, 6, 3, 7]
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tensor = tensor.view(-1, 8)[:, reverse_awq_pack_order]
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tensor = tensor.view(size0, -1)
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# 4. transpose, shape -> (4 * b * pack_factor_bit8, a)
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tensor = tensor.T.contiguous()
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# 5. repack (only when weight_bits == 4)
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# qweight shape -> (4 * b * pack_factor_bit8, a // pack_factor_bit8)
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# qzeros shape -> (4 * b, a)
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if tensor_type == "qweight":
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tensor = tensor[:, 1::2] * 16 + tensor[:, ::2]
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elif tensor_type == "qzeros":
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tensor = tensor[1::2, :] * 16 + tensor[::2, :]
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return tensor
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def convert_gptq_int4_qzeros(tensor):
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tensor = tensor.view(torch.uint8)
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shifter = torch.tensor([0, 4],
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dtype=torch.uint8,
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device=tensor.device)
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tensor = (tensor[:, :, None] >> shifter) & 0xF
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tensor = tensor + 1
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tensor = tensor[:, :, 0] + tensor[:, :, 1] * 16
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return tensor
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def moe_wna16_weight_loader(param: torch.nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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shard_id: str,
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expert_id: int,
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return_success: bool = False):
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if "g_idx" in weight_name:
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return False if return_success else None
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if not layer.quant_config.has_zp and "qzeros" in weight_name:
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return False if return_success else None
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device = get_tp_group().device
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tp_rank = get_tensor_model_parallel_rank()
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loaded_weight = loaded_weight.to(device)
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shard_size = layer.intermediate_size_per_partition
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# convert gptq and awq weight to a standard format
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if layer.quant_config.linear_quant_method == "awq":
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assert layer.quant_config.weight_bits == 4
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if "weight" in weight_name:
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loaded_weight = convert_awq_tensor(loaded_weight,
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"qweight")
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elif "zeros" in weight_name:
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loaded_weight = convert_awq_tensor(loaded_weight, "qzeros")
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else:
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loaded_weight = loaded_weight.T
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elif layer.quant_config.linear_quant_method == "gptq":
|
|
assert layer.quant_config.weight_bits in [4, 8]
|
|
if "weight" in weight_name:
|
|
loaded_weight = loaded_weight.T.contiguous().view(
|
|
torch.uint8)
|
|
elif "zeros" in weight_name:
|
|
# add 1 to gptq qzeros to align with awq
|
|
loaded_weight = loaded_weight.view(torch.uint8)
|
|
if layer.quant_config.weight_bits == 4:
|
|
loaded_weight = convert_gptq_int4_qzeros(
|
|
loaded_weight).T
|
|
else:
|
|
loaded_weight = loaded_weight.T + 1
|
|
else:
|
|
loaded_weight = loaded_weight.T
|
|
|
|
# repeat the qzeros/scales to fit new group size
|
|
if layer.group_size_div_factor > 1 and \
|
|
"qzeros" in weight_name or "scales" in weight_name:
|
|
loaded_weight = loaded_weight.repeat_interleave(
|
|
layer.group_size_div_factor, 1)
|
|
|
|
if "w13_qzeros" in weight_name:
|
|
tensor = loaded_weight.view(layer.tp_size, -1,
|
|
loaded_weight.size(1))[tp_rank]
|
|
if shard_id == "w1":
|
|
param.data[expert_id, :shard_size // 2] = tensor
|
|
else:
|
|
param.data[expert_id, shard_size // 2:] = tensor
|
|
return True if return_success else None
|
|
elif "w2_qzeros" in weight_name:
|
|
param.data[expert_id] = loaded_weight.view(
|
|
loaded_weight.size(0), layer.tp_size, -1)[:, tp_rank]
|
|
return True if return_success else None
|
|
else:
|
|
# Delegate to the original loader, passing return_success
|
|
return weight_loader(param,
|
|
loaded_weight,
|
|
weight_name,
|
|
shard_id,
|
|
expert_id,
|
|
return_success=return_success)
|
|
|
|
return moe_wna16_weight_loader
|