[3/3] fix dsv3 awq issue (#4719)

Co-authored-by: AniZpZ <aniz1905@gmail.com>

python/sglang/srt/layers/quantization/__init__.py

@@ -9,7 +9,6 @@ import torch
 
 try:
     from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
-    from vllm.model_executor.layers.quantization.awq import AWQConfig
     from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
     from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
     from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfig
@@ -33,14 +32,15 @@ except ImportError:
     class DummyConfig:
         pass
 
-    AQLMConfig = AWQConfig = AWQMarlinConfig = BitsAndBytesConfig = (
+    AQLMConfig = AWQMarlinConfig = BitsAndBytesConfig = CompressedTensorsConfig = (
-        CompressedTensorsConfig
+        DummyConfig
-    ) = DummyConfig
+    )
     DeepSpeedFPConfig = ExpertsInt8Config = FBGEMMFp8Config = GGUFConfig = (
         GPTQMarlin24Config
     ) = DummyConfig
     MarlinConfig = QQQConfig = Int8TpuConfig = DummyConfig
 
+from sglang.srt.layers.quantization.awq import AWQConfig
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
 from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import (

python/sglang/srt/layers/quantization/awq.py

@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: Apache-2.0
+import logging
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+from sgl_kernel import awq_dequantize
+
+from sglang.srt.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+
+logger = logging.getLogger(__name__)
+
+
+def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
+    return any(module_name in prefix for module_name in modules_to_not_convert)
+
+
+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: Optional[List[str]] = 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_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_name(self) -> str:
+        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
+    ) -> Optional["LinearMethodBase"]:
+
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
+                return UnquantizedLinearMethod()
+            return AWQLinearMethod(self)
+        return None
+
+
+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,
+    ):
+        if input_size_per_partition % self.quant_config.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,
+        )
+
+        qzeros = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.group_size,
+                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(
+                input_size_per_partition // self.quant_config.group_size,
+                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: Optional[torch.Tensor] = 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])
+
+        out = awq_dequantize(qweight, scales, qzeros)
+        out = torch.matmul(reshaped_x, out)
+
+        if bias is not None:
+            out.add_(bias)
+        return out.reshape(out_shape)