add qwen3

vllm-v0.6.2/vllm/model_executor/layers/quantization/ipex_quant.py

@@ -0,0 +1,160 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.awq import AWQLinearMethod
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.platforms import current_platform
+
+
+class IPEXConfig(QuantizationConfig):
+    """INT8 quantization config class using IPEX for the CPU backend,
+    including AWQ.
+    """
+
+    IPEX_QUANT_METHOD_MAP = {
+        "awq": 1,
+        "gptq": 2,
+    }
+
+    def __init__(
+        self,
+        method: str,
+        weight_bits: int,
+        group_size: int,
+    ) -> None:
+        self.method = method
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.pack_factor = 32 // self.weight_bits
+
+        if self.weight_bits not in [4]:
+            raise ValueError(f"IPEX quantization supports weight bits [4], "
+                             f"but got {self.weight_bits}.")
+
+        if self.method == "awq":
+            self.quant_method = IPEXAWQLinearMethod
+        else:
+            raise ValueError(f"IPEX quantization supports [awq], "
+                             f"but got {self.method}.")
+
+    def __repr__(self) -> str:
+        return (f"IPEXConfig(method={self.method}"
+                f"weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}")
+
+    def get_ipex_quant_method_id(self) -> int:
+        return IPEXConfig.IPEX_QUANT_METHOD_MAP[self.method]
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "ipex"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.float16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return -1
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "IPEXConfig":
+        method = cls.get_from_keys(config, ["quant_method"]).lower()
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        return cls(method, weight_bits, group_size)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        if not current_platform.is_cpu():
+            return None
+
+        quant_method = hf_quant_cfg.get("quant_method", "").lower()
+
+        if quant_method in ["awq"]:
+            return cls.get_name()
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["LinearMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return self.quant_method(self)
+        return None
+
+
+class IPEXAWQLinearMethod(AWQLinearMethod):
+    """AWQ linear method using IPEX for the CPU backend.
+    """
+
+    def __init__(self, quant_config: IPEXConfig):
+        self.quant_config = quant_config  # type: ignore
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer=layer)
+
+        bias = layer.bias if not layer.skip_bias_add else None
+
+        try:
+            import intel_extension_for_pytorch as ipex
+            if ipex.__version__ < "2.4.0":
+                raise ImportError("intel_extension_for_pytorch version is "
+                                  "wrong. Please install "
+                                  "intel_extension_for_pytorch>=2.4.0.")
+        except ImportError as err:
+            raise ImportError(
+                "Please install "
+                "intel_extension_for_pytorch>=2.4.0 via "
+                "`pip install intel_extension_for_pytorch>=2.4.0`"
+                " to use IPEX-AWQ linear method.") from err
+
+        # Using the compute dtype (lowp_mode) as INT8 to leverage instructions
+        # with better performance.
+        lowp_mode = ipex.quantization.WoqLowpMode.INT8
+        # The weight will be de-packed from INT4 to INT8.
+        weight_dtype = ipex.quantization.WoqWeightDtype.INT4
+        # The float activation will be quantized (dynamic, per-token) to INT8.
+        act_quant_mode = ipex.quantization.WoqActQuantMode.PER_BATCH
+
+        qconfig = ipex.quantization.get_weight_only_quant_qconfig_mapping(
+            weight_dtype=weight_dtype,
+            lowp_mode=lowp_mode,
+            act_quant_mode=act_quant_mode,
+            group_size=self.quant_config.group_size,
+        )
+
+        layer.ipex_output_size = layer.qweight.size(
+            1) * self.quant_config.pack_factor
+        layer.ipex_qlinear = ipex.nn.modules.weight_only_quantization.\
+            WeightOnlyQuantizedLinear.from_weight(
+                layer.qweight,
+                layer.scales,
+                layer.qzeros,
+                layer.qweight.size(0),
+                layer.ipex_output_size,
+                qconfig=qconfig,
+                bias=bias,
+                group_size=self.quant_config.group_size,
+                quant_method=
+                    self.quant_config.get_ipex_quant_method_id() # type: ignore
+            )
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = layer.ipex_qlinear(reshaped_x)
+
+        return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))