[Hardware][XPU] AWQ/GPTQ support for xpu backend (#10107)

Signed-off-by: yan ma <yan.ma@intel.com>

docs/source/quantization/supported_hardware.rst

@@ -27,7 +27,7 @@ The table below shows the compatibility of various quantization implementations
      - ✅︎
      - ✅︎
      - ✗
-     - ✗
+     - ✅︎
      - ✅︎
      - ✗
      - ✗
@@ -38,8 +38,8 @@ The table below shows the compatibility of various quantization implementations
      - ✅︎
      - ✅︎
      - ✗
-     - ✗
-     - ✗
+     - ✅︎
+     - ✅︎
      - ✗
      - ✗
    * - Marlin (GPTQ/AWQ/FP8)
@@ -129,4 +129,4 @@ Notes:
 
 Please note that this compatibility chart may be subject to change as vLLM continues to evolve and expand its support for different hardware platforms and quantization methods.
 
-For the most up-to-date information on hardware support and quantization methods, please check the `quantization directory <https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization>`_ or consult with the vLLM development team.
+For the most up-to-date information on hardware support and quantization methods, please check the `quantization directory <https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization>`_ or consult with the vLLM development team.

tests/quantization/test_ipex_quant.py

@@ -1,5 +1,5 @@
 """Test model set-up and inference for quantized HF models supported
- on the CPU backend using IPEX (including AWQ).
+ on the CPU/GPU backend using IPEX (including AWQ/GPTQ).
  
  Validating the configuration and printing results for manual checking.
 
@@ -11,13 +11,15 @@ import pytest
 from vllm.platforms import current_platform
 
 MODELS = [
-    "casperhansen/llama-3-8b-instruct-awq",
+    "AMead10/Llama-3.2-1B-Instruct-AWQ",
+    "shuyuej/Llama-3.2-1B-Instruct-GPTQ",  # with g_idx
 ]
 DTYPE = ["bfloat16"]
 
 
-@pytest.mark.skipif(not current_platform.is_cpu(),
-                    reason="only supports the CPU backend.")
+@pytest.mark.skipif(not current_platform.is_cpu()
+                    and not current_platform.is_xpu(),
+                    reason="only supports Intel CPU/XPU backend.")
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", DTYPE)
 def test_ipex_quant(vllm_runner, model, dtype):

vllm/model_executor/layers/linear.py

@@ -27,7 +27,7 @@ WEIGHT_LOADER_V2_SUPPORTED = [
     "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod",
     "MarlinLinearMethod", "QQQLinearMethod", "GPTQMarlin24LinearMethod",
     "TPUInt8LinearMethod", "GPTQLinearMethod", "FBGEMMFp8LinearMethod",
-    "ModelOptFp8LinearMethod", "IPEXAWQLinearMethod"
+    "ModelOptFp8LinearMethod", "IPEXAWQLinearMethod", "IPEXGPTQLinearMethod"
 ]
 
 

vllm/model_executor/layers/quantization/gptq.py

@@ -210,7 +210,6 @@ class GPTQLinearMethod(LinearMethodBase):
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         # for torch.compile
-        layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
         layer.qzeros = Parameter(layer.qzeros.data, requires_grad=False)
         layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
         layer.g_idx = Parameter(layer.g_idx.data, requires_grad=False)

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -23,6 +23,7 @@ from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
                                            PackedColumnParameter,
                                            PackedvLLMParameter,
                                            RowvLLMParameter)
+from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 
 logger = init_logger(__name__)
@@ -134,6 +135,9 @@ class GPTQMarlinConfig(QuantizationConfig):
         sym = quant_config.get("sym")
         desc_act = quant_config.get("desc_act")
 
+        if not current_platform.is_cuda():
+            return False
+
         if quant_method != "gptq":
             return False
 

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -2,21 +2,26 @@ 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.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.awq import (AWQLinearMethod,
+                                                         is_layer_skipped_awq)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
 from vllm.platforms import current_platform
 
+MIN_IPEX_VERSION = "2.5.0"
+
 
 class IPEXConfig(QuantizationConfig):
-    """INT8 quantization config class using IPEX for the CPU backend,
-    including AWQ.
+    """INT8 quantization config class using IPEX for the CPU/XPU backend,
+    including AWQ, GPTQ.
     """
 
     IPEX_QUANT_METHOD_MAP = {
         "awq": 1,
-        "gptq": 2,
+        "gptq": 0,
     }
 
     def __init__(
@@ -24,29 +29,30 @@ class IPEXConfig(QuantizationConfig):
         method: str,
         weight_bits: int,
         group_size: int,
+        modules_to_not_convert: Optional[List[str]] = None,
+        desc_act: Optional[bool] = None,
+        lm_head_quantized: Optional[bool] = None,
     ) -> None:
         self.method = method
         self.weight_bits = weight_bits
         self.group_size = group_size
+        self.modules_to_not_convert = modules_to_not_convert or []
+        self.desc_act = desc_act
+        self.lm_head_quantized = lm_head_quantized
         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], "
+        if self.method not in ["awq", "gptq"]:
+            raise ValueError(f"IPEX quantization supports [awq, gptq], "
                              f"but got {self.method}.")
 
     def __repr__(self) -> str:
-        return (f"IPEXConfig(method={self.method}"
+        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]
+                f"group_size={self.group_size})")
 
     @classmethod
     def get_name(cls) -> str:
@@ -70,19 +76,32 @@ class IPEXConfig(QuantizationConfig):
     @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)
+        if method == "awq":
+            weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+            group_size = cls.get_from_keys(config,
+                                           ["q_group_size", "group_size"])
+            modules_to_not_convert = cls.get_from_keys_or(
+                config, ["modules_to_not_convert"], None)
+            return cls(method, weight_bits, group_size, modules_to_not_convert,
+                       False, False)
+        # otherwise for gptq
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        desc_act = cls.get_from_keys_or(config, ["desc_act"], default=False)
+        return cls(method, weight_bits, group_size, [], desc_act,
+                   lm_head_quantized)
 
     @classmethod
     def override_quantization_method(cls, hf_quant_cfg,
                                      user_quant) -> Optional[str]:
-        if not current_platform.is_cpu():
+        if not current_platform.is_cpu() and not current_platform.is_xpu():
             return None
 
         quant_method = hf_quant_cfg.get("quant_method", "").lower()
 
-        if quant_method in ["awq"]:
+        if quant_method in ["awq", "gptq"]:
             return cls.get_name()
 
         return None
@@ -90,12 +109,81 @@ class IPEXConfig(QuantizationConfig):
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["LinearMethodBase"]:
         if isinstance(layer, LinearBase):
-            return self.quant_method(self)
+            if self.method == "awq":
+                if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
+                    return UnquantizedLinearMethod()
+                return IPEXAWQLinearMethod(self)
+            if self.method == "gptq":
+                return IPEXGPTQLinearMethod(self)
         return None
 
 
+class IPEXGPTQLinearMethod(GPTQLinearMethod):
+    """GPTQ linear method using IPEX for the CPU/XPU 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:
+        bias = layer.bias if not layer.skip_bias_add else None
+
+        try:
+            import intel_extension_for_pytorch as ipex
+            if ipex.__version__ < MIN_IPEX_VERSION:
+                raise ImportError(
+                    "intel_extension_for_pytorch version is "
+                    "wrong. Please install "
+                    f"intel_extension_for_pytorch>={MIN_IPEX_VERSION}.")
+        except ImportError as err:
+            raise ImportError(
+                "Please install "
+                f"intel_extension_for_pytorch>={MIN_IPEX_VERSION} via "
+                f"`pip install intel_extension_for_pytorch>={MIN_IPEX_VERSION}`"
+                " 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_IC_BLOCK
+
+        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.shape[-1]
+        g_idx = layer.g_idx if self.quant_config.desc_act else None
+        layer.ipex_qlinear = ipex.llm.quantization.woq_linear. \
+            IPEXWeightOnlyQuantizedLinear.from_weight(
+            layer.qweight,
+            layer.scales,
+            layer.qzeros,
+            layer.qweight.size(0),
+            layer.ipex_output_size,
+            qconfig=qconfig,
+            g_idx=g_idx,
+            bias=bias,
+            group_size=self.quant_config.group_size,
+            quant_method=IPEXConfig.IPEX_QUANT_METHOD_MAP["gptq"]
+        )
+
+    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)
+        if bias is not None:
+            out.add_(bias)
+        return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))
+
+
 class IPEXAWQLinearMethod(AWQLinearMethod):
-    """AWQ linear method using IPEX for the CPU backend.
+    """AWQ linear method using IPEX for the CPU/XPU backend.
     """
 
     def __init__(self, quant_config: IPEXConfig):
@@ -108,15 +196,16 @@ class IPEXAWQLinearMethod(AWQLinearMethod):
 
         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.")
+            if ipex.__version__ < MIN_IPEX_VERSION:
+                raise ImportError(
+                    "intel_extension_for_pytorch version is "
+                    "wrong. Please install "
+                    f"intel_extension_for_pytorch>={MIN_IPEX_VERSION}.")
         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`"
+                f"intel_extension_for_pytorch>={MIN_IPEX_VERSION} via "
+                f"`pip install intel_extension_for_pytorch>={MIN_IPEX_VERSION}`"
                 " to use IPEX-AWQ linear method.") from err
 
         # Using the compute dtype (lowp_mode) as INT8 to leverage instructions
@@ -136,19 +225,18 @@ class IPEXAWQLinearMethod(AWQLinearMethod):
 
         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
-            )
+        layer.ipex_qlinear = ipex.llm.quantization.woq_linear. \
+            IPEXWeightOnlyQuantizedLinear.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=IPEXConfig.IPEX_QUANT_METHOD_MAP["awq"]  # type: ignore
+        )
 
     def apply(self,
               layer: torch.nn.Module,
@@ -156,5 +244,4 @@ class IPEXAWQLinearMethod(AWQLinearMethod):
               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, ))

vllm/model_executor/model_loader/loader.py

@@ -29,6 +29,8 @@ from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (ReplicatedLinear,
                                                RowParallelLinear)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizeMethodBase)
 from vllm.model_executor.model_loader.tensorizer import (
     TensorizerConfig, is_vllm_tensorized, load_with_tensorizer,
     serialize_vllm_model, tensorizer_weights_iterator)
@@ -348,7 +350,7 @@ class DefaultModelLoader(BaseModelLoader):
 
             for _, module in model.named_modules():
                 quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None:
+                if isinstance(quant_method, QuantizeMethodBase):
                     # When quant methods need to process weights after loading
                     # (for repacking, quantizing, etc), they expect parameters
                     # to be on the global target device. This scope is for the