[Hardware][CPU] Support AWQ for CPU backend

.buildkite/run-cpu-test.sh

@@ -27,13 +27,19 @@ docker exec cpu-test bash -c "
   pytest -v -s tests/models/decoder_only/language \
     --ignore=tests/models/test_fp8.py \
     --ignore=tests/models/decoder_only/language/test_jamba.py \
+    --ignore=tests/models/decoder_only/language/test_granitemoe.py \
     --ignore=tests/models/decoder_only/language/test_danube3_4b.py" # Mamba and Danube3-4B on CPU is not supported
 
 # Run compressed-tensor test
+# docker exec cpu-test bash -c "
+#   pytest -s -v \
+#   tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
+#   tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynanmic_per_token"
+
+# Run AWQ test
 docker exec cpu-test bash -c "
   pytest -s -v \
-  tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
-  tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynanmic_per_token"
+  tests/quantization/test_ipex_quant.py"
 
 # online inference
 docker exec cpu-test bash -c "

Dockerfile.cpu

@@ -22,7 +22,7 @@ ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/usr/local/li
 
 RUN echo 'ulimit -c 0' >> ~/.bashrc
 
-RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/cpu/intel_extension_for_pytorch-2.4.0%2Bgitfbaa4bc-cp310-cp310-linux_x86_64.whl
+RUN pip install intel_extension_for_pytorch==2.4.0
 
 WORKDIR /workspace
 

docs/source/quantization/supported_hardware.rst

@@ -28,7 +28,7 @@ The table below shows the compatibility of various quantization implementations
      - ✅︎
      - ✗
      - ✗
-     - ✗
+     - ✅︎
      - ✗
      - ✗
    * - GPTQ
@@ -61,7 +61,7 @@ The table below shows the compatibility of various quantization implementations
      - ✅︎
      - ✗
      - ✗
-     - ✗
+     - ✅︎
      - ✗
      - ✗
    * - FP8 (W8A8)

tests/quantization/test_ipex_quant.py

@@ -0,0 +1,28 @@
+"""Test model set-up and inference for quantized HF models supported
+ on the CPU backend using IPEX (including AWQ).
+
+ Validating the configuration and printing results for manual checking.
+
+ Run `pytest tests/quantization/test_ipex_quant.py`.
+"""
+
+import pytest
+
+from vllm.platforms import current_platform
+
+MODELS = [
+    "casperhansen/llama-3-8b-instruct-awq",
+]
+DTYPE = ["bfloat16"]
+
+
+@pytest.mark.skipif(not current_platform.is_cpu(),
+                    reason="only supports the CPU backend.")
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", DTYPE)
+def test_ipex_quant(vllm_runner, model, dtype):
+    with vllm_runner(model, dtype=dtype) as llm:
+        output = llm.generate_greedy(["The capital of France is"],
+                                     max_tokens=32)
+    assert output
+    print(output)

vllm/model_executor/layers/linear.py

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

vllm/model_executor/layers/quantization/__init__.py

@@ -21,6 +21,7 @@
     GPTQMarlinConfig)
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
     GPTQMarlin24Config)
+from vllm.model_executor.layers.quantization.ipex_quant import IPEXConfig
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.modelopt import ModelOptFp8Config
 from vllm.model_executor.layers.quantization.neuron_quant import (
@@ -49,6 +50,7 @@
     "qqq": QQQConfig,
     "experts_int8": ExpertsInt8Config,
     "neuron_quant": NeuronQuantConfig,
+    "ipex": IPEXConfig,
 }
 
 

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -20,6 +20,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
                                            PackedvLLMParameter)
+from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 
 logger = init_logger(__name__)
@@ -123,6 +124,9 @@ def is_awq_marlin_compatible(cls, quant_config: Dict[str, Any]):
         group_size = quant_config.get("group_size")
         has_zp = quant_config.get("zero_point")
 
+        if not current_platform.is_cuda():
+            return False
+
         if quant_method != "awq":
             return False
 

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -0,0 +1,155 @@
+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]
+
+    @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
+
+    def get_scaled_act_names(self) -> List[str]:
+        if self.method == "awq":
+            return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+        else:
+            return []
+
+
+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
+
+        import intel_extension_for_pytorch as ipex
+
+        # 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, ))

vllm/worker/cpu_worker.py

@@ -215,7 +215,8 @@ def _is_encoder_decoder_model(self):
     def init_device(self) -> None:
         if self.local_omp_cpuid != "all":
             ret = torch.ops._C_utils.init_cpu_threads_env(self.local_omp_cpuid)
-            logger.info(ret)
+            if ret:
+                logger.info(ret)
 
         self.init_distributed_environment()
         # Set random seed.