Migrate INCConfig for HPU

vllm_gaudi/extension/ops.py

@@ -13,10 +13,12 @@
 from vllm_gaudi.extension.runtime import get_config
 from vllm_gaudi.extension.utils import get_kv_fetch_extra_args
 from vllm_gaudi.extension.scales import ConvertScaleToHwAligned
-
+import vllm.model_executor.layers.quantization as vllm_quant
 import habana_frameworks.torch.utils.experimental as htexp
 import types
 from vllm.model_executor.layers.fused_moe import FusedMoeWeightScaleSupported
+from vllm.model_executor.layers.quantization import get_quantization_config as vllm_get_quantization_config
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 
 is_hpu_gaudi2 = htexp._get_device_type() == htexp.synDeviceType.synDeviceGaudi2
 is_hpu_gaudi3 = htexp._get_device_type() == htexp.synDeviceType.synDeviceGaudi3
@@ -1330,3 +1332,13 @@ def forward(
             else:
                 final_hidden_states += slice_final_hidden_states
         return final_hidden_states
+
+
+def oot_get_quantization_config(quantization: str) -> QuantizationConfig:
+    from .quant import _FakeINCConfig
+    if quantization == "inc":
+        return _FakeINCConfig
+    return vllm_get_quantization_config(quantization)
+
+
+vllm_quant.get_quantization_config = oot_get_quantization_config

vllm_gaudi/extension/quant.py

@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Optional
+
+import torch
+
+from vllm.model_executor.layers.fused_moe.layer import (FusedMoE, UnquantizedFusedMoEMethod)
+from vllm.model_executor.layers.linear import (LinearBase, UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (QuantizationConfig, QuantizeMethodBase)
+
+
+class _FakeINCConfig(QuantizationConfig):
+    """Placeholder INC quantization config class for FP8 using Intel Neural Compressor."""
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "inc"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "_FakeINCConfig":
+        raise AssertionError
+
+    def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return UnquantizedLinearMethod()
+        elif isinstance(layer, FusedMoE):
+            return UnquantizedFusedMoEMethod(layer.moe_config)
+        return None
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise AssertionError
+
+    @staticmethod
+    def get_config_filenames() -> list[str]:
+        return []