init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/_310p/quantization/modelslim_config.py

@@ -0,0 +1,151 @@
+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from vllm.config import get_current_vllm_config
+from vllm.logger import logger
+from vllm.model_executor.layers.linear import LinearBase
+from vllm.model_executor.layers.quantization import register_quantization_config
+from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding,
+)
+
+from vllm_ascend._310p.quantization.methods.registry import (
+    get_scheme_class,
+)
+from vllm_ascend.quantization.method_adapters import AscendFusedMoEMethod, AscendLinearMethod
+from vllm_ascend.quantization.modelslim_config import (
+    AscendModelSlimConfig,
+    get_quant_type_for_layer,
+    packed_modules_model_mapping,
+)
+from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD, vllm_version_is
+
+if vllm_version_is("0.23.0"):
+    from vllm.model_executor.layers.fused_moe import FusedMoE
+else:
+    from vllm.model_executor.layers.fused_moe import MoERunner, RoutedExperts
+
+
+def _is_fused_moe_layer(layer: torch.nn.Module) -> bool:
+    if vllm_version_is("0.23.0"):
+        return isinstance(layer, FusedMoE)
+    else:
+        return isinstance(layer, (MoERunner, RoutedExperts))
+
+
+def create_scheme_for_layer(
+    quant_description: dict[str, Any],
+    prefix: str,
+    layer_type: str,
+    packed_modules_mapping: dict[str, Any] | None = None,
+):
+    """Create a quantization scheme instance for a layer.
+
+    Args:
+        quant_description: The quantization description dictionary.
+        prefix: The layer prefix.
+        layer_type: The type of layer ("linear", "moe", "attention").
+        packed_modules_mapping: Mapping for packed/fused modules.
+
+    Returns:
+        An instance of the appropriate quantization scheme class.
+    """
+    logger.info_once("Using vLLM Ascend ModelSlim quantization.")
+    quant_type = get_quant_type_for_layer(quant_description, prefix, layer_type, packed_modules_mapping)
+
+    if quant_type is None:
+        err_msg = f"Could not determine quantization type for layer {prefix} (layer_type={layer_type})."
+        logger.error(err_msg)
+        raise ValueError(err_msg)
+
+    # Use registry to get scheme class
+    scheme_cls = get_scheme_class(quant_type, layer_type)
+    if scheme_cls is not None:
+        return scheme_cls()
+
+    err_msg = f"Unsupported quant_type={quant_type} for layer_type={layer_type}."
+    logger.error(err_msg)
+    raise NotImplementedError(err_msg)
+
+
+@register_quantization_config(ASCEND_QUANTIZATION_METHOD)
+class AscendModelSlimConfig310(AscendModelSlimConfig):
+    """310P override for ModelSlim quantization config.
+
+    - Uses 310P-local scheme registry to create scheme by (quant_type, layer_type).
+    - MUST keep packed_modules_mapping behavior consistent with base, otherwise
+      fused modules (qkv_proj / gate_up_proj) will miss and fallback to base,
+      causing NZ/transpose issues on 310P.
+    """
+
+    def get_quant_method(
+        self,
+        layer: torch.nn.Module,
+        prefix: str,
+        tid2eid: Any = None,
+    ) -> QuantizeMethodBase | None:
+        vllm_config = get_current_vllm_config()
+        model_type = vllm_config.model_config.hf_config.model_type
+
+        if model_type in packed_modules_model_mapping:
+            self.packed_modules_mapping = packed_modules_model_mapping[model_type]
+
+        prefix = self.quant_prefix_mapper(model_type, prefix)
+
+        if isinstance(layer, LinearBase):
+            packed = getattr(self, "packed_modules_mapping", {})
+            if self.is_layer_skipped_ascend(prefix, packed):
+                from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
+
+                logger.debug("Select AscendUnquantizedLinearMethod for %s (layer=%s)", prefix, "LinearBase")
+                return AscendUnquantizedLinearMethod()
+
+            scheme = create_scheme_for_layer(
+                quant_description=self.quant_description,
+                prefix=prefix,
+                layer_type="linear",
+                packed_modules_mapping=packed,
+            )
+            logger.debug("Select AscendLinearMethod for %s (layer=%s)", prefix, "LinearBase")
+            return AscendLinearMethod(scheme)
+
+        elif _is_fused_moe_layer(layer):
+            if self.is_layer_skipped_ascend(prefix, self.packed_modules_mapping):
+                from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
+
+                logger.debug("Select AscendUnquantizedFusedMoEMethod310 for %s (layer=%s)", prefix, "FusedMoE")
+                return AscendUnquantizedFusedMoEMethod310(layer.moe_config)
+            scheme = create_scheme_for_layer(self.quant_description, prefix, "moe", self.packed_modules_mapping)
+            logger.debug("Select AscendFusedMoEMethod for %s (layer=%s)", prefix, "FusedMoE")
+            return AscendFusedMoEMethod(scheme, layer.moe_config)
+
+        elif isinstance(layer, VocabParallelEmbedding):
+            from vllm_ascend._310p.ops.vocab_parallel_embedding import AscendUnquantizedEmbeddingMethod310
+
+            logger.debug(
+                "Select AscendUnquantizedEmbeddingMethod310 for %s (layer=%s)", prefix, "VocabParallelEmbedding"
+            )
+            return AscendUnquantizedEmbeddingMethod310()
+
+        logger.debug("No quant method matched for %s, falling back to base", prefix)
+        return super().get_quant_method(layer, prefix)