init v0.23.0

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

vllm_ascend/_310p/sharded_state_loader_310p.py

@@ -0,0 +1,80 @@
+#
+# 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.
+#
+import json
+import os
+from pathlib import Path
+
+import torch
+from vllm.config.load import LoadConfig
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.model_loader import ShardedStateLoader
+
+
+class ShardedStateLoader310(ShardedStateLoader):
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+
+    @staticmethod
+    def save_model(
+        model: torch.nn.Module,
+        path: str,
+        pattern: str | None = None,
+        max_size: int | None = None,
+    ) -> None:
+        from safetensors.torch import save_file
+        from vllm.distributed import get_tensor_model_parallel_rank
+
+        rank = get_tensor_model_parallel_rank()
+        part_idx = 0
+        state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
+
+        filename = ShardedStateLoader.DEFAULT_PATTERN.format(rank=rank, part=part_idx)
+        save_file(
+            state_dict,
+            os.path.join(path, filename),
+        )
+
+    @staticmethod
+    def generate_quant_description(
+        model: torch.nn.Module,
+        path: str,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        """Generate a mapping of parameter names to their corresponding quantization types."""
+        quant_description = {}
+        if quant_config is None:
+            quantize_type = "FLOAT"
+        else:
+            try:
+                quantize_type = quant_config.quant_description.get("model_quant_type", "FLOAT")
+            except AttributeError:
+                quantize_type = "FLOAT"
+        quant_description["model_quant_type"] = quantize_type
+        quant_description["version"] = "1.0.0"
+        state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
+        for name, tensor in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                if tensor.dtype in [torch.int8, torch.int32, torch.int64]:
+                    quant_description[name] = quantize_type
+                else:
+                    quant_description[name] = "FLOAT"
+            else:
+                quant_description[name] = "FLOAT"
+
+        json_path = Path(path) / "parameters_type_map.json"
+        with json_path.open("w", encoding="utf-8") as f:
+            json.dump(quant_description, f, indent=2)