大改

vllm_npu/eplb/utils.py

@@ -0,0 +1,77 @@
+#
+# Copyright (c) 2025 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.
+#
+# Todo: Once https://github.com/vllm-project/vllm/pull/23553 is merged in vllm. Remove this model register.
+import types
+
+import torch
+
+
+def get_expert_map(self, layer_id):
+    return self.model.layers[layer_id].mlp.experts.get_map()
+
+
+def get_log2phy_map(self, layer_id):
+    return self.model.layers[layer_id].mlp.experts.get_log2phy_map()
+
+
+def get_all_expert_map(self, num_moe_layers):
+    all_loads = []
+    num_dense_layers = self.num_dense_layers if hasattr(
+        self, "num_dense_layers") else 0
+    for layer_id in range(num_moe_layers):
+        load_tensor = self.get_expert_map(
+            layer_id + num_dense_layers)  # (num_experts_per_layer,)
+        all_loads.append(load_tensor)
+
+    return torch.stack(all_loads, dim=0)
+
+
+def get_all_moe_loads(self):
+    num_dense_layers = self.num_dense_layers if hasattr(
+        self, "num_dense_layers") else 0
+    all_moe_loads = torch.stack(
+        [self.model.layers[layer_id + num_dense_layers].mlp.experts.moe_load \
+            for layer_id in range(self.num_moe_layers)],
+        dim=0
+    )
+    return all_moe_loads
+
+
+def clear_all_moe_loads(self):
+    num_dense_layers = self.num_dense_layers if hasattr(
+        self, "num_dense_layers") else 0
+    for layer_id in range(self.num_moe_layers):
+        self.model.layers[layer_id +
+                          num_dense_layers].mlp.experts.clear_moe_load()
+
+
+def model_register(model, model_config):
+    model.get_expert_map = types.MethodType(get_expert_map, model)
+    model.get_log2phy_map = types.MethodType(get_log2phy_map, model)
+    model.get_all_expert_map = types.MethodType(get_all_expert_map, model)
+    model.get_all_moe_loads = types.MethodType(get_all_moe_loads, model)
+    model.clear_all_moe_loads = types.MethodType(clear_all_moe_loads, model)
+
+    config = model_config.hf_config
+
+    if config.model_type == "qwen3_moe":
+        model.num_moe_layers = config.num_hidden_layers
+    elif config.model_type == "deepseek_v2" or config.model_type == "deepseek_v3":
+        model.num_dense_layers = config.first_k_dense_replace
+        model.num_moe_layers = config.num_hidden_layers - model.num_dense_layers
+    else:
+        raise NotImplementedError("EPLB is not supported.")