大改

vllm_npu/eplb/adaptor/abstract_adaptor.py

@@ -0,0 +1,44 @@
+#
+# 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/issues/22246 is merged in vllm. Remove this adaptor.
+from abc import abstractmethod
+from typing import Any
+
+
+class EplbAdaptor():
+
+    def __init__(self, **args):
+        pass
+
+    @abstractmethod
+    def get_rank_expert_workload(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_init_expert_map(self, num_moe_layers: Any) -> Any:
+        raise NotImplementedError
+
+    @abstractmethod
+    def do_update_expert_map(self, layer_id: Any,
+                             updated_expert_map: Any) -> Any:
+        raise NotImplementedError
+
+    @abstractmethod
+    def do_update_expert_weight(self, layer_id: Any,
+                                local_expert_to_replace: Any,
+                                buffer_tensor_id: Any) -> Any:
+        raise NotImplementedError

vllm_npu/eplb/adaptor/vllm_adaptor.py

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+#
+# 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/issues/22246 is merged in vllm. Remove this adaptor.
+import json
+from typing import Any
+
+import torch
+import torch.distributed as dist
+from vllm.logger import logger
+
+from vllm_npu.ascend_config import get_ascend_config
+from vllm_npu.eplb.adaptor.abstract_adaptor import EplbAdaptor
+
+
+class VllmEplbAdaptor(EplbAdaptor):
+
+    def __init__(self, model, **args):
+        super().__init__(**args)
+        self.model = model
+        self.rank_id = dist.get_rank()
+        self.world_size = dist.get_world_size()
+        self.param_dict = dict(self.model.named_parameters())
+        if self.model.config.model_type == "qwen3_moe":
+            self.num_dense_layers = 0
+            self.global_expert_num = self.model.config.num_experts
+        else:
+            self.num_dense_layers = self.model.config.first_k_dense_replace
+            self.global_expert_num = self.model.config.n_routed_experts
+        self.num_moe_layers = self.model.config.num_hidden_layers - self.num_dense_layers
+        self.init_redundancy_expert = get_ascend_config(
+        ).init_redundancy_expert
+
+        # TODO: init self.expert_weight_names depending on different model types, only deepseek v3 w8a8 and qwen3-moe is supported here
+        if self.model.quant_config is not None:
+            self.expert_weight_names = [
+                "w13_weight", "w2_weight", "w13_weight_scale",
+                "w13_weight_offset", "w2_weight_scale", "w2_weight_offset"
+            ]
+        else:
+            self.expert_weight_names = ["w13_weight", "w2_weight"]
+
+        self.expert_map_per_layer = dict(
+        )  # reference to expert map on device for expert map update
+        self.expert_map_per_layer_cpu = dict(
+        )  # copy of expert map on CPU to avoid device synchronize frequently
+        for layer_idx in range(self.num_moe_layers):
+            self.expert_map_per_layer[self.num_dense_layers + layer_idx] = \
+                self.model.get_expert_map(self.num_dense_layers + layer_idx)
+
+        # TODO: here we set number of buffer tensor equal to number of expert in each laryer, which can be improved
+        num_buffer_tensor = torch.where(
+            self.expert_map_per_layer[self.num_dense_layers] != -1)[0].numel()
+        self.buffer_tensor_list: list[list[Any]] = [
+            [] for _ in range(num_buffer_tensor)
+        ]
+        self.init_buffer_tensor(num_buffer_tensor)
+
+        self.expert_param_per_layer = dict()
+        self.init_expert_param_per_layer()
+
+        self.log2phy_map_per_layer = dict()
+        for layer_idx in range(self.num_moe_layers):
+            self.log2phy_map_per_layer[self.num_dense_layers + layer_idx] = \
+                self.model.get_log2phy_map(self.num_dense_layers + layer_idx)
+
+        self.all_topk_ids = []
+
+    def init_buffer_tensor(self, num_buffer_tensor):
+        for buffer_id in range(num_buffer_tensor):
+            for name in self.expert_weight_names:
+                complete_name = "model.layers." + str(
+                    self.num_dense_layers) + ".mlp.experts." + name
+                expert_tensor = self.param_dict[complete_name].data[0]
+                if name in ["w13_weight", "w2_weight"]:
+                    expert_tensor = expert_tensor.clone()
+                buffer_tensor = torch.empty_like(expert_tensor)
+                self.buffer_tensor_list[buffer_id].append(buffer_tensor)
+
+    def init_expert_param_per_layer(self):
+        num_local_expert = self.param_dict["model.layers." + str(self.num_dense_layers) + \
+                                           ".mlp.experts." + self.expert_weight_names[0]].data.shape[0]
+        for moe_layer_id in range(self.num_moe_layers):
+            layer_idx = self.num_dense_layers + moe_layer_id
+            self.expert_param_per_layer[layer_idx] = list()
+            for local_expert_id in range(num_local_expert):
+                self.expert_param_per_layer[layer_idx].append([
+                    self.param_dict["model.layers." + str(layer_idx) +
+                                    ".mlp.experts." +
+                                    name].data[local_expert_id]
+                    for name in self.expert_weight_names
+                ])
+
+    def get_rank_expert_workload(self) -> torch.Tensor:
+        self.moe_load = self.model.get_all_moe_loads()
+        return self.moe_load
+
+    def get_init_expert_map(self, num_moe_layers):
+        expert_map = self.model.get_all_expert_map(num_moe_layers)
+        if dist.is_initialized():
+            world_size = dist.get_world_size()
+
+        gathered = torch.empty(
+            (world_size, *expert_map.shape),  # [W, L, E]
+            dtype=expert_map.dtype,
+            device=expert_map.device)
+
+        dist.all_gather_into_tensor(gathered, expert_map)
+        all_maps = gathered.permute(1, 0, 2)
+        all_expert_maps = all_maps.cpu()
+
+        for layer_idx in range(num_moe_layers):
+            self.expert_map_per_layer_cpu[self.num_dense_layers + layer_idx] = \
+                all_expert_maps[layer_idx][self.rank_id]
+
+        return all_expert_maps
+
+    def get_init_expert_map_from_file(self, num_moe_layers, expert_map_path):
+
+        try:
+            expert_map_tensor, layers_num, ranks_num = self._expert_file_to_tensor(
+                expert_map_path)
+            expert_map_all = self.local2global(expert_map_tensor)
+        except (TypeError, FileNotFoundError, OSError):
+            expert_map_all = self.determine_expert_map_all()
+
+        for layer_idx in range(num_moe_layers):
+            if self.model.config.model_type == "qwen3_moe":
+                self.expert_map_per_layer_cpu[layer_idx] = \
+                    expert_map_all[layer_idx][self.rank_id]
+            else:
+                self.expert_map_per_layer_cpu[layer_idx + self.num_dense_layers] = \
+                    expert_map_all[layer_idx][self.rank_id]
+        return expert_map_all
+
+    def _expert_file_to_tensor(self, expert_map_path: str):
+        with open(expert_map_path, "r") as f:
+            data = json.load(f)
+            layers_num = data["moe_layer_count"]
+            gpus_num = data["layer_list"][0]["device_count"]
+
+            tensor_data = []
+            for layer in data["layer_list"]:
+                device_data = []
+                for device in layer["device_list"]:
+                    device_data.append(device["device_expert"])
+                tensor_data.append(device_data)
+            expert_map_tensor = torch.tensor(tensor_data, dtype=torch.int32)
+            return expert_map_tensor, layers_num, gpus_num
+        logger.error(f"failed to read expert_map_path: {expert_map_path}")
+
+    def _export_tensor_to_file(self, expert_maps, expert_map_record_path: str):
+        if self.rank_id == 0:
+            num_local_experts = expert_maps.max() + 1
+            expert_maps_local = self.global2local(expert_maps,
+                                                  num_local_experts)
+
+            expert_maps_list = expert_maps_local.tolist()
+            record: dict[str, Any] = {
+                "moe_layer_count": len(expert_maps_list),
+                "layer_list": []
+            }
+
+            for layer_idx, layer_data in enumerate(expert_maps_list):
+                layer_record: dict[str, Any] = {
+                    "layer_id": layer_idx,
+                    "device_count": len(layer_data),
+                    "device_list": []
+                }
+
+                for device_idx, experts in enumerate(layer_data):
+                    device_record = {
+                        "device_id": device_idx,
+                        "device_expert": experts
+                    }
+                    layer_record["device_list"].append(device_record)
+
+                record["layer_list"].append(layer_record)
+
+            with open(expert_map_record_path, "w") as f:
+                json.dump(record, f, indent=4)
+
+    def do_update_expert_map(self, layer_id, updated_expert_map):
+        self.expert_map_per_layer[layer_id].copy_(updated_expert_map)
+        self.expert_map_per_layer_cpu[layer_id].copy_(updated_expert_map)
+
+    def do_update_expert_weight(self, layer_id, local_expert_to_replace,
+                                buffer_tensor_id):
+        for expert_tensor, buffer_tensor in zip(
+                self.expert_param_per_layer[layer_id][local_expert_to_replace],
+                self.buffer_tensor_list[buffer_tensor_id]):
+            expert_tensor.copy_(buffer_tensor)
+            logger.debug(f"Expert tensor shape is :{expert_tensor.shape}")
+
+    def do_update_log2phy_map(self, layer_id, updated_log2phy_map):
+        if self.log2phy_map_per_layer[layer_id] is not None:
+            self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map)
+
+    def global2local(self, placement: torch.Tensor,
+                     E_local: int) -> torch.Tensor:
+
+        L, G, _ = placement.shape
+        device = placement.device
+
+        pt_local = torch.full((L, G, E_local),
+                              fill_value=-1,
+                              dtype=torch.long,
+                              device=device)
+
+        valid = placement >= 0
+        l_idx, g_idx, k_idx = valid.nonzero(as_tuple=True)
+
+        slot_idx = placement[l_idx, g_idx, k_idx]
+
+        pt_local[l_idx, g_idx, slot_idx] = k_idx
+
+        return pt_local
+
+    def local2global(self, placement_local: torch.Tensor) -> torch.Tensor:
+
+        L, G, E_local = placement_local.shape
+        device = placement_local.device
+
+        max_id = torch.max(placement_local)
+        E_global = (max_id + 1).item() if max_id >= 0 else 0
+
+        if E_global == 0:
+            return torch.empty((L, G, 0), dtype=torch.long, device=device)
+
+        placement_global = torch.full((L, G, E_global),
+                                      fill_value=-1,
+                                      dtype=torch.long,
+                                      device=device)
+
+        valid = placement_local >= 0
+        l_idx, g_idx, slot_idx = valid.nonzero(as_tuple=True)
+        gid_idx = placement_local[l_idx, g_idx, slot_idx]
+
+        placement_global[l_idx, g_idx, gid_idx] = slot_idx
+
+        return placement_global
+
+    def determine_expert_map_all(self):
+        if self.world_size == 1:
+            local_ids = torch.arange(self.global_expert_num, dtype=torch.int32)
+            return local_ids.view(1, 1, -1).expand(self.num_moe_layers, 1, -1)
+
+        local_num_experts = self.global_expert_num // self.world_size
+
+        expert_map_all = torch.full(
+            (self.num_moe_layers, self.world_size, self.global_expert_num),
+            -1,
+            dtype=torch.int32)
+
+        for r in range(self.world_size):
+            if r < self.world_size - 1:
+                start = r * local_num_experts
+                end = (r + 1) * local_num_experts
+                local_count = local_num_experts
+            else:
+                start = r * local_num_experts
+                end = self.global_expert_num
+                local_count = self.global_expert_num - r * local_num_experts
+
+            if r < self.init_redundancy_expert:
+                local_count += 1
+                if end < self.global_expert_num:
+                    end += 1
+                else:
+                    start -= 1
+
+            local_ids = torch.arange(local_count, dtype=torch.int32)
+            expert_map_all[:, r, start:end] = local_ids.unsqueeze(0).expand(
+                self.num_moe_layers, -1)
+
+        return expert_map_all