205 lines
9.1 KiB
Python
205 lines
9.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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# Todo: Once https://github.com/vllm-project/vllm/issues/22246 is merged in vllm. Remove this adaptor.
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import json
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from typing import Any
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import torch
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import torch.distributed as dist
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from vllm.logger import logger
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.quantization.quant_type import QuantType
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EPLB_EXPERT_WEIGHT_NAMES = {
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(QuantType.NONE, False): ("w13_weight", "w2_weight"),
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(QuantType.NONE, True): ("w13_weight_list", "w2_weight_list"),
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(QuantType.W8A8, False): (
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"w13_weight_list",
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"w2_weight_list",
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"w13_weight_scale_fp32_list",
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"w2_weight_scale_list",
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),
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(QuantType.W8A8, True): (
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"w13_weight_list",
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"w2_weight_list",
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"w13_weight_scale_fp32_list",
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"w2_weight_scale_list",
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"fused_w1_scale_list",
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"fused_w2_scale_list",
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),
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(QuantType.W4A8, True): (
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"w13_weight_list",
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"w2_weight_list",
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"w13_weight_scale_list",
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"w2_weight_scale_list",
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"w13_scale_bias_list",
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"w2_scale_bias_list",
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),
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(QuantType.MXFP4, False): ("w13_weight", "w2_weight", "w13_weight_scale", "w2_weight_scale"),
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(QuantType.MXFP4, True): ("w13_weight", "w2_weight", "w13_weight_scale", "w2_weight_scale"),
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(QuantType.MXFP8, False): ("w13_weight", "w2_weight", "w13_weight_scale", "w2_weight_scale"),
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(QuantType.MXFP8, True): ("w13_weight", "w2_weight", "w13_weight_scale", "w2_weight_scale"),
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}
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class VllmEplbAdaptor:
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_registered_moe_layers: list["torch.nn.Module"] = []
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@staticmethod
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def register_layer(layer: "torch.nn.Module") -> None:
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"""Register a MoE layer for EPLB. Called during layer initialization.
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Only real layers call this; PPMissingLayer won't, so the registry
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naturally contains only layers on this PP rank.
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"""
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VllmEplbAdaptor._registered_moe_layers.append(layer)
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def __init__(self, model, **args):
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super().__init__(**args)
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if hasattr(model, "language_model"):
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self.model = model.language_model
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self.config = model.config.text_config
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else:
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self.model = model
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self.config = model.config
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self.rank_id = dist.get_rank()
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self.world_size = dist.get_world_size()
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self.num_dense_layers = getattr(self.config, "first_k_dense_replace", 0)
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self.moe_layers = VllmEplbAdaptor._registered_moe_layers
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self.num_moe_layers = len(self.moe_layers)
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self.expert_map_per_layer_cpu = dict() # copy of expert map on CPU to avoid device synchronize frequently
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# Get num_local_experts from first real MoE layer
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first_layer = self.moe_layers[0]
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self.num_local_experts = first_layer.local_num_experts
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self.ep_rank = first_layer.ep_rank
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self.expert_param_per_layer = dict()
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self.expert_weight_key_per_layer = dict()
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self.init_expert_param_per_layer()
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num_buffer_tensor = self.num_local_experts
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self.buffer_tensor_list: dict[Any, list[list[Any]]] = dict()
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self.init_buffer_tensor(num_buffer_tensor)
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self.log2phy_map_per_layer = dict()
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for local_idx, layer in enumerate(self.moe_layers):
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self.log2phy_map_per_layer[local_idx] = layer.get_log2phy_map()
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def init_buffer_tensor(self, num_buffer_tensor):
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buffer_tensor_shapes: dict[Any, list[torch.Size]] = dict()
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for local_idx, _ in enumerate(self.moe_layers):
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expert_weight_key = self.expert_weight_key_per_layer[local_idx]
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expert_weight_names = EPLB_EXPERT_WEIGHT_NAMES[expert_weight_key]
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expert_tensors = [self.param_dict[f"{local_idx}.{name}"][0] for name in expert_weight_names]
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expert_tensor_shapes = [tensor.shape for tensor in expert_tensors]
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if expert_weight_key in self.buffer_tensor_list:
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assert expert_tensor_shapes == buffer_tensor_shapes[expert_weight_key], (
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f"EPLB expert weight shapes mismatch for {expert_weight_key}: "
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f"expected {buffer_tensor_shapes[expert_weight_key]}, got {expert_tensor_shapes}"
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)
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continue
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buffer_tensor_shapes[expert_weight_key] = expert_tensor_shapes
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self.buffer_tensor_list[expert_weight_key] = [[] for _ in range(num_buffer_tensor)]
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for buffer_id in range(num_buffer_tensor):
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for expert_tensor in expert_tensors:
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buffer_tensor = torch.empty_like(expert_tensor)
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self.buffer_tensor_list[expert_weight_key][buffer_id].append(buffer_tensor)
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def init_expert_param_per_layer(self):
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self.param_dict = dict()
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for local_idx, layer in enumerate(self.moe_layers):
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quant_type = QuantType.NONE if self.model.quant_config is None else layer.quant_type
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expert_weight_key = (quant_type, get_ascend_config().enable_fused_mc2 == 1)
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if expert_weight_key[0] == QuantType.W4A8MXFP:
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raise RuntimeError(f"EPLB not support {quant_type}")
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if expert_weight_key not in EPLB_EXPERT_WEIGHT_NAMES:
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raise ValueError(f"EPLB not support {quant_type} with fused MC2 {expert_weight_key[1]}")
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expert_weight_names = EPLB_EXPERT_WEIGHT_NAMES[expert_weight_key]
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self.expert_weight_key_per_layer[local_idx] = expert_weight_key
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self.expert_param_per_layer[local_idx] = list()
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for name in expert_weight_names:
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param_key = f"{local_idx}.{name}"
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self.param_dict[param_key] = getattr(layer, name)
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for local_expert_id in range(self.num_local_experts):
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per_expert_param = list()
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for name in expert_weight_names:
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per_expert_param.append(self.param_dict[f"{local_idx}.{name}"][local_expert_id])
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self.expert_param_per_layer[local_idx].append(per_expert_param)
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def get_rank_expert_workload(self) -> torch.Tensor:
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loads = [layer.moe_load for layer in self.moe_layers]
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self.moe_load = torch.stack(loads, dim=0) if loads else torch.empty(0)
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return self.moe_load
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def clear_all_moe_loads(self):
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for layer in self.moe_layers:
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layer.clear_moe_load()
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def _export_tensor_to_file(self, expert_maps, expert_map_record_path: str):
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if self.rank_id == 0:
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num_local_experts = expert_maps.max() + 1
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expert_maps_list = expert_maps.tolist()
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record: dict[str, Any] = {"moe_layer_count": len(expert_maps_list), "layer_list": []}
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for layer_idx, layer_data in enumerate(expert_maps_list):
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layer_record: dict[str, Any] = {
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"layer_id": layer_idx,
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"device_count": len(layer_data),
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"device_list": [],
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}
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for device_idx, experts in enumerate(layer_data):
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placement = [experts.index(i) for i in range(num_local_experts)]
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device_record = {"device_id": device_idx, "device_expert": placement}
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layer_record["device_list"].append(device_record)
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record["layer_list"].append(layer_record)
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with open(expert_map_record_path, "w") as f:
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json.dump(record, f, indent=4)
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def do_update_expert_map(self, layer_id, updated_expert_map):
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self.expert_map_per_layer_cpu[layer_id].copy_(updated_expert_map)
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def do_update_expert_weight(self, layer_id, local_expert_to_replace, buffer_tensor_id):
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expert_weight_key = self.expert_weight_key_per_layer[layer_id]
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for expert_tensor, buffer_tensor in zip(
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self.expert_param_per_layer[layer_id][local_expert_to_replace],
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self.buffer_tensor_list[expert_weight_key][buffer_tensor_id],
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):
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expert_tensor.copy_(buffer_tensor)
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logger.debug("Expert tensor shape is :%s", expert_tensor.shape)
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def do_update_log2phy_map(self, layer_id, updated_log2phy_map):
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if self.log2phy_map_per_layer[layer_id] is not None:
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self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map)
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def get_global_expert_map(self):
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all_layer_global_expert_map = []
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for local_idx, layer in enumerate(self.moe_layers):
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map_cpu = layer.global_expert_map.cpu()
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all_layer_global_expert_map.append(map_cpu)
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self.expert_map_per_layer_cpu[local_idx] = map_cpu[self.ep_rank]
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return torch.stack(all_layer_global_expert_map)
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