feat: Implement GPU layer allocation based on memory ratio

utils.py

@@ -1,18 +1,64 @@
 import os
-from typing import Dict, Tuple, Union, Optional
+from typing import Dict, Tuple, Union, Optional, List
 
+import torch
 from torch.nn import Module
 from transformers import AutoModel, AutoTokenizer
 from transformers.tokenization_utils import PreTrainedTokenizer
 
 
-def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
+def calculate_per_gpu_layers(gpu_list: List[int], total_layers) -> Dict[int, int]:
+    # 根据每个GPU的显存大小，计算每个GPU应分配的层数
+    # 获取每个gpu的显存大小
+    gpu_memory_map = {
+        gpu: torch.cuda.get_device_properties(gpu).total_memory
+        for gpu in gpu_list
+    }
+
+    # 计算总显存大小
+    total_memory = sum(gpu_memory_map.values())
+
+    # 计算每个GPU的显存比例
+    gpu_memory_ratios = {
+        gpu: memory / total_memory
+        for gpu, memory in gpu_memory_map.items()
+    }
+
+    # 计算每个 GPU 应分配的层数
+    per_gpu_layers = {
+        gpu: int(round(total_layers * ratio))
+        for gpu, ratio in gpu_memory_ratios.items()
+    }
+
+    # 修正分配误差，确保总层数为total_layers
+    while True:
+        diff = total_layers - sum(per_gpu_layers.values())
+        if diff > 0:
+            gpu_with_max_memory = max(gpu_memory_ratios, key=gpu_memory_ratios.get)
+            per_gpu_layers[gpu_with_max_memory] += diff
+        elif diff < 0:
+            gpu_with_min_memory = min(gpu_memory_ratios, key=gpu_memory_ratios.get)
+            per_gpu_layers[gpu_with_min_memory] -= -diff
+        else:
+            break
+
+    return per_gpu_layers
+
+
+def auto_configure_device_map(num_gpus: int, gpu_list: Optional[List[int]] = None) -> Dict[str, int]:
     # transformer.word_embeddings 占用1层
     # transformer.final_layernorm 和 lm_head 占用1层
     # transformer.layers 占用 28 层
     # 总共30层分配到num_gpus张卡上
     num_trans_layers = 28
-    per_gpu_layers = 30 / num_gpus
+
+    if gpu_list is None:
+        gpu_list = list(range(num_gpus))
+    assert len(gpu_list) <= torch.cuda.device_count(), "分配的GPU数量超过了实际可用的GPU数量"
+
+    current_gpu_index = 0
+    # 获取每个gpu的承载的层数
+    per_gpu_layer_dict = calculate_per_gpu_layers(gpu_list, total_layers=num_trans_layers + 2)
 
     # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
     # windows下 model.device 会被设置成 transformer.word_embeddings.device
@@ -20,18 +66,23 @@ def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
     # 在调用chat或者stream_chat时,input_ids会被放到model.device上
     # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
     # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
-    device_map = {'transformer.word_embeddings': 0,
-                  'transformer.final_layernorm': 0, 'lm_head': 0}
+    device_map = {'transformer.word_embeddings': gpu_list[current_gpu_index],
+                  'transformer.final_layernorm': gpu_list[current_gpu_index], 'lm_head': gpu_list[current_gpu_index]}
 
     used = 2
-    gpu_target = 0
+
+    # 分配剩余的层数
+    current_gpu = gpu_list[current_gpu_index]
     for i in range(num_trans_layers):
-        if used >= per_gpu_layers:
-            gpu_target += 1
-            used = 0
-        assert gpu_target < num_gpus
-        device_map[f'transformer.layers.{i}'] = gpu_target
-        used += 1
+        if used < per_gpu_layer_dict[current_gpu]:
+            device_map[f"transformer.layers.{i}"] = current_gpu
+            used += 1
+        else:
+            # 当前 GPU 的层数已分配完，切换到下一个 GPU
+            current_gpu_index += 1
+            current_gpu = gpu_list[current_gpu_index]
+            device_map[f"transformer.layers.{i}"] = gpu_list[current_gpu]
+            used = 1
 
     return device_map