.“

ds_config_zero3.json

@@ -11,10 +11,14 @@
     "overlap_comm": true,
     "contiguous_gradients": true,
     "reduce_scatter": true,
-    "reduce_bucket_size": 2e8,
-    "stage3_prefetch_bucket_size": 2e8,
-    "stage3_param_persistence_threshold": 1e6,
-    "stage3_gather_16bit_weights_on_model_save": false
+    "reduce_bucket_size": 50000000,
+    "stage3_prefetch_bucket_size": 50000000,
+    "stage3_param_persistence_threshold": 0,
+    "stage3_gather_16bit_weights_on_model_save": false,
+  
+    "offload_param":     { "device": "cpu", "pin_memory": true },
+    "offload_optimizer": { "device": "cpu", "pin_memory": true }
+        
   },
   "wall_clock_breakdown": false
 }

mm-zero3.sh

@@ -1,3 +1,5 @@
+export PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:128,expandable_segments:True,garbage_collection_threshold:0.9"
+
 deepspeed --hostfile hostfile \
   --num_nodes 6 --num_gpus 4 \
   /home/test/jd_train/train_sft_ds.py \

train_sft_ds.py

@@ -11,6 +11,8 @@ from typing import Dict, List, Iterable, Iterator, Tuple, Optional
 import torch
 import torch.distributed as dist
 from torch.utils.data import IterableDataset, Dataset
+from contextlib import nullcontext
+
 
 from datasets import load_dataset
 from transformers import (
@@ -482,6 +484,19 @@ def main():
             (torch.float16 if torch.cuda.is_available() else torch.float32))
 
 
+
+
+    try:
+        import deepspeed
+        zero_init_ctx = deepspeed.zero.Init(
+            remote_device="cpu",     # 参数初始驻留 CPU，安全
+            pin_memory=True,
+            dtype=dtype              # 和你上面的 dtype 一致（bf16）
+        )
+    except Exception:
+        zero_init_ctx = nullcontext()  # 没装 DS 时也能单机跑
+
+    with zero_init_ctx:
         model = AutoModelForCausalLM.from_pretrained(
             args.model_name_or_path,
             torch_dtype=dtype,
@@ -490,6 +505,18 @@ def main():
             attn_implementation="sdpa"
         )
 
+
+
+
+
+    # model = AutoModelForCausalLM.from_pretrained(
+    #     args.model_name_or_path,
+    #     torch_dtype=dtype,
+    #     low_cpu_mem_usage=True,
+    #     trust_remote_code=True,
+    #     attn_implementation="sdpa"
+    # )
+
     print(f"GC enabled? {getattr(model, 'is_gradient_checkpointing', False)}", flush=True)
     dbg(f"model loaded: dtype={next(model.parameters()).dtype} "
     f"use_cache={getattr(model.config,'use_cache',None)} "