fix hiyouga#2189

src/llmtuner/train/ppo/utils.py

@@ -1,7 +1,9 @@
 import json
+from contextlib import nullcontext
 from typing import TYPE_CHECKING, Dict, List, Literal, Optional
 
 import torch
+from transformers.integrations import is_deepspeed_zero3_enabled
 
 from ...extras.packages import is_requests_available
 
@@ -23,18 +25,22 @@ def get_rewards_from_server(server_url: str, messages: List[str]) -> List[torch.
 
 
 def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None:
-    if target == "reward":  # save default head temporarily
-        valuehead_state_dict: Dict[str, torch.Tensor] = model.v_head.state_dict()
-        setattr(model, "default_head_weight", valuehead_state_dict["summary.weight"].detach().clone())
-        setattr(model, "default_head_bias", valuehead_state_dict["summary.bias"].detach().clone())
-
-    model.pretrained_model.set_adapter(target)  # set the LoRA adapter to be active
-    model.v_head.load_state_dict(
-        {
-            "summary.weight": model.get_buffer("{}_head_weight".format(target)).detach().clone(),
-            "summary.bias": model.get_buffer("{}_head_bias".format(target)).detach().clone(),
-        }
-    )
+    if is_deepspeed_zero3_enabled():
+        import deepspeed  # type: ignore
+
+        params = [model.v_head.summary.weight, model.v_head.summary.bias]
+        context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
+    else:
+        context_maybe_zero3 = nullcontext()
+
+    with context_maybe_zero3:
+        if target == "reward":  # save default head temporarily
+            setattr(model, "default_head_weight", model.v_head.summary.weight.data.detach().clone())
+            setattr(model, "default_head_bias", model.v_head.summary.bias.data.detach().clone())
+
+        model.pretrained_model.set_adapter(target)  # set the LoRA adapter to be active
+        model.v_head.summary.weight.data = model.get_buffer("{}_head_weight".format(target)).detach().clone()
+        model.v_head.summary.bias.data = model.get_buffer("{}_head_bias".format(target)).detach().clone()
 
 
 def dump_layernorm(model: "PreTrainedModel") -> Dict[str, torch.Tensor]: