[megatron] fix: set model to eval during compute_log_prob/compute_values (verl-project#4708)

### What does this PR do?

Current code doesn't seem to set model to eval when we compute_log_prob,
ref_compute_log_prob, and compute_values for the megatron backend, which
might go wrong if the model contains dropout layers for e.g. LoRA.

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Signed-off-by: Hollow Man <hollowman@opensuse.org>

Author: HollowMan6

verl/workers/actor/megatron_actor.py

@@ -196,6 +196,9 @@ def compute_log_prob(self, data: DataProto, calculate_entropy=False) -> torch.Te
         Returns:
             DataProto: torch.Tensor: the log_prob tensor
         """
+        prev_modes = [m.training for m in self.actor_module]
+        for module in self.actor_module:
+            module.eval()
         use_dynamic_bsz = data.meta_info.get("use_dynamic_bsz", False)
         micro_batch_size = data.meta_info.get("micro_batch_size", None)
         max_token_len = data.meta_info.get("max_token_len", None)
@@ -306,6 +309,8 @@ def compute_logprobs_fn(output, data, use_dynamic_bsz=False, indices=None):
         # add empty cache after each compute
         get_torch_device().empty_cache()
 
+        for module, mode in zip(self.actor_module, prev_modes, strict=False):
+            module.train(mode)
         return log_probs, entropys, layers_topk_idx
 
     def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]:

verl/workers/critic/megatron_critic.py

@@ -87,6 +87,9 @@ def _validate_config(self, config) -> None:
 
     @GPUMemoryLogger("megatron critic", logger=logger)
     def compute_values(self, data: DataProto) -> DataProto:
+        prev_modes = [m.training for m in self.critic_module]
+        for module in self.critic_module:
+            module.eval()
         responses = data.batch["responses"]
         attention_mask = data.batch["attention_mask"]
         use_dynamic_bsz = data.meta_info.get("use_dynamic_bsz", False)
@@ -139,6 +142,8 @@ def compute_values(self, data: DataProto) -> DataProto:
         # add empty cache after each compute
         get_torch_device().empty_cache()
 
+        for module, mode in zip(self.critic_module, prev_modes, strict=False):
+            module.train(mode)
         return values
 
     def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]: