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5 changes: 5 additions & 0 deletions verl/workers/actor/megatron_actor.py
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -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]:
Expand Down
5 changes: 5 additions & 0 deletions verl/workers/critic/megatron_critic.py
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -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]:
Expand Down
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