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Merged
merged 1 commit into from
Oct 21, 2025
Merged

Remove useless comments #483

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14 changes: 1 addition & 13 deletions apps/grpo/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,6 +120,7 @@ def collate(
return inputs, targets


# Note: This is also available in losses.grpo_loss via `SimpleGRPOLoss`
def simple_grpo_loss(
logits: torch.Tensor,
response: torch.Tensor,
Expand All @@ -128,12 +129,7 @@ def simple_grpo_loss(
padding_mask: torch.Tensor,
beta: float = 0.1,
) -> torch.Tensor:
"""
Example GRPO Loss Function for RLTrainer
"""
logprobs: torch.Tensor = compute_logprobs(logits, response)

# Note: This is also available in losses.grpo_loss via `SimpleGRPOLoss`
kl = torch.exp(ref_logprobs - logprobs) - (ref_logprobs - logprobs) - 1
per_token_policy_loss = torch.exp(logprobs - logprobs.detach()) * advantages
per_token_loss = -(per_token_policy_loss - beta * kl)
Expand All @@ -146,7 +142,6 @@ def simple_grpo_loss(

@dataclass
class RewardActor(ForgeActor):
"""Reward actor that uses a list of scoring functions."""

reward_functions: list[Callable]

Expand Down Expand Up @@ -178,14 +173,12 @@ async def evaluate_response(self, prompt: str, response: str, target: str) -> fl
Reduce.STD,
)

# avg total reward
record_metric(
"reward/evaluate_response/avg_total_reward",
reward,
Reduce.MEAN,
)

# count fn calls
record_metric(
f"reward/evaluate_response/count_{reward_fn_name}_calls",
1,
Expand All @@ -198,8 +191,6 @@ async def evaluate_response(self, prompt: str, response: str, target: str) -> fl

@dataclass
class ComputeAdvantages(ForgeActor):
"""Compute advantages for GRPO using reward signals."""

@endpoint
async def compute(self, group: Group) -> list[float]:
# TODO: add batch processing
Expand Down Expand Up @@ -255,7 +246,6 @@ async def sample(self) -> dict[str, str] | None:
try:
sample = next(self._iterator)

# Record dataset metrics
record_metric("dataset/sample/count_samples_generated", 1, Reduce.SUM)
record_metric(
"dataset/sample/avg_sample_len",
Expand Down Expand Up @@ -406,13 +396,11 @@ async def continuous_rollouts():
episode.ref_logprobs = ref_logprobs[i]
del ref_logprobs, input_ids

# Calculate advantages and add to replay buffer
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@felipemello1 felipemello1 Oct 21, 2025

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:O i kind like this. It works as a visual way to segment code blocks

advantages = await compute_advantages.compute.call_one(episodes)
for episode, advantage in zip(episodes, advantages):
episode.advantage = advantage
await replay_buffer.add.call_one(episode)

# Log metrics
rollout_count += 1
record_metric(
"main/continuous_rollouts/count_rollout_iterations", 1, Reduce.SUM
Expand Down
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