Compute_rewards in PPO:rewards[j, start:ends[j]][-1] += reward_clip[j] is wrong

Inizia da compute_rewards e confronta il suo indice finale con l’action_mask utilizzato da actor_loss_fn. Riproduci l’esempio di tensore dell’issue e verifica che la ricompensa sottoposta a clipping venga assegnata all’ultimo token il cui action mask è attivo; done significa che la ricompensa contribuisce alla loss di quel token anziché a una posizione mascherata.

def compute_rewards(self, prompts, log_probs, ref_log_probs, reward_score,
                        action_mask):

        kl_divergence_estimate = -self.kl_ctl * (log_probs - ref_log_probs)
        rewards = kl_divergence_estimate
        start = prompts.shape[1] - 1
        ends = start + action_mask[:, start:].sum(1) + 1
        reward_clip = torch.clamp(reward_score, -self.clip_reward_value,
                                  self.clip_reward_value)
        batch_size = log_probs.shape[0]
        for j in range(batch_size):
            rewards[j, start:ends[j]][-1] += reward_clip[j]

        return rewards

rewards[:,ends[j]-1](advantages[:,ends[j]-1) will be mask in actor_loss_fn:

def actor_loss_fn(self, logprobs, old_logprobs, advantages, mask):
        ## policy gradient loss
        log_ratio = (logprobs - old_logprobs) * mask
        ratio = torch.exp(log_ratio)
        pg_loss1 = -advantages * ratio
        pg_loss2 = -advantages * torch.clamp(ratio, 1.0 - self.cliprange,
                                             1.0 + self.cliprange)
        pg_loss = torch.sum(torch.max(pg_loss1, pg_loss2) * mask) / mask.sum()
        return pg_loss

because mask is action_mask[:,start:] = attention_mask[:, 1:][:,start:] and rewards[:,ends[j]-1] represent the last non-padding token's reward by predicting padding token which action_mask is 0,The reward_score should give the penultimate non-padding token to reward it's action —— predicting the last non-padding token.
for example:

prompts:
tensor([[101, 102]])

start = 1 end = 5

seq:
tensor([[101, 102, 103, 104, 105,   0,   0,   0]])

attention_mask:
tensor([[1, 1, 1, 1, 1, 0, 0, 0]])

action_mask:
tensor([[1, 1, 1, 1, 0, 0, 0]])

mask = action_mask[:,start:] = tensor([[1, 1, 1, 0, 0, 0]])

reward_score = tensor([[2.5]])

old_rewards:
tensor([[ 8.1432e-03,  7.7722e-04, -4.7493e-05,  3.8694e-03,  2.5037e+00,
          0.0000e+00,  0.0000e+00]])

old_values:
tensor([[0.5000, 0.8000, 1.2000, 1.5000, 1.8000, 0.0000, 0.0000]])

advantages:
tensor([[1.4950, 1.1762, 0.9477, 0.7037, 0.0000, 0.0000]])

log_ratio= (logprobs - old_logprobs) * mask:
tensor([[ 0.0078,  0.0045, -0.0020, -0.0000,  0.0000,  0.0000]])

ratio
tensor([[1.0079, 1.0045, 0.9980, 1.0000, 1.0000, 1.0000]])

pg_loss1=-advantages * ratio:
tensor([[-1.5068, -1.1815, -0.9458, -0.7037, -0.0000, -0.0000]])

pg_loss2:
tensor([[-1.5068, -1.1815, -0.9458, -0.7037, -0.0000, -0.0000]])

torch.max(pg_loss1, pg_loss2) * mask:
tensor([[-1.5068, -1.1815, -0.9458, -0.0000, -0.0000, -0.0000]])

which u can see token 105's loss is 0 which means the most important reward 2.5037e+00 doesn't backward
rewards[j, start:ends[j]-1][-1] += reward_clip[j] is correct