change to right-padding, update reward score #803

Former-commit-id: baa90415bc8f5ebd423d001378b51c3a3a6c2ec7

src/llmtuner/tuner/ppo/trainer.py

@@ -102,6 +102,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
             # Get inputs
             queries, responses = self.get_inputs(batch, length_sampler, **gen_kwargs)
+            self.tokenizer.padding_side = "right" # change padding side
             rewards = self.get_rewards(queries, responses, unwrapped_model)
 
             # Cast to training mode
@@ -110,6 +111,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
             # Run PPO step
             stats = self.step(queries, responses, rewards)
+            self.tokenizer.padding_side = "left" # restore padding side
             loss_meter.update(stats["ppo/loss/total"], n=len(rewards))
             reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards))
 
@@ -169,7 +171,11 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
         query, response = batch["input_ids"].detach().cpu(), response[:, batch["input_ids"].size(-1):].detach().cpu()
         for i in range(len(query)):
             query_length = (query[i] != self.tokenizer.pad_token_id).nonzero()[0]
-            response_length = (response[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
+            response_index = (response[i] != self.tokenizer.pad_token_id).nonzero()
+            if len(response_index) == 0:
+                response_length = 1 # allow empty response
+            else:
+                response_length = response_index[-1] + 1
             queries.append(query[i, query_length:]) # remove padding from left
             responses.append(response[i, :response_length]) # remove padding from right
 
@@ -194,7 +200,11 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
         if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2
             values = torch.transpose(values, 0, 1)
 
-        rewards = [reward for reward in values[:, -1].float().detach().cpu()] # use fp32 type
+        rewards = []
+        for i in range(values.size(0)):
+            end_index = batch["attention_mask"][i].nonzero()[-1]
+            rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type
+
         replace_model(unwrapped_model, target="default")
         return rewards
 
@@ -241,7 +251,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
             for j in range(len(query_batch)):
                 start = len(query_batch[j]) - 1
-                if attention_mask[j, 0] == 0:  # offset left padding
+                if attention_mask[j, 0] == 0: # offset left padding
                     start += attention_mask[j, :].nonzero()[0]
                 end = start + len(response_batch[j])