tiny fix
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@ -182,9 +182,13 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
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"""
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replace_model(unwrapped_model, target="reward")
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batch = self.prepare_model_inputs(queries, responses)
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_, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
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if values.size(0) != batch["input_ids"].size(0): # adapt chatglm2
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with torch.cuda.amp.autocast(dtype=self.compute_dtype): # support bf16
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_, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
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if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2
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values = torch.transpose(values, 0, 1)
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rewards = [reward for reward in values[:, -1].float().detach().cpu()] # use fp32 type
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replace_model(unwrapped_model, target="default")
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return rewards
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@ -220,7 +224,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
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with torch.cuda.amp.autocast(dtype=self.compute_dtype): # support bf16
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logits, _, values = model(**input_kwargs)
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if values.size(0) != input_ids.size(0): # adapt chatglm2
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if values.size(0) != input_ids.size(0): # adapt to chatglm2
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values = torch.transpose(values, 0, 1)
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logprobs = logprobs_from_logits(logits[:, :-1, :], input_ids[:, 1:])
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@ -240,6 +244,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
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all_logits.append(logits)
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else:
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del logits
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all_values.append(values)
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all_logprobs.append(logprobs)
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all_masks.append(masks)
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@ -42,7 +42,7 @@ class PairwisePeftTrainer(PeftTrainer):
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"""
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batch_size = inputs["input_ids"].size(0) // 2
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_, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
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if values.size(0) != inputs["input_ids"].size(0): # adapt chatglm2
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if values.size(0) != inputs["input_ids"].size(0): # adapt to chatglm2
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values = torch.transpose(values, 0, 1)
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r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
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loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
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