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"""
module for TRL PPO training
"""
import torch
from tqdm import tqdm
from trl import PPOTrainer
class TRLPPOTrainer(PPOTrainer):
"""
wrapper for ppo trainer to handle customizations
"""
def train(
self,
reward_pipe,
resume_from_checkpoint=None, # pylint: disable=unused-argument
):
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": self.tokenizer.eos_token_id,
"max_new_tokens": 32,
}
sent_kwargs = {
"return_all_scores": True,
"function_to_apply": "none",
"batch_size": 16,
}
for epoch, batch in tqdm( # pylint: disable=unused-variable
enumerate(self.dataloader)
):
query_tensors = batch["input_ids"]
# generate model response
response_tensors, ref_response_tensors = self.generate(
query_tensors,
return_prompt=False,
generate_ref_response=True,
**generation_kwargs
)
batch["response"] = self.tokenizer.batch_decode(response_tensors)
batch["ref_response"] = self.tokenizer.batch_decode(ref_response_tensors)
# Compute sentiment score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
pipe_outputs = reward_pipe(texts, **sent_kwargs)
rewards = [torch.tensor(output[1]["score"]) for output in pipe_outputs]
ref_texts = [q + r for q, r in zip(batch["query"], batch["ref_response"])]
ref_pipe_outputs = reward_pipe(ref_texts, **sent_kwargs)
ref_rewards = [
torch.tensor(output[1]["score"]) for output in ref_pipe_outputs
]
batch["ref_rewards"] = ref_rewards
# Run PPO step
stats = self.step(query_tensors, response_tensors, rewards)
self.log_stats(
stats,
batch,
rewards,
columns_to_log=["query", "response", "ref_response", "ref_rewards"],
)
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