[BUG] Do not use generation of hf model in inferencers

[Kamichanw]

Describe the bug
As I mentioned in this issue, the default value of top_p and temperature is not guaranteed to be 1. Therefore, the code below will get a modified logits, i.e., a distribution processed depending on generation_config from hf end.

LMFlow/src/lmflow/models/hf_decoder_model.py

Lines 382 to 405 in 1b223f7

	if self.use_accelerator:
	outputs = self.backend_model.generate(
	input_ids=inputs,
	pad_token_id=self.tokenizer.pad_token_id,
	*args,
	**kwargs
	)
	else:
	if self.device == "gpu":
	outputs = self.ds_engine.module.generate(
	input_ids=inputs,
	synced_gpus=True,
	pad_token_id=self.tokenizer.pad_token_id,
	*args,
	**kwargs
	)
	elif self.device == "cpu":
	outputs = self.backend_model.generate(
	input_ids=inputs,
	synced_gpus=True,
	pad_token_id=self.tokenizer.pad_token_id,
	*args,
	**kwargs
	)

Much worse, you applied top_p and temperature again in score_to_prob, resulting unexpected distribution:

LMFlow/src/lmflow/pipeline/inferencer.py

Lines 435 to 440 in 1b223f7

	for _ in range(num_new_tokens):
	pred = self.predict_next_token(model=model, input_ids=sequence, num_new_tokens=1) # predict next one token
	prob = self.score_to_prob(pred.scores[0], temperature=temperature)
	sampled = self.sample(prob=prob, num_samples=1)
	new_tokens.append(sampled)
	sequence = torch.cat([sequence, sampled['sampled_token']], dim=1)