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The OpenAI API allows to retrieve log-probabilities per token (including both prompt and completion tokens) through the logprobs return argument. Currently, the CausalLM only provide logits return values, which should are the prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

The following code provides an example of how to retrieve the log-probabilities per token of CausalLMs for the huggingface API:

def logprobs_from_prompt(prompt, tokenizer, model):
      encoded = tokenizer(prompt, return_tensors="pt").to("cpu")
      input_ids = encoded["input_ids"]
      output = model(input_ids=input_ids)
      shift_labels = input_ids[..., 1:].contiguous()
      shift_logits = output.logits[..., :-1, :].contiguous()
      log_probs = []
      log_probs.append((tokenizer.decode(input_ids[0].tolist()[0]), None))
      for idx, (label_id, logit) in enumerate(zip(shift_labels[0].tolist(), shift_logits[0])):
            logprob = F.log_softmax(logit, dim=0).tolist()[label_id]
            log_probs.append((tokenizer.decode(label_id), float(logprob)))
      return log_probs

An example call would be:

tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt")
model = OPTForCausalLM.from_pretrained("facebook/opt")
prompt = "The horse raced past the barn fell."
logprobs = logprobs_from_prompt(prompt, tokenizer, model)

For its derivation and explanation see this discussion.

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