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62 lines
1.9 KiB
62 lines
1.9 KiB
from typing import List, Tuple
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import torch
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def greedy_sample(
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generation_config,
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logprobs: torch.Tensor,
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) -> torch.Tensor:
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"""
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Sample tokens greedyly.
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"""
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results = torch.argmax(logprobs, dim=-1)
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return results
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def multinomial_sample(
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generation_config,
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probs: torch.Tensor,
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) -> torch.Tensor:
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"""
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Sample tokens in a random phase.
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"""
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random_results = torch.multinomial(probs, num_samples=1).squeeze(1)
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return random_results
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def beam_search_sample(
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generation_config,
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logprobs: torch.Tensor,
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is_prompt: bool = False,
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) -> List[Tuple[List[int], List[int]]]:
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"""
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Sample tokens with beam search.
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We sample 2 * beam_width candidates to make sure that with high probability we can get `beam_width` candidates in addition to
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the finished sequences for the next iteration.
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ref:
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https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
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for details. See also HF reference:
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https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
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# NOTE: this beam search sample function is wrong now.
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"""
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beam_width = generation_config.num_beams
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results = []
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if is_prompt:
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# Prompt phase.
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parent_ids = [0] * (2 * beam_width)
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_, next_token_ids = torch.topk(logprobs[0], 2 * beam_width)
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next_token_ids = next_token_ids.tolist()
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else:
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# Generation phase.
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# cumulative_logprobs = [seq_data[seq_id].cumulative_logprob for seq_id in seq_ids]
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cumulative_logprobs = torch.tensor(logprobs, dtype=torch.float, device=seq_group_logprobs.device)
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seq_group_logprobs = seq_group_logprobs + cumulative_logprobs.unsqueeze(dim=1)
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_, topk_ids = torch.topk(logprobs.flatten(), 2 * beam_width)
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results.append((next_token_ids, parent_ids))
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return results
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