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112 lines
4.2 KiB
112 lines
4.2 KiB
from typing import List, Optional, Tuple, Union
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import torch
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from transformers.generation import GenerationConfig
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from colossalai.inference.logit_processors import get_logits_processor
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def greedy_sample(
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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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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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beam_width: int,
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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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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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def search_tokens(
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generation_config: Union[GenerationConfig, dict],
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logits,
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is_prompt: bool = False,
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batch_token_ids: Optional[List[List[int]]] = None,
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):
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"""
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Sample tokens for finished requests.
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"""
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# NOTE: need to decide the granularity to process logits (sequence or batch)
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# convert GenerationConfig to dict
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# temporary fix for compatibility with the usage of RPCInferenceEngine
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if isinstance(generation_config, GenerationConfig):
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generation_config = generation_config.to_dict()
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if (repetition_penalty := generation_config.get("repetition_penalty", 1.0)) != 1.0:
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logits = get_logits_processor("repetition_penalty", logits, repetition_penalty, batch_token_ids)
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if (no_repeat_ngram_size := generation_config.get("no_repeat_ngram_size", 0)) > 0:
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logits = get_logits_processor("no_repeat_ngram_size", logits, no_repeat_ngram_size, batch_token_ids)
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if (forced_eos_token_id := generation_config.get("forced_eos_token_id", None)) is not None:
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sequence_lengths = [len(batch_token_ids[i]) for i in range(len(batch_token_ids))]
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max_out_lengths = [generation_config.max_length for _ in range(len(batch_token_ids))]
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logits = get_logits_processor(
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"forced_eos_token_id", logits, sequence_lengths, max_out_lengths, forced_eos_token_id
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)
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if generation_config.get("do_sample"):
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if (temperature := generation_config.get("temperature", 1.0)) != 1.0:
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logits = get_logits_processor("temperature", logits, temperature)
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if (top_k := generation_config.get("top_k", 0)) != 0:
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logits = get_logits_processor("top_k", logits, top_k)
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if (top_p := generation_config.get("top_p", 1.0)) < 1.0:
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logits = get_logits_processor("top_p", logits, top_p)
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# calculate probs
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probs = torch.softmax(logits, dim=-1, dtype=torch.float)
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logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
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# sample the next tokens
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if generation_config.get("num_beams", 1) != 1:
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raise NotImplementedError("Beam search is not supported yet.")
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if generation_config.get("do_sample", False):
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sample_tokens = multinomial_sample(probs)
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else:
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sample_tokens = greedy_sample(logprobs)
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return sample_tokens
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