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88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed according to the terms of the GNU General Public License version 3.
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from typing import List
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import torch
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from tqdm import tqdm
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from llama.tokenizer import Tokenizer
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from llama.model import Transformer
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class LLaMA:
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def __init__(self, model: Transformer, tokenizer: Tokenizer):
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self.model = model
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self.tokenizer = tokenizer
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def generate(
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self,
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prompts: List[str],
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max_gen_len: int,
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temperature: float = 0.8,
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top_p: float = 0.95,
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) -> List[str]:
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bsz = len(prompts)
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params = self.model.params
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assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
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prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
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min_prompt_size = min([len(t) for t in prompt_tokens])
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max_prompt_size = max([len(t) for t in prompt_tokens])
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total_len = min(params.max_seq_len, max_gen_len + max_prompt_size)
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tokens = torch.full((bsz, total_len), self.tokenizer.pad_id).cpu().long()
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for k, t in enumerate(prompt_tokens):
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tokens[k, : len(t)] = torch.tensor(t).long()
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input_text_mask = tokens != self.tokenizer.pad_id
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start_pos = min_prompt_size
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prev_pos = 0
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steps = total_len - start_pos
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pbar = tqdm(total=steps)
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for cur_pos in range(start_pos, total_len):
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logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
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if temperature > 0:
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probs = torch.softmax(logits / temperature, dim=-1)
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next_token = sample_top_p(probs, top_p)
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else:
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next_token = torch.argmax(logits, dim=-1)
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next_token = next_token.reshape(-1)
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# only replace token if prompt has already been generated
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next_token = torch.where(
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input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
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)
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tokens[:, cur_pos] = next_token
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prev_pos = cur_pos
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pbar.update(1)
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pbar.close()
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decoded = []
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for i, t in enumerate(tokens.tolist()):
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# cut to max gen len
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t = t[: len(prompt_tokens[i]) + max_gen_len]
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# cut to eos tok if any
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try:
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t = t[: t.index(self.tokenizer.eos_id)]
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except ValueError:
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pass
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decoded.append(self.tokenizer.decode(t))
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return decoded
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def sample_top_p(probs, p):
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probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
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probs_sum = torch.cumsum(probs_sort, dim=-1)
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mask = probs_sum - probs_sort > p
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probs_sort[mask] = 0.0
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probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
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next_token = torch.multinomial(probs_sort, num_samples=1)
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next_token = torch.gather(probs_idx, -1, next_token)
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return next_token
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