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57fcfc975a
* support internLM * support internLM * simplify intern model files * update storage_manager * support internLM * Modify the file organization structure * support internLM * support internLM * support internLM * support internLM * change some details
128 lines
4.8 KiB
Python
128 lines
4.8 KiB
Python
from typing import Dict, List, Optional
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import numpy as np
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import torch
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from opencompass.models.base import BaseModel, LMTemplateParser
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class InternLM(BaseModel):
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def __init__(self,
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path: str,
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max_seq_len: int = 2048,
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tokenizer_only: bool = False,
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tokenizer_path: Optional[str] = None,
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model_config: Optional[str] = None,
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tokenizer_type: Optional[str] = 'v7',
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meta_template: Optional[Dict] = None):
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if tokenizer_only:
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self._load_tokenizer(tokenizer_path=tokenizer_path,
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tokenizer_type=tokenizer_type,
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max_seq_len=max_seq_len)
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else:
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self._load_model(path=path,
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max_seq_len=max_seq_len,
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tokenizer_path=tokenizer_path,
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tokenizer_type=tokenizer_type,
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model_config=model_config)
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self.template_parser = LMTemplateParser(meta_template)
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self.eos_token_id = None
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if meta_template and 'eos_token_id' in meta_template:
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self.eos_token_id = meta_template['eos_token_id']
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def _load_model(self,
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path: str,
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max_seq_len: int,
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tokenizer_path: Optional[str] = None,
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tokenizer_type: Optional[str] = None,
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model_config: Optional[str] = None):
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from internlm.load.load_model import load_llm
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from internlm.model import build_model_with_cfg
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self.model, self.tokenizer, self.generator, _ = load_llm(
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path,
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max_seq_len,
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tokenizer_path=tokenizer_path,
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tokenizer_type=tokenizer_type,
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module=build_model_with_cfg,
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model_config_path=model_config)
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def _load_tokenizer(self, tokenizer_path: str, tokenizer_type: str,
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max_seq_len: int):
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from internlm.load.tokenizer import LLMTokenizer
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from sentencepiece import SentencePieceProcessor
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tokenizer = SentencePieceProcessor()
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tokenizer.load(tokenizer_path)
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tokenizer = LLMTokenizer(tokenizer,
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max_seq_len=max_seq_len,
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tokenizer_type=tokenizer_type)
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self.tokenizer = tokenizer
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def get_token_len(self, prompt: str) -> int:
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"""Get lengths of the tokenized strings.
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Args:
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prompt (str): Input string.
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Returns:
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int: Length of the input tokens
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"""
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tokens = self.tokenizer([prompt], truncation=False)['tokens']
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return len(tokens[0])
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def generate(self, inputs: List[str], max_out_len: int) -> List[str]:
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"""Generate results given a list of inputs.
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Args:
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inputs (List[str]): A list of strings.
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max_out_len (int): The maximum length of the output.
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Returns:
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List[str]: A list of generated strings.
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"""
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return self.generator.generate(inputs,
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generation_kwargs={
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'max_gen_len': max_out_len,
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'eos_token_id': self.eos_token_id
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})
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def get_ppl(self,
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input_texts: List[str],
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mask_length: Optional[List[int]] = None) -> List[float]:
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"""Get perplexity scores given a list of inputs.
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Args:
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input_texts (List[str]): A list of strings.
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mask_length (Optional[List[int]]): A list of mask lengths. If
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provided, the perplexity scores will be calculated with the
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first mask_length[i] tokens masked out.
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Returns:
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List[float]: A list of perplexity scores.
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"""
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outputs, inputs = self.generator.get_logits(input_texts)
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shift_logits = outputs[..., :-1, :].contiguous()
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shift_labels = inputs['tokens'][..., 1:].contiguous()
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loss_fct = torch.nn.CrossEntropyLoss(
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reduction='none', ignore_index=self.tokenizer.pad_token_id)
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
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shift_labels.view(-1)).view(shift_labels.size())
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if mask_length is not None:
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mask = torch.zeros_like(shift_labels) # [batch,seqlen]
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for i in range(len(mask)):
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for j in range(mask_length[i] - 1, len(mask[i])):
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mask[i][j] = 1
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loss = loss * mask
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lens = (inputs['tokens'] !=
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self.tokenizer.pad_token_id).sum(-1).cpu().numpy()
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if mask_length is not None:
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lens -= np.array(mask_length)
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ce_loss = loss.sum(-1).cpu().detach().numpy() / lens
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return ce_loss
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