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704853e5e7
* [Feature] Update pip install * Update Configuration * Update * Update * Update * Update Internal Config * Update collect env
239 lines
9.3 KiB
Python
239 lines
9.3 KiB
Python
import copy
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import os
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from concurrent.futures import ThreadPoolExecutor
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from typing import Dict, List, Optional, Union
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import numpy as np
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from opencompass.models.base import BaseModel
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from opencompass.utils.logging import get_logger
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from opencompass.utils.prompt import PromptList
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PromptType = Union[PromptList, str]
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def valid_str(string, coding='utf-8'):
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"""decode text according to its encoding type."""
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invalid_chars = [b'\xef\xbf\xbd']
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bstr = bytes(string, coding)
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for invalid_char in invalid_chars:
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bstr = bstr.replace(invalid_char, b'')
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ret = bstr.decode(encoding=coding, errors='ignore')
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return ret
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class TurboMindModel(BaseModel):
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"""Model wrapper for TurboMind Python API.
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Args:
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path (str): path of the turbomind model
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concurrency (int): the maximum allowed concurrency of turbomind.
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max_seq_len (int): The maximum allowed sequence length of a model.
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Note that the length of prompt + generated tokens shall not exceed
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this value. Defaults to 2048.
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meta_template (Dict, optional): The model's meta prompt
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template if needed, in case the requirement of injecting or
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wrapping of any meta instructions.
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engine_config (Dict, optional): The engine config to set
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arguments like session_len, max_batch_size for TurboMind.
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gen_config (Dict, optional): Generation config to set
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arguments like top_k, top_p, temperature.
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end_str (str, optional): Whether to trim generated strings with end_str
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if the model has special ending strings that are not handled well.
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Defaults to None.
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"""
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def __init__(self,
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path: str,
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concurrency: int = 8,
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max_seq_len: int = 2048,
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meta_template: Optional[Dict] = None,
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engine_config: Dict = {},
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gen_config: Dict = {},
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end_str: Optional[str] = None):
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super().__init__(path=path,
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max_seq_len=max_seq_len,
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meta_template=meta_template)
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from lmdeploy.turbomind import TurboMind
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from lmdeploy.version import version_info
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if engine_config is not None:
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from lmdeploy.messages import TurbomindEngineConfig
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engine_config = TurbomindEngineConfig(**engine_config)
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self.logger = get_logger()
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assert os.path.exists(path), '{} is not existist'.format(path)
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tm_model = TurboMind.from_pretrained(path, engine_config=engine_config)
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self.tokenizer = tm_model.tokenizer
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self.generators = [
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tm_model.create_instance() for i in range(concurrency)
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]
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self.generator_ids = [i + 1 for i in range(concurrency)]
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self.gen_config = gen_config
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self.major_version, self.minor_version, _ = version_info
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self.end_str = end_str
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def generate(self,
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inputs: List[str],
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max_out_len: int = 512,
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stopping_criteria: List[str] = [],
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do_sample: Optional[bool] = None,
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temperature: int = 1,
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**kwargs) -> 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 prompts
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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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assert isinstance(
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inputs, List), f'List(str) is expected, but got {type(inputs)}'
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# split inputs into batches
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batch_size = len(self.generators)
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batch_inputs = [
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inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)
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]
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gen_config = copy.deepcopy(self.gen_config)
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if do_sample is not None:
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if do_sample:
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gen_config['top_k'] = 1000
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gen_config['temperature'] = temperature
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else:
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gen_config['top_k'] = 1
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if stopping_criteria:
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stop_words = gen_config.get('stop_words', [])
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for t in stopping_criteria:
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t = self.tokenizer.encode(t, add_bos=False)
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stop_words.append(t[0])
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gen_config['stop_words'] = list(set(stop_words))
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gen_config.setdefault('min_new_tokens', 1)
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from lmdeploy.messages import EngineGenerationConfig
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gen_config = EngineGenerationConfig(**gen_config)
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results = []
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for batch_input in batch_inputs:
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with ThreadPoolExecutor() as executor:
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_results = list(
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executor.map(
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self._generate,
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self.generators[:len(batch_input)],
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self.generator_ids[:len(batch_input)],
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batch_input,
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[max_out_len] * len(batch_input),
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[gen_config] * len(batch_input),
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[self.end_str] * len(batch_input),
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))
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results += _results
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if stopping_criteria:
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for s in stopping_criteria:
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results = [r.split(s)[0] for r in results]
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return results
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def get_token_len(self, prompt: str) -> int:
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input_ids = self.tokenizer.encode(prompt)
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return len(input_ids)
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def wait(self):
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"""Wait till the next query can be sent.
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Applicable in both single-thread and multi-thread environments.
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"""
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return self.token_bucket.get_token()
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def _generate(self,
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generator,
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session_id,
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prompt: PromptType,
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max_out_len: int,
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gen_config=None,
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end_str: Optional[str] = None) -> str:
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"""Generate results given a list of inputs.
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Args:
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prompt (PromptType): A string or PromptDict.
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The PromptDict should be organized in OpenCompass'
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API format.
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max_out_len (int): The maximum length of the output.
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gen_config (EngineGenerationConfig, optional): Generation
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config to set arguments like top_k, top_p, temperature.
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end_str (str, optional): Whether to trim generated strings
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with end_str if the model has special ending strings
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that are not handled well.
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Defaults to None.
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Returns:
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str: The generated string.
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"""
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assert type(
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prompt) is str, 'We only support string for TurboMind Python API'
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input_ids = self.tokenizer.encode(prompt)
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for outputs in generator.stream_infer(session_id=session_id,
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input_ids=[input_ids],
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gen_config=gen_config,
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request_output_len=max_out_len,
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sequence_start=True,
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sequence_end=True,
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step=0,
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stream_output=False):
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if self.major_version >= 0 and self.minor_version >= 4:
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output_ids = outputs.token_ids
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else:
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_, output_ids, _ = outputs
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response = self.tokenizer.decode(output_ids)
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response = valid_str(response)
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# used to trim
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if end_str:
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response = response.split(end_str)[0]
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return response
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def get_ppl(self,
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inputs: 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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inputs (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. It's okay to skip
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its implementation if advanced features in PPLInfernecer is
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not needed.
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Returns:
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np.ndarray: The perplexity scores in shape of (N,)
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"""
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assert isinstance(
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inputs, List), f'List(str) is expected, but got {type(inputs)}'
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results = []
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for text in inputs:
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input_ids = self.tokenizer.encode(text)
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res = self.generators[0].get_ppl(input_ids)
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results.append(res)
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results = np.concatenate(results)
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return results
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def get_loglikelihood(
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self,
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inputs: List[str],
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conts: List[str],
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mask_length: Optional[List[int]] = None) -> List[float]:
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assert isinstance(
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inputs, List), f'List(str) is expected, but got {type(inputs)}'
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results = []
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for text, cont in zip(inputs, conts):
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input_ids = self.tokenizer.encode(text)
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res = self.generators[0].get_ppl(input_ids)
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logit_sum = res * len(input_ids)
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input_ids = self.tokenizer.encode(text.replace(cont, ''))
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res = self.generators[0].get_ppl(input_ids)
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logit_part = res * len(input_ids)
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results.append(-(logit_sum - logit_part))
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results = np.concatenate(results)
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return results
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