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"""PPL Inferencer."""
import os
from typing import List, Optional
import mmengine
import torch
from tqdm import tqdm
from opencompass.models.base import BaseModel
from opencompass.registry import ICL_INFERENCERS
from ..icl_prompt_template import PromptTemplate
from ..icl_retriever import BaseRetriever
from ..utils import get_logger
from .icl_base_inferencer import BaseInferencer, dump_results_dict
logger = get_logger(__name__)
@ICL_INFERENCERS.register_module()
class MinKPercentInferencer(BaseInferencer):
"""PPLOnlyInferencer class to calculate PPL and PPL only, no choice is
made. This Inferencer is usually used along with AveragePPLEvaluator.
Attributes:
model (:obj:`BaseModel`, optional): The module to inference.
max_seq_len (:obj:`int`): Maximum number of tokenized words allowed by
the LM.
batch_size (:obj:`int`, optional): Batch size for the :obj:`DataLoader`
output_json_filepath (:obj:`str`, optional): File path for output
`JSON` file.
output_json_filename (:obj:`str`, optional): File name for output
`JSON` file.
save_every (:obj:`int`, optional): Save intermediate results every
"""
def __init__(
self,
model: BaseModel,
max_seq_len: Optional[int] = None,
batch_size: Optional[int] = 1,
output_json_filepath: Optional[str] = './icl_inference_output',
output_json_filename: Optional[str] = 'predictions',
save_every: Optional[int] = 1,
**kwargs) -> None:
super().__init__(
model=model,
max_seq_len=max_seq_len,
batch_size=batch_size,
output_json_filename=output_json_filename,
output_json_filepath=output_json_filepath,
**kwargs,
)
self.save_every = save_every
def inference(self,
retriever: BaseRetriever,
ice_template: Optional[PromptTemplate] = None,
prompt_template: Optional[PromptTemplate] = None,
output_json_filepath: Optional[str] = None,
output_json_filename: Optional[str] = None) -> List:
# 1. Preparation for output logs
output_handler = PPLOnlyInferencerOutputHandler()
if output_json_filepath is None:
output_json_filepath = self.output_json_filepath
if output_json_filename is None:
output_json_filename = self.output_json_filename
# 2. Get results of retrieval process
ice_idx_list = retriever.retrieve()
# 3. Generate prompts for testing input
prompt_list = self.get_generation_prompt_list_from_retriever_indices(
ice_idx_list,
retriever,
max_seq_len=self.max_seq_len,
ice_template=ice_template,
prompt_template=prompt_template)
# 3.1 Fetch and zip prompt & gold answer if output column exists
ds_reader = retriever.dataset_reader
assert ds_reader.output_column is None, (
'PPLOnlyInferencer supports `output_column=None` only.')
# Create tmp json file for saving intermediate results and future
# resuming
index = 0
tmp_json_filepath = os.path.join(output_json_filepath,
'tmp_' + output_json_filename)
if os.path.exists(tmp_json_filepath):
# TODO: move resume to output handler
try:
tmp_result_dict = mmengine.load(tmp_json_filepath)
except Exception:
pass
else:
output_handler.results_dict = tmp_result_dict
index = len(tmp_result_dict)
# 4. Wrap prompts with Dataloader
dataloader = self.get_dataloader(prompt_list[index:], self.batch_size)
# 5. Inference for prompts in each batch
logger.info('Starting inference process...')
for datum in tqdm(dataloader, disable=not self.is_main_process):
entry = datum
# 5-1. Inference with local model
with torch.no_grad():
sub_inputs = self.model.parse_template(entry, mode='ppl')
minks = self.model.get_mink_percent(sub_inputs).tolist()
parsed_entries = self.model.parse_template(entry, mode='gen')
# 5-3. Save current output
for prompt, mink, in zip(parsed_entries, minks):
output_handler.save_results(prompt, mink, index)
index = index + 1
# 5-4. Save intermediate results
if (self.save_every is not None and index % self.save_every == 0
and self.is_main_process):
output_handler.write_to_json(output_json_filepath,
'tmp_' + output_json_filename)
# 6. Output
if self.is_main_process:
os.makedirs(output_json_filepath, exist_ok=True)
output_handler.write_to_json(output_json_filepath,
output_json_filename)
if os.path.exists(tmp_json_filepath):
os.remove(tmp_json_filepath)
return [
sample['mink'] for sample in output_handler.results_dict.values()
]
def get_generation_prompt_list_from_retriever_indices(
self,
ice_idx_list: List[List[int]],
retriever: BaseRetriever,
max_seq_len: Optional[int] = None,
ice_template: Optional[PromptTemplate] = None,
prompt_template: Optional[PromptTemplate] = None):
prompt_list = []
for idx, ice_idx in enumerate(ice_idx_list):
ice = retriever.generate_ice(ice_idx, ice_template=ice_template)
prompt = retriever.generate_prompt_for_generate_task(
idx,
ice,
ice_template=ice_template,
prompt_template=prompt_template)
if max_seq_len is not None:
prompt_token_num = self.model.get_token_len_from_template(
prompt, mode='gen')
while len(ice_idx) > 0 and prompt_token_num > max_seq_len:
ice_idx = ice_idx[:-1]
ice = retriever.generate_ice(ice_idx,
ice_template=ice_template)
prompt = retriever.generate_prompt_for_generate_task(
idx,
ice,
ice_template=ice_template,
prompt_template=prompt_template)
prompt_token_num = self.model.get_token_len_from_template(
prompt, mode='gen')
prompt_list.append(prompt)
return prompt_list
class PPLOnlyInferencerOutputHandler:
origin_prompt_dict = {}
output_dict = {}
results_dict = {}
def __init__(self) -> None:
self.results_dict = {}
def write_to_json(self, save_dir: str, filename: str):
"""Dump the result to a json file."""
dump_results_dict(self.results_dict, os.path.join(save_dir, filename))
def save_results(self, origin_prompt, mink, idx):
self.results_dict[str(idx)] = {
'origin_prompt': origin_prompt,
'mink': mink,
}
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