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"""Direct Generation Inferencer."""
import inspect
import os
import os.path as osp
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 opencompass.utils import batched
from ..icl_prompt_template import PromptTemplate
from ..icl_retriever import BaseRetriever
from ..utils.logging import get_logger
from .icl_base_inferencer import BaseInferencer, GenInferencerOutputHandler
logger = get_logger(__name__)
@ICL_INFERENCERS.register_module()
class GenInferencer(BaseInferencer):
"""Generation Inferencer class to directly evaluate by generation.
Attributes:
model (:obj:`BaseModelWrapper`, optional): The module to inference.
max_seq_len (:obj:`int`, optional): Maximum number of tokenized words
allowed by the LM.
min_out_len (:obj:`int`, optional): Minimum number of generated tokens
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.
gen_field_replace_token (:obj:`str`, optional): Used to replace the
generation field token when generating prompts.
save_every (:obj:`int`, optional): Save intermediate results every
`save_every` iters. Defaults to 1.
generation_kwargs (:obj:`Dict`, optional): Parameters for the
:obj:`model.generate()` method.
"""
def __init__(
self,
model: BaseModel,
max_out_len: int,
stopping_criteria: List[str] = [],
max_seq_len: Optional[int] = None,
min_out_len: Optional[int] = None,
batch_size: Optional[int] = 1,
gen_field_replace_token: Optional[str] = '',
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.gen_field_replace_token = gen_field_replace_token
self.max_out_len = max_out_len
self.min_out_len = min_out_len
self.stopping_criteria = stopping_criteria
if self.model.is_api and save_every is None:
save_every = 1
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 = GenInferencerOutputHandler()
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,
self.gen_field_replace_token,
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
if ds_reader.output_column:
gold_ans = ds_reader.dataset['test'][ds_reader.output_column]
prompt_list = list(zip(prompt_list, gold_ans))
# 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 osp.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):
if ds_reader.output_column:
entry, golds = list(zip(*datum))
else:
entry = datum
golds = [None for _ in range(len(entry))]
# 5-1. Inference with local model
extra_gen_kwargs = {}
sig = inspect.signature(self.model.generate)
if 'stopping_criteria' in sig.parameters:
extra_gen_kwargs['stopping_criteria'] = self.stopping_criteria
if 'min_out_len' in sig.parameters:
extra_gen_kwargs['min_out_len'] = self.min_out_len
with torch.no_grad():
parsed_entries = self.model.parse_template(entry, mode='gen')
results = self.model.generate_from_template(
entry, max_out_len=self.max_out_len, **extra_gen_kwargs)
generated = results
num_return_sequences = getattr(self.model, 'generation_kwargs',
{}).get('num_return_sequences', 1)
# 5-3. Save current output
for prompt, prediction, gold in zip(
parsed_entries, batched(generated, num_return_sequences),
golds):
if num_return_sequences == 1:
prediction = prediction[0]
output_handler.save_results(prompt,
prediction,
index,
gold=gold)
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 osp.exists(tmp_json_filepath):
os.remove(tmp_json_filepath)
return [
sample['prediction']
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,
gen_field_replace_token: str,
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,
gen_field_replace_token=gen_field_replace_token,
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,
gen_field_replace_token=gen_field_replace_token,
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
@ICL_INFERENCERS.register_module()
class GLMChoiceInferencer(GenInferencer):
def __init__(self, *args, choices=['A', 'B', 'C', 'D'], **kwargs):
super().__init__(*args, **kwargs)
self.choices = choices
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 = GenInferencerOutputHandler()
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,
self.gen_field_replace_token,
max_seq_len=self.max_seq_len,
ice_template=ice_template,
prompt_template=prompt_template)
# 4. Wrap prompts with Dataloader
dataloader = self.get_dataloader(prompt_list, self.batch_size)
index = 0
# 5. Inference for prompts in each batch
logger.info('Starting inference process...')
for entry in tqdm(dataloader, disable=not self.is_main_process):
# 5-1. Inference with local model
with torch.no_grad():
parsed_entries = self.model.parse_template(entry, mode='gen')
results = self.model.choice(entry, choices=self.choices)
generated = results
# 5-3. Save current output
for prompt, prediction in zip(parsed_entries, generated):
output_handler.save_results(prompt, prediction, index)
index = index + 1
# 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)
return [
sample['prediction']
for sample in output_handler.results_dict.values()
]
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