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Running
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Zero
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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from mmengine.runner import ValLoop as MMENGINE_ValLoop
from mmengine.dist import broadcast_object_list, is_main_process, get_world_size, get_rank, barrier, collect_results
import math
import torch
from mmengine.model import is_model_wrapper
from types import MethodType
from xtuner.utils import (DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX,
PROMPT_TEMPLATE)
from xtuner.tools.utils import get_stop_criteria, is_cn_string
from transformers import GenerationConfig
TORCH_DTYPE_MAP = dict(
fp16=torch.float16, bf16=torch.bfloat16, fp32=torch.float32, auto='auto')
class TestLoop(MMENGINE_ValLoop):
def __init__(self, runner, dataloader, evaluator=None, torch_dtype='fp16', select_metric='first') -> None:
# must be concatset
super(MMENGINE_ValLoop, self).__init__(runner, dataloader)
self._runner = runner
self.torch_dtype = torch_dtype
if torch_dtype is not None:
self.torch_dtype = TORCH_DTYPE_MAP[torch_dtype]
self.select_metric = select_metric
def run(self) -> dict:
"""Launch Test."""
self.runner.logger.info('==================== Start test loop ===================')
self.runner.call_hook('before_test')
self.runner.call_hook('before_test_epoch')
if is_model_wrapper(self.runner.model):
model = self.runner.model.module
else:
model = self.runner.model
model.gradient_checkpointing_disable()
model.eval()
model.cuda()
rank = get_rank()
metrics = []
# Ensure that eta and log are displayed correctly.
current_run_total_ids = 0
for _, dataset in enumerate(self.dataloader.dataset.datasets):
if not hasattr(model, 'preparing_for_generation'):
model.preparing_for_generation = MethodType(default_preparing_for_generation, model)
print("Warning, the model do not have the preparing_for_generation() function, using the default!!!")
model.preparing_for_generation(dataset.metainfo)
# split per rank
results = []
n_samples = len(dataset)
per_rank_samples = math.ceil(n_samples / get_world_size())
per_rank_ids = range(per_rank_samples * rank,
min(n_samples, per_rank_samples * (rank + 1)))
for idx in per_rank_ids:
data_batch = dataset[idx]
self.run_iter(current_run_total_ids, data_batch, results, model)
current_run_total_ids += 1
barrier()
self.runner.logger.info('==================== Start collect results ===================')
results = collect_results(results, len(dataset))
self.runner.logger.info('========= Starting the evaluation of a data ===========')
if is_main_process():
metric = dataset.evaluate(results, self.runner.work_dir)
objects = [metric]
else:
objects = [None]
broadcast_object_list(objects)
metric = objects[0]
metrics.append(metric)
# select metrics
if self.select_metric == 'first':
metrics = metrics[0]
else:
raise NotImplementedError
self.runner.logger.info('================ Ending test loop ================')
self.runner.call_hook('after_test_epoch', metrics=metrics)
self.runner.call_hook('after_test')
return metrics
@torch.no_grad()
def run_iter(self, idx, data_batch, results, model):
assert 'text_prompts' in data_batch and 'pixel_values' in data_batch and 'img_id' in data_batch
prediction = {'img_id': data_batch['img_id']}
self.runner.call_hook(
'before_test_iter', batch_idx=idx, data_batch=data_batch)
outputs = model.predict_forward(**data_batch)
prediction.update(outputs)
results.append(prediction)
self.runner.call_hook(
'after_test_iter',
batch_idx=idx,
data_batch=data_batch,
outputs=outputs)
def default_preparing_for_generation(self, metainfo):
# set stop criteria and generation configs for model
assert hasattr(self, 'tokenizer'), "The Model does not have the tokenizer!!!"
self.bot_name = 'BOT'
template = PROMPT_TEMPLATE['internlm2_chat']
self.template = template
stop_words = []
stop_words += template.get('STOP_WORDS', [])
stop_criteria = get_stop_criteria(
tokenizer=self.tokenizer, stop_words=stop_words)
self.stop_criteria = stop_criteria
default_generation_kwargs = dict(
max_new_tokens=2048,
do_sample=False,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=(
self.tokenizer.pad_token_id
if self.tokenizer.pad_token_id is not None
else self.tokenizer.eos_token_id
),
)
default_generation_kwargs.update(metainfo.get('generation_kwargs', {}))
self.gen_config = GenerationConfig(**default_generation_kwargs)
return
class AnnoLoop(MMENGINE_ValLoop):
def __init__(self, runner, dataloader, evaluator=None, torch_dtype='fp16', select_metric='first') -> None:
# must be concatset
super(MMENGINE_ValLoop, self).__init__(runner, dataloader)
self._runner = runner
self.torch_dtype = torch_dtype
if torch_dtype is not None:
self.torch_dtype = TORCH_DTYPE_MAP[torch_dtype]
self.select_metric = select_metric
def run(self) -> dict:
"""Launch Test."""
self.runner.logger.info('==================== Start test loop ===================')
self.runner.call_hook('before_test')
self.runner.call_hook('before_test_epoch')
if is_model_wrapper(self.runner.model):
model = self.runner.model.module
else:
model = self.runner.model
model.eval()
rank = get_rank()
metrics = []
# Ensure that eta and log are displayed correctly.
current_run_total_ids = 0
for _, dataset in enumerate(self.dataloader.dataset.datasets):
# split per rank
results = []
n_samples = len(dataset)
per_rank_samples = math.ceil(n_samples / get_world_size())
per_rank_ids = range(per_rank_samples * rank,
min(n_samples, per_rank_samples * (rank + 1)))
for idx in per_rank_ids:
data_batch = dataset[idx]
self.run_iter(current_run_total_ids, data_batch, results, model)
current_run_total_ids += 1
if hasattr(model, 'save_step'):
model.save_step(last=True)
barrier()
self.runner.logger.info('==================== Start collect results ===================')
results = collect_results(results, len(dataset))
self.runner.logger.info('========= Starting the evaluation of a data ===========')
if is_main_process():
metric = dataset.evaluate(results, self.runner.work_dir)
objects = [metric]
else:
objects = [None]
broadcast_object_list(objects)
metric = objects[0]
metrics.append(metric)
# select metrics
if self.select_metric == 'first':
metrics = metrics[0]
else:
raise NotImplementedError
self.runner.logger.info('================ Ending test loop ================')
self.runner.call_hook('after_test_epoch', metrics=metrics)
self.runner.call_hook('after_test')
return metrics
@torch.no_grad()
def run_iter(self, idx, data_batch, results, model):
prediction = {}
self.runner.call_hook(
'before_test_iter', batch_idx=idx, data_batch=data_batch)
outputs = model.predict_forward(**data_batch)
prediction.update(outputs)
results.append(prediction)
self.runner.call_hook(
'after_test_iter',
batch_idx=idx,
data_batch=data_batch,
outputs=outputs) |