@@ -69,6 +69,7 @@ __all__ = [ | |||
# metrics | |||
"Metric", | |||
"Accuracy", | |||
"TransformersAccuracy", | |||
'SpanFPreRecMetric', | |||
'ClassifyFPreRecMetric', | |||
@@ -25,7 +25,7 @@ def _transfer(func): | |||
for callback_fn in manager.callback_fns[func.__name__]: | |||
try: | |||
callback_fn(*arg, **kwargs) | |||
except EarlyStopException as e: | |||
except (EarlyStopException, KeyboardInterrupt) as e: | |||
raise e | |||
except BaseException as e: | |||
logger.error(f"The following callback_fn raise exception:{_get_fun_msg(callback_fn)}.") | |||
@@ -27,19 +27,21 @@ class EvaluateBatchLoop(Loop): | |||
while True: | |||
try: | |||
batch = next(iterator) | |||
batch = match_and_substitute_params(evaluator.input_mapping, batch) | |||
batch = evaluator.move_data_to_device(batch) | |||
except StopIteration: | |||
break | |||
try: | |||
batch = match_and_substitute_params(evaluator.input_mapping, batch) | |||
batch = evaluator.move_data_to_device(batch) | |||
self.batch_step_fn(evaluator, batch) | |||
batch_idx += 1 | |||
evaluator.update_progress_bar(batch_idx, evaluator.cur_dataloader_name) | |||
except BaseException as e: | |||
if callable(getattr(dataloader, 'get_batch_indices', None)): | |||
indices = dataloader.get_batch_indices() | |||
logger.error(f"Exception happens when evaluating on samples: {indices}") | |||
raise e | |||
self.batch_step_fn(evaluator, batch) | |||
batch_idx += 1 | |||
evaluator.update_progress_bar(batch_idx, evaluator.cur_dataloader_name) | |||
# 获取metric结果。返回的dict内容示例为{'metric_name1': metric_results, 'metric_name2': metric_results, ...} | |||
results = evaluator.get_metric() | |||
return results | |||
@@ -19,30 +19,31 @@ class TrainBatchLoop(Loop): | |||
get_batch_indices = dataloader.get_batch_indices if callable(getattr(dataloader, 'get_batch_indices', None))\ | |||
else lambda *args, **kwargs: None | |||
dataloader = iter(dataloader) | |||
indices = None | |||
while trainer.batch_idx_in_epoch<=trainer.num_batches_per_epoch: | |||
try: | |||
trainer.on_fetch_data_begin() | |||
batch = next(dataloader) | |||
indices = get_batch_indices() | |||
except StopIteration: | |||
break | |||
try: | |||
trainer.on_fetch_data_end() | |||
batch = match_and_substitute_params(trainer.input_mapping, batch) | |||
batch = trainer.move_data_to_device(batch) | |||
except StopIteration: | |||
break | |||
trainer.on_train_batch_begin(batch, indices) | |||
with trainer.get_no_sync_context(): # 在多卡的时候可能需要关闭 sync | |||
self.batch_step_fn(trainer, batch) | |||
trainer.global_forward_batches += 1 | |||
trainer.batch_idx_in_epoch += 1 | |||
trainer.check_batch_step_fn() | |||
trainer.on_train_batch_end() | |||
except BaseException as e: | |||
if indices and not isinstance(e, EarlyStopException): | |||
if indices is not None and not isinstance(e, (EarlyStopException, KeyboardInterrupt)): | |||
logger.error(f"Exception happens when running on samples: {indices}") | |||
raise e | |||
trainer.on_train_batch_begin(batch, indices) | |||
with trainer.get_no_sync_context(): # 在多卡的时候可能需要关闭 sync | |||
self.batch_step_fn(trainer, batch) | |||
trainer.global_forward_batches += 1 | |||
trainer.batch_idx_in_epoch += 1 | |||
trainer.check_batch_step_fn() | |||
trainer.on_train_batch_end() | |||
trainer.step_evaluate() | |||
trainer.batch_idx_in_epoch = 0 | |||
@@ -47,7 +47,7 @@ class JittorDataLoader: | |||
提供给使用jittor框架的DataLoader函数,提供了auto_collate的功能, 支持实现了__getitem__和__len__的dataset | |||
""" | |||
def __init__(self, dataset, batch_size: int = 16, shuffle: bool = False, | |||
def __init__(self, dataset, batch_size: int = 16, shuffle: bool = True, | |||
drop_last: bool = False, num_workers: int = 0, buffer_size: int = 512 * 1024 * 1024, | |||
stop_grad: bool = True, keep_numpy_array: bool = False, endless: bool = False, | |||
collate_fn: Union[None, str, Callable] = "auto") -> None: | |||
@@ -47,7 +47,7 @@ class PaddleDataLoader(DataLoader): | |||
def __init__(self, dataset, feed_list=None, places=None, | |||
return_list: bool = True, batch_sampler=None, | |||
batch_size: int = 1, shuffle: bool = False, | |||
batch_size: int = 1, shuffle: bool = True, | |||
drop_last: bool = False, collate_fn: Union[str, Callable, None] = 'auto', | |||
num_workers: int = 0, use_buffer_reader: bool = True, | |||
use_shared_memory: bool = True, timeout: int = 0, | |||
@@ -14,7 +14,7 @@ from ...envs import FASTNLP_BACKEND, SUPPORT_BACKENDS | |||
from ..log import logger | |||
def prepare_dataloader(dataset, batch_size: int = 16, shuffle: bool = False, drop_last: bool = False, | |||
def prepare_dataloader(dataset, batch_size: int = 16, shuffle: bool = True, drop_last: bool = False, | |||
collate_fn: Union[Callable, str, None] = 'auto', num_workers: int = 0, | |||
seed: int = 0, backend: str = 'auto'): | |||
""" | |||
@@ -179,7 +179,7 @@ class TorchDataLoader(DataLoader): | |||
def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping[str, DataSet]], | |||
batch_size: int = 1, | |||
shuffle: bool = False, | |||
shuffle: bool = True, | |||
sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None, | |||
batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None, | |||
num_workers: int = 0, collate_fn: Union[str, Callable, None] = 'auto', | |||
@@ -236,8 +236,8 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
persistent_workers=persistent_workers, | |||
) | |||
else: | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size, | |||
shuffle=shuffle, sampler=non_train_sampler, | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size if non_train_batch_size else batch_size, | |||
shuffle=shuffle, sampler=non_train_sampler if non_train_sampler else sampler, | |||
batch_sampler=batch_sampler, | |||
num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory, | |||
drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn, | |||
@@ -1,9 +1,10 @@ | |||
from typing import Callable | |||
__all__ = [ | |||
"indice_collate_wrapper" | |||
] | |||
def indice_collate_wrapper(func): | |||
def indice_collate_wrapper(func:Callable): | |||
""" | |||
其功能是封装一层collate_fn,将dataset取到的tuple数据分离开,将idx打包为indices。 | |||
@@ -1,11 +1,13 @@ | |||
import os | |||
from typing import Dict, Union, Callable, Tuple, Optional | |||
from fastNLP.envs.imports import _NEED_IMPORT_TORCH | |||
if _NEED_IMPORT_TORCH: | |||
import torch | |||
from torch.nn import DataParallel | |||
from torch.nn.parallel import DistributedDataParallel | |||
from torch.utils.data import RandomSampler as TorchRandomSampler | |||
from torch.utils.data import SequentialSampler as TorchSequentialSampler | |||
__all__ = [ | |||
'TorchSingleDriver' | |||
@@ -15,7 +17,8 @@ from .torch_driver import TorchDriver | |||
from fastNLP.core.drivers.torch_driver.utils import replace_sampler, replace_batch_sampler | |||
from fastNLP.core.utils import auto_param_call | |||
from fastNLP.core.utils.utils import _get_fun_msg | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, ReproduceBatchSampler | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, \ | |||
ReproduceBatchSampler | |||
from fastNLP.core.samplers import RandomSampler | |||
from fastNLP.core.log import logger | |||
@@ -24,6 +27,7 @@ class TorchSingleDriver(TorchDriver): | |||
r""" | |||
用于 cpu 和 单卡 gpu 运算; | |||
""" | |||
def __init__(self, model, device: "torch.device", fp16: bool = False, **kwargs): | |||
if isinstance(model, DistributedDataParallel): | |||
raise ValueError("`DistributedDataParallel` is not supported in `TorchSingleDriver`") | |||
@@ -88,7 +92,8 @@ class TorchSingleDriver(TorchDriver): | |||
else: | |||
raise RuntimeError(f"There is no `{fn}` method in your {type(self.model)}.") | |||
def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleSampler]=None, | |||
def set_dist_repro_dataloader(self, dataloader, | |||
dist: Union[str, ReproducibleBatchSampler, ReproducibleSampler] = None, | |||
reproducible: bool = False): | |||
# 如果 dist 为 ReproducibleBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用; | |||
@@ -108,17 +113,24 @@ class TorchSingleDriver(TorchDriver): | |||
if reproducible: | |||
if isinstance(args.sampler, TorchRandomSampler): | |||
# 如果本来就是随机的,直接替换掉吧。 | |||
sampler = RandomSampler(args.sampler.data_source) | |||
logger.debug("Replace torch RandomSampler into fastNLP RandomSampler.") | |||
if getattr(args.sampler, '_num_samples', None) is None \ | |||
and getattr(args.sampler, 'replacements', False) is False \ | |||
and getattr(args.sampler, 'generator', None) is None: | |||
# 如果本来就是随机的,并且没有定制,直接替换掉吧。 | |||
sampler = RandomSampler(args.sampler.data_source, shuffle=True) | |||
logger.debug("Replace torch RandomSampler into fastNLP RandomSampler.") | |||
return replace_sampler(dataloader, sampler) | |||
elif isinstance(args.sampler, TorchSequentialSampler): | |||
# 需要替换为不要 shuffle 的。 | |||
sampler = RandomSampler(args.sampler.data_source, shuffle=False) | |||
logger.debug("Replace torch SequentialSampler into fastNLP RandomSampler.") | |||
return replace_sampler(dataloader, sampler) | |||
else: | |||
batch_sampler = ReproduceBatchSampler( | |||
batch_sampler=args.batch_sampler, | |||
batch_size=args.batch_size, | |||
drop_last=args.drop_last | |||
) | |||
return replace_batch_sampler(dataloader, batch_sampler) | |||
batch_sampler = ReproduceBatchSampler( | |||
batch_sampler=args.batch_sampler, | |||
batch_size=args.batch_size, | |||
drop_last=args.drop_last | |||
) | |||
return replace_batch_sampler(dataloader, batch_sampler) | |||
else: | |||
return dataloader | |||
@@ -138,9 +150,3 @@ class TorchSingleDriver(TorchDriver): | |||
def is_distributed(self): | |||
return False | |||
@@ -1,11 +1,12 @@ | |||
__all__ = [ | |||
"Metric", | |||
"Accuracy", | |||
"TransformersAccuracy", | |||
'SpanFPreRecMetric', | |||
'ClassifyFPreRecMetric', | |||
] | |||
from .metric import Metric | |||
from .accuracy import Accuracy | |||
from .accuracy import Accuracy, TransformersAccuracy | |||
from .span_f1_pre_rec_metric import SpanFPreRecMetric | |||
from .classify_f1_pre_rec_metric import ClassifyFPreRecMetric |
@@ -1,5 +1,6 @@ | |||
__all__ = [ | |||
'Accuracy' | |||
'Accuracy', | |||
"TransformersAccuracy" | |||
] | |||
from typing import Union | |||
@@ -17,9 +18,9 @@ class Accuracy(Metric): | |||
""" | |||
计算 准确率 的 metric 。 | |||
:param str backend: 目前支持四种类型的backend, ['auto', 'torch', 'paddle', 'jittor']。其中 auto 表示根据实际调用 Metric.update() | |||
:param backend: 目前支持四种类型的backend, ['auto', 'torch', 'paddle', 'jittor']。其中 auto 表示根据实际调用 Metric.update() | |||
函数时传入的参数决定具体的 backend ,一般情况下直接使用 'auto' 即可。 | |||
:param bool aggregate_when_get_metric: 在计算 metric 的时候是否自动将各个进程上的相同的 element 的数字聚合后再得到 metric, | |||
:param aggregate_when_get_metric: 在计算 metric 的时候是否自动将各个进程上的相同的 element 的数字聚合后再得到 metric, | |||
当 backend 不支持分布式时,该参数无意义。如果为 None ,将在 Evaluator 中根据 sampler 是否使用分布式进行自动设置。 | |||
""" | |||
super(Accuracy, self).__init__(backend=backend, aggregate_when_get_metric=aggregate_when_get_metric) | |||
@@ -39,11 +40,11 @@ class Accuracy(Metric): | |||
r""" | |||
update 函数将针对一个批次的预测结果做评价指标的累计 | |||
:param torch.Tensor pred: 预测的tensor, tensor的形状可以是torch.Size([B,]), torch.Size([B, n_classes]), | |||
:param pred: 预测的tensor, tensor的形状可以是torch.Size([B,]), torch.Size([B, n_classes]), | |||
torch.Size([B, max_len]), 或者torch.Size([B, max_len, n_classes]) | |||
:param torch.Tensor target: 真实值的tensor, tensor的形状可以是Element's can be: torch.Size([B,]), | |||
:param target: 真实值的tensor, tensor的形状可以是Element's can be: torch.Size([B,]), | |||
torch.Size([B,]), torch.Size([B, max_len]), 或者torch.Size([B, max_len]) | |||
:param torch.Tensor seq_len: 序列长度标记, 标记的形状可以是None, None, torch.Size([B]), 或者torch.Size([B]). | |||
:param seq_len: 序列长度标记, 标记的形状可以是None, None, torch.Size([B]), 或者torch.Size([B]). | |||
如果mask也被传进来的话seq_len会被忽略. | |||
""" | |||
# 为了兼容不同框架,我们将输入变量全部转为numpy类型来进行计算。 | |||
@@ -79,3 +80,20 @@ class Accuracy(Metric): | |||
else: | |||
self.total += np.prod(list(pred.shape)).item() | |||
self.correct += (target == pred).sum().item() | |||
class TransformersAccuracy(Accuracy): | |||
""" | |||
适配 transformers 中相关模型的 Accuracy metric 。 | |||
""" | |||
def update(self, logits, labels, attention_mask=None): | |||
r""" | |||
update 函数将针对一个批次的预测结果做评价指标的累计 | |||
:param logits: 形状为 ``[B, n_classes]`` 或 ``[B, max_len, n_classes]`` 。 | |||
:param labels: 形状为 ``[B, ]`` 或 ``[B, max_len]`` | |||
:param attention_mask: 序列长度标记。 | |||
""" | |||
seq_len = attention_mask.sum(dim=-1) | |||
super().update(pred=logits, target=labels, seq_len=seq_len) |
@@ -249,7 +249,7 @@ class DataBundle: | |||
return self | |||
def apply_field_more(self, func: Callable, field_name: str, num_proc: int = 0, modify_fields=True, | |||
ignore_miss_dataset=True, progress_desc: str = '', show_progress_bar: bool = True): | |||
ignore_miss_dataset=True, show_progress_bar: bool = True, progress_desc: str = ''): | |||
r""" | |||
对 :class:`~fastNLP.io.DataBundle` 中所有的 dataset 使用 :meth:`~fastNLP.DataSet.apply_field_more` 方法 | |||
@@ -263,8 +263,8 @@ class DataBundle: | |||
:param num_proc: 进程的数量。请注意,由于python语言的特性,多少进程就会导致多少倍内存的增长。 | |||
:param bool ignore_miss_dataset: 当某个field名称在某个dataset不存在时,如果为True,则直接忽略该DataSet; | |||
如果为False,则报错 | |||
:param show_progress_bar: 是否显示tqdm进度条 | |||
:param progress_desc: 当show_progress_barm为True时,可以显示当前tqdm正在处理的名称 | |||
:param show_progress_bar: 是否显示进度条 | |||
:param progress_desc: 当 ``show_progress_bar`` 为 ``True`` 时,可以显示 ``progress`` 的名称。 | |||
:return Dict[str:Dict[str:Field]]: 返回一个字典套字典,第一层的 key 是 dataset 的名字,第二层的 key 是 field 的名字 | |||
@@ -314,7 +314,7 @@ class PretrainedConfig: | |||
# TPU arguments | |||
if kwargs.pop("xla_device", None) is not None: | |||
logger.warning( | |||
logger.rank_zero_warning( | |||
"The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can " | |||
"safely remove it from your `config.json` file." | |||
) | |||
@@ -474,7 +474,7 @@ class PretrainedConfig: | |||
""" | |||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) | |||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: | |||
logger.warn( | |||
logger.rank_zero_warning( | |||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " | |||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." | |||
) | |||
@@ -122,7 +122,7 @@ def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_leng | |||
stopping_max_length = stopping_criteria.max_length | |||
new_stopping_criteria = deepcopy(stopping_criteria) | |||
if stopping_max_length is not None and stopping_max_length != max_length: | |||
logger.warn("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning) | |||
logger.rank_zero_warning("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning) | |||
elif stopping_max_length is None: | |||
new_stopping_criteria.append(MaxLengthCriteria(max_length=max_length)) | |||
return new_stopping_criteria |
@@ -429,7 +429,7 @@ class GenerationMixin: | |||
def _get_pad_token_id(self, pad_token_id: int = None, eos_token_id: int = None) -> int: | |||
if pad_token_id is None and eos_token_id is not None: | |||
logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") | |||
logger.rank_zero_warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") | |||
pad_token_id = eos_token_id | |||
return pad_token_id | |||
@@ -912,7 +912,7 @@ class GenerationMixin: | |||
# special case if pad_token_id is not defined | |||
if pad_token_id is None and eos_token_id is not None: | |||
logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") | |||
logger.rank_zero_warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") | |||
pad_token_id = eos_token_id | |||
# Storing encoder_input_ids for logits_processor that could use them | |||
@@ -352,7 +352,7 @@ class ModuleUtilsMixin: | |||
if token_inputs: | |||
return sum([token_input.numel() for token_input in token_inputs]) | |||
else: | |||
logger.warn( | |||
logger.rank_zero_warning( | |||
"Could not estimate the number of tokens of the input, floating-point operations will not be computed" | |||
) | |||
return 0 | |||
@@ -646,7 +646,7 @@ class PreTrainedModel(Module, ModuleUtilsMixin, GenerationMixin): | |||
# tie weights recursively | |||
tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights) | |||
if len(uninitialized_encoder_weights) > 0: | |||
logger.warning( | |||
logger.rank_zero_warning( | |||
f"The following encoder weights were not tied to the decoder {uninitialized_encoder_weights}" | |||
) | |||
@@ -1486,7 +1486,7 @@ class PreTrainedModel(Module, ModuleUtilsMixin, GenerationMixin): | |||
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}") | |||
if len(unexpected_keys) > 0: | |||
logger.warning( | |||
logger.rank_zero_warning( | |||
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when " | |||
f"initializing {model.__class__.__name__}: {unexpected_keys}\n" | |||
f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task " | |||
@@ -171,7 +171,7 @@ class BartConfig(PretrainedConfig): | |||
# ensure backward compatibility for BART CNN models | |||
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): | |||
self.forced_bos_token_id = self.bos_token_id | |||
logger.warn( | |||
logger.rank_zero_warning( | |||
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions." | |||
"The config can simply be saved and uploaded again to be fixed." | |||
) |