diff --git a/fastNLP/__init__.py b/fastNLP/__init__.py index c67e5919..e666f65f 100644 --- a/fastNLP/__init__.py +++ b/fastNLP/__init__.py @@ -12,7 +12,11 @@ fastNLP 中最常用的组件可以直接从 fastNLP 包中 import ，他们的 __all__ = [ "Instance", "FieldArray", - "Batch", + + "DataSetIter", + "BatchIter", + "TorchLoaderIter", + "Vocabulary", "DataSet", "Const", diff --git a/fastNLP/core/__init__.py b/fastNLP/core/__init__.py index d6ab8983..792bff66 100644 --- a/fastNLP/core/__init__.py +++ b/fastNLP/core/__init__.py @@ -14,7 +14,7 @@ core 模块里实现了 fastNLP 的核心框架，常用的功能都可以从 fa 介绍core 的子模块的分工，好像必要性不大 """ -from .batch import Batch +from .batch import DataSetIter, BatchIter, TorchLoaderIter from .callback import Callback, GradientClipCallback, EarlyStopCallback, TensorboardCallback, LRScheduler, ControlC from .const import Const from .dataset import DataSet diff --git a/fastNLP/core/batch.py b/fastNLP/core/batch.py index ce1a82f4..b23f81e2 100644 --- a/fastNLP/core/batch.py +++ b/fastNLP/core/batch.py @@ -3,7 +3,9 @@ batch 模块实现了 fastNLP 所需的 Batch 类。 """ __all__ = [ - "Batch" + "BatchIter", + "DataSetIter", + "TorchLoaderIter", ] import atexit @@ -15,7 +17,7 @@ import torch.multiprocessing as mp import torch.utils.data from numbers import Number -from .sampler import RandomSampler +from .sampler import SequentialSampler from .dataset import DataSet _python_is_exit = False @@ -28,14 +30,18 @@ def _set_python_is_exit(): atexit.register(_set_python_is_exit) + class DataSetGetter: def __init__(self, dataset: DataSet, as_numpy=False): self.dataset = dataset self.inputs = {n: f for n, f in dataset.get_all_fields().items() if f.is_input} self.targets = {n: f for n, f in dataset.get_all_fields().items() if f.is_target} self.as_numpy = as_numpy + self.idx_list = list(range(len(dataset))) def __getitem__(self, idx: int): + # mapping idx to sampled idx + idx = self.idx_list[idx] inputs = {n:f.get(idx) for n, f in self.inputs.items()} targets = {n:f.get(idx) for n, f in self.targets.items()} return idx, inputs, targets @@ -60,9 +66,9 @@ class DataSetGetter: if f.padder is None: batch_dict[n] = np.array(vlist) else: - data = f.padder(vlist, field_name=n, field_ele_dtype=f.dtype) + data = f.pad(vlist) if not self.as_numpy: - data = _to_tensor(data, f.dtype) + data, flag = _to_tensor(data, f.dtype) batch_dict[n] = data return batch_dict @@ -70,24 +76,40 @@ class DataSetGetter: pad_batch(batch_x, self.inputs), pad_batch(batch_y, self.targets)) + def set_idx_list(self, idx_list): + if len(idx_list) != len(self.idx_list): + raise ValueError + self.idx_list = idx_list -class Batch: - def __init__(self, dataset, batch_size, sampler=None, buffer_size=0, as_numpy=False, - num_workers=0, pin_memory=False, drop_last=False, - timeout=0, worker_init_fn=None, **kwargs): - dataset_getter = DataSetGetter(dataset, as_numpy) - self.buffer_size = buffer_size +class SamplerAdapter(torch.utils.data.Sampler): + def __init__(self, sampler, dataset): + self.sampler = sampler + self.dataset = dataset + + def __iter__(self): + return iter(self.sampler(self.dataset)) + + +class BatchIter: + def __init__(self): + self.dataiter = None + self.num_batches = None self.cur_batch_indices = None - self.num_batches = len(dataset) // batch_size + int(len(dataset) % batch_size != 0) - shuffle = isinstance(sampler, RandomSampler) - self.dataiter = torch.utils.data.DataLoader( - dataset=dataset_getter, batch_size=batch_size, shuffle=shuffle, - collate_fn=dataset_getter.collate_fn, - num_workers=num_workers, pin_memory=pin_memory, drop_last=drop_last, - timeout=timeout, worker_init_fn=worker_init_fn) + self.batch_size = None + + def init_iter(self): + pass + + @staticmethod + def get_num_batches(num_samples, batch_size, drop_last): + num_batches = num_samples // batch_size + if not drop_last and (num_samples % batch_size > 0): + num_batches += 1 + return num_batches def __iter__(self): + self.init_iter() for indices, batch_x, batch_y in self.dataiter: self.cur_batch_indices = indices yield batch_x, batch_y @@ -98,163 +120,62 @@ class Batch: def __len__(self): return self.num_batches + @property + def dataset(self): + return self.dataiter.dataset -class Batch1(object): - """ - 别名：:class:`fastNLP.Batch` :class:`fastNLP.core.batch.Batch` - - Batch 用于从 `DataSet` 中按一定的顺序, 依次按 ``batch_size`` 的大小将数据取出， - 组成 `x` 和 `y`:: - - batch = Batch(data_set, batch_size=16, sampler=SequentialSampler()) - num_batch = len(batch) - for batch_x, batch_y in batch: - # do stuff ... - - :param dataset: :class:`~fastNLP.DataSet` 对象, 数据集 - :param int batch_size: 取出的batch大小 - :param sampler: 规定使用的 :class:`~fastNLP.Sampler` 方式. 若为 ``None`` , 使用 :class:`~fastNLP.RandomSampler`. - - Default: ``None`` - :param bool as_numpy: 若为 ``True`` , 输出batch为 numpy.array. 否则为 :class:`torch.Tensor`. - - Default: ``False`` - :param bool prefetch: 若为 ``True`` 使用多进程预先取出下一batch. - - Default: ``False`` - """ - - def __init__(self, dataset, batch_size, sampler=None, as_numpy=False, prefetch=False): - self.dataset = dataset + +class DataSetIter(BatchIter): + def __init__(self, dataset, batch_size=1, sampler=None, as_numpy=False, + num_workers=0, pin_memory=False, drop_last=False, + timeout=0, worker_init_fn=None): + super().__init__() + assert isinstance(dataset, DataSet) + dataset = DataSetGetter(dataset, as_numpy) + collate_fn = dataset.collate_fn if hasattr(dataset, 'collate_fn') else None + sampler = SamplerAdapter(sampler=sampler or SequentialSampler(), dataset=dataset) + self.dataiter = torch.utils.data.DataLoader( + dataset=dataset, batch_size=batch_size, sampler=sampler, + collate_fn=collate_fn, num_workers=num_workers, + pin_memory=pin_memory, drop_last=drop_last, + timeout=timeout, worker_init_fn=worker_init_fn) + self.num_batches = self.get_num_batches(len(dataset), batch_size, drop_last) self.batch_size = batch_size - if sampler is None: - sampler = RandomSampler() - self.sampler = sampler - self.as_numpy = as_numpy - self.idx_list = None - self.curidx = 0 - self.num_batches = len(dataset) // batch_size + int(len(dataset) % batch_size != 0) - self.cur_batch_indices = None - self.prefetch = prefetch - self.lengths = 0 - - def fetch_one(self): - if self.curidx >= len(self.idx_list): - return None - else: - endidx = min(self.curidx + self.batch_size, len(self.idx_list)) - batch_x, batch_y = {}, {} - - indices = self.idx_list[self.curidx:endidx] - self.cur_batch_indices = indices - - for field_name, field in self.dataset.get_all_fields().items(): - if field.is_target or field.is_input: - batch = field.get(indices) - if not self.as_numpy and \ - field.dtype is not None and \ - issubclass(field.dtype, Number) and not isinstance(batch, torch.Tensor): - batch = _to_tensor(batch) - if field.is_target: - batch_y[field_name] = batch - if field.is_input: - batch_x[field_name] = batch - - self.curidx = endidx - return batch_x, batch_y - - def __iter__(self): - """ - Iterate on dataset, fetch batch data. Fetch process don't block the iterate process - :return: - """ - if self.prefetch: - return self._run_batch_iter(self) - - def batch_iter(): - self.init_iter() - while 1: - res = self.fetch_one() - if res is None: - break - yield res - - return batch_iter() - - def init_iter(self): - self.idx_list = self.sampler(self.dataset) - self.curidx = 0 - self.lengths = self.dataset.get_length() - - def __len__(self): - return self.num_batches - - def get_batch_indices(self): - """ - 取得当前batch在DataSet中所在的index下标序列 - :return list(int) indexes: 下标序列 - """ - return self.cur_batch_indices - - @staticmethod - def _run_fetch(batch, q): - try: - global _python_is_exit - batch.init_iter() - # print('start fetch') - while 1: - res = batch.fetch_one() - # print('fetch one') - while 1: - try: - q.put(res, timeout=3) - break - except Full: - if _python_is_exit: - return - if res is None: - # print('fetch done, waiting processing') - break - # print('fetch exit') - except Exception as e: - q.put(e) - finally: - q.join() - - @staticmethod - def _run_batch_iter(batch): - q = mp.JoinableQueue(maxsize=10) - fetch_p = mp.Process(target=Batch._run_fetch, args=(batch, q)) - fetch_p.daemon = True - fetch_p.start() - # print('fork fetch process') - while 1: - try: - res = q.get(timeout=1) - q.task_done() - # print('get fetched') - if res is None: - break - elif isinstance(res, Exception): - raise res - yield res - except Empty as e: - if fetch_p.is_alive(): - continue - else: - break - fetch_p.terminate() - fetch_p.join() - # print('iter done') + +class TorchLoaderIter(BatchIter): + def __init__(self, dataset): + super().__init__() + assert isinstance(dataset, torch.utils.data.DataLoader) + self.dataiter = dataset + self.num_batches = self.get_num_batches(len(dataset), dataset.batch_size, dataset.drop_last) + self.batch_size = dataset.batch_size + + +class OnlineDataGettter: + # TODO + pass -def _to_tensor(batch): +class OnlineDataIter(BatchIter): + # TODO + def __init__(self, dataset, batch_size=1, buffer_size=10000, sampler=None, as_numpy=False, + num_workers=0, pin_memory=False, drop_last=False, + timeout=0, worker_init_fn=None, **kwargs): + super().__init__() + + +def _to_tensor(batch, field_dtype): try: - if issubclass(batch.dtype.type, np.floating): - batch = torch.as_tensor(batch).float() # 默认使用float32 + if field_dtype is not None \ + and issubclass(field_dtype, Number) \ + and not isinstance(batch, torch.Tensor): + if issubclass(batch.dtype.type, np.floating): + new_batch = torch.as_tensor(batch).float() # 默认使用float32 + else: + new_batch = torch.as_tensor(batch) # 复用内存地址，避免复制 + return new_batch, True else: - batch = torch.as_tensor(batch) # 复用内存地址，避免复制 + return batch, False except: - pass - return batch + return batch, False diff --git a/fastNLP/core/field.py b/fastNLP/core/field.py index faa306f3..a8836b5a 100644 --- a/fastNLP/core/field.py +++ b/fastNLP/core/field.py @@ -176,7 +176,12 @@ class FieldArray: if self.padder is None or pad is False: return np.array(contents) else: - return self.padder(contents, field_name=self.name, field_ele_dtype=self.dtype, dim=self._cell_ndim) + return self.pad(contents) + + def pad(self, contents): + if self.padder is None: + raise RuntimeError + return self.padder(contents, field_name=self.name, field_ele_dtype=self.dtype, dim=self._cell_ndim) def set_padder(self, padder): """ diff --git a/fastNLP/core/predictor.py b/fastNLP/core/predictor.py index 4f37e105..06e586c6 100644 --- a/fastNLP/core/predictor.py +++ b/fastNLP/core/predictor.py @@ -6,7 +6,7 @@ from collections import defaultdict import torch -from . import Batch +from . import DataSetIter from . import DataSet from . import SequentialSampler from .utils import _build_args @@ -44,8 +44,7 @@ class Predictor(object): self.network.eval() batch_output = defaultdict(list) - data_iterator = Batch(data, batch_size=self.batch_size, sampler=SequentialSampler(), as_numpy=False, - prefetch=False) + data_iterator = DataSetIter(data, batch_size=self.batch_size, sampler=SequentialSampler(), as_numpy=False) if hasattr(self.network, "predict"): predict_func = self.network.predict diff --git a/fastNLP/core/tester.py b/fastNLP/core/tester.py index 883e0d01..398afe6b 100644 --- a/fastNLP/core/tester.py +++ b/fastNLP/core/tester.py @@ -37,7 +37,7 @@ import warnings import torch import torch.nn as nn -from .batch import Batch +from .batch import BatchIter, DataSetIter from .dataset import DataSet from .metrics import _prepare_metrics from .sampler import SequentialSampler @@ -82,7 +82,7 @@ class Tester(object): :param int verbose: 如果为0不输出任何信息; 如果为1，打印出验证结果。 """ - def __init__(self, data, model, metrics, batch_size=16, device=None, verbose=1): + def __init__(self, data, model, metrics, batch_size=16, num_workers=0, device=None, verbose=1): super(Tester, self).__init__() if not isinstance(data, DataSet): @@ -96,6 +96,14 @@ class Tester(object): self._model = _move_model_to_device(model, device=device) self.batch_size = batch_size self.verbose = verbose + + if isinstance(data, DataSet): + self.data_iterator = DataSetIter( + dataset=data, batch_size=batch_size, num_workers=num_workers) + elif isinstance(data, BatchIter): + self.data_iterator = data + else: + raise TypeError("data type {} not support".format(type(data))) # 如果是DataParallel将没有办法使用predict方法 if isinstance(self._model, nn.DataParallel): @@ -124,7 +132,7 @@ class Tester(object): self._model_device = _get_model_device(self._model) network = self._model self._mode(network, is_test=True) - data_iterator = Batch(self.data, self.batch_size, sampler=SequentialSampler(), as_numpy=False) + data_iterator = self.data_iterator eval_results = {} try: with torch.no_grad(): diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py index d7694e00..a882dbeb 100644 --- a/fastNLP/core/trainer.py +++ b/fastNLP/core/trainer.py @@ -312,7 +312,7 @@ try: except: from .utils import _pseudo_tqdm as tqdm -from .batch import Batch +from .batch import DataSetIter, BatchIter from .callback import CallbackManager, CallbackException from .dataset import DataSet from .losses import _prepare_losser @@ -394,7 +394,7 @@ class Trainer(object): """ def __init__(self, train_data, model, optimizer=None, loss=None, - batch_size=32, sampler=None, update_every=1, + batch_size=32, sampler=None, update_every=1, num_workers=0, n_epochs=10, print_every=5, dev_data=None, metrics=None, metric_key=None, validate_every=-1, save_path=None, @@ -439,9 +439,19 @@ class Trainer(object): # sampler check if sampler is not None and not isinstance(sampler, Sampler): raise ValueError("The type of sampler should be fastNLP.BaseSampler, got {}.".format(type(sampler))) + + if isinstance(train_data, DataSet): + self.data_iterator = DataSetIter( + dataset=train_data, batch_size=batch_size, num_workers=num_workers) + elif isinstance(train_data, BatchIter): + self.data_iterator = train_data + else: + raise TypeError("train_data type {} not support".format(type(train_data))) - if check_code_level > -1: - _check_code(dataset=train_data, model=model, losser=losser, metrics=metrics, dev_data=dev_data, + if check_code_level > -1 and isinstance(self.data_iterator, DataSetIter): + # TODO 考虑不同的dataset类型怎么check + _check_code(data_iterator=self.data_iterator, + model=model, losser=losser, metrics=metrics, dev_data=dev_data, metric_key=metric_key, check_level=check_code_level, batch_size=min(batch_size, DEFAULT_CHECK_BATCH_SIZE)) # _check_code 是 fastNLP 帮助你检查代码是否正确的方法 。如果你在错误栈中看到这行注释，请认真检查你的代码 @@ -493,7 +503,7 @@ class Trainer(object): self.callback_manager = CallbackManager(env={"trainer": self}, callbacks=callbacks) - + def train(self, load_best_model=True, on_exception='auto'): """ 使用该函数使Trainer开始训练。 @@ -572,8 +582,7 @@ class Trainer(object): with inner_tqdm(total=self.n_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar: self.pbar = pbar avg_loss = 0 - data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False, - prefetch=self.prefetch) + data_iterator = self.data_iterator self.batch_per_epoch = data_iterator.num_batches for epoch in range(1, self.n_epochs + 1): self.epoch = epoch @@ -786,13 +795,14 @@ def _get_value_info(_dict): return strs -def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_SIZE, +def _check_code(data_iterator, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=None, metric_key=None, check_level=0): # check get_loss 方法 model_devcie = model.parameters().__next__().device - batch = Batch(dataset=dataset, batch_size=batch_size, sampler=SequentialSampler()) + batch = data_iterator + dataset = data_iterator.dataset for batch_count, (batch_x, batch_y) in enumerate(batch): _move_dict_value_to_device(batch_x, batch_y, device=model_devcie) # forward check diff --git a/fastNLP/modules/encoder/embedding.py b/fastNLP/modules/encoder/embedding.py index e54c1980..6e7406b2 100644 --- a/fastNLP/modules/encoder/embedding.py +++ b/fastNLP/modules/encoder/embedding.py @@ -15,7 +15,7 @@ from ...io.file_utils import cached_path, _get_base_url from ._bert import _WordBertModel from typing import List -from ... import DataSet, Batch, SequentialSampler +from ... import DataSet, DataSetIter, SequentialSampler from ...core.utils import _move_model_to_device, _get_model_device @@ -226,7 +226,7 @@ class ContextualEmbedding(TokenEmbedding): with torch.no_grad(): for index, dataset in enumerate(datasets): try: - batch = Batch(dataset, batch_size=batch_size, sampler=SequentialSampler(), prefetch=False) + batch = DataSetIter(dataset, batch_size=batch_size, sampler=SequentialSampler()) for batch_x, batch_y in batch: words = batch_x['words'].to(device) words_list = words.tolist() diff --git a/test/core/test_batch.py b/test/core/test_batch.py index d1f93b9c..aa9808ee 100644 --- a/test/core/test_batch.py +++ b/test/core/test_batch.py @@ -3,7 +3,7 @@ import unittest import numpy as np import torch -from fastNLP import Batch +from fastNLP import DataSetIter from fastNLP import DataSet from fastNLP import Instance from fastNLP import SequentialSampler @@ -57,7 +57,7 @@ class TestCase1(unittest.TestCase): dataset = construct_dataset( [["FastNLP", "is", "the", "most", "beautiful", "tool", "in", "the", "world"] for _ in range(40)]) dataset.set_target() - batch = Batch(dataset, batch_size=4, sampler=SequentialSampler(), as_numpy=True) + batch = DataSetIter(dataset, batch_size=4, sampler=SequentialSampler(), as_numpy=True) cnt = 0 for _, _ in batch: @@ -68,7 +68,7 @@ class TestCase1(unittest.TestCase): ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40}) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=True) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=True) for x, y in iter: self.assertTrue(isinstance(x["x"], np.ndarray) and isinstance(y["y"], np.ndarray)) self.assertEqual(len(x["x"]), 4) @@ -81,7 +81,7 @@ class TestCase1(unittest.TestCase): "y": [[4, 3, 2, 1], [3, 2, 1], [2, 1], [1]] * 10}) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=True) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=True) for x, y in iter: self.assertEqual(x["x"].shape, (4, 4)) self.assertEqual(y["y"].shape, (4, 4)) @@ -91,7 +91,7 @@ class TestCase1(unittest.TestCase): "y": np.array([[4, 3, 2, 1], [3, 2, 1], [2, 1], [1]] * 10)}) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=True) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=True) for x, y in iter: self.assertEqual(x["x"].shape, (4, 4)) self.assertEqual(y["y"].shape, (4, 4)) @@ -101,7 +101,7 @@ class TestCase1(unittest.TestCase): "y": [[4, 3, 2, 1], [3, 2, 1], [2, 1], [1]] * 10}) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) for x, y in iter: self.assertTrue(isinstance(x["x"], torch.Tensor)) self.assertEqual(tuple(x["x"].shape), (4, 4)) @@ -113,7 +113,7 @@ class TestCase1(unittest.TestCase): "y": np.array([[4, 3, 2, 1], [3, 2, 1], [2, 1], [1]] * 10)}) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) for x, y in iter: self.assertTrue(isinstance(x["x"], torch.Tensor)) self.assertEqual(tuple(x["x"].shape), (4, 4)) @@ -125,7 +125,7 @@ class TestCase1(unittest.TestCase): [Instance(x=[1, 2, 3, 4], y=[3, 4, 5, 6]) for _ in range(2)]) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) for x, y in iter: self.assertTrue(isinstance(x["x"], torch.Tensor)) self.assertEqual(tuple(x["x"].shape), (4, 4)) @@ -137,7 +137,7 @@ class TestCase1(unittest.TestCase): [Instance(x=np.array([1, 2, 3, 4]), y=np.array([3, 4, 5, 6])) for _ in range(2)]) ds.set_input("x") ds.set_target("y") - iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) + iter = DataSetIter(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False) for x, y in iter: print(x, y) @@ -146,7 +146,7 @@ class TestCase1(unittest.TestCase): num_samples = 1000 dataset = generate_fake_dataset(num_samples) - batch = Batch(dataset, batch_size=batch_size, sampler=SequentialSampler()) + batch = DataSetIter(dataset, batch_size=batch_size, sampler=SequentialSampler()) for batch_x, batch_y in batch: pass