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@richarddwang
richarddwang committed Aug 30, 2020
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2 changes: 1 addition & 1 deletion README.md
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@@ -1,7 +1,7 @@
# hugdatafast
The elegant integration of huggingface/nlp and fastai, and some handy data transformation for huggingface/nlp.

🎓 ** Documentation ** : https://hugdatafast.readthedocs.io/en/latest/
🎓 **Documentation** : https://hugdatafast.readthedocs.io/en/latest/

# Install
`pip install hugdatafast`
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17 changes: 13 additions & 4 deletions docs/source/start.rst
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Expand Up @@ -62,9 +62,11 @@ Even you don't use fastai to train, you can still use as a normal DataLoader
Other use cases
----------------

1. Use your own dataset ? \n
- `nlp.Dataset s from local structured files (csv, json, ...) <https://huggingface.co/nlp/loading_datasets.html#from-local-files>`_
- `nlp.Dataset s from custom loading script <https://huggingface.co/nlp/add_dataset.html>`_
1. Use your own dataset ?

* `nlp.Dataset s from local structured files (csv, json, ...) <https://huggingface.co/nlp/loading_datasets.html#from-local-files>`_

* `nlp.Dataset s from custom loading script <https://huggingface.co/nlp/add_dataset.html>`_

2. Use custom tokenization or custom processing function ?
use :class:`HF_Transform`
Expand All @@ -74,4 +76,11 @@ use :class:`HF_Transform`
>>> def custom_tokenize(example):
... example['tok_ids'] = hf_tokenizer.encode(example['sentence1'], example['sentence2'])
... return example
>>> tokenized_rte = HF_Transform(rte, custom_tokenize).map()
>>> tokenized_rte = HF_Transform(rte, custom_tokenize).map()
``hugdatafast`` in practice
-------------------------
You can see how to use ``hugdatafast`` in real situations. Also, we're welcome you to share how you use
``hugdatafast`` in your project, contact me via github or twitter to put your project link here.

* `electra_pytorch <https://github.com/richarddwang/hugdatafast>`_ : Pretrain ELECTRA and finetune on GLUE benchmark
34 changes: 33 additions & 1 deletion hugdatafast/fastai.py
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Expand Up @@ -404,6 +404,8 @@ def dataloaders(self, device='cpu', cache_dir=None, cache_name=None, dl_kwargs=N
neither of them can because money is too tight.
"""
if dl_kwargs is None: dl_kwargs = [{} for _ in range(len(self.hf_dsets))]
elif isinstance(dl_kwargs, dict):
dl_kwargs = [ dl_kwargs[split] if split in dl_kwargs else {} for split in self.hf_dsets]
# infer cache file names for each dataloader if needed
dl_type = kwargs.pop('dl_type', self._dl_type)
if dl_type==MySortedDL and cache_name:
Expand All @@ -412,7 +414,9 @@ def dataloaders(self, device='cpu', cache_dir=None, cache_name=None, dl_kwargs=N
cache_dir.mkdir(exist_ok=True)
if not cache_name.endswith('.json'): cache_name += '.json'
for i, split in enumerate(self.hf_dsets):
dl_kwargs[i]['cache_file'] = cache_dir/cache_name.format(split=split)
filled_cache_name = dl_kwargs[i].pop('cache_name', cache_name.format(split=split))
if 'cache_file' not in dl_kwargs[i]:
dl_kwargs[i]['cache_file'] = cache_dir/filled_cache_name
# change default to not drop last
kwargs['drop_last'] = kwargs.pop('drop_last', False)
# when corpus like glue/ax has only testset, set it to non-train setting
Expand All @@ -421,9 +425,37 @@ def dataloaders(self, device='cpu', cache_dir=None, cache_name=None, dl_kwargs=N
kwargs['drop_last'] = False
return super().dataloaders(dl_kwargs=dl_kwargs, device=device, **kwargs)

def hf_merge_datasets(*datasets_s):
"""
Args:
*datasets_s: multiple dicts that contains :class:`nlp.Dataset`, each dict must have the same keys (split names), all datasets should have some columns with the same name.
Returns
Dict[ str : :class:`HF_MergeDataset` ]
Example:
# Just for example, you may not concates rte and wnli datasets in real.
>>> rte, wnli = nlp.load_dataset('glue', 'rte'), nlp.load_dataset('glue', 'wnli')
# rte: {'train':Dataset(schema:{...,'sentence1':...,'sentence2':...}),'validation':...}, wnli: {'train':Dataset(schema:{...,'sentence1':...,'sentence2':...}),'validation':...
>>> merge_dsets = hf_merge_datasets(rte, wnli)
{'train': HF_MergedDataset, 'validation': HF_MergedDataset, 'test': HF_MergedDataset}
"""
keys_s = [ list(dsets.keys()) for dsets in datasets_s ]
for keys in keys_s: assert keys == keys_s[0]
merged_dsets = {}
for split in keys:
merged_dsets[split] = HF_MergedDataset(*[ dsets[split] for dsets in datasets_s])
return merged_dsets

class HF_MergedDataset():
"""Merge multiple :class:`nlp.Dataset` s to be a fake :class:`nlp.Dataset` be able to passed to :class:`HF_Dataset`
Args:
*datasets : multiple :class:`nlp.Dataset` s, that all of these have some columns with the same names.
Returns:
HF_MergedDataset: a :class:`nlp.Dataset` like object that concats passed datasets, with basic functions to be turned into :class:`HF_Dataset`.
Example:
>>> tokenized_wiki_train, tokenized_bookcorpus_train
Dataset(schema: {...., 'input_ids': 'list<item: int64>', ...), Dataset(schema: {...., 'input_ids': 'list<item: int64>', ...)
Expand Down
9 changes: 6 additions & 3 deletions hugdatafast/transform.py
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Expand Up @@ -14,7 +14,8 @@ def __init__(self, hf_dsets, remove_original=False, out_cols=None):
out_cols (List[str]): output column names. If specified, it will assure they are not in the columns to be removed.
"""
# check arguments
if isinstance(hf_dsets, nlp.Dataset): hf_dsets = {'Single': hf_dsets}
if isinstance(hf_dsets, nlp.Dataset): hf_dsets = {'Single': hf_dsets}; self.single = True
else: self.single = False
assert isinstance(hf_dsets, dict)
# save attributes
self.hf_dsets = hf_dsets
Expand Down Expand Up @@ -49,12 +50,14 @@ def map(self, split_kwargs=None, cache_dir=None, cache_name=None, **kwargs):
new_dsets = {}
for split, dset in self.hf_dsets.items():
if self.remove_original: kwargs['remove_columns'] = dset.column_names
if cache_name: kwargs['cache_file_name'] = str(cache_dir/cache_name.format(split=split))
if cache_name:
if self.single: kwargs['cache_file_name'] = str(cache_dir/cache_name)
else: kwargs['cache_file_name'] = str(cache_dir/cache_name.format(split=split))
kwargs.update(split_kwargs[split])
if hasattr(kwargs, 'remove_columns'): self._check_outcols(kwargs['remove_columns'], split)
new_dsets[split] = self._map(dset, split, **kwargs)
# return
if len(new_dsets)==1 and 'Single' in new_dsets: return new_dsets['Single']
if self.single: return new_dsets['Single']
else: return new_dsets

def _check_outcols(self, out_cols, rm_cols, split):
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55 changes: 40 additions & 15 deletions tests/hf_nlp_extension_test.ipynb
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Expand Up @@ -42,6 +42,11 @@
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "8551it [00:00, 9465.16it/s]\n1043it [00:00, 10052.16it/s]\n1063it [00:00, 8299.48it/s]\n"
},
{
"output_type": "execute_result",
"data": {
Expand Down Expand Up @@ -73,6 +78,11 @@
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "2490it [00:01, 2350.48it/s]\n277it [00:00, 2447.21it/s]\n3000it [00:00, 3372.38it/s]\n"
},
{
"output_type": "execute_result",
"data": {
Expand Down Expand Up @@ -113,13 +123,13 @@
{
"output_type": "stream",
"name": "stderr",
"text": "60%|██████ | 642/1063 [00:00<00:00, 6415.30it/s]"
"text": "58%|█████▊ | 619/1063 [00:00<00:00, 6189.99it/s]"
},
{
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"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>text_idxs</th>\n <th>label</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>everybody who has ever, worked in any office which contained any typewriter which had ever been used to type any letters which had to be signed by any administrator who ever worked in any department like mine will know what i mean.</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>playing with matches is ; lots of fun, but doing, so and emptying gasoline from one can to another at the same time is a sport best reserved for arsons.</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>"
},
"metadata": {}
}
Expand Down Expand Up @@ -147,13 +157,13 @@
{
"output_type": "stream",
"name": "stderr",
"text": "55%|█████▍ | 583/1063 [00:00<00:00, 5826.69it/s]"
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},
{
"output_type": "display_data",
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"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>text_idxs</th>\n <th>label</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>everybody who has ever , worked in any office which contained any type ##writer which had ever been used to type any letters which had to be signed by any administrator who ever worked in any department like mine will know what i mean .</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>in january 2002 , a dull star in an obscure constellation suddenly became 600 , 000 times more luminous than our sun , temporarily making it the brightest star in our galaxy . [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>"
},
"metadata": {}
}
Expand Down Expand Up @@ -191,10 +201,22 @@
"tags": []
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=29413.0, style=ProgressStyle(descriptio…",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "cad85ddb77924b4182c01dbbfbc3fda7"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": "{'text': 'Mark told Pete many lies about himself, which Pete included in his book. He should have been more skeptical.', 'span1_index': 0, 'span2_index': 13, 'span1_text': 'Mark', 'span2_text': 'He', 'idx': 0, 'label': 0}\n"
"text": "\n554it [00:00, 3446.54it/s]{'text': 'Mark told Pete many lies about himself, which Pete included in his book. He should have been more skeptical.', 'span1_index': 0, 'span2_index': 13, 'span1_text': 'Mark', 'span2_text': 'He', 'idx': 0, 'label': 0}\n\n104it [00:00, 3162.25it/s]\n146it [00:00, 3802.30it/s]\n"
},
{
"output_type": "display_data",
Expand Down Expand Up @@ -245,8 +267,8 @@
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Original dataset:\nnum of samples: 1043\nsecond to last sentence: John arranged for himself to get the prize.\n last sentence: John talked to Bill about himself.\nLM dataset:\nnum of sampels: 481\nlast text (x): . john talked to bill about himself\nlast text (y): john talked to bill about himself.\n"
"name": "stderr",
"text": "100%|██████████| 2/2 [00:00<00:00, 102.43it/s]Original dataset:\nnum of samples: 1043\nsecond to last sentence: John arranged for himself to get the prize.\n last sentence: John talked to Bill about himself.\nLM dataset:\nnum of sampels: 481\nlast text (x): . john talked to bill about himself\nlast text (y): john talked to bill about himself.\n\n"
}
],
"source": [
Expand Down Expand Up @@ -300,12 +322,15 @@
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
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"metadata": {}
"output_type": "error",
"ename": "NameError",
"evalue": "name 'ELECTRADataTransform' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-83dd0cb5fe0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproc_dset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mELECTRADataTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcola\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'validation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_docs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sentence'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhf_toker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhf_tokenizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0me_dsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHF_Datasets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mproc_dset\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensorText\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'token_type_ids'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhf_toker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhf_tokenizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0me_dls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me_dsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataloaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrtkey_fc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0me_dls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'ELECTRADataTransform' is not defined"
]
}
],
"source": [
Expand Down Expand Up @@ -355,7 +380,7 @@
{
"output_type": "stream",
"name": "stderr",
"text": "92%|█████████▏| 977/1063 [00:00<00:00, 5087.53it/s]Test passed\n"
"text": "78%|███████▊ | 830/1063 [00:00<00:00, 8297.06it/s]Test passed\n"
}
],
"source": [
Expand Down Expand Up @@ -390,7 +415,7 @@
{
"output_type": "stream",
"name": "stderr",
"text": "83%|████████▎ | 882/1063 [00:00<00:00, 4539.60it/s]"
"text": "80%|███████▉ | 850/1063 [00:00<00:00, 8490.94it/s]"
}
],
"source": [
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