SUMIN/HuggingFace_transformer
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Reformat source code with black.

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This is the result of:

    $ black --line-length 119 examples templates transformers utils hubconf.py setup.py

There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.

This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
This commit is contained in:
Aymeric Augustin
2019-12-21 15:46:46 +01:00
parent 63e3827c6b
commit fa84ae26d6
200 changed files with 17452 additions and 12594 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
examples/distillation/lm_seqs_dataset.py
View File

@@ -21,6 +21,7 @@ from torch.utils.data import Dataset
import numpy as np
from utils import logger
class LmSeqsDataset(Dataset):
"""Custom Dataset wrapping language modeling sequences.
@@ -32,9 +33,7 @@ class LmSeqsDataset(Dataset):
data: `List[np.array[int]]
"""
def __init__(self,
params,
data):
def __init__(self, params, data):
self.params = params
self.token_ids = np.array(data)
@@ -57,7 +56,7 @@ class LmSeqsDataset(Dataset):
Some sanity checks
"""
assert len(self.token_ids) == len(self.lengths)
assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))
assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))
def remove_long_sequences(self):
"""
@@ -65,17 +64,17 @@ class LmSeqsDataset(Dataset):
"""
max_len = self.params.max_model_input_size
indices = self.lengths > max_len
logger.info(f'Splitting {sum(indices)} too long sequences.')
logger.info(f"Splitting {sum(indices)} too long sequences.")
def divide_chunks(l, n):
return [l[i:i + n] for i in range(0, len(l), n)]
return [l[i : i + n] for i in range(0, len(l), n)]
new_tok_ids = []
new_lengths = []
if self.params.mlm:
cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
cls_id, sep_id = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
else:
cls_id, sep_id = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
cls_id, sep_id = self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]
for seq_, len_ in zip(self.token_ids, self.lengths):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
@@ -84,7 +83,7 @@ class LmSeqsDataset(Dataset):
new_lengths.append(len_)
else:
sub_seqs = []
for sub_s in divide_chunks(seq_, max_len-2):
for sub_s in divide_chunks(seq_, max_len - 2):
if sub_s[0] != cls_id:
sub_s = np.insert(sub_s, 0, cls_id)
if sub_s[-1] != sep_id:
@@ -108,7 +107,7 @@ class LmSeqsDataset(Dataset):
self.token_ids = self.token_ids[indices]
self.lengths = self.lengths[indices]
new_size = len(self)
logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')
logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences.")
def print_statistics(self):
"""
@@ -116,7 +115,7 @@ class LmSeqsDataset(Dataset):
"""
if not self.params.is_master:
return
logger.info(f'{len(self)} sequences')
logger.info(f"{len(self)} sequences")
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
@@ -125,8 +124,7 @@ class LmSeqsDataset(Dataset):
# nb_unkown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unkown} unknown tokens (covering {100*nb_unkown/data_len:.2f}% of the data)')
def batch_sequences(self,
batch):
def batch_sequences(self, batch):
"""
Do the padding and transform into torch.tensor.
"""
@@ -139,13 +137,13 @@ class LmSeqsDataset(Dataset):
# Pad token ids
if self.params.mlm:
pad_idx = self.params.special_tok_ids['pad_token']
pad_idx = self.params.special_tok_ids["pad_token"]
else:
pad_idx = self.params.special_tok_ids['unk_token']
tk_ = [list(t.astype(int)) + [pad_idx]*(max_seq_len_-len(t)) for t in token_ids]
pad_idx = self.params.special_tok_ids["unk_token"]
tk_ = [list(t.astype(int)) + [pad_idx] * (max_seq_len_ - len(t)) for t in token_ids]
assert len(tk_) == len(token_ids)
assert all(len(t) == max_seq_len_ for t in tk_)
tk_t = torch.tensor(tk_) # (bs, max_seq_len_)
tk_t = torch.tensor(tk_) # (bs, max_seq_len_)
lg_t = torch.tensor(lengths) # (bs)
return tk_t, lg_t