text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
init_std (`float`, *optional*, defaults to 0.02):
Parameters initialized by N(0, init_std)
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers
eos_token_id (`int`, *optional*, defaults to 0):
End of stream ... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
Examples:
```python
>>> from transformers import TransfoXLConfig, TransfoXLModel
>>> # Initializing a Transformer XL configuration
>>> configuration = TransfoXLConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = TransfoXLModel(configuration)
>>> #... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
def __init__(
self,
vocab_size=267735,
cutoffs=[20000, 40000, 200000],
d_model=1024,
d_embed=1024,
n_head=16,
d_head=64,
d_inner=4096,
div_val=4,
pre_lnorm=False,
n_layer=18,
mem_len=1600,
clamp_len=1000,
sam... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
self.d_embed = d_embed
self.d_head = d_head
self.d_inner = d_inner
self.div_val = div_val
self.pre_lnorm = pre_lnorm
self.n_layer = n_layer
self.n_head = n_head
self.mem_len = mem_len
self.same_length = same_length
self.attn_type = attn_type
... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
@property
def max_position_embeddings(self):
# Message copied from Transformer-XL documentation
logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
return -1
@max_position_embeddings.setter
def max_position_embeddings(self, value):... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
class TFAdaptiveSoftmaxMask(keras.layers.Layer):
def __init__(self, vocab_size, d_embed, d_proj, cutoffs, div_val=1, keep_order=False, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [vo... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
def build(self, input_shape):
if self.n_clusters > 0:
self.cluster_weight = self.add_weight(
shape=(self.n_clusters, self.d_embed), initializer="zeros", trainable=True, name="cluster_weight"
)
self.cluster_bias = self.add_weight(
shape=(self.n_... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
if self.div_val == 1:
for i in range(len(self.cutoffs)):
if self.d_proj != self.d_embed:
weight = self.add_weight(
shape=(self.d_embed, self.d_proj),
initializer="zeros",
trainable=True,
... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
self.out_layers.append((weight, bias))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
d_emb_i = self.d_embed // (self.div_val**i) | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
weight = self.add_weight(
shape=(d_emb_i, self.d_proj), initializer="zeros", trainable=True, name=f"out_projs_._{i}"
)
self.out_projs.append(weight)
weight = self.add_weight(
shape=(r_idx - l_idx, d_emb_i),
initi... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
@staticmethod
def _gather_logprob(logprob, target):
lp_size = shape_list(logprob)
r = tf.range(lp_size[0], dtype=target.dtype)
idx = tf.stack([r, target], 1)
return tf.gather_nd(logprob, idx) | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
def call(self, hidden, target, return_mean=True, training=False):
head_logprob = 0
if self.n_clusters == 0:
output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0])
if target is not None:
loss = tf.nn.sparse_softmax_cross_entro... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
if self.div_val == 1:
cur_W = self.out_layers[0][0][l_idx:r_idx]
cur_b = self.out_layers[0][1][l_idx:r_idx]
else:
cur_W = self.out_layers[i][0]
cur_b = self.out_layers[i][1]
if i == 0:
cu... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
head_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[0])
head_logprob = tf.nn.log_softmax(head_logit)
out.append(head_logprob[..., : self.cutoffs[0]])
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
cur_tail_logprob = tf.boolean_mask(tail_logprob, mask)
cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target)
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(mask_id... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
if target is not None:
if return_mean:
loss = tf.reduce_mean(loss)
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(loss)
# Log the loss as a metric (we could log arbitrary metrics,
# including different m... | 10,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py |
class ProjectedAdaptiveLogSoftmax(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False):
super().__init__()
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_end... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
if div_val == 1:
for i in range(len(self.cutoffs)):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
else:
self.out_projs.append(None)
self.out_layers.append(nn.Linear(d_embed, n... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
def _compute_logit(self, hidden, weight, bias, proj):
if proj is None:
logit = nn.functional.linear(hidden, weight, bias=bias)
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
proj_hid = nn.functional.linear(hidden, proj.t().contiguous())
logit = nn.functio... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
Return:
if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out ::
[(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if
theirs had an option to set bias on all clusters in the native ... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
if labels is not None:
mask = labels != -100
out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)
... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
weight_i = self.out_layers[i].weight
bias_i = self.out_layers[i].bias | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
if i == 0:
weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], s... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
if labels is not None:
mask_i = (labels >= l_idx) & (labels < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = labels.index_select(0, indices_i) - l_idx
... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1)
cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
if labels is not None:
if (hasattr(self, "keep_order") and self.keep_order) or keep_order:
out.index_copy_(0, indices_i, -logprob_i)
else:
out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
offset += logp... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
- Input: \\((N, in\_features)\\)
- Output: \\((N, n\_classes)\\)
"""
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
return nn.functional.log_softmax(logit, dim=-1)
else:
... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
if i == 0:
weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], s... | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i)
tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob[:, -i] + tail_logprob_i
out[:, start_idx, stop_idx] = logprob_i
return out | 10,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py |
class TransfoXLTokenizer(PreTrainedTokenizer):
"""
Construct a Transformer-XL tokenizer adapted from Vocab class in [the original
code](https://github.com/kimiyoung/transformer-xl). The Transformer-XL tokenizer is a word-level tokenizer (no
sub-word tokenization).
This tokenizer inherits from [`Pre... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
Args:
special (`List[str]`, *optional*):
A list of special tokens (to be treated by the original implementation of this tokenizer).
min_freq (`int`, *optional*, defaults to 0):
The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise ... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
File containing the vocabulary as saved with the `save_pretrained()` method.
never_split (`List[str]`, *optional*):
List of tokens that should never be split. If no list is specified, will simply use the existing special
tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids"]
def __init__(
self,
special=None,
min_freq=0,
max_size=None,
lower_case=False,
delimiter=None,
vocab_file=None,
pretrained_vocab_file: str = None,
never_split=None,
... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
requires_backends(self, "sacremoses")
if special is None:
special = []
self.counter = Counter()
self.special = special
self.min_freq = min_freq
self.max_size = max_size
self.lower_case = lower_case
self.delimiter = delimiter
self.vocab_file = v... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
# in a library like ours, at all.
try:
vocab_dict = None
if pretrained_vocab_file is not None:
# Priority on pickle files (support PyTorch and TF)
if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
raise ValueError(
... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
# Loading a torch-saved transfo-xl vocab dict with pickle results in an integer
# Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed.
# We therefore load it with torch, if it's available.
if isinstance(vocab_dict, int):
... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
if vocab_dict is not None:
for key, value in vocab_dict.items():
if key not in self.__dict__ or key in ["sym2idx", "idx2sym"]:
self.__dict__[key] = value
elif vocab_file is not None:
self.build_vocab()
except Exception as e... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
super().__init__(
special=special,
min_freq=min_freq,
max_size=max_size,
lower_case=lower_case,
delimiter=delimiter,
vocab_file=vocab_file,
pretrained_vocab_file=pretrained_vocab_file,
never_split=never_split,
un... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def _compile_space_around_punctuation_pattern(self):
look_ahead_for_special_token = f"(?=[{self.punctuation_symbols}])"
look_ahead_to_match_all_except_space = r"(?=[^\s])"
return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space)
def count_file(self, path,... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def count_sents(self, sents, verbose=False):
"""
sents : a list of sentences, each a list of tokenized symbols
"""
if verbose:
logger.info(f"counting {len(sents)} sents ...")
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == ... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["pretrained_vocab_f... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
for sym in self.special:
self.add_special(sym)
for sym, cnt in self.counter.most_common(self.max_size):
if cnt < self.min_freq:
break
self.add_symbol(sym)
logger.info(f"Final vocab size {len(self.sym2idx)} from {len(self.count... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
if ordered:
encoded = torch.cat(encoded)
return encoded
@torch_only_method
def encode_sents(self, sents, ordered=False, verbose=False):
if verbose:
logger.info(f"encoding {len(sents)} sents ...")
encoded = []
for idx, symbols in enumerate(sents):
... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def move_added_token(self, token: str, target_idx: int):
"""
Moves an added token to a specific position in the vocab. This method should be used when resizing an embedding
layer other than the last one in the `AdaptiveEmbedding` in order to move the token in the tokenizer from the
defau... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
# Shift following indices in sym2idx
for idx in range(target_idx + 1, len(self.idx2sym)):
current_sym = self.idx2sym[idx]
self.sym2idx[current_sym] = idx
# Delete token from added_tokens
old_index = self._added_tokens_encoder.pop(token)
self._added_tokens_decoder... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def moses_pipeline(self, text: str) -> List[str]:
"""
Does basic tokenization using [`sacremoses.MosesPunctNormalizer`] and [`sacremoses.MosesTokenizer`] with
*aggressive_dash_splits=True* (see [`sacremoses.tokenize.MosesTokenizer.tokenize`]). Additionally, large
comma-separated numbers ... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def _convert_id_to_token(self, idx):
"""Converts an id in a token (BPE) using the vocab."""
assert 0 <= idx < len(self), f"Index {idx} out of vocabulary range"
return self.idx2sym[idx]
def _convert_token_to_id(self, sym):
"""Converts a token (str) in an id using the vocab."""
... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (string) in a single string. Additionally, the split numbers are converted back
into it's original form.
"""
out_string = self.moses_detokenizer.detokenize(tokens)
return detokenize_numbers(out_... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
# empty delimiter '' will evaluate False
if self.delimiter == "":
symbols = line
else:
symbols = self.moses_pipeline(line)
if add_double_eos: # lm1b
return ["<S>"] + symbols + ["<S>"]
elif add_eos:
return symbols + ["<eos>"]
else:... | 10,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
class LMOrderedIterator:
def __init__(self, data, bsz, bptt, device="cpu", ext_len=None):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = devic... | 10,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
data = self.data[beg_idx:end_idx]
target = self.data[i + 1 : i + 1 + seq_len]
data_out = data.transpose(0, 1).contiguous().to(self.device)
target_out = target.transpose(0, 1).contiguous().to(self.device)
return data_out, target_out, seq_len
def get_fixlen_iter(self, start=0):
... | 10,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
class LMShuffledIterator:
def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
"""
data -- list[LongTensor] -- there is no order among the LongTensors
"""
self.data = data
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ex... | 10,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
while True:
# data : [n_retain+bptt x bsz]
# target : [bptt x bsz]
data[n_retain:].fill_(-1)
target.fill_(-1)
valid_batch = True | 10,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
for i in range(self.bsz):
n_filled = 0
try:
while n_filled < self.bptt:
if streams[i] is None or len(streams[i]) <= 1:
streams[i] = next(sent_stream)
# number of new tokens to fill in
... | 10,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
data_out = data.transpose(0, 1).contiguous().to(self.device)
target_out = target.transpose(0, 1).contiguous().to(self.device)
yield data_out, target_out, self.bptt
n_retain = min(data.size(0), self.ext_len)
if n_retain > 0:
data[:n_retain] = data[-n_reta... | 10,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
class LMMultiFileIterator(LMShuffledIterator):
def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
self.paths = paths
self.vocab = vocab
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.dev... | 10,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
class TransfoXLCorpus:
@classmethod
@torch_only_method
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a pre-processed corpus.
"""
vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **... | 10,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
logger.info(f"loading corpus file {resolved_corpus_file}")
else:
logger.info(f"loading corpus file {CORPUS_NAME} from cache at {resolved_corpus_file}") | 10,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
# Instantiate tokenizer.
corpus = cls(*inputs, **kwargs)
corpus_dict = torch.load(resolved_corpus_file, weights_only=True)
for key, value in corpus_dict.items():
corpus.__dict__[key] = value
corpus.vocab = vocab
if corpus.train is not None:
corpus.train = ... | 10,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
if self.dataset in ["ptb", "wt2", "enwik8", "text8"]:
self.vocab.count_file(os.path.join(path, "train.txt"))
self.vocab.count_file(os.path.join(path, "valid.txt"))
self.vocab.count_file(os.path.join(path, "test.txt"))
elif self.dataset == "wt103":
self.vocab.count... | 10,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
if self.dataset in ["ptb", "wt2", "wt103"]:
self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True)
self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True)
self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered... | 10,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
def get_iterator(self, split, *args, **kwargs):
if split == "train":
if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
data_iter = LMOrderedIterator(self.train, *args, **kwargs)
elif self.dataset == "lm1b":
kwargs["shuffle"] = True
... | 10,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py |
class ErnieMEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_tok... | 10,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.LongTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if inputs_embeds is None:
inputs_embeds ... | 10,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
if past_key_values_length > 0:
position_ids = position_ids + past_key_values_length
# to mimic paddlenlp implementation
position_ids += 2
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = sel... | 10,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size})... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all ... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = q... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("b... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in ErnieMModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention sc... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
i... | 10,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self_attn = ErnieMSelfAttention(config, position_embedding_type=position_embedding_type)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.pruned_h... | 10,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
# Update hyper params and store pruned heads
self.self_attn.num_attention_heads = self.self_attn.num_attention_heads - len(heads)
self.self_attn.all_head_size = self.self_attn.attention_head_size * self.self_attn.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 10,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
# to mimic paddlenlp implementation
dropout = 0.1 if config.hidden_dropout_prob is None else config.hidden_dropout_prob
act_dropout = config.hidden_dropout_prob if config.act_dropout is None else conf... | 10,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = True,
):
re... | 10,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
else:
hidden_states = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
)
hidden_states = res... | 10,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([ErnieMEncoderLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
input_embeds: torch.Tensor,
attention_mask: O... | 10,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
output = input_embeds
if output_hidden_states:
hidden_states = hidden_states + (output,)
for i, layer in enumerate(self.layers):
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else N... | 10,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=attentions
) | 10,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hid... | 10,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ErnieMConfig
base_model_prefix = "ernie_m" | 10,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.... | 10,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMModel(ErnieMPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super(ErnieMModel, self).__init__(config)
self.initializer_range = config.initializer_range
self.embeddings = ErnieMEmbeddings(config)
self.encoder = ErnieMEncoder(config)
self.poole... | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
@add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,... | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time.") | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
# init the default bool value
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = retu... | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
# Adapted from paddlenlp.transformers.ernie_m.ErnieMModel
if attention_mask is None:
attention_mask = (input_ids == self.config.pad_token_id).to(torch.float32)
attention_mask *= torch.finfo(attention_mask.dtype).min
if past_key_values is not None:
batch_size =... | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=exten... | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
sequence_output = encoder_outputs["last_hidden_state"]
pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
hidden_states = None if not output_hidden_states else encoder_outputs["hidden_states"]
attentions = None if not output_attentions else encoder_outputs["attenti... | 10,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMForSequenceClassification(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.ernie_m = ErnieMModel(config)
classifier_dropout = (
config.classifier_dropout if conf... | 10,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
@add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def fo... | 10,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_lab... | 10,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
outputs = self.ernie_m(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
output_hidden_states=output_hidden_states,
outpu... | 10,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 10,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMForMultipleChoice(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.ernie_m = ErnieMModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
... | 10,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
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