text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 10,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLLMHeadModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). | 10,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Args:
losses (`torch.FloatTensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided):
Language modeling losses (not reduced).
prediction_scores (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction s... | 10,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. | 10,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 10,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
@property
def logits(self):
# prediction scores are the output of the adaptive softmax, see
# the file `modeling_transfo_xl_utilities`. Since the adaptive
# softmax returns the log softmax value, `self.prediction_scores`
# are strictly speaking not exactly `logits`, but behave the sa... | 10,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLModel(TransfoXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.n_token = config.vocab_size
self.d_embed = config.d_embed
self.d_model = config.d_model
self.n_head = config.n_head
self.d_head = config.d_head
self.wo... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
self.layers = nn.ModuleList()
if config.attn_type == 0: # the default attention
for i in range(config.n_layer):
self.layers.append(
RelPartialLearnableDecoderLayer(
config.n_head,
config.d_model,
... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
self.same_length = config.same_length
self.clamp_len = config.clamp_len
if self.attn_type == 0: # default attention
self.pos_emb = PositionalEmbedding(self.d_model)
else: # learnable embeddings and absolute embeddings
raise NotImplementedError # Removed these to avoid... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def init_mems(self, bsz):
if self.mem_len > 0:
mems = []
param = next(self.parameters())
for i in range(self.n_layer):
empty = torch.zeros(self.mem_len, bsz, self.config.d_model, dtype=param.dtype, device=param.device)
mems.append(empty)
... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TransfoXLModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz]
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
# and head_mask is converted to shape [num_hidden_layers x ... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if inputs_embeds is not None:
word_emb = inputs_embeds
else:
word_emb = self.word_emb(input_ids)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones((qlen, klen), dtype=torch.bool)
... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
hids = []
attentions = [] if output_attentions else None
if self.attn_type == 0: # default
pos_seq = torch.arange(klen - 1, -1, -1.0, device=word_emb.device, dtype=torch.int64).type_as(
dtype=word_emb.dtype
)
if self.clamp_len > 0:
pos... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
for i, layer in enumerate(self.layers):
hids.append(core_out)
mems_i = None if mems is None else mems[i]
layer_outputs = layer(
core_out,
pos_emb,
dec_attn_mask=dec_attn_mask,
mems=mems_i,
... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if output_hidden_states:
# Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
hids.append(core_out)
hids = tuple(t.transpose(0, 1).contiguous() for t in hids)
else:
hids = None
if output_attentions:
# Transpose to lib... | 10,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
_tied_weights_keys = [r"crit\.out_projs\.\d+", r"crit\.out_layers\.\d+\.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = TransfoXLModel(config)
self.sample_softmax = config.sample_softmax
self.... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
self.crit = ProjectedAdaptiveLogSoftmax(
config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val
)
# Initialize weights and apply final processing
self.post_init()
def tie_weights(self):
"""
Run this to be sure output and input ... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if self.config.tie_word_embeddings:
for i in range(len(self.crit.out_layers)):
self._tie_or_clone_weights(self.crit.out_layers[i], self.transformer.word_emb.emb_layers[i])
if self.config.tie_projs:
for i, tie_proj in enumerate(self.config.tie_projs):
if ti... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def reset_memory_length(self, mem_len):
self.transformer.reset_memory_length(mem_len)
def init_mems(self, bsz):
return self.transformer.init_mems(bsz) | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TransfoXLLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
transformer_outputs = self.transformer(
input_ids,
mems=mems,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if labels is not None:
losses = softmax_output.view(bsz, tgt_len - 1)
# Avoids from incorporating padding (-100) tokens into loss value
loss = losses[losses != 0].mean()
else:
losses, loss = None, None
if not return_dict:
if self.trainer_compa... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
return TransfoXLLMHeadModelOutput(
loss=loss,
prediction_scores=prediction_scores,
losses=losses,
mems=transformer_outputs.mems,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def get_outp... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def _resize_cutoffs(self, new_num_tokens, new_emb_size, new_embedding_shapes, layer):
new_cutoffs = super()._resize_cutoffs(new_num_tokens, new_emb_size, new_embedding_shapes, layer)
self.crit.cutoffs = new_cutoffs
self.crit.cutoff_ends = [0] + new_cutoffs
self.crit.n_token = new_num_to... | 10,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = TransfoXLModel(config)
self.score = nn.Linear(config.d_embed, self.num_labels, bias=False)
# Initial... | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TransfoXLSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTen... | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
transformer_outputs = self.transformer(
input_ids,
mems=mems,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
h... | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
assert (
self.config.pad_token_id is not None or batch_size == 1
), "Cannot handle batch sizes > 1 if no padding token is defined."
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token fou... | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
pooled_logits = logits[range(batch_size), sequence_lengths]
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... | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_... | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
return TransfoXLSequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
mems=transformer_outputs.mems,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 10,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TFPositionalEmbedding(keras.layers.Layer):
def __init__(self, demb, **kwargs):
super().__init__(**kwargs)
self.inv_freq = 1 / (10000 ** (tf.range(0, demb, 2.0) / demb))
def call(self, pos_seq, bsz=None):
self.inv_freq = tf.cast(self.inv_freq, dtype=pos_seq.dtype)
sinusoid... | 10,203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFPositionwiseFF(keras.layers.Layer):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs):
super().__init__(**kwargs)
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.layer_1 = keras... | 10,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
def call(self, inp, training=False):
if self.pre_lnorm:
# layer normalization + positionwise feed-forward
core_out = self.layer_norm(inp)
core_out = self.layer_1(core_out)
core_out = self.drop_1(core_out, training=training)
core_out = self.layer_2(core... | 10,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFRelPartialLearnableMultiHeadAttn(keras.layers.Layer):
def __init__(
self,
n_head,
d_model,
d_head,
dropout,
dropatt=0.0,
pre_lnorm=False,
r_r_bias=None,
r_w_bias=None,
layer_norm_epsilon=1e-5,
init_std=0.02,
outp... | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
self.layer_norm = keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layer_norm")
self.scale = 1 / (d_head**0.5)
self.pre_lnorm = pre_lnorm
if r_r_bias is not None and r_w_bias is not None: # Biases are shared
self.r_r_bias = r_r_bias
self.r_w_bias = r_... | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
def build(self, input_shape):
if self.r_r_bias is None or self.r_w_bias is None: # Biases are not shared
self.r_r_bias = self.add_weight(
shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias"
)
self.r_w_bias = self.add_weight(... | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
if mems is not None:
mems = tf.cast(mems, dtype=w.dtype)
cat = tf.concat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
r_head_k = self.r_net(r)
w_hea... | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
w_head_q = tf.reshape(w_head_q, (qlen, bsz, self.n_head, self.d_head)) # qlen x bsz x n_head x d_head
w_head_k = tf.reshape(w_head_k, (klen, bsz, self.n_head, self.d_head)) # qlen x bsz x n_head x d_head
w_head_v = tf.reshape(w_head_v, (klen, bsz, self.n_head, self.d_head)) # qlen x bsz x n_head x d_... | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
# compute attention probability
if attn_mask is not None:
attn_mask_t = attn_mask[:, :, None, None]
attn_mask_t = tf.cast(attn_mask_t, dtype=attn_score.dtype)
attn_score = attn_score * (1.0 - attn_mask_t) - 1e30 * attn_mask_t
# [qlen x klen x bsz x n_head]
at... | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
if self.pre_lnorm:
# residual connection
outputs = [w + attn_out]
else:
# residual connection + layer normalization
outputs = [self.layer_norm(w + attn_out)]
if output_attentions:
outputs.append(attn_prob)
return outputs | 10,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFRelPartialLearnableDecoderLayer(keras.layers.Layer):
def __init__(
self,
n_head,
d_model,
d_head,
d_inner,
dropout,
dropatt=0.0,
pre_lnorm=False,
r_w_bias=None,
r_r_bias=None,
layer_norm_epsilon=1e-5,
init_std=0.... | 10,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
self.dec_attn = TFRelPartialLearnableMultiHeadAttn(
n_head,
d_model,
d_head,
dropout,
dropatt=dropatt,
pre_lnorm=pre_lnorm,
r_w_bias=r_w_bias,
r_r_bias=r_r_bias,
init_std=init_std,
layer_norm_epsilon=... | 10,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
outputs = [ff_output] + attn_outputs[1:]
return outputs | 10,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoEmbeddings(keras.layers.Layer):
def __init__(self, vocab_size, emb_size, init_std, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.emb_size = emb_size
self.init_std = init_std
def build(self, input_shape):
self.weight = self.add_we... | 10,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFAdaptiveEmbedding(keras.layers.Layer):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, init_std=0.02, sample_softmax=False, **kwargs):
super().__init__(**kwargs)
self.n_token = n_token
self.d_embed = d_embed
self.init_std = init_std
self.cutoffs = c... | 10,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
if div_val == 1:
raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
d_emb_i = d_emb... | 10,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
def build(self, input_shape):
for i in range(len(self.cutoffs)):
d_emb_i = self.d_embed // (self.div_val**i)
self.emb_projs.append(
self.add_weight(
shape=(d_emb_i, self.d_proj),
initializer=get_initializer(self.init_std),
... | 10,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
inp_i = tf.boolean_mask(inp_flat, mask_i) - l_idx
emb_i = self.emb_layers[i](inp_i)
emb_i = tf.einsum("id,de->ie", emb_i, self.emb_projs[i])
mask_idx = tf.where(mask_i)
scatter = tf.scatter_nd(mask_idx, emb_i, shape_list(emb_flat))
emb_fla... | 10,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLMainLayer(keras.layers.Layer):
config_class = TransfoXLConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
self.n_layer = config.n_layer
self.mem_len = config.mem_len
self.attn_type = config.attn_type | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
self.layers = []
if config.attn_type == 0: # the default attention
for i in range(config.n_layer):
self.layers.append(
TFRelPartialLearnableDecoderLayer(
config.n_head,
config.d_model,
config... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
self.same_length = config.same_length
self.clamp_len = config.clamp_len
if self.attn_type == 0: # default attention
self.pos_emb = TFPositionalEmbedding(self.d_model, name="pos_emb")
else: # learnable embeddings and absolute embeddings
raise NotImplementedError # Remo... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
def backward_compatible(self):
self.sample_softmax = -1
def reset_memory_length(self, mem_len):
self.mem_len = mem_len
def _prune_heads(self, heads):
raise NotImplementedError
def init_mems(self, bsz):
if self.mem_len > 0:
mems = []
for i in range(s... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
# There are `mlen + qlen` steps that can be cached into mems
new_mems = []
end_idx = mlen + tf.math.maximum(0, qlen)
beg_idx = tf.math.maximum(0, end_idx - tf.convert_to_tensor(self.mem_len))
for i in range(len(hids)):
mems[i] = tf.cast(mems[i], dtype=hids[i].dtype)
... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
mems: List[tf.Tensor] | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
outpu... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
elif inputs_embeds is not None:
inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
qlen, bsz = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds") | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
if mems is None:
mems = self.init_mems(bsz)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
# Compute decoder attention mask
all_ones = tf.ones([qlen, klen], dtype=tf.int32)
upper_mask = 1 - tf.linalg.band_part(tf.ones([qlen, klen], dtype=tf.int32), -1, mlen)
if self.same_length:
mask_len = klen - self.mem_len
mask_shift_len = qlen - tf.nn.relu(mask_len) # Lazy... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
hids = []
attentions = [] if output_attentions else None
if self.attn_type == 0: # default
pos_seq = tf.range(klen - 1, -1, -1.0)
if self.clamp_len > 0:
pos_seq = tf.minimum(pos_seq, self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_ou... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
for i, layer in enumerate(self.layers):
hids.append(core_out)
mems_i = None if mems is None else mems[i]
layer_outputs = layer(
core_out,
pos_emb,
dec_attn_mask,
mems_i,
he... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
if output_hidden_states:
# Transpose to library standard shape [bsz, len, hidden_dim] and add last layer
hids = tuple(tf.transpose(t, perm=(1, 0, 2)) for t in hids)
hids = hids + (core_out,)
else:
hids = None
if output_attentions:
# Transpose t... | 10,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TransfoXLConfig
base_model_prefix = "transformer" | 10,210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). | 10,211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
mems (`List[tf.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states (key and values in the at... | 10,211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 10,211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLLMHeadModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). | 10,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Args:
losses (`tf.Tensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided):
Language modeling losses (not reduced).
prediction_scores (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the lan... | 10,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. | 10,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 10,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models. | 10,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (b... | 10,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
`(batch_size, sequence_length, hidden_size)`. | 10,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 10,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFTransfoXLMainLayer(config, name="transformer") | 10,214 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTransfoXLModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None =... | 10,214 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
training=training,
) | 10,214 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
return outputs | 10,214 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = TFTransfoXLMainLayer(config, name="transformer")
self.sample_softmax = config.sample_softmax
assert self.sample_softmax <= 0, (
"Sampling from... | 10,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
def reset_memory_length(self, mem_len):
self.transformer.reset_memory_length(mem_len)
def init_mems(self, bsz):
return self.transformer.init_mems(bsz) | 10,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTransfoXLLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | ... | 10,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
transformer_outputs = self.transformer(
input_ids,
mems,
head_mask,
inputs_embeds,
output_attentions,
output_hidden_states,
return_dict,
training=training,
)
last_hidden = transformer_outputs[0]
pred... | 10,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
# if past is defined in model kwargs then use it for faster decoding
if past_key_values:
input_ids = tf.expand_dims(input_ids[:, -1], axis=-1)
else:
input_ids = input_ids
return inputs | 10,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
# Adapted from the torch tie_weights function
def tf_to_pt_weight_rename(self, tf_weight):
if self.config.tie_word_embeddings and "crit.out_layers" in tf_weight:
return tf_weight, tf_weight.replace("crit.out_layers", "transformer.word_emb.emb_layers")
elif self.config.tie_projs and "crit... | 10,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = keras.layers.Dense(
config.num_labels,... | 10,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTransfoXLSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFMo... | 10,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
mems=mems,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
... | 10,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
in_logits = None
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
tf.argmax(tf.cast(tf.math... | 10,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
if labels is not None:
if input_ids is not None:
batch_size, sequence_length = shape_list(input_ids)[:2]
else:
batch_size, sequence_length = shape_list(inputs_embeds)[:2]
assert (
self.config.pad_token_id is not None or batch_size == 1
... | 10,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
return TFTransfoXLSequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
mems=transformer_outputs.mems,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 10,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py |
class TransfoXLConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
used to instantiate a Transformer-XL model according to the specified arguments, defining the model architecture.
Instantiating a configuration... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
Args:
vocab_size (`int`, *optional*, defaults to 267735):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`TransfoXLModel`] or [`TFTransfoXLModel`].
cutoffs (`List[int]`, *optional*, defaul... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
Divident value for adapative input and softmax
pre_lnorm (`boolean`, *optional*, defaults to `False`):
Whether or not to apply LayerNorm to the input instead of the output in the blocks.
n_layer (`int`, *optional*, defaults to 18):
Number of hidden layers in the Transformer encod... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
sample_softmax (`int`, *optional*, defaults to -1):
Number of samples in the sampled softmax.
adaptive (`boolean`, *optional*, defaults to `True`):
Whether or not to use adaptive softmax.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all ... | 10,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py |
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