text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.config.decoder_ffn_dim])
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallPreTrainedModel(TFPreTrainedModel):
config_class = BlenderbotSmallConfig
base_model_prefix = "model" | 9,393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallEncoder(keras.layers.Layer):
config_class = BlenderbotSmallConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFBlenderbotSmallEncoderLayer`].
Args:
config: BlenderbotSmallConfig
"""
def __init__(self... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
self.embed_tokens = embed_tokens
self.embed_positions = TFBlenderbotSmallLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TFBlenderbotSmallEncoderLayer(config, name=f"layers.{i}") for i in ran... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
"""
Args:
input_ids ... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
Mask to nullify selected heads of the ... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into assoc... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
i... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
raise ValueError("You have to specify either input_ids or inputs_embeds") | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.l... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
tf.debugging.assert_equal(
shape_list(head_mask)[0],
len(self.layers),
message=(
f"The head_mask should be specified for {len(self.la... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
hidden_states, attn = encoder_layer(
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
)
if output_attentions:
all_attentions += (attn,)
if output_hidden_states:
encoder_states =... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_positions", None) is not None:
with tf.name_scope(self.embed_positions.name):
self.embed_positions.build(None)
if getattr(self, "layernorm_embedding"... | 9,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallDecoder(keras.layers.Layer):
config_class = BlenderbotSmallConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBlenderbotSmallDecoderLayer`]
Args:
config: BlenderbotSmallConfig
embed_tokens: output embedding
""" | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.embed_tokens = embed_tokens
self.layerdrop = config.decoder_layerdrop
... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
position_ids=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embed... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
input... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the co... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise Va... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
# [bsz, seq_len] ... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
# embed positions
if position_ids is None:
posit... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
if attn_mask is not None:
tf.debugging.assert_equal(
sha... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_sta... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
else:
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_positions", None) is not None:
with tf.name_scope(self.embed_positions.name):
self.embed_positions.build(None)
if getattr(self, "layernorm_embedding"... | 9,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallMainLayer(keras.layers.Layer):
config_class = BlenderbotSmallConfig
def __init__(self, config: BlenderbotSmallConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
... | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
... | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple() | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
decoder_outputs = self.decoder(
decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
c... | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outp... | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
# The shared/tied weights expect to be in the model base namespace
# Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
# the current one.
... | 9,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
def __init__(self, config: BlenderbotSmallConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBlenderbotSmallMainLayer(config, name="model")
def get_encoder(self):
return self.model.encod... | 9,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 9,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
outputs = self.model(
input_ids=input_ids,
... | 9,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
return_dict=return_dict,
training=training,
) | 9,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
return outputs
# Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidd... | 9,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
... | 9,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | 9,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super(... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def get_bias(self):
return {"final_logits_bias": self.bias_layer.bias}
def set_bias(self, value):
# Replaces the existing layers containing bias for correct (de)serialization.
vocab_size = value["final_logits_bias"].shape[-1]
self.bias_layer = BiasLayer(
name="final_logi... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def call(
self,
input_ids: tf.Tensor | None = None,
... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
Returns:
"""
if labels is not None:
labels = tf.where(
labels == self.config.pad_token_id,
tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
labels,
)
use_cache = False
if decoder_input_ids is None ... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decode... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 o... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidde... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_st... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past_key_values
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no pa... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mas... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
if getattr(self, "bias_layer", None) is not None:
with t... | 9,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class BlenderbotSmallTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BlenderbotSmall tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_clas... | 9,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py |
def __init__(
self,
vocab_file=None,
merges_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs,
):
super().__init__(
ByteLevelBPETo... | 9,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BlenderbotSmall
does not make use of token type ids, ther... | 9,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py |
class BlenderbotSmallTokenizer(PreTrainedTokenizer):
"""
Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding)
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
the superclass for more information regarding methods. | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
merges_file (`str`):
Path to the merges file.
bos_token (`str`, *optional*, defaults to `"__start__"`):
The beginning of sentence token.
eos_token (`str`, *optional*, defaults to `"__end__"`):
... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
def __init__(
self,
vocab_file,
merges_file,
bos_token="__start__",
eos_token="__end__",
unk_token="__unk__",
pad_token="__null__",
**kwargs,
):
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
def bpe(self, token: str) -> str:
if token in self.cache:
return self.cache[token]
token = re.sub("([.,!?()])", r" \1", token)
token = re.sub("(')", r" \1 ", token)
token = re.sub(r"\s{2,}", " ", token)
if "\n" in token:
token = token.replace("\n", " __new... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except ValueError:
new_word.extend(word[i:])
break
if w... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
def _tokenize(self, text: str) -> List[str]:
"""Split a string into tokens using BPE."""
split_tokens = []
words = re.findall(r"\S+\n?", text)
for token in words:
split_tokens.extend(list(self.bpe(token).split(" ")))
return split_tokens
def _convert_token_to_id... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (file... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Sa... | 9,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py |
class MegatronBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position... | 9,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 9,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embedding... | 9,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertSelfAttention(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_... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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":... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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,
... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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 ... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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 MegatronBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attent... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Te... | 9,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.self = MegatronBertSelfAttention(config)
self.output = MegatronBertSelfOutput(config)
self.pruned_heads = set()
... | 9,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 9,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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,
... | 9,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
sel... | 9,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> to... | 9,407 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = MegatronBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_atte... | 9,408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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,
... | 9,408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 9,408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 9,408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = out... | 9,408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([MegatronBertLayer(config) for _ in range(config.num_hidden_layers)])
# The final layer norm. We removed the 1st LN, moved LN to each hidden layer and... | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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,
... | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = p... | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
... | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attent... | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 9,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertPooler(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 t... | 9,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
s... | 9,411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
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