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class FlaxElectraForCausalLM(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape | 3,820 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyway.
# Thus, we can create a single static att... | 3,820 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs | 3,820 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class ElectraConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ElectraModel`] or a [`TFElectraModel`]. It is
used to instantiate a ELECTRA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the de... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the ELECTRA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ElectraModel`] or [`TFElectraModel`].
embedding_size (`int`, *optional*, default... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
summary_type (`... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
Has to be one of the following options:
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of class... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
Pass `"gelu"` for a gelu activation to the output, any other value will result in no activation.
summary_last_dropout (`float`, *optional*, defaults to 0.0):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
The dropout ratio to be used after the projection and activation.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more ... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
The dropout ratio for the classification head. | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
Examples:
```python
>>> from transformers import ElectraConfig, ElectraModel
>>> # Initializing a ELECTRA electra-base-uncased style configuration
>>> configuration = ElectraConfig()
>>> # Initializing a model (with random weights) from the electra-base-uncased style configuration
>>> model =... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
def __init__(
self,
vocab_size=30522,
embedding_size=128,
hidden_size=256,
num_hidden_layers=12,
num_attention_heads=4,
intermediate_size=1024,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_positi... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.... | 3,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
class ElectraOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 3,822 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/configuration_electra.py |
class TFElectraSelfAttention(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the ... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.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... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_s... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = ... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is no... | 3,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraSelfOutput(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Lay... | 3,824 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None)... | 3,824 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraAttention(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFElectraSelfAttention(config, name="self")
self.dense_output = TFElectraSelfOutput(config, name="output")
def prune_heads(self, heads):
... | 3,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) ... | 3,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
outputs = (attention_output,) + self_outputs[1:] | 3,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "den... | 3,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraIntermediate(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | 3,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size]) | 3,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraOutput(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNo... | 3,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
if getattr(self, "LayerNorm",... | 3,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraLayer(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFElectraAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
i... | 3,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_value: Tuple[tf.Tensor] | None,
output_attentions: bool,
training... | 3,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.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
... | 3,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.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(
input_tensor=attention_output,
attention_mask=at... | 3,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output... | 3,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
... | 3,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraEncoder(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFElectraLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_sta... | 3,829 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.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,)
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_... | 3,829 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
# Add last layer
if output_hidden_s... | 3,829 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None) | 3,829 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraPooler(keras.layers.Layer):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
... | 3,830 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.max... | 3,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.config.type_vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_sco... | 3,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
past_key_values_length=0,
training: bo... | 3,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if position_ids is None:
position_ids = tf.expand_dims(
tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gathe... | 3,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraDiscriminatorPredictions(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.hidden_size, name="dense")
self.dense_prediction = keras.layers.Dense(1, name="dense_prediction")
self.config = config... | 3,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "dense_prediction"... | 3,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraGeneratorPredictions(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dense = keras.layers.Dense(config.embedding_size, name="dense")
... | 3,833 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.embedding_size])
if getattr(self, "de... | 3,833 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ElectraConfig
base_model_prefix = "electra"
# When the model is loaded from a PT model
_keys... | 3,834 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraMainLayer(keras.layers.Layer):
config_class = ElectraConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.embeddings = TFElectraEmbeddings(config, name="embeddings")
if con... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def get_extended_attention_mask(self, attention_mask, input_shape, dtype, past_key_values_length=0):
batch_size, seq_length = input_shape
if attention_mask is None:
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
# We create a 3D attention ... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
mask_seq_length = seq_length + past_key_values_length
# Copied from `modeling_tf_t5.py`
# Provided a padding mask of dimensions [batch_size, mask_seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask b... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2])
)
if past_key_values_length > 0:
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = tf.reshape(
... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# eff... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor ... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and 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(in... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
training=training,
)
extended_attention_mask... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.is_decoder and encoder_attention_mask is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# w... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
hidden_states = self.encoder(
hidden_states=hidden_states,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_va... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
... | 3,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForPreTrainingOutput(ModelOutput):
"""
Output type of [`TFElectraForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`):
Total loss of the ELECTRA objective.
logits (`tf.Tensor` of shape `(batch_size, sequence_le... | 3,836 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.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... | 3,836 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraModel(TFElectraPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra") | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
... | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`... | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**. | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
... | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_s... | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None) | 3,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForPreTraining(TFElectraPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predicti... | 3,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attenti... | 3,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFElectraForPreTraining | 3,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = TFElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
>>> outputs = model(input_... | 3,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if not return_dict:
return (logits,) + discriminator_hidden_states[1:]
return TFElectraForPreTrainingOutput(
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
)
def build(self, ... | 3,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraMaskedLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
se... | 3,839 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def call(self, hidden_states):
seq_length = shape_list(tensor=hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor... | 3,839 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.electra = TFElectraMainLayer(config, name="electra")
self.generator_predictions = TFElectraGenerator... | 3,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/electra-small-generator",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="[MASK]",
expec... | 3,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with... | 3,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
prediction_scores = self.generator_predictions(generator_sequence_output, training=training)
prediction_scores = self.generator_lm_head(prediction_scores, training=training)
loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) | 3,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if not return_dict:
output = (prediction_scores,) + generator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=generator_hidden_states.hidden_state... | 3,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_ran... | 3,841 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def call(self, inputs, **kwargs):
x = inputs[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = get_tf_activation("gelu")(x) # although BERT uses tanh here, it seems Electra authors used gelu here
x = self.dropout(x)
x = self.out_proj(... | 3,841 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.electra = TFElectraMainLayer(config, name="electra")
s... | 3,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-emotion",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_out... | 3,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` 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 regres... | 3,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
loss = None if labels is None else self.hf_compute_loss(labels, logits) | 3,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions... | 3,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
self.sequence_summary = TFSequenceSummary(
confi... | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
... | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
""" | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]
else:
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2] | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else Non... | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
return_dict=return_dict,
training=training,
)
logits = self.sequence_summary(outputs[0])
logits = self.classifier(logits)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
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