text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def __call__(
self,
hidden_states,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
# if key_value_states are provided this layer is used as a... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
query_length, key_length = query.shape[1], key.shape[1]
if self.causal:
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = ... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask =... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
# transform boolean mask into float mask
if attention_mask is not None:
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2MLP(nn.Module):
config: GPT2Config
intermediate_size: int
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
self.c_fc = FlaxConv1D(self.intermediate_size, dtype=self.dtype)
self.c_proj = FlaxConv1D(embed_dim, dtype=self.dtype)
... | 9,165 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2Block(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
hidden_size = self.config.hidden_size
inner_dim = self.config.n_inner if self.config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(epsilon=self.config.layer_norm_e... | 9,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def __call__(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):... | 9,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
# Cross-Attention Block
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
... | 9,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
outputs = outputs + cross_attn_outputs[1:] # add cross attentions if we output attention weights | 9,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states, deterministic=deterministic)
# residual connection
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,) + outputs
return ou... | 9,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__(
... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids)... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
if self.config.add_cross_attention:
encoder_hidden_states = jnp.zeros(input_shape + (self.config.n_embd,))
encoder_attention_mask = attention_mask
module_init_outputs = self.module.init(
rngs,
input_ids,
attention_mask,
... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freez... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decodin... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
params: dict = None,... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
if encoder_hidden_states is not None and encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = input_ids.shape
if position_ids is None:
... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPT2Attention module
if past_key_values:
inputs["cache"] = past_key_value... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past_key_values = outputs
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outp... | 9,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2BlockCollection(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGPT2Block(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_... | 9,168 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
for block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states,
attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=enco... | 9,168 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2Module(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embed_dim = self.config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.embed_dim,
embedding_init=jax.nn.initializers.normal(stddev=sel... | 9,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic=True,
init_cache: bool = False,
output_attentions: bool = False,
... | 9,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
outputs = self.h(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_stat... | 9,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2Model(FlaxGPT2PreTrainedModel):
module_class = FlaxGPT2Module | 9,170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2LMHeadModule(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxGPT2Module(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,
... | 9,171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = ... | 9,171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
if self.config.tie_word_embeddings:
shared_kernel = self.transformer.variables["params"]["wte"]["embedding"].T
lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
... | 9,171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2LMHeadModel(FlaxGPT2PreTrainedModel):
module_class = FlaxGPT2LMHeadModule
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 | 9,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.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 GPT2 uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_... | 9,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.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 | 9,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class TFAttention(keras.layers.Layer):
def __init__(self, nx, config, scale=False, is_cross_attention=False, **kwargs):
super().__init__(**kwargs)
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj")
self.attn_dropout = keras.layers.Dropout(config.attn_pdrop)
self.resid_dropout = keras.layers.Dropout(config.resid_pdrop)
self.pruned_heads = set()
self.embed_dim = n_state
def prune_heads... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False):
# q, k, v have shape [batch, heads, sequence, features]
w = tf.matmul(q, k, transpose_b=True)
if self.scale:
dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores
w ... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
w = stable_softmax(w, axis=-1)
w = self.attn_dropout(w, training=training)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [tf.matmul(w, v)]
if output_attentions:
outputs.append(w)
return outputs
def merge_he... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def call(
self,
x,
layer_past,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
training=False,
):
if encoder_hidden_states is not None:
if not hasattr(self, "q_... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
query = self.split_heads(query)
key = self.split_heads(key)
value = self.split_heads(value)
if layer_past is not None:
past_key, past_value = tf.unstack(layer_past, axis=0, num=2)
key = tf.concat([past_key, key], axis=-2)
value = tf.concat([past_value, value],... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if self.is_cross_attention:
c_attn_shape = 2 * self.embed_dim
else:
c_attn_shape = 3 * self.embed_dim
if getattr(self, "c_proj", None) is not None:
with tf.n... | 9,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFMLP(keras.layers.Layer):
def __init__(self, n_state, config, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initia... | 9,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "c_fc", None) is not None:
with tf.name_scope(self.c_fc.name):
self.c_fc.build([None, None, self.intermediate_size])
if getattr(self, "c_proj", None) is not... | 9,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFBlock(keras.layers.Layer):
def __init__(self, config, scale=False, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
inner_dim = config.n_inner if config.n_inner is not None else 4 * nx
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name... | 9,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def call(
self,
x,
layer_past,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
training=False,
):
a = self.ln_1(x)
output_attn = self.attn(
a,
... | 9,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
# Cross-Attention Block
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
... | 9,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
ca = self.ln_cross_attn(x)
output_cross_attn = self.crossattention(
ca,
layer_past=None,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=enc... | 9,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "ln_1", None) is not None:
with tf.name_scope(self.ln_1.name):
self.ln_1.build([None, None, self.hidden_size])
if getattr(self, "attn", None) is not None:
... | 9,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
self.ln_cross_attn.build([None, None, self.hidden_size]) | 9,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2MainLayer(keras.layers.Layer):
config_class = GPT2Config
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
self.wte = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.hidden_size,
embeddings_initializer=get_initializer(config.initializer_range),
name="wte",
)
self.wpe = keras.layers.Embedding(
input_dim=config.n_positions,
... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
raise NotImplementedError | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
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)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(i... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if past_key_values is None:
past_length = 0
past_key_values = [None] * len(self.h)
else:
past_length = shape_list(past_key_values[0][0])[-2]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.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
... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.config.add_cross_attention 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]... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.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... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.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]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if token_type_ids is not None:
token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
token_type_embeds = self.wte(token_type_ids)
else:
token_type_embeds = tf.constant(0.0)
position_embeds = tf.cast(position_embeds, dtype=inputs_embeds.dty... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
presents = () if use_cache else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if output_attentions:
all_attentions = all_attentions + (outputs[2],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (outputs[3],)
hidden_states = self.ln_f(hidden_states)
hidde... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_attentions, all_cross_attentions]
if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_sta... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "wte", None) is not None:
with tf.name_scope(self.wte.name):
self.wte.build(None)
if getattr(self, "wpe", None) is not None:
with tf.name_scope(... | 9,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpecte... | 9,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
@property
def input_signature(self):
# Although GPT-2 supports token_type_ids in theory, in practice they are rarely used, and the implementation
# means that passing token_type_ids=0 yields different outputs from token_type_ids=None.
# Therefore, we remove the token_type_ids argument by def... | 9,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2DoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head ... | 9,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):... | 9,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
mc_logits: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[t... | 9,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2Model(TFGPT2PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name="transformer") | 9,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInp... | 9,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
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)`, *optional*):
Sequence of hidden-states ... | 9,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
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` are used,... | 9,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
enc... | 9,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name="transformer")
def get_output_embeddings(self):
return self.get_input_... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if attention_mask is not None and position_ids is None:
position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
if past_key_values:
position_ids = tf.expand_dims(position_ids[:, -1], -1)
return {
"input_ids": inputs,
"attention_mask... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType |... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *opti... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**. | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.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` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have
... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
transformer_outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
loss = None
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = logits[:, :-1]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels, shifted_logits)
if not return_dict:
output = (logits,) + transfor... | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None) | 9,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
config.num_labels = 1
self.transformer = TFGPT2MainLayer(config, name="transformer")
self.multiple_choice_head = TFSequenceSummary(
... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFGPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.nd... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`. | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFGPT2DoubleHeadsModel
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = TFGPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2")... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
>>> input_ids = tf.constant(encoded_choices)[None, :] # Batch size: 1, number of choices: 2
>>> mc_token_ids = tf.constant([cls_token_location]) # Batch size: 1
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
>>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
```""... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
seq_length = input_shapes[-1]
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)) ... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
if return_dict and output_hidden_states:
... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if not return_dict:
return (lm_logits, mc_logits) + transformer_outputs[1:]
return TFGPT2DoubleHeadsModelOutput(
logits=lm_logits,
mc_logits=mc_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=all_hidden_states,
a... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "multiple_choice_head", None) is ... | 9,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, 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,
... | 9,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint="microsoft/DialogRPT-updown",
output_type=TFSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInp... | 9,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
... | 9,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
logits_shape = shape_list(logits)
in_logits = None
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
... | 9,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
if labels is not None:
assert (
self.config.pad_token_id is not None or logits_shape[0] == 1
), "Cannot handle batch sizes > 1 if no padding token is defined."
if not tf.is_tensor(sequence_lengths):
in_logits = logits[0 : logits_shape[0], sequence_len... | 9,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "score", None) is not None:
with tf.name_scope(self.score.name):
self.score.build([None, None, self.config.n_embd])
if getattr(self, "transformer", None) is... | 9,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py |
class GPT2Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
Args:
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].
n_positions (`int`, *optional*, defaults to 1024):... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
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