text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (... | 9,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
) | 9,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallDecoderLayer(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotSmallAttention(
config=self.config,
embed_dim=self.embed_dim,
... | 9,378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxBlenderbotSmallAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
... | 9,378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic:... | 9,378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
... | 9,378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs | 9,378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallDecoderLayerCollection(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotSmallDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in rang... | 9,379 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
hidden_states,
attention_mask,
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,
out... | 9,379 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probabi... | 9,379 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = [hidden_states, all_hidden_states, all_self_attns, all_cros... | 9,379 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallEncoder(nn.Module):
config: BlenderbotSmallConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.paddi... | 9,380 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
input_ids,
attention_mask,
position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_i... | 9,380 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
return FlaxBaseModelOutput(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,380 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallDecoder(nn.Module):
config: BlenderbotSmallConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.paddi... | 9,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states:... | 9,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
outputs = self.layers(
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... | 9,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallModule(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.norma... | 9,382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
determinist... | 9,382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
output_attentions=output_attenti... | 9,382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
return FlaxSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state... | 9,382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
config_class = BlenderbotSmallConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: BlenderbotSmallConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.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")
# make sure initialization pass will work for FlaxBlenderbotSmallForSequenceClassificationModule
input_ids = i... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
random_params = self.module.init(
rngs,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_di... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def init_cache(self, batch_size, max_length, encoder_outputs):
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-r... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
) | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
@add_start_docstrings(BLENDERBOT_SMALL_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=BlenderbotSmallConfig)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarr... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_ids, attention_mask, position_ids, **kwargs)
return self.module.apply(
{"params": params or self.params},
inpu... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
@add_start_docstrings(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(
output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=BlenderbotSmallConfig
)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Op... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, ret... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_st... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_l... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encod... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
decoder_position_ids: Optio... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
p... | 9,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallModel(FlaxBlenderbotSmallPreTrainedModel):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxBlenderbotSmallModule | 9,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallForConditionalGenerationModule(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxBlenderbotSmallModule(config=self.config, dtype=self.dtype)
... | 9,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
determinist... | 9,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if self.config.tie_word_embeddings:
shared_embedding = self.model.variables["params"]["shared"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
lm_logits += jax.lax.s... | 9,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallForConditionalGeneration(FlaxBlenderbotSmallPreTrainedModel):
module_class = FlaxBlenderbotSmallForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutpu... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration
>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_l... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
outputs = decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encod... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# 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 anyways.
# Thus we can create a ... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": position_ids,
}
def update_inputs_for_generation(... | 9,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class BlenderbotSmallConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BlenderbotSmallModel`]. It is used to instantiate
an BlenderbotSmall model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the d... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the BlenderbotSmall model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`BlenderbotSmallModel`] or [`TFBlenderbotSmallModel`].
d_model (`int`, *o... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight ma... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`. | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
Example:
```python
>>> from transformers import BlenderbotSmallConfig, BlenderbotSmallModel
>>> # Initializing a BlenderbotSmall facebook/blenderbot_small-90M style configuration
>>> configuration = BlenderbotSmallConfig()
>>> # Initializing a model (with random weights) from the facebook/blender... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
def __init__(
self,
vocab_size=50265,
max_position_embeddings=512,
encoder_layers=8,
encoder_ffn_dim=2048,
encoder_attention_heads=16,
decoder_layers=8,
decoder_ffn_dim=2048,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decode... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_drop... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
... | 9,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
class BlenderbotSmallOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
elif self.task == "causal-lm":
# TODO: figure this case out.
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
]
) | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
return common_inputs
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_outputs = super().outputs
else:
common_outputs = super(OnnxConfigWithPast, self).outputs
if self.use_past:
n... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
encoder_inputs = self._generate_dumm... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, encoder_seq_length = common_inputs["input_ids"].shape
decoder_seq_length = common... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
common_inputs["decoder_attention_mask"] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
)
common_inputs["past_key_values"] = []
# If the number of encoder and decoder layers are present in the model configuration,... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
for _ in range(min_num_layers):
common_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
def _generate_dummy_inputs_for_causal_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = self._generate_dummy_inputs_for_s... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_ke... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
mask_dtype = common_inputs["attention_mask"].dtype
common_inputs["attention_mask"] = torch.cat(
[common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
common_inputs["past_key_values"] = [
(torch.zeros(pa... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
# Copied fro... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
common_in... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
if self.task in ["default", "seq2seq-lm"]:
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
else:
flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_v... | 9,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py |
class TFBlenderbotSmallLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(... | 9,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_pro... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states) | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
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_states (first "if" case)
# if uni-directional self-attention (decoder... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(b... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weigh... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of siz... | 9,390 | /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, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "q_proj", None) is not N... | 9,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallEncoderLayer(keras.layers.Layer):
def __init__(self, config: BlenderbotSmallConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotSmallAttention(
self.embed_dim, config.encoder_attention_heads, dropout=co... | 9,391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: np.ndarray | tf.Tensor | None,
layer_head_mask: tf.Tensor | None,
training: Optional[bool] = False,
) -> tf.Tensor:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch... | 9,391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states ... | 9,391 | /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, "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... | 9,391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
class TFBlenderbotSmallDecoderLayer(keras.layers.Layer):
def __init__(self, config: BlenderbotSmallConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotSmallAttention(
embed_dim=self.embed_dim,
num_heads=config.dec... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFBlenderbotSmallAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cro... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
`(decoder_attention_heads,)... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
... | 9,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py |
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