text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.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... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.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... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.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=... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.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... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.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... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
class FlaxMBartAttention(nn.Module):
config: MBartConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_h... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
d... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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 =... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
... | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights | 10,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartEncoderLayer(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 | 10,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dt... | 10,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | 10,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_s... | 10,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs | 10,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartEncoderLayerCollection(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMBartEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
... | 10,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
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 (... | 10,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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
) | 10,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartDecoderLayer(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.conf... | 10,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxMBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=... | 10,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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:... | 10,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_stat... | 10,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs | 10,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartDecoderLayerCollection(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMBartDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
... | 10,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
config: MBartConfig
inner_dim: int
num_classes: int
pooler_dropout: float
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.inner_dim, dtype=self.... | 10,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
def __call__(self, hidden_states: jnp.ndarray, deterministic: bool):
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.dense(hidden_states)
hidden_states = jnp.tanh(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=determ... | 10,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartEncoder(nn.Module):
config: MBartConfig
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.padding_idx = self.config... | 10,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
# MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
self.embed_positions = nn.Embed(
self.config.max_position_embeddings + self.offset,
embed_... | 10,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(position_ids + self.offset)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic... | 10,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=last_hidden_states,
hidden_states=hidden_sta... | 10,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartDecoder(nn.Module):
config: MBartConfig
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.padding_idx = self.config... | 10,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
self.layers = FlaxMBartDecoderLayerCollection(self.config, self.dtype)
self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
input_ids,
attention_mask,
position_ids... | 10,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
hidden_states = inputs_embeds + positions
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
encoder_hidden_states,
... | 10,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=last_hidden_states,
... | 10,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartModule(nn.Module):
config: MBartConfig
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.normal(self.config.init_s... | 10,058 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,058 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,058 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,058 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartPreTrainedModel(FlaxPreTrainedModel):
config_class = MBartConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: MBartConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float3... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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 FlaxMBartForSequenceClassificationModule
input_ids = input_ids.a... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->MBart
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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
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.sh... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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,
... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
@add_start_docstrings(MBART_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MBartConfig)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
o... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
@add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MBartConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
>>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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,
... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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
... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
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... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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))
... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
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"),
position_ids=jnp.array(position_ids, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtyp... | 10,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartModel(FlaxMBartPreTrainedModel):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxMBartModule | 10,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartForConditionalGenerationModule(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
... | 10,061 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,061 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,061 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartForConditionalGeneration(FlaxMBartPreTrainedModel):
module_class = FlaxMBartForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MBartC... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
```python
>>> from transformers import AutoTokenizer, FlaxMBartForConditionalGeneration
>>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
>>> text = "My friends are cool ... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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,
... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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_... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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 ... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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(... | 10,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartForSequenceClassificationModule(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
num_labels: Optional[int] = None
def setup(self):
self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
self.classification_head = FlaxMBartClassificationHead(
... | 10,063 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,063 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
# The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation
if not isinstance(eos_mask, jax.interpreters.partial_eval.DynamicJaxprTracer):
if len(jnp.unique(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same numb... | 10,063 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
return FlaxSeq2SeqSequenceClassifierOutput(
logits=logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_stat... | 10,063 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartForSequenceClassification(FlaxMBartPreTrainedModel):
module_class = FlaxMBartForSequenceClassificationModule
dtype = jnp.float32 | 10,064 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartForQuestionAnsweringModule(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
num_labels = 2
def setup(self):
self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
self.qa_outputs = nn.Dense(
self.num_labels, dtype=self.dtype, kernel_i... | 10,065 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.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... | 10,065 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
ret... | 10,065 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class FlaxMBartForQuestionAnswering(FlaxMBartPreTrainedModel):
module_class = FlaxMBartForQuestionAnsweringModule
dtype = jnp.float32 | 10,066 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py |
class MBartTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" MBART tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] w... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
>>> tokenizer = MBartTokenizerFast.from_pretrained(
... "facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO"
... )
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară î... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
src_lang=None,
tgt_lang=None,
... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
super().__init__(
vocab_file=vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mas... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
@property
def src_lang(self) -> str:
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang: str) -> None:
self._src_lang = new_src_lang... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
separator.
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optio... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not
make use of token type ids, therefore a li... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
def _build_translation_inputs(
self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError("Tra... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
def prepare_seq2seq_batch(
self,
src_texts: List[str],
src_lang: str = "en_XX",
tgt_texts: Optional[List[str]] = None,
tgt_lang: str = "ro_RO",
**kwargs,
) -> BatchEncoding:
self.src_lang = src_lang
self.tgt_lang = tgt_lang
return super().prepa... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
self._tokenizer.post_processor = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
pair=prefix_tokens_str + [... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
self._tokenizer.post_processor = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
... | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
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