text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_outputs = super().outputs
else:
common_outputs = su... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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,... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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),
... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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_e... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
# Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
# We renamed this function because Marian models do not have a sequence classification or question answering head
def _generate_dummy_inputs_for_encoder_and_decoder(
self,
tokenizer: PreTrained... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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=... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.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... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._flatten_past_key_values_
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)
... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
class OpusState:
def __init__(self, source_dir, eos_token_id=0):
npz_path = find_model_file(source_dir)
self.state_dict = np.load(npz_path)
cfg = load_config_from_state_dict(self.state_dict)
if cfg["dim-vocabs"][0] != cfg["dim-vocabs"][1]:
raise ValueError
if "Wpo... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
# create the tokenizer here because we need to know the eos_token_id
self.source_dir = source_dir
self.tokenizer = self.load_tokenizer()
# retrieve EOS token and set correctly
tokenizer_has_eos_token_id = (
hasattr(self.tokenizer, "eos_token_id") and self.tokenizer.eos_token_... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
if cfg["tied-embeddings-src"]:
self.wemb, self.final_bias = add_emb_entries(self.state_dict["Wemb"], self.state_dict[BIAS_KEY], 1)
self.pad_token_id = self.wemb.shape[0] - 1
cfg["vocab_size"] = self.pad_token_id + 1
else:
self.wemb, _ = add_emb_entries(self.state_... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
# self.state_dict['Wemb'].sha
self.state_keys = list(self.state_dict.keys())
if "Wtype" in self.state_dict:
raise ValueError("Wtype key in state dictionary")
self._check_layer_entries()
self.cfg = cfg
hidden_size, intermediate_shape = self.state_dict["encoder_l1_ffn_W... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
# Process decoder.yml
decoder_yml = cast_marian_config(load_yaml(source_dir / "decoder.yml"))
check_marian_cfg_assumptions(cfg)
self.hf_config = MarianConfig(
vocab_size=cfg["vocab_size"],
decoder_vocab_size=cfg.get("decoder_vocab_size", cfg["vocab_size"]),
sh... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
max_position_embeddings=cfg["dim-emb"],
scale_embedding=True,
normalize_embedding="n" in cfg["transformer-preprocess"],
static_position_embeddings=not cfg["transformer-train-position-embeddings"],
tie_word_embeddings=cfg["tied-embeddings"],
dropout=0.1, # see... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
def _check_layer_entries(self):
self.encoder_l1 = self.sub_keys("encoder_l1")
self.decoder_l1 = self.sub_keys("decoder_l1")
self.decoder_l2 = self.sub_keys("decoder_l2")
if len(self.encoder_l1) != 16:
warnings.warn(f"Expected 16 keys for each encoder layer, got {len(self.enco... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
@property
def extra_keys(self):
extra = []
for k in self.state_keys:
if (
k.startswith("encoder_l")
or k.startswith("decoder_l")
or k in [CONFIG_KEY, "Wemb", "encoder_Wemb", "decoder_Wemb", "Wpos", "decoder_ff_logit_out_b"]
):
... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
if not cfg.static_position_embeddings:
raise ValueError("config.static_position_embeddings should be True")
model = MarianMTModel(cfg)
if "hidden_size" in cfg.to_dict():
raise ValueError("hidden_size is in config")
load_layers_(
model.model.encoder.layers,
... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
# handle tensors not associated with layers
if self.cfg["tied-embeddings-src"]:
wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
model.model.shared.weight = wemb_tensor
model.model.encoder.embe... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
model.final_logits_bias = bias_tensor
if "Wpos" in state_dict:
print("Unexpected: got Wpos")
wpos_tensor = torch.tensor(state_dict["Wpos"])
model.model.encoder.embed_positions.weight = wpos_tensor
model.model.decoder.embed_positions.weight = wpos_tensor
... | 3,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py |
class FlaxMarianAttention(nn.Module):
config: MarianConfig
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... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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:
... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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"... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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]
... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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 =... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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),
... | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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 | 3,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianEncoderLayer(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 | 3,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.d... | 3,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | 3,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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, attn_weights = self.self_attn(hidden_states=hidden_states,... | 3,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs | 3,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianEncoderLayerCollection(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMarianEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layer... | 3,742 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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 (... | 3,742 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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
) | 3,742 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianDecoderLayer(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.c... | 3,743 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype... | 3,743 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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:... | 3,743 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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)
... | 3,743 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs | 3,743 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianDecoderLayerCollection(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMarianDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layer... | 3,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianEncoder(nn.Module):
config: MarianConfig
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.max_source_positions = ... | 3,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
positions = jnp.take(self.embed_positions, position_ids, axis=0)
# explicitly cast the positions here, since self.embed_positions are not registered as parameters
positions = positions.astype(inputs_embeds.dtype)
hidden_st... | 3,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianDecoder(nn.Module):
config: MarianConfig
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.max_target_positions = ... | 3,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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:... | 3,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianModule(nn.Module):
config: MarianConfig
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... | 3,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianPreTrainedModel(FlaxPreTrainedModel):
config_class = MarianConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: MarianConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.flo... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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 FlaxMarianForSequenceClassificationModule
input_ids = input_ids.... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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
) | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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, **kwargs)
init_variables = self.module.init(
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
@add_start_docstrings(MARIAN_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MarianConfig)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
@add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MarianConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
>>> enc... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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,
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
@add_start_docstrings_to_model_forward(MARIAN_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_id... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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))
... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianModel(FlaxMarianPreTrainedModel):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxMarianModule | 3,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianMTModule(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxMarianModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.model... | 3,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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 += self.fina... | 3,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class FlaxMarianMTModel(FlaxMarianPreTrainedModel):
module_class = FlaxMarianMTModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MarianConfig)
def decode(
sel... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
>>> enc... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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,
... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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_... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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 ... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.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(... | 3,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py |
class MarianTokenizer(PreTrainedTokenizer):
r"""
Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
Args:
source_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the source language.
target_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally ... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
The token used for padding, for example when batching sequences of different lengths.
model_max_length (`int`, *optional*, defaults to 512):
The maximum sentence length the model accepts.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<eop>", "<eod>"]`):
Addit... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat.... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
def __init__(
self,
source_spm,
target_spm,
vocab,
target_vocab_file=None,
source_lang=None,
target_lang=None,
unk_token="<unk>",
eos_token="</s>",
pad_token="<pad>",
model_max_length=512,
sp_model_kwargs: Optional[Dict[str,... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
if separate_vocabs:
self.target_encoder = load_json(target_vocab_file)
self.decoder = {v: k for k, v in self.target_encoder.items()}
self.supported_language_codes = []
else:
self.decoder = {v: k for k, v in self.encoder.items()}
self.supported_language... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
super().__init__(
# bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id
source_lang=source_lang,
target_lang=target_lang,
unk_token=unk_token,
eos_token=eos_token,
pad_token=pad_token,
model_max_length=model_... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
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