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Make text encoder trainable property default to False for pre-trained HF encoders #2060

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May 26, 2022
Merged

Make text encoder trainable property default to False for pre-trained HF encoders #2060

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33 changes: 16 additions & 17 deletions ludwig/encoders/text_encoders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@ def __init__(
use_pretrained: bool = True,
pretrained_model_name_or_path: str = "albert-base-v2",
saved_weights_in_checkpoint: bool = False,
trainable: bool = True,
trainable: bool = False,
reduce_output: str = "cls_pooled",
vocab_size: int = 30000,
embedding_size: int = 128,
Expand Down Expand Up @@ -173,7 +173,7 @@ def __init__(
use_pretrained: bool = True,
pretrained_model_name_or_path: str = "google/mt5-base",
saved_weights_in_checkpoint: bool = False,
trainable: bool = True,
trainable: bool = False,
reduce_output: str = "cls_pooled",
vocab_size: int = 250112,
d_model: int = 512,
Expand Down Expand Up @@ -299,7 +299,7 @@ def __init__(
pretrained_model_name_or_path: str = "xlm-roberta-base",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "cls_pooled",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = None,
pad_token_id: int = 1,
bos_token_id: int = 0,
Expand Down Expand Up @@ -396,7 +396,7 @@ def __init__(
use_pretrained: bool = True,
pretrained_model_name_or_path: str = "bert-base-uncased",
saved_weights_in_checkpoint: bool = False,
trainable: bool = True,
trainable: bool = False,
reduce_output: str = "cls_pooled",
vocab_size: int = 30522,
hidden_size: int = 768,
Expand Down Expand Up @@ -519,7 +519,7 @@ def __init__(
use_pretrained: bool = True,
pretrained_model_name_or_path: str = "xlm-mlm-en-2048",
saved_weights_in_checkpoint: bool = False,
trainable: bool = True,
trainable: bool = False,
reduce_output: str = "cls_pooled",
vocab_size: int = 30145,
emb_dim: int = 2048,
Expand Down Expand Up @@ -659,7 +659,7 @@ def __init__(
use_pretrained: bool = True,
pretrained_model_name_or_path: str = "openai-gpt",
saved_weights_in_checkpoint: bool = False,
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 30522,
n_positions: int = 40478,
n_ctx: int = 512,
Expand Down Expand Up @@ -759,7 +759,7 @@ def __init__(
use_pretrained: bool = True,
pretrained_model_name_or_path: str = "gpt2",
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 50257,
n_positions: int = 1024,
n_ctx: int = 1024,
Expand Down Expand Up @@ -864,7 +864,7 @@ def __init__(
pretrained_model_name_or_path: str = "roberta-base",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "cls_pooled",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = None,
pad_token_id: int = 1,
bos_token_id: int = 0,
Expand Down Expand Up @@ -900,7 +900,6 @@ def __init__(
self.reduce_output = reduce_output
if not self.reduce_output == "cls_pooled":
self.reduce_sequence = SequenceReducer(reduce_mode=reduce_output)
self.transformer.trainable = trainable
self.transformer.resize_token_embeddings(vocab_size)

def forward(self, inputs: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
Expand Down Expand Up @@ -951,7 +950,7 @@ def __init__(
pretrained_model_name_or_path: str = "transfo-xl-wt103",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 267735,
cutoffs: List[int] = [20000, 40000, 200000],
d_model: int = 1024,
Expand Down Expand Up @@ -1074,7 +1073,7 @@ def __init__(
pretrained_model_name_or_path: str = "xlnet-base-cased",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 32000,
d_model: int = 1024,
n_layer: int = 24,
Expand Down Expand Up @@ -1411,7 +1410,7 @@ def __init__(
pretrained_model_name_or_path: str = "ctrl",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "cls-pooled",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 30522,
hidden_size: int = 768,
num_hidden_layers: int = 12,
Expand Down Expand Up @@ -1531,7 +1530,7 @@ def __init__(
pretrained_model_name_or_path: str = "t5-small",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 32128,
d_model: int = 512,
d_kv: int = 64,
Expand Down Expand Up @@ -1638,7 +1637,7 @@ def __init__(
pretrained_model_name_or_path: str = "flaubert/flaubert_small_cased",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 30145,
pre_norm: bool = False,
layerdrop: float = 0.0,
Expand Down Expand Up @@ -1773,7 +1772,7 @@ def __init__(
pretrained_model_name_or_path: str = "google/electra-small-discriminator",
saved_weights_in_checkpoint: bool = False,
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = 30522,
embedding_size: int = 128,
hidden_size: int = 256,
Expand Down Expand Up @@ -1888,7 +1887,7 @@ def __init__(
pretrained_model_name_or_path: str = "allenai/longformer-base-4096",
saved_weights_in_checkpoint: bool = False,
reduce_output: Optional[str] = "cls_pooled",
trainable: bool = True,
trainable: bool = False,
num_tokens: Optional[int] = None,
pretrained_kwargs: Dict = None,
**kwargs
Expand Down Expand Up @@ -1968,7 +1967,7 @@ def __init__(
pretrained_model_name_or_path: str,
max_sequence_length: int,
reduce_output: str = "sum",
trainable: bool = True,
trainable: bool = False,
vocab_size: int = None,
pretrained_kwargs: Dict = None,
**kwargs
Expand Down