riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 23 Packages Projects Releases Wiki Activity

Extend config with task specific configs. (#3433)

Browse Source 
* add new default configs

* change prefix default to None
This commit is contained in:
Patrick von Platen 2020-03-25 21:32:04 +01:00 committed by GitHub
parent 83272a3853
commit ffa17fe322
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 27 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
src/transformers/configuration_utils.py
View File

@ -78,9 +78,6 @@ class PretrainedConfig(object):
self.top_k = kwargs.pop("top_k", 50)
self.top_p = kwargs.pop("top_p", 1.0)
self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
self.length_penalty = kwargs.pop("length_penalty", 1.0)
self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
@ -94,6 +91,16 @@ class PretrainedConfig(object):
self.label2id = kwargs.pop("label2id", dict(zip(self.id2label.values(), self.id2label.keys())))
self.label2id = dict((key, int(value)) for key, value in self.label2id.items())
# Tokenizer arguments TODO: eventually tokenizer and models should share the same config
self.prefix = kwargs.pop("prefix", None)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
# task specific arguments
self.task_specific_params = kwargs.pop("task_specific_params", None)
# Additional attributes without default values
for key, value in kwargs.items():
try:

13
src/transformers/modeling_tf_utils.py
View File

@ -610,7 +610,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
if input_ids is not None:
batch_size = shape_list(input_ids)[0] # overriden by the input batch_size
@ -635,9 +637,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
assert (eos_token_id is None) or (
isinstance(eos_token_id, int) and (eos_token_id >= 0)
), "`eos_token_id` should be a positive integer."
assert (
decoder_start_token_id is not None or self.config.is_encoder_decoder is False
), "`decoder_start_token_id` has to be defined if model is encoder-decoder model"
assert length_penalty > 0, "`length_penalty` should be strictely positive."
assert (
isinstance(num_return_sequences, int) and num_return_sequences > 0
@ -708,8 +707,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
) # shape: (batch_size * num_return_sequences * num_beams, cur_len)
if self.config.is_encoder_decoder:
if decoder_start_token_id is None:
decoder_start_token_id = bos_token_id
assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
assert (
decoder_start_token_id is not None
), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)

14
src/transformers/modeling_utils.py
View File

@ -809,7 +809,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
if input_ids is not None:
batch_size = input_ids.shape[0] # overriden by the input batch_size
@ -831,9 +833,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
assert pad_token_id is None or (
isinstance(pad_token_id, int) and (pad_token_id >= 0)
), "`pad_token_id` should be a positive integer."
assert (
decoder_start_token_id is not None or self.config.is_encoder_decoder is False
), "`decoder_start_token_id` has to be defined if model is encoder-decoder model"
assert (eos_token_id is None) or (
isinstance(eos_token_id, int) and (eos_token_id >= 0)
), "`eos_token_id` should be a positive integer."
@ -912,7 +911,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
) # shape: (batch_size * num_return_sequences * num_beams, cur_len)
if self.config.is_encoder_decoder:
assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
if decoder_start_token_id is None:
decoder_start_token_id = bos_token_id
assert (
decoder_start_token_id is not None
), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)