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Config to Model mapping

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This commit is contained in:
Julien Chaumond 2020-01-13 20:05:20 +00:00
parent cf8a70bf68
commit b803b067bf
17 changed files with 58 additions and 55 deletions
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1
examples/summarization/configuration_bertabs.py
View File

@ -62,6 +62,7 @@ class BertAbsConfig(PretrainedConfig):
"""
pretrained_config_archive_map = BERTABS_FINETUNED_CONFIG_MAP
model_type = "bertabs"
def __init__(
self,

1
src/transformers/configuration_albert.py
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@ -37,6 +37,7 @@ class AlbertConfig(PretrainedConfig):
"""
pretrained_config_archive_map = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "albert"
def __init__(
self,

2
src/transformers/configuration_auto.py
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@ -188,7 +188,7 @@ class AutoConfig:
assert unused_kwargs == {'foo': False}
"""
config_dict, _ = PretrainedConfig.resolved_config_dict(
config_dict, _ = PretrainedConfig.get_config_dict(
pretrained_model_name_or_path, pretrained_config_archive_map=ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, **kwargs
)

1
src/transformers/configuration_bert.py
View File

@ -78,6 +78,7 @@ class BertConfig(PretrainedConfig):
layer_norm_eps: The epsilon used by LayerNorm.
"""
pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "bert"
def __init__(
self,

1
src/transformers/configuration_camembert.py
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@ -30,3 +30,4 @@ CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class CamembertConfig(RobertaConfig):
pretrained_config_archive_map = CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "camembert"

1
src/transformers/configuration_ctrl.py
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@ -48,6 +48,7 @@ class CTRLConfig(PretrainedConfig):
"""
pretrained_config_archive_map = CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "ctrl"
def __init__(
self,

1
src/transformers/configuration_distilbert.py
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@ -32,6 +32,7 @@ DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class DistilBertConfig(PretrainedConfig):
pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "distilbert"
def __init__(
self,

1
src/transformers/configuration_gpt2.py
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@ -54,6 +54,7 @@ class GPT2Config(PretrainedConfig):
"""
pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "gpt2"
def __init__(
self,

1
src/transformers/configuration_openai.py
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@ -54,6 +54,7 @@ class OpenAIGPTConfig(PretrainedConfig):
"""
pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "openai-gpt"
def __init__(
self,

1
src/transformers/configuration_t5.py
View File

@ -60,6 +60,7 @@ class T5Config(PretrainedConfig):
layer_norm_eps: The epsilon used by LayerNorm.
"""
pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "t5"
def __init__(
self,

1
src/transformers/configuration_transfo_xl.py
View File

@ -65,6 +65,7 @@ class TransfoXLConfig(PretrainedConfig):
"""
pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "transfo-xl"
def __init__(
self,

9
src/transformers/configuration_utils.py
View File

@ -46,7 +46,8 @@ class PretrainedConfig(object):
``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
``torchscript``: string, default `False`. Is the model used with Torchscript.
"""
pretrained_config_archive_map = {}
pretrained_config_archive_map: Dict[str, str] = {}
model_type: str
def __init__(self, **kwargs):
# Attributes with defaults
@ -155,11 +156,11 @@ class PretrainedConfig(object):
assert unused_kwargs == {'foo': False}
"""
config_dict, kwargs = cls.resolved_config_dict(pretrained_model_name_or_path, **kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
return cls.from_dict(config_dict, **kwargs)
@classmethod
def resolved_config_dict(
def get_config_dict(
cls, pretrained_model_name_or_path: str, pretrained_config_archive_map: Optional[Dict] = None, **kwargs
) -> Tuple[Dict, Dict]:
"""
@ -257,7 +258,7 @@ class PretrainedConfig(object):
@classmethod
def from_json_file(cls, json_file: str):
"""Constructs a `Config` from a json file of parameters."""
"""Constructs a `Config` from the path to a json file of parameters."""
config_dict = cls._dict_from_json_file(json_file)
return cls(**config_dict)

1
src/transformers/configuration_xlm.py
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@ -78,6 +78,7 @@ class XLMConfig(PretrainedConfig):
"""
pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "xlm"
def __init__(
self,

1
src/transformers/configuration_xlm_roberta.py
View File

@ -35,3 +35,4 @@ XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLMRobertaConfig(RobertaConfig):
pretrained_config_archive_map = XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "xlm-roberta"

1
src/transformers/configuration_xlnet.py
View File

@ -69,6 +69,7 @@ class XLNetConfig(PretrainedConfig):
"""
pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "xlnet"
def __init__(
self,

88
src/transformers/modeling_auto.py
View File

@ -16,6 +16,8 @@
import logging
from collections import OrderedDict
from typing import Type
from .configuration_auto import (
AlbertConfig,
@ -76,6 +78,7 @@ from .modeling_roberta import (
)
from .modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5Model, T5WithLMHeadModel
from .modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TransfoXLLMHeadModel, TransfoXLModel
from .modeling_utils import PreTrainedModel
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
XLMForQuestionAnswering,
@ -123,6 +126,35 @@ ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict(
for key, value, in pretrained_map.items()
)
MODEL_MAPPING: OrderedDict[Type[PretrainedConfig], Type[PreTrainedModel]] = OrderedDict(
[
(T5Config, T5Model),
(DistilBertConfig, DistilBertModel),
(AlbertConfig, AlbertModel),
(CamembertConfig, CamembertModel),
(RobertaConfig, XLMRobertaModel),
(XLMRobertaConfig, RobertaModel),
(BertConfig, BertModel),
(OpenAIGPTConfig, OpenAIGPTModel),
(GPT2Config, GPT2Model),
(TransfoXLConfig, TransfoXLModel),
(XLNetConfig, XLNetModel),
(XLMConfig, XLMModel),
(CTRLConfig, CTRLModel),
]
)
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING: OrderedDict[Type[PretrainedConfig], Type[PreTrainedModel]] = OrderedDict(
[
(DistilBertConfig, DistilBertForTokenClassification),
(CamembertConfig, CamembertForTokenClassification),
(RobertaConfig, XLMRobertaForTokenClassification),
(XLMRobertaConfig, RobertaForTokenClassification),
(BertConfig, BertForTokenClassification),
(XLNetConfig, XLNetForTokenClassification),
]
)
class AutoModel(object):
r"""
@ -183,30 +215,9 @@ class AutoModel(object):
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = AutoModel.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
if isinstance(config, DistilBertConfig):
return DistilBertModel(config)
elif isinstance(config, RobertaConfig):
return RobertaModel(config)
elif isinstance(config, BertConfig):
return BertModel(config)
elif isinstance(config, OpenAIGPTConfig):
return OpenAIGPTModel(config)
elif isinstance(config, GPT2Config):
return GPT2Model(config)
elif isinstance(config, TransfoXLConfig):
return TransfoXLModel(config)
elif isinstance(config, XLNetConfig):
return XLNetModel(config)
elif isinstance(config, XLMConfig):
return XLMModel(config)
elif isinstance(config, CTRLConfig):
return CTRLModel(config)
elif isinstance(config, AlbertConfig):
return AlbertModel(config)
elif isinstance(config, CamembertConfig):
return CamembertModel(config)
elif isinstance(config, XLMRobertaConfig):
return XLMRobertaModel(config)
for config_class, model_class in MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError("Unrecognized configuration class {}".format(config))
@classmethod
@ -294,32 +305,9 @@ class AutoModel(object):
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
if isinstance(config, T5Config):
return T5Model.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, DistilBertConfig):
return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, AlbertConfig):
return AlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, CamembertConfig):
return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, XLMRobertaConfig):
return XLMRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, RobertaConfig):
return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, BertConfig):
return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, OpenAIGPTConfig):
return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, GPT2Config):
return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, TransfoXLConfig):
return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, XLNetConfig):
return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, XLMConfig):
return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif isinstance(config, CTRLConfig):
return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
for config_class, model_class in MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
raise ValueError(
"Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "

1
templates/adding_a_new_model/configuration_xxx.py
View File

@ -58,6 +58,7 @@ class XxxConfig(PretrainedConfig):
layer_norm_eps: The epsilon used by LayerNorm.
"""
pretrained_config_archive_map = XXX_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "xxx"
def __init__(
self,