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Add support for multiple models for one config in auto classes (#11150)

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* Add support for multiple models for one config in auto classes

* Use get_values everywhere

* Prettier doc
This commit is contained in:
Sylvain Gugger
2021-04-08 18:41:36 -04:00
committed by GitHub
parent 97ccf67bb3
commit ba8b1f4754
26 changed files with 188 additions and 72 deletions
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27
tests/test_modeling_common.py
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@@ -24,6 +24,7 @@ from typing import List, Tuple
from transformers import is_torch_available
from transformers.file_utils import WEIGHTS_NAME
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
@@ -79,7 +80,7 @@ class ModelTesterMixin:
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict = {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim > 1
@@ -88,9 +89,9 @@ class ModelTesterMixin:
}
if return_labels:
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
elif model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
elif model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
@@ -98,18 +99,18 @@ class ModelTesterMixin:
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in [
*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values(),
*MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.values(),
*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.values(),
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
]:
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in [
*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
*MODEL_FOR_CAUSAL_LM_MAPPING.values(),
*MODEL_FOR_MASKED_LM_MAPPING.values(),
*MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
*get_values(MODEL_FOR_MASKED_LM_MAPPING),
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
]:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
@@ -229,7 +230,7 @@ class ModelTesterMixin:
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in MODEL_MAPPING.values():
if model_class in get_values(MODEL_MAPPING):
continue
model = model_class(config)
model.to(torch_device)
@@ -248,7 +249,7 @@ class ModelTesterMixin:
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in MODEL_MAPPING.values():
if model_class in get_values(MODEL_MAPPING):
continue
model = model_class(config)
model.to(torch_device)
@@ -312,7 +313,7 @@ class ModelTesterMixin:
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Question Answering model returns start_logits and end_logits
if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned