Rename ImageGPT (#14526)

* Rename

* Add MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING

docs/source/model_doc/imagegpt.rst

@@ -96,10 +96,10 @@ ImageGPTModel
     :members: forward
 
 
-ImageGPTForCausalLM
+ImageGPTForCausalImageModeling
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: transformers.ImageGPTForCausalLM
+.. autoclass:: transformers.ImageGPTForCausalImageModeling
     :members: forward
 
 

src/transformers/__init__.py

@@ -619,6 +619,7 @@
     _import_structure["models.auto"].extend(
         [
             "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
+            "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
             "MODEL_FOR_CAUSAL_LM_MAPPING",
             "MODEL_FOR_CTC_MAPPING",
             "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
@@ -977,7 +978,7 @@
     _import_structure["models.imagegpt"].extend(
         [
             "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
-            "ImageGPTForCausalLM",
+            "ImageGPTForCausalImageModeling",
             "ImageGPTForImageClassification",
             "ImageGPTModel",
             "ImageGPTPreTrainedModel",
@@ -2521,6 +2522,7 @@
         )
         from .models.auto import (
             MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
+            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
             MODEL_FOR_CAUSAL_LM_MAPPING,
             MODEL_FOR_CTC_MAPPING,
             MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
@@ -2823,7 +2825,7 @@
         )
         from .models.imagegpt import (
             IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            ImageGPTForCausalLM,
+            ImageGPTForCausalImageModeling,
             ImageGPTForImageClassification,
             ImageGPTModel,
             ImageGPTPreTrainedModel,

src/transformers/models/auto/__init__.py

@@ -32,6 +32,7 @@
 if is_torch_available():
     _import_structure["modeling_auto"] = [
         "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
+        "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
         "MODEL_FOR_CAUSAL_LM_MAPPING",
         "MODEL_FOR_CTC_MAPPING",
         "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
@@ -137,6 +138,7 @@
     if is_torch_available():
         from .modeling_auto import (
             MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
+            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
             MODEL_FOR_CAUSAL_LM_MAPPING,
             MODEL_FOR_CTC_MAPPING,
             MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,

src/transformers/models/auto/modeling_auto.py

@@ -147,7 +147,6 @@
 MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
     [
         # Model with LM heads mapping
-        ("imagegpt", "ImageGPTForCausalLM"),
         ("qdqbert", "QDQBertForMaskedLM"),
         ("fnet", "FNetForMaskedLM"),
         ("gptj", "GPTJForCausalLM"),
@@ -199,7 +198,6 @@
 MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
     [
         # Model for Causal LM mapping
-        ("imagegpt", "ImageGPTForCausalLM"),
         ("qdqbert", "QDQBertLMHeadModel"),
         ("trocr", "TrOCRForCausalLM"),
         ("gptj", "GPTJForCausalLM"),
@@ -233,6 +231,13 @@
     ]
 )
 
+MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
+    # Model for Causal Image Modeling mapping
+    [
+        ("imagegpt", "ImageGPTForCausalImageModeling"),
+    ]
+)
+
 MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Image Classification mapping
@@ -524,6 +529,9 @@
 MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES)
 MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_WITH_LM_HEAD_MAPPING_NAMES)
 MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
+MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
+    CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES
+)
 MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
     CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
 )

src/transformers/models/imagegpt/__init__.py

@@ -31,7 +31,7 @@
 if is_torch_available():
     _import_structure["modeling_imagegpt"] = [
         "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "ImageGPTForCausalLM",
+        "ImageGPTForCausalImageModeling",
         "ImageGPTForImageClassification",
         "ImageGPTModel",
         "ImageGPTPreTrainedModel",
@@ -48,7 +48,7 @@
     if is_torch_available():
         from .modeling_imagegpt import (
             IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            ImageGPTForCausalLM,
+            ImageGPTForCausalImageModeling,
             ImageGPTForImageClassification,
             ImageGPTModel,
             ImageGPTPreTrainedModel,

src/transformers/models/imagegpt/modeling_imagegpt.py

@@ -881,7 +881,7 @@ def custom_forward(*inputs):
     """,
     IMAGEGPT_START_DOCSTRING,
 )
-class ImageGPTForCausalLM(ImageGPTPreTrainedModel):
+class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel):
     _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"]
 
     def __init__(self, config):
@@ -958,13 +958,13 @@ def forward(
 
         Examples::
 
-            >>> from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalLM
+            >>> from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalImageModeling
             >>> import torch
             >>> import matplotlib.pyplot as plt
             >>> import numpy as np
 
             >>> feature_extractor = ImageGPTFeatureExtractor.from_pretrained('openai/imagegpt-small')
-            >>> model = ImageGPTForCausalLM.from_pretrained('openai/imagegpt-small')
+            >>> model = ImageGPTForCausalImageModeling.from_pretrained('openai/imagegpt-small')
             >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
             >>> model.to(device)
 

src/transformers/utils/dummy_pt_objects.py

@@ -341,6 +341,9 @@ def load_tf_weights_in_albert(*args, **kwargs):
 MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None
 
 
+MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = None
+
+
 MODEL_FOR_CAUSAL_LM_MAPPING = None
 
 
@@ -2661,7 +2664,7 @@ def forward(self, *args, **kwargs):
 IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
-class ImageGPTForCausalLM:
+class ImageGPTForCausalImageModeling:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 

tests/test_modeling_common.py

@@ -61,6 +61,7 @@
 
     from transformers import (
         BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
+        MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
         MODEL_FOR_CAUSAL_LM_MAPPING,
         MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         MODEL_FOR_MASKED_LM_MAPPING,
@@ -150,6 +151,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
             elif model_class in [
                 *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
                 *get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
+                *get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING),
                 *get_values(MODEL_FOR_MASKED_LM_MAPPING),
                 *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
             ]:

tests/test_modeling_imagegpt.py

@@ -34,7 +34,7 @@
 
     from transformers import (
         IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
-        ImageGPTForCausalLM,
+        ImageGPTForCausalImageModeling,
         ImageGPTForImageClassification,
         ImageGPTModel,
     )
@@ -207,14 +207,14 @@ def create_and_check_imagegpt_model(self, config, pixel_values, input_mask, head
         self.parent.assertEqual(len(result.past_key_values), config.n_layer)
 
     def create_and_check_lm_head_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
-        model = ImageGPTForCausalLM(config)
+        model = ImageGPTForCausalImageModeling(config)
         model.to(torch_device)
         model.eval()
 
         labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
         result = model(pixel_values, token_type_ids=token_type_ids, labels=labels)
         self.parent.assertEqual(result.loss.shape, ())
-        # ImageGPTForCausalLM doens't have tied input- and output embeddings
+        # ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
 
     def create_and_check_imagegpt_for_image_classification(
@@ -255,9 +255,9 @@ def prepare_config_and_inputs_for_common(self):
 class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
 
     all_model_classes = (
-        (ImageGPTForCausalLM, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
+        (ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
     )
-    all_generative_model_classes = (ImageGPTForCausalLM,) if is_torch_available() else ()
+    all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
     test_missing_keys = False
     input_name = "pixel_values"
 
@@ -273,7 +273,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
 
         return inputs_dict
 
-    # we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalLM doesn't have tied input- and output embeddings
+    # we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
     def _check_scores(self, batch_size, scores, length, config):
         expected_shape = (batch_size, config.vocab_size - 1)
         self.assertIsInstance(scores, tuple)
@@ -519,7 +519,7 @@ def default_feature_extractor(self):
 
     @slow
     def test_inference_causal_lm_head(self):
-        model = ImageGPTForCausalLM.from_pretrained("openai/imagegpt-small").to(torch_device)
+        model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
 
         feature_extractor = self.default_feature_extractor
         image = prepare_img()