Chameleon: minor fixes after shipping (#32037)

* fix merging

* make chameleon conditional

docs/source/en/model_doc/chameleon.md

@@ -64,13 +64,13 @@ The original code can be found [here](https://github.com/facebookresearch/chamel
 Here's how to load the model and perform inference in half-precision (`torch.float16`):
 
 ```python
-from transformers import ChameleonProcessor, ChameleonForCausalLM
+from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
 import torch
 from PIL import Image
 import requests
 
 processor = ChameleonProcessor.from_pretrained("meta-chameleon")
-model = ChameleonForCausalLM.from_pretrained("meta-chameleon", torch_dtype=torch.float16, device_map="auto") 
+model = ChameleonForConditionalGeneration.from_pretrained("meta-chameleon", torch_dtype=torch.float16, device_map="auto") 
 
 # prepare image and text prompt
 url = "https://bjiujitsu.com/wp-content/uploads/2021/01/jiu_jitsu_belt_white_1.jpg"
@@ -89,13 +89,13 @@ print(processor.decode(output[0], skip_special_tokens=True))
 Chameleon can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). Here is how you can do it:
 
 ```python
-from transformers import ChameleonProcessor, ChameleonForCausalLM
+from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
 import torch
 from PIL import Image
 import requests
 
 processor = ChameleonProcessor.from_pretrained("meta-chameleon")
-model = ChameleonForCausalLM.from_pretrained("meta-chameleon", torch_dtype=torch.float16, device_map="auto") 
+model = ChameleonForConditionalGeneration.from_pretrained("meta-chameleon", torch_dtype=torch.float16, device_map="auto") 
 
 # Get three different images
 url = "https://www.ilankelman.org/stopsigns/australia.jpg"
@@ -129,7 +129,7 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
 The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
 
 ```python
-from transformers import ChameleonForCausalLM, BitsAndBytesConfig
+from transformers import ChameleonForConditionalGeneration, BitsAndBytesConfig
 
 # specify how to quantize the model
 quantization_config = BitsAndBytesConfig(
@@ -138,17 +138,17 @@ quantization_config = BitsAndBytesConfig(
     bnb_4bit_compute_dtype=torch.float16,
 )
 
-model = ChameleonForCausalLM.from_pretrained("meta-chameleon", quantization_config=quantization_config, device_map="auto")
+model = ChameleonForConditionalGeneration.from_pretrained("meta-chameleon", quantization_config=quantization_config, device_map="auto")
 ```
 
 ### Use Flash-Attention 2 and SDPA to further speed-up generation
 
 The models supports both, Flash-Attention 2 and PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) which can be enables for optimization. SDPA is the default options when you load the model, If you want to switch for Flash Attention 2, first make sure to install flash-attn. Refer to the [original repository](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
 
 ```python
-from transformers import ChameleonForCausalLM
+from transformers import ChameleonForConditionalGeneration
 
-model = ChameleonForCausalLM.from_pretrained(
+model = ChameleonForConditionalGeneration.from_pretrained(
     model_id, 
     torch_dtype=torch.float16, 
     low_cpu_mem_usage=True,
@@ -183,7 +183,7 @@ model = ChameleonForCausalLM.from_pretrained(
 [[autodoc]] ChameleonModel
     - forward
 
-## ChameleonForCausalLM
+## ChameleonForConditionalGeneration
 
-[[autodoc]] ChameleonForCausalLM
+[[autodoc]] ChameleonForConditionalGeneration
     - forward

src/transformers/__init__.py

@@ -1616,7 +1616,7 @@
     )
     _import_structure["models.chameleon"].extend(
         [
-            "ChameleonForCausalLM",
+            "ChameleonForConditionalGeneration",
             "ChameleonModel",
             "ChameleonPreTrainedModel",
             "ChameleonProcessor",
@@ -6276,7 +6276,7 @@
             load_tf_weights_in_canine,
         )
         from .models.chameleon import (
-            ChameleonForCausalLM,
+            ChameleonForConditionalGeneration,
             ChameleonModel,
             ChameleonPreTrainedModel,
             ChameleonProcessor,

src/transformers/models/auto/modeling_auto.py

@@ -446,7 +446,6 @@
         ("blenderbot-small", "BlenderbotSmallForCausalLM"),
         ("bloom", "BloomForCausalLM"),
         ("camembert", "CamembertForCausalLM"),
-        ("chameleon", "ChameleonForCausalLM"),
         ("code_llama", "LlamaForCausalLM"),
         ("codegen", "CodeGenForCausalLM"),
         ("cohere", "CohereForCausalLM"),
@@ -703,6 +702,7 @@
     [
         ("blip", "BlipForConditionalGeneration"),
         ("blip-2", "Blip2ForConditionalGeneration"),
+        ("chameleon", "ChameleonForConditionalGeneration"),
         ("git", "GitForCausalLM"),
         ("idefics2", "Idefics2ForConditionalGeneration"),
         ("instructblip", "InstructBlipForConditionalGeneration"),

src/transformers/models/chameleon/__init__.py

@@ -36,7 +36,7 @@
     pass
 else:
     _import_structure["modeling_chameleon"] = [
-        "ChameleonForCausalLM",
+        "ChameleonForConditionalGeneration",
         "ChameleonModel",
         "ChameleonPreTrainedModel",
         "ChameleonVQVAE",
@@ -62,7 +62,7 @@
         pass
     else:
         from .modeling_chameleon import (
-            ChameleonForCausalLM,
+            ChameleonForConditionalGeneration,
             ChameleonModel,
             ChameleonPreTrainedModel,
             ChameleonVQVAE,

src/transformers/models/chameleon/modeling_chameleon.py

@@ -1279,7 +1279,8 @@ def forward(
         if pixel_values is not None:
             image_tokens = self.get_image_tokens(pixel_values)
             special_image_mask = input_ids == self.vocabulary_mapping.image_token_id
-            input_ids[special_image_mask] = image_tokens.flatten().to(input_ids.device, input_ids.dtype)
+            image_tokens = image_tokens.to(input_ids.device, input_ids.dtype)
+            input_ids = input_ids.masked_scatter(special_image_mask, image_tokens)
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
@@ -1445,7 +1446,7 @@ def _update_causal_mask(
     "Chameleon Model with a head on top used for outputting logits for next token prediction.",
     CHAMELEON_START_DOCSTRING,
 )
-class ChameleonForCausalLM(ChameleonPreTrainedModel):
+class ChameleonForConditionalGeneration(ChameleonPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
@@ -1504,12 +1505,12 @@ def forward(
         Example:
 
         ```python
-        >>> from transformers import ChameleonProcessor, ChameleonForCausalLM
+        >>> from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
         >>> import torch
         >>> import requests
         >>> from PIL import Image
 
-        >>> model = ChameleonForCausalLM.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16)
+        >>> model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16)
         >>> processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
 
         >>> prompt = "I used to know a lot about constellations when I was younger, but as I grew older, I forgot most of what I knew. These are the only two constellations that I really remember now.<image><image>I would like for you to tell me about 3 more constellations and give me a little bit of history about the constellation."

src/transformers/utils/dummy_pt_objects.py

@@ -1835,7 +1835,7 @@ def load_tf_weights_in_canine(*args, **kwargs):
     requires_backends(load_tf_weights_in_canine, ["torch"])
 
 
-class ChameleonForCausalLM(metaclass=DummyObject):
+class ChameleonForConditionalGeneration(metaclass=DummyObject):
     _backends = ["torch"]
 
     def __init__(self, *args, **kwargs):

tests/models/chameleon/test_modeling_chameleon.py

@@ -44,7 +44,7 @@
     import torch
 
     from transformers import (
-        ChameleonForCausalLM,
+        ChameleonForConditionalGeneration,
         ChameleonModel,
         ChameleonProcessor,
     )
@@ -191,7 +191,7 @@ def create_and_check_for_causal_lm(
         encoder_hidden_states,
         encoder_attention_mask,
     ):
-        model = ChameleonForCausalLM(config=config)
+        model = ChameleonForConditionalGeneration(config=config)
         model.to(torch_device)
         model.eval()
         result = model(input_ids, attention_mask=input_mask, labels=token_labels)
@@ -209,7 +209,7 @@ def create_and_check_decoder_model_past_large_inputs(
         encoder_attention_mask,
     ):
         config.is_decoder = True
-        model = ChameleonForCausalLM(config=config)
+        model = ChameleonForConditionalGeneration(config=config)
         model.to(torch_device)
         model.eval()
 
@@ -273,12 +273,12 @@ def prepare_config_and_inputs_for_common(self):
 
 @require_torch
 class ChameleonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (ChameleonModel, ChameleonForCausalLM) if is_torch_available() else ()
-    all_generative_model_classes = (ChameleonForCausalLM,) if is_torch_available() else ()
+    all_model_classes = (ChameleonModel, ChameleonForConditionalGeneration) if is_torch_available() else ()
+    all_generative_model_classes = (ChameleonForConditionalGeneration,) if is_torch_available() else ()
     pipeline_model_mapping = (
         {
             "feature-extraction": ChameleonModel,
-            "text-generation": ChameleonForCausalLM,
+            "text-generation": ChameleonForConditionalGeneration,
         }
         if is_torch_available()
         else {}
@@ -339,7 +339,7 @@ def test_flash_attn_2_generate_padding_right(self):
         """
         Overwritting the common test as the test is flaky on tiny models
         """
-        model = ChameleonForCausalLM.from_pretrained(
+        model = ChameleonForConditionalGeneration.from_pretrained(
             "facebook/chameleon-7b",
             load_in_4bit=True,
             device_map={"": 0},
@@ -355,7 +355,7 @@ def test_flash_attn_2_generate_padding_right(self):
         output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False)
         output_native = processor.tokenizer.batch_decode(output_native)
 
-        model = ChameleonForCausalLM.from_pretrained(
+        model = ChameleonForConditionalGeneration.from_pretrained(
             "facebook/chameleon-7b",
             load_in_4bit=True,
             attn_implementation="flash_attention_2",
@@ -377,7 +377,9 @@ class ChameleonIntegrationTest(unittest.TestCase):
     @require_bitsandbytes
     @require_read_token
     def test_model_7b(self):
-        model = ChameleonForCausalLM.from_pretrained("facebook/chameleon-7b", load_in_4bit=True, device_map="auto")
+        model = ChameleonForConditionalGeneration.from_pretrained(
+            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
+        )
         processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
 
         image = Image.open(
@@ -397,7 +399,9 @@ def test_model_7b(self):
     @require_bitsandbytes
     @require_read_token
     def test_model_7b_batched(self):
-        model = ChameleonForCausalLM.from_pretrained("facebook/chameleon-7b", load_in_4bit=True, device_map="auto")
+        model = ChameleonForConditionalGeneration.from_pretrained(
+            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
+        )
         processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
 
         image = Image.open(
@@ -428,7 +432,9 @@ def test_model_7b_batched(self):
     @require_bitsandbytes
     @require_read_token
     def test_model_7b_multi_image(self):
-        model = ChameleonForCausalLM.from_pretrained("facebook/chameleon-7b", load_in_4bit=True, device_map="auto")
+        model = ChameleonForConditionalGeneration.from_pretrained(
+            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
+        )
         processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
 
         image = Image.open(