Model transform docs (#1665)

docs/source/basics/model_transforms.rst

@@ -0,0 +1,170 @@
+.. _model_transform_usage_label:
+
+=====================
+Multimodal Transforms
+=====================
+
+Multimodal model transforms apply model-specific data transforms to each modality and prepares :class:`~torchtune.data.Message`
+objects to be input into the model. torchtune currently supports text + image model transforms.
+These are intended to be drop-in replacements for tokenizers in multimodal datasets and support the standard
+``encode``, ``decode``, and ``tokenize_messages``.
+
+.. code-block:: python
+
+    # torchtune.models.flamingo.FlamingoTransform
+    class FlamingoTransform(ModelTokenizer, Transform):
+        def __init__(...):
+            # Text transform - standard tokenization
+            self.tokenizer = llama3_tokenizer(...)
+            # Image transforms
+            self.transform_image = CLIPImageTransform(...)
+            self.xattn_mask = VisionCrossAttentionMask(...)
+
+
+.. code-block:: python
+
+    from torchtune.models.flamingo import FlamingoTransform
+    from torchtune.data import Message
+    from PIL import Image
+
+    sample = {
+        "messages": [
+            Message(
+                role="user",
+                content=[
+                    {"type": "image", "content": Image.new(mode="RGB", size=(224, 224))},
+                    {"type": "image", "content": Image.new(mode="RGB", size=(224, 224))},
+                    {"type": "text", "content": "What is common in these two images?"},
+                ],
+            ),
+            Message(
+                role="assistant",
+                content="A robot is in both images.",
+            ),
+        ],
+    }
+    transform = FlamingoTransform(
+        path="/tmp/Meta-Llama-3-8B-Instruct/original/tokenizer.model",
+        tile_size=224,
+        patch_size=14,
+    )
+    tokenized_dict = transform(sample)
+    print(transform.decode(tokenized_dict["tokens"], skip_special_tokens=False))
+    # '<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n<|image|><|image|>What is common in these two images?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nA robot is in both images.<|eot_id|>'
+    print(tokenized_dict["encoder_input"]["images"][0].shape)  # (num_tiles, num_channels, tile_height, tile_width)
+    # torch.Size([4, 3, 224, 224])
+
+
+Using model transforms
+----------------------
+You can pass them into any multimodal dataset builder just as you would a model tokenizer.
+
+.. code-block:: python
+
+    from torchtune.datasets.multimodal import the_cauldron_dataset
+    from torchtune.models.flamingo import FlamingoTransform
+
+    transform = FlamingoTransform(
+        path="/tmp/Meta-Llama-3-8B-Instruct/original/tokenizer.model",
+        tile_size=224,
+        patch_size=14,
+    )
+    ds = the_cauldron_dataset(
+        model_transform=transform,
+        subset="ai2d",
+    )
+    tokenized_dict = ds[0]
+    print(transform.decode(tokenized_dict["tokens"], skip_special_tokens=False))
+    # <|begin_of_text|><|start_header_id|>user<|end_header_id|>
+    #
+    # <|image|>Question: What do respiration and combustion give out
+    # Choices:
+    # A. Oxygen
+    # B. Carbon dioxide
+    # C. Nitrogen
+    # D. Heat
+    # Answer with the letter.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+    #
+    # Answer: B<|eot_id|>
+    print(tokenized_dict["encoder_input"]["images"][0].shape)  # (num_tiles, num_channels, tile_height, tile_width)
+    # torch.Size([4, 3, 224, 224])
+
+Creating model transforms
+-------------------------
+Model transforms are expected to process both text and images in the sample dictionary.
+Both should be contained in the ``"messages"`` field of the sample.
+
+The following methods are required on the model transform:
+
+- ``tokenize_messages``
+- ``__call__``
+
+.. code-block:: python
+
+    from torchtune.modules.tokenizers import ModelTokenizer
+    from torchtune.modules.transforms import Transform
+
+    class MyMultimodalTransform(ModelTokenizer, Transform):
+        def __init__(...):
+            self.tokenizer = my_tokenizer_builder(...)
+            self.transform_image = MyImageTransform(...)
+
+        def tokenize_messages(
+            self,
+            messages: List[Message],
+            add_eos: bool = True,
+        ) -> Tuple[List[int], List[bool]]:
+            # Any other custom logic here
+            ...
+
+            return self.tokenizer.tokenize_messages(
+                messages=messages,
+                add_eos=add_eos,
+            )
+
+        def __call__(
+            self, sample: Mapping[str, Any], inference: bool = False
+        ) -> Mapping[str, Any]:
+            # Expected input parameters for vision encoder
+            encoder_input = {"images": [], "aspect_ratio": []}
+            messages = sample["messages"]
+
+            # Transform all images in sample
+            for message in messages:
+                for image in message.get_media():
+                    out = self.transform_image({"image": image}, inference=inference)
+                    encoder_input["images"].append(out["image"])
+                    encoder_input["aspect_ratio"].append(out["aspect_ratio"])
+            sample["encoder_input"] = encoder_input
+
+            # Transform all text - returns same dictionary with additional keys "tokens" and "mask"
+            sample = self.tokenizer(sample, inference=inference)
+
+            return sample
+
+    transform = MyMultimodalTransform(...)
+    sample = {
+        "messages": [
+            Message(
+                role="user",
+                content=[
+                    {"type": "image", "content": Image.new(mode="RGB", size=(224, 224))},
+                    {"type": "image", "content": Image.new(mode="RGB", size=(224, 224))},
+                    {"type": "text", "content": "What is common in these two images?"},
+                ],
+            ),
+            Message(
+                role="assistant",
+                content="A robot is in both images.",
+            ),
+        ],
+    }
+    tokenized_dict = transform(sample)
+    print(tokenized_dict)
+    # {'encoder_input': {'images': ..., 'aspect_ratio': ...}, 'tokens': ..., 'mask': ...}
+
+
+Example model transforms
+------------------------
+- Flamingo
+    - :class:`~torchtune.models.flamingo.FlamingoTransform`

docs/source/index.rst

@@ -120,6 +120,7 @@ torchtune tutorials.
    basics/tokenizers
    basics/prompt_templates
    basics/preference_datasets
+   basics/model_transforms
 
 .. toctree::
    :glob:

torchtune/models/flamingo/_transform.py

@@ -27,7 +27,7 @@ class FlamingoTransform(ModelTokenizer, Transform):
 
     Args:
         path (str): Path to pretrained tiktoken tokenizer file.
-        tile_size (int): Size of the tiles to divide the image into. Default 224.
+        tile_size (int): Size of the tiles to divide the image into.
         patch_size (int): Size of the patches used in the CLIP vision tranformer model. This is
             used to calculate the number of image embeddings per image.
         max_num_tiles (int): Only used if possible_resolutions is NOT given.