[llama-mm] Onboard torchtune vision encoder to ExecuTorch/AOTI (#6807)

[llama-mm] Onboard torchtune vision encoder to ExecuTorch

Summary: As titled. This PR adds `llama3_2_vision_encoder` to
`examples/models/llama3_2_vision/vision_encoder` and add CI jobs.

Test Plan: Rely on newly added CI jobs

Reviewers:

Subscribers:

Tasks:

Tags:

[ghstack-poisoned]

Author: larryliu0820

.ci/scripts/gather_test_models.py

@@ -24,6 +24,7 @@
         "ic4": "linux.12xlarge",
         "resnet50": "linux.12xlarge",
         "llava": "linux.12xlarge",
+        "llama3_2_vision_encoder": "linux.12xlarge",
         # This one causes timeout on smaller runner, the root cause is unclear (T161064121)
         "dl3": "linux.12xlarge",
         "emformer_join": "linux.12xlarge",

examples/models/__init__.py

@@ -18,6 +18,7 @@
     "emformer_join": ("emformer_rnnt", "EmformerRnntJoinerModel"),
     "llama2": ("llama", "Llama2Model"),
     "llama": ("llama", "Llama2Model"),
+    "llama3_2_vision_encoder": ("llama3_2_vision", "FlamingoVisionEncoderModel"),
     "lstm": ("lstm", "LSTMModel"),
     "mobilebert": ("mobilebert", "MobileBertModelExample"),
     "mv2": ("mobilenet_v2", "MV2Model"),

examples/models/llama3_2_vision/vision_encoder/__init__.py

@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .model import FlamingoVisionEncoderModel, VisionEncoderConfig
+
+__all__ = [
+    "FlamingoVisionEncoderModel",
+    "VisionEncoderConfig",
+]

examples/models/llama3_2_vision/vision_encoder/model.py

@@ -0,0 +1,90 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+
+from executorch.examples.models.model_base import EagerModelBase
+from executorch.extension.llm.modules._position_embeddings import (
+    replace_tile_positional_embedding,
+    replace_tiled_token_positional_embedding,
+)
+from torchtune.models.llama3_2_vision._component_builders import llama3_2_vision_encoder
+
+max_seq_len = 8192
+in_channels = 3
+tile_size = 560
+max_num_tiles = 4
+# how many tokens per image generated by the vision encoder
+tokens_per_image = 6404
+# how many images to cache in the kv cache in cross attention
+kv_cache_image_num = 1
+# maximum number of tokens generated by encoder and thus stored in the kv cache in cross attention
+encoder_max_seq_len = tokens_per_image * kv_cache_image_num
+
+
+@dataclass
+class VisionEncoderConfig:
+    patch_size: int = 14
+    num_heads: int = 16
+    clip_embed_dim: int = 1280
+    clip_num_layers: int = 32
+    clip_hidden_states: list[int] = field(default_factory=lambda: [3, 7, 15, 23, 30])
+    decoder_embed_dim: int = 4096
+    num_layers_projection: int = 8
+    tile_size: int = 560
+    max_num_tiles: int = 4
+    in_channels: int = 3
+
+
+class FlamingoVisionEncoderModel(EagerModelBase):
+    def __init__(self, config: Optional[VisionEncoderConfig] = None):
+        super().__init__()
+        if config is None:
+            config = VisionEncoderConfig()
+        self.config = config
+        self.model = llama3_2_vision_encoder(
+            patch_size=config.patch_size,
+            num_heads=config.num_heads,
+            clip_embed_dim=config.clip_embed_dim,
+            clip_num_layers=config.clip_num_layers,
+            clip_hidden_states=config.clip_hidden_states,
+            decoder_embed_dim=config.decoder_embed_dim,
+            num_layers_projection=config.num_layers_projection,
+            tile_size=config.tile_size,
+            max_num_tiles=config.max_num_tiles,
+            in_channels=config.in_channels,
+        )
+        self.model = replace_tile_positional_embedding(self.model)
+        self.model = replace_tiled_token_positional_embedding(self.model)
+        self.image = torch.randn(
+            1, 1, 4, 3, self.config.tile_size, self.config.tile_size
+        )
+        self.aspect_ratio = torch.tensor([[[1, 2]]])
+        self.sample_inputs = (
+            self.image,
+            self.aspect_ratio,
+        )
+
+    def get_eager_model(self, **kwargs):
+        return self.model
+
+    def get_example_inputs(self):
+        return self.sample_inputs
+
+    def get_dynamic_shapes(self):
+        dim = torch.export.Dim("num_tiles", min=1, max=self.config.max_num_tiles)
+        image_dynamic_dim = {
+            0: 1,
+            1: 1,
+            2: dim,
+            3: 3,
+            4: self.config.tile_size,
+            5: self.config.tile_size,
+        }
+        return (image_dynamic_dim, None)

examples/models/llama3_2_vision/vision_encoder/test/__init__.py

@@ -0,0 +1,46 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Export and ExecuTorch tests for CLIP vision encoder are covered by test_models.sh.
+# Only test AOTI in this file
+import os
+import tempfile
+import unittest
+
+import torch
+
+from executorch.examples.models.llama3_2_vision.vision_encoder import (
+    FlamingoVisionEncoderModel,
+)
+from torch.testing import assert_close
+
+
+class FlamingoVisionEncoderTest(unittest.TestCase):
+    def setUp(self) -> None:
+        super().setUp()
+
+    def test_flamingo_vision_encoder(self) -> None:
+        model = FlamingoVisionEncoderModel()
+        encoder = model.model
+        eager_res = encoder.forward(*model.get_example_inputs())
+
+        # AOTI
+        ep = torch.export.export(
+            encoder,
+            model.get_example_inputs(),
+            dynamic_shapes=model.get_dynamic_shapes(),
+        )
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = torch._inductor.aoti_compile_and_package(
+                ep,
+                model.get_example_inputs(),
+                package_path=os.path.join(tmpdir, "vision_encoder.pt2"),
+            )
+            print(path)
+            encoder_aoti = torch._inductor.aoti_load_package(path)
+
+            y = encoder_aoti(*model.get_example_inputs())
+        assert_close(y, eager_res, rtol=1e-4, atol=1e-4)

examples/models/llama3_2_vision/vision_encoder/test/test_vision_encoder.py

@@ -16,7 +16,8 @@ addopts =
     devtools/
     # examples
     examples/models/llama/tests
-    examples/models/llama3_2_vision/preprocess
+    examples/models/llama3_2_vision/preprocess/test
+    examples/models/llama3_2_vision/vision_encoder/test
     # examples/models/llava/test TODO: enable this
     # exir
     exir/_serialize/test