Add vision transformer

backends/cadence/aot/ops_registrations.py

@@ -10,7 +10,7 @@
 from executorch.exir.scalar_type import ScalarType
 from torch.library import impl, Library
 
-from .utils import get_conv1d_output_size
+from .utils import get_conv1d_output_size, get_conv2d_output_size
 
 lib = Library("cadence", "DEF")
 
@@ -122,16 +122,22 @@ def quantized_conv_meta(
     out_multiplier: torch.Tensor,
     out_shift: torch.Tensor,
     channel_last: bool = False,
-):
+) -> torch.Tensor:
     out_channels, _in_channels, *kernel_size = weight.shape
     in_size = input.shape
     # Assert that the input tensor has at least 3 dimensions, and at most 6
     assert len(in_size) > 2
     assert len(in_size) < 6
 
     # Compute the output tensor size
-    output_size = get_conv1d_output_size(
-        in_size, out_channels, stride[0], padding[0], dilation[0], kernel_size[0]
+    output_size = (
+        get_conv1d_output_size(
+            in_size, out_channels, stride[1], padding[1], dilation[1], kernel_size[0]
+        )
+        if len(in_size) == 3
+        else get_conv2d_output_size(
+            in_size, out_channels, stride, padding, dilation, kernel_size, channel_last
+        )
     )
 
     return input.new_empty(output_size, dtype=input.dtype)

backends/cadence/aot/utils.py

@@ -6,7 +6,7 @@
 
 import logging
 import operator
-from typing import Dict
+from typing import Dict, List, Tuple
 
 import torch
 from executorch.exir import memory
@@ -49,6 +49,33 @@ def get_conv1d_output_size(
     return torch.Size((in_size[0], out_channels, lout))
 
 
+# Get the output size of a 2D convolution given the input size and parameters
+def get_conv2d_output_size(
+    in_size: torch.Size,
+    out_channels: int,
+    stride: Tuple[int],
+    padding: Tuple[int],
+    dilation: Tuple[int],
+    kernel_size: List[int],
+    channel_last: bool,
+) -> torch.Size:
+    assert len(in_size) == 4
+    if channel_last:
+        N, H, W, C = in_size
+    else:
+        N, C, H, W = in_size
+
+    # Reference: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
+    hout = (H + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) // stride[
+        0
+    ] + 1
+    wout = (W + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) // stride[
+        1
+    ] + 1
+
+    return torch.Size((in_size[0], out_channels, hout, wout))
+
+
 # Return the overload packet for the edge op
 def get_edge_overload_packet(edge_op: EdgeOpOverload) -> EdgeOpOverloadPacket:
     edge_op_namespace, edge_op_name = (

examples/cadence/models/vision_transformer.py

@@ -0,0 +1,28 @@
+# 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.
+
+# Example script for exporting simple models to flatbuffer
+
+import logging
+
+from executorch.backends.cadence.aot.ops_registrations import *  # noqa
+
+import torch
+import torchvision
+
+from executorch.backends.cadence.aot.export_example import export_model
+
+
+FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+
+
+if __name__ == "__main__":
+
+    model = torchvision.models.vit_b_16()
+    example_inputs = (torch.randn(1, 3, 224, 224),)
+
+    export_model(model, example_inputs)