diff --git a/extension/llm/custom_ops/TARGETS b/extension/llm/custom_ops/TARGETS
index 195df3bb931..ff3fde6e2cc 100644
--- a/extension/llm/custom_ops/TARGETS
+++ b/extension/llm/custom_ops/TARGETS
@@ -21,3 +21,16 @@ runtime.python_test(
         "//caffe2:torch",
     ],
 )
+
+runtime.python_test(
+    name = "test_preprocess_custom_ops",
+    srcs = [
+        "test_preprocess_custom_ops.py",
+    ],
+    preload_deps = [
+        ":preprocess_custom_ops_py",
+    ],
+    deps = [
+        "//caffe2:torch",
+    ],
+)
diff --git a/extension/llm/custom_ops/op_tile_crop.cpp b/extension/llm/custom_ops/op_tile_crop.cpp
new file mode 100644
index 00000000000..094bb9beec9
--- /dev/null
+++ b/extension/llm/custom_ops/op_tile_crop.cpp
@@ -0,0 +1,33 @@
+/*
+ * 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.
+ */
+
+#include <executorch/extension/kernel_util/make_boxed_from_unboxed_functor.h>
+#include <executorch/extension/llm/custom_ops/op_tile_crop.h>
+#include <executorch/runtime/kernel/kernel_includes.h>
+
+namespace torch {
+namespace executor {
+namespace native {
+
+Tensor& tile_crop_out_impl(
+    RuntimeContext& ctx,
+    const Tensor& input, // NOLINT
+    const int64_t tile_size, // NOLINT
+    Tensor& out) {
+  (void)ctx;
+  return out;
+}
+
+} // namespace native
+} // namespace executor
+} // namespace torch
+
+EXECUTORCH_LIBRARY(
+    preprocess,
+    "tile_crop.out",
+    torch::executor::native::tile_crop_out_impl);
diff --git a/extension/llm/custom_ops/op_tile_crop.h b/extension/llm/custom_ops/op_tile_crop.h
new file mode 100644
index 00000000000..445e443f9af
--- /dev/null
+++ b/extension/llm/custom_ops/op_tile_crop.h
@@ -0,0 +1,26 @@
+/*
+ * 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.
+ */
+
+#pragma once
+
+#include <executorch/runtime/kernel/kernel_includes.h>
+
+namespace torch {
+namespace executor {
+
+namespace native {
+
+Tensor& tile_crop_out_impl(
+    RuntimeContext& ctx,
+    const Tensor& input,
+    const int64_t tile_size,
+    Tensor& out);
+
+} // namespace native
+} // namespace executor
+} // namespace torch
diff --git a/extension/llm/custom_ops/preprocess_custom_ops.py b/extension/llm/custom_ops/preprocess_custom_ops.py
new file mode 100644
index 00000000000..aea8c09b0ef
--- /dev/null
+++ b/extension/llm/custom_ops/preprocess_custom_ops.py
@@ -0,0 +1,110 @@
+# 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.
+
+# pyre-unsafe
+
+
+from typing import List
+
+import torch
+
+from torch.library import impl, Library
+
+preprocess_op_lib = Library("preprocess", "DEF")
+
+# Register and define pad and out variant.
+# Note: pad doesn't require an explicit meta kernel because
+# CompositeExplicitAutograd automatically registers the implementation to meta,
+# and meta kernels do not go through functionalization. The implementation
+# does not export due to issues during functionalization.
+# See: https://github.com/pytorch/pytorch/issues/120288
+preprocess_op_lib.define("pad(Tensor image, SymInt[] padding) -> Tensor")
+
+
+@impl(preprocess_op_lib, "pad", dispatch_key="CompositeExplicitAutograd")
+def pad_impl(
+    image: torch.Tensor,
+    padding: List[int],
+) -> torch.Tensor:
+    output = torch.empty(
+        [image.shape[0], image.shape[1] + padding[3], image.shape[2] + padding[1]],
+        dtype=image.dtype,
+        device=image.device,
+        requires_grad=False,
+    )
+    output = torch.fill(output, 0)
+    output.narrow(1, 0, image.shape[1]).narrow(2, 0, image.shape[2]).copy_(image)
+    return output
+
+
+preprocess_op_lib.define(
+    "pad.out(Tensor image, SymInt[] padding, *, Tensor(a!) out) -> Tensor(a!)"
+)
+
+
+@impl(preprocess_op_lib, "pad.out", dispatch_key="CompositeExplicitAutograd")
+def pad_out_impl(
+    image: torch.Tensor,
+    padding: List[int],
+    out: torch.Tensor,
+) -> torch.Tensor:
+    out = torch.empty(
+        [image.shape[0], image.shape[1] + padding[3], image.shape[2] + padding[1]],
+        dtype=image.dtype,
+        device=image.device,
+        requires_grad=False,
+    )
+    out = torch.fill(out, 0)
+    out.narrow(1, 0, image.shape[1]).narrow(2, 0, image.shape[2]).copy_(image)
+    return out
+
+
+# Register and define tile_crop and out variant.
+preprocess_op_lib.define("tile_crop(Tensor input, int tile_size) -> Tensor")
+
+
+@impl(preprocess_op_lib, "tile_crop", dispatch_key="CompositeExplicitAutograd")
+def tile_crop_impl(input: torch.Tensor, tile_size: int) -> torch.Tensor:
+    c = input.shape[0]
+    h = input.shape[1]
+    w = input.shape[2]
+    tiles_height = h // tile_size
+    tiles_width = w // tile_size
+    tile_cropped = input.view(c, tiles_height, tile_size, tiles_width, tile_size)
+    transposed = tile_cropped.permute(1, 3, 0, 2, 4)
+    tiles = transposed.contiguous().view(
+        tiles_height * tiles_width, c, tile_size, tile_size
+    )
+    return tiles
+
+
+preprocess_op_lib.define(
+    "tile_crop.out(Tensor input, int tile_size, *, Tensor(a!) out) -> Tensor(a!)"
+)
+
+
+@impl(preprocess_op_lib, "tile_crop.out", dispatch_key="CompositeExplicitAutograd")
+def tile_crop_out_impl(
+    input: torch.Tensor, tile_size: int, out: torch.Tensor
+) -> torch.Tensor:
+    out = input.clone()
+    c = out.shape[0]
+    h = out.shape[1]
+    w = out.shape[2]
+    tiles_height = h // tile_size
+    tiles_width = w // tile_size
+    out = out.view(c, tiles_height, tile_size, tiles_width, tile_size)
+    out = out.permute(1, 3, 0, 2, 4)
+    out = out.contiguous().view(tiles_height * tiles_width, c, tile_size, tile_size)
+    return out
+
+
+# Register meta kernel to prevent export tracing into the tile_crop impl.
+@torch.library.register_fake("preprocess::tile_crop")
+def tile_crop(output: torch.Tensor, tile_size: int) -> torch.Tensor:
+    # Returned tensor is of size [n, 3, 224, 224], where n is the number of tiles.
+    # We should export with n = max_num_tiles. Set 50 for now.
+    return torch.empty([50, output.size(0), 224, 224])
diff --git a/extension/llm/custom_ops/targets.bzl b/extension/llm/custom_ops/targets.bzl
index 8c38eb6a0a0..ee277515b1d 100644
--- a/extension/llm/custom_ops/targets.bzl
+++ b/extension/llm/custom_ops/targets.bzl
@@ -86,3 +86,36 @@ def define_common_targets():
             ":custom_ops",
         ],
     )
+
+    ## For preprocess
+    runtime.python_library(
+        name = "preprocess_custom_ops_py",
+        srcs = [
+            "preprocess_custom_ops.py",
+        ],
+        visibility = [
+            "//executorch/...",
+            "@EXECUTORCH_CLIENTS",
+        ],
+        deps = [
+            "//caffe2:torch",
+        ],
+    )
+
+    runtime.cxx_library(
+        name = "op_tile_crop",
+        srcs = ["op_tile_crop.cpp"],
+        exported_headers = ["op_tile_crop.h"],
+        exported_deps = [
+            "//executorch/runtime/kernel:kernel_includes",
+            "//executorch/extension/kernel_util:kernel_util",
+        ],
+        compiler_flags = ["-Wno-missing-prototypes", "-Wno-global-constructors"],
+        visibility = [
+            "//executorch/...",
+            "@EXECUTORCH_CLIENTS",
+        ],
+        # @lint-ignore BUCKLINT link_whole
+        link_whole = True,
+        force_static = True,
+    )
diff --git a/extension/llm/custom_ops/test_preprocess_custom_ops.py b/extension/llm/custom_ops/test_preprocess_custom_ops.py
new file mode 100644
index 00000000000..50e149ece16
--- /dev/null
+++ b/extension/llm/custom_ops/test_preprocess_custom_ops.py
@@ -0,0 +1,54 @@
+# 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.
+
+# pyre-unsafe
+
+import unittest
+from typing import List, Tuple
+
+import torch
+
+from .preprocess_custom_ops import preprocess_op_lib  # noqa
+
+
+class PreprocessTest(unittest.TestCase):
+
+    def setUp(self):
+        # pad
+        self.pad_input = torch.ones(3, 200, 300)
+
+        # tile_crop
+        self.tile_size = 224
+
+    def _compare_pad(self, image: torch.Tensor, padding: List[int]) -> None:
+        output = torch.ops.preprocess.pad.default(image, padding)
+        output_ref = torch.nn.functional.pad(image, padding)
+        self.assertTrue(torch.allclose(output_ref, output, 1e-6))
+
+    def _test_tile_crop(self, image: torch.Tensor, expected_shape: Tuple[int]) -> None:
+        output = torch.ops.preprocess.tile_crop.default(image, self.tile_size)
+        self.assertTrue(output.shape == expected_shape)
+
+    def test_op_pad_without_padding(self):
+        self._compare_pad(self.pad_input, [0, 0, 0, 0])
+
+    def test_op_pad_with_right_bottom_padding(self):
+        self._compare_pad(self.pad_input, [0, 124, 0, 148])
+
+    def test_op_pad_with_right_padding(self):
+        self._compare_pad(self.pad_input, [0, 124, 0, 0])
+
+    def test_op_pad_with_bottom_padding(self):
+        self._compare_pad(self.pad_input, [0, 0, 0, 148])
+
+    def test_op_tile_crop_2x2(self):
+        self._test_tile_crop(torch.ones(3, 448, 448), (4, 3, 224, 224))
+
+    def test_op_tile_crop_1x3(self):
+        self._test_tile_crop(torch.ones(3, 224, 672), (3, 3, 224, 224))
+
+    def test_op_tile_crop_4x2(self):
+        self._test_tile_crop(torch.ones(3, 896, 448), (8, 3, 224, 224))