Verify input dim order (#18643)

extension/pybindings/test/test_pybindings.py

@@ -54,6 +54,20 @@ def setUp(self):
         self.load_prog_fn = runtime._load_program_from_buffer
         self.runtime = runtime
 
+    def _make_non_dense_strided_input(self) -> torch.Tensor:
+        strided_input = torch.arange(1 * 2 * 3 * 8, dtype=torch.float32).reshape(
+            1, 2, 3, 8
+        )[..., ::2]
+        self.assertEqual(tuple(strided_input.shape), (1, 2, 3, 4))
+        self.assertFalse(strided_input.is_contiguous())
+        self.assertFalse(strided_input.is_contiguous(memory_format=torch.channels_last))
+        return strided_input
+
+    def _create_program_with_inputs(
+        self, eager_module: torch.nn.Module, inputs: tuple[torch.Tensor, ...]
+    ):
+        return to_edge(export(eager_module, inputs, strict=True)).to_executorch()
+
     def test_e2e(self):
         exported_program, inputs = create_program(ModuleAdd())
         executorch_module = self.load_fn(exported_program.buffer)
@@ -204,6 +218,37 @@ def test_channels_last_in_default_out(self) -> None:
         expected = model(inputs[0])
         self.assertTrue(torch.allclose(expected, executorch_output))
 
+    def test_non_contiguous_input_dim_order_mismatch(self) -> None:
+        """Contiguous tensor passed to a channels-last model raises on dim order mismatch."""
+        model = ModuleChannelsLast()
+        exported_program, inputs = create_program(model)
+        executorch_module = self.load_fn(exported_program.buffer)
+
+        # Model expects channels-last; passing contiguous triggers mismatch.
+        contiguous_input = inputs[0].contiguous()
+        self.assertRaises(RuntimeError, executorch_module, contiguous_input)
+
+    def test_channels_last_input_dim_order_mismatch(self) -> None:
+        """Channels-last tensor passed to a contiguous model raises on dim order mismatch."""
+        model = ModuleChannelsLast()
+        inputs = (torch.ones(1, 2, 3, 4),)
+        exported_program = self._create_program_with_inputs(model, inputs)
+        executorch_module = self.load_fn(exported_program.buffer)
+
+        channels_last_input = inputs[0].to(memory_format=torch.channels_last)
+        self.assertRaises(RuntimeError, executorch_module, channels_last_input)
+
+    def test_strided_input_dim_order_error(self) -> None:
+        model = ModuleChannelsLast()
+        exported_program = self._create_program_with_inputs(
+            model, (torch.ones(1, 2, 3, 4),)
+        )
+        executorch_module = self.load_fn(exported_program.buffer)
+
+        self.assertRaises(
+            RuntimeError, executorch_module, self._make_non_dense_strided_input()
+        )
+
     def test_method_meta(self) -> None:
         exported_program, inputs = create_program(ModuleAdd())
 
@@ -466,6 +511,40 @@ def test_method_channels_last_in_default_out(self) -> None:
         expected = model(inputs[0])
         self.assertTrue(torch.allclose(expected, executorch_output))
 
+    def test_method_non_contiguous_input_dim_order_mismatch(self) -> None:
+        """Contiguous tensor passed to a channels-last method raises on dim order mismatch."""
+        model = ModuleChannelsLast()
+        exported_program, inputs = create_program(model)
+        executorch_program = self.load_prog_fn(exported_program.buffer)
+        executorch_method = executorch_program.load_method("forward")
+
+        # Model expects channels-last; passing contiguous triggers mismatch.
+        contiguous_input = inputs[0].contiguous()
+        self.assertRaises(RuntimeError, executorch_method, contiguous_input)
+
+    def test_method_channels_last_input_dim_order_mismatch(self) -> None:
+        """Channels-last tensor passed to a contiguous method raises on dim order mismatch."""
+        model = ModuleChannelsLast()
+        inputs = (torch.ones(1, 2, 3, 4),)
+        exported_program = self._create_program_with_inputs(model, inputs)
+        executorch_program = self.load_prog_fn(exported_program.buffer)
+        executorch_method = executorch_program.load_method("forward")
+
+        channels_last_input = inputs[0].to(memory_format=torch.channels_last)
+        self.assertRaises(RuntimeError, executorch_method, channels_last_input)
+
+    def test_method_strided_input_dim_order_error(self) -> None:
+        model = ModuleChannelsLast()
+        exported_program = self._create_program_with_inputs(
+            model, (torch.ones(1, 2, 3, 4),)
+        )
+        executorch_program = self.load_prog_fn(exported_program.buffer)
+        executorch_method = executorch_program.load_method("forward")
+
+        self.assertRaises(
+            RuntimeError, executorch_method, self._make_non_dense_strided_input()
+        )
+
     def test_method_bad_name(self) -> None:
         exported_program, inputs = create_program(ModuleAdd())
         executorch_program = self.load_prog_fn(exported_program.buffer)

runtime/core/exec_aten/util/tensor_util.h

@@ -1140,6 +1140,13 @@ bool extract_scalar_tensor(executorch::aten::Tensor tensor, BOOL_T* out_val) {
 
 /// These APIs should not be used outside of Executor.cpp.
 namespace internal {
+/**
+ * Validate that t_src matches the model's expected dim order.
+ */
+ET_NODISCARD Error check_tensor_data_layout(
+    const executorch::aten::Tensor& t_src,
+    executorch::runtime::Span<const uint8_t> expected_dim_order);
+
 /**
  * Share t_src's data_ptr with t_dst.
  */

runtime/core/exec_aten/util/tensor_util_aten.cpp

@@ -1,3 +1,3 @@
 /*
  * Copyright (c) Meta Platforms, Inc. and affiliates.
  * All rights reserved.
@@ -120,7 +120,20 @@
 
 namespace internal {
 
-Error share_tensor_data(const at::Tensor& t_dst, const at::Tensor& t_src) {
+Error check_tensor_data_layout(
+    const at::Tensor& t_src,
+    executorch::runtime::Span<const uint8_t> expected_dim_order) {
+  // Theres some annoyance on teams doing weird stuff today thats hard to
+  // migrate. ATen mode isnt supported in the CMake build and its really only
+  // used for testing. So we can just skip this check for now.
+  (void)t_src;
+  (void)expected_dim_order;
+  return Error::Ok;
+}
+
+Error share_tensor_data(
+    const at::Tensor& t_dst,
+    const at::Tensor& t_src) {
   at::StorageImpl* storage =
       t_dst.unsafeGetTensorImpl()->unsafe_storage().unsafeGetStorageImpl();
 
@@ -143,7 +156,9 @@
   return Error::Ok;
 }
 
-Error copy_tensor_data(const at::Tensor& t_dst, const at::Tensor& t_src) {
+Error copy_tensor_data(
+    const at::Tensor& t_dst,
+    const at::Tensor& t_src) {
   void* dst_data_ptr = t_dst.unsafeGetTensorImpl()
                            ->unsafe_storage()
                            .unsafeGetStorageImpl()

runtime/core/exec_aten/util/tensor_util_portable.cpp

@@ -137,6 +137,32 @@ bool tensors_have_same_dim_order(
 
 namespace internal {
 
+Error check_tensor_data_layout(
+    const torch::executor::Tensor& t_src,
+    executorch::runtime::Span<const uint8_t> expected_dim_order) {
+  // Input tensors don't actually have to have a dim order, so we can't check.
+  // Tech debt from dim order being a later addition to the tensor spec.
+  if (t_src.dim_order().data() == nullptr) {
+    return Error::Ok;
+  }
+
+  auto src_dim_order = t_src.dim_order();
+  ET_CHECK_OR_RETURN_ERROR(
+      expected_dim_order.size() == src_dim_order.size(),
+      InvalidArgument,
+      "Input dim order size (%zu) != expected (%zu).",
+      src_dim_order.size(),
+      expected_dim_order.size());
+  ET_CHECK_OR_RETURN_ERROR(
+      std::equal(
+          expected_dim_order.begin(),
+          expected_dim_order.end(),
+          src_dim_order.data()),
+      InvalidArgument,
+      "Input dim order does not match the model.");
+  return Error::Ok;
+}
+
 Error share_tensor_data(
     const torch::executor::Tensor& t_dst,
     const torch::executor::Tensor& t_src) {

runtime/executor/method.cpp

@@ -1184,6 +1184,12 @@ Method::set_input(const EValue& input_evalue, size_t input_idx) {
         "Error resizing tensor at input %" ET_PRIsize_t,
         input_idx);
     auto tensor_meta = this->method_meta().input_tensor_meta(input_idx);
+    ET_CHECK_OK_OR_RETURN_ERROR(tensor_meta.error());
+    auto expected_dim_order = tensor_meta->dim_order();
+    ET_CHECK_OK_OR_RETURN_ERROR(
+        internal::check_tensor_data_layout(t_src, expected_dim_order),
+        "Error validating tensor layout at input %" ET_PRIsize_t,
+        input_idx);
     if (tensor_meta->is_memory_planned()) {
       ET_CHECK_OK_OR_RETURN_ERROR(
           internal::copy_tensor_data(t_dst, t_src),