Metal backend: Materialize non-packed tensor views in reinterpret_tensor (#19033)

AOTI generates reinterpret_tensor views with non-packed strides (e.g.
chunk/split for RoPE rotation) that have holes in memory. ExecuTorch's
make_tensor_ptr requires densely packed layouts.

When aoti_torch__reinterpret_tensor encounters non-packed strides,
allocate a new contiguous Metal buffer and copy elements using strided
access from the source.

Author: manuelcandales

backends/apple/metal/runtime/shims/memory.cpp

@@ -367,6 +367,84 @@ AOTITorchError aoti_torch_copy_(
   return Error::Ok;
 }
 
+// Check if a strided view is densely packed (no holes in memory).
+// A densely packed tensor's storage extent equals its numel.
+static bool is_packed_strides(
+    const std::vector<aten::SizesType>& sizes,
+    const std::vector<aten::StridesType>& strides) {
+  int64_t ndim = static_cast<int64_t>(sizes.size());
+  if (ndim == 0)
+    return true;
+
+  // Compute numel
+  int64_t numel = 1;
+  for (int64_t i = 0; i < ndim; i++) {
+    numel *= sizes[i];
+  }
+  if (numel <= 1)
+    return true;
+
+  // Compute storage extent: max offset + 1
+  int64_t max_offset = 0;
+  for (int64_t i = 0; i < ndim; i++) {
+    if (sizes[i] > 1) {
+      max_offset += (sizes[i] - 1) * strides[i];
+    }
+  }
+  return (max_offset + 1) == numel;
+}
+
+// Materialize a non-packed strided view into a new contiguous Metal buffer.
+// Copies elements from source using strided access. The caller must free the
+// returned buffer. On failure returns nullptr.
+static void* materialize_packed(
+    void* src,
+    const std::vector<aten::SizesType>& sizes,
+    const std::vector<aten::StridesType>& strides,
+    size_t element_size) {
+  int64_t ndim = static_cast<int64_t>(sizes.size());
+  int64_t numel = 1;
+  for (int64_t i = 0; i < ndim; i++) {
+    numel *= sizes[i];
+  }
+
+  void* dst = metal_allocate_buffer(numel * element_size);
+  if (!dst)
+    return nullptr;
+
+  // Ensure pending GPU writes to the source buffer are complete
+  if (metal_is_device_pointer(src)) {
+    auto* stream = getCurrentMetalStream();
+    if (stream) {
+      stream->synchronize(SyncType::COMMIT_AND_WAIT);
+    }
+  }
+
+  // Element-by-element strided copy
+  char* src_bytes = static_cast<char*>(src);
+  char* dst_bytes = static_cast<char*>(dst);
+  std::vector<int64_t> coord(ndim, 0);
+  for (int64_t flat = 0; flat < numel; flat++) {
+    // Compute source offset from strides
+    int64_t src_offset = 0;
+    for (int64_t d = 0; d < ndim; d++) {
+      src_offset += coord[d] * strides[d];
+    }
+    std::memcpy(
+        dst_bytes + flat * element_size,
+        src_bytes + src_offset * element_size,
+        element_size);
+
+    // Increment coordinate (last dim fastest)
+    for (int64_t d = ndim - 1; d >= 0; d--) {
+      if (++coord[d] < sizes[d])
+        break;
+      coord[d] = 0;
+    }
+  }
+  return dst;
+}
+
 AOTITorchError aoti_torch__reinterpret_tensor(
     AOTITensorHandle self,
     int64_t ndim,
@@ -377,6 +455,12 @@ AOTITorchError aoti_torch__reinterpret_tensor(
   ET_LOG(Debug, "aoti_torch__reinterpret_tensor: entered");
 
   // Validate input parameters first
+  ET_CHECK_OR_RETURN_ERROR(
+      ndim >= 0,
+      InvalidArgument,
+      "aoti_torch__reinterpret_tensor failed: ndim must be >= 0, got %lld",
+      ndim);
+
   ET_CHECK_OR_RETURN_ERROR(
       self != nullptr,
       InvalidArgument,
@@ -430,8 +514,9 @@ AOTITorchError aoti_torch__reinterpret_tensor(
       data_ptr);
 
   // Handle storage offset by adjusting the data pointer
-  void* adjusted_data = static_cast<char*>(data_ptr) +
-      (storage_offset * dtype_to_element_size(dtype));
+  size_t element_size = dtype_to_element_size(dtype);
+  void* adjusted_data =
+      static_cast<char*>(data_ptr) + (storage_offset * element_size);
 
   // Convert sizes using utility function from utils.h
   std::vector<aten::SizesType> sizes = convert_sizes_to_vector(ndim, sizes_ptr);
@@ -440,14 +525,35 @@ AOTITorchError aoti_torch__reinterpret_tensor(
   std::vector<aten::StridesType> strides =
       convert_strides_to_vector(ndim, sizes_ptr, strides_ptr);
 
-  // Create new tensor view that reinterprets the same memory with different
-  // shape/strides This creates a view, not a copy - the data pointer is shared
+  // If the view is not densely packed (e.g. chunk/split creating holes),
+  // materialize it into a new contiguous buffer.
+  void* tensor_data = adjusted_data;
+  bool owns_buffer = false;
+  if (!is_packed_strides(sizes, strides)) {
+    ET_LOG(
+        Debug,
+        "aoti_torch__reinterpret_tensor: non-packed strides, "
+        "materializing to packed buffer");
+    tensor_data =
+        materialize_packed(adjusted_data, sizes, strides, element_size);
+    ET_CHECK_OR_RETURN_ERROR(
+        tensor_data != nullptr,
+        MemoryAllocationFailed,
+        "Failed to materialize non-packed tensor");
+    owns_buffer = true;
+
+    // Compute contiguous strides for the packed buffer
+    strides.resize(ndim);
+    if (ndim > 0) {
+      strides[ndim - 1] = 1;
+      for (int64_t i = ndim - 2; i >= 0; i--) {
+        strides[i] = strides[i + 1] * sizes[i + 1];
+      }
+    }
+  }
+
   std::shared_ptr<Tensor> tensor = executorch::extension::from_blob(
-      adjusted_data, // Use adjusted data pointer with storage offset applied
-      sizes, // New sizes with explicit SizesType
-      strides, // New strides with explicit StridesType
-      dtype_to_scalar_type(dtype) // Convert dtype with explicit type casting
-  );
+      tensor_data, sizes, strides, dtype_to_scalar_type(dtype));
 
   ET_CHECK_OR_RETURN_ERROR(
       tensor != nullptr,
@@ -456,32 +562,36 @@ AOTITorchError aoti_torch__reinterpret_tensor(
 
   // Store the tensor so it doesn't get destroyed
   tensors[tensor.get()] = tensor;
-
   *ret_new_tensor = tensor.get();
 
-  if (adjusted_data != data_ptr) {
-    ET_LOG(
-        Debug,
-        "aoti_torch__reinterpret_tensor: Adjusted original_data=%p, storage_offset=%lld, element_size=%zu, adjusted_data=%p",
-        data_ptr,
-        storage_offset,
-        dtype_to_element_size(dtype),
-        adjusted_data);
-
-    ET_CHECK_OR_RETURN_ERROR(
-        metal_buffer_nocopy(adjusted_data, tensor->nbytes(), true),
-        Internal,
-        "metal_buffer_nocopy failed for adjusted_data=%p, nbytes=%zu",
-        adjusted_data,
-        static_cast<size_t>(tensor->nbytes()));
-
-    memory_to_n_tensor[adjusted_data] = NOT_OWN;
-  }
+  if (owns_buffer) {
+    // The materialized buffer is a new allocation owned by this tensor
+    memory_to_n_tensor[tensor_data] = 1;
+  } else {
+    if (adjusted_data != data_ptr) {
+      ET_LOG(
+          Debug,
+          "aoti_torch__reinterpret_tensor: Adjusted original_data=%p, "
+          "storage_offset=%lld, element_size=%zu, adjusted_data=%p",
+          data_ptr,
+          storage_offset,
+          element_size,
+          adjusted_data);
+
+      ET_CHECK_OR_RETURN_ERROR(
+          metal_buffer_nocopy(adjusted_data, tensor->nbytes(), true),
+          Internal,
+          "metal_buffer_nocopy failed for adjusted_data=%p, nbytes=%zu",
+          adjusted_data,
+          static_cast<size_t>(tensor->nbytes()));
+
+      memory_to_n_tensor[adjusted_data] = NOT_OWN;
+    }
 
-  // Increment the reference count for this memory address only if it is owned
-  // by tensor
-  if (memory_to_n_tensor[data_ptr] != NOT_OWN) {
-    memory_to_n_tensor[data_ptr] += 1;
+    // Increment the reference count for this memory address only if it is owned
+    if (memory_to_n_tensor[data_ptr] != NOT_OWN) {
+      memory_to_n_tensor[data_ptr] += 1;
+    }
   }
 
   ET_LOG(Debug, "aoti_torch__reinterpret_tensor: successful");

backends/apple/metal/tests/test_modules.py

@@ -664,6 +664,30 @@ def forward(
 }
 
 
+# -------------------------------------------------------------------------
+# Narrow (non-packed reinterpret_tensor materialization)
+# -------------------------------------------------------------------------
+
+
+class NarrowLastDim(nn.Module):
+    """Splits the last dimension into two halves via narrow, producing
+    non-packed strided views that the Metal backend must materialize
+    into contiguous buffers."""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        half = x.shape[-1] // 2
+        a = x.narrow(-1, 0, half)
+        b = x.narrow(-1, half, half)
+        return a * 2.0 + b
+
+
+MODULE_REGISTRY["narrow_last_dim"] = {
+    "model_class": NarrowLastDim,
+    "input_shapes": [(2, 4, 16)],
+    "description": "Non-packed reinterpret_tensor views from last-dim split",
+}
+
+
 # -------------------------------------------------------------------------
 # Top-k (MoE expert routing)
 # -------------------------------------------------------------------------