[ET][Portable] Implement output broadcasting for split_with_sizes_copy

kernels/portable/cpu/op_split_with_sizes_copy.cpp

@@ -9,6 +9,7 @@
 #include <cstdint>
 #include <cstring>
 
+#include <executorch/kernels/portable/cpu/util/broadcast_util.h>
 #include <executorch/kernels/portable/cpu/util/copy_ops_util.h>
 #include <executorch/runtime/kernel/kernel_includes.h>
 
@@ -38,16 +39,25 @@ void split_with_sizes_copy_out(
       InvalidArgument,
       out);
 
-  Tensor::SizesType expected_out_size[kTensorDimensionLimit];
-  size_t expected_out_dim = 0;
+  // If out is empty, then nothing needs to be done after checking the args.
+  // Valid args implies that in.size(dim) == 0 and split_sizes is also empty.
+  if (out.size() == 0) {
+    return;
+  }
+
+  // Check that all chunks broadcast to their respective out tensor
+  Tensor::SizesType target_out_sizes[kTensorDimensionLimit];
+  size_t target_out_ndim = in.dim();
+  for (size_t d = 0; d < in.dim(); ++d) {
+    target_out_sizes[d] = static_cast<Tensor::SizesType>(in.size(d));
+  }
+
   for (size_t i = 0; i < split_sizes.size(); i++) {
-    expected_out_size[expected_out_dim++] = split_sizes[i];
-    get_split_with_sizes_copy_out_target_size(
-        in, split_sizes[i], dim, expected_out_size, &expected_out_dim);
+    target_out_sizes[dim] = static_cast<Tensor::SizesType>(split_sizes[i]);
     ET_KERNEL_CHECK(
         ctx,
-        resize_tensor(out[i], {expected_out_size, expected_out_dim}) ==
-            Error::Ok,
+        tensor_is_broadcastable_to(
+            {target_out_sizes, target_out_ndim}, out[i].sizes()),
         InvalidArgument,
         out);
   }
@@ -62,21 +72,65 @@ void split_with_sizes_copy_out(
   ET_SWITCH_REAL_TYPES_AND(Bool, in_type, ctx, __func__, CTYPE_IN, [&]() {
     ET_SWITCH_REAL_TYPES_AND(Bool, out_type, ctx, __func__, CTYPE_OUT, [&]() {
       const CTYPE_IN* in_data = in.const_data_ptr<CTYPE_IN>();
-      for (size_t i = 0, e = out.size(); i < e; ++i) {
-        size_t out_step = out[i].size(dim) * trailing_dims;
-        if (out_step == 0) {
+
+      // Iterate through list of out tensors
+      for (size_t i = 0; i < out.size(); ++i) {
+        const Tensor& out_tensor = out[i];
+
+        // If out tensor is empty, no action is required
+        if (out_tensor.numel() == 0) {
           continue;
         }
-        const CTYPE_IN* src = in_data;
-        CTYPE_OUT* dest = out[i].mutable_data_ptr<CTYPE_OUT>();
-        for (size_t j = 0; j < leading_dims; ++j) {
-          for (size_t k = 0; k < out_step; ++k) {
-            dest[k] = convert<CTYPE_OUT, CTYPE_IN>(src[k]);
+
+        size_t chunk_step = split_sizes[i] * trailing_dims;
+
+        // Update target out shape
+        target_out_sizes[dim] = static_cast<Tensor::SizesType>(split_sizes[i]);
+        ArrayRef<Tensor::SizesType> target_shape(
+            {target_out_sizes, target_out_ndim});
+
+        // Check if output involves broadcasting
+        const bool is_broadcasted = !out_tensor.sizes().equals(target_shape);
+
+        CTYPE_OUT* out_data = out_tensor.mutable_data_ptr<CTYPE_OUT>();
+
+        // Simpler logic if there's no broadcasting
+        if (!is_broadcasted) {
+          const CTYPE_IN* src = in_data;
+          for (size_t j = 0; j < leading_dims; ++j) {
+            for (size_t k = 0; k < chunk_step; ++k) {
+              out_data[k] = convert<CTYPE_OUT, CTYPE_IN>(src[k]);
+            }
+            src += step;
+            out_data += chunk_step;
+          }
+        } else { // Otherwise, we need to do a copy with broadcasting
+          // Compute target strides
+          Tensor::StridesType target_out_strides[kTensorDimensionLimit];
+          target_out_strides[in.dim() - 1] = 1;
+          for (int d = in.dim() - 2; d >= 0; --d) {
+            target_out_strides[d] = target_out_strides[d + 1] *
+                static_cast<Tensor::StridesType>(target_out_sizes[d + 1]);
+          }
+          ArrayRef<Tensor::StridesType> target_strides(
+              {target_out_strides, target_out_ndim});
+
+          // For each element in the out tensor, find its corresponding index
+          // in the input tensor and copy it over
+          for (size_t ix = 0; ix < out_tensor.numel(); ++ix) {
+            size_t out_coord[kTensorDimensionLimit];
+            delinearize_index(ix, out_tensor, out_coord, kTensorDimensionLimit);
+
+            size_t in_linear_index = linearize_access_indexes(
+                out_coord, out_tensor.dim(), target_shape, target_strides);
+
+            out_data[ix] =
+                convert<CTYPE_OUT, CTYPE_IN>(in_data[in_linear_index]);
           }
-          src += step;
-          dest += out_step;
         }
-        in_data += out_step;
+
+        // Move input data pointer
+        in_data += chunk_step;
       }
     });
   });

kernels/portable/cpu/targets.bzl

@@ -701,6 +701,7 @@ _ATEN_OPS = (
     op_target(
         name = "op_split_with_sizes_copy",
         deps = [
+            "//executorch/kernels/portable/cpu/util:broadcast_util",
             "//executorch/kernels/portable/cpu/util:copy_ops_util",
         ],
     ),

kernels/portable/cpu/util/broadcast_util.cpp

@@ -77,11 +77,9 @@ Tensor make_tensor(
 } // namespace
 
 bool tensor_is_broadcastable_to(
-    const Tensor& broadcast_from,
-    const Tensor& broadcast_to) {
+    const exec_aten::ArrayRef<Tensor::SizesType> broadcast_from_shape,
+    const exec_aten::ArrayRef<Tensor::SizesType> broadcast_to_shape) {
   bool feasible_bcast = true;
-  auto broadcast_to_shape = broadcast_to.sizes();
-  auto broadcast_from_shape = broadcast_from.sizes();
 
   if (broadcast_to_shape.size() < broadcast_from_shape.size()) {
     return false;
@@ -103,6 +101,13 @@ bool tensor_is_broadcastable_to(
   return feasible_bcast;
 }
 
+bool tensor_is_broadcastable_to(
+    const Tensor& broadcast_from,
+    const Tensor& broadcast_to) {
+  return tensor_is_broadcastable_to(
+      broadcast_from.sizes(), broadcast_to.sizes());
+}
+
 bool tensors_are_broadcastable_between(
     const exec_aten::ArrayRef<Tensor::SizesType> a_shape,
     const exec_aten::ArrayRef<Tensor::SizesType> b_shape) {
@@ -264,27 +269,39 @@ void delinearize_index(
 size_t linearize_access_indexes(
     ArrayRef<size_t> indexes_broadcast_to,
     ssize_t broadcast_to_ndim,
-    const Tensor& broadcast_from) {
-  size_t num_skip_dims = broadcast_to_ndim - broadcast_from.dim();
+    exec_aten::ArrayRef<Tensor::SizesType> broadcast_from_shape,
+    exec_aten::ArrayRef<Tensor::StridesType> broadcast_from_strides) {
+  size_t num_skip_dims = broadcast_to_ndim - broadcast_from_shape.size();
   ArrayRef<size_t> indexes_broadcast_from = indexes_broadcast_to.slice(
       num_skip_dims, broadcast_to_ndim - num_skip_dims);
 
-  ET_CHECK(indexes_broadcast_from.size() == broadcast_from.dim());
+  ET_CHECK(indexes_broadcast_from.size() == broadcast_from_shape.size());
 
   size_t linear_index = 0;
   for (size_t i = 0; i < indexes_broadcast_from.size(); ++i) {
     // If this dimension is broadcasted, add zero to the linear address.
-    if (indexes_broadcast_from[i] >= broadcast_from.size(i)) {
+    if (indexes_broadcast_from[i] >= broadcast_from_shape[i]) {
       ET_CHECK_MSG(
-          broadcast_from.size(i) == 1,
-          "Expected dim size == 1 if broadcasted, but actual dim size is %zd",
-          broadcast_from.size(i));
+          broadcast_from_shape[i] == 1,
+          "Expected dim size == 1 if broadcasted, but actual dim size is %zu",
+          static_cast<size_t>(broadcast_from_shape[i]));
       continue;
     }
-    linear_index += indexes_broadcast_from[i] * broadcast_from.strides()[i];
+    linear_index += indexes_broadcast_from[i] * broadcast_from_strides[i];
   }
   return linear_index;
 }
 
+size_t linearize_access_indexes(
+    ArrayRef<size_t> indexes_broadcast_to,
+    ssize_t broadcast_to_ndim,
+    const Tensor& broadcast_from) {
+  return linearize_access_indexes(
+      indexes_broadcast_to,
+      broadcast_to_ndim,
+      broadcast_from.sizes(),
+      broadcast_from.strides());
+}
+
 } // namespace executor
 } // namespace torch

kernels/portable/cpu/util/broadcast_util.h

@@ -14,12 +14,25 @@
 namespace torch {
 namespace executor {
 
+/**
+ * Check whether or not the broadcast_from_shape can be broadcasted onto the
+ * broadcast_to_shape.
+ *
+ * @param[in] broadcast_from_shape The tensor shape which we want to broadcast.
+ * @param[in] broadcast_to_shape The tensor shape which we want to broadcast to.
+ * @returns A bool to indicate whether or not the shape can be broadcasted.
+ *
+ */
+bool tensor_is_broadcastable_to(
+    const exec_aten::ArrayRef<Tensor::SizesType> broadcast_from_shape,
+    const exec_aten::ArrayRef<Tensor::SizesType> broadcast_to_shape);
+
 /**
  * Check whether or not the broadcast_from tensor should and can be broadcasted
  * onto the broadcast_to tensor. broadcast_tensor should only be called if this
  * returns true.
  *
- * @param[in] broadcast_from The tensor to which we want to broadcast from.
+ * @param[in] broadcast_from The tensor which we want to broadcast from.
  * @param[in] broadcast_to The tensor to which we want to broadcast to.
  * @returns A bool to indicate whether or not the tensor can be broadcasted.
  *
@@ -29,11 +42,11 @@ bool tensor_is_broadcastable_to(
     const Tensor& broadcast_to);
 
 /**
- * Returns true if the two tensors can both be broadcasted to a common shape.
+ * Returns true if the two tensor shapes can both be broadcasted to a common
+ * shape.
  *
  * @param[in] a_shape The sizes of the first tensor going to be test.
  * @param[in] b_shape The sizes of the second tensor going to be test.
- *
  * @returns true if the tensors are broadcastable, false otherwise.
  */
 bool tensors_are_broadcastable_between(
@@ -45,7 +58,6 @@ bool tensors_are_broadcastable_between(
  *
  * @param[in] a The first tensor going to be test.
  * @param[in] b The second tensor going to be test.
- *
  * @returns true if the tensors are broadcastable, false otherwise.
  */
 bool tensors_are_broadcastable_between(const Tensor& a, const Tensor& b);
@@ -195,12 +207,29 @@ void delinearize_index(
     size_t* out_indexes,
     const size_t out_indexes_len);
 
+/**
+ * Return the linear index for broatcast_from tensor, given the indexes and
+ * number of dimensions of broadcast_to tensor, and the shape and strides
+ * of broadcast_from tensor.
+ *
+ * @param[in] indexes_broadcast_to The access indexes of broadcast_to tensor.
+ * @param[in] broadcast_to_ndim The number of dims of broadcast_to tensor.
+ * @param[in] broadcast_from_shape The shape of the broadcasted tensor.
+ * @param[in] broadcast_from_strides The strides of the broadcasted tensor.
+ * @returns The flattend index for broadcast_from tensor.
+ */
+size_t linearize_access_indexes(
+    ArrayRef<size_t> indexes_broadcast_to,
+    ssize_t broadcast_to_ndim,
+    exec_aten::ArrayRef<Tensor::SizesType> broadcast_from_shape,
+    exec_aten::ArrayRef<Tensor::StridesType> broadcast_from_strides);
+
 /**
  * Return the linear index for broatcast_from tensor, given the indexes of
  * broadcast_to tensor and itself.
  *
- * @param[in] indexes The tensor access indexes of broadcast_to tensor
- * @param[in] broadcast_to_ndim The number of dims of the broadcasted shape.
+ * @param[in] indexes_broadcast_to The access indexes of broadcast_to tensor.
+ * @param[in] broadcast_to_ndim The number of dims of broadcast_to tensor.
  * @param[in] broadcast_from The tensor to be broadcasted.
  * @returns The flattend index for broadcast_from tensor.
  */