Revert "Implement GLU using internal views to avoid copying (#11295)" (#11464)

This reverts commit 1f52982.

Breaking internal tests:
[D75980858](https://www.internalfb.com/diff/D75980858)

Author: lucylq

kernels/portable/cpu/op_glu.cpp

@@ -8,7 +8,6 @@
 
 #include <c10/util/irange.h>
 #include <executorch/kernels/portable/cpu/util/activation_ops_util.h>
-#include <executorch/kernels/portable/cpu/util/elementwise_util.h>
 #include <executorch/runtime/kernel/kernel_includes.h>
 #include <executorch/runtime/platform/assert.h>
 #include <cinttypes>
@@ -24,6 +23,93 @@ using ScalarType = executorch::aten::ScalarType;
 
 namespace {
 
+double exp_overload(double d) {
+  return exp(d);
+}
+
+float exp_overload(float f) {
+  return expf(f);
+}
+
+/**
+ * In-place element-wise sigmoid function , i.e., f(x) = 1 / (1 + e^{-x})
+ */
+// TODO: T146333648, refactor this as a common helper function
+template <typename CTYPE_OUT>
+void sigmoid_tensor(Tensor& out) {
+  CTYPE_OUT* out_data = out.mutable_data_ptr<CTYPE_OUT>();
+  for (const auto i : c10::irange(out.numel())) {
+    out_data[i] = 1.0 / (1.0 + exp_overload(-out_data[i]));
+  }
+}
+
+/**
+ * Element-wise multiplication of the first half of `in` along the specified
+ * dimension and `out`, overwriting `out`.
+ */
+template <typename CTYPE_IN, typename CTYPE_OUT>
+void mul_tensors(const Tensor& in, int64_t dim, Tensor& out) {
+  size_t num_values = static_cast<size_t>(in.size(dim)) / 2;
+  size_t dim_length_in = static_cast<size_t>(in.size(dim));
+  size_t dim_length_out = static_cast<size_t>(out.size(dim));
+  size_t leading_dims = getLeadingDims(in, dim);
+  size_t trailing_dims = getTrailingDims(in, dim);
+
+  const CTYPE_IN* input_data_base = in.const_data_ptr<CTYPE_IN>();
+  CTYPE_OUT* output_data_base = out.mutable_data_ptr<CTYPE_OUT>();
+
+  for (const auto i : c10::irange(leading_dims)) {
+    const CTYPE_IN* input_data =
+        input_data_base + i * dim_length_in * trailing_dims;
+    CTYPE_OUT* output_data =
+        output_data_base + i * dim_length_out * trailing_dims;
+    for ([[maybe_unused]] const auto j : c10::irange(num_values)) {
+      for (const auto k : c10::irange(trailing_dims)) {
+        output_data[k] = static_cast<CTYPE_OUT>(input_data[k]) * output_data[k];
+      }
+      input_data += trailing_dims;
+      output_data += trailing_dims;
+    }
+  }
+}
+
+/**
+ * Slice the tensor in the given dim, from start to end, assume tensor in and
+ * out have same shape and dtype, the dim is a non-negative number and start,
+ * end are valid non-negative number
+ */
+template <typename CTYPE_IN, typename CTYPE_OUT>
+void slice_tensor(
+    const Tensor& in,
+    int64_t dim,
+    int64_t start,
+    int64_t end,
+    Tensor& out) {
+  size_t num_values = static_cast<size_t>(end - start);
+  size_t dim_length_in = static_cast<size_t>(in.size(dim));
+  size_t dim_length_out = static_cast<size_t>(out.size(dim));
+  size_t non_negative_start = static_cast<size_t>(start);
+  size_t leading_dims = getLeadingDims(in, dim);
+  size_t trailing_dims = getTrailingDims(in, dim);
+
+  const CTYPE_IN* input_data_base = in.const_data_ptr<CTYPE_IN>();
+  CTYPE_OUT* output_data_base = out.mutable_data_ptr<CTYPE_OUT>();
+
+  for (const auto i : c10::irange(leading_dims)) {
+    const CTYPE_IN* input_data = input_data_base +
+        (i * dim_length_in + non_negative_start) * trailing_dims;
+    CTYPE_OUT* output_data =
+        output_data_base + i * dim_length_out * trailing_dims;
+    for ([[maybe_unused]] const auto j : c10::irange(num_values)) {
+      for (const auto k : c10::irange(trailing_dims)) {
+        output_data[k] = static_cast<CTYPE_OUT>(input_data[k]);
+      }
+      input_data += trailing_dims;
+      output_data += trailing_dims;
+    }
+  }
+}
+
 /**
  * Applies the gated linear unit function
  *
@@ -34,63 +120,11 @@ namespace {
  *  2. The output shall be in float types (Float, Double)
  */
 template <typename CTYPE_IN, typename CTYPE_OUT>
-Tensor& glu_out_tensor(
-    KernelRuntimeContext& ctx,
-    const Tensor& self,
-    int64_t dim,
-    Tensor& out) {
+Tensor& glu_out_tensor(const Tensor& self, int64_t dim, Tensor& out) {
   const auto self_size = self.size(dim);
-  ET_KERNEL_CHECK(
-      ctx,
-      self.dim() <= static_cast<ssize_t>(kTensorDimensionLimit),
-      InvalidArgument,
-      out);
-  std::array<executorch::aten::SizesType, kTensorDimensionLimit> half_sizes;
-  std::copy(self.sizes().begin(), self.sizes().end(), half_sizes.begin());
-  half_sizes[dim] /= 2;
-  TensorImpl first_half_impl(
-      self.scalar_type(),
-      self.dim(),
-      half_sizes.data(),
-      self.mutable_data_ptr(),
-      const_cast<executorch::aten::DimOrderType*>(self.dim_order().data()),
-      const_cast<executorch::aten::StridesType*>(self.strides().data()),
-      self.shape_dynamism());
-  TensorImpl second_half_impl(
-      self.scalar_type(),
-      self.dim(),
-      half_sizes.data(),
-      reinterpret_cast<char*>(self.mutable_data_ptr()) +
-          self.strides()[dim] * self_size / 2 * self.element_size(),
-      const_cast<executorch::aten::DimOrderType*>(self.dim_order().data()),
-      const_cast<executorch::aten::StridesType*>(self.strides().data()),
-      self.shape_dynamism());
-  Tensor first_half(&first_half_impl);
-  Tensor second_half(&second_half_impl);
-  ScalarType compute_type =
-      executorch::runtime::isFloatingType(self.scalar_type())
-      ? self.scalar_type()
-      : ScalarType::Float;
-  // @lint-ignore CLANGTIDY facebook-hte-CArray
-  static constexpr const char op_name[] = "glu.out";
-  ET_SWITCH_FLOATHBF16_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<
-        CTYPE_COMPUTE,
-        op_name,
-        utils::SupportedTensorDtypes::FLOATHBF16>(
-        [](const auto val_a, const auto val_b) -> CTYPE_COMPUTE {
-          // TODO: rewrite this to be vectorization-capable.
-          const auto one = static_cast<decltype(val_a)>(1.0);
-          return val_a * (one / (one + std::exp(-val_b)));
-        },
-        ctx,
-        first_half,
-        utils::SupportedTensorDtypes::FLOATHBF16,
-        second_half,
-        utils::SupportedTensorDtypes::FLOATHBF16,
-        out,
-        utils::internal::SupportNoncontiguousTensors());
-  });
+  slice_tensor<CTYPE_IN, CTYPE_OUT>(self, dim, self_size / 2, self_size, out);
+  sigmoid_tensor<CTYPE_OUT>(out);
+  mul_tensors<CTYPE_IN, CTYPE_OUT>(self, dim, out);
   return out;
 }
 } // namespace
@@ -124,7 +158,7 @@ Tensor& glu_out(
 
   ET_SWITCH_FLOATHBF16_TYPES(in_dtype, ctx, "glu", CTYPE_IN, [&]() {
     ET_SWITCH_FLOATHBF16_TYPES(out.scalar_type(), ctx, "glu", CTYPE_OUT, [&]() {
-      glu_out_tensor<CTYPE_IN, CTYPE_OUT>(ctx, self, non_negative_dim, out);
+      glu_out_tensor<CTYPE_IN, CTYPE_OUT>(self, non_negative_dim, out);
     });
   });
 

kernels/portable/cpu/util/broadcast_indexes_range.h

@@ -43,7 +43,7 @@ inline bool sizes_match_ignoring_leading_1s(
       std::equal(lhs_begin, lhs_end, rhs_begin);
 }
 
-template <std::size_t kNumInputs, bool support_noncontiguous_tensors = false>
+template <std::size_t kNumInputs>
 class BroadcastIndexesIterator {
  public:
   using difference_type = ssize_t;
@@ -57,20 +57,16 @@ class BroadcastIndexesIterator {
   template <typename... Args>
   explicit BroadcastIndexesIterator(const Tensor& output, const Args&... args)
       : output_dim_or_zero_if_no_broadcasting_(
-            !support_noncontiguous_tensors &&
-                    (sizes_match_ignoring_leading_1s(
-                         args.sizes(),
-                         output.sizes()) &&
-                     ...)
+            (sizes_match_ignoring_leading_1s(args.sizes(), output.sizes()) &&
+             ...)
                 ? 0
                 : output.dim()),
         output_shape_(output.sizes()) {
     static_assert(
         sizeof...(args) == kNumInputs && (std::is_same_v<Args, Tensor> && ...),
         "BroadcastIndexesIterator constructor requires kNumInputs input tensor"
         "arguments!");
-    if (support_noncontiguous_tensors ||
-        output_dim_or_zero_if_no_broadcasting_ != 0) {
+    if (output_dim_or_zero_if_no_broadcasting_ != 0) {
       effective_input_broadcast_strides_ = {
           effective_input_broadcast_stride(output, args)...};
     }
@@ -253,17 +249,11 @@ class BroadcastIndexesIterator {
  * Unlike looping using delinearize_index() and
  * linearize_access_indexes(), BroadcastIndexesRange avoids expensive
  * division and modulo operations on each iteration.
- *
- * The support_noncontiguous_tensors argument disables an optimization
- * that causes the iterators not to respect strides in some
- * cases. This optimization is normally safe because ExecuTorch
- * tensors are contiguous.
  */
-template <std::size_t kNumInputs, bool support_noncontiguous_tensors = false>
+template <std::size_t kNumInputs>
 class BroadcastIndexesRange {
  public:
-  using iterator = internal::
-      BroadcastIndexesIterator<kNumInputs, support_noncontiguous_tensors>;
+  using iterator = internal::BroadcastIndexesIterator<kNumInputs>;
 
   template <typename... Args>
   BroadcastIndexesRange(const Tensor& output, const Args&... args)