#include <ATen/FunctionalInverses.h>

#include <ATen/ATen.h>
#include <ATen/ExpandUtils.h>
#include <ATen/WrapDimUtilsMulti.h>

#include <utility>
namespace at::functionalization {

// This logic is similar to autograd code for view backwards calls.
// We can't easily share it though, because (eventually) these functions
// will all call `permute/unsqueeze_copy()` instead of `permute/unsqueeze`.

static Tensor permute_inverse(const Tensor& self, IntArrayRef dims, InverseReturnMode inverse_return_mode) {
  // invert the permutation
  auto ndims = static_cast<int64_t>(dims.size());
  std::vector<int64_t> dims_(ndims);
  for(const auto i : c10::irange(ndims)) {
    dims_[at::maybe_wrap_dim(dims[i], ndims)] = i;
  }
  if (inverse_return_mode != InverseReturnMode::NeverView) {
    return at::permute(self, dims_);
  } else {
    return at::permute_copy(self, dims_);
  }
}

static Tensor unsqueeze_copy_to(const Tensor & self, c10::SymIntArrayRef sizes, InverseReturnMode inverse_return_mode) {
  auto result = self;
  bool need_alias = (inverse_return_mode == InverseReturnMode::AlwaysView);
  int64_t nDims = static_cast<int64_t>(sizes.size());
  for(const auto dim : c10::irange(nDims)) {
    if (sizes[dim] == 1) {
      need_alias = false;
      if (inverse_return_mode != InverseReturnMode::NeverView) {
        result = at::unsqueeze(result, dim);
      } else {
        result = at::unsqueeze_copy(result, dim);
      }
    }
  }

  // return an alias to ensure the output is a view when necessary
  return need_alias ? at::alias(result) : result;
}

static Tensor unsqueeze_copy_to(const Tensor & self, IntArrayRef dim, c10::SymIntArrayRef sizes, InverseReturnMode inverse_return_mode) {
  const auto ndim = static_cast<int64_t>(sizes.size());
  const auto mask = at::dim_list_to_bitset(dim, ndim);
  Tensor result = self;
  bool need_alias = (inverse_return_mode == InverseReturnMode::AlwaysView);
  // in NumPy it's not an error to unsqueeze a scalar, but we still need to avoided
  // unsqueezing in the backward.
  if (ndim == 0) {
    // return an alias to ensure the output is a view when necessary
    return need_alias ? at::alias(result) : result;
  }

  for (const auto d : c10::irange(ndim)) {
    if (mask.test(d) && sizes[d] == 1) {
      need_alias = false;
      if (inverse_return_mode != InverseReturnMode::NeverView) {
        result = at::unsqueeze(result, d);
      } else {
        result = at::unsqueeze_copy(result, d);
      }
    }
  }

  // return an alias to ensure the output is a view when necessary
  return need_alias ? at::alias(result) : result;
}

// Note [Functionalization Pass: View Inverses].
// This file contains the implementation of each "view inverse".
// These aren't really true inverses in the mathematically sense: each view inverse describes how to undo
// the original view (although it takes in different arguments).
//
// E.g. Below is an example of a program that has alias operations removed, and the role that view inverses play:
//
// normal program with views and mutations:
// view1 = input1.view_op(args...)
// view1.add_(1) (perform a mutation on the view, which should also modify input)

// version of the program with no aliasing, that instead uses view_inverse functions:
// view_copy1 = input1.view_copy_op(args...)
// view_copy1.add_(1) (perform a mutation on view_copy1. At this point, input1 is NOT modified)
// x = view_op_inverse(input1, view_copy1, args...)
//
// at this point, input1 and x should be equal
//
// Note that input1 is also passed as an argument to view_op_inverse in the above example.
// This isn't actually required for most view operators: it's only required for view ops
// where you can't figure out what the size of the base tensor is given just the view tensor and arguments.
// Examples are slice/select/scatter/squeeze/as_strided.
// We happen to be passing in the base tensor in all cases, mostly to make the codegen simpler.
// But you'll see below that the "base" argument is ignored by most view_inverse implementations.

// ----------------------------------------------------------
// Implementations of each view_inverse() function are below.
// One of these needs to be implemented for every existing non-composite view operator.
// The codegen automatically generates the corresponding function declaration.
// ----------------------------------------------------------

Tensor FunctionalInverses::_fw_primal_inverse(const at::Tensor& base, const at::Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t level) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call _fw_primal() during the functionalization pass. For now, this is not supported.");
    return Tensor();
}

Tensor FunctionalInverses::_make_dual_inverse(const at::Tensor& base, const at::Tensor& mutated_view, InverseReturnMode inverse_return_mode, const at::Tensor& tangent, int64_t level) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call _make_dual() during the functionalization pass. For now, this is not supported.");
    return Tensor();
}

Tensor FunctionalInverses::view_as_real_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::view_as_complex(mutated_view);
    } else {
      return at::view_as_complex_copy(mutated_view);
    }
}

Tensor FunctionalInverses::view_as_complex_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::view_as_real(mutated_view.resolve_conj());
    } else {
      return at::view_as_real_copy(mutated_view.resolve_conj());
    }
}

Tensor FunctionalInverses::_conj_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::_conj(mutated_view);
    } else {
      return at::_conj_copy(mutated_view);
    }
}

Tensor FunctionalInverses::_neg_view_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::_neg_view(mutated_view);
    } else {
      return at::_neg_view_copy(mutated_view);
    }
}

Tensor FunctionalInverses::as_strided_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::SymIntArrayRef size, at::SymIntArrayRef stride, std::optional<c10::SymInt> storage_offset) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.as_strided_symint(
          base.sym_sizes(), base.sym_strides(), base.sym_storage_offset());
    } else {
      return base.as_strided_scatter_symint(mutated_view, size, stride, std::move(storage_offset));
    }
}

Tensor FunctionalInverses::diagonal_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t offset, int64_t dim1, int64_t dim2) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.as_strided_symint(
          base.sym_sizes(), base.sym_strides(), base.sym_storage_offset());
    } else {
      return base.diagonal_scatter(mutated_view, offset, dim1, dim2);
    }
}

Tensor FunctionalInverses::expand_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::SymIntArrayRef size, bool implicit) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is an expanded view of base.
      // We should NOT do this for functionalization
      return mutated_view.as_strided_symint(
          base.sym_sizes(), base.sym_strides(), base.sym_storage_offset());
    } else {
      return base + at::sum_to(
          mutated_view - base,
          base.sym_sizes(),
          /*always_return_non_view=*/inverse_return_mode == InverseReturnMode::NeverView
      );
    }
}

Tensor FunctionalInverses::permute_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::IntArrayRef dims) {
    return at::functionalization::permute_inverse(mutated_view, dims, inverse_return_mode);
}

Tensor FunctionalInverses::_reshape_alias_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::SymIntArrayRef size, at::SymIntArrayRef stride) {
    // Note that I'm directly calling reshape(), and ignoring the strides.
    // _reshape_alias() isn't available from user code, and is an implementation detail of reshape().
    // Specifically, passing in the strides directly can get us into trouble in cases like:
    // b = a[0]; c = b.reshape(...); c.add_(1); print(a)
    // When we eventually run the _reshape_alias_inverse() call here, if we were to pass in both sizes and strides,
    // The call would fail because `mutated_view` doesn't have enough bytes of storage.
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::_reshape_alias_symint(mutated_view, base.sym_sizes(), base.sym_strides());
    } else {
      return at::_reshape_alias_copy_symint(mutated_view, base.sym_sizes(), base.sym_strides());
    }
}

Tensor FunctionalInverses::select_int_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t dim, c10::SymInt index) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.as_strided_symint(
          base.sym_sizes(), base.sym_strides(), base.sym_storage_offset());
    } else {
      return base.select_scatter_symint(mutated_view, dim, std::move(index));
    }
}

Tensor FunctionalInverses::detach_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    // the functionalization pass doesn't care about autograd metadata - as a view, I think detach() is just an identity function
    return mutated_view;
}

Tensor FunctionalInverses::lift_fresh_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    return mutated_view;
}

Tensor FunctionalInverses::slice_Tensor_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t dim, std::optional<c10::SymInt> start, std::optional<c10::SymInt> end, c10::SymInt step) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.slice_inverse_symint(
          base, dim, std::move(start), std::move(end), std::move(step));
    } else {
      return base.slice_scatter_symint(mutated_view, dim, std::move(start), std::move(end), std::move(step));
    }
}

Tensor FunctionalInverses::split_Tensor_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t mutated_view_idx, c10::SymInt split_size, int64_t dim) {
    // It would be nice if this logic could be re-used from autograd's split_backward(), but I don't think it can.
    // For functionalization, we have only have one of the tensors from the TensorList outputed by split(), and we want to layer i
    // on top of the base tensor.
    // For autograd, we have all of the tensors outputted by split() and we just want to stack them.
    dim = at::maybe_wrap_dim(dim, base.dim());
    auto dim_size = base.sym_size(dim);
    auto start = split_size * mutated_view_idx;
    auto end = split_size + start;
    if (end > dim_size) end = dim_size;

    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.slice_inverse_symint(base, dim, start, end, 1);
    } else {
      return base.slice_scatter_symint(mutated_view, dim, start, end, 1);
    }
}

Tensor FunctionalInverses::split_with_sizes_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t mutated_view_idx, c10::SymIntArrayRef split_sizes, int64_t dim) {
    dim = at::maybe_wrap_dim(dim, base.dim());
    auto dim_size = base.sym_size(dim);
    c10::SymInt start = 0;
    for (auto i = 0; i < mutated_view_idx; ++i) {
        start += split_sizes[i];
    }
    auto end = start + split_sizes[mutated_view_idx];
    if (end > dim_size) end = dim_size;

    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.slice_inverse_symint(base, dim, start, end, 1);
    } else {
      return base.slice_scatter_symint(mutated_view, dim, start, end, 1);
    }
}

Tensor FunctionalInverses::squeeze_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    return unsqueeze_copy_to(mutated_view, base.sym_sizes(), inverse_return_mode);
}

Tensor FunctionalInverses::squeeze_dim_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t dim) {
    return unsqueeze_copy_to(mutated_view, dim, base.sym_sizes(), inverse_return_mode);
}

Tensor FunctionalInverses::squeeze_dims_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, IntArrayRef dim) {
    return unsqueeze_copy_to(mutated_view, dim, base.sym_sizes(), inverse_return_mode);
}

Tensor FunctionalInverses::t_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::t(mutated_view);
    } else {
      return at::t_copy(mutated_view);
    }
}

Tensor FunctionalInverses::transpose_int_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t dim0, int64_t dim1) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return transpose(mutated_view, dim0, dim1);
    } else {
      return transpose_copy(mutated_view, dim0, dim1);
    }
}

Tensor FunctionalInverses::_nested_view_from_buffer_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, const Tensor& nested_sizes, const Tensor& nested_strides, const Tensor& storage_offsets) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call _nested_view_from_buffer() during the functionalization pass. For now, nested tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::_nested_view_from_jagged_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, const Tensor& offsets, const Tensor& dummy, const std::optional<Tensor>& lengths, int64_t ragged_idx, const std::optional<Tensor>& min_seqlen, const std::optional<Tensor>& max_seqlen) {
  auto values = at::_nested_get_values(mutated_view);
  if (inverse_return_mode != InverseReturnMode::NeverView) {
    return values;
  } else {
    return values.clone(/*memory_format=*/at::MemoryFormat::Contiguous);
  }
}

Tensor FunctionalInverses::_nested_get_values_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
  auto offsets = at::_nested_get_offsets(base);
  auto lengths = at::_nested_get_lengths(base);
  auto ragged_idx = at::_nested_get_ragged_idx(base);
  auto dummy = at::_nested_get_jagged_dummy(base);
  auto min_seqlen = at::_nested_get_min_seqlen(base);
  auto max_seqlen = at::_nested_get_max_seqlen(base);
  auto nt = at::_nested_view_from_jagged(
      mutated_view, offsets, dummy, lengths, ragged_idx,
      (min_seqlen.defined() ? std::optional<Tensor>(min_seqlen) : std::nullopt),
      (max_seqlen.defined() ? std::optional<Tensor>(max_seqlen) : std::nullopt));

  if (inverse_return_mode != InverseReturnMode::NeverView) {
    return nt;
  } else {
    return nt.clone(/*memory_format=*/at::MemoryFormat::Contiguous);
  }
}

Tensor FunctionalInverses::unsqueeze_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t dim) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::squeeze(mutated_view, dim);
    } else {
      return at::squeeze_copy(mutated_view, dim);
    }
}

Tensor FunctionalInverses::_indices_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call _indices() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::_values_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call _values() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::indices_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call indices() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::values_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call values() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::_sparse_broadcast_to_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::IntArrayRef size) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call _sparse_broadcast_to() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::crow_indices_inverse(const at::Tensor& base, const at::Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call crow_indices() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::col_indices_inverse(const at::Tensor& base, const at::Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call col_indices() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::ccol_indices_inverse(const at::Tensor& base, const at::Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call ccol_indices() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::row_indices_inverse(const at::Tensor& base, const at::Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    TORCH_INTERNAL_ASSERT(false, "Attempted to call row_indices() during the functionalization pass. For now, sparse tensors aren't supported during functionalization");
    return Tensor();
}

Tensor FunctionalInverses::unbind_int_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t mutated_view_idx, int64_t dim) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.as_strided_symint(
          base.sym_sizes(), base.sym_strides(), base.sym_storage_offset());
    } else {
      dim = at::maybe_wrap_dim(dim, static_cast<int64_t>(base.sizes().size()));
      return base.select_scatter(mutated_view, dim, mutated_view_idx);
    }
}

Tensor FunctionalInverses::view_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::SymIntArrayRef size) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return mutated_view.view_symint(base.sym_sizes());
    } else {
      return at::view_copy_symint(mutated_view, base.sym_sizes());
    }
}


Tensor FunctionalInverses::view_dtype_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, at::ScalarType dtype) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return mutated_view.view(base.scalar_type());
    } else {
      return at::view_copy(mutated_view, base.scalar_type());
    }
}

Tensor FunctionalInverses::unfold_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t dimension, int64_t size, int64_t step) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.as_strided_symint(
          base.sym_sizes(), base.sym_strides(), base.sym_storage_offset());
    } else {
      // I think autograd and the functionalization pass want the exact same thing here, but need to test to confirm.
      // unfold_backward() is safe to use here because it is NOT a view op.
      // (note: technically, we'll have an extra memory copy.
      // We'd need to add an aliasing version of unfold_backward to fix that though).
      TORCH_CHECK(
        !(inverse_return_mode == InverseReturnMode::ViewOrScatterInverse && size > step),
        "While executing unfold, functionalization encountered a tensor being mutated that has internal overlap. \
When using torch.compile (or running functionalization directly), this is banned \
as the behavior is not well defined. Consider cloning the tensor before mutating it, \
or removing the mutation from your model."
          );
      return unfold_backward(mutated_view, base.sizes(), dimension, size, step);
    }
}

Tensor FunctionalInverses::alias_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode) {
    if (inverse_return_mode != InverseReturnMode::NeverView) {
      return at::alias(mutated_view);
    } else {
      return at::alias_copy(mutated_view);
    }
}

Tensor FunctionalInverses::chunk_inverse(const at::Tensor & base, const at::Tensor & mutated_view, InverseReturnMode inverse_return_mode, int64_t mutated_view_idx, int chunks, int dim) {
    // TODO: Can the logic from TensorShape.cpp be reused here somehow?
    const auto dim_size = base.sym_size(dim);
    auto split_size = (dim_size + chunks - 1) / chunks;
    std::vector<c10::SymInt> split_sizes(chunks, split_size);
    split_sizes[chunks - 1] = split_size - (split_size * chunks - dim_size);
    return split_with_sizes_inverse(base, mutated_view, inverse_return_mode, mutated_view_idx, split_sizes, dim);
}

Tensor FunctionalInverses::narrow_inverse(const at::Tensor & base, const at::Tensor & mutated_view, InverseReturnMode inverse_return_mode, int dim, c10::SymInt start, c10::SymInt length) {
    if (inverse_return_mode == InverseReturnMode::AlwaysView) {
      // NB: assumes mutated_view is a narrowed view of base.
      // We should NOT do this for functionalization
      return mutated_view.slice_inverse_symint(base, dim, start, start + length, 1);
    } else {
      return base.slice_scatter_symint(
          mutated_view, dim, start, start + length, 1);
    }
}

Tensor FunctionalInverses::slice_inverse_inverse(const at::Tensor & base, const at::Tensor & mutated_view, InverseReturnMode inverse_return_mode, const at::Tensor & src, int64_t dim, std::optional<c10::SymInt> start, std::optional<c10::SymInt> end, c10::SymInt step) {
    // slice_inverse() inverse is just slice()
    if (inverse_return_mode == InverseReturnMode::NeverView) {
      return at::slice_copy_symint(
          mutated_view, dim, std::move(start), std::move(end), std::move(step));
    } else {
      return mutated_view.slice_symint(
          dim, std::move(start), std::move(end), std::move(step));
    }
}

} // namespace at::functionalization