Apply some more missing moves in aten native (#92983)

Add some additional missing moves to further improve vmap and related operators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92983
Approved by: https://github.com/ezyang

aten/src/ATen/FunctionalInverses.cpp

@@ -172,7 +172,7 @@ Tensor FunctionalInverses::_reshape_alias_copy_inverse(const Tensor& base, const
 
 Tensor FunctionalInverses::select_copy_int_inverse(const Tensor& base, const Tensor& mutated_view, bool reapply_views, int64_t dim, c10::SymInt index) {
     // Pessimism: we can't reapply views for slice_scatter.
-    return base.select_scatter_symint(mutated_view, dim, index);
+    return base.select_scatter_symint(mutated_view, dim, std::move(index));
 }
 
 Tensor FunctionalInverses::detach_copy_inverse(const Tensor& base, const Tensor& mutated_view, bool reapply_views) {

aten/src/ATen/functorch/BatchRulesBinaryOps.cpp

@@ -9,6 +9,8 @@
 #include <ATen/Operators.h>
 #include <ATen/core/dispatch/Dispatcher.h>
 
+#include <utility>
+
 namespace at { namespace functorch {
 
 template <typename F, F Func, typename... ExtraArgs>
@@ -306,7 +308,7 @@ std::tuple<Tensor, optional<int64_t>> log_sigmoid_backward_batch_rule(
 }
 
 Tensor binomial_wrapper(const Tensor& count, const Tensor& prob, c10::optional<Generator> gen) {
-  return at::binomial(count, prob.contiguous(), gen); // Bug in PyTorch, prob shouldn't need to be contiguous
+  return at::binomial(count, prob.contiguous(), std::move(gen)); // Bug in PyTorch, prob shouldn't need to be contiguous
 }
 
 TORCH_LIBRARY_IMPL(aten, FuncTorchVmapMode, m) {

aten/src/ATen/functorch/BatchRulesHelper.h

@@ -19,6 +19,8 @@
 #include <ATen/core/dispatch/Dispatcher.h>
 #include <ATen/VmapGeneratedPlumbing.h>
 
+#include <utility>
+
 // This file contains helper functions for batching rules.
 
 namespace at { namespace functorch {
@@ -339,7 +341,7 @@ inline void boxed_all_tensors_have_optional_bdim(
       if (tensor_idx == contig_tensor_index) {
         value_ = value_.contiguous();
       }
-      (*stack)[args_begin + tensor_pos[tensor_idx]] = value_;
+      (*stack)[args_begin + tensor_pos[tensor_idx]] = std::move(value_);
       continue;
     }
     TORCH_INTERNAL_ASSERT(logical_rank == feature_rank + 1);
@@ -347,7 +349,7 @@ inline void boxed_all_tensors_have_optional_bdim(
     if (tensor_idx == contig_tensor_index) {
       value_ = value_.contiguous();
     }
-    (*stack)[args_begin + tensor_pos[tensor_idx]] = value_;
+    (*stack)[args_begin + tensor_pos[tensor_idx]] = std::move(value_);
   }
 
   op.callBoxed(stack);

aten/src/ATen/functorch/BatchRulesViews.cpp

@@ -6,6 +6,7 @@
 
 #include <ATen/functorch/BatchRulesHelper.h>
 #include <iostream>
+#include <utility>
 
 #include <ATen/Operators.h>
 #include <ATen/functorch/PlumbingHelper.h>
@@ -236,7 +237,7 @@ std::tuple<Tensor, optional<int64_t>> squeeze_batch_rule(const Tensor& self, opt
   }
 
   auto result = self.view(squeezed_sizes);
-  return std::make_tuple(result, c10::optional<int64_t>(new_batch_idx));
+  return std::make_tuple(std::move(result), c10::optional<int64_t>(new_batch_idx));
 }
 
 std::tuple<Tensor, optional<int64_t>> squeeze_dims_batch_rule(
@@ -284,13 +285,13 @@ std::tuple<std::vector<Tensor>, optional<int64_t>> chunk_batching_rule(const Ten
 
 std::tuple<Tensor, optional<int64_t>> select_batching_rule(const Tensor& self, optional<int64_t> bdim, int64_t dim, c10::SymInt index) {
   if (!bdim) {
-    return std::make_tuple(self.select_symint(dim, index), nullopt);
+    return std::make_tuple(self.select_symint(dim, std::move(index)), nullopt);
   }
 
   auto _self = moveBatchDimToFront(self, bdim);
   auto dim_physical = getPhysicalDim(_self, true, dim);
-  auto result = _self.select_symint(dim_physical, index);
-  return std::make_tuple(result, 0);
+  auto result = _self.select_symint(dim_physical, std::move(index));
+  return std::make_tuple(std::move(result), 0);
 }
 
 std::tuple<Tensor, optional<int64_t>> _reshape_alias_batch_rule(const Tensor& self, optional<int64_t> bdim, const c10::SymIntArrayRef shape, const c10::SymIntArrayRef strides) {
@@ -359,8 +360,8 @@ std::tuple<Tensor,optional<int64_t>> slice_batch_rule(
   auto self_ = moveBatchDimToFront(self, self_bdim);
   dim = getPhysicalDim(self, self_bdim.has_value(), dim);
 
-  auto result = self_.slice_symint(dim, start, end, step);
-  return std::make_tuple(result, 0);
+  auto result = self_.slice_symint(dim, std::move(start), std::move(end), std::move(step));
+  return std::make_tuple(std::move(result), 0);
 }
 
 static bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
@@ -386,7 +387,7 @@ transpose_int_batch_rule(
   dim0 = getPhysicalDim(self, self_bdim.has_value(), dim0);
   dim1 = getPhysicalDim(self, self_bdim.has_value(), dim1);
   auto result = self_.transpose(dim0, dim1);
-  return std::make_tuple(result, 0);
+  return std::make_tuple(std::move(result), 0);
 }
 
 std::tuple<Tensor, optional<int64_t>> permute_batching_rule(
@@ -416,7 +417,7 @@ std::tuple<Tensor,optional<int64_t>> select_backward_batch_rule(
   c10::SymDimVector input_sizes_(input_sizes.size() + 1);
   input_sizes_[0] = grad_input_.sym_size(0);
   std::copy(input_sizes.begin(), input_sizes.end(), input_sizes_.begin() + 1);
-  auto result = at::select_backward_symint(grad_input_, input_sizes_, dim, index);
+  auto result = at::select_backward_symint(grad_input_, input_sizes_, dim, std::move(index));
   return std::make_tuple(std::move(result), 0);
 }
 
@@ -429,7 +430,7 @@ std::tuple<Tensor,optional<int64_t>> slice_backward_batch_rule(
   c10::SymDimVector input_sizes_(input_sizes.size() + 1);
   input_sizes_[0] = grad_input_.size(0);
   std::copy(input_sizes.begin(), input_sizes.end(), input_sizes_.begin() + 1);
-  auto result = at::slice_backward_symint(grad_input_, input_sizes_, dim, start, end, step);
+  auto result = at::slice_backward_symint(grad_input_, input_sizes_, dim, std::move(start), std::move(end), std::move(step));
   return std::make_tuple(std::move(result), 0);
 }
 
@@ -507,7 +508,7 @@ std::tuple<Tensor, optional<int64_t>> unfold_batch_rule(
   if (logical_rank==0) {
     result = result.squeeze(-1);
   }
-  return std::make_tuple(result, 0);
+  return std::make_tuple(std::move(result), 0);
 }
 
 std::tuple<Tensor, optional<int64_t>> narrow_copy_batch_rule(
@@ -517,9 +518,9 @@ std::tuple<Tensor, optional<int64_t>> narrow_copy_batch_rule(
   auto self_ = moveBatchDimToFront(self, self_bdim);
   auto logical_rank = rankWithoutBatchDim(self, self_bdim);
   dim = maybe_wrap_dim(dim, logical_rank) + 1;
-  auto result = self_.narrow_copy_symint(dim, start, length);
+  auto result = self_.narrow_copy_symint(dim, std::move(start), std::move(length));
 
-  return std::make_tuple(result, 0);
+  return std::make_tuple(std::move(result), 0);
 }
 
 std::tuple<std::vector<Tensor>, optional<int64_t>> unsafe_split_batch_rule(
@@ -531,8 +532,8 @@ std::tuple<std::vector<Tensor>, optional<int64_t>> unsafe_split_batch_rule(
   auto self_ = moveBatchDimToFront(self, self_bdim);
   auto logical_rank = rankWithoutBatchDim(self, self_bdim);
   dim = maybe_wrap_dim(dim, logical_rank) + 1;
-  auto result = self_.unsafe_split_symint(split_size, dim);
-  return std::make_tuple(result, 0);
+  auto result = self_.unsafe_split_symint(std::move(split_size), dim);
+  return std::make_tuple(std::move(result), 0);
 }
 
 std::tuple<Tensor, optional<int64_t>> movedim_batch_rule(const Tensor& self, optional<int64_t> self_bdim, IntArrayRef source, IntArrayRef destination) {

aten/src/ATen/functorch/Interpreter.cpp

@@ -6,6 +6,8 @@
 #include <ATen/functorch/ADInterpreters.h>
 #include <ATen/functorch/DynamicLayer.h>
 
+#include <utility>
+
 namespace at { namespace functorch {
 
 static DispatchKeySet get_all_dynlayer_keyset() {
@@ -92,7 +94,7 @@ void sanityCheckStack(const c10::OperatorHandle& op, torch::jit::Stack* stack) {
         auto result = unwrapIfDead(tensor);
         auto* wrapper = maybeGetTensorWrapper(result);
         TORCH_INTERNAL_ASSERT(wrapper == nullptr);
-        auto* batched = maybeGetBatchedImpl(result);
+        auto* batched = maybeGetBatchedImpl(std::move(result));
         TORCH_INTERNAL_ASSERT(batched == nullptr);
         return tensor;
       });

aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp

@@ -16,6 +16,8 @@
 #include <ATen/functorch/BatchedFallback.h>
 #include <ATen/functorch/BatchRulesHelper.h>
 
+#include <utility>
+
 namespace at {
 namespace functorch {
 
@@ -476,7 +478,7 @@ Tensor as_strided_batching_rule(
     optional<c10::SymInt> storage_offset) {
   if (!participatesInCurrentLevel(tensor)) {
     c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched);
-    return at::as_strided_symint(tensor, sizes, strides, storage_offset);
+    return at::as_strided_symint(tensor, sizes, strides, std::move(storage_offset));
   }
   auto physical_view = MultiBatchVmapTransform::logicalToPhysical(tensor);
   auto num_batch_dims = physical_view.numBatchDims();
@@ -511,7 +513,7 @@ Tensor as_strided_batching_rule(
   // and creates a tensor y such that each y[i] references the same memory
   // locations as zi. See NOTE: [When will the as_strided batching rule fail?]
   auto result = physical_view.tensor().as_strided_symint(
-      physical_sizes, physical_strides, storage_offset);
+      physical_sizes, physical_strides, std::move(storage_offset));
   return physical_view.getPhysicalToLogicalMap().apply(result);
 }
 

aten/src/ATen/native/ComplexHelper.h

@@ -8,6 +8,8 @@
 #else
 #include <ATen/ops/view_as_real_native.h>
 #include <ATen/ops/view_as_complex_native.h>
+
+#include <utility>
 #endif
 
 // WARNING: this header contains non-inline functions and should be only
@@ -47,7 +49,7 @@ Tensor _view_as_real_physical(const Tensor& self) {
   auto new_strides = computeStrideForViewAsReal(self.sym_strides());
   auto new_storage_offset = self.sym_storage_offset() * 2;
   const auto float_type = c10::toRealValueType(self.scalar_type());
-  auto real_tensor = view_tensor(self, float_type, new_storage_offset, new_sizes, new_strides);
+  auto real_tensor = view_tensor(self, float_type, std::move(new_storage_offset), new_sizes, new_strides);
   return real_tensor;
 }
 

aten/src/ATen/native/Normalization.cpp

@@ -48,8 +48,9 @@
 #include <ATen/ops/sqrt.h>
 #endif
 
-#include <vector>
 #include <c10/core/SymIntArrayRef.h>
+#include <utility>
+#include <vector>
 
 static const int MIOPEN_DIM_MAX = 5;
 
@@ -490,7 +491,7 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, int64_t> _batch_norm_impl_index(
     auto options = input.options().dtype(
         at::toAccumulateType(input.scalar_type(), /*is_cuda=*/input.is_cuda()));
     auto save_mean = at::empty_symint(c10::SymIntArrayRef({num_features}), options);
-    auto save_invstd = at::empty_symint(c10::SymIntArrayRef({num_features}), options);
+    auto save_invstd = at::empty_symint(c10::SymIntArrayRef({std::move(num_features)}), options);
 
     // don't return view of input, don't return empty tensor because it will break gradient chain
     auto out = input.clone();
@@ -514,7 +515,7 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, int64_t> _batch_norm_impl_index(
     check_dims_match_num_input_features("weight", num_features, weight.sym_numel());
   }
   if (bias.defined()) {
-    check_dims_match_num_input_features("bias", num_features, bias.sym_numel());
+    check_dims_match_num_input_features("bias", std::move(num_features), bias.sym_numel());
   }
 
   const bool use_cudnn = (
@@ -672,7 +673,7 @@ Tensor instance_norm(
     at::alias(running_mean).copy_(running_mean_.view_symint({ b, c }).mean(0, false));
   }
   if (running_var.defined()) {
-    at::alias(running_var).copy_(running_var_.view_symint({ b, c }).mean(0, false));
+    at::alias(running_var).copy_(running_var_.view_symint({ std::move(b), std::move(c) }).mean(0, false));
   }
 
   return out.view_symint(input.sym_sizes());

aten/src/ATen/native/Pool.h

@@ -4,6 +4,8 @@
 #include <ATen/native/DispatchStub.h>
 #include <c10/util/irange.h>
 
+#include <utility>
+
 #pragma once
 
 namespace at {
@@ -93,7 +95,7 @@ inline std::pair<int64_t, int64_t> pooling_same_mode_padding_lr(
 
 inline std::pair<c10::SymInt, c10::SymInt> pooling_same_mode_padding_lr(
     c10::SymInt inputSize, c10::SymInt kernelSize, int64_t stride, int64_t dilation) {
-  return _pooling_same_mode_padding_lr(inputSize, kernelSize, stride, dilation);
+  return _pooling_same_mode_padding_lr(std::move(inputSize), std::move(kernelSize), stride, dilation);
 }
 
 // AveragePool2d/DilatedMaxPool2d (forward)

aten/src/ATen/native/Resize.h

@@ -7,6 +7,8 @@
 
 #include <c10/core/CPUAllocator.h>
 
+#include <utility>
+
 
 namespace at { namespace native {
 
@@ -130,7 +132,7 @@ static inline void checkSetStorage(Tensor& result, Storage storage, T storage_of
                 "Attempted to set the storage of a tensor on device \"", result.storage().device(),
                 "\" to a storage on different device \"", storage.device(),
                 "\".  This is no longer allowed; the devices must match.");
-    result.unsafeGetTensorImpl()->set_storage_keep_dtype(storage);
+    result.unsafeGetTensorImpl()->set_storage_keep_dtype(std::move(storage));
   }
 
   // storageOffset

aten/src/ATen/native/TensorShape.cpp

@@ -1804,7 +1804,7 @@ Tensor select_symint(const Tensor& self, int64_t dim, c10::SymInt index) {
 
 Tensor select_backward_symint(const Tensor& grad, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) {
   auto grad_input = at::zeros_symint(input_sizes, grad.options());
-  grad_input.select_symint(dim, index).copy_(grad);
+  grad_input.select_symint(dim, std::move(index)).copy_(grad);
   return grad_input;
 }
 
@@ -3879,7 +3879,7 @@ at::Tensor clone_preserve_strides(const at::Tensor& self) {
   auto nbytes = self.storage().sym_nbytes();
   TORCH_INTERNAL_ASSERT(nbytes % dtype_size == 0);
   auto numel = nbytes / dtype_size;
-  auto self_full_size = self.as_strided_symint({numel}, {1}, 0);
+  auto self_full_size = self.as_strided_symint({std::move(numel)}, {1}, 0);
   auto clone = self_full_size.clone();
   auto out = clone.as_strided_symint(self.sym_sizes(), self.sym_strides(), self.sym_storage_offset());
   return out;
@@ -3896,7 +3896,7 @@ at::Tensor slice_scatter(const at::Tensor& self, const at::Tensor& src, int64_t
 }
 at::Tensor select_scatter_symint(const at::Tensor& self, const at::Tensor& src, int64_t dim, c10::SymInt index) {
     auto output = clone_preserve_strides(self);
-    auto slice = output.select_symint(dim, index);
+    auto slice = output.select_symint(dim, std::move(index));
     TORCH_CHECK(slice.sizes() == src.sizes(), "expected src to have a size equal to the slice of self. src size = ", src.sizes(), ", slice size = ", slice.sizes());
     slice.copy_(src);
     return output;
@@ -4039,7 +4039,7 @@ at::Tensor& _reshape_alias_copy_out(const at::Tensor & self, at::IntArrayRef siz
 
 
 at::Tensor& select_copy_symint_out(const at::Tensor & self, int64_t dim, c10::SymInt index, at::Tensor & out) {
-  auto tmp = self.select_symint(dim, index);
+  auto tmp = self.select_symint(dim, std::move(index));
   out.copy_(tmp);
   return out;
 }