fix aminmax output resize issue when input is a zero dimension tensor

aten/src/ATen/functorch/BatchRulesReduceOps.cpp

@@ -316,38 +316,6 @@ std::tuple<Tensor,optional<int64_t>> _log_softmax_backward_batch_rule(
   return std::make_tuple(at::_log_softmax_backward_data(grad_output_, output_, dim, input_dtype), 0);
 }
 
-// aminmax has divergent behavior for 0-d tenosrs.
-// reference: https://github.com/pytorch/pytorch/issues/64008
-// TODO: Once the divergent behavior for 0-d scalar is fixed, we should use REDUCTION_BOXED_ARGS
-std::tuple<Tensor, optional<int64_t>, Tensor, optional<int64_t>> aminmax_batching_rule(
-    const Tensor &self, optional<int64_t> self_bdim, optional<int64_t> dim, bool keep_dim)
-{
-  auto self_ = moveBatchDimToFront(self, self_bdim);
-  auto logical_rank = rankWithoutBatchDim(self_, self_bdim);
-  if (logical_rank == 0) {
-    self_ = self_.unsqueeze(-1);
-  }
-
-  if (dim.has_value()) {
-    dim = maybe_wrap_dim(dim.value(), logical_rank) + 1;
-  } else {
-    // flatten the input except for batch-dim
-    auto bsize = self_.size(0);
-    self_ = self_.view({bsize, -1});
-    dim = 1;
-  }
-
-  Tensor min, max;
-  std::tie(min, max) = at::aminmax(self_, dim, keep_dim);
-
-  if (logical_rank == 0 && self_.device().is_cuda()) {
-    // behaviour diverges between cpu and cuda
-    min = min.squeeze(-1);
-    max = max.squeeze(-1);
-  }
-  return std::make_tuple(min, 0, max, 0);
-}
-
 std::tuple<Tensor,optional<int64_t>> searchsorted_batch_rule(
     const Tensor& sorted_sequence,
     optional<int64_t> sorted_sequence_bdim,
@@ -466,6 +434,7 @@ TORCH_LIBRARY_IMPL(aten, FuncTorchBatched, m) {
   REDUCTION_NO_KEEPDIM_ARG(_fft_c2c);
   REDUCTION_WITH_KEEPDIM_ARG(amax);
   REDUCTION_WITH_KEEPDIM_ARG(amin);
+  REDUCTION_WITH_KEEPDIM_ARG(aminmax);
   m.impl("all", all_decomp);
   REDUCTION_WITH_KEEPDIM_ARG(all.dim);
   m.impl("any", any_decomp);
@@ -511,7 +480,6 @@ TORCH_LIBRARY_IMPL(aten, FuncTorchBatched, m) {
   REDUCTION_BOXED_ARGS(var_mean.correction, 1, KEEPDIM_CASE_VARIABLE, 3);
   REDUCTION_NO_KEEPDIM_ARG(_log_softmax);
   REDUCTION_BOXED_ARGS(rot90, 2, KEEPDIM_CASE_TRUE, -1);
-  VMAP_SUPPORT(aminmax, aminmax_batching_rule);
   VMAP_SUPPORT(_log_softmax_backward_data, _log_softmax_backward_batch_rule);
   VMAP_SUPPORT(_softmax_backward_data, _softmax_backward_batch_rule);
   VMAP_SUPPORT(_is_all_true, _is_all_true_batch_rule);

aten/src/ATen/native/cpu/TensorCompareKernel.cpp

@@ -177,6 +177,14 @@ static void aminmax_kernel(
     "Expect min and max dtype ", self.scalar_type(),
     " but got ", min_result.scalar_type(), " and ", max_result.scalar_type());
 
+  if (self.numel() == 1 && self.ndimension() == 0) {
+    min_result.resize_({});
+    max_result.resize_({});
+    min_result.fill_(self);
+    max_result.fill_(self);
+    return;
+  }
+
   AT_DISPATCH_ALL_TYPES_AND(ScalarType::Bool, self.scalar_type(), "aminmax_cpu", [&] {
     compare_base_kernel<scalar_t, scalar_t>(min_result, max_result, self, wrap_dim, keepdim, [&] (
       scalar_t* min_result_data, scalar_t* max_result_data,

torch/_decomp/decompositions.py

@@ -3337,16 +3337,6 @@ def upsample_bicubic2d_vec(
 def aminmax(self, *, dim=None, keepdim=False):
     amin = torch.amin(self, dim=dim, keepdim=keepdim)
     amax = torch.amax(self, dim=dim, keepdim=keepdim)
-    if (
-        keepdim
-        and dim is not None
-        and self.ndimension() == 0
-        and self.device.type == "cpu"
-    ):
-        # the behavior of aminmax differs from amin/amax for 0D tensors on CPU
-        # https://github.com/pytorch/pytorch/issues/96042
-        amin = amin.expand([1])
-        amax = amax.expand([1])
     return amin, amax
 
 

torch/testing/_internal/common_methods_invocations.py

@@ -11503,11 +11503,7 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
            decorators=(onlyNativeDeviceTypes,),
            supports_autograd=False,
            sample_inputs_func=sample_inputs_aminmax,
-           error_inputs_func=error_inputs_aminmax_amax_amin,
-           skips=(
-               # AssertionError: Resizing an out= argument with no elements threw a resize warning!
-               DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out', device_type='cpu'),
-           )),
+           error_inputs_func=error_inputs_aminmax_amax_amin),
     OpInfo('as_strided',
            dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf),
            supports_out=False,