Make as_strided_copy materialize a new tensor with index.

test/test_operations.py

@@ -67,6 +67,22 @@ def _is_on_eager_debug_mode():
                                    'skip on eager debug mode')
 
 
+def _skipIfFunctionalization(value=True, reason=""):
+  verb = "is" if value else "is not"
+  reason = f" Reason: {reason}" if reason else ""
+  return unittest.skipIf(
+      XLA_DISABLE_FUNCTIONALIZATION is value,
+      f'Works only when functionalization {verb} disabled.{reason}.')
+
+
+def skipIfFunctionalizationEnabled(reason):
+  return _skipIfFunctionalization(value=False, reason=reason)
+
+
+def skipIfFunctionalizationDisabled(reason):
+  return _skipIfFunctionalization(value=True, reason=reason)
+
+
 def _gen_tensor(*args, **kwargs):
   return torch.randn(*args, **kwargs)
 
@@ -978,8 +994,8 @@ def func(a, b):
 
   # TODO - upstream behavior has changed and results in expected DestroyXlaTensor
   # counter as of 11/13/2023. Re-enable after reviewing the change.
-  @unittest.skipIf(True or XLA_DISABLE_FUNCTIONALIZATION,
-                   'Metrics differ when functionalization is disabled.')
+  # @skipIfFunctionalizationDisabled("metrics differ")
+  @unittest.skip
   def test_set(self):
     met.clear_all()
 
@@ -997,8 +1013,7 @@ def test_set(self):
     # shouldn't crash
     self.assertTrue(torch.allclose(t2.cpu(), torch.zeros(10)))
 
-  @unittest.skipIf(XLA_DISABLE_FUNCTIONALIZATION,
-                   'Metrics differ when functionalization is disabled.')
+  @skipIfFunctionalizationDisabled("metrics differ")
   def test_replace_xla_tensor(self):
     met.clear_all()
 
@@ -1341,8 +1356,7 @@ def test_fn(t, c):
         ), dtype=torch.int64)
     self.runAtenTest([token_type_ids, cat_ids], test_fn)
 
-  @unittest.skipIf(not XLA_DISABLE_FUNCTIONALIZATION,
-                   'When functionalization is enabled, views do not exist.')
+  @skipIfFunctionalizationEnabled("views do not exist")
   def test_save_view_alias_check(self):
 
     class Nested(object):
@@ -1498,6 +1512,63 @@ def test_fn(r):
 
     self.runAtenTest([torch.arange(144, dtype=torch.int32)], test_fn)
 
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_gap(self):
+
+    def test_fn(r):
+      return torch.as_strided(r, (4, 4), (8, 1))
+
+    self.runAtenTest([torch.arange(28, dtype=torch.int32)], test_fn)
+
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_gap_no_unit_stride(self):
+
+    def test_fn(r):
+      return torch.as_strided(r, (4, 4), (8, 2))
+
+    self.runAtenTest([torch.arange(31, dtype=torch.int32)], test_fn)
+
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_overlap(self):
+
+    def test_fn(r):
+      return torch.as_strided(r, (4, 4), (2, 1))
+
+    self.runAtenTest([torch.arange(10, dtype=torch.int32)], test_fn)
+
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_overlap_and_gap(self):
+
+    def test_fn(r):
+      return torch.as_strided(r, (4, 4), (4, 2))
+
+    self.runAtenTest([torch.arange(19, dtype=torch.int32)], test_fn)
+
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_overlap_zero_stride(self):
+
+    def test_fn(r):
+      return torch.as_strided(r, (4, 4), (0, 1))
+
+    self.runAtenTest([torch.arange(19, dtype=torch.int32)], test_fn)
+
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_gap_no_unit_stride(self):
+
+    def test_fn(r):
+      x = r.view(8, 4)
+      return torch.as_strided(r, (4, 4), (6, 2))
+
+    self.runAtenTest([torch.arange(32, dtype=torch.int32)], test_fn)
+
+  @skipIfFunctionalizationDisabled("arbitrary as_strided unsupported")
+  def test_as_strided_with_empty_args(self):
+
+    def test_fn(r):
+      return torch.as_strided(r, tuple(), tuple())
+
+    self.runAtenTest([torch.arange(32, dtype=torch.int32)], test_fn)
+
   def test_basic_bfloat16(self):
 
     def test_fn(s):

test/test_ops.py

@@ -29,6 +29,7 @@ def __new__(cls, name, variant_test_name=""):
         {
             AllowedOpInfoEntry('abs'),
             AllowedOpInfoEntry('add'),
+            AllowedOpInfoEntry('as_strided'),
             AllowedOpInfoEntry('mul'),
             AllowedOpInfoEntry('sub'),
             AllowedOpInfoEntry('addmm'),
@@ -349,6 +350,12 @@ def __new__(cls, name, variant_test_name=""):
             # AllowedOpInfoEntry('var_mean'),
             # AllowedOpInfoEntry('pow'), # for int64 don't work, likely rounding issue
             # AllowedOpInfoEntry('__rpow__'),
+
+            # In theory, this should work.
+            # However, the problem is the way we prepare the reference (CPU) inputs:
+            # we clone them. If they were a view, they are not anymore.
+            #
+            # AllowedOpInfoEntry('as_strided', 'partial_views'),
         }))
 
 

torch_xla/csrc/aten_xla_type.cpp

@@ -697,24 +697,88 @@ at::Tensor XLANativeFunctions::as_strided_copy(
   // This function actually operates on the tensor's storage. Since XLA does not
   // expose the actual storage, we use the originally allocated tensor.
   const at::Tensor& base = bridge::GetXlaTensor(self)->Base();
-  const at::Tensor& tensor = base.defined() ? base : self;
-  XLATensorPtr self_tensor = bridge::GetXlaTensor(tensor);
-  auto xsize = XlaHelpers::I64List(size);
-  auto xstride = XlaHelpers::I64List(stride);
-  if (!AsStrided::StrideIsSupported(self_tensor->shape(), xsize, xstride,
-                                    storage_offset.value_or(0))) {
-    return at::native::call_fallback_fn<
-        &xla_cpu_fallback, ATEN_OP(as_strided)>::call(self, size, stride,
-                                                      storage_offset);
-  }
-  // Sets the base tensor as tensor.
-  // Even though this function copies (without aliasing) tensor, it's still
-  // treated as a view function in the functionalization layer.
-  return bridge::AtenFromXlaTensor(bridge::SetBaseTensor(
-      tensor_methods::as_strided(self_tensor, std::move(xsize),
-                                 std::move(xstride),
-                                 XlaHelpers::I64Optional(storage_offset)),
-      tensor));
+  at::Tensor tensor = base.defined() ? base : self;
+
+  auto dim = size.size();
+  auto itemsize = tensor.dtype().itemsize();
+  int64_t storage_size =
+      at::detail::computeStorageNbytes(size, stride, itemsize);
+
+  XLA_CHECK(tensor.numel() * itemsize >= storage_size)
+      << "as_strided: storage not big enough for size " << size << ": "
+      << storage_size << " (needed) vs " << tensor.numel() << " (actual).";
+
+  if (dim == 0 && tensor.numel() > 0) {
+    // If there's no specified dimension, return the first element of the
+    // storage. This behavior is consistent with eager.
+    return select_copy(view_copy_symint(tensor, {tensor.numel()}), 0, 0);
+  }
+
+  if (storage_size == 0) {
+    // Return an empty tensor, if no storage is actually needed.
+    return empty_symint(c10::fromIntArrayRefSlow(size), tensor.scalar_type(),
+                        /* layout= */ c10::nullopt, tensor.device(),
+                        /* pin_memory= */ c10::nullopt,
+                        /*  memory_format= */ c10::nullopt);
+  }
+
+  // At this point, the following is true:
+  XLA_CHECK(storage_size > 0);
+  XLA_CHECK(tensor.numel() > 0);
+  XLA_CHECK(dim > 0);
+
+  // Index tensor for gathering the needed elements into contiguous data.
+  //
+  // PyTorch/XLA, by default, assumes dense and contiguous data. However, when
+  // specifying strides, that might not be the case.
+  //
+  // Therefore, we gather the elements selected by following the size, stride,
+  // and storage offset, materializing it into contiguous elements.
+  //
+  // In order to accomplish that, we create an index tensor. Specifically, we
+  // create an n-dimensional tensor (n is the number of dimensions of the
+  // output) of indices. Each element represent the at which position of the
+  // flattened tensor the desired element is in.
+
+  // Example: arange(13).as_strided((2, 2, 2), (3, 4, 5))
+  //
+  // Start with a 1-element n-dimensional tensor, initialized with 0:
+  //
+  //     [[[0]]]
+  //
+  std::vector<int64_t> view_shape(dim, 1);
+  auto index_tensor =
+      at::tensor({storage_offset.value_or(self.storage_offset())},
+                 at::TensorOptions().dtype(at::kLong))
+          .view(view_shape);
+
+  // Then, add to the index_tensor the offset value introduced for each possible
+  // index of that corresponding dimension.
+  //
+  //   - Iteration i=0:
+  //        [[[0]]] + [[[0 * 3]], [[1 * 3]]]
+  //        = [[[0 * 3]], [[1 * 3]]]
+  //        = [[[0]], [[3]]]
+  //
+  //   - Iteration i=1:
+  //        [[[0]], [[3]]] + [[[0 * 4], [1 * 4]]]
+  //        = [[[0 + 0 * 4], [0 + 1 * 4]], [[3 + 0 * 4], [3 + 1 * 4]]]
+  //        = [[[0], [4]], [[3], [7]]]
+  //
+  //   - Iteration i=2:
+  //        [[[0], [4]], [[3], [7]]] + [[[0 * 5, 1 * 5]]]
+  //        =[[[0 + 0 * 5, 0 + 1 * 5], [4 + 0 * 5, 4 + 1 * 5]],
+  //          [[3 + 0 * 5, 3 + 1 * 5], [7 + 0 * 5, 7 + 1 * 5]]]
+  //        =[[[0, 5], [4, 9]], [[3, 8], [7, 12]]]
+  for (int i = 0; i < dim; i++) {
+    auto vshape = view_shape;
+    vshape[i] = size[i];
+    index_tensor =
+        index_tensor.add((at::arange(size[i]) * stride[i]).view(vshape));
+  }
+
+  // Finally, index the tensor with the computed indices.
+  return take(tensor, index_tensor.to(tensor.device()));
 }
 
 at::Tensor XLANativeFunctions::as_strided_scatter(