Use expect_true to make split with unbacked sizes work.

aten/src/ATen/native/TensorShape.cpp

@@ -1308,9 +1308,12 @@ Tensor& narrow_copy_dense_cpu_out(
 
   // wrap start and do bound check
   const auto cur_size = self_sizes[dim];
-  if (start != cur_size && start < 0) { // start being the end is valid, but
-                                        // not a valid dim specification.
-    start = at::maybe_wrap_dim(start, cur_size);
+  TORCH_CHECK_INDEX(
+    -cur_size <= start && start <= cur_size,
+    "start out of range (expected to be in range of [", -cur_size, ", ", cur_size, "], but got ", start, ")"
+  )
+  if (start < 0) {
+    start = start + cur_size;
   }
   TORCH_CHECK(
       length >= 0 && start <= cur_size - length,
@@ -1374,8 +1377,12 @@ Tensor narrow(const Tensor& self, int64_t dim, int64_t start, int64_t length) {
   TORCH_CHECK(self.dim() > 0, "narrow() cannot be applied to a 0-dim tensor.");
   TORCH_CHECK(length >= 0, "narrow(): length must be non-negative.");
   auto cur_size = self.size(dim);
-  if (start != cur_size) {  // start being the end is valid, but not a valid dim specification.
-    start = maybe_wrap_dim(start, cur_size);
+  TORCH_CHECK_INDEX(
+    -cur_size <= start && start <= cur_size,
+    "start out of range (expected to be in range of [", -cur_size, ", ", cur_size, "], but got ", start, ")"
+  )
+  if (start < 0) {
+    start = start + cur_size;
   }
   TORCH_CHECK(start <= cur_size - length,
            "start (", start, ") + length (", length, ") exceeds dimension size (", cur_size, ").");
@@ -1384,12 +1391,16 @@ Tensor narrow(const Tensor& self, int64_t dim, int64_t start, int64_t length) {
 
 Tensor narrow_symint(const Tensor& self, int64_t dim, SymInt start, SymInt length) {
   TORCH_CHECK(self.dim() > 0, "narrow() cannot be applied to a 0-dim tensor.");
-  TORCH_CHECK(length >= 0, "narrow(): length must be non-negative.");
+  TORCH_SYM_CHECK(length.sym_ge(0), "narrow(): length must be non-negative.");
   auto cur_size = self.sym_size(dim);
-  if (start != cur_size) {  // start being the end is valid, but not a valid dim specification.
-    start = maybe_wrap_dim(start, cur_size);
+  TORCH_CHECK_INDEX(
+    ((-cur_size).sym_le(start).sym_and(start.sym_le(cur_size))).expect_true(__FILE__, __LINE__),
+    "start out of range (expected to be in range of [", -cur_size, ", ", cur_size, "], but got ", start, ")"
+  )
+  if (start < 0) {
+    start = start + cur_size;
   }
-  TORCH_CHECK(start <= cur_size - length,
+  TORCH_SYM_CHECK(start.sym_le(cur_size - length),
            "start (", start, ") + length (", length, ") exceeds dimension size (", cur_size, ").");
   return at::slice_symint(self, dim, start, start + length, 1);
 }

test/dynamo/test_misc.py

@@ -6296,6 +6296,21 @@ def mapper(x):
         self.assertEqual(counter.frame_count, 1)
         self.assertEqual(counter.op_count, 9)
 
+    @torch._dynamo.config.patch(
+        capture_scalar_outputs=True, capture_dynamic_output_shape_ops=True
+    )
+    def test_unbacked_symint(self):
+        from torch._export.constraints import constrain_as_size
+
+        @torch.compile(backend="eager")
+        def f(lengths, values):
+            sizes = lengths.tolist()
+            for s in sizes:
+                constrain_as_size(s, min=2, max=100)
+            return torch.split(values, sizes)
+
+        f(torch.tensor([2, 3, 4]), torch.randn(9))
+
     def test_simple_set_usage(self):
         def foo(x, y):
             setty = {x, y}

test/test_proxy_tensor.py

@@ -1174,6 +1174,32 @@ def forward(self, a_1):
     empty = torch.ops.aten.empty.memory_format([add], device = device(type='cpu'), pin_memory = False);  add = None
     return empty""")
 
+    def test_split_unbacked_sizes(self):
+        def f(lengths, values):
+            # tolist not directly supported atm
+            sizes = [lengths[i].item() for i in range(lengths.size(0))]
+            for s in sizes:
+                constrain_range(s, min=2)  # TODO: better as constrain_as_size
+            return torch.split(values, sizes)
+
+        r = str(make_fx(f, tracing_mode="symbolic")(
+            torch.tensor([2, 3, 4]),
+            torch.randn(9)
+        ).code).strip()
+        self.assertExpectedInline(r, """\
+def forward(self, lengths_1, values_1):
+    select = torch.ops.aten.select.int(lengths_1, 0, 0)
+    _local_scalar_dense = torch.ops.aten._local_scalar_dense.default(select);  select = None
+    select_1 = torch.ops.aten.select.int(lengths_1, 0, 1)
+    _local_scalar_dense_1 = torch.ops.aten._local_scalar_dense.default(select_1);  select_1 = None
+    select_2 = torch.ops.aten.select.int(lengths_1, 0, 2);  lengths_1 = None
+    _local_scalar_dense_2 = torch.ops.aten._local_scalar_dense.default(select_2);  select_2 = None
+    split_with_sizes = torch.ops.aten.split_with_sizes.default(values_1, [_local_scalar_dense, _local_scalar_dense_1, _local_scalar_dense_2]);  values_1 = _local_scalar_dense = _local_scalar_dense_1 = _local_scalar_dense_2 = None
+    getitem = split_with_sizes[0]
+    getitem_1 = split_with_sizes[1]
+    getitem_2 = split_with_sizes[2];  split_with_sizes = None
+    return (getitem, getitem_1, getitem_2)""")  # noqa: B950
+
     def test_invalidate_nonzero(self):
         ok = False
 

torch/__init__.py

@@ -948,7 +948,7 @@ def _check_with(error_type, cond: Union[builtins.bool, SymBool], message: Callab
     if not isinstance(cond, (builtins.bool, torch.SymBool)):
         raise TypeError(f'cond must be a bool, but got {type(cond)}')
 
-    if cond:
+    if torch.fx.experimental.symbolic_shapes.expect_true(cond):
         return
 
     # error_type must be a subclass of Exception and not subclass of Warning

torch/_decomp/decompositions.py

@@ -19,7 +19,7 @@
     _safe_copy_out,
     out_wrapper,
 )
-from torch.fx.experimental.symbolic_shapes import guard_int
+from torch.fx.experimental.symbolic_shapes import expect_true, guard_int
 from torch.utils._pytree import tree_flatten, tree_map
 
 DispatchKey = torch._C.DispatchKey  # type: ignore[attr-defined]
@@ -727,12 +727,12 @@ def slice_forward(
 
     if start_val < 0:
         start_val = 0
-    elif start_val >= sizes[dim]:
+    elif start_val > sizes[dim]:
         start_val = sizes[dim]
 
     if end_val < start_val:
         end_val = start_val
-    elif end_val >= sizes[dim]:
+    elif end_val > sizes[dim]:
         end_val = sizes[dim]
 
     storage_offset = self.storage_offset() + start_val * strides[dim]
@@ -1182,15 +1182,23 @@ def prod(x: List[int]):
 def split_with_sizes(
     self: Tensor, split_sizes: List[int], dim: int = 0
 ) -> List[Tensor]:
-    if sum(split_sizes) != self.shape[dim]:
-        raise ValueError(
-            "Split sizes don't add up to the tensor's size in the given dimension"
-        )
+    torch._check_with(
+        ValueError,
+        sum(split_sizes) == self.shape[dim],
+        lambda: "Split sizes don't add up to the tensor's size in the given dimension",
+    )
     num_splits = len(split_sizes)
     splits = []
     start_idx = 0
     for i in range(num_splits):
         length = split_sizes[i]
+        torch._check(
+            length >= 0,
+            lambda: "split_with_sizes expects split_sizes have only non-negative entries",
+        )
+        # We know this is true thanks to the sum, but this assertion helps
+        # out our internal reasoning
+        expect_true(start_idx + length <= self.shape[dim])
         splits.append(self.narrow(dim, start_idx, length))
         start_idx += length
     return splits

torch/_refs/__init__.py

@@ -2923,14 +2923,13 @@ def narrow(
     torch._check(length >= 0, lambda: "narrow(): length must be non-negative.")
     dim = utils.canonicalize_dim(a.ndim, dim)
     dim_length = a.size(dim)
-    # Start being the end is usually invalid since it's out of bounds. So it's
-    # not allowed by canonicalize_dim. But for narrow it's valid as long as
-    # the length is 0, which is handled by the check below.
-    if start != dim_length:
-        # Negative start means indexing from the end of dim.
-        # Note: a dimension isn't being canonicalized here, this reuses
-        # canonicalize_dim because the semantics are similar.
-        start = utils.canonicalize_dim(dim_length, start)  # type: ignore[arg-type]
+    torch._check_with(
+        IndexError,
+        -dim_length <= start and start <= dim_length,  # type: ignore[arg-type]
+        lambda: f"start out of range (expected to be in range of [{-dim_length}, {dim_length}], but got {start})",
+    )
+    if start < 0:
+        start = start + dim_length
     torch._check(
         start <= dim_length - length,  # type: ignore[arg-type]
         lambda: f"start ({start}) + length ({length}) exceeds dimension size ({dim_length}).",

torch/csrc/jit/python/init.cpp

@@ -1210,27 +1210,25 @@ void initJITBindings(PyObject* module) {
       SYMNODE_SIZES_STRIDES(is_channels_last_strides_2d)
       SYMNODE_SIZES_STRIDES(is_channels_last_strides_3d)
       SYMNODE_SIZES_STRIDES(is_non_overlapping_and_dense)
-      // Intentionally don't set file line, as the
-      // Python backtrace matters more here
       .def(
           "guard_int",
-          [](c10::SymNode a) {
-            return a->guard_int(nullptr, 0);
+          [](c10::SymNode a, const char* file, int64_t line) {
+            return a->guard_int(file, line);
           })
       .def(
           "guard_bool",
-          [](c10::SymNode a) {
-            return a->guard_bool(nullptr, 0);
+          [](c10::SymNode a, const char* file, int64_t line) {
+            return a->guard_bool(file, line);
           })
       .def(
           "guard_float",
-          [](c10::SymNode a) {
-            return a->guard_float(nullptr, 0);
+          [](c10::SymNode a, const char* file, int64_t line) {
+            return a->guard_float(file, line);
           })
       .def(
           "expect_true",
-          [](c10::SymNode a) {
-            return a->expect_true(nullptr, 0);
+          [](c10::SymNode a, const char* file, int64_t line) {
+            return a->expect_true(file, line);
           })
       .def(
           "has_hint",

torch/testing/_internal/common_methods_invocations.py

@@ -5013,19 +5013,13 @@ def error_inputs_narrow_narrow_copy(op_info, device, *, is_narrow, is_ref):
                      error_regex=r"Dimension out of range \(expected to be in range of \[-3, 2\], but got -4\)")
 
     # out of bounds start
-    if not is_narrow and not is_ref and torch.device(device).type == 'cpu':
-        # narrow_copy_dense_cpu_out
-        yield ErrorInput(SampleInput(make_arg((L, M, S)), 1, M + 1, 0),
-                         error_type=RuntimeError,
-                         error_regex=r"start \(11\) \+ length \(0\) exceeds dimension size \(10\)\.")
-    else:
-        yield ErrorInput(SampleInput(make_arg((L, M, S)), 1, M + 1, 0),
-                         error_type=IndexError,
-                         error_regex=r"Dimension out of range \(expected to be in range of \[-10, 9\], but got 11\)")
+    yield ErrorInput(SampleInput(make_arg((L, M, S)), 1, M + 1, 0),
+                     error_type=IndexError,
+                     error_regex=r"start out of range \(expected to be in range of \[-10, 10\], but got 11\)")
     # out of bounds start (negative)
     yield ErrorInput(SampleInput(make_arg((L, M, S)), 1, -M - 1, 0),
                      error_type=IndexError,
-                     error_regex=r"Dimension out of range \(expected to be in range of \[-10, 9\], but got -11\)")
+                     error_regex=r"start out of range \(expected to be in range of \[-10, 10\], but got -11\)")
 
     # out of bounds length
     yield ErrorInput(SampleInput(make_arg((S, L, M)), 2, 0, M + 1),