Avoid DDE in narrow with unbacked start (#166361)

Slice knows how to handle unbacked start, we do not need to offset start before calling slice, we can leave it for slice.
The only edge case is when start<0 and start+length ==0 in that case slice and narrow would deviate,
for that case we shall pass dim_size instead of start+length

Pull Request resolved: https://github.com/pytorch/pytorch/pull/166361
Approved by: https://github.com/aorenste

aten/src/ATen/native/TensorShape.cpp

@@ -1,5 +1,6 @@
 #include <ATen/core/ATen_fwd.h>
 #include <c10/core/ScalarType.h>
+#include <c10/core/SymInt.h>
 #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
 #include <ATen/AccumulateType.h>
 #include <ATen/Dispatch.h>
@@ -1710,11 +1711,14 @@ Tensor narrow_symint(
       "], but got ",
       start,
       ")")
-  if (start < 0) {
-    start = start + cur_size;
-  }
+  // Bounds check without converting start:
+  // - If start < 0: need (start + cur_size) + length <= cur_size, i.e., start +
+  // length <= 0
+  // - If start >= 0: need start + length <= cur_size
+  auto end = start + length;
   TORCH_SYM_CHECK(
-      start.sym_le(cur_size - length),
+      (start.sym_lt(0).sym_and((end).sym_le(0)))
+          .sym_or(start.sym_ge(0).sym_and((end).sym_le(cur_size))),
       "start (",
       start,
       ") + length (",
@@ -1722,7 +1726,31 @@ Tensor narrow_symint(
       ") exceeds dimension size (",
       cur_size,
       ").");
-  return at::slice_symint(self, dim, start, start + length, 1);
+
+  if (TORCH_GUARD_OR_FALSE(start.sym_ge(0).sym_or(end.sym_ne(0)))) {
+    return at::slice_symint(self, dim, start, end, 1);
+  } else if (TORCH_GUARD_OR_FALSE(start.sym_lt(0))) {
+    // Avoid the complex symbolic expressions path for non-unbacked.
+    return at::slice_symint(self, dim, start + cur_size, end + cur_size, 1);
+  } else {
+    // Cannot statically determine the condition due to unbacked.
+    // This is an interesting situation; when start is negative and
+    // start + length == 0, slice and narrow do different things.
+    // i.e., x.narrow(0, -2, 2) != x[-2:0]; in that case, we want to
+    // pass curr_size instead of 0. Otherwise, they would do the same thing.
+    // This says at runtime: if start < 0 and end == 0, then pass curr_size
+    // instead of 0.
+
+    auto use_different = start.sym_lt(0).sym_and(end.sym_eq(0)).toSymInt();
+    auto result =
+        at::slice_symint(self, dim, start, end + use_different * cur_size, 1);
+
+    // Ensure slice allocated unbacked size is specialized to length.
+    SymInt new_size = result.sym_size(dim);
+    TORCH_SYM_CHECK(new_size.sym_eq(length), "")
+
+    return result;
+  }
 }
 
 // This overload exists purely for XLA, because they wanted to pass in

c10/core/SymBool.cpp

@@ -1,4 +1,5 @@
 #include <c10/core/SymBool.h>
+#include <c10/core/SymInt.h>
 #include <c10/core/SymNodeImpl.h>
 
 namespace c10 {
@@ -111,4 +112,17 @@ bool SymBool::has_hint() const {
   return toSymNodeImpl()->has_hint();
 }
 
+SymInt SymBool::toSymInt() const {
+  // If concrete bool, return concrete SymInt
+  if (auto ma = maybe_as_bool()) {
+    return SymInt(*ma ? 1 : 0);
+  }
+
+  // Symbolic case: use sym_ite to convert bool to int (0 or 1)
+  auto node = toSymNodeImpl();
+  auto one_node = node->wrap_int(1);
+  auto zero_node = node->wrap_int(0);
+  return SymInt(node->sym_ite(one_node, zero_node));
+}
+
 } // namespace c10

c10/core/SymBool.h

@@ -12,6 +12,8 @@
 
 namespace c10 {
 
+class SymInt;
+
 class C10_API SymBool {
  public:
   /*implicit*/ SymBool(bool b) : data_(b) {}
@@ -80,6 +82,10 @@ class C10_API SymBool {
     return toSymNodeImplUnowned()->constant_bool();
   }
 
+  // Convert SymBool to SymInt (0 or 1)
+  // This is the C++ equivalent of Python's cast_symbool_to_symint_guardless
+  SymInt toSymInt() const;
+
   bool is_heap_allocated() const {
     return ptr_;
   }

test/export/test_export.py

@@ -6093,26 +6093,19 @@ def forward(self, p_linear_weight, p_linear_bias, b_buffer, x):
         retry_export(
             cf_implicitsize(),
             (torch.tensor(2), torch.randn(10)),
-            fixes=[
-                # Could not guard on data-dependent expression u0 < 0
-                "torch._check(i >= 0)",
-            ],
+            fixes=[],
         )
 
         class cf_stacklist(torch.nn.Module):
             def forward(self, xs, y, fixes):
                 i = y.item()
                 eval(fixes)
-                # instead of xs[i]
                 return torch.stack(xs, 0).narrow(0, i, 1).squeeze()
 
         retry_export(
             cf_stacklist(),
             ([torch.ones(5) * i for i in range(10)], torch.tensor(2)),
-            fixes=[
-                # Could not guard on data-dependent expression u0 < 0
-                "torch._check(i >= 0)",
-            ],
+            fixes=[],
         )
 
         class cf_tensorsplit(torch.nn.Module):
@@ -6166,6 +6159,11 @@ def forward(self, p_linear_weight, p_linear_bias, b_buffer, x):
         class cf_stacklist(torch.nn.Module):
             def forward(self, xs, y):
                 # y.item() is not a local, so we can't suggest a fix
+                if y.item() < 0:
+                    return (
+                        torch.stack(xs, 0).narrow(0, y.item() + xs.size(), 1).squeeze()
+                    )
+                else:
                     return torch.stack(xs, 0).narrow(0, y.item(), 1).squeeze()
 
         with self.assertRaisesRegex(
@@ -6196,7 +6194,18 @@ def forward(self, p_linear_weight, p_linear_bias, b_buffer, x):
             def forward(self, xs, y):
                 box = Box(y.item())
                 # box.content is not a local, so we can't suggest a fix
-                return torch.stack(xs, 0).narrow(0, box.content, 1).squeeze()
+                if box.content < 0:
+                    return (
+                        torch.stack(xs, 0)
+                        .narrow(0, box.content + xs.size(), 1)
+                        .squeeze()
+                    )
+                else:
+                    return (
+                        torch.stack(xs, 0)
+                        .narrow(0, box.content + xs.size(), 1)
+                        .squeeze()
+                    )
 
         with self.assertRaisesRegex(
             error_type,

test/test_dynamic_shapes.py

@@ -4401,6 +4401,57 @@ def forward(self, arg0_1: "i64[1][1]cpu", arg1_1: "Sym(u1)", arg2_1: "i64[u1][1]
 
         self.assertEqual(compiled(a, b), func(a, b))
 
+    @fresh_cache()
+    @torch._dynamo.config.patch("capture_scalar_outputs", True)
+    def test_narrow_unbacked_start(self):
+        def func(x, start, length):
+            # unbacked start
+            u0 = start.item()
+            return torch.narrow(x, 0, u0, length)
+
+        compiled_func = torch.compile(func, fullgraph=True, backend="inductor")
+
+        x = torch.tensor([1, 2, 3, 4, 5, 6])
+
+        # Test cases: (start, length)
+        test_cases = [
+            # Negative starts
+            (-2, 2),  # Start from second-to-last element
+            (-1, 1),  # Start from last element
+            (-3, 3),  # Start from third-to-last element
+            (-6, 2),  # Start from beginning (negative)
+            (-4, 1),  # Start from fourth-to-last element
+            # Positive starts
+            (0, 2),  # Start from beginning
+            (1, 3),  # Start from second element
+            (2, 2),  # Start from third element
+            (4, 2),  # Start near end
+            # Edge cases
+            (0, 6),  # Full tensor
+            (0, 1),  # Single element from start
+            (5, 1),  # Single element from end
+        ]
+
+        for start_val, length in test_cases:
+            with self.subTest(start=start_val, length=length):
+                start = torch.tensor([start_val])
+
+                # Test with compiled function
+                result_compiled = compiled_func(x, start, length)
+
+                # Test with eager function (expected behavior)
+                result_eager = func(x, start, length)
+
+                # Compare results
+                self.assertEqual(result_compiled, result_eager)
+
+    @fresh_cache()
+    @torch._dynamo.config.patch("capture_scalar_outputs", True)
+    @torch._inductor.config.patch("cpp_wrapper", True)
+    def test_narrow_unbacked_start_cpp_wrapper(self):
+        """Test narrow with unbacked start with cpp_wrapper"""
+        self.test_narrow_unbacked_start()
+
 
 instantiate_parametrized_tests(TestUnbacked)
 

torch/_inductor/codegen/wrapper.py

@@ -2058,7 +2058,8 @@ class PythonWrapperCodegen(CodeGen):
             neg = self.codegen_sizevar(
                 sympy.Max(0, sympy.Min(x + node.size, node.size))
             )
-            return f"{pos} if {x} >= 0 else {neg}"
+            x_cond = self.codegen_sizevar(x)
+            return f"{pos} if {x_cond} >= 0 else {neg}"
 
         def codegen_with_step(start_var, end_var, step):
             if step == 1:

torch/fx/experimental/symbolic_shapes.py

@@ -547,6 +547,7 @@ def rebind_unbacked(
         assert shape_env is not None
         for raw_u0, path in bindings.items():
             u1 = pytree.key_get(result, path)
+
             # Sometimes, things were previously unbacked bindings become constants.
             # There are two situations this can happen.
             #
@@ -602,7 +603,23 @@ def rebind_unbacked(
             if u1.node.hint is not None:
                 continue
 
-            raw_u1 = u1.node.expr
+            # unbacked symbols bindings might be replaced to other backed or
+            # unbacked replacements.
+            #
+            # Example:
+            #   u = x.item()
+            #   torch._check(u == 5)
+            #
+            # The safest approach is to retrieve raw_u1 from u1.node._expr
+            # and perform the rebinding on the original unbacked symbol,
+            # even if it’s no longer directly referenced.
+            #
+            # In other words, we should always rebind the original symbol
+            # before any replacements are applied.
+            #   u0 -> u0 == s1
+            raw_u1 = u1.node._expr
+
+            # TODO Do we still need this logic below?
             # Simplify SymBool binding
             if (
                 isinstance(raw_u1, sympy.Piecewise)

torch/utils/_sympy/printers.py

@@ -306,6 +306,24 @@ class PythonPrinter(ExprPrinter):
             raise TypeError("ndigits must be an instance of sympy.Integer")
         return f"round({self._print(number)}, {ndigits})"
 
+    def _print_Piecewise(self, expr: sympy.Expr) -> str:
+        # Convert Piecewise(expr_cond_pairs) to nested ternary expressions
+        # Piecewise((e1, c1), (e2, c2), ..., (eN, cN))
+        # becomes: e1 if c1 else (e2 if c2 else (... else eN))
+        result = None
+        for expr_i, cond_i in reversed(expr.args):
+            expr_str = self._print(expr_i)
+            if cond_i == True:  # noqa: E712
+                # This is the default case
+                result = expr_str
+            else:
+                cond_str = self._print(cond_i)
+                if result is None:
+                    result = expr_str
+                else:
+                    result = f"({expr_str} if {cond_str} else {result})"
+        return result if result else "0"
+
 
 class CppPrinter(ExprPrinter):
     def _print_Integer(self, expr: sympy.Expr) -> str:
@@ -327,6 +345,24 @@ class CppPrinter(ExprPrinter):
         )
         return f"{c} ? {p} : {q}"
 
+    def _print_Piecewise(self, expr: sympy.Expr) -> str:
+        # Convert Piecewise(expr_cond_pairs) to nested ternary operators
+        # Piecewise((e1, c1), (e2, c2), ..., (eN, cN))
+        # becomes: c1 ? e1 : (c2 ? e2 : (... : eN))
+        result = None
+        for expr_i, cond_i in reversed(expr.args):
+            expr_str = self.parenthesize(expr_i, PRECEDENCE["Atom"] - 0.5)
+            if cond_i == True:  # noqa: E712
+                # This is the default case
+                result = expr_str
+            else:
+                cond_str = self.parenthesize(cond_i, PRECEDENCE["Atom"] - 0.5)
+                if result is None:
+                    result = expr_str
+                else:
+                    result = f"{cond_str} ? {expr_str} : {result}"
+        return f"({result})" if result else "0"
+
     def _print_ModularIndexing(self, expr: sympy.Expr) -> str:
         x, div, mod = expr.args
         x = self.doprint(x)