fix: Fix unbacked sym int not found issue

py/torch_tensorrt/dynamo/_tracer.py

@@ -81,6 +81,7 @@ def trace(
         tuple(torch_arg_inputs),
         kwargs=torch_kwarg_inputs,
         dynamic_shapes=dynamic_shapes,
+        strict=kwargs.get("strict", False),
     )
 
     return exp_program

py/torch_tensorrt/dynamo/runtime/meta_ops/register_meta_ops.py

@@ -60,7 +60,7 @@ def fake_tensorrt_execute_engine(
                     output_sym_int = ctx.new_dynamic_size(min=min_val, max=max_val)
                     # Update var to val (hint)
                     output_sym_int_shape_env = output_sym_int.node.shape_env
-                    output_sym_int_shape_env.add_var_to_val(
+                    output_sym_int_shape_env.set_unbacked_var_to_val(
                         output_sym_int.node.expr, opt_val
                     )
                     output_shape.append(output_sym_int)
@@ -152,7 +152,7 @@ def __getstate__(self) -> Any:
         pass
 
 
-@torch.library.custom_op(
+@torch.library.custom_op(  # type: ignore
     "tensorrt::no_op_placeholder_for_execute_engine", mutates_args=()
 )
 def no_op_placeholder_for_execute_engine(

py/torch_tensorrt/dynamo/utils.py

@@ -375,8 +375,10 @@ def extract_var_range_info(symbolic_integer: torch.SymInt) -> Dict[str, int]:
     # https://pytorch.org/docs/stable/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html#torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy
     # expr.xreplace replaces the symbolic variables with their current values and computes the expression.
     var_range = shape_env.var_to_range.get(expr, None) or shape_env.bound_sympy(expr)
-    var_val = shape_env.var_to_val.get(expr, None) or expr.xreplace(
-        shape_env.var_to_val
+    var_val = (
+        shape_env.var_to_val.get(expr, None)
+        or shape_env.unbacked_var_to_val.get(expr, None)
+        or expr.xreplace(shape_env.var_to_val)
     )
     assert var_range, var_val
     min_val, max_val = int(var_range.lower), int(var_range.upper)
@@ -385,8 +387,9 @@ def extract_var_range_info(symbolic_integer: torch.SymInt) -> Dict[str, int]:
     min_max_opt = {}
     min_max_opt["min"] = min_val
     min_max_opt["max"] = max_val
-    if isinstance(var_val, sympy.core.numbers.Integer):
+    if isinstance(var_val, (sympy.core.numbers.Integer, int)):
         min_max_opt["opt"] = int(var_val)
+
     return min_max_opt
 
 
@@ -447,9 +450,9 @@ def get_graph_io_attrs(
             metadata = node.meta["val"]
             if isinstance(metadata, (tuple, list)):
                 for tensor in metadata:
-                    graph_io_attrs.append(attr_fn(tensor))  # type: ignore
+                    graph_io_attrs.append(attr_fn(tensor))
             else:
-                graph_io_attrs.append(attr_fn(metadata))  # type: ignore
+                graph_io_attrs.append(attr_fn(metadata))
 
     return graph_io_attrs
 

tests/py/dynamo/models/test_reexport.py

@@ -458,7 +458,7 @@ def test_resnet18_dynamic(ir):
 
     dyn_batch = torch.export.Dim("batch", min=1, max=8)
     exp_program = torch.export.export(
-        model, (input_bs2,), dynamic_shapes=({0: dyn_batch},)
+        model, (input_bs2,), dynamic_shapes=({0: dyn_batch},), strict=False
     )
     trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
 
@@ -532,8 +532,9 @@ def test_resnet18_dynamic_fallback(ir):
     }
 
     dyn_batch = torch.export.Dim("batch", min=1, max=8)
+
     exp_program = torch.export.export(
-        model, (input_bs2,), dynamic_shapes=({0: dyn_batch},)
+        model, (input_bs2,), dynamic_shapes=({0: dyn_batch},), strict=False
     )
     trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
 
@@ -610,6 +611,7 @@ def forward(self, lhs_val, rhs_val):
         model,
         inputs_4,
         dynamic_shapes={"lhs_val": {1: dyn_dim}, "rhs_val": {0: dyn_dim}},
+        strict=False,
     )
     trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
 
@@ -699,13 +701,16 @@ def forward(self, x):
 
     dyn_dim = torch.export.Dim("batch", min=1, max=64)
     exp_program = torch.export.export(
-        model, torch_inputs_bs50, dynamic_shapes=({0: dyn_dim},)
+        model, torch_inputs_bs50, dynamic_shapes=({0: dyn_dim},), strict=False
     )
     trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
 
     # Reexport with dynamic dimensions
     trt_exp_program = torch.export.export(
-        trt_module, torch_inputs_bs50, strict=False, dynamic_shapes=({0: dyn_dim},)
+        trt_module,
+        torch_inputs_bs50,
+        strict=False,
+        dynamic_shapes=({0: dyn_dim},),
     )
     torch.export.save(trt_exp_program, trt_ep_path)