chore: Test update for int type dynamic shape input

Author: keehyuna

py/torch_tensorrt/_Input.py

@@ -364,9 +364,25 @@ def example_tensor(
                     )
 
                 if isinstance(self.shape, dict):
-                    return torch.rand(self.shape[optimization_profile_field]).to(
-                        dtype=self.dtype.to(torch.dtype, use_default=True)
-                    )
+                    if (
+                        self.dtype == dtype.u8
+                        or self.dtype == dtype.i8
+                        or self.dtype == dtype.i32
+                        or self.dtype == dtype.i64
+                    ):
+                        type = self.dtype.to(torch.dtype, use_default=True)
+                        min_value = torch.iinfo(type).min
+                        max_value = torch.iinfo(type).max
+                        return torch.randint(
+                            min_value,
+                            max_value,
+                            self.shape[optimization_profile_field],
+                            dtype=type,
+                        )
+                    else:
+                        return torch.rand(self.shape[optimization_profile_field]).to(
+                            dtype=self.dtype.to(torch.dtype, use_default=True)
+                        )
                 else:
                     raise RuntimeError(
                         f"Input shape is dynamic but shapes are not provided as dictionary (found: {self.shape})"

tests/py/dynamo/conversion/harness.py

@@ -338,7 +338,7 @@ def run_test_with_dynamic_shape(
         input_specs,
         rtol=1e-03,
         atol=1e-03,
-        output_dtypes=None,
+        check_dtype=True,
         use_dynamo_tracer=False,
         enable_passes=False,
     ):
@@ -355,13 +355,23 @@ def run_test_with_dynamic_shape(
         # We replicate this behavior here
         compilation_settings = CompilationSettings(truncate_double=True)
 
+        output_dtypes = None
+        if check_dtype:
+            output_dtypes = infer_module_output_dtypes(
+                mod,
+                input_specs,
+                compilation_settings.device,
+                truncate_double=compilation_settings.truncate_double,
+            )
+
         interp = TRTInterpreter(
             mod,
             input_specs,
             output_dtypes=output_dtypes,
             compilation_settings=compilation_settings,
         )
+
         # Since the lowering is based on optimal shape. We need to test with
         # different shape(for ex. max shape) for testing dynamic shape
         inputs_max = [spec.example_tensor("max_shape") for spec in input_specs]
-        super().run_test(mod, inputs_max, inputs_max, interp, rtol, atol)
+        super().run_test(mod, inputs_max, inputs_max, interp, rtol, atol, check_dtype)