bugfix: allow empty tuple for inputs or arg_inputs (#3122)

Author: jiwoong-choi

py/torch_tensorrt/dynamo/_compiler.py

@@ -604,10 +604,10 @@ def convert_exported_program_to_serialized_trt_engine(
             DeprecationWarning,
             stacklevel=2,
         )
-    if not arg_inputs and not inputs:
+    if arg_inputs is None and inputs is None:
         raise AssertionError("'arg_inputs' and 'inputs' should not both be None.")
 
-    elif arg_inputs and inputs:
+    elif arg_inputs is not None and inputs is not None:
         raise AssertionError(
             "'arg_inputs' and 'inputs' should not be used at the same time."
         )

tests/py/dynamo/models/test_export_kwargs_serde.py

@@ -525,3 +525,50 @@ def forward(self, x, b=5, c=None, d=None):
     engine = convert_exported_program_to_serialized_trt_engine(
         exp_program, **compile_spec
     )
+
+
+def test_custom_model_compile_engine_with_pure_kwarg_inputs():
+    class net(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv1 = nn.Conv2d(3, 12, 3, padding=1)
+            self.bn = nn.BatchNorm2d(12)
+            self.conv2 = nn.Conv2d(12, 12, 3, padding=1)
+            self.fc1 = nn.Linear(12 * 56 * 56, 10)
+
+        def forward(self, x, b=5, c=None, d=None):
+            x = self.conv1(x)
+            x = F.relu(x)
+            x = self.bn(x)
+            x = F.max_pool2d(x, (2, 2))
+            x = self.conv2(x)
+            x = F.relu(x)
+            x = F.max_pool2d(x, (2, 2))
+            x = torch.flatten(x, 1)
+            x = x + b
+            if c is not None:
+                x = x * c
+            if d is not None:
+                x = x - d["value"]
+            return self.fc1(x)
+
+    model = net().eval().to("cuda")
+    kwargs = {
+        "x": torch.rand((1, 3, 224, 224)).to("cuda"),
+        "b": torch.tensor(6).to("cuda"),
+        "d": {"value": torch.tensor(8).to("cuda")},
+    }
+
+    compile_spec = {
+        "arg_inputs": (),
+        "kwarg_inputs": kwargs,
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.float},
+        "pass_through_build_failures": True,
+        "optimization_level": 1,
+        "min_block_size": 1,
+        "ir": "dynamo",
+    }
+
+    exp_program = torch.export.export(model, args=(), kwargs=kwargs)
+    _ = convert_exported_program_to_serialized_trt_engine(exp_program, **compile_spec)