Reorg for converters in hardtanh(FX Converter Refactor [5/N]) <Target: converter_reorg_proto>

py/torch_tensorrt/fx/converters/acc_ops_converters.py

@@ -3585,23 +3585,15 @@ def acc_ops_hardtanh(
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    input_val = kwargs["input"]
 
-    if not isinstance(input_val, TRTTensor):
-        raise RuntimeError(
-            f"hardtanh received input {input_val} that is not part "
-            "of the TensorRT region!"
-        )
-
-    return activation.convert_activation(
+    return activation.hardtanh(
         network,
         target,
         SourceIR.ACC,
         name,
-        trt.ActivationType.CLIP,
-        input_val,
-        alpha=kwargs["min_val"],
-        beta=kwargs["max_val"],
+        kwargs["input"],
+        kwargs["min_val"],
+        kwargs["max_val"],
     )
 
 

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -201,6 +201,20 @@ def aten_ops_fmod(
     return acc_ops_converters.acc_ops_fmod(network, target, None, kwargs_new, name)
 
 
+@tensorrt_converter(torch.ops.aten.hardtanh.default)
+def aten_ops_hardtanh(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+
+    return activation.hardtanh(
+        network, target, SourceIR.ATEN, name, args[0], args[1], args[2]
+    )
+
+
 @tensorrt_converter(torch.ops.aten.linear)
 def aten_ops_linear(
     network: TRTNetwork,

py/torch_tensorrt/fx/converters/impl/activation.py

@@ -69,6 +69,37 @@ def convert_activation(
     return layer.get_output(0)
 
 
+def hardtanh(
+    network: TRTNetwork,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input_val: TRTTensor,
+    alpha: Optional[Any] = None,
+    beta: Optional[Any] = None,
+):
+    operation_type = trt.ActivationType.CLIP
+
+    def hardtanh_dyn_range_fn(dyn_range):
+        def hardtanh_fn(x):
+            # TODO: Called torch.nn.functional.hardtanh
+            return torch.nn.functional.hardtanh(x)
+
+        return hardtanh_dyn_range_fn(dyn_range[0]), hardtanh_dyn_range_fn(dyn_range[1])
+
+    return convert_activation(
+        network,
+        target,
+        source_ir,
+        name,
+        operation_type,
+        input_val,
+        alpha,
+        beta,
+        dyn_range_fn=hardtanh_dyn_range_fn,
+    )
+
+
 def relu(
     network: TRTNetwork,
     target: Target,

py/torch_tensorrt/fx/converters/nn_ops_converters.py

@@ -36,3 +36,18 @@ def sigmoid(network, submod, args, kwargs, layer_name):
         name=layer_name,
         input_val=kwargs["input"],
     )
+
+
+@tensorrt_converter(torch.nn.functional.hardtanh)
+@tensorrt_converter(torch.nn.modules.activation.Hardtanh)
+def hardtanh(network, submod, args, kwargs, layer_name):
+    # args/kwargs should have already been normalized to kwargs
+    assert len(args) == 0
+
+    return activation.hardtanh(
+        network=network,
+        target="torch.nn.modules.activation.Hardtanh",
+        source_ir=SourceIR.NN,
+        name=layer_name,
+        input_val=kwargs["input"],
+    )

py/torch_tensorrt/fx/test/converters/aten_op/test_hardtanh_aten.py

@@ -0,0 +1,53 @@
+import torch
+import torch.nn as nn
+from torch.testing._internal.common_utils import run_tests
+from torch_tensorrt.fx.tools.common_fx2trt import DispatchTestCase, InputTensorSpec
+
+
+class TestHardTanHConverter(DispatchTestCase):
+    def test_hardtanh(self):
+        class TestModule(nn.Module):
+            def forward(self, x):
+                return nn.functional.hardtanh(x)
+
+        inputs = [torch.randn(1, 10)]
+        self.run_test(
+            TestModule(), inputs, expected_ops={torch.ops.aten.hardtanh.default}
+        )
+
+    def test_hardtanh_with_dynamic_shape(self):
+        class TestModule(nn.Module):
+            def forward(self, x):
+                return nn.functional.hardtanh(x)
+
+        input_specs = [
+            InputTensorSpec(
+                shape=(-1, -1, -1),
+                dtype=torch.float32,
+                shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
+            ),
+        ]
+        self.run_test_with_dynamic_shape(
+            TestModule(), input_specs, expected_ops={torch.ops.aten.hardtanh.default}
+        )
+
+    def test_hardtanh_with_dynamic_shape_four_dimensions(self):
+        class TestModule(nn.Module):
+            def forward(self, x):
+                return nn.functional.hardtanh(x)
+
+        input_specs = [
+            InputTensorSpec(
+                shape=(-1, -1, -1, -1),
+                dtype=torch.float32,
+                shape_ranges=[((1, 1, 1, 5), (1, 2, 3, 5), (3, 3, 3, 5))],
+            ),
+        ]
+
+        self.run_test_with_dynamic_shape(
+            TestModule(), input_specs, expected_ops={torch.ops.aten.hardtanh.default}
+        )
+
+
+if __name__ == "__main__":
+    run_tests()