converter reorg for leaky relu

correcting nn_ops for leaky_relu and correcting linting error

Author: apbose

py/torch_tensorrt/fx/converters/acc_ops_converters.py

@@ -1026,17 +1026,9 @@ def acc_ops_leaky_relu(
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    input_val = kwargs["input"]
-    negative_slope = kwargs["negative_slope"]
-    operation_type = trt.ActivationType.LEAKY_RELU
-    return activation.convert_activation(
-        network,
-        target,
-        SourceIR.ACC,
-        name,
-        operation_type,
-        input_val,
-        alpha=negative_slope,
+
+    return activation.leaky_relu(
+        network, target, SourceIR.ACC, name, kwargs["input"], kwargs["negative_slope"]
     )
 
 

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -215,6 +215,33 @@ def aten_ops_hardtanh(
     )
 
 
+@tensorrt_converter(torch.ops.aten.fmod.Tensor)
+def aten_ops_fmod(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    kwargs_new = {
+        "input": args[0],
+        "other": args[1],
+    }
+    return acc_ops_converters.acc_ops_fmod(network, target, None, kwargs_new, name)
+
+
+@tensorrt_converter(torch.ops.aten.leaky_relu.default)
+def aten_ops_leaky_relu(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+
+    return activation.leaky_relu(network, target, SourceIR.ATEN, name, args[0], args[1])
+
+
 @tensorrt_converter(torch.ops.aten.linear)
 def aten_ops_linear(
     network: TRTNetwork,

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

@@ -148,3 +148,30 @@ def sigmoid_fn(x):
         input_val,
         dyn_range_fn=sigmoid_dyn_range_fn,
     )
+
+
+def leaky_relu(
+    network: TRTNetwork,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input_val: TRTTensor,
+    alpha: Optional[Any],
+):
+    operation_type = trt.ActivationType.LEAKY_RELU
+
+    def leaky_relu_dyn_range_fn(dyn_range):
+        return (max(0, dyn_range[0]) + alpha * min(0, dyn_range[0])), (
+            max(0, dyn_range[1]) + alpha * min(0, dyn_range[1])
+        )
+
+    return convert_activation(
+        network,
+        target,
+        source_ir,
+        name,
+        operation_type,
+        input_val,
+        alpha,
+        dyn_range_fn=leaky_relu_dyn_range_fn,
+    )

py/torch_tensorrt/fx/converters/nn_ops_converters.py

@@ -51,3 +51,19 @@ def hardtanh(network, submod, args, kwargs, layer_name):
         name=layer_name,
         input_val=kwargs["input"],
     )
+
+
+@tensorrt_converter(torch.nn.functional.leaky_relu)
+@tensorrt_converter(torch.nn.modules.activation.LeakyReLU)
+def leaky_relu(network, submod, args, kwargs, layer_name):
+    # args/kwargs should have already been normalized to kwargs
+    assert len(args) == 0
+
+    return activation.leaky_relu(
+        network=network,
+        target="torch.nn.functional.leaky_relu",
+        source_ir=SourceIR.NN,
+        name=layer_name,
+        input_val=kwargs["input"],
+        alpha=kwargs["negative_slope"],
+    )

py/torch_tensorrt/fx/test/converters/aten_op/test_leaky_relu_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 TestLeakyReLUConverter(DispatchTestCase):
+    def test_leaky_relu(self):
+        class TestModule(nn.Module):
+            def forward(self, x):
+                return nn.functional.leaky_relu(x, negative_slope=0.05)
+
+        inputs = [torch.randn(1, 10)]
+        self.run_test(
+            TestModule(), inputs, expected_ops={torch.ops.aten.leaky_relu.default}
+        )
+
+    def test_leaky_relu_with_dynamic_shape(self):
+        class TestModule(nn.Module):
+            def forward(self, x):
+                return nn.functional.leaky_relu(x, negative_slope=0.05)
+
+        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.leaky_relu.default}
+        )
+
+    def test_leaky_relu_with_dynamic_shape_four_dimensions(self):
+        class TestModule(nn.Module):
+            def forward(self, x):
+                return nn.functional.leaky_relu(x, negative_slope=0.05)
+
+        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.leaky_relu.default}
+        )
+
+
+if __name__ == "__main__":
+    run_tests()