Reorg for converters leaky_relu (FX Converter Refactor [6/N]) <Target: converter_reorg_proto> #1902
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There are some changes that do not conform to Python style guidelines:
--- py/torch_tensorrt/fx/converters/aten_ops_converters.py 2023-05-10 05:50:01.758314 +0000
+++ py/torch_tensorrt/fx/converters/aten_ops_converters.py 2023-05-10 05:50:17.483655 +0000
@@ -214,28 +214,20 @@
"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]
- )
+ 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 2023-05-10 05:50:01.758314 +0000
+++ py/torch_tensorrt/fx/converters/impl/activation.py 2023-05-10 05:50:17.702542 +0000
@@ -96,16 +96,18 @@
network: TRTNetwork,
target: Target,
source_ir: Optional[SourceIR],
name: str,
input_val: TRTTensor,
- alpha: Optional[Any]
+ 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 (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,
--- py/torch_tensorrt/fx/converters/nn_ops_converters.py 2023-05-10 05:50:01.758314 +0000
+++ py/torch_tensorrt/fx/converters/nn_ops_converters.py 2023-05-10 05:50:17.768262 +0000
@@ -34,7 +34,7 @@
network=network,
target="torch.nn.functional.leaky_relu",
source_ir=SourceIR.NN,
name=layer_name,
input_val=kwargs["input"],
- alpha=kwargs["negative_slope"]
- )
\ No newline at end of file
+ alpha=kwargs["negative_slope"],
+ )
--- py/torch_tensorrt/fx/converters/acc_ops_converters.py 2023-05-10 05:50:01.758314 +0000
+++ py/torch_tensorrt/fx/converters/acc_ops_converters.py 2023-05-10 05:50:19.367247 +0000
@@ -1023,16 +1023,11 @@
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
return activation.leaky_relu(
- network,
- target,
- SourceIR.ACC,
- name,
- kwargs["input"],
- kwargs["negative_slope"]
+ network, target, SourceIR.ACC, name, kwargs["input"], kwargs["negative_slope"]
)
@tensorrt_converter(acc_ops.elu)
def acc_ops_elu(
--- py/torch_tensorrt/fx/test/converters/aten_op/test_leaky_relu_aten.py 2023-05-10 05:50:01.762314 +0000
+++ py/torch_tensorrt/fx/test/converters/aten_op/test_leaky_relu_aten.py 2023-05-10 05:50:19.646364 +0000
@@ -48,6 +48,6 @@
TestModule(), input_specs, expected_ops={torch.ops.aten.leaky_relu.default}
)
if __name__ == "__main__":
- run_tests()
\ No newline at end of file
+ run_tests()
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correcting nn_ops for leaky_relu and correcting linting error
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…: converter_reorg_proto> (#1902)
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