Removing the aten.slice and add_slice

Author: apbose

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -481,24 +481,6 @@ def aten_ops_sym_size(
     return slice_layer.get_output(0)
 
 
-@tensorrt_converter(torch.ops.aten.slice.Tensor)
-def aten_ops_slice(
-    network: TRTNetwork,
-    target: Target,
-    args: Tuple[Argument, ...],
-    kwargs: Dict[str, Argument],
-    name: str,
-) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    kwargs_new = {
-        "input": args[0],
-        "dim": args[1],
-        "start": args[2],
-        "stop": args[3],
-        "step": args[4],
-    }
-    return add_slice(network, target.kwargs_new, name)
-
-
 @tensorrt_converter(torch.ops.aten.leaky_relu.default)
 def aten_ops_leaky_relu(
     network: TRTNetwork,

py/torch_tensorrt/fx/converters/operator.py

@@ -1016,52 +1016,3 @@ def add_expand(network, target, kwargs, name):
     layer = network.add_slice(input_val, start=start, shape=shape, stride=stride)
     set_layer_name(layer, target, name)
     return layer.get_output(0)
-
-
-def add_slice(network, target, kwargs, name):
-    input_val = kwargs["input"]
-
-    if not isinstance(input_val, TRTTensor):
-        raise RuntimeError(
-            f"slice_tensor received input {input_val} that is not part "
-            "of the TensorRT region!"
-        )
-
-    ranks = len(input_val.shape) + (1 if network.has_implicit_batch_dimension else 0)
-    dim = get_positive_dim(cast(int, kwargs["dim"]), ranks)
-    dynamic_shape = has_dynamic_shape(input_val.shape)
-    if network.has_implicit_batch_dimension:
-        if dim == 0:
-            raise RuntimeError(
-                f"We do not support slice_tensor at batch dim when it's implicit, got {dim}!"
-            )
-        dim = dim - 1
-    else:
-        if dynamic_shape:
-            # Check whether slice target dim is dynamic shape dim
-            assert input_val.shape[dim] != -1, "Can't chunk on dynamic shape dimension!"
-
-    start_int = cast(int, kwargs["start"])
-    stop_int = cast(int, kwargs["stop"])
-    step_int = cast(int, kwargs["step"])
-    start = [0] * len(input_val.shape)
-    start[dim] = start_int
-    stride = [1] * len(start)
-    stride[dim] = step_int
-    output_shape = list(input_val.shape)
-    output_shape[dim] = (stop_int - start_int) // step_int
-
-    if dynamic_shape > 0:
-        output_shape = get_shape_with_dynamic_shape(
-            network, output_shape, input_val, target, name
-        )
-    layer = network.add_slice(
-        input_val,
-        start=start,
-        shape=[] if dynamic_shape else output_shape,
-        stride=stride,
-    )
-    if dynamic_shape:
-        layer.set_input(2, output_shape)
-    set_layer_name(layer, target, name)
-    return layer.get_output(0)