🐛 [Bug] AssertionError: end must be an integer

[Fulitcher]

Bug Description

To Reproduce

Steps to reproduce the behavior:

1. Prepare torch-tensorrt convert code

model = MyModel(opt)
model.load_staste_dict(torch.load("model.pth"))
inputs = torch_tensorrt.Input(min_shape=[int(min_batch_size), channel, height, width],
                                   opt_shape=[int(max_batch_size // 2), channel, height, width],
                                   max_shape=[int(max_batch_size), channel, height, width],
                                   dtype=torch.float32)
trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "rt_model.ep", inputs=inputs)

2. Error occured as below,

[WARNING  | py.warnings        ]: /usr/local/lib/python3.12/dist-packages/torch/fx/graph.py:1801: UserWarning: Node prediction_src_pe_lifted_tensor_0 target Prediction.src_pe.lifted_tensor_0 lifted_tensor_0 of Prediction.src_pe does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
  warnings.warn(

[WARNING  | py.warnings        ]: /usr/local/lib/python3.12/dist-packages/torch/fx/graph.py:1801: UserWarning: Node prediction_src_pe_lifted_tensor_1 target Prediction.src_pe.lifted_tensor_1 lifted_tensor_1 of Prediction.src_pe does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
  warnings.warn(

[WARNING  | py.warnings        ]: /usr/local/lib/python3.12/dist-packages/torch/fx/graph.py:1801: UserWarning: Node prediction_trg_pe_lifted_tensor_2 target Prediction.trg_pe.lifted_tensor_2 lifted_tensor_2 of Prediction.trg_pe does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
  warnings.warn(

[WARNING  | py.warnings        ]: /usr/local/lib/python3.12/dist-packages/torch/fx/graph.py:1801: UserWarning: Node prediction_trg_pe_lifted_tensor_3 target Prediction.trg_pe.lifted_tensor_3 lifted_tensor_3 of Prediction.trg_pe does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
  warnings.warn(

[WARNING  | py.warnings        ]: /usr/local/lib/python3.12/dist-packages/torch/fx/graph.py:1801: UserWarning: Node prediction_trg_pe_lifted_tensor_4 target Prediction.trg_pe.lifted_tensor_4 lifted_tensor_4 of Prediction.trg_pe does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
  warnings.warn(

[WARNING  | py.warnings        ]: /usr/local/lib/python3.12/dist-packages/torch/fx/graph.py:1810: UserWarning: Additional 22 warnings suppressed about get_attr references
  warnings.warn(

Traceback (most recent call last):
  File "convert_torch_tensorrt.py", line 146, in <module>
    convert_tensorrt(opt)
  File "convert_torch_tensorrt.py", line 50, in convert_tensorrt
    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch_tensorrt/_compile.py", line 289, in compile
    trt_graph_module = dynamo_compile(
                       ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch_tensorrt/dynamo/_compiler.py", line 670, in compile
    exported_program = exported_program.run_decompositions(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/export/exported_program.py", line 128, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/export/exported_program.py", line 1310, in run_decompositions
    return _decompose_exported_program(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/export/exported_program.py", line 784, in _decompose_exported_program
    ) = _decompose_and_get_gm_with_new_signature_constants(
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/export/exported_program.py", line 472, in _decompose_and_get_gm_with_new_signature_constants
    aten_export_artifact = _export_to_aten_ir(
                           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/export/_trace.py", line 743, in _export_to_aten_ir
    gm, graph_signature = transform(aot_export_module)(
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/aot_autograd.py", line 1357, in aot_export_module
    fx_g, metadata, in_spec, out_spec = _aot_export_function(
                                        ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/aot_autograd.py", line 1596, in _aot_export_function
    fx_g, meta = create_aot_dispatcher_function(
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/aot_autograd.py", line 582, in create_aot_dispatcher_function
    return _create_aot_dispatcher_function(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/aot_autograd.py", line 832, in _create_aot_dispatcher_function
    compiled_fn, fw_metadata = compiler_fn(
                               ^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 118, in aot_dispatch_export
    graph, _, _ = aot_dispatch_base_graph(
                  ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py", line 153, in aot_dispatch_base_graph
    fw_module = _create_graph(
                ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py", line 55, in _create_graph
    fx_g = make_fx(
           ^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 2200, in wrapped
    return make_fx_tracer.trace(f, *args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 2138, in trace
    return self._trace_inner(f, *args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 2109, in _trace_inner
    t = dispatch_trace(
        ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_compile.py", line 51, in inner
    return disable_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_dynamo/eval_frame.py", line 755, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 1142, in dispatch_trace
    graph = tracer.trace(root, concrete_args)  # type: ignore[arg-type]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 1698, in trace
    res = super().trace(root, concrete_args)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_dynamo/eval_frame.py", line 755, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/_symbolic_trace.py", line 843, in trace
    (self.create_arg(fn(*args)),),
                     ^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 1197, in wrapped
    out = f(*tensors)  # type:ignore[call-arg]
          ^^^^^^^^^^^
  File "<string>", line 1, in <lambda>
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 693, in inner_fn
    outs = fn(*args)
           ^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 413, in _functionalized_f_helper
    f_outs = fn(*f_args)
             ^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 78, in inner_fn
    outs = fn(*args)
           ^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/utils.py", line 184, in flat_fn
    tree_out = fn(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 875, in functional_call
    out = PropagateUnbackedSymInts(mod).run(
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/interpreter.py", line 167, in run
    self.env[node] = self.run_node(node)
                     ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/symbolic_shapes.py", line 6826, in run_node
    result = super().run_node(n)
             ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/interpreter.py", line 230, in run_node
    return getattr(self, n.op)(n.target, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/interpreter.py", line 310, in call_function
    return target(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 758, in __call__
    return self._op(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 1245, in __torch_function__
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 758, in __call__
    return self._op(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/functional_tensor.py", line 527, in __torch_dispatch__
    outs_unwrapped = func._op_dk(
                     ^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_stats.py", line 26, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 1347, in __torch_dispatch__
    return proxy_call(self, func, self.pre_dispatch, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 793, in proxy_call
    r = maybe_handle_decomp(proxy_mode, func, args, kwargs)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 2268, in maybe_handle_decomp
    out = CURRENT_DECOMPOSITION_TABLE[op](*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch_tensorrt/dynamo/lowering/_decompositions.py", line 208, in slice_scatter_decomposition
    assert isinstance(end, int), "end must be an integer"
           ^^^^^^^^^^^^^^^^^^^^
AssertionError: end must be an integer

While executing %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%slice_11, %clone_1), kwargs = {})
Original traceback:
  File "/model.py", line 60, in forward
    outs, probs = self.Prediction(features)
  File "prediction.py", line 85, in forward
    probs[:, step, :] = prob.clone().detach()

3. My code for model(prediction.py) is like below,

    def forward(self, src):
        memory = self.encode(src)
        b = memory.size(0)

        # filling with [BOS](index=1)
        outs = torch.ones(b, 1).fill_(1).long().to(self.device)
        probs = torch.zeros(b, self.max_len, self.num_class).to(self.device)

        for step in range(self.max_len - 1):
            probs = probs.clone().detach()
            # [B, step+1, d_model]
            out = self.decode(memory, outs, subsequent_mask(outs.size(1)).long().to(self.device))
            prob = self.generator(out[:, -1])  # [B, num_class]
            _, next_word = torch.max(prob, dim=1)   # [B]

            outs = torch.cat([outs, next_word.unsqueeze(1)], dim=1)  # [B, step+2]
            probs[:, step, :] = prob.clone().detach()  # <--- Error occur at this line

        return outs, probs

Expected behavior

"rt_model.ep" model file must be created and saved.

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0): 2.6.0a0
• PyTorch Version (e.g. 1.0): 2.7.0a0+ecf3bae40a.nv25.2
• CPU Architecture: x86_64
• OS (e.g., Linux): Ubuntu 24.04.1 LTS
• How you installed PyTorch (conda, pip, libtorch, source): Docker image "PyTorch Release 25.02" at link
• Python version: 3.12.3
• CUDA version: 12.8
• GPU models and configuration: RTX4000

Additional context

Even if I do with adding or removing functions such as clone() and detach() to the 'probs' variable in the example code where the error occurred, the same error occurs.

!!! important !!!
Model transformation just succeeds when static batch is set as inputs like below.

inputs = [torch.randn((1, channel, height, width)).cuda()]

Setting dynamic batch as inputs result the error as suggested.
Is there a way to take dynamic batch as inputs like the sample code provided as an example?

[narendasan]

@apbose please look at this bug

[apbose]

Thanks for the issue. Trying to repro the above.
Couple of questions- what are the values of min_batch_size, max_batch_size, channel, height and width you are using. Also the subsequent_mask? Do you have a simple repro?

[Fulitcher]

@apbose Thank you for reply,
min_batch_size = 1
max_batch_size = 200
channel = 3
height = 32
width = 128

subsequent_mask() looks like,

def subsequent_mask(size):
    attn_shape = (1, size, size)
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0

The whole repo of my project is customized and hard to summary,
but I wish given sample code enough to reproduce the issue.

[apbose]

There are a couple of other things missing for the repro. The opt in model = MyModel(opt) is missing. Also I do not have the model.pth which is used in model.load_state_dict(torch.load("model.pth"))

[Fulitcher]

@apbose
MyModel is a model composed of a transformer encoder and a decoder. However, it is difficult to share the entire structure of the model. Is it impossible to debug with only the given example?
opt is nothing special. It is an arguments with the following values ​​defined:
--img_w, --img_h, --transformer_encoder_layer_num, ...