🐛 [Bug] ValueError: Trying to flatten user inputs with exported input tree spec

[Hukongtao]

Bug Description

To Reproduce

The code comes from the official documentation：
https://pytorch.org/TensorRT/user_guide/dynamic_shapes.html#custom-dynamic-shape-constraints

import torch
import torch_tensorrt

class MatMul(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, query, key):
        attn_weight = torch.matmul(query, key.transpose(-1, -2))
        return attn_weight

model = MatMul().eval().cuda()
inputs = [torch.randn(1, 12, 7, 64).cuda(), torch.randn(1, 12, 7, 64).cuda()]
seq_len = torch.export.Dim("seq_len", min=1, max=10)
dynamic_shapes=({2: seq_len}, {2: seq_len})
# Export the model first with custom dynamic shape constraints
exp_program = torch.export.export(model, tuple(inputs), dynamic_shapes=dynamic_shapes)
trt_gm = torch_tensorrt.dynamo.compile(exp_program, [inputs])
# Run inference
trt_gm(inputs)

Expected behavior

run successfully

Environment

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

• Torch-TensorRT Version (e.g. 1.0.0):
• PyTorch Version (e.g. 1.0):
• CPU Architecture:
• OS (e.g., Linux):
• How you installed PyTorch (conda, pip, libtorch, source):
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version:
• CUDA version:
• GPU models and configuration:
• Any other relevant information:

Additional context

INFO:torch_tensorrt [TensorRT Conversion Context]:[MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 1 MiB
INFO:torch_tensorrt [TensorRT Conversion Context]:[MemUsageStats] Peak memory usage during Engine building and serialization: CPU: 7909 MiB
INFO:torch_tensorrt [TensorRT Conversion Context]:Serialized 26 bytes of code generator cache.
INFO:torch_tensorrt [TensorRT Conversion Context]:Serialized 81 timing cache entries
INFO:torch_tensorrt.dynamo.conversion._TRTInterpreter:Build TRT engine elapsed time: 0:00:00.846883
INFO:torch_tensorrt.dynamo.conversion._TRTInterpreter:TRT Engine uses: 123972 bytes of Memory
WARNING: [Torch-TensorRT] - CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage and speed up TensorRT initialization. See "Lazy Loading" section of CUDA documentation https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#lazy-loading
Traceback (most recent call last):
  File "/mnt/bn/hukongtao-infer-speed/mlx/users/kongtao.hu/codebase/EasyGuard_0617/speed_test.py", line 18, in <module>
    trt_gm = torch_tensorrt.dynamo.compile(exp_program, [inputs])
  File "/usr/local/lib/python3.9/dist-packages/torch_tensorrt/dynamo/_compiler.py", line 227, in compile
    trt_gm = compile_module(gm, inputs, settings)
  File "/usr/local/lib/python3.9/dist-packages/torch_tensorrt/dynamo/_compiler.py", line 421, in compile_module
    sample_outputs = gm(
  File "/usr/local/lib/python3.9/dist-packages/torch/fx/graph_module.py", line 737, in call_wrapped
    return self._wrapped_call(self, *args, **kwargs)
  File "/usr/local/lib/python3.9/dist-packages/torch/fx/graph_module.py", line 317, in __call__
    raise e
  File "/usr/local/lib/python3.9/dist-packages/torch/fx/graph_module.py", line 304, in __call__
    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
  File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1561, in _call_impl
    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
  File "/usr/local/lib/python3.9/dist-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.9/dist-packages/torch/_dynamo/external_utils.py", line 36, in inner
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.9/dist-packages/torch/export/_unlift.py", line 25, in _check_input_constraints_pre_hook
    raise ValueError(  # noqa: TRY200
ValueError: Trying to flatten user inputs with exported input tree spec: 
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
    *]),
  TreeSpec(dict, [], [])])
but actually got inputs with tree spec of: 
TreeSpec(tuple, None, [TreeSpec(tuple, None, [TreeSpec(list, None, [*,
      *])]),
  TreeSpec(dict, [], [])])

[Hukongtao]

Did the code examples provided by the official documentation also have errors?

[peri044]

Thanks for catching this. The correct usage is as follows:

trt_gm = torch_tensorrt.dynamo.compile(exp_program, inputs)
# Run inference
trt_gm(*inputs)

inputs is already a list and [inputs] was being passed which is incorrect. Raised a PR here : #3036