torch and cuda version?

Hello,

Thanks for sharing your this project. I had some issues running the examples and seem to be related to torch version. I am currently using torch 1.2.0 and CUDA10.0, could you please tell me the latest torch and cuda version used with this project?

I am getting errors like this:

(.mpc) user@userlabpc:~/git_clone/mpc.pytorch/examples$ python pendulum.py 
Tmp dir: /tmp/tmp7hokco9j
  0%|                                                                                      | 0/100 [00:00<?, ?it/s]/pytorch/torch/csrc/autograd/python_function.cpp:638: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
  0%|                                                                                      | 0/100 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "pendulum.py", line 80, in <module>
    )(x, QuadCost(Q, p), dx)
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/torch/nn/modules/module.py", line 547, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/mpc.py", line 265, in forward
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/mpc.py", line 362, in solve_lqr_subproblem
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/lqr_step.py", line 112, in forward
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/lqr_step.py", line 303, in lqr_backward
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/pnqp.py", line 31, in pnqp
  File "/home/user/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/torch/tensor.py", line 325, in __rsub__
    return _C._VariableFunctions.rsub(self, other)
RuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `bitwise_not()` operator instead.

Traceback (most recent call last):
  File "/home/fer/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/fer/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/mpc.py", line 265, in forward
  File "/home/fer/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/mpc.py", line 362, in solve_lqr_subproblem
  File "/home/fer/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/lqr_step.py", line 114, in forward
  File "/home/fer/git_clone/mpc.pytorch/.mpc/lib/python3.5/site-packages/mpc-0.0.3-py3.5.egg/mpc/lqr_step.py", line 344, in lqr_forward
AttributeError: module 'torch' has no attribute 'any'

Thank you very much.

[bamos]

The GPU’s usually useful for OptNet/MPC acceleration here if you’re solving many problems at the same time since the operations are batched, but otherwise if you’re just solving a single problem it’s probably not going to be faster than the CPU version – no cpu-gpu transfers happen that slows it down, just the sequential nature.

Also the torch JIT wasn’t really around/used much when I was developing this library but it should give very significant performance gains here on the CPU and GPU

[juanmed]

I see, there is something I have not yet understand about your Optnet and Differentiable MPC papers then. I thought about doing trajectory-tracking real-time control using MPC for a linear system on the GPU based on your work.

It should work if you pass in all cuda tensors: https://github.com/locuslab/mpc.pytorch/blob/master/tests/test_mpc.py#L274

But the sequential/iterative nature of the code doesn’t make it very GPU-amenable so you might not see much gains from running on the GPU

Could you please be more specific on the issue here? Are you talking about the problem of cpu-gpu loading/offloading in a loop? Thanks.