Fix pytorch gradients

[Simone-Bordoni]

Fix #1449

Checklist:

• Reviewers confirm new code works as expected.
• Tests are passing.
• Coverage does not decrease.
• Documentation is updated.

[BrunoLiegiBastonLiegi]

I found a potential problem when you cast the parameters of parametrized gates:

qibo/src/qibo/backends/pytorch.py

Line 32 in 1b6eb8a

return self.np.tensor(x, dtype=self.dtype, requires_grad=self.requires_grad)

here you cast to self.dtype which is usually torch.complex128 but it should actually be torch.float32/64 since this is a parameter. I don't know whether this is the origin of the problem with the gradients though.

EDIT: nevermind, you necessarily have to cast to complex otherwise when you build the matrix elements pytorch complains about mismatching dtypes...

[Simone-Bordoni]

I found a potential problem when you cast the parameters of parametrized gates:

qibo/src/qibo/backends/pytorch.py

Line 32 in 1b6eb8a

return self.np.tensor(x, dtype=self.dtype, requires_grad=self.requires_grad)

here you cast to self.dtype which is usually torch.complex128 but it should actually be torch.float32/64 since this is a parameter. I don't know whether this is the origin of the problem with the gradients though.
EDIT: nevermind, you necessarily have to cast to complex otherwise when you build the matrix elements pytorch complains about mismatching dtypes...

Actually this may be part of the problem, i am rewriting in a better way the whole casting function differentiating the matrix_dtype from the parameters_dtype

[Simone-Bordoni]

Now the gradients are passing however their final value is always zero. I have tried visualizing the computational graph (shown below) and all the operations seems to be performed correctly.
I am currently tsting a simple circuit with just one rotation gate (test_torch_gradients.py).
In the next days I will not be able to work furthermore on this issue, I will resume working on the gradients next week. In the meantime if you find any possible reason for this problemlet me know, it would be very helpful.

[Simone-Bordoni]

Now the gradients are passing however their final value is always zero. I have tried visualizing the computational graph (shown below) and all the operations seems to be performed correctly. I am currently tsting a simple circuit with just one rotation gate (test_torch_gradients.py). In the next days I will not be able to work furthermore on this issue, I will resume working on the gradients next week. In the meantime if you find any possible reason for this problemlet me know, it would be very helpful.

Now the gradients are passing however their final value is always zero. I have tried visualizing the computational graph (shown below) and all the operations seems to be performed correctly. I am currently tsting a simple circuit with just one rotation gate (test_torch_gradients.py). In the next days I will not be able to work furthermore on this issue, I will resume working on the gradients next week. In the meantime if you find any possible reason for this problemlet me know, it would be very helpful.

I found out the problem was with the target state. now the gradients are passing correctly,
as soon as possible I will clean up the code and it should be ready for a review.

[Simone-Bordoni]

I think that now everything has been fixed.
The changes required were bigger than expected, I had to add the backend in the gate decompositions and in other parts.
I have added a test to check the correct backpropagation in test_torch_gradients.py so that thay can be easily moved to qiboml.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 99.94%. Comparing base (c92f49c) to head (ad25c2c).
Report is 4 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1450      +/-   ##
==========================================
- Coverage   99.94%   99.94%   -0.01%     
==========================================
  Files          81       81              
  Lines       11740    11715      -25     
==========================================
- Hits        11733    11708      -25     
  Misses          7        7              

Flag	Coverage Δ
unittests	99.94% <100.00%> (-0.01%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[BrunoLiegiBastonLiegi]

Thanks @Simone-Bordoni, #1449 is still failing even with this patch, but I think that's more related to a problem with the circuit.set/get_parameters().

[Simone-Bordoni]

Thanks @Simone-Bordoni, #1449 is still failing even with this patch, but I think that's more related to a problem with the circuit.set/get_parameters().

In the new version the backend.cast has requires_grad=False by default, you need to modify #1449 and activate the gradients when casting.

[BrunoLiegiBastonLiegi]

Like this?

self.circuit_parameters = torch.nn.Parameter(BACKEND.cast(circuit.get_parameters(), dtype=torch.float64, requires_grad=True))

because this fails anyway.

EDIT: sorry maybe I should have specified better, but it doesn't fail in the sense that the gradients don't pass, it actually crashes with an error:

RuntimeError: Output 0 of UnbindBackward0 is a view and its base or another view of its base has been modified inplace. This view is the output of a function that returns multiple views. Such functions do not allow the output views to be modified inplace. You should replace the inplace operation by an out-of-place one.

[Simone-Bordoni]

Like this?

self.circuit_parameters = torch.nn.Parameter(BACKEND.cast(circuit.get_parameters(), dtype=torch.float64, requires_grad=True))

because this fails anyway.

EDIT: sorry maybe I should have specified better, but it doesn't fail in the sense that the gradients don't pass, it actually crashes with an error:

RuntimeError: Output 0 of UnbindBackward0 is a view and its base or another view of its base has been modified inplace. This view is the output of a function that returns multiple views. Such functions do not allow the output views to be modified inplace. You should replace the inplace operation by an out-of-place one.

OK, I thought that the problem was the gradient not passing. Is it a different error with respect to what you were getting before this update?

[BrunoLiegiBastonLiegi]

Yes, if I remember correctly before it used to run without crashing but had the gradients flow problem.