Fix release action

[thisac]

Context:
Automatic PyPI release action is failing due to failing tests when simply running tests with pytest tests (instead of make tests).

Description of the Change:

• Adds a random seed to the tests run on the upload action.
• Sets the env var SYMPY_USE_CACHE to "no" to avoid issue with sympy caching (see Set SYMPY_USE_CACHE environment variable to "no" #684 for details).
• Fixes tolerances in a few tests that were failing too often (specifically when running without setting a seed).
• Replaces _einsum_v1 with tf.einsum in all places but one (still an issue with sf.backends.tfbackend.ops.conditional_state() which I wasn't able to solve) and also removed the einsum test marked XFAIL (was XPASSing).

Benefits:

• It should now be possible to run all tests directly using pytest tests.
• The automatic PyPI release on GitHub published should now work.

Possible Drawbacks:
None

Related GitHub Issues:
None

[thisac]

This should work using tf.einsum, but there were issues with the shape of the einsum_args arrays. It looked like they were of the wrong shape, but I couldn't figure it out. It works (for now) using _einsum_v1 as before, but it'd be nicer to simply use tf.einsum instead.

[sduquemesa]

Unless the shape of the input tensors in changed, I there's no way around the issue. You're correct in the sense that the shape is the problem, from the new einsum (_einsum_v2) code comments

    # Validate and sanitize the equation and resolve static input shapes, as
    # opt_einsum requires that all shapes be a tuple of positive integers.
    # Also remove ellipsis from the equation as opt_einsum will replace them
    # with named labels. Then broadcasting between different shapes or ranks
    # wouldn't work. (E.g. [1, 1, 2] wouldn't broadcast with [3, 1]).

Some of the inputs are, for example, of sizes [1, 6, 6, 6, 6, 6, 6] and [1, 6], and then won't broadcast and work with einsum.

[codecov]

Codecov Report

Merging #722 (606a50b) into master (d2d5a3c) will increase coverage by 0.02%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #722      +/-   ##
==========================================
+ Coverage   98.30%   98.33%   +0.02%     
==========================================
  Files          78       78              
  Lines        9614     9603      -11     
==========================================
- Hits         9451     9443       -8     
+ Misses        163      160       -3     

Impacted Files	Coverage Δ
strawberryfields/backends/tfbackend/circuit.py	96.62% <ø> (+0.18%)	⬆️
strawberryfields/backends/tfbackend/states.py	98.97% <ø> (+0.48%)	⬆️
strawberryfields/backends/tfbackend/ops.py	98.27% <100.00%> (+0.12%)	⬆️
strawberryfields/decompositions.py	99.44% <100.00%> (ø)

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[thisac]

This takes ~3 seconds to run on my laptop (probably longer on CI). Test failed often with only 10000 samples, and even more than 20000 would be better, and would allow for a lower tolerance below, but would take much longer to run.

[sduquemesa]

Thanks @thisac! Hopefully this will make the release pipeline more streamlined 🚀 I left some comments just as potential future references.

[sduquemesa]

Unless the shape of the input tensors in changed, I there's no way around the issue. You're correct in the sense that the shape is the problem, from the new einsum (_einsum_v2) code comments

    # Validate and sanitize the equation and resolve static input shapes, as
    # opt_einsum requires that all shapes be a tuple of positive integers.
    # Also remove ellipsis from the equation as opt_einsum will replace them
    # with named labels. Then broadcasting between different shapes or ranks
    # wouldn't work. (E.g. [1, 1, 2] wouldn't broadcast with [3, 1]).

Some of the inputs are, for example, of sizes [1, 6, 6, 6, 6, 6, 6] and [1, 6], and then won't broadcast and work with einsum.

[sduquemesa]

For future reference I'm attaching this comment from PR #480:

Optimally, we would remove the randomly-seed=42 from the tests, causing the seed to be set randomly each time, helping us discover potential bugs due to the stochastic nature of some tests. By resetting the seed before each test, we may miss failures that might be present. This might cause some headaches, though.