Migration to Poetry #257
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Migration to Poetry #257
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Additional details and impacted files@@ Coverage Diff @@
## develop #257 +/- ##
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+ Coverage 77.69% 77.73% +0.03%
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Files 53 53
Lines 4376 4383 +7
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+ Hits 3400 3407 +7
Misses 976 976
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Fantastic, thank you Gabriele! |
ziofil
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Jul 17, 2023
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ziofil
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Jul 26, 2023
# Release 0.5.0 ### New features * Optimization callback functionalities has been improved. A dedicated `Callback` class is added which is able to access the optimizer, the cost function, the parameters as well as gradients, during the optimization. In addition, multiple callbacks can be specified. This opens up the endless possiblities of customizing the the optimization progress with schedulers, trackers, heuristics, tricks, etc. [(#219)](#219) * Tensorboard-based optimization tracking is added as a builtin `Callback` class: `TensorboardCallback`. It can automatically track costs as well as all trainable parameters during optimization in realtime. Tensorboard can be most conveniently viewed from VScode. [(#219)](#219) ```python import numpy as np from mrmustard.training import Optimizer, TensorboardCallback def cost_fn(): ... def as_dB(cost): delta = np.sqrt(np.log(1 / (abs(cost) ** 2)) / (2 * np.pi)) cost_dB = -10 * np.log10(delta**2) return cost_dB tb_cb = TensorboardCallback(cost_converter=as_dB, track_grads=True) opt = Optimizer(euclidean_lr = 0.001); opt.minimize(cost_fn, max_steps=200, by_optimizing=[...], callbacks=tb_cb) # Logs will be stored in `tb_cb.logdir` which defaults to `./tb_logdir/...` but can be customized. # VScode can be used to open the Tensorboard frontend for live monitoring. # Or, in command line: `tensorboard --logdir={tb_cb.logdir}` and open link in browser. ``` * Gaussian states support a `bargmann` method for returning the bargmann representation. [(#235)](#235) * The `ket` method of `State` now supports new keyword arguments `max_prob` and `max_photons`. Use them to speed-up the filling of a ket array up to a certain probability or *total* photon number. [(#235)](#235) ```python from mrmustard.lab import Gaussian # Fills the ket array up to 99% probability or up to the |0,3>, |1,2>, |2,1>, |3,0> subspace, whichever is reached first. # The array has the autocutoff shape, unless the cutoffs are specified explicitly. ket = Gaussian(2).ket(max_prob=0.99, max_photons=3) ``` * Gaussian transformations support a `bargmann` method for returning the bargmann representation. [(#239)](#239) * BSGate.U now supports method='vanilla' (default) and 'schwinger' (slower, but stable to any cutoff) [(#248)](#248) ### Breaking Changes * The previous `callback` argument to `Optimizer.minimize` is now `callbacks` since we can now pass multiple callbacks to it. [(#219)](#219) * The `opt_history` attribute of `Optimizer` does not have the placeholder at the beginning anymore. [(#235)](#235) ### Improvements * The math module now has a submodule `lattice` for constructing recurrence relation strategies in the Fock lattice. There are a few predefined strategies in `mrmustard.math.lattice.strategies`. [(#235)](#235) * Gradients in the Fock lattice are now computed using the vector-jacobian product. This saves a lot of memory and speeds up the optimization process by roughly 4x. [(#235)](#235) * Tests of the compact_fock module now use hypothesis. [(#235)](#235) * Faster implementation of the fock representation of `BSgate`, `Sgate` and `SqueezedVacuum`, ranging from 5x to 50x. [(#239)](#239) * More robust implementation of cutoffs for States. [(#239)](#239) * Dependencies and versioning are now managed using Poetry. [(#257)](#257) ### Bug fixes * Fixed a bug that would make two progress bars appear during an optimization [(#235)](#235) * The displacement of the dual of an operation had the wrong sign [(#239)](#239) * When projecting a Gaussian state onto a Fock state, the upper limit of the autocutoff now respect the Fock projection. [(#246)](#246) * Fixed a bug for the algorithms that allow faster PNR sampling from Gaussian circuits using density matrices. When the cutoff of the first detector is equal to 1, the resulting density matrix is now correct. ### Documentation ### Contributors [Filippo Miatto](https://github.com/ziofil), [Zeyue Niu](https://github.com/zeyueN), [Robbe De Prins](https://github.com/rdprins), [Gabriele Gullì](https://github.com/ggulli), [Richard A. Wolf](https://github.com/ryk-wolf)
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sduquemesa
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Jul 27, 2023
# Release 0.5.0 ### New features * Optimization callback functionalities has been improved. A dedicated `Callback` class is added which is able to access the optimizer, the cost function, the parameters as well as gradients, during the optimization. In addition, multiple callbacks can be specified. This opens up the endless possiblities of customizing the the optimization progress with schedulers, trackers, heuristics, tricks, etc. [(#219)](#219) * Tensorboard-based optimization tracking is added as a builtin `Callback` class: `TensorboardCallback`. It can automatically track costs as well as all trainable parameters during optimization in realtime. Tensorboard can be most conveniently viewed from VScode. [(#219)](#219) ```python import numpy as np from mrmustard.training import Optimizer, TensorboardCallback def cost_fn(): ... def as_dB(cost): delta = np.sqrt(np.log(1 / (abs(cost) ** 2)) / (2 * np.pi)) cost_dB = -10 * np.log10(delta**2) return cost_dB tb_cb = TensorboardCallback(cost_converter=as_dB, track_grads=True) opt = Optimizer(euclidean_lr = 0.001); opt.minimize(cost_fn, max_steps=200, by_optimizing=[...], callbacks=tb_cb) # Logs will be stored in `tb_cb.logdir` which defaults to `./tb_logdir/...` but can be customized. # VScode can be used to open the Tensorboard frontend for live monitoring. # Or, in command line: `tensorboard --logdir={tb_cb.logdir}` and open link in browser. ``` * Gaussian states support a `bargmann` method for returning the bargmann representation. [(#235)](#235) * The `ket` method of `State` now supports new keyword arguments `max_prob` and `max_photons`. Use them to speed-up the filling of a ket array up to a certain probability or *total* photon number. [(#235)](#235) ```python from mrmustard.lab import Gaussian # Fills the ket array up to 99% probability or up to the |0,3>, |1,2>, |2,1>, |3,0> subspace, whichever is reached first. # The array has the autocutoff shape, unless the cutoffs are specified explicitly. ket = Gaussian(2).ket(max_prob=0.99, max_photons=3) ``` * Gaussian transformations support a `bargmann` method for returning the bargmann representation. [(#239)](#239) * BSGate.U now supports method='vanilla' (default) and 'schwinger' (slower, but stable to any cutoff) [(#248)](#248) ### Breaking Changes * The previous `callback` argument to `Optimizer.minimize` is now `callbacks` since we can now pass multiple callbacks to it. [(#219)](#219) * The `opt_history` attribute of `Optimizer` does not have the placeholder at the beginning anymore. [(#235)](#235) ### Improvements * The math module now has a submodule `lattice` for constructing recurrence relation strategies in the Fock lattice. There are a few predefined strategies in `mrmustard.math.lattice.strategies`. [(#235)](#235) * Gradients in the Fock lattice are now computed using the vector-jacobian product. This saves a lot of memory and speeds up the optimization process by roughly 4x. [(#235)](#235) * Tests of the compact_fock module now use hypothesis. [(#235)](#235) * Faster implementation of the fock representation of `BSgate`, `Sgate` and `SqueezedVacuum`, ranging from 5x to 50x. [(#239)](#239) * More robust implementation of cutoffs for States. [(#239)](#239) * Dependencies and versioning are now managed using Poetry. [(#257)](#257) ### Bug fixes * Fixed a bug that would make two progress bars appear during an optimization [(#235)](#235) * The displacement of the dual of an operation had the wrong sign [(#239)](#239) * When projecting a Gaussian state onto a Fock state, the upper limit of the autocutoff now respect the Fock projection. [(#246)](#246) * Fixed a bug for the algorithms that allow faster PNR sampling from Gaussian circuits using density matrices. When the cutoff of the first detector is equal to 1, the resulting density matrix is now correct. ### Documentation ### Contributors [Filippo Miatto](https://github.com/ziofil), [Zeyue Niu](https://github.com/zeyueN), [Robbe De Prins](https://github.com/rdprins), [Gabriele Gullì](https://github.com/ggulli), [Richard A. Wolf](https://github.com/ryk-wolf) --------- Co-authored-by: Sebastián Duque Mesa <[email protected]> Co-authored-by: JacobHast <[email protected]> Co-authored-by: elib20 <[email protected]> Co-authored-by: Luke Helt <[email protected]> Co-authored-by: zeyueN <[email protected]> Co-authored-by: Robbe De Prins <[email protected]> Co-authored-by: Robbe De Prins (UGent-imec) <[email protected]> Co-authored-by: Yuan <[email protected]> Co-authored-by: Ryk <[email protected]> Co-authored-by: Gabriele Gullì <[email protected]>
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Context:
This PR is responsible for migrating the dependencies management from
piptopoetry. The goal is also to use Poetry for versioning, adopting as a single source of truth the version defined in thepyproject.tomlfile.Description of the Change:
pyproject.tomlandpoetry.lock: these two files have been introduced to properly configure and lock the dependencies of the project. The version used by poetry for the lock file is1.4.0.requirementsfiles: the requirements previously defined in the different requirements.txt files have been imported inpyproject.toml. The files have been removed from the project. The additional dependencies have been imported respectively under the groupsdev(requirements-dev.txt) anddoc(doc/requirements.txt). The versions imported are the ones previously defined in the requirements files. The only change introduced is related to a submodule of tensorflow to make it work between ARM/x86_64 architectures.setup.py: the package configurations have been also imported inpyproject.toml. No change has been introduced in the different attributes such as authors, classifiers and so on. Probably some of the are out of date._version.py: some changes introduced to retrieve the package version frompyproject.toml.Documentation: text changes introduced describing where poetry is now required to perform some actions.
.readthedocs.ymlhas been also updated to build the documentation using poetry.Makefile: commands to build or install the package updated to use poetry.devcontainers:
Dockerfileand script updated to use poetry.CI: all GitHub Actions workflows have been updated to use Poetry. An additional change has been introduced in those workflows using a third party action to cancel previous runs, they now use a concurrency setting to do so.
Benefits: