diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5be91f9 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.ipynb linguist-vendored diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index d018580..d13ae8a 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -21,7 +21,7 @@ jobs: - precommit strategy: matrix: - python-version: [3.11] + python-version: [ 3.12 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -41,7 +41,7 @@ jobs: - precommit strategy: matrix: - python-version: [3.11] + python-version: [ 3.12 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -61,7 +61,7 @@ jobs: - precommit strategy: matrix: - python-version: [3.11] + python-version: [ 3.12, 3.13 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} diff --git a/.github/workflows/examples.yaml b/.github/workflows/examples.yaml index 36f56ba..755fb5d 100644 --- a/.github/workflows/examples.yaml +++ b/.github/workflows/examples.yaml @@ -19,7 +19,7 @@ jobs: - precommit strategy: matrix: - python-version: [3.11] + python-version: [ 3.12, 3.13 ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 3c918b9..d2634ee 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.12, 3.13] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} diff --git a/.gitignore b/.gitignore index ca9ee37..be0d3b8 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,5 @@ cython_debug/ .vscode/ poetry.lock + +.DS_Store diff --git a/.gitlint b/.gitlint new file mode 100644 index 0000000..1abf77d --- /dev/null +++ b/.gitlint @@ -0,0 +1,8 @@ +[general] +ignore=body-is-missing + +[title-min-length] +min-length=10 + +[title-must-not-contain-word] +words=wip,todo diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 815e351..aab908b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,22 +9,12 @@ repos: - id: check-toml - id: check-yaml - id: debug-statements + - id: end-of-file-fixer - id: requirements-txt-fixer - id: trailing-whitespace -- repo: https://github.com/pycqa/bandit - rev: 1.7.1 - hooks: - - id: bandit - language: python - language_version: python3 - types: [python] - args: ["-c", "pyproject.toml"] - additional_dependencies: ["toml"] - files: "(surjectors|examples)" - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.910-1 + rev: v1.15.0 hooks: - id: mypy args: ["--ignore-missing-imports"] @@ -35,4 +25,4 @@ repos: hooks: - id: ruff args: [ --fix ] - - id: ruff-format \ No newline at end of file + - id: ruff-format diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..3b2cfc0 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12.10 diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 3332335..136cfe4 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -3,7 +3,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.11" + python: "3.12" sphinx: builder: html diff --git a/Makefile b/Makefile index f8802c2..f462304 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,14 @@ -PKG_VERSION=`hatch version` +.PHONY: tests, lints, docs, format -tag: - git tag -a v${PKG_VERSION} -m v${PKG_VERSION} - git push --tag +tests: + uv run pytest + +lints: + uv run ruff check surjectors examples + +format: + uv run ruff check --fix surjectors examples + uv run ruff format surjectors examples + +docs: + cd docs && make html diff --git a/README.md b/README.md index db21dea..3be54d5 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,9 @@ # surjectors -[![active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![ci](https://github.com/dirmeier/surjectors/actions/workflows/ci.yaml/badge.svg)](https://github.com/dirmeier/surjectors/actions/workflows/ci.yaml) [![version](https://img.shields.io/pypi/v/surjectors.svg?colorB=black&style=flat)](https://pypi.org/project/surjectors/) [![doi](https://joss.theoj.org/papers/10.21105/joss.06188/status.svg)](https://doi.org/10.21105/joss.06188) -> Surjection layers for density estimation with normalizing flows - ## About Surjectors is a light-weight library for density estimation using @@ -82,12 +79,26 @@ Contributions in the form of pull requests are more than welcome. A good way to In order to contribute: -1) Clone `Surjectors` and install `hatch` via `pip install hatch`, -2) create a new branch locally `git checkout -b feature/my-new-feature` or `git checkout -b issue/fixes-bug`, -3) implement your contribution and ideally a test case, -4) test it by calling `hatch run test` on the (Unix) command line, -5) submit a PR 🙂 - +1) Clone `surjectors` and install `uv` from [here](https://docs.astral.sh/uv/getting-started/installation/). +2) Install all dependencies using `uv sync --all-groups`. +3) Install `pre-commit` and `gitlint` via: + + ```shell + pre-commit install + gitlint install-hook + ``` +4) Create a new branch locally `git checkout -b feature/my-new-feature` or `git checkout -b issue/fixes-bug`. +5) Implement your contribution and ideally a test case. +6) Test it by calling `make tests`, `make lints` and `make format` on the (Unix) command line. +7) Submit a PR 🙂. + + ```shell + pre-commit install + gitlint install-hook + ``` +6) Implement your contribution and ideally a test case. +7) Test it by calling `make format`, `make lints` and `make tests` on the (Unix) command line. +8) Submit a PR 🙂. ## Citing Surjectors @@ -109,4 +120,4 @@ If you find our work relevant to your research, please consider citing: ## Author -Simon Dirmeier sfyrbnd @ pm me +Simon Dirmeier simd23 @ pm me diff --git a/docs/_static/theme.css b/docs/_static/theme.css index 9dc0715..374ede6 100644 --- a/docs/_static/theme.css +++ b/docs/_static/theme.css @@ -5,11 +5,25 @@ html[data-theme="light"] { --pst-color-inline-code-links: #b26679; } +pre > span { + line-height: 20px; +} + +span.kn { + color: rgb(0, 120, 161) !important; +} h1 > code > span { - font-family: var(--pst-font-family-monospace); - font-weight: 700; + font-weight: 300 !important; } +pre { + border: 0; + font-size: 13px; + background-color: rgb(245, 245, 245) !important; +} +h1 { + margin-bottom: 50px; +} nav > li > a > code.literal { padding-top: 0; padding-bottom: 0; @@ -20,17 +34,18 @@ nav > li > a > code.literal { nav.bd-links p.caption { text-transform: uppercase; } - code.literal { background-color: white; border: 0; border-radius: 0; } +a > code { + font-weight: 575; +} a:hover { text-decoration-thickness: 1px !important; } - ul.bd-breadcrumbs li.breadcrumb-item a:hover { text-decoration-thickness: 1px; } @@ -45,4 +60,4 @@ nav.bd-links li > a:hover { button.theme-switch-button { display: none !important; -} \ No newline at end of file +} diff --git a/docs/conf.py b/docs/conf.py index 00db2a4..2669a0c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -50,7 +50,8 @@ "repository_url": "https://github.com/dirmeier/surjectors", "use_repository_button": True, "use_download_button": False, - "extra_navbar": "" + "use_fullscreen_button": False, + "launch_buttons": {"colab_url": "https://colab.research.google.com"}, } html_title = "Surjectors 🚀" @@ -63,4 +64,4 @@ def skip(app, what, name, obj, would_skip, options): def setup(app): - app.connect("autodoc-skip-member", skip) \ No newline at end of file + app.connect("autodoc-skip-member", skip) diff --git a/docs/index.rst b/docs/index.rst index 2e9c0c6..243aaf9 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -84,11 +84,12 @@ Contributions in the form of pull requests are more than welcome. A good way to In order to contribute: -1) Clone :code:`Surjectors` and install :code:`hatch` via :code:`pip install hatch`, -2) create a new branch locally :code:`git checkout -b feature/my-new-feature` or :code:`git checkout -b issue/fixes-bug`, -3) implement your contribution and ideally a test case, -4) test it by calling :code:`hatch run test` on the (Unix) command line, -5) submit a PR 🙂 +1) Clone :code:`surjectors` and install :code:`uv` from `here `_, +2) install all dependencies using ``uv sync``, +3) create a new branch locally :code:`git checkout -b feature/my-new-feature` or :code:`git checkout -b issue/fixes-bug`, +4) implement your contribution and ideally a test case, +5) test it by calling ``make format``, ``make lints`` and ``make tests`` on the (Unix) command line, +6) submit a PR 🙂 Citing Surjectors ----------------- @@ -117,16 +118,21 @@ Surjectors is licensed under the Apache 2.0 License. :hidden: 🏠 Home - 📰 News .. toctree:: - :caption: 🎓 Examples + :caption: 🎓 Tutorials :maxdepth: 1 :hidden: Introduction Unconditional and conditional density estimation Dimensionality reduction using surjectors + +.. toctree:: + :caption: 🚀 Examples + :maxdepth: 1 + :hidden: + Self-contained scripts .. toctree:: diff --git a/docs/news.rst b/docs/news.rst deleted file mode 100644 index 1542a62..0000000 --- a/docs/news.rst +++ /dev/null @@ -1,11 +0,0 @@ -📰 News -======= - -Latest news on the development of `Surjectors`. - -------- - -.. note:: - - - 29.02.2024. Surjectors has been accepted for publication in the Journal of Open Source Software 🎉🎉🎉. - Find the paper here `10.21105/joss.06188 `_. diff --git a/examples/autoregressive_inference_surjection.py b/examples/autoregressive_inference_surjection.py index 36bf32b..880f2d6 100644 --- a/examples/autoregressive_inference_surjection.py +++ b/examples/autoregressive_inference_surjection.py @@ -1,7 +1,6 @@ import argparse from collections import namedtuple -import distrax import haiku as hk import jax import numpy as np @@ -9,114 +8,116 @@ from jax import numpy as jnp from jax import random as jr from matplotlib import pyplot as plt +from tensorflow_probability.substrates.jax import distributions as tfd +import surjectors from surjectors import ( - AffineMaskedAutoregressiveInferenceFunnel, - Chain, - MaskedAutoregressive, - TransformedDistribution, + AffineMaskedAutoregressiveInferenceFunnel, + Chain, + MaskedAutoregressive, + TransformedDistribution, ) from surjectors.nn import MADE, make_mlp from surjectors.util import as_batch_iterator, unstack def _decoder_fn(n_dim): - decoder_net = make_mlp([4, 4, n_dim * 2]) + decoder_net = make_mlp((4, 4, n_dim * 2)) - def _fn(z): - params = decoder_net(z) - mu, log_scale = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) + def _fn(z): + params = decoder_net(z) + mu, log_scale = jnp.split(params, 2, -1) + return tfd.Independent(tfd.Normal(mu, jnp.exp(log_scale))) - return _fn + return _fn def _made_bijector_fn(params): - means, log_scales = unstack(params, -1) - return distrax.Inverse(distrax.ScalarAffine(means, jnp.exp(log_scales))) + means, log_scales = unstack(params, -1) + return surjectors.Inverse(surjectors.ScalarAffine(means, jnp.exp(log_scales))) def make_model(n_dimensions): - def _flow(**kwargs): - n_dim = n_dimensions - layers = [] - for i in range(3): - if i != 1: - layer = AffineMaskedAutoregressiveInferenceFunnel( - n_keep=int(n_dim / 2), - decoder=_decoder_fn(int(n_dim / 2)), - conditioner=MADE(int(n_dim / 2), [8, 8], 2), - ) - n_dim = int(n_dim / 2) - else: - layer = MaskedAutoregressive( - conditioner=MADE(n_dim, [8, 8], 2), - bijector_fn=_made_bijector_fn, - ) - layers.append(layer) - # TODO(simon): needs to change order - # layers.append(Permutation(order, 1)) - chain = Chain(layers) - - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_dim), jnp.ones(n_dim)), - reinterpreted_batch_ndims=1, + def _flow(**kwargs): + n_dim = n_dimensions + layers = [] + for i in range(3): + if i != 1: + layer = AffineMaskedAutoregressiveInferenceFunnel( + n_keep=int(n_dim / 2), + decoder=_decoder_fn(int(n_dim / 2)), + conditioner=MADE(int(n_dim / 2), [8, 8], 2), ) - td = TransformedDistribution(base_distribution, chain) - return td.log_prob(**kwargs) + n_dim = int(n_dim / 2) + else: + layer = MaskedAutoregressive( + conditioner=MADE(n_dim, [8, 8], 2), + bijector_fn=_made_bijector_fn, + ) + layers.append(layer) + # TODO(simon): needs to change order + # layers.append(Permutation(order, 1)) + chain = Chain(layers) + + base_distribution = tfd.Independent( + tfd.Normal(jnp.zeros(n_dim), jnp.ones(n_dim)), + reinterpreted_batch_ndims=1, + ) + td = TransformedDistribution(base_distribution, chain) + return td.log_prob(**kwargs) - td = hk.transform(_flow) - td = hk.without_apply_rng(td) - return td + td = hk.transform(_flow) + td = hk.without_apply_rng(td) + return td -def train(rng_seq, data, model, max_n_iter=1000): - train_iter = as_batch_iterator(next(rng_seq), data, 100, True) - params = model.init(next(rng_seq), **train_iter(0)) +def train(rng_seq, data, model, n_iter=1000): + train_iter = as_batch_iterator(next(rng_seq), data, 100, True) + params = model.init(next(rng_seq), **train_iter(0)) - optimizer = optax.adam(1e-4) - state = optimizer.init(params) + optimizer = optax.adam(1e-4) + state = optimizer.init(params) - @jax.jit - def step(params, state, **batch): - def loss_fn(params): - lp = model.apply(params, **batch) - return -jnp.sum(lp) + @jax.jit + def step(params, state, **batch): + def loss_fn(params): + lp = model.apply(params, **batch) + return -jnp.sum(lp) - loss, grads = jax.value_and_grad(loss_fn)(params) - updates, new_state = optimizer.update(grads, state, params) - new_params = optax.apply_updates(params, updates) - return loss, new_params, new_state + loss, grads = jax.value_and_grad(loss_fn)(params) + updates, new_state = optimizer.update(grads, state, params) + new_params = optax.apply_updates(params, updates) + return loss, new_params, new_state - losses = np.zeros(max_n_iter) - for i in range(max_n_iter): - train_loss = 0.0 - for j in range(train_iter.num_batches): - batch = train_iter(j) - batch_loss, params, state = step(params, state, **batch) - train_loss += batch_loss - losses[i] = train_loss + losses = np.zeros(n_iter) + for i in range(n_iter): + train_loss = 0.0 + for j in range(train_iter.num_batches): + batch = train_iter(j) + batch_loss, params, state = step(params, state, **batch) + train_loss += batch_loss + losses[i] = train_loss - return params, losses + return params, losses def run(n_iter): - n, p = 1000, 20 - rng_seq = hk.PRNGSequence(2) - y = jr.normal(next(rng_seq), shape=(n, p)) - data = namedtuple("named_dataset", "y")(y) + n, p = 1000, 20 + rng_seq = hk.PRNGSequence(2) + y = jr.normal(next(rng_seq), shape=(n, p)) + data = namedtuple("named_dataset", "y")(y) - model = make_model(p) - params, losses = train(rng_seq, data, model, n_iter) - plt.plot(losses) - plt.show() + model = make_model(p) + params, losses = train(rng_seq, data, model, n_iter) + plt.plot(losses) + plt.show() - y = jr.normal(next(rng_seq), shape=(10, p)) - print(model.apply(params, **{"y": y})) + y = jr.normal(next(rng_seq), shape=(10, p)) + print(model.apply(params, **{"y": y})) if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--n-iter", type=int, default=1_000) - args = parser.parse_args() - run(args.n_iter) + parser = argparse.ArgumentParser() + parser.add_argument("--n-iter", type=int, default=1_000) + args = parser.parse_args() + run(args.n_iter) diff --git a/examples/conditional_density_estimation.py b/examples/conditional_density_estimation.py index f4b5f58..e21c73f 100644 --- a/examples/conditional_density_estimation.py +++ b/examples/conditional_density_estimation.py @@ -1,138 +1,138 @@ import argparse -import distrax import haiku as hk import jax import numpy as np import optax -from jax import numpy as jnp -from jax import random +from jax import numpy as jnp, random as jr from matplotlib import pyplot as plt +from tensorflow_probability.substrates.jax import distributions as tfd from surjectors import ( - Chain, - MaskedAutoregressive, - MaskedCoupling, - Permutation, - TransformedDistribution, + Chain, + MaskedAutoregressive, + MaskedCoupling, + Permutation, + ScalarAffine, + TransformedDistribution, ) from surjectors.nn import MADE, make_mlp from surjectors.util import ( - as_batch_iterator, - make_alternating_binary_mask, - named_dataset, - unstack, + as_batch_iterator, + make_alternating_binary_mask, + named_dataset, + unstack, ) def make_model(dim, model="coupling"): - def _bijector_fn(params): - means, log_scales = unstack(params, -1) - return distrax.ScalarAffine(means, jnp.exp(log_scales)) - - def _flow(method, **kwargs): - layers = [] - order = jnp.arange(2) - for i in range(2): - if model == "coupling": - mask = make_alternating_binary_mask(2, i % 2 == 0) - layer = MaskedCoupling( - mask=mask, - bijector_fn=_bijector_fn, - conditioner=hk.Sequential( - [ - make_mlp([8, 8, dim * 2]), - hk.Reshape((dim, dim)), - ] - ), - ) - layers.append(layer) - else: - layer = MaskedAutoregressive( - bijector_fn=_bijector_fn, - conditioner=MADE( - 2, [32, 32], 2, - w_init=hk.initializers.TruncatedNormal(0.01), - b_init=jnp.zeros, - ), - ) - order = order[::-1] - layers.append(layer) - layers.append(Permutation(order, 1)) - if model != "coupling": - layers = layers[:-1] - chain = Chain(layers) - - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(dim), jnp.ones(dim)), - reinterpreted_batch_ndims=1, + def _bijector_fn(params): + means, log_scales = unstack(params, -1) + return ScalarAffine(means, jnp.exp(log_scales)) + + def _flow(method, **kwargs): + layers = [] + order = jnp.arange(2) + for i in range(2): + if model == "coupling": + mask = make_alternating_binary_mask(2, i % 2 == 0) + layer = MaskedCoupling( + mask=mask, + bijector_fn=_bijector_fn, + conditioner=hk.Sequential( + layers=[ + make_mlp((8, 8, dim * 2)), + hk.Reshape((dim, dim)), + ] + ), ) - td = TransformedDistribution(base_distribution, chain) - return td(method=method, **kwargs) + layers.append(layer) + else: + layer = MaskedAutoregressive( + bijector_fn=_bijector_fn, + conditioner=MADE( + 2, + [32, 32], + 2, + w_init=hk.initializers.TruncatedNormal(0.01), + b_init=jnp.zeros, + ), + ) + order = order[::-1] + layers.append(layer) + layers.append(Permutation(order, 1)) + if model != "coupling": + layers = layers[:-1] + chain = Chain(layers) + + base_distribution = tfd.Independent( + tfd.Normal(jnp.zeros(dim), jnp.ones(dim)), + reinterpreted_batch_ndims=1, + ) + td = TransformedDistribution(base_distribution, chain) + return td(method=method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td -def train(rng_seq, data, model, max_n_iter=1000): - train_iter = as_batch_iterator(next(rng_seq), data, 100, True) - params = model.init(next(rng_seq), method="log_prob", **train_iter(0)) +def train(rng_seq, data, model, n_iter=1_000): + train_iter = as_batch_iterator(next(rng_seq), data, 100, True) + params = model.init(next(rng_seq), method="log_prob", **train_iter(0)) - optimizer = optax.adam(1e-4) - state = optimizer.init(params) + optimizer = optax.adam(1e-4) + state = optimizer.init(params) - @jax.jit - def step(params, state, **batch): - def loss_fn(params): - lp = model.apply(params, None, method="log_prob", **batch) - return -jnp.sum(lp) + @jax.jit + def step(params, state, **batch): + def loss_fn(params): + lp = model.apply(params, None, method="log_prob", **batch) + return -jnp.sum(lp) - loss, grads = jax.value_and_grad(loss_fn)(params) - updates, new_state = optimizer.update(grads, state, params) - new_params = optax.apply_updates(params, updates) - return loss, new_params, new_state + loss, grads = jax.value_and_grad(loss_fn)(params) + updates, new_state = optimizer.update(grads, state, params) + new_params = optax.apply_updates(params, updates) + return loss, new_params, new_state - losses = np.zeros(max_n_iter) - for i in range(max_n_iter): - train_loss = 0.0 - for j in range(train_iter.num_batches): - batch = train_iter(j) - batch_loss, params, state = step(params, state, **batch) - train_loss += batch_loss - losses[i] = train_loss + losses = np.zeros(n_iter) + for i in range(n_iter): + train_loss = 0.0 + for j in range(train_iter.num_batches): + batch = train_iter(j) + batch_loss, params, state = step(params, state, **batch) + train_loss += batch_loss + losses[i] = train_loss - return params, losses + return params, losses def run(n_iter, model): - n = 10000 - thetas = distrax.Normal(jnp.zeros(2), jnp.full(2, 10)).sample( - seed=random.PRNGKey(0), sample_shape=(n,) - ) - y = 2 * thetas + distrax.Normal(jnp.zeros_like(thetas), 0.1).sample( - seed=random.PRNGKey(1) - ) - data = named_dataset(y, thetas) - - model = make_model(2, model) - params, losses = train(hk.PRNGSequence(2), data, model, n_iter) - samples = model.apply( - params, - random.PRNGKey(2), - method="sample", - x=jnp.full_like(thetas, -2.0), - ) - - plt.hist(samples[:, 0]) - plt.hist(samples[:, 1]) - plt.show() + n = 10000 + thetas = tfd.Normal(jnp.zeros(2), jnp.full(2, 10.0)).sample( + seed=jr.key(0), sample_shape=(n,) + ) + y = 2 * thetas + tfd.Normal(jnp.zeros_like(thetas), jnp.array(0.1)).sample( + seed=jr.key(1) + ) + data = named_dataset(y, thetas) + + model = make_model(2, model) + params, losses = train(hk.PRNGSequence(2), data, model, n_iter) + samples = model.apply( + params, + jr.key(2), + method="sample", + x=jnp.full_like(thetas, -2.0), + ) + + plt.hist(samples[:, 0]) + plt.hist(samples[:, 1]) + plt.show() if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--n-iter", type=int, default=1_000) - parser.add_argument("--model", type=str, default="coupling") - args = parser.parse_args() - run(args.n_iter, args.model) - - + parser = argparse.ArgumentParser() + parser.add_argument("--n-iter", type=int, default=1_000) + parser.add_argument("--model", type=str, default="coupling") + args = parser.parse_args() + run(args.n_iter, args.model) diff --git a/examples/coupling_inference_surjection.py b/examples/coupling_inference_surjection.py index 38725f0..24c0b22 100644 --- a/examples/coupling_inference_surjection.py +++ b/examples/coupling_inference_surjection.py @@ -1,7 +1,6 @@ import argparse from collections import namedtuple -import distrax import haiku as hk import jax import numpy as np @@ -9,116 +8,116 @@ from jax import numpy as jnp from jax import random as jr from matplotlib import pyplot as plt +from tensorflow_probability.substrates.jax import distributions as tfd +import surjectors from surjectors import ( - AffineMaskedCouplingInferenceFunnel, - Chain, - MaskedCoupling, - TransformedDistribution, + AffineMaskedCouplingInferenceFunnel, + Chain, + MaskedCoupling, + TransformedDistribution, ) from surjectors.nn import make_mlp from surjectors.util import as_batch_iterator, make_alternating_binary_mask def _decoder_fn(n_dim): - decoder_net = make_mlp([4, 4, n_dim * 2]) + decoder_net = make_mlp((4, 4, n_dim * 2)) - def _fn(z): - params = decoder_net(z) - mu, log_scale = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) + def _fn(z): + params = decoder_net(z) + mu, log_scale = jnp.split(params, 2, -1) + return tfd.Independent(tfd.Normal(mu, jnp.exp(log_scale))) - return _fn + return _fn def bijector_fn(params): - shift, log_scale = jnp.split(params, 2, axis=-1) - return distrax.ScalarAffine(shift, jnp.exp(log_scale)) + shift, log_scale = jnp.split(params, 2, axis=-1) + return surjectors.ScalarAffine(shift, jnp.exp(log_scale)) def make_model(n_dimensions): - def _flow(**kwargs): - n_dim = n_dimensions - layers = [] - for i in range(3): - if i != 1: - layer = AffineMaskedCouplingInferenceFunnel( - n_keep=int(n_dim / 2), - decoder=_decoder_fn(int(n_dim / 2)), - conditioner=make_mlp([8, 8, n_dim * 2]), - ) - n_dim = int(n_dim / 2) - else: - mask = make_alternating_binary_mask(n_dim, i % 2 == 0) - layer = MaskedCoupling( - mask=mask, - bijector_fn=bijector_fn, - conditioner=make_mlp([8, 8, n_dim * 2]), - ) - layers.append(layer) - chain = Chain(layers) - - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_dim), jnp.ones(n_dim)), - reinterpreted_batch_ndims=1, + def _flow(**kwargs): + n_dim = n_dimensions + layers = [] + for i in range(3): + if i != 1: + layer = AffineMaskedCouplingInferenceFunnel( + n_keep=int(n_dim / 2), + decoder=_decoder_fn(int(n_dim / 2)), + conditioner=make_mlp((8, 8, n_dim * 2)), ) - td = TransformedDistribution(base_distribution, chain) - return td.log_prob(**kwargs) + n_dim = int(n_dim / 2) + else: + mask = make_alternating_binary_mask(n_dim, i % 2 == 0) + layer = MaskedCoupling( + mask=mask, + bijector_fn=bijector_fn, + conditioner=make_mlp([8, 8, n_dim * 2]), + ) + layers.append(layer) + chain = Chain(layers) + + base_distribution = tfd.Independent( + tfd.Normal(jnp.zeros(n_dim), jnp.ones(n_dim)), + reinterpreted_batch_ndims=1, + ) + td = TransformedDistribution(base_distribution, chain) + return td.log_prob(**kwargs) - td = hk.transform(_flow) - td = hk.without_apply_rng(td) - return td + td = hk.transform(_flow) + td = hk.without_apply_rng(td) + return td -def train(rng_seq, data, model, max_n_iter=1000): - train_iter = as_batch_iterator(next(rng_seq), data, 100, True) - params = model.init(next(rng_seq), **train_iter(0)) +def train(rng_seq, data, model, n_iter=1000): + train_iter = as_batch_iterator(next(rng_seq), data, 100, True) + params = model.init(next(rng_seq), **train_iter(0)) - optimizer = optax.adam(1e-4) - state = optimizer.init(params) + optimizer = optax.adam(1e-4) + state = optimizer.init(params) - @jax.jit - def step(params, state, **batch): - def loss_fn(params): - lp = model.apply(params, **batch) - return -jnp.sum(lp) + @jax.jit + def step(params, state, **batch): + def loss_fn(params): + lp = model.apply(params, **batch) + return -jnp.sum(lp) - loss, grads = jax.value_and_grad(loss_fn)(params) - updates, new_state = optimizer.update(grads, state, params) - new_params = optax.apply_updates(params, updates) - return loss, new_params, new_state + loss, grads = jax.value_and_grad(loss_fn)(params) + updates, new_state = optimizer.update(grads, state, params) + new_params = optax.apply_updates(params, updates) + return loss, new_params, new_state - losses = np.zeros(max_n_iter) - for i in range(max_n_iter): - train_loss = 0.0 - for j in range(train_iter.num_batches): - batch = train_iter(j) - batch_loss, params, state = step(params, state, **batch) - train_loss += batch_loss - losses[i] = train_loss + losses = np.zeros(n_iter) + for i in range(n_iter): + train_loss = 0.0 + for j in range(train_iter.num_batches): + batch = train_iter(j) + batch_loss, params, state = step(params, state, **batch) + train_loss += batch_loss + losses[i] = train_loss - return params, losses + return params, losses def run(n_iter): - n, p = 1000, 20 - rng_seq = hk.PRNGSequence(2) - y = jr.normal(next(rng_seq), shape=(n, p)) - data = namedtuple("named_dataset", "y")(y) + n, p = 1000, 20 + rng_seq = hk.PRNGSequence(2) + y = jr.normal(next(rng_seq), shape=(n, p)) + data = namedtuple("named_dataset", "y")(y) - model = make_model(p) - params, losses = train(rng_seq, data, model, n_iter) - plt.plot(losses) - plt.show() + model = make_model(p) + params, losses = train(rng_seq, data, model, n_iter) + plt.plot(losses) + plt.show() - y = jr.normal(next(rng_seq), shape=(10, p)) - print(model.apply(params, **{"y": y})) + y = jr.normal(next(rng_seq), shape=(10, p)) + print(model.apply(params, **{"y": y})) if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--n-iter", type=int, default=1_000) - args = parser.parse_args() - run(args.n_iter) - - + parser = argparse.ArgumentParser() + parser.add_argument("--n-iter", type=int, default=1_000) + args = parser.parse_args() + run(args.n_iter) diff --git a/paper/paper.bib b/paper/paper.bib index fb039b6..5319bfd 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -140,4 +140,3 @@ @inproceedings{oliva18transform booktitle = {Proceedings of the 35th International Conference on Machine Learning}, year = {2018} } - diff --git a/pyproject.toml b/pyproject.toml index 00d0471..a726274 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,67 +1,90 @@ [build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +requires = ["setuptools", "setuptools-scm"] +build-backend = "setuptools.build_meta" [project] name = "surjectors" description = "Surjection layers for density estimation with normalizing flows" -authors = [{name = "Simon Dirmeier", email = "sfyrbnd@pm.me"}] +authors = [{name = "Simon Dirmeier", email = "simd23@pm.me"}] readme = "README.md" -license = "Apache-2.0" -homepage = "https://github.com/dirmeier/surjectors" +license = { file = "LICENCE" } keywords = ["normalizing flows", "surjections", "density estimation"] classifiers = [ - "Development Status :: 3 - Alpha", + "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] -requires-python = ">=3.9" +requires-python = ">=3.12" dependencies = [ - "distrax>=0.1.4", - "dm-haiku>=0.0.10", - "jaxlib>=0.4.18", - "jax>=0.4.18", - "optax>=0.1.7" + "distrax>=0.1.7", + "dm-haiku>=0.0.16", + "jaxlib>=0.8.11", + "jax>=0.8.1", + "optax>=0.1.7", + "tfp-nightly>=0.26.0.dev20250828", ] dynamic = ["version"] -[project.urls] -homepage = "https://github.com/dirmeier/surjectors" - -[tool.hatch.version] -path = "surjectors/__init__.py" - -[tool.hatch.build.targets.sdist] -exclude = [ - "/.github", - "./gitignore", - "/.pre-commit-config.yaml" +[dependency-groups] +dev = [ + "gitlint>=0.19.1", + "jupyter>=1.1.1", + "pre-commit>=4.5.1", + "pytest>=9.0.2", + "pytest-cov>=7.0.0", + "ruff>=0.15.6", ] - -[tool.hatch.envs.examples] -dependencies = [ - "matplotlib>=3.6.1" +docs = [ + "ipykernel>=7.2.0", + "ipython>=9.11.0", + "nbsphinx>=0.9.8", + "pandas>=2.3.3", + "scikit-learn>=1.8.0", + "session-info>=1.0.1", + "sphinx>=9.1.0", + "sphinx-autobuild>=2025.8.25", + "sphinx-autodoc-typehints>=3.9.8", + "sphinx-book-theme==1.1.0", + "sphinx-copybutton>=0.5.2", + "sphinx-design>=0.7.0", + "sphinx-fontawesome>=0.0.6", + "sphinx-gallery>=0.20.0", + "sphinx-math-dollar>=1.3", + "sphinxcontrib-bibtex>=2.6.5", + "sphinxcontrib-fulltoc>=1.2.0", ] - -[tool.hatch.envs.test] -dependencies = [ - "ruff>=0.3.0", - "pytest>=7.2.0", - "pytest-cov>=4.0.0" +examples = [ + "matplotlib>=3.10.5", ] -[tool.hatch.envs.test.scripts] -lint = 'ruff check surjectors' -test = 'pytest -v --cov=./surjectors --cov-report=xml surjectors' +[project.urls] +Homepage = "https://github.com/dirmeier/surjectors" +Documentation = "https://surjectors.readthedocs.io" + +[tool.setuptools] +packages = ["surjectors"] + +[tool.setuptools.dynamic] +version = {attr = "surjectors.__init__.__version__"} [tool.bandit] -skips = ["B101"] +skips = ["B101", "B310"] + +[tool.mypy] +show_error_codes = true +no_implicit_optional = true + +[tool.pytest.ini_options] +addopts = ["-v", "--doctest-modules", "--cov=./surjectors", "--cov-report=xml"] +testpaths = [ + "surjectors" +] [tool.ruff] +indent-width = 2 line-length = 80 exclude = ["*_test.py", "docs/**", "examples/**"] @@ -73,3 +96,6 @@ extend-select = [ [tool.ruff.lint.pydocstyle] convention= 'google' + +[tool.uv] +upgrade-package = ["jax", "jaxlib", "dm-haiku"] diff --git a/surjectors/__init__.py b/surjectors/__init__.py index bb945c5..5afa959 100644 --- a/surjectors/__init__.py +++ b/surjectors/__init__.py @@ -1,66 +1,68 @@ """surjectors: Surjection layers for density estimation with normalizing flows.""" -__version__ = "0.3.3" +__version__ = "0.3.4" -from distrax import ScalarAffine +from distrax import Inverse, RationalQuadraticSpline, ScalarAffine from surjectors._src.bijectors.affine_masked_autoregressive import ( - AffineMaskedAutoregressive, + AffineMaskedAutoregressive, ) from surjectors._src.bijectors.affine_masked_coupling import ( - AffineMaskedCoupling, + AffineMaskedCoupling, ) from surjectors._src.bijectors.lu_linear import LULinear from surjectors._src.bijectors.masked_autoregressive import MaskedAutoregressive from surjectors._src.bijectors.masked_coupling import MaskedCoupling from surjectors._src.bijectors.permutation import Permutation from surjectors._src.bijectors.rq_masked_coupling import ( - RationalQuadraticSplineMaskedCoupling, + RationalQuadraticSplineMaskedCoupling, ) from surjectors._src.distributions.transformed_distribution import ( - TransformedDistribution, + TransformedDistribution, ) from surjectors._src.surjectors.affine_masked_autoregressive_inference_funnel import ( # noqa: E501 - AffineMaskedAutoregressiveInferenceFunnel, + AffineMaskedAutoregressiveInferenceFunnel, ) from surjectors._src.surjectors.affine_masked_coupling_inference_funnel import ( - AffineMaskedCouplingInferenceFunnel, + AffineMaskedCouplingInferenceFunnel, ) from surjectors._src.surjectors.chain import Chain from surjectors._src.surjectors.masked_autoregressive_inference_funnel import ( # noqa: E501 - MaskedAutoregressiveInferenceFunnel, + MaskedAutoregressiveInferenceFunnel, ) from surjectors._src.surjectors.masked_coupling_inference_funnel import ( - MaskedCouplingInferenceFunnel, + MaskedCouplingInferenceFunnel, ) from surjectors._src.surjectors.mlp import MLPInferenceFunnel from surjectors._src.surjectors.rq_masked_autoregressive_inference_funnel import ( # noqa: E501 - RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel, + RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel, ) from surjectors._src.surjectors.rq_masked_coupling_inference_funnel import ( - RationalQuadraticSplineMaskedCouplingInferenceFunnel, + RationalQuadraticSplineMaskedCouplingInferenceFunnel, ) from surjectors._src.surjectors.slice import Slice __all__ = [ - "Chain", - "Permutation", - "TransformedDistribution", - "AffineMaskedAutoregressive", - "MaskedAutoregressive", - "MaskedAutoregressiveInferenceFunnel", - "AffineMaskedAutoregressiveInferenceFunnel", - "RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel", - "MaskedCoupling", - "AffineMaskedCoupling", - "RationalQuadraticSplineMaskedCoupling", - "MaskedCouplingInferenceFunnel", - "AffineMaskedCouplingInferenceFunnel", - "RationalQuadraticSplineMaskedCouplingInferenceFunnel", - # "AffineMaskedCouplingGenerativeFunnel", - "LULinear", - "MLPInferenceFunnel", - "Slice", - # "Augment", - "ScalarAffine", + "Chain", + "Permutation", + "TransformedDistribution", + "AffineMaskedAutoregressive", + "MaskedAutoregressive", + "MaskedAutoregressiveInferenceFunnel", + "AffineMaskedAutoregressiveInferenceFunnel", + "RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel", + "MaskedCoupling", + "AffineMaskedCoupling", + "RationalQuadraticSplineMaskedCoupling", + "MaskedCouplingInferenceFunnel", + "AffineMaskedCouplingInferenceFunnel", + "RationalQuadraticSplineMaskedCouplingInferenceFunnel", + # "AffineMaskedCouplingGenerativeFunnel", + "LULinear", + "MLPInferenceFunnel", + "Slice", + # "Augment", + "ScalarAffine", + "RationalQuadraticSpline", + "Inverse", ] diff --git a/surjectors/_src/_transform.py b/surjectors/_src/_transform.py index 67ea65a..fb0c32f 100644 --- a/surjectors/_src/_transform.py +++ b/surjectors/_src/_transform.py @@ -4,4 +4,4 @@ class Transform(jittable.Jittable, metaclass=ABCMeta): - """Transformation of a random variable.""" + """Transformation of a random variable.""" diff --git a/surjectors/_src/bijectors/affine_masked_autoregressive.py b/surjectors/_src/bijectors/affine_masked_autoregressive.py index fbdd9dd..b847444 100644 --- a/surjectors/_src/bijectors/affine_masked_autoregressive.py +++ b/surjectors/_src/bijectors/affine_masked_autoregressive.py @@ -8,38 +8,51 @@ # pylint: disable=too-many-arguments,arguments-renamed class AffineMaskedAutoregressive(MaskedAutoregressive): - """An affine masked autoregressive layer. - - Args: - conditioner: a MADE network - event_ndims: the number of array dimensions the bijector operates on - inner_event_ndims: tthe number of array dimensions the bijector - operates on - - References: - .. [1] Papamakarios, George, et al. "Masked Autoregressive Flow for - Density Estimation". Advances in Neural Information Processing - Systems, 2017. - - Examples: - >>> import distrax - >>> from surjectors import AffineMaskedAutoregressive - >>> - >>> layer = AffineMaskedAutoregressive( - >>> conditioner=MADE(10, [8, 8], 2), - >>> ) - """ - - def __init__( - self, - conditioner: MADE, - event_ndims: int = 1, - inner_event_ndims: int = 0, - ): - def bijector_fn(params): - means, log_scales = unstack(params, -1) - return distrax.ScalarAffine(means, jnp.exp(log_scales)) - - super().__init__( - conditioner, bijector_fn, event_ndims, inner_event_ndims - ) + """An affine masked autoregressive layer. + + Args: + conditioner: a MADE network + event_ndims: the number of array dimensions the bijector operates on + inner_event_ndims: tthe number of array dimensions the bijector + operates on + + References: + .. [1] Papamakarios, George, et al. "Masked Autoregressive Flow for + Density Estimation". Advances in Neural Information Processing + Systems, 2017. + + Examples: + >>> import haiku as hk + >>> from jax import random as jr + >>> from tensorflow_probability.substrates.jax import distributions as tfd + >>> from surjectors import AffineMaskedAutoregressive, TransformedDistribution + + >>> @hk.without_apply_rng + ... @hk.transform + ... def fn(inputs): + ... base_distribution = tfd.Independent( + ... tfd.Normal(jnp.zeros(10), jnp.ones(10)), + ... reinterpreted_batch_ndims=1, + ... ) + ... td = TransformedDistribution( + ... base_distribution, + ... AffineMaskedAutoregressive(MADE(10, (64, 64), 2)) + ... ) + ... return td.log_prob(inputs) + + >>> data = jr.normal(jr.PRNGKey(1), shape=(10, 10)) + >>> params = fn.init(jr.key(0), data) + >>> lps = fn.apply(params, data) + """ + + def __init__( + self, + conditioner: MADE, + event_ndims: int = 1, + inner_event_ndims: int = 0, + ): + def bijector_fn(params): + means, log_scales = unstack(params, -1) + return distrax.ScalarAffine(means, jnp.exp(log_scales)) + + super().__init__(conditioner, bijector_fn, event_ndims, inner_event_ndims) diff --git a/surjectors/_src/bijectors/affine_masked_autoregressive_test.py b/surjectors/_src/bijectors/affine_masked_autoregressive_test.py index 863e1bb..52f1bb3 100644 --- a/surjectors/_src/bijectors/affine_masked_autoregressive_test.py +++ b/surjectors/_src/bijectors/affine_masked_autoregressive_test.py @@ -10,44 +10,44 @@ def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def make_bijector(n_dimension): - def _transformation_fn(n_dimension): - bij = AffineMaskedAutoregressive( - MADE(n_dimension, [8, 8], 2), - ) - return bij + def _transformation_fn(n_dimension): + bij = AffineMaskedAutoregressive( + MADE(n_dimension, (32, 32), 2), + ) + return bij - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_dimension), _transformation_fn(n_dimension) - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_dimension), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_affine_masked_autoregressive(): - n_dimension = 4 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension = 4 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_bijector(n_dimension) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_bijector(n_dimension) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_affine_masked_autoregressive(): - n_dimension = 4 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension = 4 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_bijector(n_dimension) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_bijector(n_dimension) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/bijectors/affine_masked_coupling.py b/surjectors/_src/bijectors/affine_masked_coupling.py index fe0ccec..8da8fe8 100644 --- a/surjectors/_src/bijectors/affine_masked_coupling.py +++ b/surjectors/_src/bijectors/affine_masked_coupling.py @@ -1,4 +1,4 @@ -from typing import Callable, Optional +from collections.abc import Callable import distrax from jax import numpy as jnp @@ -9,44 +9,44 @@ # pylint: disable=too-many-arguments, arguments-renamed,too-many-ancestors class AffineMaskedCoupling(MaskedCoupling): - """An affine masked coupling layer. - - Args: - mask: a boolean mask of length n_dim. A value - of True indicates that the corresponding input remains unchanged - conditioner: a function that computes the parameters of the inner - bijector - event_ndims: the number of array dimensions the bijector operates on - inner_event_ndims: the number of array dimensions the inner bijector - operates on - - References: - .. [1] Dinh, Laurent, et al. "Density estimation using RealNVP". - International Conference on Learning Representations, 2017. - - Examples: - >>> import distrax - >>> from surjectors import AffineMaskedCoupling - >>> from surjectors.nn import make_mlp - >>> from surjectors.util import make_alternating_binary_mask - >>> - >>> layer = MaskedCoupling( - >>> mask=make_alternating_binary_mask(10, True), - >>> conditioner=make_mlp([8, 8, 10 * 2]), - >>> ) - """ - - def __init__( - self, - mask: Array, - conditioner: Callable, - event_ndims: Optional[int] = None, - inner_event_ndims: int = 0, - ): - def _bijector_fn(params): - means, log_scales = jnp.split(params, 2, -1) - return distrax.ScalarAffine(means, jnp.exp(log_scales)) - - super().__init__( - mask, conditioner, _bijector_fn, event_ndims, inner_event_ndims - ) + """An affine masked coupling layer. + + Args: + mask: a boolean mask of length n_dim. A value + of True indicates that the corresponding input remains unchanged + conditioner: a function that computes the parameters of the inner + bijector + event_ndims: the number of array dimensions the bijector operates on + inner_event_ndims: the number of array dimensions the inner bijector + operates on + + References: + .. [1] Dinh, Laurent, et al. "Density estimation using RealNVP". + International Conference on Learning Representations, 2017. + + Examples: + >>> import distrax + >>> from surjectors import AffineMaskedCoupling + >>> from surjectors.nn import make_mlp + >>> from surjectors.util import make_alternating_binary_mask + >>> + >>> layer = MaskedCoupling( + ... mask=make_alternating_binary_mask(10, True), + ... conditioner=make_mlp([8, 8, 10 * 2]), + ... ) + """ + + def __init__( + self, + mask: Array, + conditioner: Callable, + event_ndims: int | None = None, + inner_event_ndims: int = 0, + ): + def _bijector_fn(params): + means, log_scales = jnp.split(params, 2, -1) + return distrax.ScalarAffine(means, jnp.exp(log_scales)) + + super().__init__( + mask, conditioner, _bijector_fn, event_ndims, inner_event_ndims + ) diff --git a/surjectors/_src/bijectors/affine_masked_coupling_test.py b/surjectors/_src/bijectors/affine_masked_coupling_test.py index f53d045..07fb21c 100644 --- a/surjectors/_src/bijectors/affine_masked_coupling_test.py +++ b/surjectors/_src/bijectors/affine_masked_coupling_test.py @@ -11,45 +11,45 @@ def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def make_bijector(n_dimension): - def _transformation_fn(n_dimension): - bij = AffineMaskedCoupling( - make_alternating_binary_mask(n_dimension, 0 % 2 == 0), - make_mlp([8, 8, n_dimension * 2]), - ) - return bij + def _transformation_fn(n_dimension): + bij = AffineMaskedCoupling( + make_alternating_binary_mask(n_dimension, 0 % 2 == 0), + make_mlp([8, 8, n_dimension * 2]), + ) + return bij - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_dimension), _transformation_fn(n_dimension) - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_dimension), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_affine_masked_coupling(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_bijector(n_dimension) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_bijector(n_dimension) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_affine_masked_coupling(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_bijector(n_dimension) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_bijector(n_dimension) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/bijectors/bijector.py b/surjectors/_src/bijectors/bijector.py index 11cbec3..cacfb17 100644 --- a/surjectors/_src/bijectors/bijector.py +++ b/surjectors/_src/bijectors/bijector.py @@ -7,34 +7,34 @@ # pylint: disable=too-many-arguments class Bijector(Surjector, ABC): - """Bijector base class.""" - - def inverse_and_log_det(self, y: Array, x: Array = None, **kwargs): - """Compute the inverse transformation and its Jacobian determinant. - - Args: - y: event for which the inverse and likelihood contribution is - computed - x: event to condition on - kwargs: additional keyword arguments - - Returns: - tuple of two arrays of floats. The first one is the inverse - transformation, the second one its likelihood contribution - """ - return self.inverse_and_likelihood_contribution(y, x=x, **kwargs) - - def forward_and_log_det(self, z: Array, x: Array = None, **kwargs): - """Compute the forward transformation and its Jacobian determinant. - - Args: - z: event for which the forward transform and likelihood contribution - is computed - x: event to condition on - kwargs: additional keyword arguments - - Returns: - tuple of two arrays of floats. The first one is the forward - transformation, the second one its likelihood contribution - """ - return self.forward_and_likelihood_contribution(z, x=x, **kwargs) + """Bijector base class.""" + + def inverse_and_log_det(self, y: Array, x: Array = None, **kwargs): + """Compute the inverse transformation and its Jacobian determinant. + + Args: + y: event for which the inverse and likelihood contribution is + computed + x: event to condition on + kwargs: additional keyword arguments + + Returns: + tuple of two arrays of floats. The first one is the inverse + transformation, the second one its likelihood contribution + """ + return self.inverse_and_likelihood_contribution(y, x=x, **kwargs) + + def forward_and_log_det(self, z: Array, x: Array = None, **kwargs): + """Compute the forward transformation and its Jacobian determinant. + + Args: + z: event for which the forward transform and likelihood contribution + is computed + x: event to condition on + kwargs: additional keyword arguments + + Returns: + tuple of two arrays of floats. The first one is the forward + transformation, the second one its likelihood contribution + """ + return self.forward_and_likelihood_contribution(z, x=x, **kwargs) diff --git a/surjectors/_src/bijectors/lu_linear.py b/surjectors/_src/bijectors/lu_linear.py index f8dc1e9..ffc48f8 100644 --- a/surjectors/_src/bijectors/lu_linear.py +++ b/surjectors/_src/bijectors/lu_linear.py @@ -8,70 +8,86 @@ # pylint: disable=arguments-differ,too-many-instance-attributes class LULinear(Bijector, hk.Module): - """An bijection based on the LU composition. - - Args: - n_dimension: number of dimensions to keep - with_bias: use a bias term or not - dtype: parameter dtype - - References: - .. [1] Oliva, Junier, et al. "Transformation Autoregressive Networks". - Proceedings of the 35th International Conference on - Machine Learning, 2018. - - Examples: - >>> from surjectors import LULinear - >>> layer = LULinear(10) - """ - - def __init__(self, n_dimension, with_bias=False, dtype=jnp.float32): - super().__init__() - if with_bias: - raise NotImplementedError() - - self.n_dimension = n_dimension - self.with_bias = with_bias - self.dtype = dtype - n_triangular_entries = ((n_dimension - 1) * n_dimension) // 2 - - self._lower_indices = np.tril_indices(n_dimension, k=-1) - self._upper_indices = np.triu_indices(n_dimension, k=1) - self._diag_indices = np.diag_indices(n_dimension) - - self._lower_entries = hk.get_parameter( - "lower_entries", [n_triangular_entries], dtype=dtype, init=jnp.zeros - ) - self._upper_entries = hk.get_parameter( - "upper_entries", [n_triangular_entries], dtype=dtype, init=jnp.zeros - ) - self._unconstrained_upper_diag_entries = hk.get_parameter( - "diag_entries", [n_dimension], dtype=dtype, init=jnp.ones - ) - - def _to_lower_and_upper_matrices(self): - L = jnp.zeros((self.n_dimension, self.n_dimension), dtype=self.dtype) - L = L.at[self._lower_indices].set(self._lower_entries) - L = L.at[self._diag_indices].set(1.0) - - U = jnp.zeros((self.n_dimension, self.n_dimension), dtype=self.dtype) - U = U.at[self._upper_indices].set(self._upper_entries) - U = U.at[self._diag_indices].set(self._upper_diag) - - return L, U - - @property - def _upper_diag(self): - return jax.nn.softplus(self._unconstrained_upper_diag_entries) + 1e-4 - - def _inverse_likelihood_contribution(self): - return jnp.sum(jnp.log(self._upper_diag)) - - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - L, U = self._to_lower_and_upper_matrices() - z = jnp.dot(jnp.dot(y, U), L) - lc = self._inverse_likelihood_contribution() * jnp.ones(z.shape[0]) - return z, lc - - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - raise NotImplementedError() + """An bijection based on the LU composition. + + Args: + n_dimension: number of dimensions to keep + with_bias: use a bias term or not + dtype: parameter dtype + + References: + .. [1] Oliva, Junier, et al. "Transformation Autoregressive Networks". + Proceedings of the 35th International Conference on + Machine Learning, 2018. + + Examples: + >>> import haiku as hk + >>> from jax import random as jr + >>> from tensorflow_probability.substrates.jax import distributions as tfd + >>> from surjectors import LULinear, TransformedDistribution + + >>> @hk.without_apply_rng + ... @hk.transform + ... def fn(inputs): + ... base_distribution = tfd.Independent( + ... tfd.Normal(jnp.zeros(5), jnp.ones(5)), + ... reinterpreted_batch_ndims=1, + ... ) + ... td = TransformedDistribution(base_distribution, LULinear(5)) + ... return td.log_prob(inputs) + >>> + >>> data = jr.normal(jr.PRNGKey(1), shape=(10, 5)) + >>> params = fn.init(jr.key(0), data) + >>> lps = fn.apply(params, data) + """ + + def __init__(self, n_dimension, with_bias=False, dtype=jnp.float32): + super().__init__() + if with_bias: + raise NotImplementedError() + + self.n_dimension = n_dimension + self.with_bias = with_bias + self.dtype = dtype + n_triangular_entries = ((n_dimension - 1) * n_dimension) // 2 + + self._lower_indices = np.tril_indices(n_dimension, k=-1) + self._upper_indices = np.triu_indices(n_dimension, k=1) + self._diag_indices = np.diag_indices(n_dimension) + + self._lower_entries = hk.get_parameter( + "lower_entries", [n_triangular_entries], dtype=dtype, init=jnp.zeros + ) + self._upper_entries = hk.get_parameter( + "upper_entries", [n_triangular_entries], dtype=dtype, init=jnp.zeros + ) + self._unconstrained_upper_diag_entries = hk.get_parameter( + "diag_entries", [n_dimension], dtype=dtype, init=jnp.ones + ) + + def _to_lower_and_upper_matrices(self): + L = jnp.zeros((self.n_dimension, self.n_dimension), dtype=self.dtype) + L = L.at[self._lower_indices].set(self._lower_entries) + L = L.at[self._diag_indices].set(1.0) + + U = jnp.zeros((self.n_dimension, self.n_dimension), dtype=self.dtype) + U = U.at[self._upper_indices].set(self._upper_entries) + U = U.at[self._diag_indices].set(self._upper_diag) + + return L, U + + @property + def _upper_diag(self): + return jax.nn.softplus(self._unconstrained_upper_diag_entries) + 1e-4 + + def _inverse_likelihood_contribution(self): + return jnp.sum(jnp.log(self._upper_diag)) + + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + L, U = self._to_lower_and_upper_matrices() + z = jnp.dot(jnp.dot(y, U), L) + lc = self._inverse_likelihood_contribution() * jnp.ones(z.shape[0]) + return z, lc + + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + raise NotImplementedError() diff --git a/surjectors/_src/bijectors/lu_liner_test.py b/surjectors/_src/bijectors/lu_liner_test.py new file mode 100644 index 0000000..cbbd595 --- /dev/null +++ b/surjectors/_src/bijectors/lu_liner_test.py @@ -0,0 +1,25 @@ +# pylint: skip-file + +import haiku as hk +from jax import numpy as jnp +from jax import random as jr +from tensorflow_probability.substrates.jax import distributions as tfd + +from surjectors import LULinear, TransformedDistribution + + +@hk.without_apply_rng +@hk.transform +def fn(**kwargs): + base_distribution = tfd.Independent( + tfd.Normal(jnp.zeros(5), jnp.ones(5)), + reinterpreted_batch_ndims=1, + ) + td = TransformedDistribution(base_distribution, LULinear(5)) + return td.log_prob(**kwargs) + + +def test_lu_linear(): + y = jr.normal(jr.PRNGKey(1), shape=(10, 5)) + params = fn.init(jr.key(0), y=y) + _ = fn.apply(params, y=y) diff --git a/surjectors/_src/bijectors/masked_autoregressive.py b/surjectors/_src/bijectors/masked_autoregressive.py index 59d1e87..3595b39 100644 --- a/surjectors/_src/bijectors/masked_autoregressive.py +++ b/surjectors/_src/bijectors/masked_autoregressive.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable from distrax._src.utils import math from jax import numpy as jnp @@ -9,72 +9,71 @@ # pylint: disable=too-many-arguments,arguments-renamed class MaskedAutoregressive(Bijector): - """A masked autoregressive layer. + """A masked autoregressive layer. - Args: - conditioner: a MADE network - bijector_fn: a callable that returns the inner bijector that will - be used to transform the input - event_ndims: the number of array dimensions the bijector operates on - inner_event_ndims: tthe number of array dimensions the bijector - operates on + Args: + conditioner: a MADE network + bijector_fn: a callable that returns the inner bijector that will + be used to transform the input + event_ndims: the number of array dimensions the bijector operates on + inner_event_ndims: tthe number of array dimensions the bijector + operates on - References: - .. [1] Papamakarios, George, et al. "Masked Autoregressive Flow for - Density Estimation". Advances in Neural Information Processing - Systems, 2017. + References: + .. [1] Papamakarios, George, et al. "Masked Autoregressive Flow for + Density Estimation". Advances in Neural Information Processing + Systems, 2017. - Examples: - >>> import distrax - >>> from surjectors import MaskedAutoregressive - >>> from surjectors.util import unstack - >>> - >>> def bijector_fn(params): - >>> means, log_scales = unstack(params, -1) - >>> return distrax.ScalarAffine(means, jnp.exp(log_scales)) - >>> - >>> layer = MaskedAutoregressive( - >>> conditioner=MADE(10, [8, 8], 2), - >>> bijector_fn=bijector_fn - >>> ) - """ + Examples: + >>> import distrax + >>> from surjectors import MaskedAutoregressive + >>> from surjectors.util import unstack + >>> + >>> def bijector_fn(params): + >>> means, log_scales = unstack(params, -1) + >>> return distrax.ScalarAffine(means, jnp.exp(log_scales)) + >>> + >>> layer = MaskedAutoregressive( + >>> conditioner=MADE(10, [8, 8], 2), + >>> bijector_fn=bijector_fn + >>> ) + """ - def __init__( - self, - conditioner: MADE, - bijector_fn: Callable, - event_ndims: int = 1, - inner_event_ndims: int = 0, - ): - if event_ndims is not None and event_ndims < inner_event_ndims: - raise ValueError( - f"'event_ndims={event_ndims}' should be at least as" - f" large as 'inner_event_ndims={inner_event_ndims}'." - ) - if not isinstance(conditioner, MADE): - raise ValueError( - "conditioner should be a MADE when used " - "MaskedAutoregressive flow" - ) - self._event_ndims = event_ndims - self._inner_event_ndims = inner_event_ndims - self.conditioner = conditioner - self._inner_bijector = bijector_fn + def __init__( + self, + conditioner: MADE, + bijector_fn: Callable, + event_ndims: int = 1, + inner_event_ndims: int = 0, + ): + if event_ndims is not None and event_ndims < inner_event_ndims: + raise ValueError( + f"'event_ndims={event_ndims}' should be at least as" + f" large as 'inner_event_ndims={inner_event_ndims}'." + ) + if not isinstance(conditioner, MADE): + raise ValueError( + "conditioner should be a MADE when used MaskedAutoregressive flow" + ) + self._event_ndims = event_ndims + self._inner_event_ndims = inner_event_ndims + self.conditioner = conditioner + self._inner_bijector = bijector_fn - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - y = jnp.zeros_like(z) - for _ in jnp.arange(z.shape[-1]): - params = self.conditioner(y, x) - y, log_det = self._inner_bijector(params).forward_and_log_det(z) - log_det = math.sum_last( - log_det, self._event_ndims - self._inner_event_ndims - ) - return y, log_det + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + y = jnp.zeros_like(z) + for _ in jnp.arange(z.shape[-1]): + params = self.conditioner(y, x) + y, log_det = self._inner_bijector(params).forward_and_log_det(z) + log_det = math.sum_last( + log_det, self._event_ndims - self._inner_event_ndims + ) + return y, log_det - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - params = self.conditioner(y, x) - z, log_det = self._inner_bijector(params).inverse_and_log_det(y) - log_det = math.sum_last( - log_det, self._event_ndims - self._inner_event_ndims - ) - return z, log_det + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + params = self.conditioner(y, x) + z, log_det = self._inner_bijector(params).inverse_and_log_det(y) + log_det = math.sum_last( + log_det, self._event_ndims - self._inner_event_ndims + ) + return z, log_det diff --git a/surjectors/_src/bijectors/masked_autoregressive_test.py b/surjectors/_src/bijectors/masked_autoregressive_test.py index a6e7d71..8d1cc23 100644 --- a/surjectors/_src/bijectors/masked_autoregressive_test.py +++ b/surjectors/_src/bijectors/masked_autoregressive_test.py @@ -14,188 +14,188 @@ def _affine_bijector_fn(params): - means, log_scales = unstack(params, -1) - return distrax.Inverse(distrax.ScalarAffine(means, jnp.exp(log_scales))) + means, log_scales = unstack(params, -1) + return distrax.Inverse(distrax.ScalarAffine(means, jnp.exp(log_scales))) def _rq_bijector_fn(params): - return distrax.Inverse(distrax.RationalQuadraticSpline(params, -2.0, 2.0)) + return distrax.Inverse(distrax.RationalQuadraticSpline(params, -2.0, 2.0)) def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def masked_autoregressive_bijector(n_dim, bijector_fn, n_params, n_hidden): - def _transformation_fn(n_dim): - layer = MaskedAutoregressive( - bijector_fn=bijector_fn, - conditioner=MADE( - n_dim, - [n_hidden], - n_params, - w_init=hk.initializers.TruncatedNormal(stddev=1.0), - b_init=jnp.ones, - ), - ) - - return layer - - def _flow(**kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_dim), _transformation_fn(n_dim) - ) - return td.log_prob(**kwargs) - - td = hk.transform(_flow) - td = hk.without_apply_rng(td) - return td + def _transformation_fn(n_dim): + layer = MaskedAutoregressive( + bijector_fn=bijector_fn, + conditioner=MADE( + n_dim, + [n_hidden], + n_params, + w_init=hk.initializers.TruncatedNormal(stddev=1.0), + b_init=jnp.ones, + ), + ) + return layer -@pytest.fixture() -def forward_affine_masked_autoregressive_flow(request): - def _affine_bijector_fn(params): - means, log_scales = unstack(params, -1) - return distrax.ScalarAffine(means, jnp.exp(log_scales)) + def _flow(**kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_dim), _transformation_fn(n_dim) + ) + return td.log_prob(**kwargs) - input_shape, hidden_shapes, output_shape = request.param + td = hk.transform(_flow) + td = hk.without_apply_rng(td) + return td - @hk.without_apply_rng - @hk.transform - def _flow(**kwargs): - layer = MaskedAutoregressive( - bijector_fn=_affine_bijector_fn, - conditioner=MADE( - input_shape, - hidden_shapes, - output_shape, - w_init=jnp.ones, - b_init=jnp.zeros, - activation=lambda x: x, - ), - ) - return layer.forward(**kwargs) +@pytest.fixture() +def forward_affine_masked_autoregressive_flow(request): + def _affine_bijector_fn(params): + means, log_scales = unstack(params, -1) + return distrax.ScalarAffine(means, jnp.exp(log_scales)) + + input_shape, hidden_shapes, output_shape = request.param + + @hk.without_apply_rng + @hk.transform + def _flow(**kwargs): + layer = MaskedAutoregressive( + bijector_fn=_affine_bijector_fn, + conditioner=MADE( + input_shape, + hidden_shapes, + output_shape, + w_init=jnp.ones, + b_init=jnp.zeros, + activation=lambda x: x, + ), + ) + + return layer.forward(**kwargs) - return _flow + return _flow @pytest.fixture( - params=[(_rq_bijector_fn, 4), (_affine_bijector_fn, 2)], - ids=["rq_masked_autoregressive", "affine_masked_autoregressive"], + params=[(_rq_bijector_fn, 4), (_affine_bijector_fn, 2)], + ids=["rq_masked_autoregressive", "affine_masked_autoregressive"], ) def bijection(request): - yield request.param + yield request.param def test_unconditional_bijector_shape(bijection): - rng_seq = hk.PRNGSequence(0) - bijector_fn, n_params = bijection - n_dim, n_hidden = 2, 8 - flow = masked_autoregressive_bijector(2, bijector_fn, n_params, n_hidden) - y = distrax.Normal(0.0, 1.0).sample( - seed=random.PRNGKey(2), sample_shape=(100, n_dim) - ) + rng_seq = hk.PRNGSequence(0) + bijector_fn, n_params = bijection + n_dim, n_hidden = 2, 8 + flow = masked_autoregressive_bijector(2, bijector_fn, n_params, n_hidden) + y = distrax.Normal(0.0, 1.0).sample( + seed=random.PRNGKey(2), sample_shape=(100, n_dim) + ) - params = flow.init(next(rng_seq), y=y) - chex.assert_shape( - params["made/~/masked_linear_1"]["w"], (8, n_dim * n_params) - ) + params = flow.init(next(rng_seq), y=y) + chex.assert_shape( + params["made/~/masked_linear_1"]["w"], (8, n_dim * n_params) + ) @pytest.mark.parametrize( - "forward_affine_masked_autoregressive_flow,expected", - [ - ((3, [2, 4, 2], 2), [[1.0000000e00, 9.3890562e00, 4.2617353e11]]), - ((3, [2], 2), [[1.0, 3.7182817, 310.09976]]), - ], - indirect=["forward_affine_masked_autoregressive_flow"], + "forward_affine_masked_autoregressive_flow,expected", + [ + ((3, [2, 4, 2], 2), [[1.0000000e00, 9.3890562e00, 4.2617353e11]]), + ((3, [2], 2), [[1.0, 3.7182817, 310.09976]]), + ], + indirect=["forward_affine_masked_autoregressive_flow"], ) def test_unconditional_affine_masked_autoregressive_flow( - forward_affine_masked_autoregressive_flow, expected + forward_affine_masked_autoregressive_flow, expected ): - """ - Tested using: - - import numpy as np - z = np.ones((1, 3), dtype=np.float32) - ma = AutoregressiveNetwork( - 2, - 3, - hidden_units=[2, 4, 2], # or [2] - conditional_input_layers="first_layer", - kernel_initializer="ones", - activation=lambda x: x - ) - flow = MaskedAutoregressiveFlow(ma) - y = flow.forward(z) - print(y) - """ - z = jnp.ones((1, 3)) - params = forward_affine_masked_autoregressive_flow.init( - random.PRNGKey(0), z=z - ) - y = forward_affine_masked_autoregressive_flow.apply(params, z=z) - assert jnp.allclose(y, jnp.array(expected)) + """ + Tested using: + + import numpy as np + z = np.ones((1, 3), dtype=np.float32) + ma = AutoregressiveNetwork( + 2, + 3, + hidden_units=[2, 4, 2], # or [2] + conditional_input_layers="first_layer", + kernel_initializer="ones", + activation=lambda x: x + ) + flow = MaskedAutoregressiveFlow(ma) + y = flow.forward(z) + print(y) + """ + z = jnp.ones((1, 3)) + params = forward_affine_masked_autoregressive_flow.init( + random.PRNGKey(0), z=z + ) + y = forward_affine_masked_autoregressive_flow.apply(params, z=z) + assert jnp.allclose(y, jnp.array(expected)) @pytest.mark.parametrize( - "forward_affine_masked_autoregressive_flow,expected", - [ - ((3, [2, 4, 2], 2), [[1.0000000e00, 1.6063736e01, 2.9474317e18]]), - ((3, [2], 2), [[1.0000000e00, 4.9692965e00, 1.9453466e03]]), - ], - indirect=["forward_affine_masked_autoregressive_flow"], + "forward_affine_masked_autoregressive_flow,expected", + [ + ((3, [2, 4, 2], 2), [[1.0000000e00, 1.6063736e01, 2.9474317e18]]), + ((3, [2], 2), [[1.0000000e00, 4.9692965e00, 1.9453466e03]]), + ], + indirect=["forward_affine_masked_autoregressive_flow"], ) def test_conditional_affine_masked_autoregressive_flow( - forward_affine_masked_autoregressive_flow, expected + forward_affine_masked_autoregressive_flow, expected ): - """ - Tested using: - - import numpy as np - z = np.ones((1, 3), dtype=np.float32) - x = np.array([[0.1, 0.2]], dtype=np.float32) - ma = AutoregressiveNetwork( - 2, - 3, - hidden_units=[2, 4, 2], # or [2] - conditional_input_layers="first_layer", - kernel_initializer="ones", - activation=lambda x: x - ) - flow = MaskedAutoregressiveFlow(ma) - y = flow.forward(z, conditional_input=x) - print(y) - """ - - z = jnp.ones((1, 3)) - x = jnp.array([[0.1, 0.2]]) - params = forward_affine_masked_autoregressive_flow.init( - random.PRNGKey(0), z=z, x=x - ) - y = forward_affine_masked_autoregressive_flow.apply(params, z=z, x=x) - assert jnp.allclose(y, jnp.array(expected)) + """ + Tested using: + + import numpy as np + z = np.ones((1, 3), dtype=np.float32) + x = np.array([[0.1, 0.2]], dtype=np.float32) + ma = AutoregressiveNetwork( + 2, + 3, + hidden_units=[2, 4, 2], # or [2] + conditional_input_layers="first_layer", + kernel_initializer="ones", + activation=lambda x: x + ) + flow = MaskedAutoregressiveFlow(ma) + y = flow.forward(z, conditional_input=x) + print(y) + """ + + z = jnp.ones((1, 3)) + x = jnp.array([[0.1, 0.2]]) + params = forward_affine_masked_autoregressive_flow.init( + random.PRNGKey(0), z=z, x=x + ) + y = forward_affine_masked_autoregressive_flow.apply(params, z=z, x=x) + assert jnp.allclose(y, jnp.array(expected)) def test_conditional_bijector_shape(bijection): - rng_seq = hk.PRNGSequence(0) - - bijector_fn, n_params = bijection - n_dim, n_hidden = 2, 8 - flow = masked_autoregressive_bijector(2, bijector_fn, n_params, n_hidden) - y = distrax.Normal(0.0, 1.0).sample( - seed=random.PRNGKey(2), sample_shape=(100, n_dim) - ) - x = distrax.Normal(0.0, 1.0).sample( - seed=random.PRNGKey(2), sample_shape=(100, 1) - ) - - params = flow.init(next(rng_seq), y=y, x=x) - chex.assert_shape( - params["made/~/masked_linear_1"]["w"], (8, n_dim * n_params) - ) + rng_seq = hk.PRNGSequence(0) + + bijector_fn, n_params = bijection + n_dim, n_hidden = 2, 8 + flow = masked_autoregressive_bijector(2, bijector_fn, n_params, n_hidden) + y = distrax.Normal(0.0, 1.0).sample( + seed=random.PRNGKey(2), sample_shape=(100, n_dim) + ) + x = distrax.Normal(0.0, 1.0).sample( + seed=random.PRNGKey(2), sample_shape=(100, 1) + ) + + params = flow.init(next(rng_seq), y=y, x=x) + chex.assert_shape( + params["made/~/masked_linear_1"]["w"], (8, n_dim * n_params) + ) diff --git a/surjectors/_src/bijectors/masked_coupling.py b/surjectors/_src/bijectors/masked_coupling.py index 73fdd3a..595f020 100644 --- a/surjectors/_src/bijectors/masked_coupling.py +++ b/surjectors/_src/bijectors/masked_coupling.py @@ -1,4 +1,4 @@ -from typing import Callable, Optional +from collections.abc import Callable import distrax from distrax._src.utils import math @@ -10,76 +10,76 @@ # ruff: noqa: PLR0913 class MaskedCoupling(Bijector, distrax.MaskedCoupling): - """A masked coupling layer. + """A masked coupling layer. - Args: - mask: a boolean mask of length n_dim. A value - of True indicates that the corresponding input remains unchanged - conditioner: a function that computes the parameters of the inner - bijector - bijector_fn: a callable that returns the inner bijector that will be - used to transform the input - event_ndims: the number of array dimensions the bijector operates on - inner_event_ndims: the number of array dimensions the inner bijector - operates on + Args: + mask: a boolean mask of length n_dim. A value + of True indicates that the corresponding input remains unchanged + conditioner: a function that computes the parameters of the inner + bijector + bijector_fn: a callable that returns the inner bijector that will be + used to transform the input + event_ndims: the number of array dimensions the bijector operates on + inner_event_ndims: the number of array dimensions the inner bijector + operates on - Examples: - >>> import distrax - >>> from surjectors import MaskedCoupling - >>> from surjectors.nn import make_mlp - >>> from surjectors.util import make_alternating_binary_mask - >>> - >>> def bijector_fn(params): - >>> means, log_scales = jnp.split(params, 2, -1) - >>> return distrax.ScalarAffine(means, jnp.exp(log_scales)) - >>> - >>> layer = MaskedCoupling( - >>> mask=make_alternating_binary_mask(10, True), - >>> bijector_fn=bijector_fn, - >>> conditioner=make_mlp([8, 8, 10 * 2]), - >>> ) + Examples: + >>> import distrax + >>> from surjectors import MaskedCoupling + >>> from surjectors.nn import make_mlp + >>> from surjectors.util import make_alternating_binary_mask + >>> + >>> def bijector_fn(params): + >>> means, log_scales = jnp.split(params, 2, -1) + >>> return distrax.ScalarAffine(means, jnp.exp(log_scales)) + >>> + >>> layer = MaskedCoupling( + >>> mask=make_alternating_binary_mask(10, True), + >>> bijector_fn=bijector_fn, + >>> conditioner=make_mlp([8, 8, 10 * 2]), + >>> ) - References: - .. [1] Dinh, Laurent, et al. "Density estimation using RealNVP". - International Conference on Learning Representations, 2017. - """ + References: + .. [1] Dinh, Laurent, et al. "Density estimation using RealNVP". + International Conference on Learning Representations, 2017. + """ - def __init__( - self, - mask: Array, - conditioner: Callable, - bijector_fn: Callable, - event_ndims: Optional[int] = None, - inner_event_ndims: int = 0, - ): - super().__init__( - mask, conditioner, bijector_fn, event_ndims, inner_event_ndims - ) + def __init__( + self, + mask: Array, + conditioner: Callable, + bijector_fn: Callable, + event_ndims: int | None = None, + inner_event_ndims: int = 0, + ): + super().__init__( + mask, conditioner, bijector_fn, event_ndims, inner_event_ndims + ) - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - self._check_forward_input_shape(z) - masked_z = jnp.where(self._event_mask, z, 0.0) - if x is not None: - masked_z = jnp.concatenate([masked_z, x], axis=-1) - params = self._conditioner(masked_z) - y0, log_d = self._inner_bijector(params).forward_and_log_det(z) - y = jnp.where(self._event_mask, z, y0) - logdet = math.sum_last( - jnp.where(self._mask, 0.0, log_d), - self._event_ndims - self._inner_event_ndims, - ) - return y, logdet + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + self._check_forward_input_shape(z) + masked_z = jnp.where(self._event_mask, z, 0.0) + if x is not None: + masked_z = jnp.concatenate([masked_z, x], axis=-1) + params = self._conditioner(masked_z) + y0, log_d = self._inner_bijector(params).forward_and_log_det(z) + y = jnp.where(self._event_mask, z, y0) + logdet = math.sum_last( + jnp.where(self._mask, 0.0, log_d), + self._event_ndims - self._inner_event_ndims, + ) + return y, logdet - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - self._check_inverse_input_shape(y) - masked_y = jnp.where(self._event_mask, y, 0.0) - if x is not None: - masked_y = jnp.concatenate([masked_y, x], axis=-1) - params = self._conditioner(masked_y) - z0, log_d = self._inner_bijector(params).inverse_and_log_det(y) - z = jnp.where(self._event_mask, y, z0) - logdet = math.sum_last( - jnp.where(self._mask, 0.0, log_d), - self._event_ndims - self._inner_event_ndims, - ) - return z, logdet + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + self._check_inverse_input_shape(y) + masked_y = jnp.where(self._event_mask, y, 0.0) + if x is not None: + masked_y = jnp.concatenate([masked_y, x], axis=-1) + params = self._conditioner(masked_y) + z0, log_d = self._inner_bijector(params).inverse_and_log_det(y) + z = jnp.where(self._event_mask, y, z0) + logdet = math.sum_last( + jnp.where(self._mask, 0.0, log_d), + self._event_ndims - self._inner_event_ndims, + ) + return z, logdet diff --git a/surjectors/_src/bijectors/masked_coupling_test.py b/surjectors/_src/bijectors/masked_coupling_test.py index 9e71918..0ac61e2 100644 --- a/surjectors/_src/bijectors/masked_coupling_test.py +++ b/surjectors/_src/bijectors/masked_coupling_test.py @@ -15,243 +15,243 @@ def simple_dataset(rng_key, batch_size, n_dimension, n_latent): - means_sample_key, rng_key = random.split(rng_key, 2) - pz_mean = distrax.Normal(0.0, 10.0).sample( - seed=means_sample_key, sample_shape=(n_latent) - ) - pz = distrax.MultivariateNormalDiag( - loc=pz_mean, scale_diag=jnp.ones_like(pz_mean) - ) - p_loadings = distrax.Normal(0.0, 10.0) - make_noise = distrax.Normal(0.0, 1) - - loadings_sample_key, rng_key = random.split(rng_key, 2) - loadings = p_loadings.sample( - seed=loadings_sample_key, sample_shape=(n_dimension, len(pz_mean)) + means_sample_key, rng_key = random.split(rng_key, 2) + pz_mean = distrax.Normal(0.0, 10.0).sample( + seed=means_sample_key, sample_shape=(n_latent) + ) + pz = distrax.MultivariateNormalDiag( + loc=pz_mean, scale_diag=jnp.ones_like(pz_mean) + ) + p_loadings = distrax.Normal(0.0, 10.0) + make_noise = distrax.Normal(0.0, 1) + + loadings_sample_key, rng_key = random.split(rng_key, 2) + loadings = p_loadings.sample( + seed=loadings_sample_key, sample_shape=(n_dimension, len(pz_mean)) + ) + + def _fn(rng_key): + z_sample_key, noise_sample_key = random.split(rng_key, 2) + z = pz.sample(seed=z_sample_key, sample_shape=(batch_size,)) + noise = make_noise.sample( + seed=noise_sample_key, sample_shape=(batch_size, n_dimension) ) + y = (loadings @ z.T).T + noise + return {"y": y, "x": noise} - def _fn(rng_key): - z_sample_key, noise_sample_key = random.split(rng_key, 2) - z = pz.sample(seed=z_sample_key, sample_shape=(batch_size,)) - noise = make_noise.sample( - seed=noise_sample_key, sample_shape=(batch_size, n_dimension) - ) - y = (loadings @ z.T).T + noise - return {"y": y, "x": noise} - - return _fn + return _fn def slcp(rng_key, batch_size): - prior = distrax.Uniform(jnp.full(2, -2), jnp.full(2, 2)) + prior = distrax.Uniform(jnp.full(2, -2), jnp.full(2, 2)) - def _fn(rng_key): - y_sample_key, noise_sample_key = random.split(rng_key, 2) - theta = prior.sample(seed=noise_sample_key, sample_shape=(batch_size,)) - likelihood = distrax.MultivariateNormalDiag(theta, jnp.ones_like(theta)) - y = likelihood.sample(seed=y_sample_key) - return {"y": y, "x": theta} + def _fn(rng_key): + y_sample_key, noise_sample_key = random.split(rng_key, 2) + theta = prior.sample(seed=noise_sample_key, sample_shape=(batch_size,)) + likelihood = distrax.MultivariateNormalDiag(theta, jnp.ones_like(theta)) + y = likelihood.sample(seed=y_sample_key) + return {"y": y, "x": theta} - return _fn + return _fn def _bijector_fn(params): - means, log_scales = jnp.split(params, 2, -1) - return distrax.ScalarAffine(means, jnp.exp(log_scales)) + means, log_scales = jnp.split(params, 2, -1) + return distrax.ScalarAffine(means, jnp.exp(log_scales)) def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def masked_coupling_bijector(n_dim, td_ctor, flow_ctor): - def _transformation_fn(n_dimension): - mask = make_alternating_binary_mask(n_dimension, 0 % 2 == 0) - try: - layer = flow_ctor( - mask=mask, - bijector_fn=_bijector_fn, - conditioner=make_mlp( - [8, n_dim * 2], - w_init=hk.initializers.TruncatedNormal(stddev=1.0), - b_init=jnp.ones, - ), - ) - except TypeError: - layer = flow_ctor( - mask=mask, - bijector=_bijector_fn, - conditioner=make_mlp( - [8, n_dim * 2], - w_init=hk.initializers.TruncatedNormal(stddev=1.0), - b_init=jnp.ones, - ), - ) - return layer - - def _flow(y): - td = td_ctor(_base_distribution_fn(n_dim), _transformation_fn(n_dim)) - return td.log_prob(y) - - td = hk.transform(_flow) - td = hk.without_apply_rng(td) - return td + def _transformation_fn(n_dimension): + mask = make_alternating_binary_mask(n_dimension, 0 % 2 == 0) + try: + layer = flow_ctor( + mask=mask, + bijector_fn=_bijector_fn, + conditioner=make_mlp( + [8, n_dim * 2], + w_init=hk.initializers.TruncatedNormal(stddev=1.0), + b_init=jnp.ones, + ), + ) + except TypeError: + layer = flow_ctor( + mask=mask, + bijector=_bijector_fn, + conditioner=make_mlp( + [8, n_dim * 2], + w_init=hk.initializers.TruncatedNormal(stddev=1.0), + b_init=jnp.ones, + ), + ) + return layer + + def _flow(y): + td = td_ctor(_base_distribution_fn(n_dim), _transformation_fn(n_dim)) + return td.log_prob(y) + + td = hk.transform(_flow) + td = hk.without_apply_rng(td) + return td def masked_conditional_coupling_bijector(n_dim): - def _flow(**kwargs): - layers = [] - for i in range(2): - mask = make_alternating_binary_mask(n_dim, i % 2 == 0) - layer = surjectors.MaskedCoupling( - mask=mask, - bijector_fn=_bijector_fn, - conditioner=make_mlp([8, 8, n_dim * 2]), - ) - layers.append(layer) - chain = surjectors.Chain(layers) - - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_dim), jnp.ones(n_dim)), - reinterpreted_batch_ndims=1, - ) - td = surjectors.TransformedDistribution(base_distribution, chain) - return td.log_prob(**kwargs) - - td = hk.transform(_flow) - td = hk.without_apply_rng(td) - return td + def _flow(**kwargs): + layers = [] + for i in range(2): + mask = make_alternating_binary_mask(n_dim, i % 2 == 0) + layer = surjectors.MaskedCoupling( + mask=mask, + bijector_fn=_bijector_fn, + conditioner=make_mlp([8, 8, n_dim * 2]), + ) + layers.append(layer) + chain = surjectors.Chain(layers) + + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_dim), jnp.ones(n_dim)), + reinterpreted_batch_ndims=1, + ) + td = surjectors.TransformedDistribution(base_distribution, chain) + return td.log_prob(**kwargs) + + td = hk.transform(_flow) + td = hk.without_apply_rng(td) + return td def _train_unconditional(rng_key, params, model, sampler, n_iter=5): - @jax.jit - def step(params, state, y): - def loss_fn(params): - lp = model.apply(params, y) - return -jnp.sum(lp) + @jax.jit + def step(params, state, y): + def loss_fn(params): + lp = model.apply(params, y) + return -jnp.sum(lp) - loss, grads = jax.value_and_grad(loss_fn)(params) - updates, new_state = adam.update(grads, state, params) - new_params = optax.apply_updates(params, updates) - return loss, new_params, new_state + loss, grads = jax.value_and_grad(loss_fn)(params) + updates, new_state = adam.update(grads, state, params) + new_params = optax.apply_updates(params, updates) + return loss, new_params, new_state - adam = optax.adamw(0.001) - state = adam.init(params) - for i in range(n_iter): - rng = random.fold_in(rng_key, i) - batch = sampler(rng) - _, params, state = step(params, state, batch["y"]) + adam = optax.adamw(0.001) + state = adam.init(params) + for i in range(n_iter): + rng = random.fold_in(rng_key, i) + batch = sampler(rng) + _, params, state = step(params, state, batch["y"]) - return params + return params def _train_conditional(rng_key, params, model, sampler, n_iter=1000): - @jax.jit - def step(params, state, **batch): - def loss_fn(params): - lp = model.apply(params, **batch) - return -jnp.sum(lp) + @jax.jit + def step(params, state, **batch): + def loss_fn(params): + lp = model.apply(params, **batch) + return -jnp.sum(lp) - loss, grads = jax.value_and_grad(loss_fn)(params) - updates, new_state = adam.update(grads, state, params) - new_params = optax.apply_updates(params, updates) - return loss, new_params, new_state + loss, grads = jax.value_and_grad(loss_fn)(params) + updates, new_state = adam.update(grads, state, params) + new_params = optax.apply_updates(params, updates) + return loss, new_params, new_state - adam = optax.adamw(0.001) - state = adam.init(params) - for i in range(n_iter): - rng = random.fold_in(rng_key, i) - batch = sampler(rng) - _, params, state = step(params, state, **batch) + adam = optax.adamw(0.001) + state = adam.init(params) + for i in range(n_iter): + rng = random.fold_in(rng_key, i) + batch = sampler(rng) + _, params, state = step(params, state, **batch) - return params + return params @pytest.fixture( - params=[ - ( - masked_coupling_bijector, - (distrax.Transformed, distrax.MaskedCoupling), - (surjectors.TransformedDistribution, surjectors.MaskedCoupling), - ), - ], - ids=["masked_coupling"], + params=[ + ( + masked_coupling_bijector, + (distrax.Transformed, distrax.MaskedCoupling), + (surjectors.TransformedDistribution, surjectors.MaskedCoupling), + ), + ], + ids=["masked_coupling"], ) def bijection(request): - yield request.param + yield request.param def test_params_against_distrax_bijector(bijection): - rng_seq = hk.PRNGSequence(0) - n_dim, n_dim_latent = 2, 2 + rng_seq = hk.PRNGSequence(0) + n_dim, n_dim_latent = 2, 2 - bijector_fn, distrax_ctors, surjectors_ctors = bijection - distrax_model = bijector_fn(n_dim, *distrax_ctors) - surjectors_model = bijector_fn(n_dim, *surjectors_ctors) + bijector_fn, distrax_ctors, surjectors_ctors = bijection + distrax_model = bijector_fn(n_dim, *distrax_ctors) + surjectors_model = bijector_fn(n_dim, *surjectors_ctors) - sampling_fn = simple_dataset(next(rng_seq), 64, n_dim, n_dim_latent) - init_data = sampling_fn(next(rng_seq)) + sampling_fn = simple_dataset(next(rng_seq), 64, n_dim, n_dim_latent) + init_data = sampling_fn(next(rng_seq)) - rng = next(rng_seq) - params_distrax = distrax_model.init(rng, init_data["y"]) - params_surjectors = surjectors_model.init(rng, y=init_data["y"]) + rng = next(rng_seq) + params_distrax = distrax_model.init(rng, init_data["y"]) + params_surjectors = surjectors_model.init(rng, y=init_data["y"]) - # TODO(simon): fix this. this isn't an issue, but why does it happen - # chex.assert_trees_all_equal(params_distrax, params_surjectors) - jnp.array_equal( - distrax_model.apply(params_distrax, init_data["y"]), - surjectors_model.apply(params_surjectors, y=init_data["y"]), - ) - # TODO(simon): fix this. this isn't an issue, but why does it happen - # jnp.array_equal( - # distrax_model.apply(params_surjectors, init_data["y"]), - # surjectors_model.apply(params_distrax, y=init_data["y"]), - # ) + # TODO(simon): fix this. this isn't an issue, but why does it happen + # chex.assert_trees_all_equal(params_distrax, params_surjectors) + jnp.array_equal( + distrax_model.apply(params_distrax, init_data["y"]), + surjectors_model.apply(params_surjectors, y=init_data["y"]), + ) + # TODO(simon): fix this. this isn't an issue, but why does it happen + # jnp.array_equal( + # distrax_model.apply(params_surjectors, init_data["y"]), + # surjectors_model.apply(params_distrax, y=init_data["y"]), + # ) def test_against_distrax_bijector_after_training(bijection): - rng_seq = hk.PRNGSequence(0) - n_dim, n_dim_latent = 2, 2 + rng_seq = hk.PRNGSequence(0) + n_dim, n_dim_latent = 2, 2 - bijector_fn, distrax_ctors, surjectors_ctors = bijection - distrax_model = bijector_fn(n_dim, *distrax_ctors) - surjectors_model = bijector_fn(n_dim, *surjectors_ctors) + bijector_fn, distrax_ctors, surjectors_ctors = bijection + distrax_model = bijector_fn(n_dim, *distrax_ctors) + surjectors_model = bijector_fn(n_dim, *surjectors_ctors) - sampling_fn = simple_dataset(next(rng_seq), 64, n_dim, n_dim_latent) - init_data = sampling_fn(next(rng_seq)) + sampling_fn = simple_dataset(next(rng_seq), 64, n_dim, n_dim_latent) + init_data = sampling_fn(next(rng_seq)) - init_rng = next(rng_seq) - train_rng = next(rng_seq) - params_distrax = distrax_model.init(init_rng, init_data["y"]) - params_distrax = _train_unconditional( - train_rng, params_distrax, distrax_model, sampling_fn - ) + init_rng = next(rng_seq) + train_rng = next(rng_seq) + params_distrax = distrax_model.init(init_rng, init_data["y"]) + params_distrax = _train_unconditional( + train_rng, params_distrax, distrax_model, sampling_fn + ) - params_surjectors = surjectors_model.init(init_rng, y=init_data["y"]) - params_surjectors = _train_unconditional( - train_rng, params_surjectors, surjectors_model, sampling_fn - ) + params_surjectors = surjectors_model.init(init_rng, y=init_data["y"]) + params_surjectors = _train_unconditional( + train_rng, params_surjectors, surjectors_model, sampling_fn + ) - # chex.assert_trees_all_equal(params_distrax, params_surjectors) + # chex.assert_trees_all_equal(params_distrax, params_surjectors) def test_conditional_masked_bijector(): - rng_seq = hk.PRNGSequence(0) - n_dim = 2 - model = masked_conditional_coupling_bijector(n_dim) + rng_seq = hk.PRNGSequence(0) + n_dim = 2 + model = masked_conditional_coupling_bijector(n_dim) - sampling_fn = slcp(next(rng_seq), 64) - init_data = sampling_fn(next(rng_seq)) + sampling_fn = slcp(next(rng_seq), 64) + init_data = sampling_fn(next(rng_seq)) - params = model.init(next(rng_seq), **init_data) - params = _train_conditional(next(rng_seq), params, model, sampling_fn) + params = model.init(next(rng_seq), **init_data) + params = _train_conditional(next(rng_seq), params, model, sampling_fn) - theta = jnp.ones((5, 2)) - data = jnp.repeat(jnp.arange(5), 2).reshape(-1, 2) - out = model.apply(params, **{"y": data, "x": theta}) - max_lp_idx = jnp.argmax(out) - chex.assert_equal(max_lp_idx, 1) + theta = jnp.ones((5, 2)) + data = jnp.repeat(jnp.arange(5), 2).reshape(-1, 2) + out = model.apply(params, **{"y": data, "x": theta}) + max_lp_idx = jnp.argmax(out) + chex.assert_equal(max_lp_idx, 1) diff --git a/surjectors/_src/bijectors/permutation.py b/surjectors/_src/bijectors/permutation.py index ec48ba3..0158a31 100644 --- a/surjectors/_src/bijectors/permutation.py +++ b/surjectors/_src/bijectors/permutation.py @@ -5,33 +5,33 @@ # pylint: disable=arguments-renamed class Permutation(Bijector): - """Permute the dimensions of a vector. - - Args: - permutation: a vector of integer indexes representing the order of - the elements - event_ndims_in: number of input event dimensions - - Examples: - >>> from surjectors import Permutation - >>> from jax import numpy as jnp - >>> - >>> order = jnp.arange(10) - >>> perm = Permutation(order, 1) - """ - - def __init__(self, permutation, event_ndims_in: int): - self.permutation = permutation - self.event_ndims_in = event_ndims_in - - def _forward_and_likelihood_contribution(self, z, **kwargs): - return z[..., self.permutation], jnp.full(jnp.shape(z)[:-1], 0.0) - - def _inverse_and_likelihood_contribution(self, y, **kwargs): - size = self.permutation.size - permutation_inv = ( - jnp.zeros(size, dtype=jnp.result_type(int)) - .at[self.permutation] - .set(jnp.arange(size)) - ) - return y[..., permutation_inv], jnp.full(jnp.shape(y)[:-1], 0.0) + """Permute the dimensions of a vector. + + Args: + permutation: a vector of integer indexes representing the order of + the elements + event_ndims_in: number of input event dimensions + + Examples: + >>> from surjectors import Permutation + >>> from jax import numpy as jnp + >>> + >>> order = jnp.arange(10) + >>> perm = Permutation(order, 1) + """ + + def __init__(self, permutation, event_ndims_in: int): + self.permutation = permutation + self.event_ndims_in = event_ndims_in + + def _forward_and_likelihood_contribution(self, z, **kwargs): + return z[..., self.permutation], jnp.full(jnp.shape(z)[:-1], 0.0) + + def _inverse_and_likelihood_contribution(self, y, **kwargs): + size = self.permutation.size + permutation_inv = ( + jnp.zeros(size, dtype=jnp.result_type(int)) + .at[self.permutation] + .set(jnp.arange(size)) + ) + return y[..., permutation_inv], jnp.full(jnp.shape(y)[:-1], 0.0) diff --git a/surjectors/_src/bijectors/rq_masked_coupling.py b/surjectors/_src/bijectors/rq_masked_coupling.py index 7bccd25..c67ca64 100644 --- a/surjectors/_src/bijectors/rq_masked_coupling.py +++ b/surjectors/_src/bijectors/rq_masked_coupling.py @@ -1,4 +1,4 @@ -from typing import Callable, Optional +from collections.abc import Callable import distrax @@ -8,58 +8,58 @@ # ruff: noqa: PLR0913 class RationalQuadraticSplineMaskedCoupling(MaskedCoupling): - """A rational quadratic spline masked coupling layer. + """A rational quadratic spline masked coupling layer. - References: - .. [1] Dinh, Laurent, et al. "Density estimation using RealNVP". - International Conference on Learning Representations, 2017. - .. [2] Durkan, Conor, et al. "Neural Spline Flows". - Advances in Neural Information Processing Systems, 2019. + References: + .. [1] Dinh, Laurent, et al. "Density estimation using RealNVP". + International Conference on Learning Representations, 2017. + .. [2] Durkan, Conor, et al. "Neural Spline Flows". + Advances in Neural Information Processing Systems, 2019. - Examples: - >>> import distrax - >>> from surjectors import RationalQuadraticSplineMaskedCoupling - >>> from surjectors.nn import make_mlp - >>> from surjectors.util import make_alternating_binary_mask - >>> - >>> layer = RationalQuadraticSplineMaskedCoupling( - >>> mask=make_alternating_binary_mask(10, True), - >>> conditioner=make_mlp([8, 8, 10 * 2]), - >>> range_min=-1.0, - >>> range_max=1.0 - >>> ) - """ + Examples: + >>> import distrax + >>> from surjectors import RationalQuadraticSplineMaskedCoupling + >>> from surjectors.nn import make_mlp + >>> from surjectors.util import make_alternating_binary_mask + >>> + >>> layer = RationalQuadraticSplineMaskedCoupling( + >>> mask=make_alternating_binary_mask(10, True), + >>> conditioner=make_mlp([8, 8, 10 * 2]), + >>> range_min=-1.0, + >>> range_max=1.0 + >>> ) + """ - def __init__( - self, - mask: Array, - conditioner: Callable, - range_min: float, - range_max: float, - event_ndims: Optional[int] = None, - inner_event_ndims: int = 0, - ): - """Construct a rational quadratic spline masked coupling layer. + def __init__( + self, + mask: Array, + conditioner: Callable, + range_min: float, + range_max: float, + event_ndims: int | None = None, + inner_event_ndims: int = 0, + ): + """Construct a rational quadratic spline masked coupling layer. - Args: - mask: a boolean mask of length n_dim. A value - of True indicates that the corresponding input remains unchanged - conditioner: a function that computes the parameters of the inner - bijector - range_min: minimum range of the spline - range_max: maximum range of the spline - event_ndims: the number of array dimensions the bijector operates on - inner_event_ndims: the number of array dimensions the inner bijector - operates on - """ - self.range_min = range_min - self.range_max = range_max + Args: + mask: a boolean mask of length n_dim. A value + of True indicates that the corresponding input remains unchanged + conditioner: a function that computes the parameters of the inner + bijector + range_min: minimum range of the spline + range_max: maximum range of the spline + event_ndims: the number of array dimensions the bijector operates on + inner_event_ndims: the number of array dimensions the inner bijector + operates on + """ + self.range_min = range_min + self.range_max = range_max - def _bijector_fn(params: Array): - return distrax.RationalQuadraticSpline( - params, self.range_min, self.range_max - ) + def _bijector_fn(params: Array): + return distrax.RationalQuadraticSpline( + params, self.range_min, self.range_max + ) - super().__init__( - mask, conditioner, _bijector_fn, event_ndims, inner_event_ndims - ) + super().__init__( + mask, conditioner, _bijector_fn, event_ndims, inner_event_ndims + ) diff --git a/surjectors/_src/bijectors/rq_masked_coupling_test.py b/surjectors/_src/bijectors/rq_masked_coupling_test.py index b8ad3bf..c7489b0 100644 --- a/surjectors/_src/bijectors/rq_masked_coupling_test.py +++ b/surjectors/_src/bijectors/rq_masked_coupling_test.py @@ -6,60 +6,60 @@ from jax import random from surjectors import ( - RationalQuadraticSplineMaskedCoupling, - TransformedDistribution, + RationalQuadraticSplineMaskedCoupling, + TransformedDistribution, ) from surjectors.nn import make_mlp from surjectors.util import make_alternating_binary_mask def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def make_bijector(n_dimension): - def _transformation_fn(n_dimension): - bij = RationalQuadraticSplineMaskedCoupling( - make_alternating_binary_mask(n_dimension, 0 % 2 == 0), - hk.Sequential( - [ - make_mlp([8, 8, n_dimension * 10]), - hk.Reshape((n_dimension, 10)), - ] - ), - -1.0, - 1.0, - ) - return bij + def _transformation_fn(n_dimension): + bij = RationalQuadraticSplineMaskedCoupling( + make_alternating_binary_mask(n_dimension, 0 % 2 == 0), + hk.Sequential( + [ + make_mlp([8, 8, n_dimension * 10]), + hk.Reshape((n_dimension, 10)), + ] + ), + -1.0, + 1.0, + ) + return bij - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_dimension), _transformation_fn(n_dimension) - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_dimension), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_rq_masked_coupling(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_bijector(n_dimension) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_bijector(n_dimension) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_rq_masked_coupling(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_bijector(n_dimension) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_bijector(n_dimension) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/conditioners/mlp.py b/surjectors/_src/conditioners/mlp.py index 9d24f1b..8b35521 100644 --- a/surjectors/_src/conditioners/mlp.py +++ b/surjectors/_src/conditioners/mlp.py @@ -5,25 +5,25 @@ # type: ignore[B008] def make_mlp( - dims, - activation=jax.nn.gelu, - w_init=hk.initializers.TruncatedNormal(stddev=0.01), - b_init=jnp.zeros, + dims: tuple[int, ...], + activation=jax.nn.swish, + w_init=hk.initializers.TruncatedNormal(stddev=0.01), + b_init=jnp.zeros, ): - """Create a conditioner network based on an MLP. + """Create a conditioner network based on an MLP. - Args: - dims: dimensions of hidden layers and last layer - activation: a JAX activation function - w_init: a haiku initializer - b_init: a haiku initializer + Args: + dims: dimensions of hidden layers and last layer + activation: a JAX activation function + w_init: a haiku initializer + b_init: a haiku initializer - Returns: - a transformable haiku neural network module - """ - return hk.nets.MLP( - output_sizes=dims, - w_init=w_init, - b_init=b_init, - activation=activation, - ) + Returns: + a transformable haiku neural network module + """ + return hk.nets.MLP( + output_sizes=dims, + w_init=w_init, + b_init=b_init, + activation=activation, + ) diff --git a/surjectors/_src/conditioners/nn/made.py b/surjectors/_src/conditioners/nn/made.py index 0dc29aa..fb12d78 100644 --- a/surjectors/_src/conditioners/nn/made.py +++ b/surjectors/_src/conditioners/nn/made.py @@ -1,11 +1,11 @@ -from typing import Callable, Optional, Union +from collections.abc import Callable import haiku as hk import jax from jax import Array from jax import numpy as jnp from tensorflow_probability.substrates.jax.bijectors.masked_autoregressive import ( # noqa: E501 - _make_dense_autoregressive_masks, + _make_dense_autoregressive_masks, ) from surjectors._src.conditioners.nn.masked_linear import MaskedLinear @@ -13,79 +13,79 @@ # ruff: noqa: PLR0913 class MADE(hk.Module): - """Masked Autoregressive Density Estimator. + """Masked Autoregressive Density Estimator. - Passing a value through a MADE will output a tensor of shape - [..., input_size, n_params] + Passing a value through a MADE will output a tensor of shape + [..., input_size, n_params] - Examples: - >>> from surjectors.nn import MADE - >>> made = MADE(10, [32, 32], 2) - """ + Examples: + >>> from surjectors.nn import MADE + >>> made = MADE(10, (32, 32), 2) + """ - def __init__( - self, - input_size: int, - hidden_layer_sizes: Union[list[int], tuple[int]], - n_params: int, - w_init: Optional[hk.initializers.Initializer] = None, - b_init: Optional[hk.initializers.Initializer] = None, - activation: Callable[[jnp.ndarray], jnp.ndarray] = jax.nn.relu, - ): - """Construct a MADE network. + def __init__( + self, + input_size: int, + hidden_layer_sizes: tuple[int, ...], + n_params: int, + w_init: hk.initializers.Initializer | None = None, + b_init: hk.initializers.Initializer | None = None, + activation: Callable[[jnp.ndarray], jnp.ndarray] = jax.nn.relu, + ): + """Construct a MADE network. - Args: - input_size: number of input features - hidden_layer_sizes: list/tuple of ints describing the number of - nodes in the hidden layers - n_params: number of output parameters. For instance, if used as - a conditioner of an affine bijector should be 2 (mean and scale) - w_init: a Haiku initializer - b_init: a Haiku initializer - activation: n activation function - """ - super().__init__() - self.input_size = input_size - self.output_sizes = hidden_layer_sizes - self.n_params = n_params - self.w_init = w_init - self.b_init = b_init - self.activation = activation - masks = _make_dense_autoregressive_masks( - n_params, self.input_size, self.output_sizes - ) + Args: + input_size: number of input features + hidden_layer_sizes: list/tuple of ints describing the number of + nodes in the hidden layers + n_params: number of output parameters. For instance, if used as + a conditioner of an affine bijector should be 2 (mean and scale) + w_init: a Haiku initializer + b_init: a Haiku initializer + activation: n activation function + """ + super().__init__() + self.input_size = input_size + self.output_sizes = hidden_layer_sizes + self.n_params = n_params + self.w_init = w_init + self.b_init = b_init + self.activation = activation + masks = _make_dense_autoregressive_masks( + n_params, self.input_size, self.output_sizes + ) - layers = [] - for mask in masks: - layers.append( - MaskedLinear( - mask=mask.astype(jnp.float_), - w_init=w_init, - b_init=b_init, - ) - ) - self.layers = tuple(layers) + layers = [] + for mask in masks: + layers.append( + MaskedLinear( + mask=mask.astype(jnp.float_), + w_init=w_init, + b_init=b_init, + ) + ) + self.layers = tuple(layers) - def __call__(self, y: Array, x: Array = None): - """Apply the MADE network. + def __call__(self, y: Array, x: Array = None): + """Apply the MADE network. - Args: - y: input to be transformed - x: conditioning variable + Args: + y: input to be transformed + x: conditioning variable - Returns: - the transformed value - """ - output = self.layers[0](y) - if x is not None: - context = hk.Linear( - self.output_sizes[0], w_init=self.w_init, b_init=self.b_init - )(x) - output += context + Returns: + the transformed value + """ + output = self.layers[0](y) + if x is not None: + context = hk.Linear( + self.output_sizes[0], w_init=self.w_init, b_init=self.b_init + )(x) + output += context + output = self.activation(output) + for i, layer in enumerate(self.layers[1:]): + output = layer(output) + if i < len(self.layers[1:]) - 1: output = self.activation(output) - for i, layer in enumerate(self.layers[1:]): - output = layer(output) - if i < len(self.layers[1:]) - 1: - output = self.activation(output) - output = hk.Reshape((self.input_size, self.n_params))(output) - return output + output = hk.Reshape((self.input_size, self.n_params))(output) + return output diff --git a/surjectors/_src/conditioners/nn/made_test.py b/surjectors/_src/conditioners/nn/made_test.py index a4ea520..3e165b3 100644 --- a/surjectors/_src/conditioners/nn/made_test.py +++ b/surjectors/_src/conditioners/nn/made_test.py @@ -12,97 +12,96 @@ @pytest.fixture() def made(request): + input_shape, hidden_shapes, output_shape = request.param + + @hk.without_apply_rng + @hk.transform + def _made(**kwargs): + made = MADE( + input_shape, + hidden_shapes, + output_shape, + w_init=jnp.ones, + activation=lambda x: x, + ) + return made(**kwargs) - input_shape, hidden_shapes, output_shape = request.param - - @hk.without_apply_rng - @hk.transform - def _made(**kwargs): - made = MADE( - input_shape, - hidden_shapes, - output_shape, - w_init=jnp.ones, - activation=lambda x: x, - ) - return made(**kwargs) - - return _made + return _made @pytest.mark.parametrize("made", [(3, [2, 4, 2], 2)], indirect=True) def test_made_shape(made): - x = jnp.ones((1, 3)) - params = made.init(random.PRNGKey(0), y=x) - y = made.apply(params, y=x) - chex.assert_shape(y, (1, 3, 2)) + x = jnp.ones((1, 3)) + params = made.init(random.PRNGKey(0), y=x) + y = made.apply(params, y=x) + chex.assert_shape(y, (1, 3, 2)) @pytest.mark.parametrize( - "made,expected", - [ - ((3, [2, 4, 2], 2), [[0.0, 2.0, 10.0]]), - ((3, [2], 2), [[0.0, 1.0, 3.0]]), - ], - indirect=["made"], + "made,expected", + [ + ((3, [2, 4, 2], 2), [[0.0, 2.0, 10.0]]), + ((3, [2], 2), [[0.0, 1.0, 3.0]]), + ], + indirect=["made"], ) def test_made_output(made, expected): - """ - Tested using: - - y = np.ones((1, 3)) - made = AutoregressiveNetwork( - 2, - 3, - hidden_units=[2, 4, 2], # or [2] - conditional_input_layers="first_layer", - kernel_initializer="ones", - activation=lambda x: x - ) - y = made(y) - a, b = unstack(y, -1) - """ - - y = jnp.ones((1, 3)) - params = made.init(random.PRNGKey(0), y=y) - y = made.apply(params, y=y) - a, b = unstack(y, -1) - chex.assert_trees_all_equal(a, jnp.array(expected)) - chex.assert_trees_all_equal(b, jnp.array(expected)) - - -@pytest.mark.parametrize( - "made,expected", - [ - ((3, [2, 4, 2], 2), [[0.0, 6.0, 26.0]]), - ((3, [2], 2), [[0.0, 3.0, 7.0]]), - ], - indirect=["made"], -) -def test_conditional_made_output(made, expected): - """ - Tested using: + """ + Tested using: - y = np.ones((1, 3)) - x = np.ones((1, 2)) - made = AutoregressiveNetwork( + y = np.ones((1, 3)) + made = AutoregressiveNetwork( 2, 3, - True, - 2, hidden_units=[2, 4, 2], # or [2] conditional_input_layers="first_layer", kernel_initializer="ones", activation=lambda x: x - ) - y = made(y, x) - a, b = unstack(y, -1) - """ - - y = jnp.ones((1, 3)) - x = jnp.ones((1, 2)) - params = made.init(random.PRNGKey(0), y=y, x=x) - y = made.apply(params, y=y, x=x) - a, b = unstack(y, -1) - chex.assert_trees_all_equal(a, jnp.array(expected)) - chex.assert_trees_all_equal(b, jnp.array(expected)) + ) + y = made(y) + a, b = unstack(y, -1) + """ + + y = jnp.ones((1, 3)) + params = made.init(random.PRNGKey(0), y=y) + y = made.apply(params, y=y) + a, b = unstack(y, -1) + chex.assert_trees_all_equal(a, jnp.array(expected)) + chex.assert_trees_all_equal(b, jnp.array(expected)) + + +@pytest.mark.parametrize( + "made,expected", + [ + ((3, [2, 4, 2], 2), [[0.0, 6.0, 26.0]]), + ((3, [2], 2), [[0.0, 3.0, 7.0]]), + ], + indirect=["made"], +) +def test_conditional_made_output(made, expected): + """ + Tested using: + + y = np.ones((1, 3)) + x = np.ones((1, 2)) + made = AutoregressiveNetwork( + 2, + 3, + True, + 2, + hidden_units=[2, 4, 2], # or [2] + conditional_input_layers="first_layer", + kernel_initializer="ones", + activation=lambda x: x + ) + y = made(y, x) + a, b = unstack(y, -1) + """ + + y = jnp.ones((1, 3)) + x = jnp.ones((1, 2)) + params = made.init(random.PRNGKey(0), y=y, x=x) + y = made.apply(params, y=y, x=x) + a, b = unstack(y, -1) + chex.assert_trees_all_equal(a, jnp.array(expected)) + chex.assert_trees_all_equal(b, jnp.array(expected)) diff --git a/surjectors/_src/conditioners/nn/masked_linear.py b/surjectors/_src/conditioners/nn/masked_linear.py index aa499fc..d3b870d 100644 --- a/surjectors/_src/conditioners/nn/masked_linear.py +++ b/surjectors/_src/conditioners/nn/masked_linear.py @@ -1,5 +1,3 @@ -from typing import Optional - import chex import haiku as hk import numpy as np @@ -9,52 +7,52 @@ # pylint: disable=too-many-arguments,too-few-public-methods class MaskedLinear(hk.Module): - """Linear layer that masks some weights.""" - - def __init__( - self, - mask: chex.Array, - with_bias: bool = True, - w_init: Optional[hk.initializers.Initializer] = None, - b_init: Optional[hk.initializers.Initializer] = None, - ): - """Construct a MaskedLinear layer. - - Args: - mask: boolean mask - with_bias: boolean - w_init: haiku initializer - b_init: haiku initializer - """ - super().__init__() - self.input_size = None - self.output_size = mask.shape[-1] - self.with_bias = with_bias - self.w_init = w_init - self.b_init = b_init or jnp.zeros - self.mask = mask - - def __call__( - self, inputs, *, precision: Optional[lax.Precision] = None - ) -> jnp.ndarray: - """Apply the layer.""" - if not inputs.shape: - raise ValueError("Input must not be scalar.") - dtype = inputs.dtype - - w_init = self.w_init - if w_init is None: - stddev = 1.0 / np.sqrt(jnp.shape(self.mask)[0]) - w_init = hk.initializers.TruncatedNormal(stddev=stddev) - w = hk.get_parameter("w", jnp.shape(self.mask), dtype, init=w_init) - - outputs = jnp.dot(inputs, jnp.multiply(w, self.mask), precision=None) - - if self.with_bias: - b = hk.get_parameter( - "b", (jnp.shape(self.mask)[-1],), dtype, init=self.b_init - ) - b = jnp.broadcast_to(b, outputs.shape) - outputs = outputs + b - - return outputs + """Linear layer that masks some weights.""" + + def __init__( + self, + mask: chex.Array, + with_bias: bool = True, + w_init: hk.initializers.Initializer | None = None, + b_init: hk.initializers.Initializer | None = None, + ): + """Construct a MaskedLinear layer. + + Args: + mask: boolean mask + with_bias: boolean + w_init: haiku initializer + b_init: haiku initializer + """ + super().__init__() + self.input_size = None + self.output_size = mask.shape[-1] + self.with_bias = with_bias + self.w_init = w_init + self.b_init = b_init or jnp.zeros + self.mask = mask + + def __call__( + self, inputs, *, precision: lax.Precision | None = None + ) -> jnp.ndarray: + """Apply the layer.""" + if not inputs.shape: + raise ValueError("Input must not be scalar.") + dtype = inputs.dtype + + w_init = self.w_init + if w_init is None: + stddev = 1.0 / np.sqrt(jnp.shape(self.mask)[0]) + w_init = hk.initializers.TruncatedNormal(stddev=stddev) + w = hk.get_parameter("w", jnp.shape(self.mask), dtype, init=w_init) + + outputs = jnp.dot(inputs, jnp.multiply(w, self.mask), precision=None) + + if self.with_bias: + b = hk.get_parameter( + "b", (jnp.shape(self.mask)[-1],), dtype, init=self.b_init + ) + b = jnp.broadcast_to(b, outputs.shape) + outputs = outputs + b + + return outputs diff --git a/surjectors/_src/conditioners/transformer.py b/surjectors/_src/conditioners/transformer.py index 1967e27..9eadab6 100644 --- a/surjectors/_src/conditioners/transformer.py +++ b/surjectors/_src/conditioners/transformer.py @@ -1,5 +1,5 @@ import dataclasses -from typing import Callable, Optional +from collections.abc import Callable import haiku as hk import jax @@ -7,90 +7,84 @@ @dataclasses.dataclass class _EncoderLayer(hk.Module): - num_heads: int - num_layers: int - key_size: int - dropout_rate: float - widening_factor: int = 4 - initializer: Callable = hk.initializers.TruncatedNormal(stddev=0.01) - name: Optional[str] = None + num_heads: int + num_layers: int + key_size: int + dropout_rate: float + widening_factor: int = 4 + initializer: Callable = hk.initializers.TruncatedNormal(stddev=0.01) + name: str | None = None - def __call__(self, inputs, *, is_training): - dropout_rate = self.dropout_rate if is_training else 0.0 - # causal_mask = np.tril(np.ones((1, 1, seq_len, seq_len))) - causal_mask = None - model_size = self.key_size * self.num_heads + def __call__(self, inputs, *, is_training): + dropout_rate = self.dropout_rate if is_training else 0.0 + # causal_mask = np.tril(np.ones((1, 1, seq_len, seq_len))) + causal_mask = None + model_size = self.key_size * self.num_heads - h = inputs - for _ in range(self.num_layers): - attn_block = hk.MultiHeadAttention( - num_heads=self.num_heads, - key_size=self.key_size, - model_size=model_size, - w_init=self.initializer, - ) - h_norm = hk.LayerNorm( - axis=-1, create_scale=True, create_offset=True - )(h) - h_attn = attn_block(h_norm, h_norm, h_norm, mask=causal_mask) - h_attn = hk.dropout(hk.next_rng_key(), dropout_rate, h_attn) - h = h + h_attn + h = inputs + for _ in range(self.num_layers): + attn_block = hk.MultiHeadAttention( + num_heads=self.num_heads, + key_size=self.key_size, + model_size=model_size, + w_init=self.initializer, + ) + h_norm = hk.LayerNorm(axis=-1, create_scale=True, create_offset=True)(h) + h_attn = attn_block(h_norm, h_norm, h_norm, mask=causal_mask) + h_attn = hk.dropout(hk.next_rng_key(), dropout_rate, h_attn) + h = h + h_attn - mlp = hk.nets.MLP( - [self.widening_factor * model_size, model_size], - w_init=self.initializer, - activation=jax.nn.gelu, - ) - h_norm = hk.LayerNorm( - axis=-1, create_scale=True, create_offset=True - )(h) - h_dense = mlp(h_norm) - h_dense = hk.dropout(hk.next_rng_key(), dropout_rate, h_dense) - h = h + h_dense + mlp = hk.nets.MLP( + [self.widening_factor * model_size, model_size], + w_init=self.initializer, + activation=jax.nn.gelu, + ) + h_norm = hk.LayerNorm(axis=-1, create_scale=True, create_offset=True)(h) + h_dense = mlp(h_norm) + h_dense = hk.dropout(hk.next_rng_key(), dropout_rate, h_dense) + h = h + h_dense - return hk.LayerNorm(axis=-1, create_scale=True, create_offset=True)(h) + return hk.LayerNorm(axis=-1, create_scale=True, create_offset=True)(h) @dataclasses.dataclass class _AutoregressiveTransformerEncoder(hk.Module): - linear: hk.Linear - transformer: _EncoderLayer - output_size: int - name: Optional[str] = None + linear: hk.Linear + transformer: _EncoderLayer + output_size: int + name: str | None = None - def __call__(self, inputs, *, is_training=True): - h = self.linear(inputs) - h = self.transformer(h, is_training=is_training) - return hk.Linear(self.output_size)(h) + def __call__(self, inputs, *, is_training=True): + h = self.linear(inputs) + h = self.transformer(h, is_training=is_training) + return hk.Linear(self.output_size)(h) # ruff: noqa: PLR0913 def make_transformer( - output_size, - num_heads=4, - num_layers=4, - key_size=32, - dropout_rate=0.1, - widening_factor=4, + output_size, + num_heads=4, + num_layers=4, + key_size=32, + dropout_rate=0.1, + widening_factor=4, ): - """Create a conditioner network based on a transformer. + """Create a conditioner network based on a transformer. - Args: - output_size: output size of the last layer - num_heads: number of heads of the attention - num_layers: number of layers - key_size: size of the key - dropout_rate: rate of dropout - widening_factor: factor by which MLP after attention is widened + Args: + output_size: output size of the last layer + num_heads: number of heads of the attention + num_layers: number of layers + key_size: size of the key + dropout_rate: rate of dropout + widening_factor: factor by which MLP after attention is widened - Returns: - a transformable haiku neural network module - """ - linear = hk.Linear(key_size * num_heads) - encoder = _EncoderLayer( - num_heads, num_layers, key_size, dropout_rate, widening_factor - ) - transformer = _AutoregressiveTransformerEncoder( - linear, encoder, output_size - ) - return transformer + Returns: + a transformable haiku neural network module + """ + linear = hk.Linear(key_size * num_heads) + encoder = _EncoderLayer( + num_heads, num_layers, key_size, dropout_rate, widening_factor + ) + transformer = _AutoregressiveTransformerEncoder(linear, encoder, output_size) + return transformer diff --git a/surjectors/_src/distributions/transformed_distribution.py b/surjectors/_src/distributions/transformed_distribution.py index b9d1f29..725e4b4 100644 --- a/surjectors/_src/distributions/transformed_distribution.py +++ b/surjectors/_src/distributions/transformed_distribution.py @@ -1,5 +1,3 @@ -from typing import Union - import chex import distrax import haiku as hk @@ -11,131 +9,131 @@ class TransformedDistribution: - """Distribution of a random variable transformed by a function. + """Distribution of a random variable transformed by a function. + + Can be used to define a pushforward measure. + + Args: + base_distribution: a distribution object + transform: some transformation + + Examples: + >>> import distrax + >>> from jax import numpy as jnp + >>> from surjectors import Slice, Chain, TransformedDistribution + >>> + >>> a = Slice(10) + >>> b = Slice(5) + >>> ab = Chain([a, b]) + >>> + >>> TransformedDistribution( + >>> distrax.Normal(jnp.zeros(5), jnp.ones(5)), + >>> Chain([a, b]) + >>> ) + """ + + def __init__( + self, + base_distribution: Distribution | tfd.Distribution, + transform: Surjector, + ): + self.base_distribution = base_distribution + self.transform = transform + + def __call__(self, method, **kwargs): + """Call the TransformedDistribution object. + + Depending on "method", computes log-probability of an event or samples + from the distribution. + + Args: + method: either "sample" or "log_prob" + **kwargs: several keyword arguments that are dispatched to + whatever method is called. - Can be used to define a pushforward measure. + Returns: + returns whatever 'method' returns + """ + return getattr(self, method)(**kwargs) + + def log_prob(self, y: Array, x: Array = None): + """Calculate the log probability of an event conditional on another. Args: - base_distribution: a distribution object - transform: some transformation - - Examples: - >>> import distrax - >>> from jax import numpy as jnp - >>> from surjectors import Slice, Chain, TransformedDistribution - >>> - >>> a = Slice(10) - >>> b = Slice(5) - >>> ab = Chain([a, b]) - >>> - >>> TransformedDistribution( - >>> distrax.Normal(jnp.zeros(5), jnp.ones(5)), - >>> Chain([a, b]) - >>> ) + y: event for which the log probability is computed + x: optional event that is used to condition + + Returns: + array of floats of log probabilities + """ + _, lp = self.inverse_and_log_prob(y, x) + return lp + + def inverse_and_log_prob(self, y: Array, x: Array = None): + """Compute the inverse transformation and its log probability. + + Args: + y: event for which the inverse and log probability is computed + x: optional event that is used to condition + + Returns: + tuple of two arrays of floats. The first one is the inverse + transformation, the second one is the log probability """ + if x is not None: + chex.assert_equal_rank([y, x]) + chex.assert_axis_dimension(y, 0, x.shape[0]) - def __init__( - self, - base_distribution: Union[Distribution, tfd.Distribution], - transform: Surjector, - ): - self.base_distribution = base_distribution - self.transform = transform - - def __call__(self, method, **kwargs): - """Call the TransformedDistribution object. - - Depending on "method", computes log-probability of an event or samples - from the distribution. - - Args: - method: either "sample" or "log_prob" - **kwargs: several keyword arguments that are dispatched to - whatever method is called. - - Returns: - returns whatever 'method' returns - """ - return getattr(self, method)(**kwargs) - - def log_prob(self, y: Array, x: Array = None): - """Calculate the log probability of an event conditional on another. - - Args: - y: event for which the log probability is computed - x: optional event that is used to condition - - Returns: - array of floats of log probabilities - """ - _, lp = self.inverse_and_log_prob(y, x) - return lp - - def inverse_and_log_prob(self, y: Array, x: Array = None): - """Compute the inverse transformation and its log probability. - - Args: - y: event for which the inverse and log probability is computed - x: optional event that is used to condition - - Returns: - tuple of two arrays of floats. The first one is the inverse - transformation, the second one is the log probability - """ - if x is not None: - chex.assert_equal_rank([y, x]) - chex.assert_axis_dimension(y, 0, x.shape[0]) - - if isinstance(self.transform, distrax.Bijector): - z, lc = self.transform.inverse_and_log_det(y) - else: - z, lc = self.transform.inverse_and_likelihood_contribution(y, x=x) - lp_z = self.base_distribution.log_prob(z) - lp = lp_z + lc - return z, lp - - def sample(self, sample_shape=(), x: Array = None): - """Sample an event. - - Args: - sample_shape: the size of the sample to be drawn - x: optional event that is used to condition the samples. - If x is given sample_shape is ignored - - Returns: - a sample from the transformed distribution - """ - y, _ = self.sample_and_log_prob(sample_shape, x) - return y - - def sample_and_log_prob(self, sample_shape=(), x: Array = None): - """Sample an event and compute its log probability. - - Args: - sample_shape: the size of the sample to be drawn - x: optional event that is used to condition the samples. - If x is given sample_shape is ignored - - Returns: - tuple of two arrays of floats. The first one is the drawn sample - transformation, the second one is its log probability - """ - if x is not None and len(sample_shape) == 0: - sample_shape = (x.shape[0],) - if x is not None: - chex.assert_equal(sample_shape[0], x.shape[0]) - - try: - z, lp_z = self.base_distribution.sample_and_log_prob( - seed=hk.next_rng_key(), - sample_shape=sample_shape, - ) - except AttributeError: - z, lp_z = self.base_distribution.experimental_sample_and_log_prob( - seed=hk.next_rng_key(), - sample_shape=sample_shape, - ) - - y, fldj = self.transform.forward_and_likelihood_contribution(z, x=x) - lp = lp_z - fldj - return y, lp + if isinstance(self.transform, distrax.Bijector): + z, lc = self.transform.inverse_and_log_det(y) + else: + z, lc = self.transform.inverse_and_likelihood_contribution(y, x=x) + lp_z = self.base_distribution.log_prob(z) + lp = lp_z + lc + return z, lp + + def sample(self, sample_shape=(), x: Array = None): + """Sample an event. + + Args: + sample_shape: the size of the sample to be drawn + x: optional event that is used to condition the samples. + If x is given sample_shape is ignored + + Returns: + a sample from the transformed distribution + """ + y, _ = self.sample_and_log_prob(sample_shape, x) + return y + + def sample_and_log_prob(self, sample_shape=(), x: Array = None): + """Sample an event and compute its log probability. + + Args: + sample_shape: the size of the sample to be drawn + x: optional event that is used to condition the samples. + If x is given sample_shape is ignored + + Returns: + tuple of two arrays of floats. The first one is the drawn sample + transformation, the second one is its log probability + """ + if x is not None and len(sample_shape) == 0: + sample_shape = (x.shape[0],) + if x is not None: + chex.assert_equal(sample_shape[0], x.shape[0]) + + try: + z, lp_z = self.base_distribution.sample_and_log_prob( + seed=hk.next_rng_key(), + sample_shape=sample_shape, + ) + except AttributeError: + z, lp_z = self.base_distribution.experimental_sample_and_log_prob( + seed=hk.next_rng_key(), + sample_shape=sample_shape, + ) + + y, fldj = self.transform.forward_and_likelihood_contribution(z, x=x) + lp = lp_z - fldj + return y, lp diff --git a/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel.py b/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel.py index 8e4de9c..836f03a 100644 --- a/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel.py +++ b/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable import distrax from chex import Array @@ -6,60 +6,60 @@ from surjectors._src.conditioners.nn.made import MADE from surjectors._src.surjectors.masked_autoregressive_inference_funnel import ( - MaskedAutoregressiveInferenceFunnel, + MaskedAutoregressiveInferenceFunnel, ) from surjectors.util import unstack class AffineMaskedAutoregressiveInferenceFunnel( - MaskedAutoregressiveInferenceFunnel + MaskedAutoregressiveInferenceFunnel ): - """A masked affine autoregressive funnel layer. + """A masked affine autoregressive funnel layer. - The AffineMaskedAutoregressiveInferenceFunnel is an autoregressive funnel, - i.e., dimensionality reducing transformation, that uses an affine - transformation from data to latent space using a masking mechanism as in - MaskedAutoegressive. + The AffineMaskedAutoregressiveInferenceFunnel is an autoregressive funnel, + i.e., dimensionality reducing transformation, that uses an affine + transformation from data to latent space using a masking mechanism as in + MaskedAutoegressive. - Args: - n_keep: number of dimensions to keep - decoder: a callable that returns a conditional probabiltiy - distribution when called - conditioner: a MADE neural network + Args: + n_keep: number of dimensions to keep + decoder: a callable that returns a conditional probabiltiy + distribution when called + conditioner: a MADE neural network - Examples: - >>> import distrax - >>> from surjectors import AffineMaskedAutoregressiveInferenceFunnel - >>> from surjectors.nn import MADE, make_mlp - >>> from surjectors.util import unstack - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> layer = AffineMaskedAutoregressiveInferenceFunnel( - >>> n_keep=10, - >>> decoder=decoder_fn(10), - >>> conditioner=MADE(10, [8, 8], 2), - >>> ) + Examples: + >>> import distrax + >>> from surjectors import AffineMaskedAutoregressiveInferenceFunnel + >>> from surjectors.nn import MADE, make_mlp + >>> from surjectors.util import unstack + >>> + >>> def decoder_fn(n_dim): + >>> def _fn(z): + >>> params = make_mlp([4, 4, n_dim * 2])(z) + >>> mu, log_scale = jnp.split(params, 2, -1) + >>> return distrax.Independent( + >>> distrax.Normal(mu, jnp.exp(log_scale)) + >>> ) + >>> return _fn + >>> + >>> layer = AffineMaskedAutoregressiveInferenceFunnel( + >>> n_keep=10, + >>> decoder=decoder_fn(10), + >>> conditioner=MADE(10, [8, 8], 2), + >>> ) - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - .. [2] Papamakarios, George, et al. "Masked Autoregressive Flow for - Density Estimation". Advances in Neural Information Processing - Systems, 2017. - """ + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + .. [2] Papamakarios, George, et al. "Masked Autoregressive Flow for + Density Estimation". Advances in Neural Information Processing + Systems, 2017. + """ - def __init__(self, n_keep: int, decoder: Callable, conditioner: MADE): - def bijector_fn(params: Array): - shift, log_scale = unstack(params, axis=-1) - return distrax.ScalarAffine(shift, jnp.exp(log_scale)) + def __init__(self, n_keep: int, decoder: Callable, conditioner: MADE): + def bijector_fn(params: Array): + shift, log_scale = unstack(params, axis=-1) + return distrax.ScalarAffine(shift, jnp.exp(log_scale)) - super().__init__(n_keep, decoder, conditioner, bijector_fn) + super().__init__(n_keep, decoder, conditioner, bijector_fn) diff --git a/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel_test.py b/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel_test.py index cb9bb41..426b189 100644 --- a/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel_test.py +++ b/surjectors/_src/surjectors/affine_masked_autoregressive_inference_funnel_test.py @@ -9,66 +9,66 @@ from surjectors._src.conditioners.mlp import make_mlp from surjectors._src.conditioners.nn.made import MADE from surjectors._src.surjectors.affine_masked_autoregressive_inference_funnel import ( # noqa: E501 - AffineMaskedAutoregressiveInferenceFunnel, + AffineMaskedAutoregressiveInferenceFunnel, ) def _decoder_fn(n_dim): - decoder_net = make_mlp([4, 4, n_dim * 2]) + decoder_net = make_mlp([4, 4, n_dim * 2]) - def _fn(z): - params = decoder_net(z) - mu, log_scale = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) + def _fn(z): + params = decoder_net(z) + mu, log_scale = jnp.split(params, 2, -1) + return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) - return _fn + return _fn def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def _get_funnel_surjector(n_latent, n_dimension): - return AffineMaskedAutoregressiveInferenceFunnel( - n_latent, - _decoder_fn(n_dimension - n_latent), - MADE(n_latent, [4, 4], 2), - ) + return AffineMaskedAutoregressiveInferenceFunnel( + n_latent, + _decoder_fn(n_dimension - n_latent), + MADE(n_latent, [4, 4], 2), + ) def make_surjector(n_dimension, n_latent): - def _transformation_fn(n_dimension): - funnel = _get_funnel_surjector(n_latent, n_dimension) - return funnel + def _transformation_fn(n_dimension): + funnel = _get_funnel_surjector(n_latent, n_dimension) + return funnel - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_latent), _transformation_fn(n_dimension) - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_latent), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_affine_masked_autoregressive_inference_funnel(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_affine_masked_autoregressive_inference_funnel(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/surjectors/affine_masked_coupling_generative_funnel.py b/surjectors/_src/surjectors/affine_masked_coupling_generative_funnel.py index d0b8894..8624119 100644 --- a/surjectors/_src/surjectors/affine_masked_coupling_generative_funnel.py +++ b/surjectors/_src/surjectors/affine_masked_coupling_generative_funnel.py @@ -8,54 +8,50 @@ class AffineMaskedCouplingGenerativeFunnel(Surjector): - """A Generative funnel layer using masked affine coupling. - - Args: - n_keep: number of dimensions to keep - encoder: callable - conditioner: callable - """ - - def __init__(self, n_keep, encoder, conditioner): - self.n_keep = n_keep - self.encoder = encoder - self.conditioner = conditioner - - def _mask(self, array): - mask = jnp.arange(array.shape[-1]) >= self.n_keep - mask = mask.astype(jnp.bool_) - return mask - - def _inner_bijector(self, mask): - def _bijector_fn(params: Array): - shift, log_scale = jnp.split(params, 2, axis=-1) - return distrax.ScalarAffine(shift, jnp.exp(log_scale)) - - return MaskedCoupling(mask, self.conditioner, _bijector_fn) - - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - y_condition = y - # TODO(simon) : fixme - if x is not None: - y_condition = jnp.concatenate([y, x], axis=-1) - z_minus, lc = self.encoder(y_condition).sample_and_log_prob( - seed=hk.next_rng_key() - ) - arr = jnp.concatenate([y, z_minus], axis=-1) - # TODO(simon): remote the conditioning here? - z, jac_det = self._inner_bijector(self._mask(arr)).inverse_and_log_det( - arr - ) - return z, -lc + jac_det - - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - # TODO(simon): remote the conditioning here? - faux, jac_det = self._inner_bijector(self._mask(z)).inverse_and_log_det( - z - ) - y = faux[..., : self.n_keep] - y_condition = y - if x is not None: - y_condition = jnp.concatenate([y_condition, x], axis=-1) - lc = self.encoder(y_condition).log_prob(faux[..., self.n_keep :]) - return y, -lc + jac_det + """A Generative funnel layer using masked affine coupling. + + Args: + n_keep: number of dimensions to keep + encoder: callable + conditioner: callable + """ + + def __init__(self, n_keep, encoder, conditioner): + self.n_keep = n_keep + self.encoder = encoder + self.conditioner = conditioner + + def _mask(self, array): + mask = jnp.arange(array.shape[-1]) >= self.n_keep + mask = mask.astype(jnp.bool_) + return mask + + def _inner_bijector(self, mask): + def _bijector_fn(params: Array): + shift, log_scale = jnp.split(params, 2, axis=-1) + return distrax.ScalarAffine(shift, jnp.exp(log_scale)) + + return MaskedCoupling(mask, self.conditioner, _bijector_fn) + + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + y_condition = y + # TODO(simon) : fixme + if x is not None: + y_condition = jnp.concatenate([y, x], axis=-1) + z_minus, lc = self.encoder(y_condition).sample_and_log_prob( + seed=hk.next_rng_key() + ) + arr = jnp.concatenate([y, z_minus], axis=-1) + # TODO(simon): remote the conditioning here? + z, jac_det = self._inner_bijector(self._mask(arr)).inverse_and_log_det(arr) + return z, -lc + jac_det + + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + # TODO(simon): remote the conditioning here? + faux, jac_det = self._inner_bijector(self._mask(z)).inverse_and_log_det(z) + y = faux[..., : self.n_keep] + y_condition = y + if x is not None: + y_condition = jnp.concatenate([y_condition, x], axis=-1) + lc = self.encoder(y_condition).log_prob(faux[..., self.n_keep :]) + return y, -lc + jac_det diff --git a/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel.py b/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel.py index 0a74524..b8dba36 100644 --- a/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel.py +++ b/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel.py @@ -1,55 +1,55 @@ -from typing import Callable +from collections.abc import Callable import distrax from chex import Array from jax import numpy as jnp from surjectors._src.surjectors.masked_coupling_inference_funnel import ( - MaskedCouplingInferenceFunnel, + MaskedCouplingInferenceFunnel, ) class AffineMaskedCouplingInferenceFunnel(MaskedCouplingInferenceFunnel): - """A masked coupling inference funnel that uses an affine transformation. - - Args: - n_keep: number of dimensions to keep - decoder: a callable that returns a conditional probabiltiy - distribution when called - conditioner: a conditioning neural network - - Examples: - >>> import distrax - >>> from jax import numpy as jnp - >>> from surjectors import AffineMaskedCouplingInferenceFunnel - >>> from surjectors.nn import make_mlp - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> layer = AffineMaskedCouplingInferenceFunnel( - >>> n_keep=10, - >>> decoder=decoder_fn(10), - >>> conditioner=make_mlp([4, 4, 10 * 2])(z), - >>> ) - - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - .. [2] Dinh, Laurent, et al. "Density estimation using RealNVP". - International Conference on Learning Representations, 2017. - """ - - def __init__(self, n_keep: int, decoder: Callable, conditioner: Callable): - def bijector_fn(params: Array): - shift, log_scale = jnp.split(params, 2, axis=-1) - return distrax.ScalarAffine(shift, jnp.exp(log_scale)) - - super().__init__(n_keep, decoder, conditioner, bijector_fn) + """A masked coupling inference funnel that uses an affine transformation. + + Args: + n_keep: number of dimensions to keep + decoder: a callable that returns a conditional probabiltiy + distribution when called + conditioner: a conditioning neural network + + Examples: + >>> import distrax + >>> from jax import numpy as jnp + >>> from surjectors import AffineMaskedCouplingInferenceFunnel + >>> from surjectors.nn import make_mlp + >>> + >>> def decoder_fn(n_dim): + >>> def _fn(z): + >>> params = make_mlp([4, 4, n_dim * 2])(z) + >>> mu, log_scale = jnp.split(params, 2, -1) + >>> return distrax.Independent( + >>> distrax.Normal(mu, jnp.exp(log_scale)) + >>> ) + >>> return _fn + >>> + >>> layer = AffineMaskedCouplingInferenceFunnel( + >>> n_keep=10, + >>> decoder=decoder_fn(10), + >>> conditioner=make_mlp([4, 4, 10 * 2])(z), + >>> ) + + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + .. [2] Dinh, Laurent, et al. "Density estimation using RealNVP". + International Conference on Learning Representations, 2017. + """ + + def __init__(self, n_keep: int, decoder: Callable, conditioner: Callable): + def bijector_fn(params: Array): + shift, log_scale = jnp.split(params, 2, axis=-1) + return distrax.ScalarAffine(shift, jnp.exp(log_scale)) + + super().__init__(n_keep, decoder, conditioner, bijector_fn) diff --git a/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel_test.py b/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel_test.py index 0e8d200..0a55928 100644 --- a/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel_test.py +++ b/surjectors/_src/surjectors/affine_masked_coupling_inference_funnel_test.py @@ -8,66 +8,66 @@ from surjectors import TransformedDistribution from surjectors._src.conditioners.mlp import make_mlp from surjectors._src.surjectors.affine_masked_coupling_inference_funnel import ( # noqa: E501 - AffineMaskedCouplingInferenceFunnel, + AffineMaskedCouplingInferenceFunnel, ) def _decoder_fn(n_dim): - decoder_net = make_mlp([4, 4, n_dim * 2]) + decoder_net = make_mlp([4, 4, n_dim * 2]) - def _fn(z): - params = decoder_net(z) - mu, log_scale = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) + def _fn(z): + params = decoder_net(z) + mu, log_scale = jnp.split(params, 2, -1) + return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) - return _fn + return _fn def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def _get_funnel_surjector(n_latent, n_dimension): - return AffineMaskedCouplingInferenceFunnel( - n_latent, - _decoder_fn(n_dimension - n_latent), - make_mlp([4, 4, n_dimension * 2]), - ) + return AffineMaskedCouplingInferenceFunnel( + n_latent, + _decoder_fn(n_dimension - n_latent), + make_mlp([4, 4, n_dimension * 2]), + ) def make_surjector(n_dimension, n_latent): - def _transformation_fn(n_dimension): - funnel = _get_funnel_surjector(n_latent, n_dimension) - return funnel + def _transformation_fn(n_dimension): + funnel = _get_funnel_surjector(n_latent, n_dimension) + return funnel - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_latent), _transformation_fn(n_dimension) - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_latent), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_affine_masked_coupling_inference_funnel(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_affine_masked_coupling_inference_funnel(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/surjectors/augment.py b/surjectors/_src/surjectors/augment.py index 9526e0f..1e0f5d9 100644 --- a/surjectors/_src/surjectors/augment.py +++ b/surjectors/_src/surjectors/augment.py @@ -5,35 +5,35 @@ class Augment(Surjector): - """Augment generative funnel. - - Args: - n_keep: number of dimensions to keep - encoder: encoder callable - """ - - def __init__(self, n_keep, encoder): - self.n_keep = n_keep - self.encoder = encoder - - def _split_input(self, array): - spl = jnp.split(array, [self.n_keep], axis=-1) - return spl - - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - z_plus = y_condition = y - if x is not None: - y_condition = jnp.concatenate([y_condition, x], axis=-1) - z_minus, lc = self.encoder(y_condition).sample_and_log_prob( - seed=hk.next_rng_key() - ) - z = jnp.concatenate([z_plus, z_minus], axis=-1) - return z, -lc - - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - z_plus, z_minus = self._split_input(z) - y_condition = y = z_plus - if x is not None: - y_condition = jnp.concatenate([y_condition, x], axis=-1) - lc = self.encoder(y_condition).log_prob(z_minus) - return y, -lc + """Augment generative funnel. + + Args: + n_keep: number of dimensions to keep + encoder: encoder callable + """ + + def __init__(self, n_keep, encoder): + self.n_keep = n_keep + self.encoder = encoder + + def _split_input(self, array): + spl = jnp.split(array, [self.n_keep], axis=-1) + return spl + + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + z_plus = y_condition = y + if x is not None: + y_condition = jnp.concatenate([y_condition, x], axis=-1) + z_minus, lc = self.encoder(y_condition).sample_and_log_prob( + seed=hk.next_rng_key() + ) + z = jnp.concatenate([z_plus, z_minus], axis=-1) + return z, -lc + + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + z_plus, z_minus = self._split_input(z) + y_condition = y = z_plus + if x is not None: + y_condition = jnp.concatenate([y_condition, x], axis=-1) + lc = self.encoder(y_condition).log_prob(z_minus) + return y, -lc diff --git a/surjectors/_src/surjectors/chain.py b/surjectors/_src/surjectors/chain.py index 98e9166..4248319 100644 --- a/surjectors/_src/surjectors/chain.py +++ b/surjectors/_src/surjectors/chain.py @@ -4,64 +4,64 @@ # type: ignore[B009] class Chain(Surjector): - """Chain of normalizing flows. + """Chain of normalizing flows. - Can be used to concatenate several normalizing flows together. + Can be used to concatenate several normalizing flows together. - Args: - transforms: a list of transformations, such as bijections or - surjections + Args: + transforms: a list of transformations, such as bijections or + surjections - Examples: - >>> from surjectors import Slice, Chain - >>> a = Slice(10) - >>> b = Slice(5) - >>> ab = Chain([a, b]) - """ + Examples: + >>> from surjectors import Slice, Chain + >>> a = Slice(10) + >>> b = Slice(5) + >>> ab = Chain([a, b]) + """ - def __init__(self, transforms: list[Transform]): - self._transforms = transforms + def __init__(self, transforms: list[Transform]): + self._transforms = transforms - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - z, lcs = self._inverse_and_log_contribution_dispatch( - self._transforms[0], y, x - ) - for transform in self._transforms[1:]: - z, lc = self._inverse_and_log_contribution_dispatch(transform, z, x) - lcs += lc - return z, lcs + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + z, lcs = self._inverse_and_log_contribution_dispatch( + self._transforms[0], y, x + ) + for transform in self._transforms[1:]: + z, lc = self._inverse_and_log_contribution_dispatch(transform, z, x) + lcs += lc + return z, lcs - @staticmethod - def _inverse_and_log_contribution_dispatch(transform, y, x): - if isinstance(transform, Surjector): - if hasattr(transform, "inverse_and_likelihood_contribution"): - fn = getattr(transform, "inverse_and_likelihood_contribution") - else: - fn = getattr(transform, "inverse_and_log_det") - z, lc = fn(y, x) - else: - fn = getattr(transform, "inverse_and_log_det") - z, lc = fn(y) - return z, lc + @staticmethod + def _inverse_and_log_contribution_dispatch(transform, y, x): + if isinstance(transform, Surjector): + if hasattr(transform, "inverse_and_likelihood_contribution"): + fn = getattr(transform, "inverse_and_likelihood_contribution") + else: + fn = getattr(transform, "inverse_and_log_det") + z, lc = fn(y, x) + else: + fn = getattr(transform, "inverse_and_log_det") + z, lc = fn(y) + return z, lc - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - y, log_det = self._forward_and_log_contribution_dispatch( - self._transforms[-1], z, x - ) - for transform in reversed(self._transforms[:-1]): - y, lc = self._forward_and_log_contribution_dispatch(transform, y, x) - log_det += lc - return y, log_det + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + y, log_det = self._forward_and_log_contribution_dispatch( + self._transforms[-1], z, x + ) + for transform in reversed(self._transforms[:-1]): + y, lc = self._forward_and_log_contribution_dispatch(transform, y, x) + log_det += lc + return y, log_det - @staticmethod - def _forward_and_log_contribution_dispatch(transform, z, x): - if isinstance(transform, Surjector): - if hasattr(transform, "forward_and_likelihood_contribution"): - fn = getattr(transform, "forward_and_likelihood_contribution") - else: - fn = getattr(transform, "forward_and_log_det") - y, lc = fn(z, x) - else: - fn = getattr(transform, "forward_and_log_det") - y, lc = fn(z) - return y, lc + @staticmethod + def _forward_and_log_contribution_dispatch(transform, z, x): + if isinstance(transform, Surjector): + if hasattr(transform, "forward_and_likelihood_contribution"): + fn = getattr(transform, "forward_and_likelihood_contribution") + else: + fn = getattr(transform, "forward_and_log_det") + y, lc = fn(z, x) + else: + fn = getattr(transform, "forward_and_log_det") + y, lc = fn(z) + return y, lc diff --git a/surjectors/_src/surjectors/masked_autoregressive_inference_funnel.py b/surjectors/_src/surjectors/masked_autoregressive_inference_funnel.py index 8044c36..40c4b24 100644 --- a/surjectors/_src/surjectors/masked_autoregressive_inference_funnel.py +++ b/surjectors/_src/surjectors/masked_autoregressive_inference_funnel.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable import haiku as hk from jax import numpy as jnp @@ -9,100 +9,100 @@ class MaskedAutoregressiveInferenceFunnel(Surjector): - """A masked autoregressive funnel layer. - - The MaskedAutoregressiveInferenceFunnel is an autoregressive funnel, - i.e., dimensionality reducing transformation, that uses a masking mechanism - as in MaskedAutoegressive. Its inner bijectors needs to be specified in - comparison to AffineMaskedAutoregressiveInferenceFunnel and - RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel. - - Args: - n_keep: number of dimensions to keep - decoder: a callable that returns a conditional probabiltiy - distribution when called - conditioner: a MADE neural network - bijector_fn: an inner bijector function to be used - - Examples: - >>> import distrax - >>> from surjectors import MaskedAutoregressiveInferenceFunnel - >>> from surjectors.nn import MADE, make_mlp - >>> from surjectors.util import unstack - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> def bijector_fn(params: Array): - >>> shift, log_scale = unstack(params, axis=-1) - >>> return distrax.ScalarAffine(shift, jnp.exp(log_scale)) - >>> - >>> layer = MaskedAutoregressiveInferenceFunnel( - >>> n_keep=10, - >>> decoder=decoder_fn(10), - >>> conditioner=MADE(10, [8, 8], 2), - >>> bijector_fn=bijector_fn - >>> ) - - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - .. [2] Papamakarios, George, et al. "Masked Autoregressive Flow for - Density Estimation". Advances in Neural Information Processing - Systems, 2017. - """ - - def __init__( - self, - n_keep: int, - decoder: Callable, - conditioner: MADE, - bijector_fn: Callable, - ): - self.n_keep = n_keep - self.decoder = decoder - self.conditioner = conditioner - self.bijector_fn = bijector_fn - - def _inner_bijector(self): - return MaskedAutoregressive(self.conditioner, self.bijector_fn) - - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - y_plus, y_minus = y[..., : self.n_keep], y[..., self.n_keep :] - - y_cond = y_minus - if x is not None: - y_cond = jnp.concatenate([y_cond, x], axis=-1) - z, jac_det = self._inner_bijector().inverse_and_log_det(y_plus, y_cond) - - z_condition = z - if x is not None: - z_condition = jnp.concatenate([z, x], axis=-1) - lc = self.decoder(z_condition).log_prob(y_minus) - - return z, lc + jac_det - - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - z_condition = z - if x is not None: - z_condition = jnp.concatenate([z, x], axis=-1) - y_minus, jac_det = self.decoder(z_condition).sample_and_log_prob( - seed=hk.next_rng_key() - ) - - y_cond = y_minus - if x is not None: - y_cond = jnp.concatenate([y_cond, x], axis=-1) - # TODO(simon): need to sort the indexes correctly (?) - # TODO(simon): remote the conditioning here? - y_plus, lc = self._inner_bijector().forward_and_log_det(z, y_cond) - - y = jnp.concatenate([y_plus, y_minus]) - return y, lc + jac_det + """A masked autoregressive funnel layer. + + The MaskedAutoregressiveInferenceFunnel is an autoregressive funnel, + i.e., dimensionality reducing transformation, that uses a masking mechanism + as in MaskedAutoegressive. Its inner bijectors needs to be specified in + comparison to AffineMaskedAutoregressiveInferenceFunnel and + RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel. + + Args: + n_keep: number of dimensions to keep + decoder: a callable that returns a conditional probabiltiy + distribution when called + conditioner: a MADE neural network + bijector_fn: an inner bijector function to be used + + Examples: + >>> import distrax + >>> from surjectors import MaskedAutoregressiveInferenceFunnel + >>> from surjectors.nn import MADE, make_mlp + >>> from surjectors.util import unstack + >>> + >>> def decoder_fn(n_dim): + >>> def _fn(z): + >>> params = make_mlp([4, 4, n_dim * 2])(z) + >>> mu, log_scale = jnp.split(params, 2, -1) + >>> return distrax.Independent( + >>> distrax.Normal(mu, jnp.exp(log_scale)) + >>> ) + >>> return _fn + >>> + >>> def bijector_fn(params: Array): + >>> shift, log_scale = unstack(params, axis=-1) + >>> return distrax.ScalarAffine(shift, jnp.exp(log_scale)) + >>> + >>> layer = MaskedAutoregressiveInferenceFunnel( + >>> n_keep=10, + >>> decoder=decoder_fn(10), + >>> conditioner=MADE(10, [8, 8], 2), + >>> bijector_fn=bijector_fn + >>> ) + + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + .. [2] Papamakarios, George, et al. "Masked Autoregressive Flow for + Density Estimation". Advances in Neural Information Processing + Systems, 2017. + """ + + def __init__( + self, + n_keep: int, + decoder: Callable, + conditioner: MADE, + bijector_fn: Callable, + ): + self.n_keep = n_keep + self.decoder = decoder + self.conditioner = conditioner + self.bijector_fn = bijector_fn + + def _inner_bijector(self): + return MaskedAutoregressive(self.conditioner, self.bijector_fn) + + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + y_plus, y_minus = y[..., : self.n_keep], y[..., self.n_keep :] + + y_cond = y_minus + if x is not None: + y_cond = jnp.concatenate([y_cond, x], axis=-1) + z, jac_det = self._inner_bijector().inverse_and_log_det(y_plus, y_cond) + + z_condition = z + if x is not None: + z_condition = jnp.concatenate([z, x], axis=-1) + lc = self.decoder(z_condition).log_prob(y_minus) + + return z, lc + jac_det + + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + z_condition = z + if x is not None: + z_condition = jnp.concatenate([z, x], axis=-1) + y_minus, jac_det = self.decoder(z_condition).sample_and_log_prob( + seed=hk.next_rng_key() + ) + + y_cond = y_minus + if x is not None: + y_cond = jnp.concatenate([y_cond, x], axis=-1) + # TODO(simon): need to sort the indexes correctly (?) + # TODO(simon): remote the conditioning here? + y_plus, lc = self._inner_bijector().forward_and_log_det(z, y_cond) + + y = jnp.concatenate([y_plus, y_minus]) + return y, lc + jac_det diff --git a/surjectors/_src/surjectors/masked_coupling_inference_funnel.py b/surjectors/_src/surjectors/masked_coupling_inference_funnel.py index 7e071e1..16728f7 100644 --- a/surjectors/_src/surjectors/masked_coupling_inference_funnel.py +++ b/surjectors/_src/surjectors/masked_coupling_inference_funnel.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable import haiku as hk from jax import numpy as jnp @@ -9,98 +9,98 @@ # pylint: disable=too-many-function-args class MaskedCouplingInferenceFunnel(Surjector): - """A masked coupling inference funnel. + """A masked coupling inference funnel. - The MaskedCouplingInferenceFunnel is a coupling funnel, - i.e., dimensionality reducing transformation, that uses a masking mechanism - as in MaskedCouplingI. Its inner bijectors needs to be specified in - comparison to ASffineMaskedCouplingInferenceFunnel and - RationalQuadraticSplineMaskedCouplingInferenceFunnel. + The MaskedCouplingInferenceFunnel is a coupling funnel, + i.e., dimensionality reducing transformation, that uses a masking mechanism + as in MaskedCouplingI. Its inner bijectors needs to be specified in + comparison to ASffineMaskedCouplingInferenceFunnel and + RationalQuadraticSplineMaskedCouplingInferenceFunnel. - Args: - n_keep: number of dimensions to keep - decoder: a callable that returns a conditional probabiltiy - distribution when called - conditioner: a conditioning neural network - bijector_fn: an inner bijector function to be used + Args: + n_keep: number of dimensions to keep + decoder: a callable that returns a conditional probabiltiy + distribution when called + conditioner: a conditioning neural network + bijector_fn: an inner bijector function to be used - Examples: - >>> import distrax - >>> from surjectors import MaskedCouplingInferenceFunnel - >>> from surjectors.nn import make_mlp - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> def bijector_fn(params): - >>> shift, log_scale = jnp.split(params, 2, -1) - >>> return distrax.ScalarAffine(shift, jnp.exp(log_scale)) - >>> - >>> layer = MaskedCouplingInferenceFunnel( - >>> n_keep=10, - >>> decoder=decoder_fn(10), - >>> conditioner=make_mlp([4, 4, 10 * 2]), - >>> bijector_fn=bijector_fn - >>> ) + Examples: + >>> import distrax + >>> from surjectors import MaskedCouplingInferenceFunnel + >>> from surjectors.nn import make_mlp + >>> + >>> def decoder_fn(n_dim): + >>> def _fn(z): + >>> params = make_mlp([4, 4, n_dim * 2])(z) + >>> mu, log_scale = jnp.split(params, 2, -1) + >>> return distrax.Independent( + >>> distrax.Normal(mu, jnp.exp(log_scale)) + >>> ) + >>> return _fn + >>> + >>> def bijector_fn(params): + >>> shift, log_scale = jnp.split(params, 2, -1) + >>> return distrax.ScalarAffine(shift, jnp.exp(log_scale)) + >>> + >>> layer = MaskedCouplingInferenceFunnel( + >>> n_keep=10, + >>> decoder=decoder_fn(10), + >>> conditioner=make_mlp([4, 4, 10 * 2]), + >>> bijector_fn=bijector_fn + >>> ) - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - .. [2] Dinh, Laurent, et al. "Density estimation using RealNVP". - International Conference on Learning Representations, 2017. - """ + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + .. [2] Dinh, Laurent, et al. "Density estimation using RealNVP". + International Conference on Learning Representations, 2017. + """ - def __init__( - self, - n_keep: int, - decoder: Callable, - conditioner: Callable, - bijector_fn: Callable, - ): - self.n_keep = n_keep - self.decoder = decoder - self.conditioner = conditioner - self.bijector_fn = bijector_fn + def __init__( + self, + n_keep: int, + decoder: Callable, + conditioner: Callable, + bijector_fn: Callable, + ): + self.n_keep = n_keep + self.decoder = decoder + self.conditioner = conditioner + self.bijector_fn = bijector_fn - def _mask(self, array): - mask = jnp.arange(array.shape[-1]) >= self.n_keep - mask = mask.astype(jnp.bool_) - return mask + def _mask(self, array): + mask = jnp.arange(array.shape[-1]) >= self.n_keep + mask = mask.astype(jnp.bool_) + return mask - def _inner_bijector(self, mask): - return MaskedCoupling(mask, self.conditioner, self.bijector_fn) + def _inner_bijector(self, mask): + return MaskedCoupling(mask, self.conditioner, self.bijector_fn) - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - # TODO(simon): remote the conditioning here? - faux, jac_det = self._inner_bijector(self._mask(y)).inverse_and_log_det( - y, x=x - ) - # TODO(simon): this should be relegated to the base class: - # MaskedCouplingInferenceFunnel (see issue #14 on GitHub) - z_condition = z = faux[:, : self.n_keep] - if x is not None: - z_condition = jnp.concatenate([z, x], axis=-1) - lc = self.decoder(z_condition).log_prob(y[:, self.n_keep :]) - return z, lc + jac_det + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + # TODO(simon): remote the conditioning here? + faux, jac_det = self._inner_bijector(self._mask(y)).inverse_and_log_det( + y, x=x + ) + # TODO(simon): this should be relegated to the base class: + # MaskedCouplingInferenceFunnel (see issue #14 on GitHub) + z_condition = z = faux[:, : self.n_keep] + if x is not None: + z_condition = jnp.concatenate([z, x], axis=-1) + lc = self.decoder(z_condition).log_prob(y[:, self.n_keep :]) + return z, lc + jac_det - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - z_condition = z - if x is not None: - z_condition = jnp.concatenate([z, x], axis=-1) - y_minus, jac_det = self.decoder(z_condition).sample_and_log_prob( - seed=hk.next_rng_key() - ) - # TODO(simon): need to sort the indexes correctly (?) - z_tilde = jnp.concatenate([z, y_minus], axis=-1) - # TODO(simon): remote the conditioning here? - y, lc = self._inner_bijector(self._mask(z_tilde)).forward_and_log_det( - z_tilde, x - ) - return y, lc + jac_det + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + z_condition = z + if x is not None: + z_condition = jnp.concatenate([z, x], axis=-1) + y_minus, jac_det = self.decoder(z_condition).sample_and_log_prob( + seed=hk.next_rng_key() + ) + # TODO(simon): need to sort the indexes correctly (?) + z_tilde = jnp.concatenate([z, y_minus], axis=-1) + # TODO(simon): remote the conditioning here? + y, lc = self._inner_bijector(self._mask(z_tilde)).forward_and_log_det( + z_tilde, x + ) + return y, lc + jac_det diff --git a/surjectors/_src/surjectors/mlp.py b/surjectors/_src/surjectors/mlp.py index 719f4ea..5c7252b 100644 --- a/surjectors/_src/surjectors/mlp.py +++ b/surjectors/_src/surjectors/mlp.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable import haiku as hk from jax import numpy as jnp @@ -8,52 +8,52 @@ class MLPInferenceFunnel(Surjector, hk.Module): - """A multilayer perceptron inference funnel. - - Args: - n_keep: number of dimensions to keep - decoder: a conditional probability function - - Examples: - >>> import distrax - >>> from surjectors import MLPInferenceFunnel - >>> from surjectors.nn import make_mlp - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> decoder = decoder_fn(5) - >>> a = MLPInferenceFunnel(10, decoder) - - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - """ - - def __init__(self, n_keep: int, decoder: Callable): - super().__init__() - self._r = LULinear(n_keep, False) - self._w_prime = hk.Linear(n_keep, True) - self.decoder = decoder - self.n_keep = n_keep - - def _split_input(self, array): - spl = jnp.split(array, [self.n_keep], axis=-1) - return spl - - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - y_plus, y_minus = self._split_input(y) - z, jac_det = self._r.inverse_and_likelihood_contribution(y_plus) - z += self._w_prime(y_minus) - lp = self.decoder(z).log_prob(y_minus) - return z, lp + jac_det - - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - raise NotImplementedError() + """A multilayer perceptron inference funnel. + + Args: + n_keep: number of dimensions to keep + decoder: a conditional probability function + + Examples: + >>> import distrax + >>> from surjectors import MLPInferenceFunnel + >>> from surjectors.nn import make_mlp + >>> + >>> def decoder_fn(n_dim): + >>> def _fn(z): + >>> params = make_mlp([4, 4, n_dim * 2])(z) + >>> mu, log_scale = jnp.split(params, 2, -1) + >>> return distrax.Independent( + >>> distrax.Normal(mu, jnp.exp(log_scale)) + >>> ) + >>> return _fn + >>> + >>> decoder = decoder_fn(5) + >>> a = MLPInferenceFunnel(10, decoder) + + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + """ + + def __init__(self, n_keep: int, decoder: Callable): + super().__init__() + self._r = LULinear(n_keep, False) + self._w_prime = hk.Linear(n_keep, True) + self.decoder = decoder + self.n_keep = n_keep + + def _split_input(self, array): + spl = jnp.split(array, [self.n_keep], axis=-1) + return spl + + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + y_plus, y_minus = self._split_input(y) + z, jac_det = self._r.inverse_and_likelihood_contribution(y_plus) + z += self._w_prime(y_minus) + lp = self.decoder(z).log_prob(y_minus) + return z, lp + jac_det + + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + raise NotImplementedError() diff --git a/surjectors/_src/surjectors/rq_masked_autoregressive_inference_funnel.py b/surjectors/_src/surjectors/rq_masked_autoregressive_inference_funnel.py index 79aa4f2..2609898 100644 --- a/surjectors/_src/surjectors/rq_masked_autoregressive_inference_funnel.py +++ b/surjectors/_src/surjectors/rq_masked_autoregressive_inference_funnel.py @@ -1,5 +1,5 @@ import warnings -from typing import Callable +from collections.abc import Callable import distrax from chex import Array @@ -7,75 +7,75 @@ from surjectors._src.bijectors.masked_autoregressive import MaskedAutoregressive from surjectors._src.conditioners.nn.made import MADE from surjectors._src.surjectors.masked_autoregressive_inference_funnel import ( - MaskedAutoregressiveInferenceFunnel, + MaskedAutoregressiveInferenceFunnel, ) # ruff: noqa: PLR0913 class RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel( - MaskedAutoregressiveInferenceFunnel + MaskedAutoregressiveInferenceFunnel ): - """A masked autoregressive inference funnel that uses RQ-NSFs. + """A masked autoregressive inference funnel that uses RQ-NSFs. - Args: - n_keep: number of dimensions to keep - decoder: a callable that returns a conditional probabiltiy - distribution when called - conditioner: a conditioning neural network - range_min: minimum range of the spline - range_max: maximum range of the spline + Args: + n_keep: number of dimensions to keep + decoder: a callable that returns a conditional probabiltiy + distribution when called + conditioner: a conditioning neural network + range_min: minimum range of the spline + range_max: maximum range of the spline - Examples: - >>> import distrax - >>> from jax import numpy as jnp - >>> from surjectors import \ + Examples: + >>> import distrax + >>> from jax import numpy as jnp + >>> from surjectors import \ >>> RationalQuadraticSplineMaskedCouplingInferenceFunnel - >>> from surjectors.nn import make_mlp - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> layer = RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel( - >>> n_keep=10, - >>> decoder=decoder_fn(10), - >>> conditioner=MADE(10, [8, 8], 2), - >>> ) + >>> from surjectors.nn import make_mlp + >>> + >>> def decoder_fn(n_dim): + >>> def _fn(z): + >>> params = make_mlp([4, 4, n_dim * 2])(z) + >>> mu, log_scale = jnp.split(params, 2, -1) + >>> return distrax.Independent( + >>> distrax.Normal(mu, jnp.exp(log_scale)) + >>> ) + >>> return _fn + >>> + >>> layer = RationalQuadraticSplineMaskedAutoregressiveInferenceFunnel( + >>> n_keep=10, + >>> decoder=decoder_fn(10), + >>> conditioner=MADE(10, [8, 8], 2), + >>> ) - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - .. [2] Durkan, Conor, et al. "Neural Spline Flows". - Advances in Neural Information Processing Systems, 2019. - .. [3] Papamakarios, George, et al. "Masked Autoregressive Flow for - Density Estimation". Advances in Neural Information Processing - Systems, 2017. - """ + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + .. [2] Durkan, Conor, et al. "Neural Spline Flows". + Advances in Neural Information Processing Systems, 2019. + .. [3] Papamakarios, George, et al. "Masked Autoregressive Flow for + Density Estimation". Advances in Neural Information Processing + Systems, 2017. + """ - def __init__( - self, - n_keep: int, - decoder: Callable, - conditioner: MADE, - range_min: float, - range_max: float, - ): - warnings.warn("class has not been tested properly. use at own risk") - self.range_min = range_min - self.range_max = range_max + def __init__( + self, + n_keep: int, + decoder: Callable, + conditioner: MADE, + range_min: float, + range_max: float, + ): + warnings.warn("class has not been tested properly. use at own risk") + self.range_min = range_min + self.range_max = range_max - def _inner_bijector(self): - def _bijector_fn(params: Array): - return distrax.RationalQuadraticSpline( - params, self.range_min, self.range_max - ) + def _inner_bijector(self): + def _bijector_fn(params: Array): + return distrax.RationalQuadraticSpline( + params, self.range_min, self.range_max + ) - return MaskedAutoregressive(self.conditioner, _bijector_fn) + return MaskedAutoregressive(self.conditioner, _bijector_fn) - super().__init__(n_keep, decoder, conditioner, _inner_bijector) + super().__init__(n_keep, decoder, conditioner, _inner_bijector) diff --git a/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel.py b/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel.py index e61a789..2ac0b74 100644 --- a/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel.py +++ b/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel.py @@ -3,63 +3,84 @@ # pylint: disable=too-many-arguments, arguments-renamed from surjectors._src.surjectors.masked_coupling_inference_funnel import ( - MaskedCouplingInferenceFunnel, + MaskedCouplingInferenceFunnel, ) # ruff: noqa: PLR0913 class RationalQuadraticSplineMaskedCouplingInferenceFunnel( - MaskedCouplingInferenceFunnel + MaskedCouplingInferenceFunnel ): - """A masked coupling inference funnel that uses a rational quatratic spline. + """A masked coupling inference funnel that uses a rational quatratic spline. - Args: - n_keep: number of dimensions to keep - decoder: a callable that returns a conditional probabiltiy - distribution when called - conditioner: a conditioning neural network - range_min: minimum range of the spline - range_max: maximum range of the spline + Args: + n_keep: number of dimensions to keep + decoder: a callable that returns a conditional probabiltiy + distribution when called + conditioner: a conditioning neural network + range_min: minimum range of the spline + range_max: maximum range of the spline - Examples: - >>> import distrax - >>> from jax import numpy as jnp - >>> from surjectors import \ - >>> RationalQuadraticSplineMaskedCouplingInferenceFunnel - >>> from surjectors.nn import make_mlp - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> layer = RationalQuadraticSplineMaskedCouplingInferenceFunnel( - >>> n_keep=10, - >>> decoder=decoder_fn(10), - >>> conditioner=make_mlp([4, 4, 10 * 2])(z), - >>> ) + Examples: + >>> import haiku as hk + >>> from jax import numpy as jnp + >>> from jax import random as jr + >>> from tensorflow_probability.substrates.jax import distributions as tfd + >>> from surjectors import TransformedDistribution + >>> from surjectors import RationalQuadraticSplineMaskedCouplingInferenceFunnel + >>> from surjectors.nn import make_mlp + >>> + >>> def decoder_fn(n_dim): + ... def _fn(z): + ... params = make_mlp((64, 64, n_dim * 2))(z) + ... mu, log_scale = jnp.split(params, 2, -1) + ... return tfd.Independent( + ... tfd.Normal(mu, jnp.exp(log_scale)) + ... ) + ... return _fn + >>> + >>> @hk.without_apply_rng + ... @hk.transform + ... def fn(inputs, num_params=3): + ... base_distribution = tfd.Independent( + ... tfd.Normal(jnp.zeros(4), jnp.ones(4)), + ... reinterpreted_batch_ndims=1, + ... ) + ... td = TransformedDistribution( + ... base_distribution, + ... RationalQuadraticSplineMaskedCouplingInferenceFunnel( + ... n_keep=4, + ... decoder=decoder_fn(10 - 4), + ... conditioner=hk.Sequential([ + ... make_mlp((64, 64, 10 * (3 * num_params + 1))), + ... hk.Reshape((10, 3 * num_params + 1), preserve_dims=-1) + ... ]), + ... range_min=-2, range_max=2 + ... ) + ... ) + ... return td.log_prob(inputs) + >>> + >>> data = jr.normal(jr.PRNGKey(1), shape=(10, 10)) + >>> params = fn.init(jr.key(0), data) + >>> lps = fn.apply(params, data) - References: - .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood - with dimensionality reduction". Workshop on Bayesian Deep Learning, - Advances in Neural Information Processing Systems, 2021. - .. [2] Durkan, Conor, et al. "Neural Spline Flows". - Advances in Neural Information Processing Systems, 2019. - .. [3] Dinh, Laurent, et al. "Density estimation using RealNVP". - International Conference on Learning Representations, 2017. - """ + References: + .. [1] Klein, Samuel, et al. "Funnels: Exact maximum likelihood + with dimensionality reduction". Workshop on Bayesian Deep Learning, + Advances in Neural Information Processing Systems, 2021. + .. [2] Durkan, Conor, et al. "Neural Spline Flows". + Advances in Neural Information Processing Systems, 2019. + .. [3] Dinh, Laurent, et al. "Density estimation using RealNVP". + International Conference on Learning Representations, 2017. + """ - def __init__(self, n_keep, decoder, conditioner, range_min, range_max): - self.range_min = range_min - self.range_max = range_max + def __init__(self, n_keep, decoder, conditioner, range_min, range_max): + self.range_min = range_min + self.range_max = range_max - def _bijector_fn(params: Array): - return distrax.RationalQuadraticSpline( - params, self.range_min, self.range_max - ) + def _bijector_fn(params: Array): + return distrax.RationalQuadraticSpline( + params, self.range_min, self.range_max + ) - super().__init__(n_keep, decoder, conditioner, _bijector_fn) + super().__init__(n_keep, decoder, conditioner, _bijector_fn) diff --git a/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel_test.py b/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel_test.py index e4d1e10..3b7b770 100644 --- a/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel_test.py +++ b/surjectors/_src/surjectors/rq_masked_coupling_inference_funnel_test.py @@ -6,76 +6,76 @@ from jax import random from surjectors import ( - RationalQuadraticSplineMaskedCouplingInferenceFunnel, - TransformedDistribution, + RationalQuadraticSplineMaskedCouplingInferenceFunnel, + TransformedDistribution, ) from surjectors._src.conditioners.mlp import make_mlp def _conditional_fn(n_dim): - decoder_net = make_mlp([4, 4, n_dim * 2]) + decoder_net = make_mlp([4, 4, n_dim * 2]) - def _fn(z): - params = decoder_net(z) - mu, log_scale = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) + def _fn(z): + params = decoder_net(z) + mu, log_scale = jnp.split(params, 2, -1) + return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) - return _fn + return _fn def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def _get_funnel_surjector(n_latent, n_dimension): - return RationalQuadraticSplineMaskedCouplingInferenceFunnel( - n_latent, - _conditional_fn(n_dimension - n_latent), - hk.Sequential( - [ - hk.nets.MLP([4, 4], activate_final=True), - hk.Linear(n_dimension * 4), - hk.Reshape((n_dimension, 4)), - ] - ), - -5.0, - 5.0, - ) + return RationalQuadraticSplineMaskedCouplingInferenceFunnel( + n_latent, + _conditional_fn(n_dimension - n_latent), + hk.Sequential( + [ + hk.nets.MLP([4, 4], activate_final=True), + hk.Linear(n_dimension * 4), + hk.Reshape((n_dimension, 4)), + ] + ), + -5.0, + 5.0, + ) def make_surjector(n_dimension, n_latent): - def _transformation_fn(n_dimension): - funnel = _get_funnel_surjector(n_latent, n_dimension) - return funnel + def _transformation_fn(n_dimension): + funnel = _get_funnel_surjector(n_latent, n_dimension) + return funnel - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_latent), _transformation_fn(n_dimension) - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_latent), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_rq_masked_coupling_inference_funnel(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_rq_masked_coupling_inference_funnel(): - n_dimension, n_latent = 4, 2 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension, n_latent = 4, 2 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/surjectors/slice.py b/surjectors/_src/surjectors/slice.py index 6d3b024..13db807 100644 --- a/surjectors/_src/surjectors/slice.py +++ b/surjectors/_src/surjectors/slice.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable import haiku as hk from jax import numpy as jnp @@ -7,56 +7,72 @@ class Slice(Surjector): - """A slice funnel. - - Args: - n_keep: number if dimensions to keep - decoder: callable - - Examples: - >>> import distrax - >>> from surjectors import Slice - >>> from surjectors.nn import make_mlp - >>> - >>> def decoder_fn(n_dim): - >>> def _fn(z): - >>> params = make_mlp([4, 4, n_dim * 2])(z) - >>> mu, log_scale = jnp.split(params, 2, -1) - >>> return distrax.Independent( - >>> distrax.Normal(mu, jnp.exp(log_scale)) - >>> ) - >>> return _fn - >>> - >>> layer = Slice(10, decoder_fn(10)) - - References: - .. [1] Nielsen, Didrik, et al. "SurVAE Flows: Surjections to Bridge the - Gap between VAEs and Flows". Advances in Neural Information - Processing Systems, 2020. - """ - - def __init__(self, n_keep: int, decoder: Callable): - self.n_keep = n_keep - self.decoder = decoder - - def _split_input(self, array): - spl = jnp.split(array, [self.n_keep], axis=-1) - return spl - - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - z, y_minus = self._split_input(y) - z_condition = z - if x is not None: - z_condition = jnp.concatenate([z, x], axis=-1) - lc = self.decoder(z_condition).log_prob(y_minus) - return z, lc - - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - z_condition = z - if x is not None: - z_condition = jnp.concatenate([z, x], axis=-1) - y_minus, lc = self.decoder(z_condition).sample_and_log_prob( - seed=hk.next_rng_key() - ) - y = jnp.concatenate([z, y_minus], axis=-1) - return y, lc + """A slice funnel. + + Args: + n_keep: number of dimensions to keep + decoder: callable + + Examples: + >>> import haiku as hk + >>> from jax import random as jr + >>> from tensorflow_probability.substrates.jax import distributions as tfd + >>> from surjectors.nn import make_mlp + >>> from surjectors import Slice, TransformedDistribution + + >>> def decoder_fn(n_dim): + ... def fn(z): + ... params = make_mlp((64, 64, n_dim * 2))(z) + ... mu, log_scale = jnp.split(params, 2, -1) + ... return tfd.Independent( + ... tfd.Normal(mu, jnp.exp(log_scale)) + ... ) + ... return fn + >>> + >>> @hk.without_apply_rng + ... @hk.transform + ... def fn(inputs): + ... base_distribution = tfd.Independent( + ... tfd.Normal(jnp.zeros(4), jnp.ones(4)), + ... reinterpreted_batch_ndims=1, + ... ) + ... td = TransformedDistribution( + ... base_distribution, + ... Slice(4, decoder_fn(10 - 4)) + ... ) + ... return td.log_prob(inputs) + >>> data = jr.normal(jr.PRNGKey(1), shape=(10, 10)) + >>> params = fn.init(jr.key(0), data) + >>> lps = fn.apply(params, data) + + References: + .. [1] Nielsen, Didrik, et al. "SurVAE Flows: Surjections to Bridge the + Gap between VAEs and Flows". Advances in Neural Information + Processing Systems, 2020. + """ + + def __init__(self, n_keep: int, decoder: Callable): + self.n_keep = n_keep + self.decoder = decoder + + def _split_input(self, array): + spl = jnp.split(array, [self.n_keep], axis=-1) + return spl + + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + z, y_minus = self._split_input(y) + z_condition = z + if x is not None: + z_condition = jnp.concatenate([z, x], axis=-1) + lc = self.decoder(z_condition).log_prob(y_minus) + return z, lc + + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + z_condition = z + if x is not None: + z_condition = jnp.concatenate([z, x], axis=-1) + y_minus, lc = self.decoder(z_condition).sample_and_log_prob( + seed=hk.next_rng_key() + ) + y = jnp.concatenate([z, y_minus], axis=-1) + return y, lc diff --git a/surjectors/_src/surjectors/slice_test.py b/surjectors/_src/surjectors/slice_test.py index aa60b59..19daedd 100644 --- a/surjectors/_src/surjectors/slice_test.py +++ b/surjectors/_src/surjectors/slice_test.py @@ -10,51 +10,51 @@ def _decoder_fn(n_dim): - def _fn(z): - params = make_mlp([32, 32, n_dim * 2])(z) - means, log_scales = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(means, jnp.exp(log_scales))) + def _fn(z): + params = make_mlp([32, 32, n_dim * 2])(z) + means, log_scales = jnp.split(params, 2, -1) + return distrax.Independent(distrax.Normal(means, jnp.exp(log_scales))) - return _fn + return _fn def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def make_surjector(n_dimension, n_latent): - def _transformation_fn(): - slice = Slice(n_latent, _decoder_fn(n_dimension - n_latent)) - return slice + def _transformation_fn(): + slice = Slice(n_latent, _decoder_fn(n_dimension - n_latent)) + return slice - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_latent), _transformation_fn() - ) - return td(method, **kwargs) + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_latent), _transformation_fn() + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def test_slice(): - n_dimension, n_latent = 10, 3 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + n_dimension, n_latent = 10, 3 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y) - _ = flow.apply(params, None, method="log_prob", y=y) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y) + _ = flow.apply(params, None, method="log_prob", y=y) def test_conditional_slice(): - n_dimension, n_latent = 10, 3 - y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) - x = random.normal(random.PRNGKey(1), shape=(10, 2)) + n_dimension, n_latent = 10, 3 + y = random.normal(random.PRNGKey(1), shape=(10, n_dimension)) + x = random.normal(random.PRNGKey(1), shape=(10, 2)) - flow = make_surjector(n_dimension, n_latent) - params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) - _ = flow.apply(params, None, method="log_prob", y=y, x=x) + flow = make_surjector(n_dimension, n_latent) + params = flow.init(random.PRNGKey(0), method="log_prob", y=y, x=x) + _ = flow.apply(params, None, method="log_prob", y=y, x=x) diff --git a/surjectors/_src/surjectors/surjector.py b/surjectors/_src/surjectors/surjector.py index 9c8709e..4ed8406 100644 --- a/surjectors/_src/surjectors/surjector.py +++ b/surjectors/_src/surjectors/surjector.py @@ -7,93 +7,93 @@ # pylint: disable=too-many-arguments class Surjector(Transform, ABC): - """A surjective transformation.""" - - def __call__(self, method, **kwargs): - """Call the surjector. - - Depending on "method", computes - - inverse, - - forward, - - inverse_and_likelihood_contribution - - forward_and_likelihood_contribution - - Args: - method: either of the methods above - **kwargs: several keyword arguments that are dispatched to - whatever method is called. - - Returns: - returns whatever 'method' returns - """ - return getattr(self, method)(**kwargs) - - def inverse_and_likelihood_contribution( - self, y: Array, x: Array = None, **kwargs - ): - """Compute the inverse transformation and its likelihood contribution. - - Args: - y: event for which the inverse and likelihood contribution is - computed - x: event to condition on - kwargs: additional keyword arguments - - Returns: - tuple of two arrays of floats. The first one is the inverse - transformation, the second one its likelihood contribution - """ - return self._inverse_and_likelihood_contribution(y, x=x, **kwargs) - - @abstractmethod - def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): - pass - - def forward_and_likelihood_contribution( - self, z: Array, x: Array = None, **kwargs - ): - """Compute the forward transformation and its likelihood contribution. - - Args: - z: event for which the forward transform and likelihood contribution - is computed - x: event to condition on - kwargs: additional keyword arguments - - Returns: - tuple of two arrays of floats. The first one is the forward - transformation, the second one its likelihood contribution - """ - return self._forward_and_likelihood_contribution(z, x=x, **kwargs) - - @abstractmethod - def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): - pass - - def forward(self, z: Array, x: Array = None, **kwargs): - """Computes the forward transformation. - - Args: - z: event for which the forward transform is computed - x: event to condition on - kwargs: additional keyword arguments - - Returns: - result of the forward transformation - """ - y, _ = self.forward_and_likelihood_contribution(z, x=x, **kwargs) - return y - - def inverse(self, y: Array, x: Array = None, **kwargs): - """Compute the inverse transformation. - - Args: - y: event for which the inverse transform is computed - x: event to condition on - kwargs: additional keyword arguments - - Returns: - result of the inverse transformation - """ - z, _ = self.inverse_and_likelihood_contribution(y, x=x, **kwargs) - return z + """A surjective transformation.""" + + def __call__(self, method, **kwargs): + """Call the surjector. + + Depending on "method", computes + - inverse, + - forward, + - inverse_and_likelihood_contribution + - forward_and_likelihood_contribution + + Args: + method: either of the methods above + **kwargs: several keyword arguments that are dispatched to + whatever method is called. + + Returns: + returns whatever 'method' returns + """ + return getattr(self, method)(**kwargs) + + def inverse_and_likelihood_contribution( + self, y: Array, x: Array = None, **kwargs + ): + """Compute the inverse transformation and its likelihood contribution. + + Args: + y: event for which the inverse and likelihood contribution is + computed + x: event to condition on + kwargs: additional keyword arguments + + Returns: + tuple of two arrays of floats. The first one is the inverse + transformation, the second one its likelihood contribution + """ + return self._inverse_and_likelihood_contribution(y, x=x, **kwargs) + + @abstractmethod + def _inverse_and_likelihood_contribution(self, y, x=None, **kwargs): + pass + + def forward_and_likelihood_contribution( + self, z: Array, x: Array = None, **kwargs + ): + """Compute the forward transformation and its likelihood contribution. + + Args: + z: event for which the forward transform and likelihood contribution + is computed + x: event to condition on + kwargs: additional keyword arguments + + Returns: + tuple of two arrays of floats. The first one is the forward + transformation, the second one its likelihood contribution + """ + return self._forward_and_likelihood_contribution(z, x=x, **kwargs) + + @abstractmethod + def _forward_and_likelihood_contribution(self, z, x=None, **kwargs): + pass + + def forward(self, z: Array, x: Array = None, **kwargs): + """Computes the forward transformation. + + Args: + z: event for which the forward transform is computed + x: event to condition on + kwargs: additional keyword arguments + + Returns: + result of the forward transformation + """ + y, _ = self.forward_and_likelihood_contribution(z, x=x, **kwargs) + return y + + def inverse(self, y: Array, x: Array = None, **kwargs): + """Compute the inverse transformation. + + Args: + y: event for which the inverse transform is computed + x: event to condition on + kwargs: additional keyword arguments + + Returns: + result of the inverse transformation + """ + z, _ = self.inverse_and_likelihood_contribution(y, x=x, **kwargs) + return z diff --git a/surjectors/_src/surjectors/surjectors_test.py b/surjectors/_src/surjectors/surjectors_test.py index 716a924..35f1dfc 100644 --- a/surjectors/_src/surjectors/surjectors_test.py +++ b/surjectors/_src/surjectors/surjectors_test.py @@ -12,13 +12,13 @@ from surjectors._src.bijectors.masked_coupling import MaskedCoupling from surjectors._src.conditioners.mlp import make_mlp from surjectors._src.distributions.transformed_distribution import ( - TransformedDistribution, + TransformedDistribution, ) from surjectors._src.surjectors.affine_masked_coupling_generative_funnel import ( # noqa: E501 - AffineMaskedCouplingGenerativeFunnel, + AffineMaskedCouplingGenerativeFunnel, ) from surjectors._src.surjectors.affine_masked_coupling_inference_funnel import ( - AffineMaskedCouplingInferenceFunnel, + AffineMaskedCouplingInferenceFunnel, ) from surjectors._src.surjectors.augment import Augment from surjectors._src.surjectors.chain import Chain @@ -27,168 +27,168 @@ def simple_dataset(rng_key, batch_size, n_dimension, n_latent): - means_sample_key, rng_key = random.split(rng_key, 2) - pz_mean = distrax.Normal(0.0, 10.0).sample( - seed=means_sample_key, sample_shape=(n_latent) + means_sample_key, rng_key = random.split(rng_key, 2) + pz_mean = distrax.Normal(0.0, 10.0).sample( + seed=means_sample_key, sample_shape=(n_latent) + ) + pz = distrax.MultivariateNormalDiag( + loc=pz_mean, scale_diag=jnp.ones_like(pz_mean) + ) + p_loadings = distrax.Normal(0.0, 10.0) + make_noise = distrax.Normal(0.0, 1) + + loadings_sample_key, rng_key = random.split(rng_key, 2) + loadings = p_loadings.sample( + seed=loadings_sample_key, sample_shape=(n_dimension, len(pz_mean)) + ) + + def _fn(rng_key): + z_sample_key, noise_sample_key = random.split(rng_key, 2) + z = pz.sample(seed=z_sample_key, sample_shape=(batch_size,)) + noise = make_noise.sample( + seed=noise_sample_key, sample_shape=(batch_size, n_dimension) ) - pz = distrax.MultivariateNormalDiag( - loc=pz_mean, scale_diag=jnp.ones_like(pz_mean) - ) - p_loadings = distrax.Normal(0.0, 10.0) - make_noise = distrax.Normal(0.0, 1) - - loadings_sample_key, rng_key = random.split(rng_key, 2) - loadings = p_loadings.sample( - seed=loadings_sample_key, sample_shape=(n_dimension, len(pz_mean)) - ) - - def _fn(rng_key): - z_sample_key, noise_sample_key = random.split(rng_key, 2) - z = pz.sample(seed=z_sample_key, sample_shape=(batch_size,)) - noise = make_noise.sample( - seed=noise_sample_key, sample_shape=(batch_size, n_dimension) - ) - y = (loadings @ z.T).T + noise - return {"y": y, "x": noise} + y = (loadings @ z.T).T + noise + return {"y": y, "x": noise} - return _fn + return _fn def _conditional_fn(n_dim): - decoder_net = make_mlp([32, 32, n_dim * 2]) + decoder_net = make_mlp([32, 32, n_dim * 2]) - def _fn(z): - params = decoder_net(z) - mu, log_scale = jnp.split(params, 2, -1) - return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) + def _fn(z): + params = decoder_net(z) + mu, log_scale = jnp.split(params, 2, -1) + return distrax.Independent(distrax.Normal(mu, jnp.exp(log_scale))) - return _fn + return _fn def _bijector_fn(params): - means, log_scales = jnp.split(params, 2, -1) - return distrax.ScalarAffine(means, jnp.exp(log_scales)) + means, log_scales = jnp.split(params, 2, -1) + return distrax.ScalarAffine(means, jnp.exp(log_scales)) def _base_distribution_fn(n_latent): - base_distribution = distrax.Independent( - distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), - reinterpreted_batch_ndims=1, - ) - return base_distribution + base_distribution = distrax.Independent( + distrax.Normal(jnp.zeros(n_latent), jnp.ones(n_latent)), + reinterpreted_batch_ndims=1, + ) + return base_distribution def _get_augment_surjector(n_latent, n_dimension): - return Augment(n_dimension, _conditional_fn(n_latent - n_dimension)) + return Augment(n_dimension, _conditional_fn(n_latent - n_dimension)) def _get_generative_funnel_surjector(n_latent, n_dimension): - return AffineMaskedCouplingGenerativeFunnel( - n_dimension, - _conditional_fn(n_latent - n_dimension), - make_mlp([32, 32, n_latent * 2]), - ) + return AffineMaskedCouplingGenerativeFunnel( + n_dimension, + _conditional_fn(n_latent - n_dimension), + make_mlp([32, 32, n_latent * 2]), + ) def make_surjector(n_dimension, n_latent, surjector_fn): - def _transformation_fn(n_dimension): - layers = [] - for i in range(5): - if i != 3: - mask = make_alternating_binary_mask(n_dimension, i % 2 == 0) - layer = MaskedCoupling( - mask=mask, - bijector_fn=_bijector_fn, - conditioner=make_mlp([32, 32, n_dimension * 2]), - ) - else: - layer = surjector_fn(n_latent, n_dimension) - n_dimension = n_latent - layers.append(layer) - return Chain(layers) - - def _flow(method, **kwargs): - td = TransformedDistribution( - _base_distribution_fn(n_latent), _transformation_fn(n_dimension) + def _transformation_fn(n_dimension): + layers = [] + for i in range(5): + if i != 3: + mask = make_alternating_binary_mask(n_dimension, i % 2 == 0) + layer = MaskedCoupling( + mask=mask, + bijector_fn=_bijector_fn, + conditioner=make_mlp([32, 32, n_dimension * 2]), ) - return td(method, **kwargs) + else: + layer = surjector_fn(n_latent, n_dimension) + n_dimension = n_latent + layers.append(layer) + return Chain(layers) + + def _flow(method, **kwargs): + td = TransformedDistribution( + _base_distribution_fn(n_latent), _transformation_fn(n_dimension) + ) + return td(method, **kwargs) - td = hk.transform(_flow) - return td + td = hk.transform(_flow) + return td def _get_slice_surjector(n_latent, n_dimension): - return Slice(n_latent, _conditional_fn(n_dimension - n_latent)) + return Slice(n_latent, _conditional_fn(n_dimension - n_latent)) def _get_inference_funnel_surjector(n_latent, n_dimension): - return AffineMaskedCouplingInferenceFunnel( - n_latent, - _conditional_fn(n_dimension - n_latent), - make_mlp([32, 32, n_dimension * 2]), - ) + return AffineMaskedCouplingInferenceFunnel( + n_latent, + _conditional_fn(n_dimension - n_latent), + make_mlp([32, 32, n_dimension * 2]), + ) @pytest.fixture( - params=[ - (_get_generative_funnel_surjector, 5, 10), - (_get_augment_surjector, 5, 10), - ], - ids=["funnel", "augment"], + params=[ + (_get_generative_funnel_surjector, 5, 10), + (_get_augment_surjector, 5, 10), + ], + ids=["funnel", "augment"], ) def generative_surjection(request): - yield request.param + yield request.param @pytest.fixture( - params=[ - (_get_inference_funnel_surjector, 10, 5), - (_get_slice_surjector, 10, 5), - ], - ids=["funnel", "slice"], + params=[ + (_get_inference_funnel_surjector, 10, 5), + (_get_slice_surjector, 10, 5), + ], + ids=["funnel", "slice"], ) def inference_surjection(request): - yield request.param + yield request.param def train(rng_seq, model, sampler, n_iter=5): - @jax.jit - def step(rng, params, state, **batch): - def loss_fn(params): - lp = model.apply(params, rng, method="log_prob", **batch) - return -jnp.sum(lp) + @jax.jit + def step(rng, params, state, **batch): + def loss_fn(params): + lp = model.apply(params, rng, method="log_prob", **batch) + return -jnp.sum(lp) - loss, grads = jax.value_and_grad(loss_fn)(params) - updates, new_state = adam.update(grads, state, params) - new_params = optax.apply_updates(params, updates) - return loss, new_params, new_state + loss, grads = jax.value_and_grad(loss_fn)(params) + updates, new_state = adam.update(grads, state, params) + new_params = optax.apply_updates(params, updates) + return loss, new_params, new_state - init_data = sampler(next(rng_seq)) - params = model.init(next(rng_seq), method="log_prob", **init_data) + init_data = sampler(next(rng_seq)) + params = model.init(next(rng_seq), method="log_prob", **init_data) - adam = optax.adamw(0.001) - state = adam.init(params) + adam = optax.adamw(0.001) + state = adam.init(params) - losses = [0] * n_iter - for i in range(n_iter): - batch = sampler(next(rng_seq)) - loss, params, state = step(next(rng_seq), params, state, **batch) - losses[i] = loss + losses = [0] * n_iter + for i in range(n_iter): + batch = sampler(next(rng_seq)) + loss, params, state = step(next(rng_seq), params, state, **batch) + losses[i] = loss - return params + return params def _surjection(surjector_fn, n_data, n_latent): - rng_seq = hk.PRNGSequence(0) - sampling_fn = simple_dataset(next(rng_seq), 64, n_data, n_latent) - model = make_surjector(n_data, n_latent, surjector_fn) - train(rng_seq, model, sampling_fn) + rng_seq = hk.PRNGSequence(0) + sampling_fn = simple_dataset(next(rng_seq), 64, n_data, n_latent) + model = make_surjector(n_data, n_latent, surjector_fn) + train(rng_seq, model, sampling_fn) def test_generative_surjection(generative_surjection): - _surjection(*generative_surjection) + _surjection(*generative_surjection) def test_inference_surjection(inference_surjection): - _surjection(*inference_surjection) + _surjection(*inference_surjection) diff --git a/surjectors/util.py b/surjectors/util.py index 828ffcf..d4469c7 100644 --- a/surjectors/util.py +++ b/surjectors/util.py @@ -14,107 +14,107 @@ # pylint: disable=too-few-public-methods class _DataLoader: - """Dataloader class.""" + """Dataloader class.""" - def __init__(self, num_batches, idxs, get_batch): - self.num_batches = num_batches - self.idxs = idxs - self.get_batch = get_batch + def __init__(self, num_batches, idxs, get_batch): + self.num_batches = num_batches + self.idxs = idxs + self.get_batch = get_batch - def __call__(self, idx, idxs=None): - if idxs is None: - idxs = self.idxs - return self.get_batch(idx, idxs) + def __call__(self, idx, idxs=None): + if idxs is None: + idxs = self.idxs + return self.get_batch(idx, idxs) def make_alternating_binary_mask(n_dim: int, even_idx_as_true: bool = False): - """Create a binary masked array. + """Create a binary masked array. - Args: - n_dim: length of the masked array to be created - even_idx_as_true: a boolean indicating which indices are set to zero. - If even_idx_as_true=True sets all even indices [0, 2, 4, ...] - to True + Args: + n_dim: length of the masked array to be created + even_idx_as_true: a boolean indicating which indices are set to zero. + If even_idx_as_true=True sets all even indices [0, 2, 4, ...] + to True - Returns: - boolean masked array where every even or uneven index is True - """ - mask = jnp.arange(0, np.prod(n_dim)) % 2 - mask = jnp.reshape(mask, n_dim) - mask = mask.astype(bool) - if even_idx_as_true: - mask = jnp.logical_not(mask) - return mask + Returns: + boolean masked array where every even or uneven index is True + """ + mask = jnp.arange(0, np.prod(n_dim)) % 2 + mask = jnp.reshape(mask, n_dim) + mask = mask.astype(bool) + if even_idx_as_true: + mask = jnp.logical_not(mask) + return mask def as_batch_iterator( - rng_key: jr.PRNGKey, data: named_dataset, batch_size: int, shuffle=True -): - """Create a batch iterator for a data set. - - Args: - rng_key: a JAX random key - data: a data set for which an iterator is created. The data set needs - to be a NamedTuple with at least one element being called `y`. If a - conditional flow is to be trained, the second element has to be - called `x`. - batch_size: size of each batch of data that is returned by the iterator - shuffle: if true shuffles the data before creating batches - - Examples: - >>> from collections import namedtuple - >>> from jax import numpy as jnp, random as jr - >>> - >>> y = jr.normal(jr.PRNGKey(0), (1000, 2)) - >>> as_batch_iterator(jr.PRNGKey(1), namedtuple("data", "y")(y), 100) - >>> - >>> x = jr.normal(jr.PRNGKey(1), (1000, 2)) - >>> as_batch_iterator( - >>> jr.PRNGKey(1), namedtuple("data", "y x")(y, x), 100 - >>> ) - - Returns: - a data loader object - """ - n = data.y.shape[0] - if n < batch_size: - num_batches = 1 - batch_size = n - elif n % batch_size == 0: - num_batches = int(n // batch_size) - else: - num_batches = int(n // batch_size) + 1 - - idxs = jnp.arange(n) - if shuffle: - idxs = jr.permutation(rng_key, idxs) - - def get_batch(idx, idxs=idxs): - start_idx = idx * batch_size - step_size = jnp.minimum(n - start_idx, batch_size) - ret_idx = lax.dynamic_slice_in_dim(idxs, idx * batch_size, step_size) - batch = { - name: lax.index_take(array, (ret_idx,), axes=(0,)) - for name, array in zip(data._fields, data) - } - return batch - - return _DataLoader(num_batches, idxs, get_batch) + rng_key: jr.PRNGKey, data: named_dataset, batch_size: int, shuffle=True +) -> _DataLoader: + """Create a batch iterator for a data set. + + Args: + rng_key: a JAX random key + data: a data set for which an iterator is created. The data set needs + to be a NamedTuple with at least one element being called `y`. If a + conditional flow is to be trained, the second element has to be + called `x`. + batch_size: size of each batch of data that is returned by the iterator + shuffle: if true shuffles the data before creating batches + + Examples: + >>> from collections import namedtuple + >>> from jax import numpy as jnp, random as jr + >>> + >>> y = jr.normal(jr.PRNGKey(0), (1000, 2)) + >>> loader = as_batch_iterator( + ... jr.PRNGKey(1), namedtuple("data", "y")(y), 100 + ... ) + >>> x = jr.normal(jr.PRNGKey(1), (1000, 2)) + >>> loader = as_batch_iterator( + ... jr.PRNGKey(1), namedtuple("data", "y x")(y, x), 100 + ... ) + + Returns: + a data loader object + """ + n = data.y.shape[0] + if n < batch_size: + num_batches = 1 + batch_size = n + elif n % batch_size == 0: + num_batches = int(n // batch_size) + else: + num_batches = int(n // batch_size) + 1 + + idxs = jnp.arange(n) + if shuffle: + idxs = jr.permutation(rng_key, idxs) + + def get_batch(idx, idxs=idxs): + start_idx = idx * batch_size + step_size = jnp.minimum(n - start_idx, batch_size) + ret_idx = lax.dynamic_slice_in_dim(idxs, idx * batch_size, step_size) + batch = { + name: lax.index_take(array, (ret_idx,), axes=(0,)) + for name, array in zip(data._fields, data) + } + return batch + + return _DataLoader(num_batches, idxs, get_batch) def unstack(x, axis=0): - """Unstack a tensor. + """Unstack a tensor. - Unstack a tensor as tf.unstack does + Unstack a tensor as tf.unstack does - Args: - x: array to unstack - axis: the axis as integer index + Args: + x: array to unstack + axis: the axis as integer index - Returns: - unstacked array - """ - return [ - lax.index_in_dim(x, i, axis, keepdims=False) - for i in range(x.shape[axis]) - ] + Returns: + unstacked array + """ + return [ + lax.index_in_dim(x, i, axis, keepdims=False) for i in range(x.shape[axis]) + ] diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..b0db1fa --- /dev/null +++ b/uv.lock @@ -0,0 +1,3065 @@ +version = 1 +revision = 3 +requires-python 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