Reduce the peak memory usage of skillmodels (#76) and refactor the li…

…kelihood functions modules (#77)

* Add a version of mem consumption test that fails if there are increases in the repo.
* Decorate kalman_update, _calculate_sigma_points,  with jax.checkpoint, can use `prevent_cse=False` due to being inside lax.scan, as per https://jax.readthedocs.io/en/latest/_autosummary/jax.checkpoint.html\#jax.checkpoint.
* Use checkpoint() on kalman_predict as well, requires changing `transition_func` to be its first argument and using `jnp.array` in tests.
* Provision for testing on GPUs, timing information is not useful yet.

.github/workflows/main.yml

@@ -28,14 +28,14 @@ jobs:
       - uses: actions/checkout@v4
       - uses: prefix-dev/setup-pixi@v0.8.0
         with:
-          pixi-version: v0.28.2
+          pixi-version: v0.29.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
-          environments: test
+          environments: test-cpu
           activate-environment: true
       - name: Run pytest
         shell: bash -l {0}
-        run: pixi run -e test tests-with-cov
+        run: pixi run -e test-cpu tests-with-cov
       - name: Upload coverage report
         if: runner.os == 'Linux' && matrix.python-version == '3.12'
         uses: codecov/codecov-action@v4

README.md

@@ -73,10 +73,10 @@ entry for a suggested citation. The suggested citation will be updated once the
 becomes part of a published paper.
 
 ```
-@Unpublished{Gabler2018,
+@Unpublished{Gabler2024,
   Title                    = {A Python Library to Estimate Nonlinear Dynamic Latent Factor Models},
   Author                   = {Janos Gabler},
-  Year                     = {2018},
+  Year                     = {2024},
   Url                      = {https://github.com/OpenSourceEconomics/skillmodels}
 }
 ```

docs/source/getting_started/tutorial.ipynb

@@ -20,7 +20,7 @@
     "import yaml\n",
     "\n",
     "from skillmodels.config import TEST_DIR\n",
-    "from skillmodels.likelihood_function import get_maximization_inputs"
+    "from skillmodels.maximization_inputs import get_maximization_inputs"
    ]
   },
   {

docs/source/how_to_guides/how_to_visualize_pairwise_factor_distribution.ipynb

@@ -22,7 +22,7 @@
     "import yaml\n",
     "\n",
     "from skillmodels.config import TEST_DIR\n",
-    "from skillmodels.likelihood_function import get_maximization_inputs\n",
+    "from skillmodels.maximization_inputs import get_maximization_inputs\n",
     "from skillmodels.simulate_data import simulate_dataset\n",
     "from skillmodels.visualize_factor_distributions import (\n",
     "    bivariate_density_contours,\n",

pyproject.toml

@@ -106,15 +106,33 @@ scipy = "<=1.13"
 # Development Dependencies (pypi)
 # --------------------------------------------------------------------------------------
 
-[tool.pixi.pypi-dependencies]
-jax = { version = ">=0.4.20", extras = ["cpu"] }
+[tool.pixi.target.unix.dependencies]
+jax = ">=0.4.20"
 jaxlib = ">=0.4.20"
+
+# Development Dependencies (pypi)
+# --------------------------------------------------------------------------------------
+
+[tool.pixi.pypi-dependencies]
 pdbp = "*"
 skillmodels = {path = ".", editable = true}
 
+[tool.pixi.target.win-64.pypi-dependencies]
+jax = { version = ">=0.4.20", extras = ["cpu"] }
+jaxlib = ">=0.4.20"
+
 # Features and Tasks
 # --------------------------------------------------------------------------------------
 
+[tool.pixi.feature.cuda]
+platforms = ["linux-64"]
+system-requirements = {cuda = "12"}
+
+[tool.pixi.feature.cuda.target.linux-64.dependencies]
+cuda-nvcc = ">=12"
+jax = ">=0.4.20"
+jaxlib = { version = ">=0.4.20", build = "cuda12*" }
+
 [tool.pixi.feature.test.dependencies]
 pytest = "*"
 pytest-cov = "*"
@@ -128,6 +146,10 @@ pytest-memray = "*"
 tests = "pytest tests"
 tests-with-cov = "pytest tests --cov-report=xml --cov=./"
 mem = "pytest -x -s --pdb --memray --fail-on-increase tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs"
+mem-on-clean-repo = "git status --porcelain && git diff-index --quiet HEAD -- && git rev-parse HEAD && pytest -x -s --pdb --memray --fail-on-increase tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs"
+
+[tool.pixi.feature.cuda.tasks]
+mem-cuda = "pytest -x -s --pdb --memray --fail-on-increase tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs"
 
 [tool.pixi.feature.mypy.dependencies]
 mypy = "*"
@@ -141,8 +163,10 @@ mypy = "mypy src"
 # --------------------------------------------------------------------------------------
 
 [tool.pixi.environments]
+cuda = {features = ["cuda"], solve-group = "cuda"}
 mypy = {features = ["mypy"], solve-group = "default"}
-test = {features = ["test"], solve-group = "default"}
+test-cpu = {features = ["test"], solve-group = "default"}
+test-gpu = {features = ["test", "cuda"], solve-group = "cuda"}
 
 
 # ======================================================================================

src/skillmodels/__init__.py

@@ -6,7 +6,7 @@
     contextlib.suppress(Exception)
 
 from skillmodels.filtered_states import get_filtered_states
-from skillmodels.likelihood_function import get_maximization_inputs
+from skillmodels.maximization_inputs import get_maximization_inputs
 from skillmodels.simulate_data import simulate_dataset
 
 __all__ = ["get_maximization_inputs", "simulate_dataset", "get_filtered_states"]

src/skillmodels/filtered_states.py

@@ -1,7 +1,7 @@
 import jax.numpy as jnp
 import numpy as np
 
-from skillmodels.likelihood_function import get_maximization_inputs
+from skillmodels.maximization_inputs import get_maximization_inputs
 from skillmodels.params_index import get_params_index
 from skillmodels.parse_params import create_parsing_info, parse_params
 from skillmodels.process_debug_data import create_state_ranges

src/skillmodels/kalman_filters.py

@@ -1,3 +1,5 @@
+import functools
+
 import jax
 import jax.numpy as jnp
 
@@ -9,6 +11,7 @@
 # ======================================================================================
 
 
+@functools.partial(jax.checkpoint, prevent_cse=False)
 def kalman_update(
     states,
     upper_chols,
@@ -152,12 +155,13 @@ def calculate_sigma_scaling_factor_and_weights(n_states, kappa=2):
     return scaling_factor, weights
 
 
+@functools.partial(jax.checkpoint, static_argnums=0, prevent_cse=False)
 def kalman_predict(
+    transition_func,
     states,
     upper_chols,
     sigma_scaling_factor,
     sigma_weights,
-    transition_info,
     trans_coeffs,
     shock_sds,
     anchoring_scaling_factors,
@@ -167,6 +171,7 @@ def kalman_predict(
     """Make a unscented Kalman predict.
 
     Args:
+        transition_func (Callable): The transition function.
         states (jax.numpy.array): Array of shape (n_obs, n_mixtures, n_states) with
             pre-update states estimates.
         upper_chols (jax.numpy.array): Array of shape (n_obs, n_mixtures, n_states,
@@ -177,9 +182,6 @@ def kalman_predict(
             the sigma_point algorithm chosen.
         sigma_weights (jax.numpy.array): 1d array of length n_sigma with non-negative
             sigma weights.
-        transition_info (dict): Dict with the entries "func" (the actual transition
-            function) and "columns" (a dictionary mapping factors that are needed
-            as individual columns to positions in the factor array).
         trans_coeffs (tuple): Tuple of 1d jax.numpy.arrays with transition parameters.
         anchoring_scaling_factors (jax.numpy.array): Array of shape (2, n_fac) with
             the scaling factors for anchoring. The first row corresponds to the input
@@ -203,7 +205,7 @@ def kalman_predict(
     )
     transformed = transform_sigma_points(
         sigma_points,
-        transition_info,
+        transition_func,
         trans_coeffs,
         anchoring_scaling_factors,
         anchoring_constants,
@@ -225,6 +227,7 @@ def kalman_predict(
     return predicted_states, predicted_covs
 
 
+@functools.partial(jax.checkpoint, prevent_cse=False)
 def _calculate_sigma_points(states, upper_chols, scaling_factor, observed_factors):
     """Calculate the array of sigma_points for the unscented transform.
 
@@ -272,7 +275,7 @@ def _calculate_sigma_points(states, upper_chols, scaling_factor, observed_factor
 
 def transform_sigma_points(
     sigma_points,
-    transition_info,
+    transition_func,
     trans_coeffs,
     anchoring_scaling_factors,
     anchoring_constants,
@@ -281,9 +284,7 @@ def transform_sigma_points(
 
     Args:
         sigma_points (jax.numpy.array) of shape n_obs, n_mixtures, n_sigma, n_fac.
-        transition_info (dict): Dict with the entries "func" (the actual transition
-            function) and "columns" (a dictionary mapping factors that are needed
-            as individual columns to positions in the factor array).
+        transition_func (Callable): The transition function.
         trans_coeffs (tuple): Tuple of 1d jax.numpy.arrays with transition parameters.
         anchoring_scaling_factors (jax.numpy.array): Array of shape (2, n_states) with
             the scaling factors for anchoring. The first row corresponds to the input
@@ -303,9 +304,7 @@ def transform_sigma_points(
 
     anchored = flat_sigma_points * anchoring_scaling_factors[0] + anchoring_constants[0]
 
-    transition_function = transition_info["func"]
-
-    transformed_anchored = transition_function(trans_coeffs, anchored)
+    transformed_anchored = transition_func(trans_coeffs, anchored)
 
     n_observed = transformed_anchored.shape[-1]