From 23f6efb48af7d4d9e59642e6ff5472d8effb79b3 Mon Sep 17 00:00:00 2001 From: ealt Date: Wed, 12 Nov 2025 01:42:58 +0000 Subject: [PATCH 01/37] pylint (#98) --- .github/workflows/simplexity.yaml | 3 + examples/configs/demo_config.py | 11 + examples/managed_run_demo.py | 11 + pyproject.toml | 17 + simplexity/__init__.py | 9 + simplexity/data_structures/collection.py | 13 +- simplexity/data_structures/heap.py | 29 +- simplexity/data_structures/queue.py | 2 + simplexity/data_structures/stack.py | 2 + simplexity/exceptions.py | 9 +- simplexity/generative_processes/builder.py | 656 +- .../factored_beliefs/topology/chain.py | 141 + .../factored_beliefs/topology/symmetric.py | 196 + .../factored_beliefs/topology/topology.py | 80 + .../topology/transition_coupled.py | 233 + .../factored_generative_process.py | 240 + .../generalized_hidden_markov_model.py | 15 +- .../generative_process.py | 10 +- simplexity/generative_processes/generator.py | 11 + .../hidden_markov_model.py | 15 +- .../mixed_state_presentation.py | 19 +- .../generative_processes/torch_generator.py | 11 + .../transition_matrices.py | 31 +- simplexity/logging/file_logger.py | 14 +- simplexity/logging/logger.py | 11 +- simplexity/logging/mlflow_logger.py | 22 +- simplexity/logging/print_logger.py | 11 + .../persistence/local_equinox_persister.py | 2 + .../persistence/local_penzai_persister.py | 6 +- simplexity/persistence/local_persister.py | 7 +- .../persistence/local_pytorch_persister.py | 8 +- simplexity/persistence/mlflow_persister.py | 36 +- simplexity/persistence/model_persister.py | 6 +- simplexity/persistence/s3_persister.py | 18 +- simplexity/predictive_models/types.py | 14 +- simplexity/run_management/components.py | 11 + simplexity/run_management/run_logging.py | 13 +- simplexity/run_management/run_management.py | 9 + .../run_management/structured_configs.py | 1 - simplexity/utils/config_utils.py | 2 + simplexity/utils/equinox_utils.py | 2 + simplexity/utils/factoring_utils.py | 173 + simplexity/utils/{git.py => git_utils.py} | 73 +- simplexity/utils/{jnp.py => jnp_utils.py} | 2 + .../utils/{penzai.py => penzai_utils.py} | 4 + simplexity/utils/pip_utils.py | 6 +- simplexity/utils/pytorch_utils.py | 10 +- simplexity/utils/subprocess_utils.py | 25 + tests/assertions.py | 4 +- tests/data_structures/test_queue.py | 9 +- tests/data_structures/test_stack.py | 9 +- ...p5_pR2_0p5.npz => rrxor_p1_0p5_p2_0p5.npz} | Bin tests/generative_processes/test_builder.py | 11 + .../test_generalized_hidden_markov_model.py | 26 + tests/generative_processes/test_generator.py | 5 + .../test_hidden_markov_model.py | 21 + .../test_mixed_state_presentation.py | 13 +- .../test_mixed_state_tree.py | 12 +- .../test_torch_generator.py | 5 + .../test_transition_matrices.py | 45 +- tests/logging/test_artifact_logging.py | 9 + tests/logging/test_file_logger.py | 11 + tests/logging/test_plot_logging.py | 9 + tests/persistence/s3_mocks.py | 10 +- .../test_local_equinox_persister.py | 4 + .../test_local_penzai_persister.py | 3 + .../test_local_pytorch_persister.py | 19 +- tests/persistence/test_mlflow_persister.py | 19 +- tests/persistence/test_s3_persister.py | 49 +- tests/predictive_models/test_types.py | 27 +- tests/run_management/test_base_config.py | 19 +- .../test_generative_process_config.py | 33 +- tests/run_management/test_instance_config.py | 13 +- tests/run_management/test_logging_config.py | 31 +- tests/run_management/test_mlflow_config.py | 21 +- tests/run_management/test_optimizer_config.py | 35 +- .../run_management/test_persistence_config.py | 29 +- .../test_predictive_model_config.py | 63 +- tests/utils/test_config_utils.py | 2 + tests/utils/test_equinox_utils.py | 2 + .../utils/{test_jnp.py => test_jnp_utils.py} | 14 +- tests/utils/test_mlflow_utils.py | 1 + tests/utils/test_penzai.py | 13 +- tests/utils/test_pip_utils.py | 26 +- tests/utils/test_pytorch_utils.py | 2 + uv.lock | 5427 +++++++---------- 86 files changed, 4682 insertions(+), 3619 deletions(-) create mode 100644 simplexity/generative_processes/factored_beliefs/topology/chain.py create mode 100644 simplexity/generative_processes/factored_beliefs/topology/symmetric.py create mode 100644 simplexity/generative_processes/factored_beliefs/topology/topology.py create mode 100644 simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py create mode 100644 simplexity/generative_processes/factored_generative_process.py create mode 100644 simplexity/utils/factoring_utils.py rename simplexity/utils/{git.py => git_utils.py} (58%) rename simplexity/utils/{jnp.py => jnp_utils.py} (99%) rename simplexity/utils/{penzai.py => penzai_utils.py} (99%) create mode 100644 simplexity/utils/subprocess_utils.py rename tests/generative_processes/goldens/mixed_state_trees/{rrxor_pR1_0p5_pR2_0p5.npz => rrxor_p1_0p5_p2_0p5.npz} (100%) rename tests/utils/{test_jnp.py => test_jnp_utils.py} (72%) diff --git a/.github/workflows/simplexity.yaml b/.github/workflows/simplexity.yaml index 7b4ac7af..271308f7 100644 --- a/.github/workflows/simplexity.yaml +++ b/.github/workflows/simplexity.yaml @@ -19,6 +19,9 @@ jobs: - name: ruff format run: uv run --extra dev ruff format --check + - name: pylint + run: uv run --extra dev --extra penzai pylint simplexity tests + - name: pyright run: uv run --extra aws --extra dev --extra penzai pyright diff --git a/examples/configs/demo_config.py b/examples/configs/demo_config.py index 37ab373a..47202ee9 100644 --- a/examples/configs/demo_config.py +++ b/examples/configs/demo_config.py @@ -1,3 +1,14 @@ +"""Configuration for the managed run demo.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from dataclasses import dataclass from simplexity.run_management.structured_configs import ( diff --git a/examples/managed_run_demo.py b/examples/managed_run_demo.py index 57ef6092..db5b820f 100644 --- a/examples/managed_run_demo.py +++ b/examples/managed_run_demo.py @@ -1,3 +1,14 @@ +"""Managed run demo.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import logging import logging.config import time diff --git a/pyproject.toml b/pyproject.toml index 300fa91b..b43c88f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,6 +45,8 @@ cuda = [ dev = [ "diff-cover", "jaxtyping", + "pylint", + "pylint-per-file-ignores", "pyright", "pytest", "pytest-cov", @@ -87,6 +89,21 @@ convention = "google" "test_*.py" = ["D"] "*.ipynb" = ["D"] +[tool.pylint.MASTER] +# Add project root to Python path so pylint can resolve simplexity imports +init-hook = 'import sys; import os; sys.path.insert(0, os.path.abspath("."))' +load-plugins = ["pylint_per_file_ignores"] + +[tool.pylint."MESSAGES CONTROL"] +# Disable line-too-long check since ruff handles line length (120 chars) +disable = ["line-too-long", "duplicate-code"] +per-file-ignores = ["tests/**:redefined-outer-name"] + +[tool.pylint.IMPORTS] +# Ignore simplexity module to prevent astroid crashes during import resolution +# This is a known bug: https://github.com/pylint-dev/pylint/issues/10185 +ignored-modules = ["simplexity"] + [tool.pyright] typeCheckingMode = "standard" diff --git a/simplexity/__init__.py b/simplexity/__init__.py index df9aac74..ad758e54 100644 --- a/simplexity/__init__.py +++ b/simplexity/__init__.py @@ -1,5 +1,14 @@ """Simplexity package for machine learning experiments.""" +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from .run_management.components import Components from .run_management.run_management import managed_run diff --git a/simplexity/data_structures/collection.py b/simplexity/data_structures/collection.py index 00fed526..dc33da20 100644 --- a/simplexity/data_structures/collection.py +++ b/simplexity/data_structures/collection.py @@ -1,5 +1,7 @@ +"""Collection data structure.""" + from abc import abstractmethod -from typing import Generic, TypeVar +from typing import TypeVar import equinox as eqx import jax @@ -7,7 +9,7 @@ Element = TypeVar("Element") -class Collection(eqx.Module, Generic[Element]): +class Collection[Element](eqx.Module): """Generic collection for any PyTree structure.""" def __init__(self, *args, **kwargs): @@ -17,36 +19,29 @@ def __init__(self, *args, **kwargs): @abstractmethod def size(self) -> jax.Array: """The number of elements in the collection.""" - ... @property @abstractmethod def is_empty(self) -> jax.Array: """Whether the collection is empty.""" - ... @property @abstractmethod def is_full(self) -> jax.Array: """Whether the collection is full.""" - ... @abstractmethod def add(self, element: Element) -> "Collection[Element]": """Add an element to the collection.""" - ... @abstractmethod def remove(self) -> tuple["Collection[Element]", Element]: """Remove an element from the collection.""" - ... @abstractmethod def peek(self) -> Element: """Look at the next element without removing it.""" - ... @abstractmethod def clear(self) -> "Collection[Element]": """Clear the collection.""" - ... diff --git a/simplexity/data_structures/heap.py b/simplexity/data_structures/heap.py index c733d0d2..079aa677 100644 --- a/simplexity/data_structures/heap.py +++ b/simplexity/data_structures/heap.py @@ -1,5 +1,7 @@ +"""Heap data structure.""" + from collections.abc import Callable -from typing import cast +from typing import Protocol, cast import equinox as eqx import jax @@ -9,15 +11,26 @@ from simplexity.data_structures.stack import Stack +class ArrayLike(Protocol): # pylint: disable=too-few-public-methods + """A protocol for array-like objects.""" + + shape: tuple[int, ...] + dtype: jnp.dtype + + class Heap(Stack[Element]): """Generic heap for any PyTree structure.""" compare: Callable[[Element, Element], jax.Array] - def __init__(self, max_size: int, default_element: Element, compare: Callable[[Element, Element], jax.Array]): + def __init__(self, max_size: int, default_element: Element, compare: Callable[[Element, Element], jax.Array]): # pylint: disable=super-init-not-called """Initialize empty queue/stack.""" self.default_element = default_element - self.data = jax.tree.map(lambda x: jnp.zeros((max_size,) + x.shape, dtype=x.dtype), default_element) + + def init_data(element: ArrayLike) -> jax.Array: + return jnp.zeros((max_size,) + element.shape, dtype=element.dtype) + + self.data = jax.tree.map(init_data, default_element) self._size = jnp.array(0, dtype=jnp.int32) self.max_size = jnp.array(max_size, dtype=jnp.int32) self.compare = compare @@ -27,7 +40,7 @@ def push(self, element: Element) -> "Heap[Element]": """Push a new element onto the stack.""" heap = super().push(element) heap = cast(Heap[Element], heap) - heap = heap._bubble_up(heap.size - 1) + heap = heap._bubble_up(heap.size - 1) # pylint: disable=protected-access return heap @eqx.filter_jit @@ -36,7 +49,7 @@ def pop(self) -> tuple["Heap[Element]", Element]: heap = self._swap(jnp.array(0, dtype=jnp.int32), self.size - 1) heap, element = super(Heap, heap).pop() heap = cast(Heap[Element], heap) - heap = heap._bubble_down(jnp.array(0, dtype=jnp.int32)) + heap = heap._bubble_down(jnp.array(0, dtype=jnp.int32)) # pylint: disable=protected-access return heap, element @eqx.filter_jit @@ -92,7 +105,7 @@ def do_nothing(heap: "Heap[Element]") -> "Heap[Element]": def do_bubble_up(heap: "Heap[Element]") -> "Heap[Element]": heap = self._swap(child_idx, parent_idx) - return heap._bubble_up(parent_idx) + return heap._bubble_up(parent_idx) # pylint: disable=protected-access return jax.lax.cond( self.compare(child, parent) > 0, @@ -114,11 +127,11 @@ def do_nothing(heap: "Heap[Element]") -> "Heap[Element]": def do_left_bubble_down(heap: "Heap[Element]") -> "Heap[Element]": heap = self._swap(parent_idx, left_child_idx) - return heap._bubble_down(left_child_idx) + return heap._bubble_down(left_child_idx) # pylint: disable=protected-access def do_right_bubble_down(heap: "Heap[Element]") -> "Heap[Element]": heap = self._swap(parent_idx, right_child_idx) - return heap._bubble_down(right_child_idx) + return heap._bubble_down(right_child_idx) # pylint: disable=protected-access return jax.lax.cond( self.compare(parent, left_child) > 0, diff --git a/simplexity/data_structures/queue.py b/simplexity/data_structures/queue.py index 8a1414e3..b938eba3 100644 --- a/simplexity/data_structures/queue.py +++ b/simplexity/data_structures/queue.py @@ -1,3 +1,5 @@ +"""Queue data structure.""" + import equinox as eqx import jax diff --git a/simplexity/data_structures/stack.py b/simplexity/data_structures/stack.py index c7d3d767..0d860448 100644 --- a/simplexity/data_structures/stack.py +++ b/simplexity/data_structures/stack.py @@ -1,3 +1,5 @@ +"""Stack data structure.""" + import equinox as eqx import jax import jax.numpy as jnp diff --git a/simplexity/exceptions.py b/simplexity/exceptions.py index dd0d8daa..c459ad70 100644 --- a/simplexity/exceptions.py +++ b/simplexity/exceptions.py @@ -1,16 +1,13 @@ +"""Custom exception hierarchy for the simplexity package.""" + + class SimplexityException(Exception): """Base exception for Simplexity.""" - pass - class ConfigValidationError(SimplexityException): """Exception raised when a config is invalid.""" - pass - class DeviceResolutionError(SimplexityException): """Exception raised when a device resolution fails.""" - - pass diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 29548368..894487b0 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -1,17 +1,33 @@ +"""Builder for generative processes.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import inspect from collections.abc import Callable, Mapping, Sequence -from typing import Any +from typing import Any, Literal import jax import jax.numpy as jnp +from simplexity.generative_processes.factored_generative_process import ComponentType, FactoredGenerativeProcess from simplexity.generative_processes.generalized_hidden_markov_model import GeneralizedHiddenMarkovModel from simplexity.generative_processes.hidden_markov_model import HiddenMarkovModel from simplexity.generative_processes.transition_matrices import ( GHMM_MATRIX_FUNCTIONS, HMM_MATRIX_FUNCTIONS, - stationary_state, + get_stationary_state, ) +from simplexity.generative_processes.factored_beliefs.topology.chain import ChainTopology +from simplexity.generative_processes.factored_beliefs.topology.symmetric import SymmetricTopology +from simplexity.generative_processes.factored_beliefs.topology.transition_coupled import TransitionCoupledTopology + def build_transition_matrices(matrix_functions: dict[str, Callable], process_name: str, **kwargs) -> jax.Array: @@ -38,7 +54,7 @@ def add_begin_of_sequence_token(transition_matrix: jax.Array, initial_state: jax augmented_matrix = jnp.zeros((base_vocab_size + 1, num_states + 1, num_states + 1), dtype=transition_matrix.dtype) augmented_matrix = augmented_matrix.at[:base_vocab_size, :num_states, :num_states].set(transition_matrix) if initial_state is None: - initial_state = stationary_state(transition_matrix.sum(axis=0).T) + initial_state = get_stationary_state(transition_matrix.sum(axis=0).T) return augmented_matrix.at[base_vocab_size, num_states, :num_states].set(initial_state) @@ -104,7 +120,7 @@ def build_nonergodic_hidden_markov_model( ] composite_transition_matrix = build_nonergodic_transition_matrices(component_transition_matrices, vocab_maps) component_initial_states = [ - stationary_state(transition_matrix.sum(axis=0).T) for transition_matrix in component_transition_matrices + get_stationary_state(transition_matrix.sum(axis=0).T) for transition_matrix in component_transition_matrices ] initial_state = build_nonergodic_initial_state(component_initial_states, jnp.array(process_weights)) if add_bos_token: @@ -113,3 +129,635 @@ def build_nonergodic_hidden_markov_model( initial_state = jnp.zeros((num_states,), dtype=composite_transition_matrix.dtype) initial_state = initial_state.at[num_states - 1].set(1) return HiddenMarkovModel(composite_transition_matrix, initial_state) + + +def build_chain_process( + *, + component_types: Sequence[ComponentType], + transition_matrices: Sequence[jnp.ndarray], + normalizing_eigenvectors: Sequence[jnp.ndarray], + initial_states: Sequence[jnp.ndarray], + control_maps: Sequence[jnp.ndarray | None], +) -> FactoredGenerativeProcess: + """Build a chain/conditional factored process. + + Factor i>0 selects its parameter variant based on factor i-1's emitted token. + + Args: + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) + normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) + initial_states: Initial state per factor (shape [S_i]) + control_maps: Control maps for variant selection. control_maps[0] should + be None. control_maps[i] for i>0 should have shape [V_{i-1}]. + + Returns: + FactoredGenerativeProcess with chain topology + """ + # Extract vocab sizes from transition matrices + vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) + + topology = ChainTopology(control_maps=tuple(control_maps), vocab_sizes=vocab_sizes) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + topology=topology, + ) + + +def build_symmetric_process( + *, + component_types: Sequence[ComponentType], + transition_matrices: Sequence[jnp.ndarray], + normalizing_eigenvectors: Sequence[jnp.ndarray], + initial_states: Sequence[jnp.ndarray], + control_maps: Sequence[jnp.ndarray], +) -> FactoredGenerativeProcess: + """Build a symmetric/fully-bidirectional factored process. + + Each factor selects its variant based on all other factors' tokens. + + Args: + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) + normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) + initial_states: Initial state per factor (shape [S_i]) + control_maps: Control maps for variant selection. control_maps[i] should + have shape [prod(V_j for j!=i)]. + + Returns: + FactoredGenerativeProcess with symmetric topology + """ + # Extract vocab sizes from transition matrices + vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) + + topology = SymmetricTopology( + control_maps=tuple(control_maps), + vocab_sizes=vocab_sizes, + ) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + topology=topology, + ) + + +def build_transition_coupled_process( + *, + component_types: Sequence[ComponentType], + transition_matrices: Sequence[jnp.ndarray], + normalizing_eigenvectors: Sequence[jnp.ndarray], + initial_states: Sequence[jnp.ndarray], + control_maps_transition: Sequence[jnp.ndarray], + emission_variant_indices: jnp.ndarray | Sequence[int], + emission_control_maps: Sequence[jnp.ndarray | None] | None = None, +) -> FactoredGenerativeProcess: + """Build a transition-coupled factored process. + + Emissions can be independent or chain-style. Transitions are coupled + based on other factors' tokens. + + Args: + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) + normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) + initial_states: Initial state per factor (shape [S_i]) + control_maps_transition: Transition control maps. control_maps_transition[i] + should have shape [prod(V_j for j!=i)]. + emission_variant_indices: Fixed emission variant per factor (shape [F]) + emission_control_maps: Optional chain-style emission control maps. + If provided, emission_control_maps[i] should have shape + [prod(V_j for j0. + + Returns: + FactoredGenerativeProcess with transition-coupled topology + """ + # Extract vocab sizes from transition matrices + vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) + + topology = TransitionCoupledTopology( + control_maps_transition=tuple(control_maps_transition), + emission_variant_indices=emission_variant_indices, + vocab_sizes=vocab_sizes, + emission_control_maps=tuple(emission_control_maps) if emission_control_maps else None, + ) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + topology=topology, + ) + + +def build_factored_process( + topology_type: Literal["chain", "symmetric", "transition_coupled"], + component_types: Sequence[ComponentType], + transition_matrices: Sequence[jnp.ndarray], + normalizing_eigenvectors: Sequence[jnp.ndarray], + initial_states: Sequence[jnp.ndarray], + **topology_kwargs, +) -> FactoredGenerativeProcess: + """Factory function for building factored processes with different topologies. + + Args: + topology_type: Type of coupling topology to use + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) + normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) + initial_states: Initial state per factor (shape [S_i]) + **topology_kwargs: Topology-specific keyword arguments: + - For "chain": control_maps + - For "symmetric": control_maps + - For "transition_coupled": control_maps_transition, + emission_variant_indices, emission_control_maps (optional) + + Returns: + FactoredGenerativeProcess with the specified topology + + Raises: + ValueError: If topology_type is invalid or required kwargs are missing + """ + if topology_type == "chain": + return build_chain_process( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps=topology_kwargs["control_maps"], + ) + elif topology_type == "symmetric": + return build_symmetric_process( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps=topology_kwargs["control_maps"], + ) + elif topology_type == "transition_coupled": + return build_transition_coupled_process( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps_transition=topology_kwargs["control_maps_transition"], + emission_variant_indices=topology_kwargs["emission_variant_indices"], + emission_control_maps=topology_kwargs.get("emission_control_maps"), + ) + else: + raise ValueError( + f"Unknown topology_type: {topology_type}. " + f"Must be one of: 'chain', 'symmetric', 'transition_coupled'" + ) + + +def build_chain_process_from_spec( + chain: Sequence[dict[str, Any]], +) -> FactoredGenerativeProcess: + """Build chain process directly from specification. + + This is a convenience function that combines spec parsing and process building. + + Args: + chain: List of factor specifications. Each should have: + - component_type: "hmm" or "ghmm" + - variants: List of variant specs with process_name and parameters + - control_map (for i>0): List mapping parent token -> variant index + + Returns: + FactoredGenerativeProcess with chain topology + + Example: + ```python + chain = [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + }, + { + "component_type": "hmm", + "variants": [ + {"process_name": "mess3", "x": 0.15, "a": 0.6}, + {"process_name": "mess3", "x": 0.5, "a": 0.6}, + ], + "control_map": [0, 1, 0], + }, + ] + process = build_chain_process_from_spec(chain) + ``` + """ + component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps = ( + build_chain_from_spec(chain) + ) + + return build_chain_process( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps=control_maps, + ) + + +def build_symmetric_process_from_spec( + components: Sequence[dict[str, Any]], + control_maps: Sequence[list[int]], +) -> FactoredGenerativeProcess: + """Build symmetric process directly from specification. + + Args: + components: List of factor specifications + control_maps: Control maps for each factor + + Returns: + FactoredGenerativeProcess with symmetric topology + + Example: + ```python + components = [ + { + "component_type": "hmm", + "variants": [ + {"process_name": "mess3", "x": 0.15, "a": 0.6}, + {"process_name": "mess3", "x": 0.5, "a": 0.6}, + ], + }, + # ... more components + ] + control_maps = [[0, 1, 0, 1], [1, 0, 1, 0]] + process = build_symmetric_process_from_spec(components, control_maps) + ``` + """ + component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps_arrays = ( + build_symmetric_from_spec(components, control_maps) + ) + + return build_symmetric_process( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps=control_maps_arrays, + ) + + +def build_transition_coupled_process_from_spec( + components: Sequence[dict[str, Any]], + control_maps_transition: Sequence[list[int]], + emission_variant_indices: Sequence[int], + emission_control_maps: Sequence[list[int] | None] | None = None, +) -> FactoredGenerativeProcess: + """Build transition-coupled process directly from specification. + + Args: + components: List of factor specifications + control_maps_transition: Transition control maps + emission_variant_indices: Fixed emission variant per factor + emission_control_maps: Optional chain-style emission control maps + + Returns: + FactoredGenerativeProcess with transition-coupled topology + + Example: + ```python + components = [...] + control_maps_transition = [[0, 1, 0, 1], [1, 0, 1, 0]] + emission_variant_indices = [0, 0] + process = build_transition_coupled_process_from_spec( + components, control_maps_transition, emission_variant_indices + ) + ``` + """ + ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps_arrays, + emission_variant_indices_array, + emission_control_maps_arrays, + ) = build_transition_coupled_from_spec( + components, control_maps_transition, emission_variant_indices, emission_control_maps + ) + + return build_transition_coupled_process( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps_transition=control_maps_arrays, + emission_variant_indices=emission_variant_indices_array, + emission_control_maps=emission_control_maps_arrays, + ) + + +def build_matrices_from_spec( + spec: Sequence[dict[str, Any]], +) -> tuple[ + list[ComponentType], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray], +]: + """Build transition matrices, eigenvectors, and initial states from spec. + + This is a generic helper that works for all topologies. Each element of spec + should be a dict with: + - component_type: "hmm" | "ghmm" + - variants: list of dicts, each with "process_name" and process-specific kwargs + + Args: + spec: List of factor specifications + + Returns: + Tuple of (component_types, transition_matrices, normalizing_eigenvectors, initial_states) + + Example: + ```python + spec = [ + { + "component_type": "hmm", + "variants": [ + {"process_name": "mess3", "x": 0.15, "a": 0.6}, + {"process_name": "mess3", "x": 0.5, "a": 0.6}, + ] + }, + { + "component_type": "ghmm", + "variants": [ + {"process_name": "tom_quantum", "alpha": 1.0, "beta": 1.0}, + ] + }, + ] + component_types, T_mats, norms, states = build_matrices_from_spec(spec) + ``` + """ + if not spec: + raise ValueError("spec must contain at least one factor") + + component_types: list[ComponentType] = [] + transition_matrices: list[jnp.ndarray] = [] + normalizing_eigenvectors: list[jnp.ndarray] = [] + initial_states: list[jnp.ndarray] = [] + + for idx, factor_spec in enumerate(spec): + ctype: ComponentType = factor_spec.get("component_type", "ghmm") + variants: Sequence[dict[str, Any]] = factor_spec.get("variants", []) + + if not variants: + raise ValueError(f"spec[{idx}].variants must be non-empty") + + # Build all variants for this factor + built = [build_hidden_markov_model(**v) if ctype == "hmm" else build_generalized_hidden_markov_model(**v) for v in variants] + + # Validate dimensions + vocab_sizes = [b.vocab_size for b in built] + num_states = [ + b.num_states if hasattr(b, "num_states") else b.transition_matrices.shape[1] + for b in built + ] + + if len(set(vocab_sizes)) != 1: + raise ValueError( + f"All variants in spec[{idx}] must have same vocab size; got {vocab_sizes}" + ) + if len(set(num_states)) != 1: + raise ValueError( + f"All variants in spec[{idx}] must have same state dim; got {num_states}" + ) + + V = vocab_sizes[0] + S = num_states[0] + + # Stack transition matrices: [K, V, S, S] + T_stack = jnp.stack([b.transition_matrices for b in built], axis=0) + transition_matrices.append(T_stack) + + # Stack normalizing eigenvectors (or create dummy for HMM) + if ctype == "ghmm": + norms = jnp.stack([b.normalizing_eigenvector for b in built], axis=0) # [K, S] + else: # dummy (unused) vector for HMM + norms = jnp.ones((len(built), S)) + normalizing_eigenvectors.append(norms) + + # Initial state: use variant 0's initial state + initial_states.append(built[0].initial_state) + + component_types.append(ctype) + + return component_types, transition_matrices, normalizing_eigenvectors, initial_states + + +def build_chain_from_spec( + chain: Sequence[dict[str, Any]], +) -> tuple[ + list[ComponentType], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray | None], +]: + """Build all parameters for chain topology from chain specification. + + Each element of chain should be a dict with: + - component_type: "hmm" | "ghmm" + - variants: list of variant specs + - control_map (optional for index 0, required for i>0): list[int] mapping + parent token -> variant index + + Args: + chain: List of factor specifications with control maps + + Returns: + Tuple of (component_types, transition_matrices, normalizing_eigenvectors, + initial_states, control_maps) + + Example: + ```python + chain = [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + # No control_map for root + }, + { + "component_type": "hmm", + "variants": [ + {"process_name": "mess3", "x": 0.15, "a": 0.6}, + {"process_name": "mess3", "x": 0.5, "a": 0.6}, + ], + "control_map": [0, 1, 0], # Maps 3 parent tokens -> 2 variants + }, + ] + ``` + """ + if not chain: + raise ValueError("chain must contain at least one node") + + # Build base matrices + component_types, transition_matrices, normalizing_eigenvectors, initial_states = ( + build_matrices_from_spec(chain) + ) + + # Extract control maps + control_maps: list[jnp.ndarray | None] = [] + expected_prev_vocab = None + + for idx, node in enumerate(chain): + if idx == 0: + control_maps.append(None) + else: + cm = node.get("control_map", None) + if cm is None: + raise ValueError(f"chain[{idx}].control_map is required for i>0") + + cm_arr = jnp.asarray(cm, dtype=jnp.int32) + + if expected_prev_vocab is not None and int(cm_arr.shape[0]) != int(expected_prev_vocab): + raise ValueError( + f"chain[{idx}].control_map length {cm_arr.shape[0]} must equal " + f"parent vocab {expected_prev_vocab}" + ) + + control_maps.append(cm_arr) + + # Track vocab size for next iteration + expected_prev_vocab = int(transition_matrices[idx].shape[1]) + + return component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps + + +def build_symmetric_from_spec( + components: Sequence[dict[str, Any]], + control_maps: Sequence[list[int]], +) -> tuple[ + list[ComponentType], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray], +]: + """Build all parameters for symmetric topology from specification. + + Args: + components: List of factor specifications (same format as build_matrices_from_spec) + control_maps: Control maps for each factor. control_maps[i] should have + shape [prod(V_j for j!=i)] mapping other-factor tokens to variant index. + + Returns: + Tuple of (component_types, transition_matrices, normalizing_eigenvectors, + initial_states, control_maps_arrays) + + Example: + ```python + components = [ + { + "component_type": "hmm", + "variants": [ + {"process_name": "mess3", "x": 0.15, "a": 0.6}, + {"process_name": "mess3", "x": 0.5, "a": 0.6}, + ], + }, + # ... more components + ] + control_maps = [ + [0, 1, 0, 1], # Factor 0: 4 other-token combos -> variants + [1, 0, 1, 0], # Factor 1: 4 other-token combos -> variants + ] + ``` + """ + # Build base matrices + component_types, transition_matrices, normalizing_eigenvectors, initial_states = ( + build_matrices_from_spec(components) + ) + + # Convert control maps to JAX arrays + control_maps_arrays = [jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps] + + # Validate control map lengths + vocab_sizes = [int(T.shape[1]) for T in transition_matrices] + F = len(vocab_sizes) + + for i, cm in enumerate(control_maps_arrays): + # Expected length: product of all vocab sizes except i + expected = 1 + for j in range(F): + if j != i: + expected *= vocab_sizes[j] + + if int(cm.shape[0]) != expected: + raise ValueError( + f"control_maps[{i}] length {cm.shape[0]} must equal " + f"prod(V_j for j!={i}) = {expected}" + ) + + return component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps_arrays + + +def build_transition_coupled_from_spec( + components: Sequence[dict[str, Any]], + control_maps_transition: Sequence[list[int]], + emission_variant_indices: Sequence[int], + emission_control_maps: Sequence[list[int] | None] | None = None, +) -> tuple[ + list[ComponentType], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray], + list[jnp.ndarray], + jnp.ndarray, + list[jnp.ndarray | None] | None, +]: + """Build all parameters for transition-coupled topology from specification. + + Args: + components: List of factor specifications + control_maps_transition: Transition control maps (same format as symmetric) + emission_variant_indices: Fixed emission variant per factor + emission_control_maps: Optional chain-style emission control maps + + Returns: + Tuple of (component_types, transition_matrices, normalizing_eigenvectors, + initial_states, control_maps_transition_arrays, + emission_variant_indices_array, emission_control_maps_arrays) + + Example: + ```python + components = [...] + control_maps_transition = [[0, 1, 0, 1], [1, 0, 1, 0]] + emission_variant_indices = [0, 0] # Use variant 0 for emissions + emission_control_maps = None # Independent emissions + ``` + """ + # Build base matrices + component_types, transition_matrices, normalizing_eigenvectors, initial_states = ( + build_matrices_from_spec(components) + ) + + # Convert transition control maps + control_maps_arrays = [jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps_transition] + + # Convert emission variant indices + emission_variant_indices_array = jnp.asarray(emission_variant_indices, dtype=jnp.int32) + + # Convert emission control maps if provided + emission_control_maps_arrays = None + if emission_control_maps is not None: + emission_control_maps_arrays = [ + jnp.asarray(cm, dtype=jnp.int32) if cm is not None else None + for cm in emission_control_maps + ] + + return ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps_arrays, + emission_variant_indices_array, + emission_control_maps_arrays, + ) diff --git a/simplexity/generative_processes/factored_beliefs/topology/chain.py b/simplexity/generative_processes/factored_beliefs/topology/chain.py new file mode 100644 index 00000000..35ed7cfb --- /dev/null +++ b/simplexity/generative_processes/factored_beliefs/topology/chain.py @@ -0,0 +1,141 @@ +"""Chain topology: one-way conditional dependencies between factors. + +Factor i>0 selects its parameter variant based on the emitted token +of factor i-1 (parent) via a control map. +""" + +from __future__ import annotations + +import equinox as eqx +import jax +import jax.numpy as jnp + +from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext +from simplexity.utils.factoring_utils import compute_obs_dist_for_variant + + +class ChainTopology(eqx.Module): + """Chain/conditional coupling topology. + + Factors form a chain: Factor i depends on Factor i-1's emitted token. + - Factor 0 always uses variant 0 + - Factor i>0 uses control_maps[i][parent_token] to select variant + + Joint distribution: P(t0, t1, ..., tF) = P(t0) * P(t1|t0) * ... * P(tF|t_{F-1}) + + Attributes: + control_maps: Tuple of F arrays. control_maps[i] has shape [V_{i-1}] for i>0, + mapping parent token to variant index. control_maps[0] is None. + vocab_sizes_py: Python int tuple of vocab sizes (for reshape operations) + """ + + control_maps: tuple[jnp.ndarray | None, ...] + vocab_sizes_py: tuple[int, ...] | None + + def __init__( + self, + control_maps: tuple[jnp.ndarray | None, ...], + vocab_sizes: jnp.ndarray | None = None, + ): + """Initialize chain topology. + + Args: + control_maps: Control maps for variant selection. control_maps[0] + should be None (root factor). control_maps[i] for i>0 should + have shape [V_{i-1}] mapping parent token to variant index. + vocab_sizes: Optional vocab sizes for shape operations. If provided, + should be array of shape [F]. + """ + self.control_maps = tuple(control_maps) + self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) if vocab_sizes is not None else None + + def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + """Compute joint distribution using chain factorization. + + Builds P(t0, t1, ..., tF) = P(t0) * P(t1|t0) * ... * P(tF|t_{F-1}) + iteratively, then flattens to radix encoding. + + Args: + context: Coupling context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + F = len(context.vocab_sizes) + states = context.states + component_types = context.component_types + transition_matrices = context.transition_matrices + normalizing_eigenvectors = context.normalizing_eigenvectors + num_variants = context.num_variants + + # Root distribution (factor 0, variant 0) + T_root = transition_matrices[0][0] # [V_0, S_0, S_0] + norm_root = normalizing_eigenvectors[0][0] if component_types[0] == "ghmm" else None + p_root = compute_obs_dist_for_variant( + component_types[0], states[0], T_root, norm_root + ) # [V_0] + joint = p_root + + # Iteratively extend with conditional factors + for i in range(1, F): + # Compute distributions for all variants of factor i + K_i = num_variants[i] + ks = jnp.arange(K_i, dtype=jnp.int32) + + # Vectorize over variants + def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: + T_k = transition_matrices[i][k] + norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None + return compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + + all_pi = jax.vmap(get_dist_i)(ks) # [K_i, V_i] + + # Build conditional matrix [V_{i-1}, V_i] via control map + cm = self.control_maps[i] # [V_{i-1}] + cond = all_pi[cm] # [V_{i-1}, V_i] + + # Extend joint distribution + # Current joint has shape [..., V_{i-1}] + # We want to expand to [..., V_{i-1}, V_i] + # Use precomputed Python ints for reshape (JIT-compatible) + if self.vocab_sizes_py is not None: + prev_vocab_size = self.vocab_sizes_py[i - 1] + curr_vocab_size = self.vocab_sizes_py[i] + else: + prev_vocab_size = int(context.vocab_sizes[i - 1]) + curr_vocab_size = int(context.vocab_sizes[i]) + left = joint.reshape(-1, prev_vocab_size) # [P, V_{i-1}] + extended = left[..., None] * cond[None, ...] # [P, V_{i-1}, V_i] + joint = extended.reshape(joint.shape + (curr_vocab_size,)) + + return joint.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jnp.ndarray, ...], + context: CouplingContext, + ) -> tuple[jnp.ndarray, ...]: + """Select variants based on parent tokens in chain. + + Args: + obs_tuple: Tuple of observed tokens (one per factor) + context: Coupling context (unused for chain topology) + + Returns: + Tuple of variant indices (one per factor) + """ + variants = [] + for i in range(len(obs_tuple)): + if i == 0: + # Root factor always uses variant 0 + variants.append(jnp.array(0, dtype=jnp.int32)) + else: + # Select based on parent's observed token + parent_token = obs_tuple[i - 1] + k_i = self.control_maps[i][parent_token] + variants.append(k_i) + return tuple(variants) + + def get_required_params(self) -> dict[str, type]: + """Return required parameters for chain topology.""" + return {"control_maps": tuple} diff --git a/simplexity/generative_processes/factored_beliefs/topology/symmetric.py b/simplexity/generative_processes/factored_beliefs/topology/symmetric.py new file mode 100644 index 00000000..7b04ce39 --- /dev/null +++ b/simplexity/generative_processes/factored_beliefs/topology/symmetric.py @@ -0,0 +1,196 @@ +"""Symmetric topology: fully bidirectional coupling between all factors. + +Each factor's parameter variant is selected based on the tokens of +ALL OTHER factors via a control map, producing mutual dependencies. +""" + +from __future__ import annotations + +import equinox as eqx +import jax +import jax.numpy as jnp + +from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext +from simplexity.utils.factoring_utils import compute_obs_dist_for_variant + + +class SymmetricTopology(eqx.Module): + """Symmetric/fully-bidirectional coupling topology. + + Each factor i selects its variant based on all other factors' tokens. + Joint distribution uses product-of-experts with normalization. + + Attributes: + control_maps: Tuple of F arrays. control_maps[i] has shape [prod(V_j for j!=i)] + mapping flattened other-tokens to variant index for factor i. + other_multipliers: Precomputed radix multipliers for flattening other tokens + other_shapes: Reshape targets for conditioning on other factors + perms_py: Axis permutations to align conditional distributions + vocab_sizes_py: Python int tuple of vocab sizes for shape operations + joint_vocab_size: Total vocabulary size (product of all V_i) + """ + + control_maps: tuple[jnp.ndarray, ...] + other_multipliers: tuple[jnp.ndarray, ...] + other_shapes: tuple[tuple[int, ...], ...] + perms_py: tuple[tuple[int, ...], ...] + vocab_sizes_py: tuple[int, ...] + joint_vocab_size: int + + def __init__( + self, + control_maps: tuple[jnp.ndarray, ...], + vocab_sizes: jnp.ndarray, + ): + """Initialize symmetric topology. + + Args: + control_maps: Control maps for each factor. control_maps[i] should + have shape [prod(V_j for j!=i)] mapping other-factor tokens + to variant index for factor i. + vocab_sizes: Array of shape [F] with vocab sizes per factor + """ + self.control_maps = tuple(jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps) + self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) + F = len(vocab_sizes) + + # Compute joint vocab size + jv = 1 + for v in self.vocab_sizes_py: + jv *= v + self.joint_vocab_size = jv + + # Precompute indexing helpers for each factor + other_multipliers: list[jnp.ndarray] = [] + other_shapes: list[tuple[int, ...]] = [] + perms_py: list[tuple[int, ...]] = [] + + for i in range(F): + # Compute radix multipliers for "other" factors (excluding i) + mult = [] + for j in range(F): + if j == i: + mult.append(0) # Unused + else: + m = 1 + for k in range(j + 1, F): + if k == i: + continue + m *= self.vocab_sizes_py[k] + mult.append(m) + other_multipliers.append(jnp.array(mult)) + + # Shape for reshaping conditional [prod_others, V_i] -> [*others, V_i] + other_shapes.append(tuple(self.vocab_sizes_py[j] for j in range(F) if j != i)) + + # Permutation to align [*others, V_i] to [V_0, ..., V_{F-1}] + others = [j for j in range(F) if j != i] + axis_pos = {j: pos for pos, j in enumerate(others)} + perm = [] + for j in range(F): + if j == i: + perm.append(len(others)) # V_i is the last axis + else: + perm.append(axis_pos[j]) + perms_py.append(tuple(perm)) + + self.other_multipliers = tuple(other_multipliers) + self.other_shapes = tuple(other_shapes) + self.perms_py = tuple(perms_py) + + def _flatten_other_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray: + """Flatten other-factor tokens to control map index. + + Args: + tokens: Array of shape [F] with all tokens + i: Factor index to exclude + + Returns: + Scalar index for control_maps[i] + """ + mult = self.other_multipliers[i] + # Multiply elementwise and sum (mult[i] == 0) + return jnp.sum(tokens * mult) + + def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + """Compute joint distribution using product-of-experts. + + For each factor i, computes conditional P(t_i | all other t_j), + then multiplies all conditionals and normalizes. + + Args: + context: Coupling context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + F = len(context.vocab_sizes) + states = context.states + component_types = context.component_types + transition_matrices = context.transition_matrices + normalizing_eigenvectors = context.normalizing_eigenvectors + num_variants = context.num_variants + + # Compute per-factor conditionals + parts = [] + for i in range(F): + K_i = num_variants[i] + ks = jnp.arange(K_i, dtype=jnp.int32) + + # Compute all variant distributions for factor i + def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: + T_k = transition_matrices[i][k] + norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None + return compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + + all_pi = jax.vmap(get_dist_i)(ks) # [K_i, V_i] + + # Select per other-tokens using control map + cm = self.control_maps[i] # [prod_others] + cond = all_pi[cm] # [prod_others, V_i] + + # Reshape to [*others, V_i] + cond_nd = cond.reshape(self.other_shapes[i] + (self.vocab_sizes_py[i],)) + + # Permute to [V_0, ..., V_{F-1}] with V_i at position i + aligned = jnp.transpose(cond_nd, self.perms_py[i]) + parts.append(aligned) + + # Product of experts + J = parts[0] + for p in parts[1:]: + J = J * p + + # Normalize + Z = jnp.sum(J) + J_norm = jnp.where(Z > 0, J / Z, jnp.ones_like(J) / self.joint_vocab_size) + + assert isinstance(J_norm, jnp.ndarray) + + return J_norm.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jnp.ndarray, ...], + context: CouplingContext, + ) -> tuple[jnp.ndarray, ...]: + """Select variants based on all other factors' tokens. + + Args: + obs_tuple: Tuple of observed tokens (one per factor) + context: Coupling context (unused for symmetric topology) + + Returns: + Tuple of variant indices (one per factor) + """ + tokens_arr = jnp.array(obs_tuple) + variants = [] + for i in range(len(obs_tuple)): + idx = self._flatten_other_tokens_index(tokens_arr, i) + k_i = self.control_maps[i][idx] + variants.append(k_i) + return tuple(variants) + + def get_required_params(self) -> dict[str, type]: + """Return required parameters for symmetric topology.""" + return {"control_maps": tuple, "vocab_sizes": jnp.ndarray} diff --git a/simplexity/generative_processes/factored_beliefs/topology/topology.py b/simplexity/generative_processes/factored_beliefs/topology/topology.py new file mode 100644 index 00000000..906d0e37 --- /dev/null +++ b/simplexity/generative_processes/factored_beliefs/topology/topology.py @@ -0,0 +1,80 @@ +"""Coupling topology protocol for factored generative processes. + +Defines the interface for different coupling strategies between factors. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal, Protocol + +import jax.numpy as jnp + +ComponentType = Literal["hmm", "ghmm"] +FactoredState = tuple[jnp.ndarray, ...] + + +@dataclass +class CouplingContext: + """Context information needed by topologies to compute joint distributions. + + Attributes: + states: Tuple of state vectors, one per factor (shape [S_i]) + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) + normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) + vocab_sizes: Vocabulary size per factor (shape [F]) + num_variants: Number of parameter variants per factor + """ + + states: FactoredState + component_types: tuple[ComponentType, ...] + transition_matrices: tuple[jnp.ndarray, ...] + normalizing_eigenvectors: tuple[jnp.ndarray, ...] + vocab_sizes: jnp.ndarray + num_variants: tuple[int, ...] + + +class CouplingTopology(Protocol): + """Protocol for coupling topologies between factors. + + A coupling topology defines how factors interact to produce joint + observation distributions and how variant selection works. + """ + + def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + """Compute joint observation distribution across all factors. + + Args: + context: All information needed to compute the joint distribution + + Returns: + Flattened joint distribution of shape [prod(V_i)] + Uses radix encoding: token = sum(t_i * prod(V_j for j>i)) + """ + ... + + def select_variants( + self, + obs_tuple: tuple[jnp.ndarray, ...], + context: CouplingContext, + ) -> tuple[jnp.ndarray, ...]: + """Select parameter variant for each factor given observations. + + Args: + obs_tuple: Tuple of F observed tokens (one per factor) + context: Context information + + Returns: + Tuple of F variant indices (one per factor) + """ + ... + + def get_required_params(self) -> dict[str, type]: + """Return dictionary of topology-specific parameters and their types. + + Returns: + Dict mapping parameter names to expected types + (e.g., {"control_maps": tuple} for chain topology) + """ + ... diff --git a/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py b/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py new file mode 100644 index 00000000..126e7957 --- /dev/null +++ b/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py @@ -0,0 +1,233 @@ +"""Transition-coupled topology: independent/chain emissions with coupled transitions. + +Emissions can be either: +- Independent: each factor uses a fixed emission variant +- Chain-style: factor i selects emission variant based on previous tokens 0..i-1 + +Transitions are always coupled: factor i selects transition variant based on +all other factors' tokens. +""" + +from __future__ import annotations +from typing import Sequence + +import equinox as eqx +import jax +import jax.numpy as jnp + +from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext +from simplexity.utils.factoring_utils import compute_obs_dist_for_variant + + +class TransitionCoupledTopology(eqx.Module): + """Transition-coupled topology with flexible emission modes. + + Emissions can be: + - Independent (use_emission_chain=False): P(t) = ∏_i P_i(t_i | s_i, k_emit_i) + - Chain (use_emission_chain=True): P(t) = P0(t0) * ∏_{i>0} P_i(t_i | t_0..t_{i-1}, s_i) + + Transitions are always coupled: factor i selects transition variant based on + all other factors' tokens. + + Attributes: + control_maps_transition: Transition control maps. control_maps_transition[i] + has shape [prod(V_j for j!=i)] mapping other tokens to transition variant. + emission_variant_indices: Fixed emission variants per factor (shape [F]) + emission_control_maps: Optional chain-style emission control maps + use_emission_chain: Whether to use chain-style emissions + other_multipliers: Precomputed radix multipliers for other-factor indexing + prefix_multipliers: Precomputed radix multipliers for prefix indexing + vocab_sizes_py: Python int tuple of vocab sizes + """ + + control_maps_transition: tuple[jnp.ndarray, ...] + emission_variant_indices: jnp.ndarray # shape [F] + emission_control_maps: tuple[jnp.ndarray | None, ...] + use_emission_chain: bool + other_multipliers: tuple[jnp.ndarray, ...] + prefix_multipliers: tuple[jnp.ndarray, ...] + vocab_sizes_py: tuple[int, ...] + + def __init__( + self, + control_maps_transition: tuple[jnp.ndarray, ...], + emission_variant_indices: jnp.ndarray | Sequence[int], + vocab_sizes: jnp.ndarray, + emission_control_maps: tuple[jnp.ndarray | None, ...] | None = None, + ): + """Initialize transition-coupled topology. + + Args: + control_maps_transition: Transition control maps for each factor. + control_maps_transition[i] should have shape [prod(V_j for j!=i)]. + emission_variant_indices: Fixed emission variant per factor (shape [F]) + vocab_sizes: Vocabulary sizes per factor (shape [F]) + emission_control_maps: Optional chain-style emission control maps. + If provided, emission_control_maps[i] should have shape + [prod(V_j for j0. + """ + self.control_maps_transition = tuple( + jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps_transition + ) + self.emission_variant_indices = jnp.asarray(emission_variant_indices, dtype=jnp.int32) + self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) + F = len(vocab_sizes) + + # Process emission control maps + use_chain = False + ecm_list: list[jnp.ndarray | None] = [] + if emission_control_maps is not None: + for i, cm_i in enumerate(emission_control_maps): + if cm_i is None: + ecm_list.append(None) + else: + ecm_list.append(jnp.asarray(cm_i, dtype=jnp.int32)) + if i > 0: + use_chain = True + else: + ecm_list = [None] * F + self.emission_control_maps = tuple(ecm_list) + self.use_emission_chain = bool(use_chain) + + # Precompute multipliers for other-factor indexing (for transitions) + other_multipliers: list[jnp.ndarray] = [] + for i in range(F): + mult = [] + for j in range(F): + if j == i: + mult.append(0) # Unused + else: + m = 1 + for k in range(j + 1, F): + if k == i: + continue + m *= self.vocab_sizes_py[k] + mult.append(m) + other_multipliers.append(jnp.array(mult)) + self.other_multipliers = tuple(other_multipliers) + + # Precompute multipliers for prefix indexing (for chain emissions) + prefix_multipliers: list[jnp.ndarray] = [] + for i in range(F): + pmult = [] + for j in range(F): + if j >= i: + pmult.append(0) # Unused + else: + m = 1 + for k in range(j + 1, i): + m *= self.vocab_sizes_py[k] + pmult.append(m) + prefix_multipliers.append(jnp.array(pmult)) + self.prefix_multipliers = tuple(prefix_multipliers) + + def _flatten_other_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray: + """Flatten other-factor tokens to transition control map index.""" + mult = self.other_multipliers[i] + return jnp.sum(tokens * mult) + + def _flatten_prev_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray: + """Flatten prefix tokens to emission control map index.""" + mult = self.prefix_multipliers[i] + return jnp.sum(tokens * mult) + + def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + """Compute joint distribution based on emission mode. + + Args: + context: Coupling context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + F = len(context.vocab_sizes) + states = context.states + component_types = context.component_types + transition_matrices = context.transition_matrices + normalizing_eigenvectors = context.normalizing_eigenvectors + num_variants = context.num_variants + + if not self.use_emission_chain: + # Independent emissions + parts = [] + for i in range(F): + k_emit = self.emission_variant_indices[i] + T_k = transition_matrices[i][k_emit] + norm_k = normalizing_eigenvectors[i][k_emit] if component_types[i] == "ghmm" else None + p_i = compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + parts.append(p_i) + + # Product of independent factors + J = parts[0] + for i in range(1, F): + J = (J[..., None] * parts[i]).reshape(*J.shape, parts[i].shape[0]) + return J.reshape(-1) + + # Chain-style emissions + k0 = self.emission_variant_indices[0] + T0 = transition_matrices[0][k0] + norm0 = normalizing_eigenvectors[0][k0] if component_types[0] == "ghmm" else None + joint = compute_obs_dist_for_variant(component_types[0], states[0], T0, norm0) + prev_prod = context.vocab_sizes[0] + + for i in range(1, F): + K_i = num_variants[i] + ks = jnp.arange(K_i, dtype=jnp.int32) + + # Compute all variant distributions + def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: + T_k = transition_matrices[i][k] + norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None + return compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + + all_pi = jax.vmap(get_dist_i)(ks) # [K_i, V_i] + + cm = self.emission_control_maps[i] + if cm is None: + # Use fixed emission variant + fixed = self.emission_variant_indices[i] + cond = jnp.tile(all_pi[fixed][None, :], (prev_prod, 1)) # [prev_prod, V_i] + else: + # Use control map + cond = all_pi[cm] # [prev_prod, V_i] + + left = joint.reshape(prev_prod) + extended = cond * left[:, None] + curr_vocab_size = context.vocab_sizes[i] + joint = extended.reshape(*(list(joint.shape) + [curr_vocab_size])) + prev_prod *= curr_vocab_size + + return joint.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jnp.ndarray, ...], + context: CouplingContext, + ) -> tuple[jnp.ndarray, ...]: + """Select transition variants based on other factors' tokens. + + Note: This returns TRANSITION variants, not emission variants. + + Args: + obs_tuple: Tuple of observed tokens (one per factor) + context: Coupling context (unused) + + Returns: + Tuple of transition variant indices (one per factor) + """ + tokens_arr = jnp.array(obs_tuple) + variants = [] + for i in range(len(obs_tuple)): + idx = self._flatten_other_tokens_index(tokens_arr, i) + k_trans = self.control_maps_transition[i][idx] + variants.append(k_trans) + return tuple(variants) + + def get_required_params(self) -> dict[str, type]: + """Return required parameters for transition-coupled topology.""" + return { + "control_maps_transition": tuple, + "emission_variant_indices": jnp.ndarray, + "vocab_sizes": jnp.ndarray, + "emission_control_maps": tuple, # optional + } diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py new file mode 100644 index 00000000..bfb8d675 --- /dev/null +++ b/simplexity/generative_processes/factored_generative_process.py @@ -0,0 +1,240 @@ +"""Unified factored generative process with pluggable coupling topologies.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Literal + +import chex +import equinox as eqx +import jax +import jax.numpy as jnp + +from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext, CouplingTopology +from simplexity.generative_processes.generative_process import GenerativeProcess +from simplexity.utils.factoring_utils import TokenEncoder, transition_with_obs + +ComponentType = Literal["hmm", "ghmm"] +FactoredState = tuple[jnp.ndarray, ...] + + +class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): + """Unified factored generative process with pluggable coupling topology. + + This class provides a single implementation of factored generative processes + that supports different coupling patterns via the CouplingTopology protocol. + + Attributes: + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) + normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) + initial_states: Initial state per factor (shape [S_i]) + num_variants: Number of parameter variants per factor + topology: Coupling topology determining factor interactions + encoder: Token encoder for composite observations + """ + + # Static structure + component_types: tuple[ComponentType, ...] + num_variants: tuple[int, ...] + + # Per-factor parameters + transition_matrices: tuple[jnp.ndarray, ...] + normalizing_eigenvectors: tuple[jnp.ndarray, ...] + initial_states: tuple[jnp.ndarray, ...] + + # Coupling topology and encoding + topology: CouplingTopology + encoder: TokenEncoder + + def __init__( + self, + *, + component_types: Sequence[ComponentType], + transition_matrices: Sequence[jnp.ndarray], + normalizing_eigenvectors: Sequence[jnp.ndarray], + initial_states: Sequence[jnp.ndarray], + topology: CouplingTopology, + ) -> None: + """Initialize factored generative process. + + Args: + component_types: Type of each factor ("hmm" or "ghmm") + transition_matrices: Per-factor transition tensors. + transition_matrices[i] has shape [K_i, V_i, S_i, S_i] + normalizing_eigenvectors: Per-factor eigenvectors for GHMM. + normalizing_eigenvectors[i] has shape [K_i, S_i] + initial_states: Initial state per factor (shape [S_i]) + topology: Coupling topology defining factor interactions + """ + if len(component_types) == 0: + raise ValueError("Must provide at least one component") + + self.component_types = tuple(component_types) + self.transition_matrices = tuple(transition_matrices) + self.normalizing_eigenvectors = tuple(normalizing_eigenvectors) + self.initial_states = tuple(initial_states) + self.topology = topology + + # Validate shapes and compute derived sizes + vocab_sizes = [] + num_variants = [] + for i, T in enumerate(self.transition_matrices): + if T.ndim != 4: + raise ValueError( + f"transition_matrices[{i}] must have shape [K, V, S, S], got {T.shape}" + ) + K, V, S1, S2 = T.shape + if S1 != S2: + raise ValueError( + f"transition_matrices[{i}] square mismatch: {S1} vs {S2}" + ) + vocab_sizes.append(V) + num_variants.append(K) + + self.num_variants = tuple(int(k) for k in num_variants) + self.encoder = TokenEncoder(jnp.array(vocab_sizes)) + + def _make_context(self, state: FactoredState) -> CouplingContext: + """Create coupling context for topology methods.""" + return CouplingContext( + states=state, + component_types=self.component_types, + transition_matrices=self.transition_matrices, + normalizing_eigenvectors=self.normalizing_eigenvectors, + vocab_sizes=self.encoder.vocab_sizes, + num_variants=self.num_variants, + ) + + # ------------------------ GenerativeProcess API ------------------------- + @property + def vocab_size(self) -> int: + """Total vocabulary size of composite observations.""" + return self.encoder.composite_vocab_size + + @property + def initial_state(self) -> FactoredState: + """Initial state across all factors.""" + return tuple(self.initial_states) + + @eqx.filter_jit + def observation_probability_distribution(self, state: FactoredState) -> jnp.ndarray: + """Compute P(composite_token | state) under the coupling topology. + + Args: + state: Tuple of state vectors (one per factor) + + Returns: + Distribution over composite tokens, shape [prod(V_i)] + """ + context = self._make_context(state) + return self.topology.compute_joint_distribution(context) + + @eqx.filter_jit + def log_observation_probability_distribution( + self, log_belief_state: FactoredState + ) -> jnp.ndarray: + """Compute log P(composite_token | state). + + Args: + log_belief_state: Tuple of log-state vectors + + Returns: + Log-distribution over composite tokens, shape [prod(V_i)] + """ + state = tuple(jnp.exp(s) for s in log_belief_state) + probs = self.observation_probability_distribution(state) + return jnp.log(probs) + + @eqx.filter_jit + def emit_observation(self, state: FactoredState, key: jax.Array) -> jnp.ndarray: + """Sample composite observation from current state. + + Args: + state: Tuple of state vectors + key: JAX random key + + Returns: + Composite observation (scalar token) + """ + probs = self.observation_probability_distribution(state) + token_flat = jax.random.categorical(key, jnp.log(probs)) + return token_flat + + @eqx.filter_jit + def transition_states(self, state: FactoredState, obs: chex.Array) -> FactoredState: + """Update states given composite observation. + + Args: + state: Tuple of current state vectors + obs: Composite observation (scalar token) + + Returns: + Tuple of updated state vectors + """ + # Decode composite observation to per-factor tokens + obs_tuple = self.encoder.token_to_tuple(obs) + + # Select variants based on topology + context = self._make_context(state) + variants = self.topology.select_variants(obs_tuple, context) + + # Update each factor's state + new_states: list[jnp.ndarray] = [] + for i, (s_i, t_i, k_i) in enumerate(zip(state, obs_tuple, variants)): + T_k = self.transition_matrices[i][k_i] + norm_k = ( + self.normalizing_eigenvectors[i][k_i] + if self.component_types[i] == "ghmm" + else None + ) + new_state_i = transition_with_obs( + self.component_types[i], s_i, T_k, t_i, norm_k + ) + new_states.append(new_state_i) + + return tuple(new_states) + + @eqx.filter_jit + def probability(self, observations: jnp.ndarray) -> jnp.ndarray: + """Compute P(observations) by scanning through sequence. + + Args: + observations: Array of composite observations + + Returns: + Scalar probability + """ + + def step(carry: FactoredState, obs: jnp.ndarray): + state = carry + dist = self.observation_probability_distribution(state) + p = dist[obs] + new_state = self.transition_states(state, obs) + return new_state, p + + _, ps = jax.lax.scan(step, self.initial_state, observations) + return jnp.prod(ps) + + @eqx.filter_jit + def log_probability(self, observations: jnp.ndarray) -> jnp.ndarray: + """Compute log P(observations) by scanning through sequence. + + Args: + observations: Array of composite observations + + Returns: + Scalar log-probability + """ + + def step(carry: FactoredState, obs: jnp.ndarray): + state = carry + log_dist = self.log_observation_probability_distribution( + tuple(jnp.log(s) for s in state) + ) + lp = log_dist[obs] + new_state = self.transition_states(state, obs) + return new_state, lp + + _, lps = jax.lax.scan(step, self.initial_state, observations) + return jnp.sum(lps) diff --git a/simplexity/generative_processes/generalized_hidden_markov_model.py b/simplexity/generative_processes/generalized_hidden_markov_model.py index d93523c4..1ef4381e 100644 --- a/simplexity/generative_processes/generalized_hidden_markov_model.py +++ b/simplexity/generative_processes/generalized_hidden_markov_model.py @@ -1,3 +1,14 @@ +"""Generalized Hidden Markov Model class.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from typing import TypeVar, cast import chex @@ -6,7 +17,7 @@ import jax.numpy as jnp from simplexity.generative_processes.generative_process import GenerativeProcess -from simplexity.generative_processes.transition_matrices import stationary_state +from simplexity.generative_processes.transition_matrices import get_stationary_state State = TypeVar("State", bound=jax.Array) @@ -41,7 +52,7 @@ def __init__(self, transition_matrices: jax.Array, initial_state: jax.Array | No self.log_normalizing_eigenvector = jnp.log(self.normalizing_eigenvector) if initial_state is None: - initial_state = stationary_state(state_transition_matrix.T) + initial_state = get_stationary_state(state_transition_matrix.T) self._initial_state = initial_state self.log_initial_state = jnp.log(self._initial_state) diff --git a/simplexity/generative_processes/generative_process.py b/simplexity/generative_processes/generative_process.py index 7ac18a7e..e312591e 100644 --- a/simplexity/generative_processes/generative_process.py +++ b/simplexity/generative_processes/generative_process.py @@ -1,3 +1,5 @@ +"""Generative process interface.""" + from abc import abstractmethod from typing import TypeVar @@ -15,18 +17,15 @@ class GenerativeProcess[State](eqx.Module): @abstractmethod def vocab_size(self) -> int: """The number of observations that can be emitted by the generative process.""" - ... @property @abstractmethod def initial_state(self) -> State: """The initial state of the generative process.""" - ... @abstractmethod def emit_observation(self, state: State, key: chex.PRNGKey) -> chex.Array: """Emit an observation based on the state of the generative process.""" - ... @abstractmethod def transition_states(self, state: State, obs: chex.Array) -> State: @@ -36,7 +35,6 @@ def transition_states(self, state: State, obs: chex.Array) -> State: the returned state represents a posterior distribution over hidden states conditioned on the observation. """ - ... @eqx.filter_vmap(in_axes=(None, 0, 0, None, None)) def generate( @@ -78,19 +76,15 @@ def gen_states_and_obs(state: State, key: chex.PRNGKey) -> tuple[State, tuple[St @abstractmethod def observation_probability_distribution(self, state: State) -> jax.Array: """Compute the probability distribution of the observations that can be emitted by the process.""" - ... @abstractmethod def log_observation_probability_distribution(self, log_belief_state: State) -> jax.Array: """Compute the log probability distribution of the observations that can be emitted by the process.""" - ... @abstractmethod def probability(self, observations: jax.Array) -> jax.Array: """Compute the probability of the process generating a sequence of observations.""" - ... @abstractmethod def log_probability(self, observations: jax.Array) -> jax.Array: """Compute the log probability of the process generating a sequence of observations.""" - ... diff --git a/simplexity/generative_processes/generator.py b/simplexity/generative_processes/generator.py index dc7f85bc..aaddf6f2 100644 --- a/simplexity/generative_processes/generator.py +++ b/simplexity/generative_processes/generator.py @@ -1,3 +1,14 @@ +"""Generator for generative processes.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import equinox as eqx import jax import jax.numpy as jnp diff --git a/simplexity/generative_processes/hidden_markov_model.py b/simplexity/generative_processes/hidden_markov_model.py index 15b3edb5..dcf004fd 100644 --- a/simplexity/generative_processes/hidden_markov_model.py +++ b/simplexity/generative_processes/hidden_markov_model.py @@ -1,3 +1,14 @@ +"""Hidden Markov Model class.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from typing import cast import chex @@ -6,7 +17,7 @@ import jax.numpy as jnp from simplexity.generative_processes.generalized_hidden_markov_model import GeneralizedHiddenMarkovModel, State -from simplexity.generative_processes.transition_matrices import stationary_state +from simplexity.generative_processes.transition_matrices import get_stationary_state class HiddenMarkovModel(GeneralizedHiddenMarkovModel[State]): @@ -29,7 +40,7 @@ def __init__(self, transition_matrices: jax.Array, initial_state: jax.Array | No self.log_normalizing_eigenvector = jnp.zeros(self.num_states) if initial_state is None: - initial_state = stationary_state(state_transition_matrix.T) + initial_state = get_stationary_state(state_transition_matrix.T) self._initial_state = initial_state self.log_initial_state = jnp.log(self._initial_state) diff --git a/simplexity/generative_processes/mixed_state_presentation.py b/simplexity/generative_processes/mixed_state_presentation.py index 0d2ed589..6bb71fa9 100644 --- a/simplexity/generative_processes/mixed_state_presentation.py +++ b/simplexity/generative_processes/mixed_state_presentation.py @@ -1,7 +1,18 @@ +"""Mixed state presentation of a generative process.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import functools from enum import Enum from pathlib import Path -from typing import Any, Generic, NamedTuple, TypeVar, cast +from typing import Any, NamedTuple, TypeVar, cast import equinox as eqx import jax @@ -12,7 +23,7 @@ from simplexity.data_structures.stack import Stack from simplexity.generative_processes.generalized_hidden_markov_model import GeneralizedHiddenMarkovModel from simplexity.generative_processes.hidden_markov_model import HiddenMarkovModel -from simplexity.utils.jnp import entropy +from simplexity.utils.jnp_utils import entropy Sequence = tuple[int, ...] @@ -158,7 +169,7 @@ class NodeDictValue(NamedTuple): TNodeDictValue = TypeVar("TNodeDictValue", bound=tuple[float, tuple[float, ...]]) -class MixedStateTree(Generic[TTreeData, TNodeDictValue]): +class MixedStateTree[TTreeData: TreeData, TNodeDictValue: tuple[float, tuple[float, ...]]]: """A presentation of a generative process as a mixed state.""" def __init__(self, nodes: TTreeData): @@ -188,7 +199,7 @@ def add(self, sequence: jax.Array, probability: jax.Array, belief_state: jax.Arr TTree = TypeVar("TTree", bound=MixedStateTree) -class MixedStateTreeGenerator(eqx.Module, Generic[TNode, TTreeData, TTree]): +class MixedStateTreeGenerator[TNode: MixedStateNode, TTreeData: TreeData, TTree: MixedStateTree](eqx.Module): """A generator of nodes in a mixed state presentation of a generative process.""" ghmm: GeneralizedHiddenMarkovModel diff --git a/simplexity/generative_processes/torch_generator.py b/simplexity/generative_processes/torch_generator.py index c8407b46..dffbe9db 100644 --- a/simplexity/generative_processes/torch_generator.py +++ b/simplexity/generative_processes/torch_generator.py @@ -1,3 +1,14 @@ +"""Torch generator for generative processes.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import jax import torch diff --git a/simplexity/generative_processes/transition_matrices.py b/simplexity/generative_processes/transition_matrices.py index ce4b7092..f66b511c 100644 --- a/simplexity/generative_processes/transition_matrices.py +++ b/simplexity/generative_processes/transition_matrices.py @@ -1,13 +1,10 @@ +"""Transition matrices for generative processes.""" + import jax import jax.numpy as jnp -""" -Each process defines P(cur_obs, cur_state | prev_state) with a tensor of shape -[cur_obs, cur_state, prev_state] -""" - -def stationary_state(state_transition_matrix: jax.Array) -> jax.Array: +def get_stationary_state(state_transition_matrix: jax.Array) -> jax.Array: """Compute the stationary distribution of a transition matrix.""" eigenvalues, left_eigenvectors = jnp.linalg.eig(state_transition_matrix) stationary_state = left_eigenvectors[:, jnp.isclose(eigenvalues, 1)].real @@ -84,9 +81,9 @@ def fanizza(alpha: float, lamb: float) -> jax.Array: [1, 1 - lamb, 1 + lamb * (jnp.sin(alpha) - jnp.cos(alpha)), 1 - lamb * (jnp.sin(alpha) + jnp.cos(alpha))] ) - Da = jnp.outer(w, pi0) + da = jnp.outer(w, pi0) - Db = lamb * jnp.array( + db = lamb * jnp.array( [ [0, 0, 0, 0], [0, 1, 0, 0], @@ -95,7 +92,7 @@ def fanizza(alpha: float, lamb: float) -> jax.Array: ] ) - return jnp.stack([Da, Db], axis=0) + return jnp.stack([da, db], axis=0) def matching_parens(open_probs: list[float]) -> jax.Array: @@ -223,7 +220,7 @@ def no_consecutive_ones(p: float) -> jax.Array: def _validate_post_quantum_conditions(alpha: jax.Array, beta: float) -> None: - if not (alpha > 1 > beta > 0): + if not alpha > 1 > beta > 0: raise ValueError("Condition alpha > 1 > beta > 0 not satisfied") if alpha + beta == 2: raise ValueError("Condition alpha + beta ≠ 2 not satisfied") @@ -259,7 +256,7 @@ def _intermediate_matrix(val): return transition_matrices -def rrxor(pR1: float, pR2: float) -> jax.Array: +def rrxor(p1: float, p2: float) -> jax.Array: """Creates a transition matrix for the RRXOR Process. Steady-state distribution = [2, 1, 1, 1, 1] / 6 @@ -267,12 +264,12 @@ def rrxor(pR1: float, pR2: float) -> jax.Array: s = {"S": 0, "0": 1, "1": 2, "T": 3, "F": 4} transition_matrices = jnp.zeros((2, 5, 5)) - transition_matrices = transition_matrices.at[0, s["S"], s["0"]].set(pR1) - transition_matrices = transition_matrices.at[1, s["S"], s["1"]].set(1 - pR1) - transition_matrices = transition_matrices.at[0, s["0"], s["F"]].set(pR2) - transition_matrices = transition_matrices.at[1, s["0"], s["T"]].set(1 - pR2) - transition_matrices = transition_matrices.at[0, s["1"], s["T"]].set(pR2) - transition_matrices = transition_matrices.at[1, s["1"], s["F"]].set(1 - pR2) + transition_matrices = transition_matrices.at[0, s["S"], s["0"]].set(p1) + transition_matrices = transition_matrices.at[1, s["S"], s["1"]].set(1 - p1) + transition_matrices = transition_matrices.at[0, s["0"], s["F"]].set(p2) + transition_matrices = transition_matrices.at[1, s["0"], s["T"]].set(1 - p2) + transition_matrices = transition_matrices.at[0, s["1"], s["T"]].set(p2) + transition_matrices = transition_matrices.at[1, s["1"], s["F"]].set(1 - p2) transition_matrices = transition_matrices.at[1, s["T"], s["S"]].set(1.0) transition_matrices = transition_matrices.at[0, s["F"], s["S"]].set(1.0) diff --git a/simplexity/logging/file_logger.py b/simplexity/logging/file_logger.py index dd71cfb9..b11791fc 100644 --- a/simplexity/logging/file_logger.py +++ b/simplexity/logging/file_logger.py @@ -1,3 +1,14 @@ +"""FileLogger class for logging to a file.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import json import shutil from collections.abc import Mapping @@ -10,7 +21,6 @@ import PIL.Image import plotly.graph_objects from omegaconf import DictConfig, OmegaConf -from PIL import Image from simplexity.logging.logger import Logger @@ -78,7 +88,7 @@ def _save_image_to_path( if isinstance(image, PIL.Image.Image): image.save(str(image_path), **kwargs) elif isinstance(image, numpy.ndarray): - Image.fromarray(image).save(str(image_path), **kwargs) + PIL.Image.fromarray(image).save(str(image_path), **kwargs) elif isinstance(image, mlflow.Image): # MLflow Image objects need special handling - convert to PIL first pil_image = image.to_pil() diff --git a/simplexity/logging/logger.py b/simplexity/logging/logger.py index fc93a038..6638bce0 100644 --- a/simplexity/logging/logger.py +++ b/simplexity/logging/logger.py @@ -1,3 +1,5 @@ +"""Logger interface for logging to a variety of backends.""" + from abc import ABC, abstractmethod from collections.abc import Mapping from typing import Any @@ -16,22 +18,18 @@ class Logger(ABC): @abstractmethod def log_config(self, config: DictConfig, resolve: bool = False) -> None: """Log config to the logger.""" - ... @abstractmethod def log_metrics(self, step: int, metric_dict: Mapping[str, Any]) -> None: """Log metrics to the logger.""" - ... @abstractmethod def log_params(self, param_dict: Mapping[str, Any]) -> None: """Log params to the logger.""" - ... @abstractmethod def log_tags(self, tag_dict: Mapping[str, Any]) -> None: """Log tags to the logger.""" - ... @abstractmethod def log_figure( @@ -41,7 +39,6 @@ def log_figure( **kwargs, ) -> None: """Log a figure to the logger.""" - ... @abstractmethod def log_image( @@ -65,7 +62,6 @@ def log_image( Must provide either artifact_file OR both key and step parameters. Providing neither or only one of key/step will result in an error. """ - ... @abstractmethod def log_artifact(self, local_path: str, artifact_path: str | None = None) -> None: @@ -76,7 +72,6 @@ def log_artifact(self, local_path: str, artifact_path: str | None = None) -> Non artifact_path: Optional artifact path within the experiment run. If None, uses the filename from local_path. """ - ... @abstractmethod def log_json_artifact(self, data: dict | list, artifact_name: str) -> None: @@ -86,9 +81,7 @@ def log_json_artifact(self, data: dict | list, artifact_name: str) -> None: data: Dictionary or list to serialize as JSON artifact_name: Name for the artifact (e.g., "results.json") """ - ... @abstractmethod def close(self) -> None: """Close the logger.""" - ... diff --git a/simplexity/logging/mlflow_logger.py b/simplexity/logging/mlflow_logger.py index ed750463..cbf5489b 100644 --- a/simplexity/logging/mlflow_logger.py +++ b/simplexity/logging/mlflow_logger.py @@ -1,3 +1,14 @@ +"""MLFlowLogger class for logging to MLflow.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import json import os import tempfile @@ -15,7 +26,12 @@ from omegaconf import DictConfig, OmegaConf from simplexity.logging.logger import Logger -from simplexity.utils.mlflow_utils import get_experiment_id, get_run_id, maybe_terminate_run, resolve_registry_uri +from simplexity.utils.mlflow_utils import ( + get_experiment_id, + get_run_id, + maybe_terminate_run, + resolve_registry_uri, +) dotenv.load_dotenv() _DATABRICKS_HOST = os.getenv("DATABRICKS_HOST") @@ -65,7 +81,7 @@ def tracking_uri(self) -> str | None: @property def registry_uri(self) -> str | None: """Return the model registry URI associated with this logger.""" - return self.client._registry_uri + return self.client._registry_uri # pylint: disable=protected-access def log_config(self, config: DictConfig, resolve: bool = False) -> None: """Log config to MLflow.""" @@ -152,7 +168,7 @@ def log_json_artifact(self, data: dict | list, artifact_name: str) -> None: """Log a JSON object as an artifact to MLflow.""" with tempfile.TemporaryDirectory() as temp_dir: json_path = os.path.join(temp_dir, artifact_name) - with open(json_path, "w") as f: + with open(json_path, "w", encoding="utf-8") as f: json.dump(data, f, indent=2) self.client.log_artifact(self.run_id, json_path) diff --git a/simplexity/logging/print_logger.py b/simplexity/logging/print_logger.py index a8969fb3..33629e95 100644 --- a/simplexity/logging/print_logger.py +++ b/simplexity/logging/print_logger.py @@ -1,3 +1,14 @@ +"""PrintLogger class for logging to the console.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from collections.abc import Mapping from pprint import pprint from typing import Any diff --git a/simplexity/persistence/local_equinox_persister.py b/simplexity/persistence/local_equinox_persister.py index d2eea43c..882225ec 100644 --- a/simplexity/persistence/local_equinox_persister.py +++ b/simplexity/persistence/local_equinox_persister.py @@ -1,3 +1,5 @@ +"""Local Equinox persister.""" + from pathlib import Path import equinox as eqx diff --git a/simplexity/persistence/local_penzai_persister.py b/simplexity/persistence/local_penzai_persister.py index 1f2757b0..6ba03568 100644 --- a/simplexity/persistence/local_penzai_persister.py +++ b/simplexity/persistence/local_penzai_persister.py @@ -1,12 +1,14 @@ +"""Local Penzai persister.""" + from pathlib import Path import orbax.checkpoint as ocp -from penzai.nn.layer import Layer as PenzaiModel from orbax.checkpoint.handlers import DefaultCheckpointHandlerRegistry from penzai import pz +from penzai.nn.layer import Layer as PenzaiModel from simplexity.persistence.local_persister import LocalPersister -from simplexity.utils.penzai import deconstruct_variables, reconstruct_variables +from simplexity.utils.penzai_utils import deconstruct_variables, reconstruct_variables class LocalPenzaiPersister(LocalPersister): diff --git a/simplexity/persistence/local_persister.py b/simplexity/persistence/local_persister.py index fc96e82f..292049a4 100644 --- a/simplexity/persistence/local_persister.py +++ b/simplexity/persistence/local_persister.py @@ -1,3 +1,5 @@ +"""Local persister.""" + from abc import ABC, abstractmethod from pathlib import Path from typing import Any @@ -8,16 +10,13 @@ class LocalPersister(ABC): directory: Path - def cleanup(self) -> None: + def cleanup(self) -> None: # noqa: B027 """Cleans up the persister.""" - ... @abstractmethod def save_weights(self, model: Any, step: int = 0) -> None: """Saves a model.""" - ... @abstractmethod def load_weights(self, model: Any, step: int = 0) -> Any: """Load weights into an existing model instance.""" - ... diff --git a/simplexity/persistence/local_pytorch_persister.py b/simplexity/persistence/local_pytorch_persister.py index 29d31145..078a259f 100644 --- a/simplexity/persistence/local_pytorch_persister.py +++ b/simplexity/persistence/local_pytorch_persister.py @@ -1,3 +1,5 @@ +"""Local PyTorch persister.""" + from pathlib import Path import torch @@ -14,8 +16,7 @@ def __init__(self, directory: str | Path, filename: str = "model.pt"): self.directory = Path(directory) self.filename = filename - # TODO: This is a hack to get the type checker to work. - def save_weights(self, model: torch.nn.Module, step: int = 0, overwrite_existing: bool = False) -> None: # type: ignore + def save_weights(self, model: torch.nn.Module, step: int = 0, overwrite_existing: bool = False) -> None: """Saves a PyTorch model to the local filesystem.""" path = self._get_path(step) path.parent.mkdir(parents=True, exist_ok=True) @@ -25,8 +26,7 @@ def save_weights(self, model: torch.nn.Module, step: int = 0, overwrite_existing torch.save(model.state_dict(), path) - # TODO: This is a hack to get the type checker to work. - def load_weights(self, model: torch.nn.Module, step: int = 0) -> torch.nn.Module: # type: ignore + def load_weights(self, model: torch.nn.Module, step: int = 0) -> torch.nn.Module: """Loads weights into a PyTorch model from the local filesystem.""" path = self._get_path(step) device = next(model.parameters()).device if list(model.parameters()) else "cpu" diff --git a/simplexity/persistence/mlflow_persister.py b/simplexity/persistence/mlflow_persister.py index fc4fcf1f..935dd9b8 100644 --- a/simplexity/persistence/mlflow_persister.py +++ b/simplexity/persistence/mlflow_persister.py @@ -5,7 +5,7 @@ import shutil import tempfile from pathlib import Path -from typing import TYPE_CHECKING, Any +from typing import Any import mlflow from omegaconf import DictConfig, OmegaConf @@ -15,27 +15,32 @@ from simplexity.utils.config_utils import typed_instantiate from simplexity.utils.mlflow_utils import get_experiment_id, get_run_id, maybe_terminate_run, resolve_registry_uri -if TYPE_CHECKING: - from mlflow import MlflowClient - def _build_local_persister(model_framework: ModelFramework, artifact_dir: Path) -> LocalPersister: - if model_framework == ModelFramework.Equinox: - from simplexity.persistence.local_equinox_persister import LocalEquinoxPersister + if model_framework == ModelFramework.EQUINOX: + from simplexity.persistence.local_equinox_persister import ( # pylint: disable=import-outside-toplevel + LocalEquinoxPersister, + ) directory = artifact_dir / "equinox" return LocalEquinoxPersister(directory=directory) - if model_framework == ModelFramework.Penzai: - from simplexity.persistence.local_penzai_persister import LocalPenzaiPersister + if model_framework == ModelFramework.PENZAI: + from simplexity.persistence.local_penzai_persister import ( # pylint: disable=import-outside-toplevel + LocalPenzaiPersister, + ) directory = artifact_dir / "penzai" return LocalPenzaiPersister(directory=directory) - if model_framework == ModelFramework.Pytorch: - from simplexity.persistence.local_pytorch_persister import LocalPytorchPersister + if model_framework == ModelFramework.PYTORCH: + from simplexity.persistence.local_pytorch_persister import ( # pylint: disable=import-outside-toplevel + LocalPytorchPersister, + ) directory = artifact_dir / "pytorch" return LocalPytorchPersister(directory=directory) + raise ValueError(f"Unsupported model framework: {model_framework}") + def _clear_subdirectory(subdirectory: Path) -> None: if subdirectory.exists(): @@ -43,18 +48,9 @@ def _clear_subdirectory(subdirectory: Path) -> None: subdirectory.parent.mkdir(parents=True, exist_ok=True) -class MLFlowPersister: +class MLFlowPersister: # pylint: disable=too-many-instance-attributes """Persist model checkpoints as MLflow artifacts, optionally reusing an existing run.""" - _client: MlflowClient - _experiment_id: str - _run_id: str - _artifact_path: str - _temp_dir: tempfile.TemporaryDirectory - _artifact_dir: Path - _config_path: str - _local_persisters: dict[ModelFramework, LocalPersister] - def __init__( self, experiment_name: str | None = None, diff --git a/simplexity/persistence/model_persister.py b/simplexity/persistence/model_persister.py index 1e705a48..0707d87e 100644 --- a/simplexity/persistence/model_persister.py +++ b/simplexity/persistence/model_persister.py @@ -1,3 +1,5 @@ +"""Model persister protocol.""" + from typing import Any, Protocol @@ -6,12 +8,10 @@ class ModelPersister(Protocol): def cleanup(self) -> None: """Cleans up the persister.""" - ... def save_weights(self, model: Any, step: int = 0) -> None: """Saves a model.""" - ... def load_weights(self, model: Any, step: int = 0) -> Any: """Load weights into an existing model instance.""" - ... + ... # pylint: disable=unnecessary-ellipsis diff --git a/simplexity/persistence/s3_persister.py b/simplexity/persistence/s3_persister.py index 6e090086..dcf05d10 100644 --- a/simplexity/persistence/s3_persister.py +++ b/simplexity/persistence/s3_persister.py @@ -1,3 +1,5 @@ +"""S3 persister for predictive models.""" + import configparser import tempfile from collections.abc import Iterable, Mapping @@ -21,9 +23,9 @@ class S3Paginator(Protocol): Since boto3 does not currently support type checking: https://github.com/boto/boto3/issues/1055 """ - def paginate(self, Bucket: str, Prefix: str) -> Iterable[Mapping[str, Any]]: + def paginate(self, Bucket: str, Prefix: str) -> Iterable[Mapping[str, Any]]: # pylint: disable=invalid-name """Paginate over the objects in an S3 bucket.""" - ... + ... # pylint: disable=unnecessary-ellipsis class S3Client(Protocol): @@ -35,15 +37,13 @@ class S3Client(Protocol): def upload_file(self, file_name: str, bucket: str, object_name: str) -> None: """Upload a file to S3.""" - ... def download_file(self, bucket: str, object_name: str, file_name: str) -> None: """Download a file from S3.""" - ... def get_paginator(self, operation_name: str) -> S3Paginator: """Get a paginator for the given operation.""" - ... + ... # pylint: disable=unnecessary-ellipsis class S3Persister: @@ -67,7 +67,7 @@ def __init__( def from_config( cls, prefix: str, - model_framework: ModelFramework = ModelFramework.Equinox, + model_framework: ModelFramework = ModelFramework.EQUINOX, config_filename: str = "config.ini", ) -> "S3Persister": """Creates a new S3Persister from configuration parameters. @@ -85,11 +85,11 @@ def from_config( session = boto3.session.Session(profile_name=profile_name) s3_client = session.client("s3") temp_dir = tempfile.TemporaryDirectory() - if model_framework == ModelFramework.Equinox: + if model_framework == ModelFramework.EQUINOX: local_persister = LocalEquinoxPersister(directory=temp_dir.name) - elif model_framework == ModelFramework.Penzai: + elif model_framework == ModelFramework.PENZAI: local_persister = LocalPenzaiPersister(directory=temp_dir.name) - elif model_framework == ModelFramework.Pytorch: + elif model_framework == ModelFramework.PYTORCH: local_persister = LocalPytorchPersister(directory=temp_dir.name) else: raise ValueError(f"Unsupported model framework: {model_framework}") diff --git a/simplexity/predictive_models/types.py b/simplexity/predictive_models/types.py index 22a12db5..151b295f 100644 --- a/simplexity/predictive_models/types.py +++ b/simplexity/predictive_models/types.py @@ -1,3 +1,5 @@ +"""Types for predictive models.""" + from enum import StrEnum from functools import singledispatch from typing import Any @@ -8,9 +10,9 @@ class ModelFramework(StrEnum): """The type of model.""" - Equinox = "equinox" - Penzai = "penzai" - Pytorch = "pytorch" + EQUINOX = "equinox" + PENZAI = "penzai" + PYTORCH = "pytorch" @singledispatch @@ -21,7 +23,7 @@ def get_model_framework(predictive_model: Any) -> ModelFramework: @get_model_framework.register(eqx.Module) def _(predictive_model: eqx.Module) -> ModelFramework: - return ModelFramework.Equinox + return ModelFramework.EQUINOX try: @@ -32,7 +34,7 @@ def _(predictive_model: eqx.Module) -> ModelFramework: @get_model_framework.register(TorchModule) def _(predictive_model: TorchModule) -> ModelFramework: - return ModelFramework.Pytorch + return ModelFramework.PYTORCH try: @@ -43,4 +45,4 @@ def _(predictive_model: TorchModule) -> ModelFramework: @get_model_framework.register(PenzaiModel) def _(predictive_model: PenzaiModel) -> ModelFramework: - return ModelFramework.Penzai + return ModelFramework.PENZAI diff --git a/simplexity/run_management/components.py b/simplexity/run_management/components.py index 1b2b8f3b..2757702d 100644 --- a/simplexity/run_management/components.py +++ b/simplexity/run_management/components.py @@ -1,3 +1,14 @@ +"""Components for the run.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from dataclasses import dataclass from typing import Any diff --git a/simplexity/run_management/run_logging.py b/simplexity/run_management/run_logging.py index 623b4dfa..7925678e 100644 --- a/simplexity/run_management/run_logging.py +++ b/simplexity/run_management/run_logging.py @@ -1,3 +1,14 @@ +"""Run logging utilities for logging run information.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import inspect import logging import platform @@ -8,7 +19,7 @@ from hydra.core.hydra_config import HydraConfig from simplexity.logging.logger import Logger -from simplexity.utils.git import get_git_info +from simplexity.utils.git_utils import get_git_info def _get_calling_file_path() -> str | None: diff --git a/simplexity/run_management/run_management.py b/simplexity/run_management/run_management.py index acdb11c0..cbdde100 100644 --- a/simplexity/run_management/run_management.py +++ b/simplexity/run_management/run_management.py @@ -5,6 +5,15 @@ management, and cleanup via the `managed_run` decorator. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import logging import os import random diff --git a/simplexity/run_management/structured_configs.py b/simplexity/run_management/structured_configs.py index b5fecb77..67399df5 100644 --- a/simplexity/run_management/structured_configs.py +++ b/simplexity/run_management/structured_configs.py @@ -592,7 +592,6 @@ def validate_predictive_model_config(cfg: DictConfig) -> None: raise ConfigValidationError( f"PredictiveModelConfig.instance._target_ must be a predictive model target, got {target}" ) - # If this is a HookedTransformerConfig, validate it fully if we have access to the nested cfg if target == "transformer_lens.HookedTransformer" and instance.get("cfg") is not None: validate_hooked_transformer_config(instance) _validate_nonempty_str(cfg.get("name"), "PredictiveModelConfig.name", is_none_allowed=True) diff --git a/simplexity/utils/config_utils.py b/simplexity/utils/config_utils.py index b802f8d5..3e4945f7 100644 --- a/simplexity/utils/config_utils.py +++ b/simplexity/utils/config_utils.py @@ -1,3 +1,5 @@ +"""Config utilities.""" + import importlib import logging from collections.abc import Callable diff --git a/simplexity/utils/equinox_utils.py b/simplexity/utils/equinox_utils.py index 96b87218..bb9cdf24 100644 --- a/simplexity/utils/equinox_utils.py +++ b/simplexity/utils/equinox_utils.py @@ -1,3 +1,5 @@ +"""Equinox utilities.""" + from collections.abc import Callable from typing import Any diff --git a/simplexity/utils/factoring_utils.py b/simplexity/utils/factoring_utils.py new file mode 100644 index 00000000..440a7fc0 --- /dev/null +++ b/simplexity/utils/factoring_utils.py @@ -0,0 +1,173 @@ +"""Core computational kernels for HMM/GHMM factor operations. + +These functions implement the observation and transition dynamics +for individual factors, supporting both HMM and GHMM variants. +""" + +from __future__ import annotations + +from typing import Literal + +import chex +import jax.numpy as jnp +import equinox as eqx +import jax.numpy as jnp + +ComponentType = Literal["hmm", "ghmm"] + + +def compute_obs_dist_for_variant( + component_type: ComponentType, + state: jnp.ndarray, + transition_matrix: jnp.ndarray, + normalizing_eigenvector: jnp.ndarray | None = None, +) -> jnp.ndarray: + """Compute observation distribution for a single factor variant. + + Args: + component_type: "hmm" or "ghmm" + state: State vector of shape [S] + transition_matrix: Transition tensor of shape [V, S, S] + normalizing_eigenvector: For GHMM only, shape [S]. Ignored for HMM. + + Returns: + Distribution over observations, shape [V] + """ + if component_type == "hmm": + # HMM: normalize by sum + obs_state = state @ transition_matrix # [V, S] + return jnp.sum(obs_state, axis=1) # [V] + else: # ghmm + # GHMM: normalize by eigenvector + if normalizing_eigenvector is None: + raise ValueError("GHMM requires normalizing_eigenvector") + numer = state @ transition_matrix @ normalizing_eigenvector # [V] + denom = jnp.sum(state * normalizing_eigenvector) # scalar + return numer / denom + + +def transition_with_obs( + component_type: ComponentType, + state: jnp.ndarray, + transition_matrix: jnp.ndarray, + obs: jnp.ndarray, + normalizing_eigenvector: jnp.ndarray | None = None, +) -> jnp.ndarray: + """Update state after observing a token. + + Args: + component_type: "hmm" or "ghmm" + state: Current state vector of shape [S] + transition_matrix: Transition tensor of shape [V, S, S] + obs: Observed token (scalar int) + normalizing_eigenvector: For GHMM only, shape [S]. Ignored for HMM. + + Returns: + New normalized state vector of shape [S] + """ + new_state = state @ transition_matrix[obs] # [S] + + if component_type == "hmm": + # HMM: normalize by sum + return new_state / jnp.sum(new_state) + else: # ghmm + # GHMM: normalize by eigenvector + if normalizing_eigenvector is None: + raise ValueError("GHMM requires normalizing_eigenvector") + return new_state / (new_state @ normalizing_eigenvector) + +"""Token encoding utilities for factored observations. + +Handles conversion between composite tokens and per-factor token tuples +using radix/base conversion. +""" + + +class TokenEncoder(eqx.Module): + """Encodes/decodes composite observations from per-factor tokens. + + Uses radix encoding: given vocab sizes [V_0, V_1, ..., V_{F-1}], + a tuple (t_0, t_1, ..., t_{F-1}) maps to: + composite = t_0 * (V_1 * V_2 * ... * V_{F-1}) + t_1 * (V_2 * ... * V_{F-1}) + ... + t_{F-1} + + Attributes: + vocab_sizes: Array of shape [F] with vocabulary size per factor + radix_multipliers: Array of shape [F] with multipliers for encoding + """ + + vocab_sizes: jnp.ndarray # shape [F] + radix_multipliers: jnp.ndarray # shape [F] + + def __init__(self, vocab_sizes: jnp.ndarray): + """Initialize encoder with vocab sizes. + + Args: + vocab_sizes: Array of shape [F] with vocabulary size per factor + """ + self.vocab_sizes = jnp.asarray(vocab_sizes) + + # Compute radix multipliers + F = len(vocab_sizes) + multipliers = [] + for i in range(F): + m = 1 + for j in range(i + 1, F): + m *= int(vocab_sizes[j]) + multipliers.append(m) + self.radix_multipliers = jnp.array(multipliers) + + @property + def num_factors(self) -> int: + """Number of factors.""" + return int(self.vocab_sizes.shape[0]) + + @property + def composite_vocab_size(self) -> int: + """Total vocabulary size of composite observation.""" + return int(jnp.prod(self.vocab_sizes)) + + def tuple_to_token(self, token_tuple: tuple[jnp.ndarray, ...]) -> jnp.ndarray: + """Convert per-factor tokens to composite token. + + Args: + token_tuple: Tuple of F scalar arrays, each in [0, V_i) + + Returns: + Scalar array with composite token in [0, prod(V_i)) + """ + token = jnp.array(0) + multiplier = jnp.array(1) + for i in reversed(range(len(token_tuple))): + token += token_tuple[i] * multiplier + multiplier *= self.vocab_sizes[i] + return token + + def token_to_tuple(self, token: chex.Array) -> tuple[jnp.ndarray, ...]: + """Convert composite token to per-factor tokens. + + Args: + token: Scalar array with composite token + + Returns: + Tuple of F scalar arrays with per-factor tokens + """ + result = [] + remaining = jnp.array(token) + for i in reversed(range(self.num_factors)): + v = self.vocab_sizes[i] + t_i = remaining % v + result.append(t_i) + remaining = remaining // v + return tuple(reversed(result)) + + def extract_factors_vectorized(self, tokens: jnp.ndarray) -> jnp.ndarray: + """Extract per-factor tokens from batch of composite tokens. + + Args: + tokens: Array of shape [N] with composite tokens + + Returns: + Array of shape [N, F] with per-factor tokens + """ + tokens = jnp.atleast_1d(tokens) + return (tokens[:, None] // self.radix_multipliers[None, :]) % self.vocab_sizes[None, :] diff --git a/simplexity/utils/git.py b/simplexity/utils/git_utils.py similarity index 58% rename from simplexity/utils/git.py rename to simplexity/utils/git_utils.py index 08068a4c..3bbd7068 100644 --- a/simplexity/utils/git.py +++ b/simplexity/utils/git_utils.py @@ -1,9 +1,13 @@ +"""Git utilities.""" + import re import subprocess from functools import partial from pathlib import Path from urllib.parse import urlsplit +from simplexity.utils.subprocess_utils import handle_subprocess_errors + def _sanitize_remote(remote: str) -> str: """Sanitize git remote URL to remove potential credentials. @@ -19,14 +23,15 @@ def _sanitize_remote(remote: str) -> str: # Try URL-like first: http(s)://..., ssh://..., git+https://... try: parts = urlsplit(remote) + except (TypeError, ValueError): + pass + else: if parts.scheme: # rebuild without username/password, query, fragment host = parts.hostname or "" port = f":{parts.port}" if parts.port else "" path = parts.path or "" return f"{parts.scheme}://{host}{port}{path}" - except Exception: - pass # SCP-like: user@host:path m = re.match(r"^[^@]+@([^:]+):(.*)$", remote) @@ -52,10 +57,11 @@ def _find_git_root(start: Path) -> Path | None: capture_output=True, text=True, timeout=1, + check=False, ) if r.returncode == 0: return Path(r.stdout.strip()) - except Exception: + except (subprocess.TimeoutExpired, subprocess.CalledProcessError): pass for parent in [start.resolve(), *start.resolve().parents]: if (parent / ".git").exists(): @@ -63,6 +69,7 @@ def _find_git_root(start: Path) -> Path | None: return None +@handle_subprocess_errors(default_return={}) def get_git_info(repo_path: Path | None = None) -> dict[str, str]: """Get git repository information. @@ -75,35 +82,31 @@ def get_git_info(repo_path: Path | None = None) -> dict[str, str]: repo_path = _find_git_root(Path.cwd()) if repo_path is None: return {} - try: - # Create a partial function with common arguments - run = partial(subprocess.run, capture_output=True, text=True, timeout=2.0) - - # Get commit hash - result = run(["git", "-C", str(repo_path), "rev-parse", "HEAD"]) - commit_full = result.stdout.strip() if result.returncode == 0 else "unknown" - commit_short = commit_full[:8] if commit_full != "unknown" else "unknown" - - # Check if working directory is dirty (has uncommitted changes) - result = run(["git", "-C", str(repo_path), "diff-index", "--quiet", "HEAD", "--"]) - is_dirty = result.returncode != 0 - - # Get current branch name - result = run(["git", "-C", str(repo_path), "branch", "--show-current"]) - branch = result.stdout.strip() if result.returncode == 0 else "unknown" - - # Get remote URL and sanitize it - result = run(["git", "-C", str(repo_path), "remote", "get-url", "origin"]) - remote_url = result.stdout.strip() if result.returncode == 0 else "unknown" - remote_url = _sanitize_remote(remote_url) - - return { - "commit": commit_short, - "commit_full": commit_full, - "dirty": str(is_dirty), - "branch": branch, - "remote": remote_url, - } - except (subprocess.TimeoutExpired, FileNotFoundError, Exception): - # Return empty dict if git is not available or repo is not a git repo - return {} + # Create a partial function with common arguments + run = partial(subprocess.run, capture_output=True, text=True, timeout=2.0) + + # Get commit hash + result = run(["git", "-C", str(repo_path), "rev-parse", "HEAD"]) + commit_full = result.stdout.strip() if result.returncode == 0 else "unknown" + commit_short = commit_full[:8] if commit_full != "unknown" else "unknown" + + # Check if working directory is dirty (has uncommitted changes) + result = run(["git", "-C", str(repo_path), "diff-index", "--quiet", "HEAD", "--"]) + is_dirty = result.returncode != 0 + + # Get current branch name + result = run(["git", "-C", str(repo_path), "branch", "--show-current"]) + branch = result.stdout.strip() if result.returncode == 0 else "unknown" + + # Get remote URL and sanitize it + result = run(["git", "-C", str(repo_path), "remote", "get-url", "origin"]) + remote_url = result.stdout.strip() if result.returncode == 0 else "unknown" + remote_url = _sanitize_remote(remote_url) + + return { + "commit": commit_short, + "commit_full": commit_full, + "dirty": str(is_dirty), + "branch": branch, + "remote": remote_url, + } diff --git a/simplexity/utils/jnp.py b/simplexity/utils/jnp_utils.py similarity index 99% rename from simplexity/utils/jnp.py rename to simplexity/utils/jnp_utils.py index eab6db00..fc22a796 100644 --- a/simplexity/utils/jnp.py +++ b/simplexity/utils/jnp_utils.py @@ -1,3 +1,5 @@ +"""JAX NumPy utilities.""" + import equinox as eqx import jax import jax.numpy as jnp diff --git a/simplexity/utils/penzai.py b/simplexity/utils/penzai_utils.py similarity index 99% rename from simplexity/utils/penzai.py rename to simplexity/utils/penzai_utils.py index 13f58a7f..42c4bc2e 100644 --- a/simplexity/utils/penzai.py +++ b/simplexity/utils/penzai_utils.py @@ -1,3 +1,7 @@ +"""Penzai utilities.""" + +# pylint: disable-all + from collections import OrderedDict from collections.abc import Callable, Mapping from dataclasses import dataclass diff --git a/simplexity/utils/pip_utils.py b/simplexity/utils/pip_utils.py index 93458b2b..e5ac9ee9 100644 --- a/simplexity/utils/pip_utils.py +++ b/simplexity/utils/pip_utils.py @@ -76,7 +76,7 @@ def get_minimal_requirements(requirements_path: Path | str = "requirements.txt") # ==, !=, >=, <=, ~=, ===, >, <, = # This matches the operators in order (longest first) to avoid partial matches version_specifier_pattern = r"(===|~=|==|!=|>=|<=|>|<|=)" - with open(requirements_path) as f: + with open(requirements_path, encoding="utf-8") as f: for line in f: line = line.strip() if line and not line.startswith("#"): @@ -91,7 +91,7 @@ def create_minimal_requirements_file(requirements_path: Path | str = "requiremen minimal_requirements = get_minimal_requirements(requirements_path) requirements_path = Path(requirements_path) minimal_path = requirements_path.parent / "requirements_minimal.txt" - with minimal_path.open("w") as f: + with minimal_path.open("w", encoding="utf-8") as f: f.write(minimal_requirements) return str(minimal_path) @@ -112,6 +112,6 @@ def create_conda_yaml_file(pyproject_path: str | Path = "pyproject.toml") -> str raise FileNotFoundError(f"pyproject.toml not found at {pyproject_path}") python_dependency = get_python_version(pyproject_path) conda_yaml_path = pyproject_path.parent / "conda.yaml" - with conda_yaml_path.open("w") as f: + with conda_yaml_path.open("w", encoding="utf-8") as f: f.write(CONDA_YAML_CONTENT.format(python_dependency=python_dependency)) return str(conda_yaml_path) diff --git a/simplexity/utils/pytorch_utils.py b/simplexity/utils/pytorch_utils.py index 55015aa3..61491e1f 100644 --- a/simplexity/utils/pytorch_utils.py +++ b/simplexity/utils/pytorch_utils.py @@ -28,15 +28,12 @@ def jax_to_torch(jax_array: jax.Array) -> torch.Tensor: Returns: PyTorch tensor - - Raises: - ImportError: If JAX or PyTorch is not available """ try: torch_tensor = torch_dlpack.from_dlpack(jax_array) return torch_tensor - except Exception as e: + except TypeError as e: warnings.warn( f"DLPack conversion failed ({e}), falling back to numpy. This may cause GPU-to-CPU transfer.", UserWarning, @@ -58,16 +55,13 @@ def torch_to_jax(torch_tensor: torch.Tensor) -> jax.Array: Returns: JAX array - - Raises: - ImportError: If JAX or PyTorch is not available """ try: dlpack_tensor = torch_dlpack.to_dlpack(torch_tensor) # type: ignore jax_array = jax_dlpack.from_dlpack(dlpack_tensor) return jax_array - except Exception as e: + except TypeError as e: warnings.warn( f"DLPack conversion failed ({e}), falling back to numpy. This may cause GPU-to-CPU transfer.", UserWarning, diff --git a/simplexity/utils/subprocess_utils.py b/simplexity/utils/subprocess_utils.py new file mode 100644 index 00000000..a5dda043 --- /dev/null +++ b/simplexity/utils/subprocess_utils.py @@ -0,0 +1,25 @@ +"""Subprocess utilities.""" + +import subprocess +from functools import wraps +from typing import Any + + +def handle_subprocess_errors(default_return: Any | None = None): + """Decorator to catch subprocess errors and return a default value. + + Args: + default_return: Value to return on subprocess errors. Defaults to empty dict. + """ + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except (subprocess.TimeoutExpired, subprocess.CalledProcessError): + return default_return + + return wrapper + + return decorator diff --git a/tests/assertions.py b/tests/assertions.py index eeb9bb32..8317b78e 100644 --- a/tests/assertions.py +++ b/tests/assertions.py @@ -1,9 +1,11 @@ +"""Assertions for testing.""" + import chex import jax import jax.numpy as jnp -def assert_proportional(a: jax.Array, b: jax.Array, rtol: float = 1e-6, atol: float = 0): +def assert_proportional(a: jax.Array, b: jax.Array, rtol: float = 1e-6, atol: float = 0) -> None: """Assert that two arrays are proportional.""" def normalize(x: jax.Array) -> jax.Array: diff --git a/tests/data_structures/test_queue.py b/tests/data_structures/test_queue.py index f7d80987..483546c8 100644 --- a/tests/data_structures/test_queue.py +++ b/tests/data_structures/test_queue.py @@ -1,3 +1,5 @@ +"""Test the Queue data structure.""" + import chex import equinox as eqx import jax @@ -7,7 +9,9 @@ from simplexity.data_structures.queue import Queue -class Element(eqx.Module): +class Element(eqx.Module): # pylint: disable=too-few-public-methods + """An element for the queue.""" + x: jax.Array y: jax.Array z: jax.Array @@ -15,16 +19,19 @@ class Element(eqx.Module): @pytest.fixture def default_element() -> Element: + """A default element for the queue.""" return Element(x=jnp.zeros(4), y=jnp.zeros((2, 3)), z=jnp.array(0)) @pytest.fixture def queue(default_element: Element) -> Queue: + """A queue for the test.""" return Queue(max_size=2, default_element=default_element) @pytest.fixture def elements() -> list[Element]: + """A list of elements for the test.""" num_elements = 3 key = jax.random.PRNGKey(0) keys = jax.random.split(key, 3 * num_elements) diff --git a/tests/data_structures/test_stack.py b/tests/data_structures/test_stack.py index c5bb59ad..0618d2f6 100644 --- a/tests/data_structures/test_stack.py +++ b/tests/data_structures/test_stack.py @@ -1,3 +1,5 @@ +"""Test the Stack data structure.""" + import chex import equinox as eqx import jax @@ -7,7 +9,9 @@ from simplexity.data_structures.stack import Stack -class Element(eqx.Module): +class Element(eqx.Module): # pylint: disable=too-few-public-methods + """An element stored in the stack.""" + x: jax.Array y: jax.Array z: jax.Array @@ -15,16 +19,19 @@ class Element(eqx.Module): @pytest.fixture def default_element() -> Element: + """Return a default element for stack tests.""" return Element(x=jnp.zeros(4), y=jnp.zeros((2, 3)), z=jnp.array(0)) @pytest.fixture def stack(default_element: Element) -> Stack: + """Return a stack preconfigured with the default element.""" return Stack(max_size=2, default_element=default_element) @pytest.fixture def elements() -> list[Element]: + """Return a list of random elements for stack tests.""" num_elements = 3 key = jax.random.PRNGKey(0) keys = jax.random.split(key, 3 * num_elements) diff --git a/tests/generative_processes/goldens/mixed_state_trees/rrxor_pR1_0p5_pR2_0p5.npz b/tests/generative_processes/goldens/mixed_state_trees/rrxor_p1_0p5_p2_0p5.npz similarity index 100% rename from tests/generative_processes/goldens/mixed_state_trees/rrxor_pR1_0p5_pR2_0p5.npz rename to tests/generative_processes/goldens/mixed_state_trees/rrxor_p1_0p5_p2_0p5.npz diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 77bfff9a..c074f7e9 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -1,3 +1,5 @@ +"""Test the builder module.""" + import chex import jax.numpy as jnp import pytest @@ -16,6 +18,7 @@ def test_build_transition_matrices(): + """Test the build_transition_matrices function.""" transition_matrices = build_transition_matrices(HMM_MATRIX_FUNCTIONS, "coin", p=0.6) assert transition_matrices.shape == (2, 1, 1) expected = jnp.array([[[0.6]], [[0.4]]]) @@ -23,6 +26,7 @@ def test_build_transition_matrices(): def test_add_begin_of_sequence_token(): + """Test the add_begin_of_sequence_token function.""" transition_matrix = jnp.array( [ [ @@ -61,6 +65,7 @@ def test_add_begin_of_sequence_token(): def test_build_hidden_markov_model(): + """Test the build_hidden_markov_model function.""" hmm = build_hidden_markov_model("even_ones", p=0.5) assert hmm.vocab_size == 2 @@ -72,6 +77,7 @@ def test_build_hidden_markov_model(): def test_build_generalized_hidden_markov_model(): + """Test the build_generalized_hidden_markov_model function.""" ghmm = build_generalized_hidden_markov_model("even_ones", p=0.5) assert ghmm.vocab_size == 2 @@ -83,6 +89,7 @@ def test_build_generalized_hidden_markov_model(): def test_build_nonergodic_transition_matrices(): + """Test the build_nonergodic_transition_matrices function.""" coin_1 = build_transition_matrices(HMM_MATRIX_FUNCTIONS, "coin", p=0.6) coin_2 = build_transition_matrices(HMM_MATRIX_FUNCTIONS, "coin", p=0.3) transition_matrices = build_nonergodic_transition_matrices([coin_1, coin_2], [[0, 1], [0, 2]]) @@ -107,6 +114,7 @@ def test_build_nonergodic_transition_matrices(): def test_build_nonergodic_initial_state(): + """Test the build_nonergodic_initial_state function.""" state_1 = jnp.array([0.25, 0.40, 0.35]) state_2 = jnp.array([0.7, 0.3]) initial_state = build_nonergodic_initial_state([state_1, state_2], jnp.array([0.8, 0.2])) @@ -116,6 +124,7 @@ def test_build_nonergodic_initial_state(): def test_build_nonergodic_hidden_markov_model(): + """Test the build_nonergodic_hidden_markov_model function.""" hmm = build_nonergodic_hidden_markov_model( process_names=["coin", "coin"], process_kwargs=[{"p": 0.6}, {"p": 0.3}], @@ -147,6 +156,7 @@ def test_build_nonergodic_hidden_markov_model(): def test_build_nonergodic_hidden_markov_model_with_nonergodic_process(): + """Test the build_nonergodic_hidden_markov_model function with a nonergodic process.""" kwargs = {"p": 0.4, "q": 0.25} hmm = build_nonergodic_hidden_markov_model( process_names=["mr_name", "mr_name"], @@ -162,6 +172,7 @@ def test_build_nonergodic_hidden_markov_model_with_nonergodic_process(): def test_build_nonergodic_hidden_markov_model_bos(): + """Test the build_nonergodic_hidden_markov_model function with a BOS token.""" hmm = build_nonergodic_hidden_markov_model( process_names=["coin", "coin"], process_kwargs=[{"p": 0.6}, {"p": 0.3}], diff --git a/tests/generative_processes/test_generalized_hidden_markov_model.py b/tests/generative_processes/test_generalized_hidden_markov_model.py index 33a60c59..a1460875 100644 --- a/tests/generative_processes/test_generalized_hidden_markov_model.py +++ b/tests/generative_processes/test_generalized_hidden_markov_model.py @@ -1,3 +1,14 @@ +"""Tests for generalized hidden Markov models.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import chex import equinox as eqx import jax @@ -11,22 +22,26 @@ @pytest.fixture def z1r() -> GeneralizedHiddenMarkovModel: + """Return the zero-one random generalized HMM.""" return build_generalized_hidden_markov_model("zero_one_random", p=0.5) @pytest.fixture def fanizza_model() -> GeneralizedHiddenMarkovModel: + """Return the fanizza generalized HMM.""" return build_generalized_hidden_markov_model("fanizza", alpha=2000, lamb=0.49) @pytest.mark.parametrize(("model_name", "vocab_size", "num_states"), [("z1r", 2, 3), ("fanizza_model", 2, 4)]) def test_properties(model_name: str, vocab_size: int, num_states: int, request: pytest.FixtureRequest): + """Test vocab size and state count for the available fixtures.""" model: GeneralizedHiddenMarkovModel = request.getfixturevalue(model_name) assert model.vocab_size == vocab_size assert model.num_states == num_states def test_normalize_belief_state(z1r: GeneralizedHiddenMarkovModel): + """Test belief-state normalization under linear probabilities.""" state = jnp.array([2, 5, 1]) belief_state = z1r.normalize_belief_state(state) chex.assert_trees_all_close(belief_state, jnp.array([0.25, 0.625, 0.125])) @@ -37,6 +52,7 @@ def test_normalize_belief_state(z1r: GeneralizedHiddenMarkovModel): def test_normalize_log_belief_state(z1r: GeneralizedHiddenMarkovModel): + """Test log-space belief-state normalization.""" state = jnp.log(jnp.array([2, 5, 1])) log_belief_state = z1r.normalize_log_belief_state(state) chex.assert_trees_all_close(log_belief_state, jnp.log(jnp.array([0.25, 0.625, 0.125]))) @@ -47,6 +63,7 @@ def test_normalize_log_belief_state(z1r: GeneralizedHiddenMarkovModel): def test_hmm_single_transition(z1r: GeneralizedHiddenMarkovModel): + """Test single-step transitions from each canonical state.""" zero_state = jnp.array([[1.0, 0.0, 0.0]]) one_state = jnp.array([[0.0, 1.0, 0.0]]) random_state = jnp.array([[0.0, 0.0, 1.0]]) @@ -88,6 +105,7 @@ def test_hmm_single_transition(z1r: GeneralizedHiddenMarkovModel): @pytest.mark.parametrize("model_name", ["z1r", "fanizza_model"]) def test_generate(model_name: str, request: pytest.FixtureRequest): + """Test sampling sequences without intermediate states.""" model: GeneralizedHiddenMarkovModel = request.getfixturevalue(model_name) batch_size = 4 sequence_len = 10 @@ -106,6 +124,7 @@ def test_generate(model_name: str, request: pytest.FixtureRequest): @pytest.mark.parametrize("model_name", ["z1r", "fanizza_model"]) def test_generate_with_intermediate_states(model_name: str, request: pytest.FixtureRequest): + """Test sampling sequences while also returning intermediates.""" model: GeneralizedHiddenMarkovModel = request.getfixturevalue(model_name) batch_size = 4 sequence_len = 10 @@ -123,6 +142,7 @@ def test_generate_with_intermediate_states(model_name: str, request: pytest.Fixt def test_hmm_observation_probability_distribution(z1r: GeneralizedHiddenMarkovModel): + """Test observation probability distribution in probability space.""" state = jnp.array([0.3, 0.1, 0.6]) obs_probs = z1r.observation_probability_distribution(state) chex.assert_trees_all_close(obs_probs, jnp.array([0.6, 0.4])) @@ -133,6 +153,7 @@ def test_hmm_observation_probability_distribution(z1r: GeneralizedHiddenMarkovMo def test_ghmm_observation_probability_distribution(fanizza_model: GeneralizedHiddenMarkovModel): + """Test observation probabilities for the fanizza GHMM.""" valid_state = fanizza_model.initial_state obs_probs = fanizza_model.observation_probability_distribution(valid_state) assert jnp.isclose(jnp.sum(obs_probs), 1) @@ -146,6 +167,7 @@ def test_ghmm_observation_probability_distribution(fanizza_model: GeneralizedHid def test_hmm_log_observation_probability_distribution(z1r: GeneralizedHiddenMarkovModel): + """Test observation probabilities in log space.""" log_belief_state = jnp.log(jnp.array([0.3, 0.1, 0.6])) log_obs_probs = z1r.log_observation_probability_distribution(log_belief_state) assert jnp.isclose(jax.nn.logsumexp(log_obs_probs), 0, atol=2e-7) @@ -158,6 +180,7 @@ def test_hmm_log_observation_probability_distribution(z1r: GeneralizedHiddenMark def test_hmm_probability(z1r: GeneralizedHiddenMarkovModel): + """Test forward probability of a fixed observation sequence.""" observations = jnp.array([1, 0, 0, 1, 1, 0]) expected_probability = 1 / 12 @@ -166,6 +189,7 @@ def test_hmm_probability(z1r: GeneralizedHiddenMarkovModel): def test_ghmm_probability(fanizza_model: GeneralizedHiddenMarkovModel): + """Test probability bounds for random observations.""" key = jax.random.PRNGKey(0) observations = jax.random.randint(key, (10,), 0, fanizza_model.vocab_size) @@ -174,6 +198,7 @@ def test_ghmm_probability(fanizza_model: GeneralizedHiddenMarkovModel): def test_hmm_log_probability(z1r: GeneralizedHiddenMarkovModel): + """Test log-probability of a fixed observation sequence.""" observations = jnp.array([1, 0, 0, 1, 1, 0]) expected_probability = 1 / 12 @@ -182,6 +207,7 @@ def test_hmm_log_probability(z1r: GeneralizedHiddenMarkovModel): def test_ghmm_log_probability(fanizza_model: GeneralizedHiddenMarkovModel): + """Test that log probabilities remain non-positive.""" key = jax.random.PRNGKey(0) observations = jax.random.randint(key, (10,), 0, fanizza_model.vocab_size) diff --git a/tests/generative_processes/test_generator.py b/tests/generative_processes/test_generator.py index cab049cd..615be620 100644 --- a/tests/generative_processes/test_generator.py +++ b/tests/generative_processes/test_generator.py @@ -1,3 +1,5 @@ +"""Test the generator module.""" + import chex import jax import jax.numpy as jnp @@ -7,6 +9,7 @@ def test_generate_data_batch(): + """Test the generate_data_batch function.""" hmm = build_hidden_markov_model("zero_one_random", p=0.5) batch_size = 10 sequence_len = 10 @@ -25,6 +28,7 @@ def test_generate_data_batch(): def test_generate_data_batch_with_bos_token(): + """Test the generate_data_batch function with a BOS token.""" hmm = build_hidden_markov_model("zero_one_random", p=0.5) batch_size = 10 sequence_len = 10 @@ -45,6 +49,7 @@ def test_generate_data_batch_with_bos_token(): def test_generate_data_batch_with_eos_token(): + """Test the generate_data_batch function with an EOS token.""" hmm = build_hidden_markov_model("zero_one_random", p=0.5) batch_size = 10 sequence_len = 10 diff --git a/tests/generative_processes/test_hidden_markov_model.py b/tests/generative_processes/test_hidden_markov_model.py index 4c495bcb..4943d9ba 100644 --- a/tests/generative_processes/test_hidden_markov_model.py +++ b/tests/generative_processes/test_hidden_markov_model.py @@ -1,3 +1,14 @@ +"""Tests for standard hidden Markov models.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import chex import equinox as eqx import jax @@ -11,10 +22,12 @@ @pytest.fixture def z1r() -> HiddenMarkovModel: + """Return the zero-one random HMM.""" return build_hidden_markov_model("zero_one_random", p=0.5) def test_properties(z1r: HiddenMarkovModel): + """Test key properties of the base HMM.""" assert z1r.vocab_size == 2 assert z1r.num_states == 3 assert_proportional(z1r.normalizing_eigenvector, jnp.ones(3)) @@ -22,6 +35,7 @@ def test_properties(z1r: HiddenMarkovModel): def test_normalize_belief_state(z1r: HiddenMarkovModel): + """Test normalization in probability space.""" state = jnp.array([2, 5, 1]) belief_state = z1r.normalize_belief_state(state) chex.assert_trees_all_close(belief_state, jnp.array([0.25, 0.625, 0.125])) @@ -32,6 +46,7 @@ def test_normalize_belief_state(z1r: HiddenMarkovModel): def test_normalize_log_belief_state(z1r: HiddenMarkovModel): + """Test normalization in log space.""" state = jnp.log(jnp.array([2, 5, 1])) log_belief_state = z1r.normalize_log_belief_state(state) chex.assert_trees_all_close(log_belief_state, jnp.log(jnp.array([0.25, 0.625, 0.125]))) @@ -42,6 +57,7 @@ def test_normalize_log_belief_state(z1r: HiddenMarkovModel): def test_single_transition(z1r: HiddenMarkovModel): + """Test single transition outcomes and observations.""" zero_state = jnp.array([[1.0, 0.0, 0.0]]) one_state = jnp.array([[0.0, 1.0, 0.0]]) random_state = jnp.array([[0.0, 0.0, 1.0]]) @@ -82,6 +98,7 @@ def test_single_transition(z1r: HiddenMarkovModel): def test_generate(z1r: HiddenMarkovModel): + """Test multi-step generation without intermediates.""" batch_size = 4 sequence_len = 10 @@ -98,6 +115,7 @@ def test_generate(z1r: HiddenMarkovModel): def test_observation_probability_distribution(z1r: HiddenMarkovModel): + """Test probability-space observation distribution.""" state = jnp.array([0.3, 0.1, 0.6]) obs_probs = z1r.observation_probability_distribution(state) chex.assert_trees_all_close(obs_probs, jnp.array([0.6, 0.4])) @@ -108,6 +126,7 @@ def test_observation_probability_distribution(z1r: HiddenMarkovModel): def test_log_observation_probability_distribution(z1r: HiddenMarkovModel): + """Test log-space observation distribution.""" log_belief_state = jnp.log(jnp.array([0.3, 0.1, 0.6])) log_obs_probs = z1r.log_observation_probability_distribution(log_belief_state) assert jnp.isclose(jax.nn.logsumexp(log_obs_probs), 0, atol=1e-7) @@ -120,6 +139,7 @@ def test_log_observation_probability_distribution(z1r: HiddenMarkovModel): def test_probability(z1r: HiddenMarkovModel): + """Test probability of a fixed observation sequence.""" observations = jnp.array([1, 0, 0, 1, 1, 0]) expected_probability = 1 / 12 @@ -128,6 +148,7 @@ def test_probability(z1r: HiddenMarkovModel): def test_log_probability(z1r: HiddenMarkovModel): + """Test log probability of a fixed observation sequence.""" observations = jnp.array([1, 0, 0, 1, 1, 0]) expected_probability = 1 / 12 diff --git a/tests/generative_processes/test_mixed_state_presentation.py b/tests/generative_processes/test_mixed_state_presentation.py index b940fe38..49361373 100644 --- a/tests/generative_processes/test_mixed_state_presentation.py +++ b/tests/generative_processes/test_mixed_state_presentation.py @@ -1,3 +1,5 @@ +"""Tests for mixed-state presentation utilities.""" + import math from typing import cast @@ -152,17 +154,20 @@ @pytest.fixture def generator() -> MixedStateTreeGenerator: + """Return the standard mixed-state tree generator.""" ghmm = build_generalized_hidden_markov_model("no_consecutive_ones", p=0.5) return MixedStateTreeGenerator(ghmm, max_sequence_length=2) @pytest.fixture def log_generator() -> LogMixedStateTreeGenerator: + """Return the log-space mixed-state tree generator.""" hmm = build_hidden_markov_model("no_consecutive_ones", p=0.5) return LogMixedStateTreeGenerator(hmm, max_sequence_length=2, prob_threshold=-jnp.inf) def get_sequences_in_collection(collection: Queue[MixedStateNode]) -> list[tuple[int, ...]]: + """Extract readable sequences from a collection of nodes.""" nodes = cast(MixedStateNode, collection.data) sequences = [] for i in range(collection.size): @@ -173,6 +178,7 @@ def get_sequences_in_collection(collection: Queue[MixedStateNode]) -> list[tuple @pytest.mark.parametrize(("generator_name", "expected_nodes"), [("generator", NODES), ("log_generator", LOG_NODES)]) def test_get_child(generator_name: str, expected_nodes: dict[str, MixedStateNode], request: pytest.FixtureRequest): + """Test retrieving child nodes for both generator types.""" generator = cast(MixedStateTreeGenerator, request.getfixturevalue(generator_name)) child = generator.get_child(generator.root, jnp.array(0)) chex.assert_trees_all_close(child, expected_nodes["0"], atol=ABS_TOL) @@ -183,6 +189,7 @@ def test_get_child(generator_name: str, expected_nodes: dict[str, MixedStateNode @pytest.mark.parametrize("generator_name", ["generator", "log_generator"]) def test_get_all_children(generator_name: str, request: pytest.FixtureRequest): + """Test generating all children from the search queue.""" generator = cast(MixedStateTreeGenerator, request.getfixturevalue(generator_name)) search_nodes = Queue(max_size=7, default_element=generator.root) search_nodes = search_nodes.enqueue(generator.root) @@ -210,12 +217,13 @@ def test_next_node( expected_sequences: list[str], request: pytest.FixtureRequest, ): + """Test finding the next node in a mixed-state tree.""" generator = cast(MixedStateTreeGenerator, request.getfixturevalue(generator_name)) search_nodes = data_structure(max_size=7, default_element=generator.root) search_nodes = search_nodes.add(generator.root) for expected_sequence in expected_sequences: - search_nodes, node = generator._next_node(search_nodes) + search_nodes, node = generator._next_node(search_nodes) # pylint: disable=protected-access expected_node = expected_nodes[expected_sequence] chex.assert_trees_all_close(node, expected_node, atol=ABS_TOL) @@ -224,6 +232,7 @@ def test_next_node( @pytest.mark.parametrize("search_algorithm", [SearchAlgorithm.BREADTH_FIRST, SearchAlgorithm.DEPTH_FIRST]) def test_generate(generator: MixedStateTreeGenerator, search_algorithm: SearchAlgorithm): + """Test generating a mixed-state tree.""" tree = generator.generate(search_algorithm) assert isinstance(tree, MixedStateTree) expected_nodes: dict[Sequence, NodeDictValue] = { @@ -253,6 +262,7 @@ def assert_node_dict_values_close(actual: NodeDictValue, expected: NodeDictValue @pytest.mark.parametrize("search_algorithm", [SearchAlgorithm.BREADTH_FIRST, SearchAlgorithm.DEPTH_FIRST]) def test_log_generate(log_generator: LogMixedStateTreeGenerator, search_algorithm: SearchAlgorithm): + """Test generating a log-space mixed-state tree.""" tree = log_generator.generate(search_algorithm) assert isinstance(tree, LogMixedStateTree) log_1 = 0.0 @@ -288,6 +298,7 @@ def assert_node_dict_values_close(actual: LogNodeDictValue, expected: LogNodeDic @pytest.mark.parametrize("generator_name", ["generator", "log_generator"]) def test_myopic_entropy(generator_name: str, request: pytest.FixtureRequest): + """Test computing myopic entropy for a mixed-state tree.""" generator = cast(MixedStateTreeGenerator, request.getfixturevalue(generator_name)) myopic_entropies = generator.compute_myopic_entropy() assert myopic_entropies.sequence_lengths.shape == (generator.max_sequence_length + 1,) diff --git a/tests/generative_processes/test_mixed_state_tree.py b/tests/generative_processes/test_mixed_state_tree.py index bb83608e..b90f232f 100644 --- a/tests/generative_processes/test_mixed_state_tree.py +++ b/tests/generative_processes/test_mixed_state_tree.py @@ -1,3 +1,5 @@ +"""Test the mixed-state tree module.""" + from pathlib import Path import chex @@ -28,8 +30,8 @@ "beta": 0.5, }, "rrxor": { - "pR1": 0.5, - "pR2": 0.5, + "p1": 0.5, + "p2": 0.5, }, "tom_quantum": { "alpha": 1, @@ -74,7 +76,8 @@ def load_golden(process_name: str) -> MixedStateTree[TreeData, NodeDictValue]: @pytest.mark.parametrize("process_name", ["mess3", "rrxor", "zero_one_random"]) -def test_hmm_mixed_state_tree(process_name): +def test_hmm_mixed_state_tree(process_name: str): + """Test generating a mixed-state tree for a hidden Markov model.""" params = PROCESS_PARAMS[process_name] model = build_hidden_markov_model(process_name, initial_state=None, **params) generator = MixedStateTreeGenerator(model, max_sequence_length=4) @@ -98,7 +101,8 @@ def test_hmm_mixed_state_tree(process_name): @pytest.mark.parametrize( "process_name", ["fanizza", "mess3", "post_quantum", "rrxor", "tom_quantum", "zero_one_random"] ) -def test_ghmm_mixed_state_tree(process_name): +def test_ghmm_mixed_state_tree(process_name: str): + """Test generating a mixed-state tree for a generalized hidden Markov model.""" params = PROCESS_PARAMS[process_name] model = build_generalized_hidden_markov_model(process_name, **params) generator = MixedStateTreeGenerator(model, max_sequence_length=4) diff --git a/tests/generative_processes/test_torch_generator.py b/tests/generative_processes/test_torch_generator.py index a3132b6a..7caa002b 100644 --- a/tests/generative_processes/test_torch_generator.py +++ b/tests/generative_processes/test_torch_generator.py @@ -1,3 +1,5 @@ +"""Test the torch generator module.""" + import jax import jax.numpy as jnp import torch @@ -7,6 +9,7 @@ def test_generate_data_batch(): + """Test generating a batch of data.""" hmm = build_hidden_markov_model("zero_one_random", p=0.5) batch_size = 10 sequence_len = 10 @@ -28,6 +31,7 @@ def test_generate_data_batch(): def test_generate_data_batch_with_bos_token(): + """Test generating a batch of data with a BOS token.""" hmm = build_hidden_markov_model("zero_one_random", p=0.5) batch_size = 10 sequence_len = 10 @@ -51,6 +55,7 @@ def test_generate_data_batch_with_bos_token(): def test_generate_data_batch_with_eos_token(): + """Test generating a batch of data with an EOS token.""" hmm = build_hidden_markov_model("zero_one_random", p=0.5) batch_size = 10 sequence_len = 10 diff --git a/tests/generative_processes/test_transition_matrices.py b/tests/generative_processes/test_transition_matrices.py index 34d615cd..2d6f0960 100644 --- a/tests/generative_processes/test_transition_matrices.py +++ b/tests/generative_processes/test_transition_matrices.py @@ -1,4 +1,7 @@ +"""Test the transition matrices module.""" + import chex +import jax import jax.numpy as jnp from simplexity.generative_processes.transition_matrices import ( @@ -6,6 +9,7 @@ days_of_week, even_ones, fanizza, + get_stationary_state, matching_parens, mess3, mr_name, @@ -13,7 +17,6 @@ post_quantum, rrxor, sns, - stationary_state, tom_quantum, zero_one_random, ) @@ -21,15 +24,15 @@ def test_stationary_state(): + """Test the get_stationary_state function.""" transition_matrix = jnp.array([[0.5, 0.5], [0.5, 0.5]]) - actual = stationary_state(transition_matrix) + actual = get_stationary_state(transition_matrix) expected = jnp.array([0.5, 0.5]) assert jnp.allclose(actual, expected) -def validate_ghmm_transition_matrices( - transition_matrices: jnp.ndarray, ergodic: bool = True, rtol: float = 1e-6, atol: float = 0 -): +def validate_ghmm_transition_matrices(transition_matrices: jax.Array, ergodic: bool = True) -> None: + """Test the validate_ghmm_transition_matrices function.""" transition_matrix = jnp.sum(transition_matrices, axis=0) num_states = transition_matrix.shape[0] @@ -49,7 +52,8 @@ def validate_ghmm_transition_matrices( def validate_hmm_transition_matrices( transition_matrices: jnp.ndarray, ergodic: bool = True, rtol: float = 1e-6, atol: float = 0 ): - validate_ghmm_transition_matrices(transition_matrices, ergodic, rtol, atol) + """Test the validate_hmm_transition_matrices function.""" + validate_ghmm_transition_matrices(transition_matrices, ergodic) assert jnp.all(transition_matrices >= 0) assert jnp.all(transition_matrices <= 1) @@ -74,30 +78,34 @@ def validate_hmm_transition_matrices( def test_coin(): + """Test the coin transition matrices.""" transition_matrices = coin(p=0.5) assert transition_matrices.shape == (2, 1, 1) validate_hmm_transition_matrices(transition_matrices) state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([1])) def test_days_of_week(): + """Test the days of week transition matrices.""" transition_matrices = days_of_week() assert transition_matrices.shape == (11, 7, 7) validate_hmm_transition_matrices(transition_matrices, rtol=2e-6) def test_even_ones(): + """Test the even ones transition matrices.""" transition_matrices = even_ones(p=0.5) assert transition_matrices.shape == (2, 2, 2) validate_hmm_transition_matrices(transition_matrices) state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([2, 1]) / 3) def test_fanizza(): + """Test the fanizza transition matrices.""" transition_matrices = fanizza(alpha=2000, lamb=0.49) assert transition_matrices.shape == (2, 4, 4) validate_ghmm_transition_matrices(transition_matrices) @@ -106,39 +114,44 @@ def test_fanizza(): def test_matching_parens(): + """Test the matching parens transition matrices.""" transition_matrices = matching_parens(open_probs=[1.0, 0.5, 0.5]) assert transition_matrices.shape == (2, 4, 4) validate_hmm_transition_matrices(transition_matrices, rtol=1e-5) state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([1, 2, 2, 1]) / 6) def test_mess3(): + """Test the mess3 transition matrices.""" transition_matrices = mess3(x=0.15, a=0.6) assert transition_matrices.shape == (3, 3, 3) validate_hmm_transition_matrices(transition_matrices) def test_mr_name(): + """Test the mr name transition matrices.""" transition_matrices = mr_name(p=0.4, q=0.25) assert transition_matrices.shape == (4, 4, 4) validate_hmm_transition_matrices(transition_matrices) state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([1, 2, 2, 1]) / 6) def test_no_consecutive_ones(): + """Test the no consecutive ones transition matrices.""" transition_matrices = no_consecutive_ones(p=0.5) assert transition_matrices.shape == (2, 2, 2) validate_hmm_transition_matrices(transition_matrices) state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([2, 1]) / 3) def test_post_quantum(): + """Test the post quantum transition matrices.""" transition_matrices = post_quantum(log_alpha=1.0, beta=0.5) assert transition_matrices.shape == (3, 3, 3) validate_ghmm_transition_matrices(transition_matrices) @@ -149,30 +162,34 @@ def test_post_quantum(): def test_rrxor(): - transition_matrices = rrxor(pR1=0.5, pR2=0.5) + """Test the rrxor transition matrices.""" + transition_matrices = rrxor(p1=0.5, p2=0.5) assert transition_matrices.shape == (2, 5, 5) validate_hmm_transition_matrices(transition_matrices, rtol=1e-5) # rtol=1e-6 barely fails state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([2, 1, 1, 1, 1]) / 6) def test_sns(): + """Test the sns transition matrices.""" transition_matrices = sns(p=0.5, q=0.5) assert transition_matrices.shape == (2, 2, 2) validate_hmm_transition_matrices(transition_matrices) def test_tom_quantum(): + """Test the tom quantum transition matrices.""" transition_matrices = tom_quantum(alpha=1.0, beta=1.0) assert transition_matrices.shape == (4, 3, 3) validate_ghmm_transition_matrices(transition_matrices) def test_zero_one_random(): + """Test the zero one random transition matrices.""" transition_matrices = zero_one_random(p=0.5) assert transition_matrices.shape == (2, 3, 3) validate_hmm_transition_matrices(transition_matrices) state_transition_matrix = jnp.sum(transition_matrices, axis=0) - stationary_distribution = stationary_state(state_transition_matrix.T) + stationary_distribution = get_stationary_state(state_transition_matrix.T) assert jnp.allclose(stationary_distribution, jnp.array([1, 1, 1]) / 3) diff --git a/tests/logging/test_artifact_logging.py b/tests/logging/test_artifact_logging.py index ce395795..2aac05da 100644 --- a/tests/logging/test_artifact_logging.py +++ b/tests/logging/test_artifact_logging.py @@ -1,5 +1,14 @@ """Tests for artifact logging functionality across all logger implementations.""" +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import json import os import tempfile diff --git a/tests/logging/test_file_logger.py b/tests/logging/test_file_logger.py index 332d524e..84b71773 100644 --- a/tests/logging/test_file_logger.py +++ b/tests/logging/test_file_logger.py @@ -1,3 +1,14 @@ +"""Test the file logger.""" + +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from pathlib import Path import jax.numpy as jnp diff --git a/tests/logging/test_plot_logging.py b/tests/logging/test_plot_logging.py index 6bfbfbc5..5d4b9475 100644 --- a/tests/logging/test_plot_logging.py +++ b/tests/logging/test_plot_logging.py @@ -1,5 +1,14 @@ """Tests for plot and image logging functionality across all logger implementations.""" +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import os import tempfile from pathlib import Path diff --git a/tests/persistence/s3_mocks.py b/tests/persistence/s3_mocks.py index 5ce4a16c..3ce2d0c1 100644 --- a/tests/persistence/s3_mocks.py +++ b/tests/persistence/s3_mocks.py @@ -1,3 +1,5 @@ +"""Mock S3 client and paginator for testing.""" + import shutil import tempfile from collections.abc import Iterable, Mapping @@ -5,13 +7,13 @@ from typing import Any -class MockS3Paginator: +class MockS3Paginator: # pylint: disable=too-few-public-methods """Local filesystem implementation of S3 paginator for testing.""" def __init__(self, root_dir: Path): self.root_dir = root_dir - def paginate(self, Bucket: str, Prefix: str) -> Iterable[Mapping[str, Any]]: + def paginate(self, Bucket: str, Prefix: str) -> Iterable[Mapping[str, Any]]: # pylint: disable=invalid-name """Paginate over the objects in an S3 bucket.""" bucket_dir = self.root_dir / Bucket bucket_dir.mkdir(exist_ok=True) @@ -39,7 +41,7 @@ def download_file(self, bucket: str, object_name: str, file_name: str) -> None: raise RuntimeError(f"File not found: {object_name}") shutil.copy(source_path, file_name) - def get_paginator(self, operation_name: str) -> MockS3Paginator: + def get_paginator(self, operation_name: str) -> MockS3Paginator: # pylint: disable=unused-argument """Get a paginator for the given operation.""" return MockS3Paginator(self.root_dir) @@ -47,7 +49,7 @@ def get_paginator(self, operation_name: str) -> MockS3Paginator: class MockBoto3Session: """Mock Boto3 Session for testing.""" - def __init__(self, profile_name: str | None = None): + def __init__(self, profile_name: str | None = None): # pylint: disable=unused-argument self._root_dir: Path | None = None self._s3_client: MockS3Client | None = None diff --git a/tests/persistence/test_local_equinox_persister.py b/tests/persistence/test_local_equinox_persister.py index 18fc5205..04de3aeb 100644 --- a/tests/persistence/test_local_equinox_persister.py +++ b/tests/persistence/test_local_equinox_persister.py @@ -1,3 +1,5 @@ +"""Test the local equinox persister.""" + from pathlib import Path import chex @@ -9,6 +11,7 @@ def get_model(seed: int) -> eqx.Module: + """Get a model for testing.""" return eqx.nn.Linear( in_features=4, out_features=3, @@ -17,6 +20,7 @@ def get_model(seed: int) -> eqx.Module: def test_local_persister(tmp_path: Path): + """Test the local equinox persister.""" directory = tmp_path filename = "test_model.eqx" persister = LocalEquinoxPersister(directory, filename) diff --git a/tests/persistence/test_local_penzai_persister.py b/tests/persistence/test_local_penzai_persister.py index 2df40610..068ee7ad 100644 --- a/tests/persistence/test_local_penzai_persister.py +++ b/tests/persistence/test_local_penzai_persister.py @@ -1,3 +1,5 @@ +"""Test the local penzai persister.""" + from pathlib import Path import chex @@ -12,6 +14,7 @@ def test_local_penzai_persister(tmp_path: Path): + """Test the local penzai persister.""" config = LlamalikeTransformerConfig( num_kv_heads=1, query_head_multiplier=1, diff --git a/tests/persistence/test_local_pytorch_persister.py b/tests/persistence/test_local_pytorch_persister.py index 5ebbb350..2d2dafbe 100644 --- a/tests/persistence/test_local_pytorch_persister.py +++ b/tests/persistence/test_local_pytorch_persister.py @@ -1,21 +1,24 @@ +"""Test the local pytorch persister.""" + from pathlib import Path import torch -import torch.nn as nn +from torch.nn import GRU, Embedding, Linear, Module from simplexity.persistence.local_pytorch_persister import LocalPytorchPersister -class SimpleLM(nn.Module): +class SimpleLM(Module): """A simple language model for testing.""" def __init__(self, vocab_size: int, embed_size: int = 64, hidden_size: int = 64): super().__init__() - self.embedding = nn.Embedding(vocab_size, embed_size) - self.gru = nn.GRU(embed_size, hidden_size, batch_first=True) - self.output = nn.Linear(hidden_size, vocab_size) + self.embedding = Embedding(vocab_size, embed_size) + self.gru = GRU(embed_size, hidden_size, batch_first=True) + self.output = Linear(hidden_size, vocab_size) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass.""" embedded = self.embedding(x) gru_out, _ = self.gru(embedded) logits = self.output(gru_out) @@ -23,6 +26,7 @@ def forward(self, x): def get_model(seed: int) -> SimpleLM: + """Get a model for testing.""" device = "cuda" if torch.cuda.is_available() else "cpu" torch.manual_seed(seed) vocab_size = 2 # Binary vocab @@ -30,7 +34,7 @@ def get_model(seed: int) -> SimpleLM: return model.to(device) -def models_equal(model1: nn.Module, model2: nn.Module) -> bool: +def models_equal(model1: Module, model2: Module) -> bool: """Check if two PyTorch models have identical parameters.""" params1 = dict(model1.named_parameters()) params2 = dict(model2.named_parameters()) @@ -42,6 +46,7 @@ def models_equal(model1: nn.Module, model2: nn.Module) -> bool: def test_local_persister(tmp_path: Path): + """Test the local pytorch persister.""" directory = tmp_path filename = "test_model.pt" persister = LocalPytorchPersister(directory, filename) diff --git a/tests/persistence/test_mlflow_persister.py b/tests/persistence/test_mlflow_persister.py index e6c502db..7100bf96 100644 --- a/tests/persistence/test_mlflow_persister.py +++ b/tests/persistence/test_mlflow_persister.py @@ -8,16 +8,16 @@ import equinox as eqx import jax import torch -import torch.nn as nn import yaml +from torch.nn import Linear, Module from simplexity.persistence.mlflow_persister import MLFlowPersister -def get_pytorch_model(seed: int) -> nn.Linear: +def get_pytorch_model(seed: int) -> Linear: """Build a small deterministic PyTorch model for serialization tests.""" torch.manual_seed(seed) - return nn.Linear(in_features=4, out_features=2) + return Linear(in_features=4, out_features=2) def get_hydra_config_for_pytorch_model() -> dict: @@ -33,7 +33,7 @@ def get_hydra_config_for_pytorch_model() -> dict: } -def pytorch_models_equal(model1: nn.Module, model2: nn.Module) -> bool: +def pytorch_models_equal(model1: Module, model2: Module) -> bool: """Check if two PyTorch models have identical parameters.""" params1 = dict(model1.named_parameters()) params2 = dict(model2.named_parameters()) @@ -120,7 +120,8 @@ def test_mlflow_persister_round_trip_from_config(tmp_path: Path) -> None: chex.assert_trees_all_equal(loaded, original) -def test_mlflow_persister_cleanup(tmp_path: Path): +def test_mlflow_persister_cleanup(tmp_path: Path) -> None: + """Test MLflow persister cleanup.""" artifact_dir = tmp_path / "mlruns" artifact_dir.mkdir() @@ -131,7 +132,8 @@ def test_mlflow_persister_cleanup(tmp_path: Path): artifact_path="models", ) - def run_status(): + def run_status() -> str: + """Get the status of the run.""" client = persister.client run_id = persister.run_id run = client.get_run(run_id) @@ -207,7 +209,7 @@ def test_mlflow_persister_pytorch_round_trip_from_config(tmp_path: Path) -> None assert pytorch_models_equal(loaded, original) # type: ignore[arg-type] -def test_mlflow_persister_pytorch_cleanup(tmp_path: Path): +def test_mlflow_persister_pytorch_cleanup(tmp_path: Path) -> None: """Test PyTorch model cleanup with MLflow persister.""" artifact_dir = tmp_path / "mlruns" artifact_dir.mkdir() @@ -219,7 +221,8 @@ def test_mlflow_persister_pytorch_cleanup(tmp_path: Path): artifact_path="models", ) - def run_status(): + def run_status() -> str: + """Get the status of the run.""" client = persister.client run_id = persister.run_id run = client.get_run(run_id) diff --git a/tests/persistence/test_s3_persister.py b/tests/persistence/test_s3_persister.py index fe5420c6..72f10927 100644 --- a/tests/persistence/test_s3_persister.py +++ b/tests/persistence/test_s3_persister.py @@ -1,3 +1,5 @@ +"""Test the S3 persister.""" + import tempfile from pathlib import Path @@ -12,6 +14,7 @@ def get_model(seed: int = 0) -> eqx.Module: + """Get a model for testing.""" return eqx.nn.Linear(in_features=4, out_features=2, key=jax.random.key(seed)) @@ -19,33 +22,35 @@ def test_s3_persister(tmp_path: Path): """Test S3Persister initialization.""" s3_client_mock = MockS3Client(tmp_path) temp_dir = tempfile.TemporaryDirectory() - local_persister = LocalEquinoxPersister(temp_dir.name) - persister = S3Persister( - bucket="test_bucket", - prefix="test_prefix", - s3_client=s3_client_mock, - temp_dir=temp_dir, - local_persister=local_persister, - ) - assert persister.bucket == "test_bucket" - assert persister.prefix == "test_prefix" - - model = get_model(0) - assert not (tmp_path / "test_bucket" / "test_prefix" / "0" / "model.eqx").exists() - persister.save_weights(model, 0) - assert (tmp_path / "test_bucket" / "test_prefix" / "0" / "model.eqx").exists() - - new_model = get_model(1) - with pytest.raises(AssertionError): - chex.assert_trees_all_close(new_model, model) - loaded_model = persister.load_weights(new_model, 0) - chex.assert_trees_all_equal(loaded_model, model) + with temp_dir: + local_persister = LocalEquinoxPersister(temp_dir.name) + persister = S3Persister( + bucket="test_bucket", + prefix="test_prefix", + s3_client=s3_client_mock, + temp_dir=temp_dir, + local_persister=local_persister, + ) + assert persister.bucket == "test_bucket" + assert persister.prefix == "test_prefix" + + model = get_model(0) + assert not (tmp_path / "test_bucket" / "test_prefix" / "0" / "model.eqx").exists() + persister.save_weights(model, 0) + assert (tmp_path / "test_bucket" / "test_prefix" / "0" / "model.eqx").exists() + + new_model = get_model(1) + with pytest.raises(AssertionError): + chex.assert_trees_all_close(new_model, model) + loaded_model = persister.load_weights(new_model, 0) + chex.assert_trees_all_equal(loaded_model, model) def test_s3_persister_from_config(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): """Test S3Persister.from_config with mocked Boto3 session.""" - def mock_session_init(profile_name=None, **kwargs): + def mock_session_init(profile_name=None, **kwargs): # pylint: disable=unused-argument + """Mock session initialization.""" return MockBoto3Session.create(tmp_path) monkeypatch.setattr("simplexity.persistence.s3_persister.boto3.session.Session", mock_session_init) diff --git a/tests/predictive_models/test_types.py b/tests/predictive_models/test_types.py index 63cfa67a..c13425aa 100644 --- a/tests/predictive_models/test_types.py +++ b/tests/predictive_models/test_types.py @@ -1,3 +1,9 @@ +"""Test the predictive models types module.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + import equinox as eqx import pytest @@ -6,32 +12,39 @@ get_model_framework, ) +if TYPE_CHECKING: + import torch + -class _DummyEqxModule(eqx.Module): +class _DummyEqxModule(eqx.Module): # pylint: disable=too-few-public-methods weight: float = 1.0 def test_get_model_framework_equinox_model(): + """Test the get_model_framework function with an equinox model.""" model = _DummyEqxModule() - assert get_model_framework(model) is ModelFramework.Equinox + assert get_model_framework(model) is ModelFramework.EQUINOX def test_get_model_framework_handles_equinox_and_pytorch_in_sequence(): + """Test the get_model_framework function with an equinox and pytorch model in sequence.""" torch = pytest.importorskip("torch", reason="torch is optional for simplexity") - class _DummyTorchModule(torch.nn.Module): - def forward(self, x): + class _DummyTorchModule(torch.nn.Module): # pylint: disable=too-few-public-methods + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass.""" return x eqx_model = _DummyEqxModule() torch_model = _DummyTorchModule() - assert get_model_framework(eqx_model) is ModelFramework.Equinox - assert get_model_framework(torch_model) is ModelFramework.Pytorch + assert get_model_framework(eqx_model) is ModelFramework.EQUINOX + assert get_model_framework(torch_model) is ModelFramework.PYTORCH # Repeat to confirm that dispatch stays consistent when frameworks alternate. - assert get_model_framework(eqx_model) is ModelFramework.Equinox + assert get_model_framework(eqx_model) is ModelFramework.EQUINOX def test_get_model_framework_unsupported_type(): + """Test the get_model_framework function with an unsupported type.""" with pytest.raises(ValueError, match="Unsupported model framework"): get_model_framework(object()) diff --git a/tests/run_management/test_base_config.py b/tests/run_management/test_base_config.py index 5a276b9a..c55d90c0 100644 --- a/tests/run_management/test_base_config.py +++ b/tests/run_management/test_base_config.py @@ -4,6 +4,15 @@ including validation of seed, tags, and MLFlow configuration fields. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import pytest from omegaconf import DictConfig @@ -12,7 +21,9 @@ class TestBaseConfig: - def test_validate_base_config_valid(self): + """Test BaseConfig.""" + + def test_validate_base_config_valid(self) -> None: """Test validate_base_config with valid configs.""" cfg = DictConfig({}) validate_base_config(cfg) @@ -26,7 +37,7 @@ def test_validate_base_config_valid(self): ) validate_base_config(cfg) - def test_validate_base_config_invalid_seed(self): + def test_validate_base_config_invalid_seed(self) -> None: """Test validate_base_config with invalid configs.""" # Non-integer seed cfg = DictConfig({"seed": "42"}) @@ -42,7 +53,7 @@ def test_validate_base_config_invalid_seed(self): with pytest.raises(ConfigValidationError, match="BaseConfig.seed must be non-negative"): validate_base_config(cfg) - def test_validate_base_config_invalid_tags(self): + def test_validate_base_config_invalid_tags(self) -> None: """Test validate_base_config with invalid tags.""" # Non-dictionary tags cfg = DictConfig({"tags": "not a dictionary"}) @@ -59,7 +70,7 @@ def test_validate_base_config_invalid_tags(self): with pytest.raises(ConfigValidationError, match="BaseConfig.tags values must be strings"): validate_base_config(cfg) - def test_validate_base_config_invalid_mlflow(self): + def test_validate_base_config_invalid_mlflow(self) -> None: """Test validate_base_config with invalid mlflow.""" # Non-MLFlowConfig mlflow cfg = DictConfig({"mlflow": "not an MLFlowConfig"}) diff --git a/tests/run_management/test_generative_process_config.py b/tests/run_management/test_generative_process_config.py index cc8760bb..d2346083 100644 --- a/tests/run_management/test_generative_process_config.py +++ b/tests/run_management/test_generative_process_config.py @@ -5,6 +5,15 @@ sequence length, batch size, and generative process configuration instances. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import re from unittest.mock import call, patch @@ -25,7 +34,7 @@ class TestGenerativeProcessConfig: """Test GenerativeProcessConfig.""" - def test_generative_process_config(self): + def test_generative_process_config(self) -> None: """Test creating generative process config from dataclass.""" cfg: DictConfig = OmegaConf.structured( GenerativeProcessConfig( @@ -45,18 +54,18 @@ def test_generative_process_config(self): assert cfg.get("sequence_len") is None assert cfg.get("batch_size") is None - def test_is_generative_process_target_valid(self): + def test_is_generative_process_target_valid(self) -> None: """Test is_generative_process_target with valid generative process targets.""" assert is_generative_process_target("simplexity.generative_processes.hidden_markov_model.HiddenMarkovModel") assert is_generative_process_target("simplexity.generative_processes.builder.build_hidden_markov_model") - def test_is_generative_process_target_invalid(self): + def test_is_generative_process_target_invalid(self) -> None: """Test is_generative_process_target with invalid targets.""" assert not is_generative_process_target("simplexity.persistence.mlflow_persister.MLFlowPersister") assert not is_generative_process_target("torch.optim.Adam") assert not is_generative_process_target("") - def test_is_generative_process_config_valid(self): + def test_is_generative_process_config_valid(self) -> None: """Test is_generative_process_config with valid generative process configs.""" cfg = DictConfig({"_target_": "simplexity.generative_processes.hidden_markov_model.HiddenMarkovModel"}) assert is_generative_process_config(cfg) @@ -71,7 +80,7 @@ def test_is_generative_process_config_valid(self): ) assert is_generative_process_config(cfg) - def test_is_generative_process_config_invalid(self): + def test_is_generative_process_config_invalid(self) -> None: """Test is_generative_process_config with invalid configs.""" # Non-generative process target cfg = DictConfig({"_target_": "simplexity.persistence.mlflow_persister.MLFlowPersister"}) @@ -97,7 +106,7 @@ def test_is_generative_process_config_invalid(self): cfg = DictConfig({}) assert not is_generative_process_config(cfg) - def test_validate_generative_process_config_valid(self): + def test_validate_generative_process_config_valid(self) -> None: """Test validate_generative_process_config with valid configs.""" cfg = DictConfig( { @@ -136,7 +145,7 @@ def test_validate_generative_process_config_valid(self): ) validate_generative_process_config(cfg) - def test_validate_generative_process_config_missing_instance(self): + def test_validate_generative_process_config_missing_instance(self) -> None: """Test validate_generative_process_config raises when instance is missing.""" cfg = DictConfig({}) with pytest.raises(ConfigValidationError, match="GenerativeProcessConfig.instance is required"): @@ -155,7 +164,7 @@ def test_validate_generative_process_config_missing_instance(self): with pytest.raises(ConfigValidationError, match="GenerativeProcessConfig.instance is required"): validate_generative_process_config(cfg) - def test_validate_generative_process_config_invalid_instance(self): + def test_validate_generative_process_config_invalid_instance(self) -> None: """Test validate_generative_process_config raises when instance is invalid.""" # Instance without _target_ cfg = DictConfig( @@ -178,7 +187,7 @@ def test_validate_generative_process_config_invalid_instance(self): with pytest.raises(ConfigValidationError, match="InstanceConfig._target_ must be a string"): validate_generative_process_config(cfg) - def test_validate_generative_process_config_non_generative_process_target(self): + def test_validate_generative_process_config_non_generative_process_target(self) -> None: """Test validate_generative_process_config raises when instance target is not a generative process target.""" cfg = DictConfig( { @@ -205,7 +214,7 @@ def test_validate_generative_process_config_non_generative_process_target(self): ): validate_generative_process_config(cfg) - def test_validate_generative_process_config_invalid_name(self): + def test_validate_generative_process_config_invalid_name(self) -> None: """Test validate_generative_process_config raises when name is invalid.""" # Empty string name cfg = DictConfig( @@ -249,7 +258,7 @@ def test_validate_generative_process_config_invalid_name(self): with pytest.raises(ConfigValidationError, match="GenerativeProcessConfig.name must be a string or None"): validate_generative_process_config(cfg) - def test_validate_generative_process_config_invalid_base_vocab_size(self): + def test_validate_generative_process_config_invalid_base_vocab_size(self) -> None: """Test validate_generative_process_config raises when base_vocab_size is invalid.""" # Non-integer base_vocab_size cfg = DictConfig( @@ -293,7 +302,7 @@ def test_validate_generative_process_config_invalid_base_vocab_size(self): validate_generative_process_config(cfg) @pytest.mark.parametrize("token_type", ["bos_token", "eos_token"]) - def test_validate_generative_process_config_invalid_special_tokens(self, token_type: str): + def test_validate_generative_process_config_invalid_special_tokens(self, token_type: str) -> None: """Test validate_generative_process_config raises when special tokens are invalid.""" # Non-integer token value cfg = DictConfig( diff --git a/tests/run_management/test_instance_config.py b/tests/run_management/test_instance_config.py index 72d23eb2..65cf2016 100644 --- a/tests/run_management/test_instance_config.py +++ b/tests/run_management/test_instance_config.py @@ -4,6 +4,15 @@ creation from dataclass and derived config classes. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + from dataclasses import dataclass from omegaconf import DictConfig, OmegaConf @@ -14,12 +23,12 @@ class TestInstanceConfig: """Test InstanceConfig.""" - def test_instance_config(self): + def test_instance_config(self) -> None: """Test creating instance config from dataclass.""" cfg: DictConfig = OmegaConf.structured(InstanceConfig(_target_="some_target")) assert cfg.get("_target_") == "some_target" - def test_instance_derived_config(self): + def test_instance_derived_config(self) -> None: """Test creating a child instance config from dataclass.""" @dataclass diff --git a/tests/run_management/test_logging_config.py b/tests/run_management/test_logging_config.py index ceecdfa2..9729c59c 100644 --- a/tests/run_management/test_logging_config.py +++ b/tests/run_management/test_logging_config.py @@ -4,6 +4,15 @@ validation of logger targets, logger configs, and logging configuration instances. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import pytest from omegaconf import DictConfig, OmegaConf @@ -18,25 +27,27 @@ class TestLoggingConfig: - def test_logging_config(self): + """Test LoggingConfig.""" + + def test_logging_config(self) -> None: """Test creating logger config from dataclass.""" cfg: DictConfig = OmegaConf.structured(LoggingConfig(instance=InstanceConfig(_target_="some_target"))) assert OmegaConf.select(cfg, "instance._target_") == "some_target" assert cfg.get("name") is None - def test_is_logger_target_valid(self): + def test_is_logger_target_valid(self) -> None: """Test is_logger_target with valid logger targets.""" assert is_logger_target("simplexity.logging.file_logger.FileLogger") assert is_logger_target("simplexity.logging.mlflow_logger.MLFlowLogger") assert is_logger_target("simplexity.logging.print_logger.PrintLogger") - def test_is_logger_target_invalid(self): + def test_is_logger_target_invalid(self) -> None: """Test is_logger_target with invalid targets.""" assert not is_logger_target("simplexity.persistence.mlflow_persister.MLFlowPersister") assert not is_logger_target("logging.Logger") assert not is_logger_target("") - def test_is_logger_config_valid(self): + def test_is_logger_config_valid(self) -> None: """Test is_logger_config with valid logger configs.""" cfg = DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"}) assert is_logger_config(cfg) @@ -51,7 +62,7 @@ def test_is_logger_config_valid(self): ) assert is_logger_config(cfg) - def test_is_logger_config_invalid(self): + def test_is_logger_config_invalid(self) -> None: """Test is_logger_config with invalid configs.""" # Non-logger target cfg = DictConfig({"_target_": "simplexity.persistence.mlflow_persister.MLFlowPersister"}) @@ -77,7 +88,7 @@ def test_is_logger_config_invalid(self): cfg = DictConfig({}) assert not is_logger_config(cfg) - def test_validate_logging_config_valid(self): + def test_validate_logging_config_valid(self) -> None: """Test validate_logging_config with valid configs.""" # Valid config without name cfg = DictConfig( @@ -126,7 +137,7 @@ def test_validate_logging_config_valid(self): ) validate_logging_config(cfg) # Should not raise - def test_validate_logging_config_missing_instance(self): + def test_validate_logging_config_missing_instance(self) -> None: """Test validate_logging_config raises when instance is missing.""" cfg = DictConfig({}) with pytest.raises(ConfigValidationError, match="LoggingConfig.instance is required"): @@ -136,7 +147,7 @@ def test_validate_logging_config_missing_instance(self): with pytest.raises(ConfigValidationError, match="LoggingConfig.instance is required"): validate_logging_config(cfg) - def test_validate_logging_config_invalid_instance(self): + def test_validate_logging_config_invalid_instance(self) -> None: """Test validate_logging_config raises when instance is invalid.""" # Instance without _target_ cfg = DictConfig({"instance": DictConfig({"other_field": "value"})}) @@ -153,7 +164,7 @@ def test_validate_logging_config_invalid_instance(self): with pytest.raises(ConfigValidationError, match="InstanceConfig._target_ must be a string"): validate_logging_config(cfg) - def test_validate_logging_config_non_logger_target(self): + def test_validate_logging_config_non_logger_target(self) -> None: """Test validate_logging_config raises when instance target is not a logger target.""" cfg = DictConfig( {"instance": DictConfig({"_target_": "simplexity.persistence.mlflow_persister.MLFlowPersister"})} @@ -165,7 +176,7 @@ def test_validate_logging_config_non_logger_target(self): with pytest.raises(ConfigValidationError, match="LoggingConfig.instance._target_ must be a logger target"): validate_logging_config(cfg) - def test_validate_logging_config_invalid_name(self): + def test_validate_logging_config_invalid_name(self) -> None: """Test validate_logging_config raises when name is invalid.""" # Empty string name cfg = DictConfig( diff --git a/tests/run_management/test_mlflow_config.py b/tests/run_management/test_mlflow_config.py index 54b7dc80..a413c10c 100644 --- a/tests/run_management/test_mlflow_config.py +++ b/tests/run_management/test_mlflow_config.py @@ -5,6 +5,15 @@ downgrade_unity_catalog fields. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import pytest from omegaconf import DictConfig, OmegaConf @@ -13,7 +22,9 @@ class TestMLFlowConfig: - def test_mlflow_config(self): + """Test MLFlowConfig.""" + + def test_mlflow_config(self) -> None: """Test creating mlflow config from dataclass.""" cfg: DictConfig = OmegaConf.structured(MLFlowConfig(experiment_name="some_experiment", run_name="some_run")) assert cfg.get("experiment_name") == "some_experiment" @@ -22,7 +33,7 @@ def test_mlflow_config(self): assert cfg.get("registry_uri") is None assert cfg.get("downgrade_unity_catalog") is None - def test_validate_mlflow_config_valid(self): + def test_validate_mlflow_config_valid(self) -> None: """Test validate_mlflow_config with valid configs.""" cfg = DictConfig( { @@ -43,7 +54,7 @@ def test_validate_mlflow_config_valid(self): ) validate_mlflow_config(cfg) - def test_validate_mlflow_config_missing_required_fields(self): + def test_validate_mlflow_config_missing_required_fields(self) -> None: """Test validate_mlflow_config with missing required fields.""" # Missing experiment_name cfg = DictConfig( @@ -69,7 +80,7 @@ def test_validate_mlflow_config_missing_required_fields(self): with pytest.raises(ConfigValidationError, match="MLFlowConfig.run_name must be a non-empty string"): validate_mlflow_config(cfg) - def test_validate_mlflow_config_invalid_downgrade_unity_catalog(self): + def test_validate_mlflow_config_invalid_downgrade_unity_catalog(self) -> None: """Test validate_mlflow_config with invalid downgrade_unity_catalog.""" cfg = DictConfig( { @@ -82,7 +93,7 @@ def test_validate_mlflow_config_invalid_downgrade_unity_catalog(self): validate_mlflow_config(cfg) @pytest.mark.parametrize("uri_type", ["tracking_uri", "registry_uri"]) - def test_validate_mlflow_config_invalid_uri(self, uri_type: str): + def test_validate_mlflow_config_invalid_uri(self, uri_type: str) -> None: """Test validate_mlflow_config with invalid tracking_uri and registry_uri.""" # Empty URI cfg = DictConfig( diff --git a/tests/run_management/test_optimizer_config.py b/tests/run_management/test_optimizer_config.py index ab33171b..8e36d5d0 100644 --- a/tests/run_management/test_optimizer_config.py +++ b/tests/run_management/test_optimizer_config.py @@ -5,6 +5,15 @@ optimizer configuration instances. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import pytest from omegaconf import DictConfig, OmegaConf @@ -20,26 +29,28 @@ class TestOptimizerConfig: - def test_optimizer_config(self): + """Test OptimizerConfig.""" + + def test_optimizer_config(self) -> None: """Test creating optimizer config from dataclass.""" cfg: DictConfig = OmegaConf.structured(OptimizerConfig(instance=InstanceConfig(_target_="some_target"))) assert OmegaConf.select(cfg, "instance._target_") == "some_target" assert cfg.get("name") is None - def test_is_optimizer_target_valid(self): + def test_is_optimizer_target_valid(self) -> None: """Test is_optimizer_target with valid optimizer targets.""" assert is_optimizer_target("torch.optim.Adam") assert is_optimizer_target("torch.optim.SGD") assert is_optimizer_target("optax.adam") assert is_optimizer_target("optax.sgd") - def test_is_optimizer_target_invalid(self): + def test_is_optimizer_target_invalid(self) -> None: """Test is_optimizer_target with invalid targets.""" assert not is_optimizer_target("simplexity.logging.mlflow_logger.MLFlowLogger") assert not is_optimizer_target("torch.nn.Linear") assert not is_optimizer_target("") - def test_is_optimizer_config_valid(self): + def test_is_optimizer_config_valid(self) -> None: """Test is_optimizer_config with valid optimizer configs.""" cfg = DictConfig({"_target_": "torch.optim.Adam"}) assert is_optimizer_config(cfg) @@ -53,7 +64,7 @@ def test_is_optimizer_config_valid(self): ) assert is_optimizer_config(cfg) - def test_is_optimizer_config_invalid(self): + def test_is_optimizer_config_invalid(self) -> None: """Test is_optimizer_config with invalid configs.""" # Non-optimizer target cfg = DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"}) @@ -75,7 +86,7 @@ def test_is_optimizer_config_invalid(self): cfg = DictConfig({}) assert not is_optimizer_config(cfg) - def test_is_pytorch_optimizer_config_valid(self): + def test_is_pytorch_optimizer_config_valid(self) -> None: """Test is_pytorch_optimizer_config with valid PyTorch optimizer configs.""" cfg = DictConfig({"_target_": "torch.optim.Adam"}) assert is_pytorch_optimizer_config(cfg) @@ -83,7 +94,7 @@ def test_is_pytorch_optimizer_config_valid(self): cfg = DictConfig({"_target_": "torch.optim.SGD"}) assert is_pytorch_optimizer_config(cfg) - def test_is_pytorch_optimizer_config_invalid(self): + def test_is_pytorch_optimizer_config_invalid(self) -> None: """Test is_pytorch_optimizer_config with invalid configs.""" cfg = DictConfig({"_target_": "optax.adam"}) assert not is_pytorch_optimizer_config(cfg) @@ -94,7 +105,7 @@ def test_is_pytorch_optimizer_config_invalid(self): cfg = DictConfig({}) assert not is_pytorch_optimizer_config(cfg) - def test_validate_optimizer_config_valid(self): + def test_validate_optimizer_config_valid(self) -> None: """Test validate_optimizer_config with valid configs.""" # Valid config without name cfg = DictConfig( @@ -138,7 +149,7 @@ def test_validate_optimizer_config_valid(self): ) validate_optimizer_config(cfg) # Should not raise - def test_validate_optimizer_config_missing_instance(self): + def test_validate_optimizer_config_missing_instance(self) -> None: """Test validate_optimizer_config raises when instance is missing.""" cfg = DictConfig({}) with pytest.raises(ConfigValidationError, match="OptimizerConfig.instance is required"): @@ -148,7 +159,7 @@ def test_validate_optimizer_config_missing_instance(self): with pytest.raises(ConfigValidationError, match="OptimizerConfig.instance is required"): validate_optimizer_config(cfg) - def test_validate_optimizer_config_invalid_instance(self): + def test_validate_optimizer_config_invalid_instance(self) -> None: """Test validate_optimizer_config raises when instance is invalid.""" # Instance without _target_ cfg = DictConfig({"instance": DictConfig({"lr": 0.001})}) @@ -165,7 +176,7 @@ def test_validate_optimizer_config_invalid_instance(self): with pytest.raises(ConfigValidationError, match="InstanceConfig._target_ must be a string"): validate_optimizer_config(cfg) - def test_validate_optimizer_config_non_optimizer_target(self): + def test_validate_optimizer_config_non_optimizer_target(self) -> None: """Test validate_optimizer_config raises when instance target is not an optimizer target.""" cfg = DictConfig({"instance": DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"})}) with pytest.raises( @@ -179,7 +190,7 @@ def test_validate_optimizer_config_non_optimizer_target(self): ): validate_optimizer_config(cfg) - def test_validate_optimizer_config_invalid_name(self): + def test_validate_optimizer_config_invalid_name(self) -> None: """Test validate_optimizer_config raises when name is invalid.""" # Empty string name cfg = DictConfig( diff --git a/tests/run_management/test_persistence_config.py b/tests/run_management/test_persistence_config.py index 9106771b..9ace5541 100644 --- a/tests/run_management/test_persistence_config.py +++ b/tests/run_management/test_persistence_config.py @@ -5,6 +5,15 @@ configuration instances. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import pytest from omegaconf import DictConfig, OmegaConf @@ -21,24 +30,24 @@ class TestPersistenceConfig: """Test PersistenceConfig.""" - def test_persistence_config(self): + def test_persistence_config(self) -> None: """Test creating persistence config from dataclass.""" cfg: DictConfig = OmegaConf.structured(PersistenceConfig(instance=InstanceConfig(_target_="some_target"))) assert OmegaConf.select(cfg, "instance._target_") == "some_target" assert cfg.get("name") is None - def test_is_model_persister_target_valid(self): + def test_is_model_persister_target_valid(self) -> None: """Test is_model_persister_target with valid persister targets.""" assert is_model_persister_target("simplexity.persistence.mlflow_persister.MLFlowPersister") assert is_model_persister_target("simplexity.persistence.local_pytorch_persister.LocalPytorchPersister") - def test_is_model_persister_target_invalid(self): + def test_is_model_persister_target_invalid(self) -> None: """Test is_model_persister_target with invalid targets.""" assert not is_model_persister_target("simplexity.logging.mlflow_logger.MLFlowLogger") assert not is_model_persister_target("torch.optim.Adam") assert not is_model_persister_target("") - def test_is_persister_config_valid(self): + def test_is_persister_config_valid(self) -> None: """Test is_persister_config with valid persister configs.""" cfg = DictConfig({"_target_": "simplexity.persistence.mlflow_persister.MLFlowPersister"}) assert is_persister_config(cfg) @@ -51,7 +60,7 @@ def test_is_persister_config_valid(self): ) assert is_persister_config(cfg) - def test_is_persister_config_invalid(self): + def test_is_persister_config_invalid(self) -> None: """Test is_persister_config with invalid configs.""" # Non-persister target cfg = DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"}) @@ -73,7 +82,7 @@ def test_is_persister_config_invalid(self): cfg = DictConfig({}) assert not is_persister_config(cfg) - def test_validate_persistence_config_valid(self): + def test_validate_persistence_config_valid(self) -> None: """Test validate_persistence_config with valid configs.""" # Valid config without name cfg = DictConfig( @@ -119,7 +128,7 @@ def test_validate_persistence_config_valid(self): ) validate_persistence_config(cfg) # Should not raise - def test_validate_persistence_config_missing_instance(self): + def test_validate_persistence_config_missing_instance(self) -> None: """Test validate_persistence_config raises when instance is missing.""" cfg = DictConfig({}) with pytest.raises(ConfigValidationError, match="PersistenceConfig.instance is required"): @@ -129,7 +138,7 @@ def test_validate_persistence_config_missing_instance(self): with pytest.raises(ConfigValidationError, match="PersistenceConfig.instance is required"): validate_persistence_config(cfg) - def test_validate_persistence_config_invalid_instance(self): + def test_validate_persistence_config_invalid_instance(self) -> None: """Test validate_persistence_config raises when instance is invalid.""" # Instance without _target_ cfg = DictConfig({"instance": DictConfig({"other_field": "value"})}) @@ -146,7 +155,7 @@ def test_validate_persistence_config_invalid_instance(self): with pytest.raises(ConfigValidationError, match="InstanceConfig._target_ must be a string"): validate_persistence_config(cfg) - def test_validate_persistence_config_non_persister_target(self): + def test_validate_persistence_config_non_persister_target(self) -> None: """Test validate_persistence_config raises when instance target is not a persister target.""" cfg = DictConfig({"instance": DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"})}) with pytest.raises( @@ -160,7 +169,7 @@ def test_validate_persistence_config_non_persister_target(self): ): validate_persistence_config(cfg) - def test_validate_persistence_config_invalid_name(self): + def test_validate_persistence_config_invalid_name(self) -> None: """Test validate_persistence_config raises when name is invalid.""" # Empty string name cfg = DictConfig( diff --git a/tests/run_management/test_predictive_model_config.py b/tests/run_management/test_predictive_model_config.py index 029d1971..ff18a83a 100644 --- a/tests/run_management/test_predictive_model_config.py +++ b/tests/run_management/test_predictive_model_config.py @@ -5,6 +5,15 @@ configuration instances. """ +# pylint: disable-all +# Temporarily disable all pylint checkers during AST traversal to prevent crash. +# The imports checker crashes when resolving simplexity package imports due to a bug +# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 +# pylint: enable=all +# Re-enable all pylint checkers for the checking phase. This allows other checks +# (code quality, style, undefined names, etc.) to run normally while bypassing +# the problematic imports checker that would crash during AST traversal. + import re from unittest.mock import ANY, call, patch @@ -27,9 +36,11 @@ class TestHookedTransformerConfig: + """Test HookedTransformerConfig.""" + """Test PredictiveModelConfig.""" - def test_hooked_transformer_config(self): + def test_hooked_transformer_config(self) -> None: """Test creating hooked transformer config from dataclass.""" cfg: DictConfig = OmegaConf.structured( PredictiveModelConfig( @@ -60,7 +71,7 @@ def test_hooked_transformer_config(self): assert cfg.get("name") is None assert cfg.get("load_checkpoint_step") is None - def test_validate_hooked_transformer_config_config_valid(self): + def test_validate_hooked_transformer_config_config_valid(self) -> None: """Test validate_hooked_transformer_config_config with valid configs.""" # Test with minimal config (n_heads defaults to -1) cfg = DictConfig( @@ -95,7 +106,7 @@ def test_validate_hooked_transformer_config_config_valid(self): ) validate_hooked_transformer_config_config(cfg) - def test_validate_hooked_transformer_config_config_incompatible_dimensions(self): + def test_validate_hooked_transformer_config_config_incompatible_dimensions(self) -> None: """Test validate_hooked_transformer_config_config raises when n_heads=-1. Specifically tests that d_model must be divisible by d_head when n_heads=-1. @@ -131,7 +142,7 @@ def test_validate_hooked_transformer_config_config_incompatible_dimensions(self) ("n_ctx", 256), ], ) - def test_validate_hooked_transformer_config_config_invalid_fields(self, field, value): + def test_validate_hooked_transformer_config_config_invalid_fields(self, field, value) -> None: """Test validate_hooked_transformer_config_config raises for invalid field values.""" cfg = DictConfig( { @@ -164,7 +175,7 @@ def test_validate_hooked_transformer_config_config_invalid_fields(self, field, v with pytest.raises(ConfigValidationError, match=f"HookedTransformerConfigConfig.{field} must be positive"): validate_hooked_transformer_config_config(cfg) - def test_validate_hooked_transformer_config_config_d_model_not_divisible_by_n_heads(self): + def test_validate_hooked_transformer_config_config_d_model_not_divisible_by_n_heads(self) -> None: """Test validate_hooked_transformer_config_config raises when d_model is not divisible by n_heads.""" cfg = DictConfig( { @@ -185,7 +196,7 @@ def test_validate_hooked_transformer_config_config_d_model_not_divisible_by_n_he with pytest.raises(ConfigValidationError, match="d_model.*must be divisible by n_heads"): validate_hooked_transformer_config_config(cfg) - def test_validate_hooked_transformer_config_config_d_head_times_n_heads_not_equal_d_model(self): + def test_validate_hooked_transformer_config_config_d_head_times_n_heads_not_equal_d_model(self) -> None: """Test validate_hooked_transformer_config_config raises when d_head * n_heads != d_model.""" cfg = DictConfig( { @@ -206,7 +217,7 @@ def test_validate_hooked_transformer_config_config_d_head_times_n_heads_not_equa with pytest.raises(ConfigValidationError, match="d_head.*n_heads.*must equal d_model"): validate_hooked_transformer_config_config(cfg) - def test_validate_hooked_transformer_config_valid(self): + def test_validate_hooked_transformer_config_valid(self) -> None: """Test validate_hooked_transformer_config with valid configs.""" cfg = DictConfig( { @@ -231,7 +242,7 @@ def test_validate_hooked_transformer_config_valid(self): ) validate_hooked_transformer_config(cfg) - def test_validate_hooked_transformer_config_missing_target(self): + def test_validate_hooked_transformer_config_missing_target(self) -> None: """Test validate_hooked_transformer_config raises when _target_ is missing.""" cfg = DictConfig( { @@ -256,13 +267,13 @@ def test_validate_hooked_transformer_config_missing_target(self): with pytest.raises(ConfigValidationError, match="InstanceConfig._target_ must be a string"): validate_hooked_transformer_config(cfg) - def test_validate_hooked_transformer_config_missing_cfg(self): + def test_validate_hooked_transformer_config_missing_cfg(self) -> None: """Test validate_hooked_transformer_config raises when cfg is missing.""" cfg = DictConfig({"_target_": "transformer_lens.HookedTransformer"}) with pytest.raises(ConfigValidationError, match="HookedTransformerConfig.cfg is required"): validate_hooked_transformer_config(cfg) - def test_resolve_hooked_transformer_config_without_kwargs(self): + def test_resolve_hooked_transformer_config_without_kwargs(self) -> None: """Test resolve_hooked_transformer_config with valid configs.""" cfg = DictConfig( { @@ -286,7 +297,7 @@ def test_resolve_hooked_transformer_config_without_kwargs(self): assert OmegaConf.is_missing(cfg, "n_ctx") assert OmegaConf.is_missing(cfg, "d_vocab") - def test_resolve_hooked_transformer_config_with_complete_values(self): + def test_resolve_hooked_transformer_config_with_complete_values(self) -> None: cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -316,7 +327,7 @@ def test_resolve_hooked_transformer_config_with_complete_values(self): assert cfg.get("d_vocab") == 4 assert cfg.get("device") == "cuda" - def test_resolve_hooked_transformer_config_with_missing_values(self): + def test_resolve_hooked_transformer_config_with_missing_values(self) -> None: cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -346,7 +357,7 @@ def test_resolve_hooked_transformer_config_with_missing_values(self): assert cfg.get("d_vocab") == 4 assert cfg.get("device") == "cuda" - def test_resolve_hooked_transformer_config_with_invalid_values(self): + def test_resolve_hooked_transformer_config_with_invalid_values(self) -> None: cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -379,7 +390,7 @@ def test_resolve_hooked_transformer_config_with_invalid_values(self): ): resolve_hooked_transformer_config(cfg, bos_token=3, sequence_len=16) - def test_resolve_hooked_transformer_config_with_conflicting_device(self): + def test_resolve_hooked_transformer_config_with_conflicting_device(self) -> None: cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -409,7 +420,7 @@ def test_resolve_hooked_transformer_config_with_conflicting_device(self): ] ) - def test_is_predictive_model_target_valid(self): + def test_is_predictive_model_target_valid(self) -> None: """Test is_predictive_model_target with valid model targets.""" assert is_predictive_model_target("transformer_lens.HookedTransformer") assert is_predictive_model_target("torch.nn.Linear") @@ -418,13 +429,13 @@ def test_is_predictive_model_target_valid(self): assert is_predictive_model_target("penzai.models.Transformer") assert is_predictive_model_target("simplexity.predictive_models.MyModel") - def test_is_predictive_model_target_invalid(self): + def test_is_predictive_model_target_invalid(self) -> None: """Test is_predictive_model_target with invalid targets.""" assert not is_predictive_model_target("simplexity.logging.mlflow_logger.MLFlowLogger") assert not is_predictive_model_target("torch.optim.Adam") assert not is_predictive_model_target("") - def test_is_predictive_model_config_valid(self): + def test_is_predictive_model_config_valid(self) -> None: """Test is_predictive_model_config with valid predictive model configs.""" cfg = DictConfig({"_target_": "transformer_lens.HookedTransformer"}) assert is_predictive_model_config(cfg) @@ -435,7 +446,7 @@ def test_is_predictive_model_config_valid(self): cfg = DictConfig({"_target_": "simplexity.predictive_models.MyModel"}) assert is_predictive_model_config(cfg) - def test_is_predictive_model_config_invalid(self): + def test_is_predictive_model_config_invalid(self) -> None: """Test is_predictive_model_config with invalid configs.""" # Non-model target cfg = DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"}) @@ -457,12 +468,12 @@ def test_is_predictive_model_config_invalid(self): cfg = DictConfig({}) assert not is_predictive_model_config(cfg) - def test_is_hooked_transformer_config_valid(self): + def test_is_hooked_transformer_config_valid(self) -> None: """Test is_hooked_transformer_config with valid HookedTransformer configs.""" cfg = DictConfig({"_target_": "transformer_lens.HookedTransformer"}) assert is_hooked_transformer_config(cfg) - def test_is_hooked_transformer_config_invalid(self): + def test_is_hooked_transformer_config_invalid(self) -> None: """Test is_hooked_transformer_config with invalid configs.""" cfg = DictConfig({"_target_": "transformer_lens.HookedTransformerConfig"}) assert not is_hooked_transformer_config(cfg) @@ -473,7 +484,7 @@ def test_is_hooked_transformer_config_invalid(self): cfg = DictConfig({}) assert not is_hooked_transformer_config(cfg) - def test_validate_predictive_model_config_valid(self): + def test_validate_predictive_model_config_valid(self) -> None: """Test validate_predictive_model_config with valid configs.""" # Valid config without name or load_checkpoint_step cfg = DictConfig( @@ -523,7 +534,7 @@ def test_validate_predictive_model_config_valid(self): ) validate_predictive_model_config(cfg) - def test_validate_predictive_model_config_missing_instance(self): + def test_validate_predictive_model_config_missing_instance(self) -> None: """Test validate_predictive_model_config raises when instance is missing.""" cfg = DictConfig({}) with pytest.raises(ConfigValidationError, match="PredictiveModelConfig.instance is required"): @@ -533,7 +544,7 @@ def test_validate_predictive_model_config_missing_instance(self): with pytest.raises(ConfigValidationError, match="PredictiveModelConfig.instance is required"): validate_predictive_model_config(cfg) - def test_validate_predictive_model_config_invalid_instance(self): + def test_validate_predictive_model_config_invalid_instance(self) -> None: """Test validate_predictive_model_config raises when instance is invalid.""" # Instance without _target_ cfg = DictConfig({"instance": DictConfig({"other_field": "value"})}) @@ -550,7 +561,7 @@ def test_validate_predictive_model_config_invalid_instance(self): with pytest.raises(ConfigValidationError, match="InstanceConfig._target_ must be a string"): validate_predictive_model_config(cfg) - def test_validate_predictive_model_config_non_model_target(self): + def test_validate_predictive_model_config_non_model_target(self) -> None: """Test validate_predictive_model_config raises when instance target is not a model target.""" cfg = DictConfig({"instance": DictConfig({"_target_": "simplexity.logging.mlflow_logger.MLFlowLogger"})}) with pytest.raises( @@ -564,7 +575,7 @@ def test_validate_predictive_model_config_non_model_target(self): ): validate_predictive_model_config(cfg) - def test_validate_predictive_model_config_invalid_name(self): + def test_validate_predictive_model_config_invalid_name(self) -> None: """Test validate_predictive_model_config raises when name is invalid.""" # Empty string name cfg = DictConfig( @@ -596,7 +607,7 @@ def test_validate_predictive_model_config_invalid_name(self): with pytest.raises(ConfigValidationError, match="PredictiveModelConfig.name must be a string or None"): validate_predictive_model_config(cfg) - def test_validate_predictive_model_config_invalid_load_checkpoint_step(self): + def test_validate_predictive_model_config_invalid_load_checkpoint_step(self) -> None: """Test validate_predictive_model_config raises when load_checkpoint_step is invalid.""" # Non-integer load_checkpoint_step cfg = DictConfig( diff --git a/tests/utils/test_config_utils.py b/tests/utils/test_config_utils.py index 18ca3747..7f331afb 100644 --- a/tests/utils/test_config_utils.py +++ b/tests/utils/test_config_utils.py @@ -1,3 +1,5 @@ +"""Tests for config utilities.""" + import pytest from omegaconf import DictConfig, ListConfig, OmegaConf from omegaconf.errors import ReadonlyConfigError diff --git a/tests/utils/test_equinox_utils.py b/tests/utils/test_equinox_utils.py index 84f3a94c..c428efe1 100644 --- a/tests/utils/test_equinox_utils.py +++ b/tests/utils/test_equinox_utils.py @@ -1,3 +1,5 @@ +"""Tests for equinox utilities.""" + import equinox as eqx import jax diff --git a/tests/utils/test_jnp.py b/tests/utils/test_jnp_utils.py similarity index 72% rename from tests/utils/test_jnp.py rename to tests/utils/test_jnp_utils.py index dfcf5b6c..b7d9752a 100644 --- a/tests/utils/test_jnp.py +++ b/tests/utils/test_jnp_utils.py @@ -1,20 +1,24 @@ +"""Tests for JAX NumPy utilities.""" + import chex import jax import jax.numpy as jnp -from simplexity.utils.jnp import log_matmul, signed_logsumexp +from simplexity.utils.jnp_utils import log_matmul, signed_logsumexp def test_log_matmul(): + """Test log_matmul function.""" key_a, key_b = jax.random.split(jax.random.PRNGKey(0)) - A = jax.random.uniform(key_a, (3, 4)) - B = jax.random.uniform(key_b, (4, 5)) - actual = log_matmul(jnp.log(A), jnp.log(B)) - expected = jnp.log(A @ B) + mat_a = jax.random.uniform(key_a, (3, 4)) + mat_b = jax.random.uniform(key_b, (4, 5)) + actual = log_matmul(jnp.log(mat_a), jnp.log(mat_b)) + expected = jnp.log(mat_a @ mat_b) chex.assert_trees_all_close(actual, expected, atol=1e-7) def test_signed_logsumexp(): + """Test signed_logsumexp function.""" # values: # [ 4, 1, 2] -> 7 # [-1, -3, -2] -> -6 diff --git a/tests/utils/test_mlflow_utils.py b/tests/utils/test_mlflow_utils.py index 9df9e997..66fa7612 100644 --- a/tests/utils/test_mlflow_utils.py +++ b/tests/utils/test_mlflow_utils.py @@ -89,6 +89,7 @@ def test_uc_registry_uri_is_downgraded_to_workspace_uri( assert "Unity Catalog URI" in str(warning.message) def test_non_databricks_tracking_uri_ignored(self, recwarn: pytest.WarningsRecorder) -> None: + """Non-Databricks tracking URIs are ignored.""" assert resolve_registry_uri(tracking_uri=FILE_URI) is None assert not recwarn.list diff --git a/tests/utils/test_penzai.py b/tests/utils/test_penzai.py index d95e3b6f..a5810fab 100644 --- a/tests/utils/test_penzai.py +++ b/tests/utils/test_penzai.py @@ -1,3 +1,5 @@ +"""Tests for penzai utilities.""" + import dataclasses import jax @@ -7,7 +9,7 @@ from penzai.models.transformer.variants.llamalike_common import LlamalikeTransformerConfig, build_llamalike_transformer from penzai.nn.layer import Layer as PenzaiModel -from simplexity.utils.penzai import ( +from simplexity.utils.penzai_utils import ( ParamCountNode, PenzaiWrapper, VariableLabelClass, @@ -20,6 +22,7 @@ def test_penzai_wrapper(): + """Test PenzaiWrapper.""" vocab_size = 4 batch_size = 2 seq_length = 16 @@ -47,6 +50,7 @@ def test_penzai_wrapper(): def test_use_penzai_model(): + """Test use_penzai_model decorator.""" vocab_size = 4 batch_size = 2 seq_length = 16 @@ -78,6 +82,7 @@ def f(model: PenzaiModel, inputs: jax.Array) -> jax.Array: def test_get_parameter_count_tree(): + """Test get_parameter_count_tree function.""" config = LlamalikeTransformerConfig( embedding_dim=2, num_decoder_blocks=3, @@ -315,15 +320,17 @@ def test_get_parameter_count_tree(): def test_deconstruct_variables(): + """Test deconstruct_variables function.""" base_rng = jax.random.PRNGKey(0) transformer = build_llamalike_transformer(CONFIG, init_base_rng=base_rng) _, variables = pz.unbind_variables(transformer, freeze=True) actual = deconstruct_variables(variables) - for key in DECONSTRUCTED_VARIABLES: - assert actual[key] == DECONSTRUCTED_VARIABLES[key] + for key, value in DECONSTRUCTED_VARIABLES.items(): + assert actual[key] == value def test_reconstruct_variables(): + """Test reconstruct_variables function.""" sequences = jax.random.randint(jax.random.PRNGKey(0), (4, 16), 0, CONFIG.vocab_size) inputs = pz.nx.wrap(sequences, "batch", "seq") unbound_transformer = build_llamalike_transformer(CONFIG) diff --git a/tests/utils/test_pip_utils.py b/tests/utils/test_pip_utils.py index 49f08b12..886f4a03 100644 --- a/tests/utils/test_pip_utils.py +++ b/tests/utils/test_pip_utils.py @@ -1,3 +1,5 @@ +"""Tests for pip utilities.""" + from pathlib import Path from unittest.mock import MagicMock, patch @@ -14,21 +16,21 @@ def test_get_python_version_with_specific_version(tmp_path: Path): - """Test get_python_version function.""" + """Test get_python_version with specific version.""" specific_version_toml = tmp_path / "specific_version.toml" specific_version_toml.write_text('[project]\nrequires-python = "3.12"') assert get_python_version(specific_version_toml) == "python==3.12" def test_get_python_version_with_range_version(tmp_path: Path): - """Test get_python_version function.""" + """Test get_python_version with range version.""" range_version_toml = tmp_path / "range_version.toml" range_version_toml.write_text('[project]\nrequires-python = ">=3.12"') assert get_python_version(range_version_toml) == "python>=3.12" def test_get_python_version_no_python(tmp_path: Path): - """Test get_python_version function.""" + """Test get_python_version with no Python version.""" no_python_toml = tmp_path / "no_python.toml" no_python_toml.write_text("[project]") with pytest.raises(ValueError, match="Python version not found in pyproject.toml"): @@ -36,13 +38,13 @@ def test_get_python_version_no_python(tmp_path: Path): def test_get_python_version_no_file(): - """Test get_python_version function.""" + """Test get_python_version with no file.""" with pytest.raises(FileNotFoundError, match="pyproject.toml not found at"): get_python_version("this_file_does_not_exist.toml") def test_create_requirements_file(tmp_path: Path): - """Test create_requirements_file function.""" + """Test create_requirements_file.""" pyproject_toml = tmp_path / "pyproject.toml" pyproject_toml.write_text('[project]\nrequires-python = "3.12"') @@ -77,13 +79,13 @@ def mock_run_uv_export(*args, **_kwargs): def test_create_requirements_file_no_file(): - """Test create_requirements_file function.""" + """Test create_requirements_file with no file.""" with pytest.raises(FileNotFoundError, match="pyproject.toml not found at"): create_requirements_file("this_file_does_not_exist.toml") def test_get_minimal_requirements(tmp_path: Path): - """Test get_minimal_requirements function.""" + """Test get_minimal_requirements.""" requirements_path = tmp_path / "requirements.txt" requirements_path.write_text(""" cloudpickle>2.0.0 @@ -117,13 +119,13 @@ def test_get_minimal_requirements(tmp_path: Path): def test_get_minimal_requirements_no_file(): - """Test get_minimal_requirements function.""" + """Test get_minimal_requirements with no file.""" with pytest.raises(FileNotFoundError, match="requirements.txt not found. Run setup_mlflow_uv.py first."): get_minimal_requirements("this_file_does_not_exist.txt") def test_create_minimal_requirements_file(tmp_path: Path): - """Test create_minimal_requirements_file function.""" + """Test create_minimal_requirements_file.""" requirements_path = tmp_path / "requirements.txt" requirements_path.write_text("torch==2.0.0") minimal_requirements_path = create_minimal_requirements_file(requirements_path) @@ -141,13 +143,13 @@ def test_create_minimal_requirements_file(tmp_path: Path): def test_fix_dependency_mismatches_no_file(): - """Test fix_dependency_mismatches function.""" + """Test fix_dependency_mismatches with no file.""" with pytest.raises(FileNotFoundError, match="requirements.txt not found. Run setup_mlflow_uv.py first."): fix_dependency_mismatches("this_file_does_not_exist.txt") def test_create_conda_yaml_file(tmp_path: Path): - """Test create_conda_yaml_file function.""" + """Test create_conda_yaml_file.""" pyproject_toml = tmp_path / "pyproject.toml" pyproject_toml.write_text('[project]\nrequires-python = "3.12"') conda_yaml_path = create_conda_yaml_file(pyproject_toml) @@ -170,6 +172,6 @@ def test_create_conda_yaml_file(tmp_path: Path): def test_create_conda_yaml_file_no_file(): - """Test create_conda_yaml_file function.""" + """Test create_conda_yaml_file with no file.""" with pytest.raises(FileNotFoundError, match="pyproject.toml not found at"): create_conda_yaml_file("this_file_does_not_exist.toml") diff --git a/tests/utils/test_pytorch_utils.py b/tests/utils/test_pytorch_utils.py index 326b8c24..2c528eb8 100644 --- a/tests/utils/test_pytorch_utils.py +++ b/tests/utils/test_pytorch_utils.py @@ -1,3 +1,5 @@ +"""Tests for PyTorch utilities.""" + from unittest.mock 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size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, ] From 26a38629bd39dc43ec17e2f73c5cb450ff2f02ff Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 12 Nov 2025 11:43:01 -0800 Subject: [PATCH 02/37] add compatibility for factored states --- simplexity/run_management/run_management.py | 24 ++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/simplexity/run_management/run_management.py b/simplexity/run_management/run_management.py index cbdde100..02da8768 100644 --- a/simplexity/run_management/run_management.py +++ b/simplexity/run_management/run_management.py @@ -263,13 +263,27 @@ def _instantiate_generative_process(cfg: DictConfig, instance_key: str) -> Gener raise KeyError -def _create_initial_state(generative_process: GenerativeProcess, batch_size: int | None) -> jax.Array: +def _create_initial_state(generative_process: GenerativeProcess, batch_size: int | None) -> jax.Array | tuple[jax.Array, ...]: """Setup the initial state.""" - if batch_size is None: - initial_state = generative_process.initial_state + # For factored processes, there initial state may be a tuple of arrays + # One for each factor. + if isinstance(generative_process.initial_state, tuple): + if batch_size is None: + initial_state = generative_process.initial_state + else: + initial_state = tuple( + jnp.repeat(state[None, :], batch_size, axis=0) for state in generative_process.initial_state + ) + SIMPLEXITY_LOGGER.info( + "[generative process] instantiated initial factored state with shapes: %s", + tuple(state.shape for state in initial_state), + ) else: - initial_state = jnp.repeat(generative_process.initial_state[None, :], batch_size, axis=0) - SIMPLEXITY_LOGGER.info("[generative process] instantiated initial state with shape: %s", initial_state.shape) + if batch_size is None: + initial_state = generative_process.initial_state + else: + initial_state = jnp.repeat(generative_process.initial_state[None, :], batch_size, axis=0) + SIMPLEXITY_LOGGER.info("[generative process] instantiated initial state with shape: %s", initial_state.shape) return initial_state From a15ef3b5ee9df7cf715d8a58819a25824ef99ecb Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Thu, 13 Nov 2025 10:27:56 -0800 Subject: [PATCH 03/37] concrete examples and alternating process --- .../unified_chain_3mess3_2tomq.yaml | 89 +++++ .../unified_chain_example.yaml | 50 +++ .../unified_symmetric_example.yaml | 86 +++++ ...ansition_and_emission_coupled_example.yaml | 85 +++++ .../unified_transition_coupled_example.yaml | 85 +++++ .../alternating_process.py | 269 ++++++++++++++ .../test_alternating_process.py | 341 ++++++++++++++++++ 7 files changed, 1005 insertions(+) create mode 100644 examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml create mode 100644 examples/configs/generative_process/unified_chain_example.yaml create mode 100644 examples/configs/generative_process/unified_symmetric_example.yaml create mode 100644 examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml create mode 100644 examples/configs/generative_process/unified_transition_coupled_example.yaml create mode 100644 simplexity/generative_processes/alternating_process.py create mode 100644 tests/generative_processes/test_alternating_process.py diff --git a/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml b/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml new file mode 100644 index 00000000..08a626d7 --- /dev/null +++ b/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml @@ -0,0 +1,89 @@ +# Unified API: Chain Topology - 3 mess3 + 2 tom_quantum +# Factor i depends on Factor i-1's emitted token (one-way conditional dependencies) + +name: unified_chain_3mess3_2tomq +base_vocab_size: 432 # (3^3) * (4^2) = 27 * 16 = 432 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_chain_process_from_spec + + chain: + # Factor 0: mess3 (unconditional, single variant) + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + + # Factor 1: mess3 (4 variants based on Factor 0's token) + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.11 + a: 0.79 + - process_name: mess3 + x: 0.5 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.21 + control_map: [2, 3, 0] # 3 parent tokens -> 4 variants + + # Factor 2: mess3 (4 variants based on Factor 1's token) + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.11 + a: 0.79 + - process_name: mess3 + x: 0.5 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.21 + control_map: [1, 3, 2] # 3 parent tokens -> 4 variants + + # Factor 3: tom_quantum (4 variants based on Factor 2's token) + - component_type: ghmm + variants: + - process_name: tom_quantum + alpha: 1.0 + beta: 1.0 + - process_name: tom_quantum + alpha: 1.0 + beta: 4.0 + - process_name: tom_quantum + alpha: 1.0 + beta: 7.141428 # sqrt(51) + - process_name: tom_quantum + alpha: 1.0 + beta: 2.0 + control_map: [2, 0, 3] # 3 parent tokens -> 4 variants + + # Factor 4: tom_quantum (4 variants based on Factor 3's token) + - component_type: ghmm + variants: + - process_name: tom_quantum + alpha: 1.0 + beta: 1.0 + - process_name: tom_quantum + alpha: 1.0 + beta: 4.0 + - process_name: tom_quantum + alpha: 1.0 + beta: 7.141428 + - process_name: tom_quantum + alpha: 1.0 + beta: 2.0 + control_map: [1, 3, 0, 2] # 4 parent tokens -> 4 variants + +bos_token: ??? +eos_token: null +batch_size: 1024 \ No newline at end of file diff --git a/examples/configs/generative_process/unified_chain_example.yaml b/examples/configs/generative_process/unified_chain_example.yaml new file mode 100644 index 00000000..d9488680 --- /dev/null +++ b/examples/configs/generative_process/unified_chain_example.yaml @@ -0,0 +1,50 @@ +# Unified API: Chain Topology +# Chain topology creates one-way conditional dependencies: +# Factor 0 -> Factor 1 -> Factor 2 -> ... +# Each factor i>0 selects its parameter variant based on factor i-1's token. + +name: unified_chain_example +base_vocab_size: 12 # 3 * 2 * 2 = 12 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_chain_process_from_spec + + chain: + # Factor 0: mess3 (root, single variant) + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + + # Factor 1: mess3 (depends on Factor 0, 2 variants) + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.6 + control_map: [0, 1, 0] # 3 parent tokens -> 2 variants + + # Factor 2: mess3 (depends on Factor 1, 2 variants) + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.6 + control_map: [1, 0] # 2 parent tokens -> 2 variants + +bos_token: ??? +eos_token: null +batch_size: 1024 + +# Example interpretation: +# - Factor 0 always uses variant 0 +# - Factor 1: parent_token 0->variant 0, token 1->variant 1, token 2->variant 0 +# - Factor 2: parent_token 0->variant 1, token 1->variant 0 \ No newline at end of file diff --git a/examples/configs/generative_process/unified_symmetric_example.yaml b/examples/configs/generative_process/unified_symmetric_example.yaml new file mode 100644 index 00000000..27e38f1f --- /dev/null +++ b/examples/configs/generative_process/unified_symmetric_example.yaml @@ -0,0 +1,86 @@ +# Unified API: Symmetric (Fully Bidirectional) Topology +# Symmetric topology creates mutual dependencies: +# Each factor's variant selection depends on ALL OTHER factors' tokens. +# Uses product-of-experts to compute joint distribution. + +name: unified_symmetric_example +base_vocab_size: 27 # 3 * 3 * 3 = 27 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_symmetric_process_from_spec + + # Component specifications + components: + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.11 + a: 0.79 + - process_name: mess3 + x: 0.5 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.21 + + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.11 + a: 0.79 + - process_name: mess3 + x: 0.5 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.21 + + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.11 + a: 0.79 + - process_name: mess3 + x: 0.5 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.21 + + # Control maps: define how other-tokens select variant + # control_maps[i] has shape [prod(V_j for j!=i)] + # For 3 factors with vocab_sizes [3,3,3]: + # - Factor 0 depends on (t1, t2): 3*3=9 combinations + # - Factor 1 depends on (t0, t2): 3*3=9 combinations + # - Factor 2 depends on (t0, t1): 3*3=9 combinations + # Each maps to one of K_i=4 variants + control_maps: + # Factor 0: 9 other-token combinations -> 4 variants + - [0, 1, 2, 3, 0, 1, 2, 3, 0] + + # Factor 1: 9 other-token combinations -> 4 variants + - [1, 2, 3, 0, 1, 2, 3, 0, 1] + + # Factor 2: 9 other-token combinations -> 4 variants + - [2, 3, 0, 1, 2, 3, 0, 1, 2] + +bos_token: ??? +eos_token: null +batch_size: 1024 + +# Example interpretation: +# At each timestep, factors emit jointly using product-of-experts: +# - Factor 0 selects variant based on what tokens factors 1&2 will emit +# - Factor 1 selects variant based on what tokens factors 0&2 will emit +# - Factor 2 selects variant based on what tokens factors 0&1 will emit +# The joint distribution is computed, normalized, and sampled from. diff --git a/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml b/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml new file mode 100644 index 00000000..47a31709 --- /dev/null +++ b/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml @@ -0,0 +1,85 @@ +# Unified API: Transition-Coupled Topology +# Transition-coupled topology separates emission and transition coupling: +# - Emissions: Can be independent OR chain-style (factor i depends on i-1's token) +# - Transitions: Always coupled (factor i's transition depends on all other factors) + +name: unified_transition_coupled_example +base_vocab_size: 36 # 3 * 3 * 4 = 24 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_transition_coupled_process_from_spec + + # Component specifications + components: + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.6 + + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.6 + + - component_type: ghmm + variants: + - process_name: tom_quantum + alpha: 1.0 + beta: 1.0 + - process_name: tom_quantum + alpha: 1.0 + beta: 4.0 + + # Transition control maps: how other-factors' tokens select transition variant + # control_maps_transition[i] has shape [prod(V_j for j!=i)] + # For 3 factors with vocab_sizes [3,3,4]: + # - Factor 0: depends on (t1,t2): 3*4=12 combinations -> 2 variants + # - Factor 1: depends on (t0,t2): 3*4=12 combinations -> 2 variants + # - Factor 2: depends on (t0,t1): 3*3=9 combinations -> 2 variants + control_maps_transition: + # Factor 0: 12 other-token combos -> 2 transition variants + - [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + + # Factor 1: 12 other-token combos -> 2 transition variants + - [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] + + # Factor 2: 9 other-token combos -> 2 transition variants + - [0, 1, 0, 1, 0, 1, 0, 1, 0] + + # Emission variant indices: which variant to use for emissions (shape [F]) + # This uses independent emission mode (each factor uses fixed variant) + emission_variant_indices: [0, 0, 0] + + # Optional: emission_control_maps for chain-style emissions + # If provided, emission mode becomes chain-style (factor i depends on previous tokens) + # Uncomment to enable chain emissions: + emission_control_maps: + - null # Factor 0: root + - [0, 1, 0] # Factor 1: depends on t0 (3 values) -> use emission variants + - [0, 1, 0, 1, 0, 1, 0, 1, 0] # Factor 2: depends on (t0,t1) (9 combos) + +bos_token: ??? +eos_token: null +batch_size: 1024 + +# Example interpretation (independent emission mode): +# - Emissions: Each factor independently emits using variant 0 +# P(t) = P0(t0|s0) * P1(t1|s1) * P2(t2|s2) +# - Transitions: After observing (t0,t1,t2): +# - Factor 0 selects transition variant based on (t1,t2) +# - Factor 1 selects transition variant based on (t0,t2) +# - Factor 2 selects transition variant based on (t0,t1) +# Each updates its state using the selected transition variant + +# If emission_control_maps is enabled (chain mode): +# - Emissions: P(t) = P0(t0|s0) * P1(t1|t0,s1) * P2(t2|t0,t1,s2) +# - Transitions: Same coupling as above diff --git a/examples/configs/generative_process/unified_transition_coupled_example.yaml b/examples/configs/generative_process/unified_transition_coupled_example.yaml new file mode 100644 index 00000000..4119c1ce --- /dev/null +++ b/examples/configs/generative_process/unified_transition_coupled_example.yaml @@ -0,0 +1,85 @@ +# Unified API: Transition-Coupled Topology +# Transition-coupled topology separates emission and transition coupling: +# - Emissions: Can be independent OR chain-style (factor i depends on i-1's token) +# - Transitions: Always coupled (factor i's transition depends on all other factors) + +name: unified_transition_coupled_example +base_vocab_size: 36 # 3 * 3 * 4 = 24 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_transition_coupled_process_from_spec + + # Component specifications + components: + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.6 + + - component_type: hmm + variants: + - process_name: mess3 + x: 0.15 + a: 0.6 + - process_name: mess3 + x: 0.5 + a: 0.6 + + - component_type: ghmm + variants: + - process_name: tom_quantum + alpha: 1.0 + beta: 1.0 + - process_name: tom_quantum + alpha: 1.0 + beta: 4.0 + + # Transition control maps: how other-factors' tokens select transition variant + # control_maps_transition[i] has shape [prod(V_j for j!=i)] + # For 3 factors with vocab_sizes [3,3,4]: + # - Factor 0: depends on (t1,t2): 3*4=12 combinations -> 2 variants + # - Factor 1: depends on (t0,t2): 3*4=12 combinations -> 2 variants + # - Factor 2: depends on (t0,t1): 3*3=9 combinations -> 2 variants + control_maps_transition: + # Factor 0: 12 other-token combos -> 2 transition variants + - [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + + # Factor 1: 12 other-token combos -> 2 transition variants + - [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] + + # Factor 2: 9 other-token combos -> 2 transition variants + - [0, 1, 0, 1, 0, 1, 0, 1, 0] + + # Emission variant indices: which variant to use for emissions (shape [F]) + # This uses independent emission mode (each factor uses fixed variant) + emission_variant_indices: [0, 0, 0] + + # Optional: emission_control_maps for chain-style emissions + # If provided, emission mode becomes chain-style (factor i depends on previous tokens) + # Uncomment to enable chain emissions: + # emission_control_maps: + # - null # Factor 0: root + # - [0, 1, 0] # Factor 1: depends on t0 (3 values) -> use emission variants + # - [0, 1, 0, 1, 0, 1, 0, 1, 0] # Factor 2: depends on (t0,t1) (9 combos) + +bos_token: ??? +eos_token: null +batch_size: 1024 + +# Example interpretation (independent emission mode): +# - Emissions: Each factor independently emits using variant 0 +# P(t) = P0(t0|s0) * P1(t1|s1) * P2(t2|s2) +# - Transitions: After observing (t0,t1,t2): +# - Factor 0 selects transition variant based on (t1,t2) +# - Factor 1 selects transition variant based on (t0,t2) +# - Factor 2 selects transition variant based on (t0,t1) +# Each updates its state using the selected transition variant + +# If emission_control_maps is enabled (chain mode): +# - Emissions: P(t) = P0(t0|s0) * P1(t1|t0,s1) * P2(t2|t0,t1,s2) +# - Transitions: Same coupling as above diff --git a/simplexity/generative_processes/alternating_process.py b/simplexity/generative_processes/alternating_process.py new file mode 100644 index 00000000..eab9858d --- /dev/null +++ b/simplexity/generative_processes/alternating_process.py @@ -0,0 +1,269 @@ +"""Alternating generative process that cycles through n different sub-processes. + +This module provides a simple wrapper around HMM/GHMM that alternates deterministically +through n different transition matrix sets based on position. +""" + +from __future__ import annotations + +from typing import Literal + +import chex +import equinox as eqx +import jax +import jax.numpy as jnp + +from simplexity.generative_processes.generative_process import GenerativeProcess + +ComponentType = Literal["hmm", "ghmm"] + +# State type: (position, belief_state) +AlternatingState = tuple[jnp.ndarray, jnp.ndarray] + + +class AlternatingProcess(GenerativeProcess[AlternatingState]): + """Generative process that alternates through n different sub-processes. + + The process cycles deterministically through n different transition matrices: + - With n_repetitions=1: position 0→variant 0, position 1→variant 1, etc. + - With n_repetitions=k: positions 0..k-1→variant 0, positions k..2k-1→variant 1, etc. + + Each sub-process has its own transition matrix but shares the same state space + and vocabulary size. + + State structure: (position_counter, belief_state) + - position_counter: int tracking which step we're on + - belief_state: probability distribution over hidden states [S] + + Attributes: + component_type: Either "hmm" or "ghmm" + transition_matrices: Array of shape [n, V, S, S] with n transition matrices + normalizing_eigenvectors: Array of shape [n, S] for GHMM normalization + initial_state_dist: Initial belief state distribution [S] + n: Number of sub-processes to cycle through + n_repetitions: Number of times each process is used before switching + vocab_size: Vocabulary size (same across all variants) + num_states: Number of hidden states (same across all variants) + """ + + component_type: ComponentType + transition_matrices: jnp.ndarray # [n, V, S, S] + normalizing_eigenvectors: jnp.ndarray # [n, S] + initial_state_dist: jnp.ndarray # [S] + n: int + n_repetitions: int + + def __init__( + self, + *, + component_type: ComponentType, + transition_matrices: jnp.ndarray, + normalizing_eigenvectors: jnp.ndarray, + initial_state: jnp.ndarray, + n_repetitions: int = 1, + ): + """Initialize alternating process. + + Args: + component_type: Either "hmm" or "ghmm" + transition_matrices: Array of shape [n, V, S, S] with n variants + normalizing_eigenvectors: Array of shape [n, S] for GHMM (ignored for HMM) + initial_state: Initial belief state [S] + n_repetitions: Number of times each variant is used consecutively + """ + if transition_matrices.ndim != 4: + raise ValueError(f"transition_matrices must have shape [n, V, S, S], got {transition_matrices.shape}") + + n, V, S1, S2 = transition_matrices.shape + if S1 != S2: + raise ValueError(f"State dimensions must match: {S1} vs {S2}") + + if initial_state.shape != (S1,): + raise ValueError(f"initial_state shape {initial_state.shape} doesn't match state dim {S1}") + + if n_repetitions <= 0: + raise ValueError(f"n_repetitions must be positive, got {n_repetitions}") + + self.component_type = component_type + self.transition_matrices = transition_matrices + self.normalizing_eigenvectors = normalizing_eigenvectors + self.initial_state_dist = initial_state + self.n = n + self.n_repetitions = n_repetitions + + @property + def vocab_size(self) -> int: + """Vocabulary size.""" + return int(self.transition_matrices.shape[1]) + + @property + def num_states(self) -> int: + """Number of hidden states.""" + return int(self.transition_matrices.shape[2]) + + @property + def initial_state(self) -> AlternatingState: + """Initial state: (position=0, initial_belief_state).""" + return (jnp.array(0, dtype=jnp.int32), self.initial_state_dist) + + def _select_variant(self, position: jnp.ndarray) -> jnp.ndarray: + """Select which variant to use based on position. + + Args: + position: Current position counter + + Returns: + Variant index k = (position // n_repetitions) % n (as JAX array) + """ + return (position // self.n_repetitions) % self.n + + def _compute_obs_dist(self, belief_state: jnp.ndarray, variant_idx: jnp.ndarray) -> jnp.ndarray: + """Compute observation distribution for given belief state and variant. + + Args: + belief_state: Current belief state [S] + variant_idx: Which variant to use (0 to n-1) + + Returns: + Distribution over observations [V] + """ + T = self.transition_matrices[variant_idx] # [V, S, S] + + if self.component_type == "hmm": + # HMM: P(obs|state) = sum_s belief[s] * sum_s' T[obs, s, s'] + obs_state = belief_state @ T # [V, S] + return jnp.sum(obs_state, axis=1) # [V] + else: # ghmm + # GHMM: weighted by normalizing eigenvector + norm = self.normalizing_eigenvectors[variant_idx] # [S] + numer = belief_state @ T @ norm # [V] + denom = jnp.sum(belief_state * norm) # scalar + return numer / denom + + def _transition(self, belief_state: jnp.ndarray, obs: chex.Array, variant_idx: jnp.ndarray) -> jnp.ndarray: + """Compute next belief state after observing a token. + + Args: + belief_state: Current belief state [S] + obs: Observed token (scalar) + variant_idx: Which variant to use + + Returns: + Updated belief state [S] + """ + T = self.transition_matrices[variant_idx, obs] # [S, S] + + if self.component_type == "hmm": + # HMM: normalize by sum + new_state = belief_state @ T # [S] + return new_state / jnp.sum(new_state) + else: # ghmm + # GHMM: normalize by eigenvector + norm = self.normalizing_eigenvectors[variant_idx] # [S] + new_state = belief_state @ T # [S] + return (new_state * norm) / jnp.sum(new_state * norm) + + @eqx.filter_jit + def observation_probability_distribution(self, state: AlternatingState) -> jnp.ndarray: + """Compute P(obs | state). + + Args: + state: (position, belief_state) + + Returns: + Distribution over observations [V] + """ + position, belief_state = state + variant_idx = self._select_variant(position) + return self._compute_obs_dist(belief_state, variant_idx) + + @eqx.filter_jit + def log_observation_probability_distribution(self, log_belief_state: AlternatingState) -> jnp.ndarray: + """Compute log P(obs | state). + + Args: + log_belief_state: (position, log_belief_state) + + Returns: + Log distribution over observations [V] + """ + position, log_belief = log_belief_state + belief_state = jnp.exp(log_belief) + variant_idx = self._select_variant(position) + probs = self._compute_obs_dist(belief_state, variant_idx) + return jnp.log(probs) + + @eqx.filter_jit + def emit_observation(self, state: AlternatingState, key: jax.Array) -> jnp.ndarray: + """Sample observation from current state. + + Args: + state: (position, belief_state) + key: JAX random key + + Returns: + Sampled observation (scalar token) + """ + probs = self.observation_probability_distribution(state) + return jax.random.categorical(key, jnp.log(probs)) + + @eqx.filter_jit + def transition_states(self, state: AlternatingState, obs: chex.Array) -> AlternatingState: + """Update state after observing a token. + + Args: + state: Current (position, belief_state) + obs: Observed token + + Returns: + Updated (position+1, new_belief_state) + """ + position, belief_state = state + variant_idx = self._select_variant(position) + new_belief_state = self._transition(belief_state, obs, variant_idx) + new_position = position + 1 + return (new_position, new_belief_state) + + @eqx.filter_jit + def probability(self, observations: jnp.ndarray) -> jnp.ndarray: + """Compute P(observations). + + Args: + observations: Array of observations + + Returns: + Scalar probability + """ + + def step(carry: AlternatingState, obs: jnp.ndarray): + state = carry + dist = self.observation_probability_distribution(state) + p = dist[obs] + new_state = self.transition_states(state, obs) + return new_state, p + + _, ps = jax.lax.scan(step, self.initial_state, observations) + return jnp.prod(ps) + + @eqx.filter_jit + def log_probability(self, observations: jnp.ndarray) -> jnp.ndarray: + """Compute log P(observations). + + Args: + observations: Array of observations + + Returns: + Scalar log probability + """ + + def step(carry: AlternatingState, obs: jnp.ndarray): + position, belief_state = carry + log_belief_state = jnp.log(belief_state) + log_state = (position, log_belief_state) + log_dist = self.log_observation_probability_distribution(log_state) + lp = log_dist[obs] + new_state = self.transition_states(carry, obs) + return new_state, lp + + _, lps = jax.lax.scan(step, self.initial_state, observations) + return jnp.sum(lps) diff --git a/tests/generative_processes/test_alternating_process.py b/tests/generative_processes/test_alternating_process.py new file mode 100644 index 00000000..ca685580 --- /dev/null +++ b/tests/generative_processes/test_alternating_process.py @@ -0,0 +1,341 @@ +"""Tests for simple (non-factored) alternating process.""" + +import jax +import jax.numpy as jnp +import pytest + +from simplexity.generative_processes.alternating_process import AlternatingProcess +from simplexity.generative_processes.builder import ( + build_simple_alternating_process, + build_simple_alternating_process_from_spec, +) + + +def test_alternating_process_init(): + """Test AlternatingProcess initialization.""" + n, V, S = 2, 3, 4 + T = jnp.ones((n, V, S, S)) * 0.5 + norm = jnp.ones((n, S)) + initial_state = jnp.ones(S) / S + + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=norm, + initial_state=initial_state, + n_repetitions=1, + ) + + assert process.vocab_size == V + assert process.num_states == S + assert process.n == n + assert process.n_repetitions == 1 + + +def test_alternating_process_invalid_shape(): + """Test that invalid transition matrix shapes are rejected.""" + # Wrong number of dimensions + with pytest.raises(ValueError, match="must have shape"): + AlternatingProcess( + component_type="hmm", + transition_matrices=jnp.ones((2, 3, 4)), # Missing a dimension + normalizing_eigenvectors=jnp.ones((2, 4)), + initial_state=jnp.ones(4) / 4, + ) + + +def test_alternating_process_invalid_state_dim(): + """Test mismatched state dimensions.""" + with pytest.raises(ValueError, match="State dimensions must match"): + AlternatingProcess( + component_type="hmm", + transition_matrices=jnp.ones((2, 3, 4, 5)), # S1=4, S2=5 mismatch + normalizing_eigenvectors=jnp.ones((2, 4)), + initial_state=jnp.ones(4) / 4, + ) + + +def test_alternating_process_invalid_initial_state(): + """Test mismatched initial state shape.""" + with pytest.raises(ValueError, match="initial_state shape"): + AlternatingProcess( + component_type="hmm", + transition_matrices=jnp.ones((2, 3, 4, 4)), + normalizing_eigenvectors=jnp.ones((2, 4)), + initial_state=jnp.ones(5) / 5, # Wrong size + ) + + +def test_alternating_process_invalid_repetitions(): + """Test invalid n_repetitions.""" + with pytest.raises(ValueError, match="n_repetitions must be positive"): + AlternatingProcess( + component_type="hmm", + transition_matrices=jnp.ones((2, 3, 4, 4)), + normalizing_eigenvectors=jnp.ones((2, 4)), + initial_state=jnp.ones(4) / 4, + n_repetitions=0, + ) + + +def test_alternating_process_initial_state(): + """Test initial state structure.""" + n, V, S = 2, 3, 4 + T = jnp.ones((n, V, S, S)) * 0.5 + initial_dist = jnp.ones(S) / S + + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=initial_dist, + ) + + position, belief_state = process.initial_state + assert position == 0 + assert belief_state.shape == (S,) + assert jnp.allclose(belief_state, initial_dist) + + +def test_alternating_process_variant_selection(): + """Test variant selection logic.""" + n, V, S = 3, 2, 2 + n_repetitions = 2 + + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + n_repetitions=n_repetitions, + ) + + # Positions 0,1 -> variant 0 + assert int(process._select_variant(jnp.array(0))) == 0 + assert int(process._select_variant(jnp.array(1))) == 0 + + # Positions 2,3 -> variant 1 + assert int(process._select_variant(jnp.array(2))) == 1 + assert int(process._select_variant(jnp.array(3))) == 1 + + # Positions 4,5 -> variant 2 + assert int(process._select_variant(jnp.array(4))) == 2 + assert int(process._select_variant(jnp.array(5))) == 2 + + # Position 6 -> wraps to variant 0 + assert int(process._select_variant(jnp.array(6))) == 0 + + +def test_alternating_process_observation_distribution(): + """Test observation probability distribution.""" + n, V, S = 2, 3, 2 + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + ) + + state = process.initial_state + dist = process.observation_probability_distribution(state) + + assert dist.shape == (V,) + assert jnp.all(dist >= 0) + + +def test_alternating_process_emission(): + """Test observation emission.""" + n, V, S = 2, 3, 2 + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + ) + + key = jax.random.PRNGKey(42) + state = process.initial_state + obs = process.emit_observation(state, key) + + assert obs.shape == () + assert 0 <= obs < V + + +def test_alternating_process_transition(): + """Test state transition.""" + n, V, S = 2, 3, 2 + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + ) + + state = process.initial_state + position, belief_state = state + + assert position == 0 + + # Transition + new_state = process.transition_states(state, jnp.array(0)) + new_position, new_belief_state = new_state + + assert new_position == 1 + assert new_belief_state.shape == (S,) + + +def test_alternating_process_position_increment(): + """Test position increments through sequence.""" + n, V, S = 2, 2, 2 + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + ) + + state = process.initial_state + + for expected_pos in range(5): + position, _ = state + assert position == expected_pos + state = process.transition_states(state, jnp.array(0)) + + +def test_alternating_process_probability(): + """Test probability computation.""" + n, V, S = 2, 2, 2 + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + ) + + observations = jnp.array([0, 1, 0, 1]) + prob = process.probability(observations) + + assert prob.shape == () + assert 0 <= prob <= 1 + + +def test_alternating_process_log_probability(): + """Test log probability computation.""" + n, V, S = 2, 2, 2 + T = jnp.ones((n, V, S, S)) * 0.5 + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + ) + + observations = jnp.array([0, 1, 0]) + log_prob = process.log_probability(observations) + + assert log_prob.shape == () + assert log_prob <= 0 + + +def test_build_simple_alternating_process(): + """Test builder function.""" + n, V, S = 2, 3, 4 + T = jnp.ones((n, V, S, S)) * 0.5 + + process = build_simple_alternating_process( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.ones(S) / S, + n_repetitions=2, + ) + + assert isinstance(process, AlternatingProcess) + assert process.vocab_size == V + assert process.num_states == S + assert process.n == n + assert process.n_repetitions == 2 + + +def test_build_simple_alternating_process_from_spec(): + """Test spec-based builder.""" + variants = [ + {"process_name": "coin", "p": 0.7}, + {"process_name": "coin", "p": 0.3}, + ] + + process = build_simple_alternating_process_from_spec( + variants=variants, + component_type="hmm", + n_repetitions=3, + ) + + assert isinstance(process, AlternatingProcess) + assert process.n == 2 + assert process.n_repetitions == 3 + assert process.vocab_size == 2 # coin has 2 tokens + + +def test_build_simple_alternating_process_from_spec_empty(): + """Test that empty variants list is rejected.""" + with pytest.raises(ValueError, match="Must provide at least one variant"): + build_simple_alternating_process_from_spec( + variants=[], + component_type="hmm", + ) + + +def test_build_simple_alternating_process_from_spec_mismatched_shapes(): + """Test that mismatched variant shapes are rejected.""" + variants = [ + {"process_name": "coin", "p": 0.7}, # 2 tokens + {"process_name": "mess3", "x": 0.5, "a": 0.6}, # 3 tokens + ] + + with pytest.raises(ValueError, match="All variants must have same shape"): + build_simple_alternating_process_from_spec( + variants=variants, + component_type="hmm", + ) + + +def test_alternating_process_with_repetitions_full_cycle(): + """Test full alternating cycle with repetitions.""" + n = 3 + n_repetitions = 2 + S = 2 + + # Create distinct matrices for each variant (deterministic for testing) + T0 = jnp.array([[[1.0, 0.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 1.0]]]) + T1 = jnp.array([[[0.0, 1.0], [0.0, 1.0]], [[1.0, 0.0], [1.0, 0.0]]]) + T2 = jnp.array([[[1.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 0.0]]]) + T = jnp.stack([T0, T1, T2]) + + process = AlternatingProcess( + component_type="hmm", + transition_matrices=T, + normalizing_eigenvectors=jnp.ones((n, S)), + initial_state=jnp.array([1.0, 0.0]), + n_repetitions=n_repetitions, + ) + + state = process.initial_state + + # Verify cycling pattern: 0,0,1,1,2,2,0,0,... + expected_variants = [0, 0, 1, 1, 2, 2, 0, 0] + + for i, expected_variant in enumerate(expected_variants): + position, _ = state + assert position == i + actual_variant = int(process._select_variant(position)) + assert actual_variant == expected_variant, f"Position {i} should use variant {expected_variant}" + + # Advance state + if i < len(expected_variants) - 1: + state = process.transition_states(state, jnp.array(0)) From c90ebbc91e83621dd74f6b2291e795397644f5ca Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Thu, 13 Nov 2025 10:34:40 -0800 Subject: [PATCH 04/37] tweaks to vocab sizes --- simplexity/generative_processes/builder.py | 191 +++++++++++++++--- .../topology/transition_coupled.py | 4 +- 2 files changed, 160 insertions(+), 35 deletions(-) diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 894487b0..b4556e80 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -16,6 +16,10 @@ import jax import jax.numpy as jnp +from simplexity.generative_processes.alternating_process import AlternatingProcess +from simplexity.generative_processes.factored_beliefs.topology.chain import ChainTopology +from simplexity.generative_processes.factored_beliefs.topology.symmetric import SymmetricTopology +from simplexity.generative_processes.factored_beliefs.topology.transition_coupled import TransitionCoupledTopology from simplexity.generative_processes.factored_generative_process import ComponentType, FactoredGenerativeProcess from simplexity.generative_processes.generalized_hidden_markov_model import GeneralizedHiddenMarkovModel from simplexity.generative_processes.hidden_markov_model import HiddenMarkovModel @@ -24,10 +28,6 @@ HMM_MATRIX_FUNCTIONS, get_stationary_state, ) -from simplexity.generative_processes.factored_beliefs.topology.chain import ChainTopology -from simplexity.generative_processes.factored_beliefs.topology.symmetric import SymmetricTopology -from simplexity.generative_processes.factored_beliefs.topology.transition_coupled import TransitionCoupledTopology - def build_transition_matrices(matrix_functions: dict[str, Callable], process_name: str, **kwargs) -> jax.Array: @@ -254,6 +254,144 @@ def build_transition_coupled_process( ) +def build_simple_alternating_process( + *, + component_type: ComponentType, + transition_matrices: jnp.ndarray, + normalizing_eigenvectors: jnp.ndarray, + initial_state: jnp.ndarray, + n_repetitions: int = 1, +) -> AlternatingProcess: + """Build a simple alternating process (non-factored). + + The process cycles through n different transition matrices. This is simpler + than the factored alternating process - it's just a single HMM/GHMM that + switches between different parameter sets. + + Args: + component_type: Either "hmm" or "ghmm" + transition_matrices: Array of shape [n, V, S, S] with n variants + normalizing_eigenvectors: Array of shape [n, S] for GHMM normalization + initial_state: Initial belief state distribution [S] + n_repetitions: Number of times each variant is used consecutively + + Returns: + AlternatingProcess that cycles through the n variants + + Example: + ```python + # Build process alternating between 2 HMM variants + # Each variant used 3 times before switching + n = 2 + V, S = 3, 4 # 3 tokens, 4 hidden states + + # Stack n transition matrices + T0 = ... # [V, S, S] + T1 = ... # [V, S, S] + transition_matrices = jnp.stack([T0, T1]) # [2, V, S, S] + + process = build_simple_alternating_process( + component_type="hmm", + transition_matrices=transition_matrices, + normalizing_eigenvectors=jnp.ones((2, S)), # dummy for HMM + initial_state=jnp.ones(S) / S, # uniform initial state + n_repetitions=3, + ) + ``` + """ + return AlternatingProcess( + component_type=component_type, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_state=initial_state, + n_repetitions=n_repetitions, + ) + + +def build_simple_alternating_process_from_spec( + variants: list[dict[str, Any]], + component_type: ComponentType = "ghmm", + n_repetitions: int = 1, +) -> AlternatingProcess: + """Build simple alternating process from specification. + + Args: + variants: List of variant specifications, one per sub-process + component_type: Either "hmm" or "ghmm" + n_repetitions: Number of times each variant is used consecutively + + Returns: + AlternatingProcess + + Example: + ```python + variants = [ + {"process_name": "coin", "p": 0.7}, # Variant 0 + {"process_name": "coin", "p": 0.3}, # Variant 1 + {"process_name": "coin", "p": 0.5}, # Variant 2 + ] + + process = build_simple_alternating_process_from_spec( + variants=variants, + component_type="hmm", + n_repetitions=2, # Each variant repeated twice + ) + ``` + """ + if not variants: + raise ValueError("Must provide at least one variant") + + # Build each variant + matrix_functions = HMM_MATRIX_FUNCTIONS if component_type == "hmm" else GHMM_MATRIX_FUNCTIONS + built_processes = [] + + for variant_spec in variants: + T = build_transition_matrices(matrix_functions, **variant_spec) + built_processes.append(T) + + # Validate all have same shape + shapes = [T.shape for T in built_processes] + if len(set(shapes)) != 1: + raise ValueError(f"All variants must have same shape, got {shapes}") + + # Stack into [n, V, S, S] + transition_matrices = jnp.stack(built_processes, axis=0) + n, V, S, _ = transition_matrices.shape + + # Get initial state from first variant (uniform if stationary fails) + try: + initial_state = get_stationary_state(built_processes[0]) + except (IndexError, ValueError): + # Fall back to uniform distribution if stationary state computation fails + initial_state = jnp.ones(S) / S + + # Build normalizing eigenvectors + if component_type == "ghmm": + eigenvectors = [] + for T in built_processes: + # Compute normalizing eigenvector for this variant + state_transition_matrix = jnp.sum(T, axis=0) + eigenvalues, right_eigenvectors = jnp.linalg.eig(state_transition_matrix) + principal_eigenvalue = jnp.max(eigenvalues) + normalizing_eigenvector = ( + right_eigenvectors[:, jnp.isclose(eigenvalues, principal_eigenvalue)].squeeze().real + ) + normalizing_eigenvector = normalizing_eigenvector / jnp.sum(normalizing_eigenvector) * S + eigenvectors.append(normalizing_eigenvector) + normalizing_eigenvectors = jnp.stack(eigenvectors, axis=0) + else: + # Dummy for HMM + normalizing_eigenvectors = jnp.ones((n, S)) + + return build_simple_alternating_process( + component_type=component_type, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_state=initial_state, + n_repetitions=n_repetitions, + ) + + def build_factored_process( topology_type: Literal["chain", "symmetric", "transition_coupled"], component_types: Sequence[ComponentType], @@ -310,8 +448,7 @@ def build_factored_process( ) else: raise ValueError( - f"Unknown topology_type: {topology_type}. " - f"Must be one of: 'chain', 'symmetric', 'transition_coupled'" + f"Unknown topology_type: {topology_type}. Must be one of: 'chain', 'symmetric', 'transition_coupled'" ) @@ -512,25 +649,20 @@ def build_matrices_from_spec( raise ValueError(f"spec[{idx}].variants must be non-empty") # Build all variants for this factor - built = [build_hidden_markov_model(**v) if ctype == "hmm" else build_generalized_hidden_markov_model(**v) for v in variants] + built = [ + build_hidden_markov_model(**v) if ctype == "hmm" else build_generalized_hidden_markov_model(**v) + for v in variants + ] # Validate dimensions vocab_sizes = [b.vocab_size for b in built] - num_states = [ - b.num_states if hasattr(b, "num_states") else b.transition_matrices.shape[1] - for b in built - ] + num_states = [b.num_states if hasattr(b, "num_states") else b.transition_matrices.shape[1] for b in built] if len(set(vocab_sizes)) != 1: - raise ValueError( - f"All variants in spec[{idx}] must have same vocab size; got {vocab_sizes}" - ) + raise ValueError(f"All variants in spec[{idx}] must have same vocab size; got {vocab_sizes}") if len(set(num_states)) != 1: - raise ValueError( - f"All variants in spec[{idx}] must have same state dim; got {num_states}" - ) + raise ValueError(f"All variants in spec[{idx}] must have same state dim; got {num_states}") - V = vocab_sizes[0] S = num_states[0] # Stack transition matrices: [K, V, S, S] @@ -599,9 +731,7 @@ def build_chain_from_spec( raise ValueError("chain must contain at least one node") # Build base matrices - component_types, transition_matrices, normalizing_eigenvectors, initial_states = ( - build_matrices_from_spec(chain) - ) + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec(chain) # Extract control maps control_maps: list[jnp.ndarray | None] = [] @@ -619,8 +749,7 @@ def build_chain_from_spec( if expected_prev_vocab is not None and int(cm_arr.shape[0]) != int(expected_prev_vocab): raise ValueError( - f"chain[{idx}].control_map length {cm_arr.shape[0]} must equal " - f"parent vocab {expected_prev_vocab}" + f"chain[{idx}].control_map length {cm_arr.shape[0]} must equal parent vocab {expected_prev_vocab}" ) control_maps.append(cm_arr) @@ -671,8 +800,8 @@ def build_symmetric_from_spec( ``` """ # Build base matrices - component_types, transition_matrices, normalizing_eigenvectors, initial_states = ( - build_matrices_from_spec(components) + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + components ) # Convert control maps to JAX arrays @@ -690,10 +819,7 @@ def build_symmetric_from_spec( expected *= vocab_sizes[j] if int(cm.shape[0]) != expected: - raise ValueError( - f"control_maps[{i}] length {cm.shape[0]} must equal " - f"prod(V_j for j!={i}) = {expected}" - ) + raise ValueError(f"control_maps[{i}] length {cm.shape[0]} must equal prod(V_j for j!={i}) = {expected}") return component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps_arrays @@ -734,8 +860,8 @@ def build_transition_coupled_from_spec( ``` """ # Build base matrices - component_types, transition_matrices, normalizing_eigenvectors, initial_states = ( - build_matrices_from_spec(components) + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + components ) # Convert transition control maps @@ -748,8 +874,7 @@ def build_transition_coupled_from_spec( emission_control_maps_arrays = None if emission_control_maps is not None: emission_control_maps_arrays = [ - jnp.asarray(cm, dtype=jnp.int32) if cm is not None else None - for cm in emission_control_maps + jnp.asarray(cm, dtype=jnp.int32) if cm is not None else None for cm in emission_control_maps ] return ( diff --git a/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py b/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py index 126e7957..c5171910 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py +++ b/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py @@ -168,7 +168,7 @@ def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: T0 = transition_matrices[0][k0] norm0 = normalizing_eigenvectors[0][k0] if component_types[0] == "ghmm" else None joint = compute_obs_dist_for_variant(component_types[0], states[0], T0, norm0) - prev_prod = context.vocab_sizes[0] + prev_prod = self.vocab_sizes_py[0] for i in range(1, F): K_i = num_variants[i] @@ -193,7 +193,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: left = joint.reshape(prev_prod) extended = cond * left[:, None] - curr_vocab_size = context.vocab_sizes[i] + curr_vocab_size = self.vocab_sizes_py[i] joint = extended.reshape(*(list(joint.shape) + [curr_vocab_size])) prev_prod *= curr_vocab_size From 731eb5d51dd4a3626c9c4c6cfec49b89b3427204 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Tue, 18 Nov 2025 10:36:32 -0800 Subject: [PATCH 05/37] update naming --- .../unified_chain_3mess3_2tomq.yaml | 2 +- .../unified_chain_example.yaml | 4 +- .../unified_symmetric_example.yaml | 4 +- ...ansition_and_emission_coupled_example.yaml | 8 ++-- .../unified_transition_coupled_example.yaml | 8 ++-- simplexity/generative_processes/builder.py | 24 ++++++------ ..._coupled.py => conditional_transitions.py} | 38 +++++++++---------- .../{symmetric.py => fully_conditional.py} | 20 +++++----- .../{chain.py => sequential_conditional.py} | 22 +++++------ .../factored_beliefs/topology/topology.py | 24 ++++++------ .../factored_generative_process.py | 34 ++++++++--------- 11 files changed, 94 insertions(+), 94 deletions(-) rename simplexity/generative_processes/factored_beliefs/topology/{transition_coupled.py => conditional_transitions.py} (86%) rename simplexity/generative_processes/factored_beliefs/topology/{symmetric.py => fully_conditional.py} (91%) rename simplexity/generative_processes/factored_beliefs/topology/{chain.py => sequential_conditional.py} (87%) diff --git a/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml b/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml index 08a626d7..7e3db3bf 100644 --- a/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml +++ b/examples/configs/generative_process/unified_chain_3mess3_2tomq.yaml @@ -1,4 +1,4 @@ -# Unified API: Chain Topology - 3 mess3 + 2 tom_quantum +# Unified API: Sequential Conditional Structure - 3 mess3 + 2 tom_quantum # Factor i depends on Factor i-1's emitted token (one-way conditional dependencies) name: unified_chain_3mess3_2tomq diff --git a/examples/configs/generative_process/unified_chain_example.yaml b/examples/configs/generative_process/unified_chain_example.yaml index d9488680..47e6c56a 100644 --- a/examples/configs/generative_process/unified_chain_example.yaml +++ b/examples/configs/generative_process/unified_chain_example.yaml @@ -1,5 +1,5 @@ -# Unified API: Chain Topology -# Chain topology creates one-way conditional dependencies: +# Unified API: Sequential Conditional Structure +# Sequential conditional structure creates one-way conditional dependencies: # Factor 0 -> Factor 1 -> Factor 2 -> ... # Each factor i>0 selects its parameter variant based on factor i-1's token. diff --git a/examples/configs/generative_process/unified_symmetric_example.yaml b/examples/configs/generative_process/unified_symmetric_example.yaml index 27e38f1f..dd594f7d 100644 --- a/examples/configs/generative_process/unified_symmetric_example.yaml +++ b/examples/configs/generative_process/unified_symmetric_example.yaml @@ -1,5 +1,5 @@ -# Unified API: Symmetric (Fully Bidirectional) Topology -# Symmetric topology creates mutual dependencies: +# Unified API: Fully Conditional Structure +# Fully conditional structure creates mutual dependencies: # Each factor's variant selection depends on ALL OTHER factors' tokens. # Uses product-of-experts to compute joint distribution. diff --git a/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml b/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml index 47a31709..7dc7f3fa 100644 --- a/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml +++ b/examples/configs/generative_process/unified_transition_and_emission_coupled_example.yaml @@ -1,7 +1,7 @@ -# Unified API: Transition-Coupled Topology -# Transition-coupled topology separates emission and transition coupling: -# - Emissions: Can be independent OR chain-style (factor i depends on i-1's token) -# - Transitions: Always coupled (factor i's transition depends on all other factors) +# Unified API: Conditional Transitions Structure +# Conditional transitions structure separates emission and transition dependencies: +# - Emissions: Can be independent OR sequential (factor i depends on i-1's token) +# - Transitions: Always mutually conditional (factor i's transition depends on all other factors) name: unified_transition_coupled_example base_vocab_size: 36 # 3 * 3 * 4 = 24 diff --git a/examples/configs/generative_process/unified_transition_coupled_example.yaml b/examples/configs/generative_process/unified_transition_coupled_example.yaml index 4119c1ce..5b77d0e7 100644 --- a/examples/configs/generative_process/unified_transition_coupled_example.yaml +++ b/examples/configs/generative_process/unified_transition_coupled_example.yaml @@ -1,7 +1,7 @@ -# Unified API: Transition-Coupled Topology -# Transition-coupled topology separates emission and transition coupling: -# - Emissions: Can be independent OR chain-style (factor i depends on i-1's token) -# - Transitions: Always coupled (factor i's transition depends on all other factors) +# Unified API: Conditional Transitions Structure +# Conditional transitions structure separates emission and transition dependencies: +# - Emissions: Can be independent OR sequential (factor i depends on i-1's token) +# - Transitions: Always mutually conditional (factor i's transition depends on all other factors) name: unified_transition_coupled_example base_vocab_size: 36 # 3 * 3 * 4 = 24 diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index b4556e80..730d413f 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -17,9 +17,9 @@ import jax.numpy as jnp from simplexity.generative_processes.alternating_process import AlternatingProcess -from simplexity.generative_processes.factored_beliefs.topology.chain import ChainTopology -from simplexity.generative_processes.factored_beliefs.topology.symmetric import SymmetricTopology -from simplexity.generative_processes.factored_beliefs.topology.transition_coupled import TransitionCoupledTopology +from simplexity.generative_processes.factored_beliefs.topology.sequential_conditional import SequentialConditional +from simplexity.generative_processes.factored_beliefs.topology.fully_conditional import FullyConditional +from simplexity.generative_processes.factored_beliefs.topology.conditional_transitions import ConditionalTransitions from simplexity.generative_processes.factored_generative_process import ComponentType, FactoredGenerativeProcess from simplexity.generative_processes.generalized_hidden_markov_model import GeneralizedHiddenMarkovModel from simplexity.generative_processes.hidden_markov_model import HiddenMarkovModel @@ -152,18 +152,18 @@ def build_chain_process( be None. control_maps[i] for i>0 should have shape [V_{i-1}]. Returns: - FactoredGenerativeProcess with chain topology + FactoredGenerativeProcess with sequential conditional structure """ # Extract vocab sizes from transition matrices vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) - topology = ChainTopology(control_maps=tuple(control_maps), vocab_sizes=vocab_sizes) + structure = SequentialConditional(control_maps=tuple(control_maps), vocab_sizes=vocab_sizes) return FactoredGenerativeProcess( component_types=component_types, transition_matrices=transition_matrices, normalizing_eigenvectors=normalizing_eigenvectors, initial_states=initial_states, - topology=topology, + structure=structure, ) @@ -188,12 +188,12 @@ def build_symmetric_process( have shape [prod(V_j for j!=i)]. Returns: - FactoredGenerativeProcess with symmetric topology + FactoredGenerativeProcess with fully conditional structure """ # Extract vocab sizes from transition matrices vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) - topology = SymmetricTopology( + structure = FullyConditional( control_maps=tuple(control_maps), vocab_sizes=vocab_sizes, ) @@ -202,7 +202,7 @@ def build_symmetric_process( transition_matrices=transition_matrices, normalizing_eigenvectors=normalizing_eigenvectors, initial_states=initial_states, - topology=topology, + structure=structure, ) @@ -234,12 +234,12 @@ def build_transition_coupled_process( [prod(V_j for j0. Returns: - FactoredGenerativeProcess with transition-coupled topology + FactoredGenerativeProcess with conditional transitions structure """ # Extract vocab sizes from transition matrices vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) - topology = TransitionCoupledTopology( + structure = ConditionalTransitions( control_maps_transition=tuple(control_maps_transition), emission_variant_indices=emission_variant_indices, vocab_sizes=vocab_sizes, @@ -250,7 +250,7 @@ def build_transition_coupled_process( transition_matrices=transition_matrices, normalizing_eigenvectors=normalizing_eigenvectors, initial_states=initial_states, - topology=topology, + structure=structure, ) diff --git a/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py b/simplexity/generative_processes/factored_beliefs/topology/conditional_transitions.py similarity index 86% rename from simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py rename to simplexity/generative_processes/factored_beliefs/topology/conditional_transitions.py index c5171910..d7b62918 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/transition_coupled.py +++ b/simplexity/generative_processes/factored_beliefs/topology/conditional_transitions.py @@ -1,10 +1,10 @@ -"""Transition-coupled topology: independent/chain emissions with coupled transitions. +"""Conditional transitions structure: independent/sequential emissions with mutually conditional transitions. Emissions can be either: - Independent: each factor uses a fixed emission variant -- Chain-style: factor i selects emission variant based on previous tokens 0..i-1 +- Sequential: factor i selects emission variant based on previous tokens 0..i-1 -Transitions are always coupled: factor i selects transition variant based on +Transitions are always mutually conditional: factor i selects transition variant based on all other factors' tokens. """ @@ -15,26 +15,26 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext +from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext from simplexity.utils.factoring_utils import compute_obs_dist_for_variant -class TransitionCoupledTopology(eqx.Module): - """Transition-coupled topology with flexible emission modes. +class ConditionalTransitions(eqx.Module): + """Conditional transitions structure with flexible emission modes. Emissions can be: - Independent (use_emission_chain=False): P(t) = ∏_i P_i(t_i | s_i, k_emit_i) - - Chain (use_emission_chain=True): P(t) = P0(t0) * ∏_{i>0} P_i(t_i | t_0..t_{i-1}, s_i) + - Sequential (use_emission_chain=True): P(t) = P0(t0) * ∏_{i>0} P_i(t_i | t_0..t_{i-1}, s_i) - Transitions are always coupled: factor i selects transition variant based on + Transitions are always mutually conditional: factor i selects transition variant based on all other factors' tokens. Attributes: control_maps_transition: Transition control maps. control_maps_transition[i] has shape [prod(V_j for j!=i)] mapping other tokens to transition variant. emission_variant_indices: Fixed emission variants per factor (shape [F]) - emission_control_maps: Optional chain-style emission control maps - use_emission_chain: Whether to use chain-style emissions + emission_control_maps: Optional sequential emission control maps + use_emission_chain: Whether to use sequential emissions other_multipliers: Precomputed radix multipliers for other-factor indexing prefix_multipliers: Precomputed radix multipliers for prefix indexing vocab_sizes_py: Python int tuple of vocab sizes @@ -55,14 +55,14 @@ def __init__( vocab_sizes: jnp.ndarray, emission_control_maps: tuple[jnp.ndarray | None, ...] | None = None, ): - """Initialize transition-coupled topology. + """Initialize conditional transitions structure. Args: control_maps_transition: Transition control maps for each factor. control_maps_transition[i] should have shape [prod(V_j for j!=i)]. emission_variant_indices: Fixed emission variant per factor (shape [F]) vocab_sizes: Vocabulary sizes per factor (shape [F]) - emission_control_maps: Optional chain-style emission control maps. + emission_control_maps: Optional sequential emission control maps. If provided, emission_control_maps[i] should have shape [prod(V_j for j0. """ @@ -106,7 +106,7 @@ def __init__( other_multipliers.append(jnp.array(mult)) self.other_multipliers = tuple(other_multipliers) - # Precompute multipliers for prefix indexing (for chain emissions) + # Precompute multipliers for prefix indexing (for sequential emissions) prefix_multipliers: list[jnp.ndarray] = [] for i in range(F): pmult = [] @@ -131,11 +131,11 @@ def _flatten_prev_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray mult = self.prefix_multipliers[i] return jnp.sum(tokens * mult) - def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: """Compute joint distribution based on emission mode. Args: - context: Coupling context with states and parameters + context: Conditional context with states and parameters Returns: Flattened joint distribution of shape [prod(V_i)] @@ -163,7 +163,7 @@ def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: J = (J[..., None] * parts[i]).reshape(*J.shape, parts[i].shape[0]) return J.reshape(-1) - # Chain-style emissions + # Sequential emissions k0 = self.emission_variant_indices[0] T0 = transition_matrices[0][k0] norm0 = normalizing_eigenvectors[0][k0] if component_types[0] == "ghmm" else None @@ -202,7 +202,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: CouplingContext, + context: ConditionalContext, ) -> tuple[jnp.ndarray, ...]: """Select transition variants based on other factors' tokens. @@ -210,7 +210,7 @@ def select_variants( Args: obs_tuple: Tuple of observed tokens (one per factor) - context: Coupling context (unused) + context: Conditional context (unused) Returns: Tuple of transition variant indices (one per factor) @@ -224,7 +224,7 @@ def select_variants( return tuple(variants) def get_required_params(self) -> dict[str, type]: - """Return required parameters for transition-coupled topology.""" + """Return required parameters for conditional transitions structure.""" return { "control_maps_transition": tuple, "emission_variant_indices": jnp.ndarray, diff --git a/simplexity/generative_processes/factored_beliefs/topology/symmetric.py b/simplexity/generative_processes/factored_beliefs/topology/fully_conditional.py similarity index 91% rename from simplexity/generative_processes/factored_beliefs/topology/symmetric.py rename to simplexity/generative_processes/factored_beliefs/topology/fully_conditional.py index 7b04ce39..c74a1358 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/symmetric.py +++ b/simplexity/generative_processes/factored_beliefs/topology/fully_conditional.py @@ -1,4 +1,4 @@ -"""Symmetric topology: fully bidirectional coupling between all factors. +"""Fully conditional structure: mutual dependencies between all factors. Each factor's parameter variant is selected based on the tokens of ALL OTHER factors via a control map, producing mutual dependencies. @@ -10,12 +10,12 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext +from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext from simplexity.utils.factoring_utils import compute_obs_dist_for_variant -class SymmetricTopology(eqx.Module): - """Symmetric/fully-bidirectional coupling topology. +class FullyConditional(eqx.Module): + """Fully conditional structure with mutual dependencies. Each factor i selects its variant based on all other factors' tokens. Joint distribution uses product-of-experts with normalization. @@ -42,7 +42,7 @@ def __init__( control_maps: tuple[jnp.ndarray, ...], vocab_sizes: jnp.ndarray, ): - """Initialize symmetric topology. + """Initialize fully conditional structure. Args: control_maps: Control maps for each factor. control_maps[i] should @@ -112,14 +112,14 @@ def _flatten_other_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarra # Multiply elementwise and sum (mult[i] == 0) return jnp.sum(tokens * mult) - def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: """Compute joint distribution using product-of-experts. For each factor i, computes conditional P(t_i | all other t_j), then multiplies all conditionals and normalizes. Args: - context: Coupling context with states and parameters + context: Conditional context with states and parameters Returns: Flattened joint distribution of shape [prod(V_i)] @@ -172,13 +172,13 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: CouplingContext, + context: ConditionalContext, ) -> tuple[jnp.ndarray, ...]: """Select variants based on all other factors' tokens. Args: obs_tuple: Tuple of observed tokens (one per factor) - context: Coupling context (unused for symmetric topology) + context: Conditional context (unused for fully conditional structure) Returns: Tuple of variant indices (one per factor) @@ -192,5 +192,5 @@ def select_variants( return tuple(variants) def get_required_params(self) -> dict[str, type]: - """Return required parameters for symmetric topology.""" + """Return required parameters for fully conditional structure.""" return {"control_maps": tuple, "vocab_sizes": jnp.ndarray} diff --git a/simplexity/generative_processes/factored_beliefs/topology/chain.py b/simplexity/generative_processes/factored_beliefs/topology/sequential_conditional.py similarity index 87% rename from simplexity/generative_processes/factored_beliefs/topology/chain.py rename to simplexity/generative_processes/factored_beliefs/topology/sequential_conditional.py index 35ed7cfb..8635c6b6 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/chain.py +++ b/simplexity/generative_processes/factored_beliefs/topology/sequential_conditional.py @@ -1,4 +1,4 @@ -"""Chain topology: one-way conditional dependencies between factors. +"""Sequential conditional structure: one-way conditional dependencies between factors. Factor i>0 selects its parameter variant based on the emitted token of factor i-1 (parent) via a control map. @@ -10,12 +10,12 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext +from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext from simplexity.utils.factoring_utils import compute_obs_dist_for_variant -class ChainTopology(eqx.Module): - """Chain/conditional coupling topology. +class SequentialConditional(eqx.Module): + """Sequential conditional structure (autoregressive chain). Factors form a chain: Factor i depends on Factor i-1's emitted token. - Factor 0 always uses variant 0 @@ -37,7 +37,7 @@ def __init__( control_maps: tuple[jnp.ndarray | None, ...], vocab_sizes: jnp.ndarray | None = None, ): - """Initialize chain topology. + """Initialize sequential conditional structure. Args: control_maps: Control maps for variant selection. control_maps[0] @@ -49,14 +49,14 @@ def __init__( self.control_maps = tuple(control_maps) self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) if vocab_sizes is not None else None - def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: - """Compute joint distribution using chain factorization. + def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: + """Compute joint distribution using sequential factorization. Builds P(t0, t1, ..., tF) = P(t0) * P(t1|t0) * ... * P(tF|t_{F-1}) iteratively, then flattens to radix encoding. Args: - context: Coupling context with states and parameters + context: Conditional context with states and parameters Returns: Flattened joint distribution of shape [prod(V_i)] @@ -113,13 +113,13 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: CouplingContext, + context: ConditionalContext, ) -> tuple[jnp.ndarray, ...]: """Select variants based on parent tokens in chain. Args: obs_tuple: Tuple of observed tokens (one per factor) - context: Coupling context (unused for chain topology) + context: Conditional context (unused for sequential conditional) Returns: Tuple of variant indices (one per factor) @@ -137,5 +137,5 @@ def select_variants( return tuple(variants) def get_required_params(self) -> dict[str, type]: - """Return required parameters for chain topology.""" + """Return required parameters for sequential conditional structure.""" return {"control_maps": tuple} diff --git a/simplexity/generative_processes/factored_beliefs/topology/topology.py b/simplexity/generative_processes/factored_beliefs/topology/topology.py index 906d0e37..372da543 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/topology.py +++ b/simplexity/generative_processes/factored_beliefs/topology/topology.py @@ -1,6 +1,6 @@ -"""Coupling topology protocol for factored generative processes. +"""Conditional structure protocol for factored generative processes. -Defines the interface for different coupling strategies between factors. +Defines the interface for different conditional dependency structures between factors. """ from __future__ import annotations @@ -15,8 +15,8 @@ @dataclass -class CouplingContext: - """Context information needed by topologies to compute joint distributions. +class ConditionalContext: + """Context information needed by conditional structures to compute joint distributions. Attributes: states: Tuple of state vectors, one per factor (shape [S_i]) @@ -35,14 +35,14 @@ class CouplingContext: num_variants: tuple[int, ...] -class CouplingTopology(Protocol): - """Protocol for coupling topologies between factors. +class ConditionalStructure(Protocol): + """Protocol for conditional dependency structures between factors. - A coupling topology defines how factors interact to produce joint - observation distributions and how variant selection works. + A conditional structure defines how factors conditionally depend on each other + to produce joint observation distributions and how variant selection works. """ - def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: """Compute joint observation distribution across all factors. Args: @@ -57,7 +57,7 @@ def compute_joint_distribution(self, context: CouplingContext) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: CouplingContext, + context: ConditionalContext, ) -> tuple[jnp.ndarray, ...]: """Select parameter variant for each factor given observations. @@ -71,10 +71,10 @@ def select_variants( ... def get_required_params(self) -> dict[str, type]: - """Return dictionary of topology-specific parameters and their types. + """Return dictionary of structure-specific parameters and their types. Returns: Dict mapping parameter names to expected types - (e.g., {"control_maps": tuple} for chain topology) + (e.g., {"control_maps": tuple} for sequential conditional) """ ... diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py index bfb8d675..2afa22e6 100644 --- a/simplexity/generative_processes/factored_generative_process.py +++ b/simplexity/generative_processes/factored_generative_process.py @@ -1,4 +1,4 @@ -"""Unified factored generative process with pluggable coupling topologies.""" +"""Unified factored generative process with pluggable conditional structures.""" from __future__ import annotations @@ -10,7 +10,7 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import CouplingContext, CouplingTopology +from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext, ConditionalStructure from simplexity.generative_processes.generative_process import GenerativeProcess from simplexity.utils.factoring_utils import TokenEncoder, transition_with_obs @@ -19,10 +19,10 @@ class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): - """Unified factored generative process with pluggable coupling topology. + """Unified factored generative process with pluggable conditional structures. This class provides a single implementation of factored generative processes - that supports different coupling patterns via the CouplingTopology protocol. + that supports different conditional dependency patterns via the ConditionalStructure protocol. Attributes: component_types: Type of each factor ("hmm" or "ghmm") @@ -30,7 +30,7 @@ class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) initial_states: Initial state per factor (shape [S_i]) num_variants: Number of parameter variants per factor - topology: Coupling topology determining factor interactions + structure: Conditional structure determining factor interactions encoder: Token encoder for composite observations """ @@ -43,8 +43,8 @@ class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): normalizing_eigenvectors: tuple[jnp.ndarray, ...] initial_states: tuple[jnp.ndarray, ...] - # Coupling topology and encoding - topology: CouplingTopology + # Conditional structure and encoding + structure: ConditionalStructure encoder: TokenEncoder def __init__( @@ -54,7 +54,7 @@ def __init__( transition_matrices: Sequence[jnp.ndarray], normalizing_eigenvectors: Sequence[jnp.ndarray], initial_states: Sequence[jnp.ndarray], - topology: CouplingTopology, + structure: ConditionalStructure, ) -> None: """Initialize factored generative process. @@ -65,7 +65,7 @@ def __init__( normalizing_eigenvectors: Per-factor eigenvectors for GHMM. normalizing_eigenvectors[i] has shape [K_i, S_i] initial_states: Initial state per factor (shape [S_i]) - topology: Coupling topology defining factor interactions + structure: Conditional structure defining factor interactions """ if len(component_types) == 0: raise ValueError("Must provide at least one component") @@ -74,7 +74,7 @@ def __init__( self.transition_matrices = tuple(transition_matrices) self.normalizing_eigenvectors = tuple(normalizing_eigenvectors) self.initial_states = tuple(initial_states) - self.topology = topology + self.structure = structure # Validate shapes and compute derived sizes vocab_sizes = [] @@ -95,9 +95,9 @@ def __init__( self.num_variants = tuple(int(k) for k in num_variants) self.encoder = TokenEncoder(jnp.array(vocab_sizes)) - def _make_context(self, state: FactoredState) -> CouplingContext: - """Create coupling context for topology methods.""" - return CouplingContext( + def _make_context(self, state: FactoredState) -> ConditionalContext: + """Create conditional context for structure methods.""" + return ConditionalContext( states=state, component_types=self.component_types, transition_matrices=self.transition_matrices, @@ -119,7 +119,7 @@ def initial_state(self) -> FactoredState: @eqx.filter_jit def observation_probability_distribution(self, state: FactoredState) -> jnp.ndarray: - """Compute P(composite_token | state) under the coupling topology. + """Compute P(composite_token | state) under the conditional structure. Args: state: Tuple of state vectors (one per factor) @@ -128,7 +128,7 @@ def observation_probability_distribution(self, state: FactoredState) -> jnp.ndar Distribution over composite tokens, shape [prod(V_i)] """ context = self._make_context(state) - return self.topology.compute_joint_distribution(context) + return self.structure.compute_joint_distribution(context) @eqx.filter_jit def log_observation_probability_distribution( @@ -175,9 +175,9 @@ def transition_states(self, state: FactoredState, obs: chex.Array) -> FactoredSt # Decode composite observation to per-factor tokens obs_tuple = self.encoder.token_to_tuple(obs) - # Select variants based on topology + # Select variants based on conditional structure context = self._make_context(state) - variants = self.topology.select_variants(obs_tuple, context) + variants = self.structure.select_variants(obs_tuple, context) # Update each factor's state new_states: list[jnp.ndarray] = [] From c88d596aba59cce77277be59a303bb721b04dbfa Mon Sep 17 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.../fully_conditional.py | 2 +- .../topology.py => structures/protocol.py} | 0 .../sequential_conditional.py | 8 +- .../unified_chain_3mess3_2tomq.yaml | 2 +- .../unified_chain_example.yaml | 2 +- .../unified_symmetric_example.yaml | 2 +- ...ansition_and_emission_coupled_example.yaml | 4 +- .../unified_transition_coupled_example.yaml | 2 +- .../test_alternating_process.py | 341 ------------------ tests/generative_processes/test_builder.py | 20 - .../test_generalized_hidden_markov_model.py | 11 - tests/generative_processes/test_generator.py | 15 - .../test_hidden_markov_model.py | 7 - .../test_mixed_state_presentation.py | 11 - .../test_mixed_state_tree.py | 13 - .../test_torch_generator.py | 15 - 22 files changed, 141 insertions(+), 893 deletions(-) delete mode 100644 simplexity/generative_processes/alternating_process.py create mode 100644 simplexity/generative_processes/structures/__init__.py rename simplexity/generative_processes/{factored_beliefs/topology => structures}/conditional_transitions.py (97%) rename simplexity/generative_processes/{factored_beliefs/topology => structures}/fully_conditional.py (98%) rename simplexity/generative_processes/{factored_beliefs/topology/topology.py => structures/protocol.py} (100%) rename simplexity/generative_processes/{factored_beliefs/topology => structures}/sequential_conditional.py (94%) delete mode 100644 tests/generative_processes/test_alternating_process.py diff --git a/simplexity/generative_processes/alternating_process.py b/simplexity/generative_processes/alternating_process.py deleted file mode 100644 index eab9858d..00000000 --- a/simplexity/generative_processes/alternating_process.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Alternating generative process that cycles through n different sub-processes. - -This module provides a simple wrapper around HMM/GHMM that alternates deterministically -through n different transition matrix sets based on position. -""" - -from __future__ import annotations - -from typing import Literal - -import chex -import equinox as eqx -import jax -import jax.numpy as jnp - -from simplexity.generative_processes.generative_process import GenerativeProcess - -ComponentType = Literal["hmm", "ghmm"] - -# State type: (position, belief_state) -AlternatingState = tuple[jnp.ndarray, jnp.ndarray] - - -class AlternatingProcess(GenerativeProcess[AlternatingState]): - """Generative process that alternates through n different sub-processes. - - The process cycles deterministically through n different transition matrices: - - With n_repetitions=1: position 0→variant 0, position 1→variant 1, etc. - - With n_repetitions=k: positions 0..k-1→variant 0, positions k..2k-1→variant 1, etc. - - Each sub-process has its own transition matrix but shares the same state space - and vocabulary size. - - State structure: (position_counter, belief_state) - - position_counter: int tracking which step we're on - - belief_state: probability distribution over hidden states [S] - - Attributes: - component_type: Either "hmm" or "ghmm" - transition_matrices: Array of shape [n, V, S, S] with n transition matrices - normalizing_eigenvectors: Array of shape [n, S] for GHMM normalization - initial_state_dist: Initial belief state distribution [S] - n: Number of sub-processes to cycle through - n_repetitions: Number of times each process is used before switching - vocab_size: Vocabulary size (same across all variants) - num_states: Number of hidden states (same across all variants) - """ - - component_type: ComponentType - transition_matrices: jnp.ndarray # [n, V, S, S] - normalizing_eigenvectors: jnp.ndarray # [n, S] - initial_state_dist: jnp.ndarray # [S] - n: int - n_repetitions: int - - def __init__( - self, - *, - component_type: ComponentType, - transition_matrices: jnp.ndarray, - normalizing_eigenvectors: jnp.ndarray, - initial_state: jnp.ndarray, - n_repetitions: int = 1, - ): - """Initialize alternating process. - - Args: - component_type: Either "hmm" or "ghmm" - transition_matrices: Array of shape [n, V, S, S] with n variants - normalizing_eigenvectors: Array of shape [n, S] for GHMM (ignored for HMM) - initial_state: Initial belief state [S] - n_repetitions: Number of times each variant is used consecutively - """ - if transition_matrices.ndim != 4: - raise ValueError(f"transition_matrices must have shape [n, V, S, S], got {transition_matrices.shape}") - - n, V, S1, S2 = transition_matrices.shape - if S1 != S2: - raise ValueError(f"State dimensions must match: {S1} vs {S2}") - - if initial_state.shape != (S1,): - raise ValueError(f"initial_state shape {initial_state.shape} doesn't match state dim {S1}") - - if n_repetitions <= 0: - raise ValueError(f"n_repetitions must be positive, got {n_repetitions}") - - self.component_type = component_type - self.transition_matrices = transition_matrices - self.normalizing_eigenvectors = normalizing_eigenvectors - self.initial_state_dist = initial_state - self.n = n - self.n_repetitions = n_repetitions - - @property - def vocab_size(self) -> int: - """Vocabulary size.""" - return int(self.transition_matrices.shape[1]) - - @property - def num_states(self) -> int: - """Number of hidden states.""" - return int(self.transition_matrices.shape[2]) - - @property - def initial_state(self) -> AlternatingState: - """Initial state: (position=0, initial_belief_state).""" - return (jnp.array(0, dtype=jnp.int32), self.initial_state_dist) - - def _select_variant(self, position: jnp.ndarray) -> jnp.ndarray: - """Select which variant to use based on position. - - Args: - position: Current position counter - - Returns: - Variant index k = (position // n_repetitions) % n (as JAX array) - """ - return (position // self.n_repetitions) % self.n - - def _compute_obs_dist(self, belief_state: jnp.ndarray, variant_idx: jnp.ndarray) -> jnp.ndarray: - """Compute observation distribution for given belief state and variant. - - Args: - belief_state: Current belief state [S] - variant_idx: Which variant to use (0 to n-1) - - Returns: - Distribution over observations [V] - """ - T = self.transition_matrices[variant_idx] # [V, S, S] - - if self.component_type == "hmm": - # HMM: P(obs|state) = sum_s belief[s] * sum_s' T[obs, s, s'] - obs_state = belief_state @ T # [V, S] - return jnp.sum(obs_state, axis=1) # [V] - else: # ghmm - # GHMM: weighted by normalizing eigenvector - norm = self.normalizing_eigenvectors[variant_idx] # [S] - numer = belief_state @ T @ norm # [V] - denom = jnp.sum(belief_state * norm) # scalar - return numer / denom - - def _transition(self, belief_state: jnp.ndarray, obs: chex.Array, variant_idx: jnp.ndarray) -> jnp.ndarray: - """Compute next belief state after observing a token. - - Args: - belief_state: Current belief state [S] - obs: Observed token (scalar) - variant_idx: Which variant to use - - Returns: - Updated belief state [S] - """ - T = self.transition_matrices[variant_idx, obs] # [S, S] - - if self.component_type == "hmm": - # HMM: normalize by sum - new_state = belief_state @ T # [S] - return new_state / jnp.sum(new_state) - else: # ghmm - # GHMM: normalize by eigenvector - norm = self.normalizing_eigenvectors[variant_idx] # [S] - new_state = belief_state @ T # [S] - return (new_state * norm) / jnp.sum(new_state * norm) - - @eqx.filter_jit - def observation_probability_distribution(self, state: AlternatingState) -> jnp.ndarray: - """Compute P(obs | state). - - Args: - state: (position, belief_state) - - Returns: - Distribution over observations [V] - """ - position, belief_state = state - variant_idx = self._select_variant(position) - return self._compute_obs_dist(belief_state, variant_idx) - - @eqx.filter_jit - def log_observation_probability_distribution(self, log_belief_state: AlternatingState) -> jnp.ndarray: - """Compute log P(obs | state). - - Args: - log_belief_state: (position, log_belief_state) - - Returns: - Log distribution over observations [V] - """ - position, log_belief = log_belief_state - belief_state = jnp.exp(log_belief) - variant_idx = self._select_variant(position) - probs = self._compute_obs_dist(belief_state, variant_idx) - return jnp.log(probs) - - @eqx.filter_jit - def emit_observation(self, state: AlternatingState, key: jax.Array) -> jnp.ndarray: - """Sample observation from current state. - - Args: - state: (position, belief_state) - key: JAX random key - - Returns: - Sampled observation (scalar token) - """ - probs = self.observation_probability_distribution(state) - return jax.random.categorical(key, jnp.log(probs)) - - @eqx.filter_jit - def transition_states(self, state: AlternatingState, obs: chex.Array) -> AlternatingState: - """Update state after observing a token. - - Args: - state: Current (position, belief_state) - obs: Observed token - - Returns: - Updated (position+1, new_belief_state) - """ - position, belief_state = state - variant_idx = self._select_variant(position) - new_belief_state = self._transition(belief_state, obs, variant_idx) - new_position = position + 1 - return (new_position, new_belief_state) - - @eqx.filter_jit - def probability(self, observations: jnp.ndarray) -> jnp.ndarray: - """Compute P(observations). - - Args: - observations: Array of observations - - Returns: - Scalar probability - """ - - def step(carry: AlternatingState, obs: jnp.ndarray): - state = carry - dist = self.observation_probability_distribution(state) - p = dist[obs] - new_state = self.transition_states(state, obs) - return new_state, p - - _, ps = jax.lax.scan(step, self.initial_state, observations) - return jnp.prod(ps) - - @eqx.filter_jit - def log_probability(self, observations: jnp.ndarray) -> jnp.ndarray: - """Compute log P(observations). - - Args: - observations: Array of observations - - Returns: - Scalar log probability - """ - - def step(carry: AlternatingState, obs: jnp.ndarray): - position, belief_state = carry - log_belief_state = jnp.log(belief_state) - log_state = (position, log_belief_state) - log_dist = self.log_observation_probability_distribution(log_state) - lp = log_dist[obs] - new_state = self.transition_states(carry, obs) - return new_state, lp - - _, lps = jax.lax.scan(step, self.initial_state, observations) - return jnp.sum(lps) diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 98db723e..7b012e57 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -16,13 +16,14 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.alternating_process import AlternatingProcess -from simplexity.generative_processes.factored_beliefs.topology.sequential_conditional import SequentialConditional -from simplexity.generative_processes.factored_beliefs.topology.fully_conditional import FullyConditional -from simplexity.generative_processes.factored_beliefs.topology.conditional_transitions import ConditionalTransitions from simplexity.generative_processes.factored_generative_process import ComponentType, FactoredGenerativeProcess from simplexity.generative_processes.generalized_hidden_markov_model import GeneralizedHiddenMarkovModel from simplexity.generative_processes.hidden_markov_model import HiddenMarkovModel +from simplexity.generative_processes.structures import ( + ConditionalTransitions, + FullyConditional, + SequentialConditional, +) from simplexity.generative_processes.transition_matrices import ( GHMM_MATRIX_FUNCTIONS, HMM_MATRIX_FUNCTIONS, @@ -276,144 +277,6 @@ def build_transition_coupled_process( ) -def build_simple_alternating_process( - *, - component_type: ComponentType, - transition_matrices: jnp.ndarray, - normalizing_eigenvectors: jnp.ndarray, - initial_state: jnp.ndarray, - n_repetitions: int = 1, -) -> AlternatingProcess: - """Build a simple alternating process (non-factored). - - The process cycles through n different transition matrices. This is simpler - than the factored alternating process - it's just a single HMM/GHMM that - switches between different parameter sets. - - Args: - component_type: Either "hmm" or "ghmm" - transition_matrices: Array of shape [n, V, S, S] with n variants - normalizing_eigenvectors: Array of shape [n, S] for GHMM normalization - initial_state: Initial belief state distribution [S] - n_repetitions: Number of times each variant is used consecutively - - Returns: - AlternatingProcess that cycles through the n variants - - Example: - ```python - # Build process alternating between 2 HMM variants - # Each variant used 3 times before switching - n = 2 - V, S = 3, 4 # 3 tokens, 4 hidden states - - # Stack n transition matrices - T0 = ... # [V, S, S] - T1 = ... # [V, S, S] - transition_matrices = jnp.stack([T0, T1]) # [2, V, S, S] - - process = build_simple_alternating_process( - component_type="hmm", - transition_matrices=transition_matrices, - normalizing_eigenvectors=jnp.ones((2, S)), # dummy for HMM - initial_state=jnp.ones(S) / S, # uniform initial state - n_repetitions=3, - ) - ``` - """ - return AlternatingProcess( - component_type=component_type, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_state=initial_state, - n_repetitions=n_repetitions, - ) - - -def build_simple_alternating_process_from_spec( - variants: list[dict[str, Any]], - component_type: ComponentType = "ghmm", - n_repetitions: int = 1, -) -> AlternatingProcess: - """Build simple alternating process from specification. - - Args: - variants: List of variant specifications, one per sub-process - component_type: Either "hmm" or "ghmm" - n_repetitions: Number of times each variant is used consecutively - - Returns: - AlternatingProcess - - Example: - ```python - variants = [ - {"process_name": "coin", "p": 0.7}, # Variant 0 - {"process_name": "coin", "p": 0.3}, # Variant 1 - {"process_name": "coin", "p": 0.5}, # Variant 2 - ] - - process = build_simple_alternating_process_from_spec( - variants=variants, - component_type="hmm", - n_repetitions=2, # Each variant repeated twice - ) - ``` - """ - if not variants: - raise ValueError("Must provide at least one variant") - - # Build each variant - matrix_functions = HMM_MATRIX_FUNCTIONS if component_type == "hmm" else GHMM_MATRIX_FUNCTIONS - built_processes = [] - - for variant_spec in variants: - T = build_transition_matrices(matrix_functions, **variant_spec) - built_processes.append(T) - - # Validate all have same shape - shapes = [T.shape for T in built_processes] - if len(set(shapes)) != 1: - raise ValueError(f"All variants must have same shape, got {shapes}") - - # Stack into [n, V, S, S] - transition_matrices = jnp.stack(built_processes, axis=0) - n, V, S, _ = transition_matrices.shape - - # Get initial state from first variant (uniform if stationary fails) - try: - initial_state = get_stationary_state(built_processes[0]) - except (IndexError, ValueError): - # Fall back to uniform distribution if stationary state computation fails - initial_state = jnp.ones(S) / S - - # Build normalizing eigenvectors - if component_type == "ghmm": - eigenvectors = [] - for T in built_processes: - # Compute normalizing eigenvector for this variant - state_transition_matrix = jnp.sum(T, axis=0) - eigenvalues, right_eigenvectors = jnp.linalg.eig(state_transition_matrix) - principal_eigenvalue = jnp.max(eigenvalues) - normalizing_eigenvector = ( - right_eigenvectors[:, jnp.isclose(eigenvalues, principal_eigenvalue)].squeeze().real - ) - normalizing_eigenvector = normalizing_eigenvector / jnp.sum(normalizing_eigenvector) * S - eigenvectors.append(normalizing_eigenvector) - normalizing_eigenvectors = jnp.stack(eigenvectors, axis=0) - else: - # Dummy for HMM - normalizing_eigenvectors = jnp.ones((n, S)) - - return build_simple_alternating_process( - component_type=component_type, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_state=initial_state, - n_repetitions=n_repetitions, - ) - - def build_factored_process( topology_type: Literal["chain", "symmetric", "transition_coupled"], component_types: Sequence[ComponentType], @@ -468,10 +331,6 @@ def build_factored_process( emission_variant_indices=topology_kwargs["emission_variant_indices"], emission_control_maps=topology_kwargs.get("emission_control_maps"), ) - else: - raise ValueError( - f"Unknown topology_type: {topology_type}. Must be one of: 'chain', 'symmetric', 'transition_coupled'" - ) def build_chain_process_from_spec( diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py index 2afa22e6..07bddbb6 100644 --- a/simplexity/generative_processes/factored_generative_process.py +++ b/simplexity/generative_processes/factored_generative_process.py @@ -10,9 +10,11 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext, ConditionalStructure from simplexity.generative_processes.generative_process import GenerativeProcess +from simplexity.generative_processes.structures import ConditionalContext, ConditionalStructure +from simplexity.logger import SIMPLEXITY_LOGGER from simplexity.utils.factoring_utils import TokenEncoder, transition_with_obs +from simplexity.utils.jnp_utils import resolve_jax_device ComponentType = Literal["hmm", "ghmm"] FactoredState = tuple[jnp.ndarray, ...] @@ -37,6 +39,7 @@ class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): # Static structure component_types: tuple[ComponentType, ...] num_variants: tuple[int, ...] + device: jax.Device # type: ignore[valid-type] # Per-factor parameters transition_matrices: tuple[jnp.ndarray, ...] @@ -55,6 +58,7 @@ def __init__( normalizing_eigenvectors: Sequence[jnp.ndarray], initial_states: Sequence[jnp.ndarray], structure: ConditionalStructure, + device: str | None = None, ) -> None: """Initialize factored generative process. @@ -66,14 +70,56 @@ def __init__( normalizing_eigenvectors[i] has shape [K_i, S_i] initial_states: Initial state per factor (shape [S_i]) structure: Conditional structure defining factor interactions + device: Device to place arrays on (e.g., "cpu", "gpu") """ if len(component_types) == 0: raise ValueError("Must provide at least one component") + self.device = resolve_jax_device(device) self.component_types = tuple(component_types) - self.transition_matrices = tuple(transition_matrices) - self.normalizing_eigenvectors = tuple(normalizing_eigenvectors) - self.initial_states = tuple(initial_states) + + # Move transition matrices to device + transition_matrices_on_device = [] + for i, T in enumerate(transition_matrices): + if T.device != self.device: + SIMPLEXITY_LOGGER.warning( + "Transition matrices[%d] on device %s but model is on device %s. Moving to model device.", + i, + T.device, + self.device, + ) + T = jax.device_put(T, self.device) + transition_matrices_on_device.append(T) + self.transition_matrices = tuple(transition_matrices_on_device) + + # Move normalizing eigenvectors to device + normalizing_eigenvectors_on_device = [] + for i, norm in enumerate(normalizing_eigenvectors): + if norm.device != self.device: + SIMPLEXITY_LOGGER.warning( + "Normalizing eigenvectors[%d] on device %s but model is on device %s. Moving to model device.", + i, + norm.device, + self.device, + ) + norm = jax.device_put(norm, self.device) + normalizing_eigenvectors_on_device.append(norm) + self.normalizing_eigenvectors = tuple(normalizing_eigenvectors_on_device) + + # Move initial states to device + initial_states_on_device = [] + for i, state in enumerate(initial_states): + if state.device != self.device: + SIMPLEXITY_LOGGER.warning( + "Initial states[%d] on device %s but model is on device %s. Moving to model device.", + i, + state.device, + self.device, + ) + state = jax.device_put(state, self.device) + initial_states_on_device.append(state) + self.initial_states = tuple(initial_states_on_device) + self.structure = structure # Validate shapes and compute derived sizes @@ -81,14 +127,10 @@ def __init__( num_variants = [] for i, T in enumerate(self.transition_matrices): if T.ndim != 4: - raise ValueError( - f"transition_matrices[{i}] must have shape [K, V, S, S], got {T.shape}" - ) + raise ValueError(f"transition_matrices[{i}] must have shape [K, V, S, S], got {T.shape}") K, V, S1, S2 = T.shape if S1 != S2: - raise ValueError( - f"transition_matrices[{i}] square mismatch: {S1} vs {S2}" - ) + raise ValueError(f"transition_matrices[{i}] square mismatch: {S1} vs {S2}") vocab_sizes.append(V) num_variants.append(K) @@ -131,9 +173,7 @@ def observation_probability_distribution(self, state: FactoredState) -> jnp.ndar return self.structure.compute_joint_distribution(context) @eqx.filter_jit - def log_observation_probability_distribution( - self, log_belief_state: FactoredState - ) -> jnp.ndarray: + def log_observation_probability_distribution(self, log_belief_state: FactoredState) -> jnp.ndarray: """Compute log P(composite_token | state). Args: @@ -181,16 +221,10 @@ def transition_states(self, state: FactoredState, obs: chex.Array) -> FactoredSt # Update each factor's state new_states: list[jnp.ndarray] = [] - for i, (s_i, t_i, k_i) in enumerate(zip(state, obs_tuple, variants)): + for i, (s_i, t_i, k_i) in enumerate(zip(state, obs_tuple, variants, strict=True)): T_k = self.transition_matrices[i][k_i] - norm_k = ( - self.normalizing_eigenvectors[i][k_i] - if self.component_types[i] == "ghmm" - else None - ) - new_state_i = transition_with_obs( - self.component_types[i], s_i, T_k, t_i, norm_k - ) + norm_k = self.normalizing_eigenvectors[i][k_i] if self.component_types[i] == "ghmm" else None + new_state_i = transition_with_obs(self.component_types[i], s_i, T_k, t_i, norm_k) new_states.append(new_state_i) return tuple(new_states) @@ -229,10 +263,9 @@ def log_probability(self, observations: jnp.ndarray) -> jnp.ndarray: def step(carry: FactoredState, obs: jnp.ndarray): state = carry - log_dist = self.log_observation_probability_distribution( - tuple(jnp.log(s) for s in state) - ) - lp = log_dist[obs] + # Compute distribution directly without converting to log and back + dist = self.observation_probability_distribution(state) + lp = jnp.log(dist[obs]) new_state = self.transition_states(state, obs) return new_state, lp diff --git a/simplexity/generative_processes/generalized_hidden_markov_model.py b/simplexity/generative_processes/generalized_hidden_markov_model.py index 21cca753..5263b2d3 100644 --- a/simplexity/generative_processes/generalized_hidden_markov_model.py +++ b/simplexity/generative_processes/generalized_hidden_markov_model.py @@ -71,17 +71,45 @@ def __init__( if initial_state is None: initial_state = get_stationary_state(state_transition_matrix.T) - self._initial_state = initial_state + + if initial_state.device != self.device: + SIMPLEXITY_LOGGER.warning( + "Initial state is on device %s but model is on device %s. Moving initial state to model device.", + initial_state.device, + self.device, + ) + self._initial_state = jax.device_put(initial_state, self.device) + else: + self._initial_state = initial_state self.log_initial_state = jnp.log(self._initial_state) self.normalizing_constant = self._initial_state @ self.normalizing_eigenvector self.log_normalizing_constant = jax.nn.logsumexp(self.log_initial_state + self.log_normalizing_eigenvector) def validate_transition_matrices(self, transition_matrices: jax.Array): - """Validate the transition matrices.""" + """Validate the transition matrices. + + For GHMM, transition matrices must be non-negative and the net transition + matrix T = sum_x T^(x) should have a dominant eigenvalue close to 1. + """ if transition_matrices.ndim != 3 or transition_matrices.shape[1] != transition_matrices.shape[2]: raise ValueError("Transition matrices must have shape (vocab_size, num_states, num_states)") + # GHMM-specific validation: matrices should be non-negative + if not jnp.all(transition_matrices >= 0): + raise ValueError("Transition matrices must be non-negative") + + # Check that net transition matrix has dominant eigenvalue close to 1 + state_transition_matrix = jnp.sum(transition_matrices, axis=0) + eigenvalues, _ = jnp.linalg.eig(state_transition_matrix) + principal_eigenvalue = jnp.max(jnp.abs(eigenvalues)) + + if not jnp.isclose(principal_eigenvalue, 1.0, rtol=1e-5): + SIMPLEXITY_LOGGER.warning( + "Net transition matrix has principal eigenvalue %.6f (expected 1.0). Matrices will be normalized.", + float(principal_eigenvalue), + ) + @property def vocab_size(self) -> int: """The number of distinct observations that can be emitted by the model.""" diff --git a/simplexity/generative_processes/structures/__init__.py b/simplexity/generative_processes/structures/__init__.py new file mode 100644 index 00000000..5f91c9d9 --- /dev/null +++ b/simplexity/generative_processes/structures/__init__.py @@ -0,0 +1,33 @@ +"""Conditional structures for factored generative processes. + +This module provides various conditional dependency structures that define +how factors in a factored generative process depend on each other. + +Available structures: +- Independent: No conditional dependencies between factors +- SequentialConditional: One-way chain dependencies (factor i depends on i-1) +- FullyConditional: Mutual dependencies between all factors +- ConditionalTransitions: Hybrid structure (independent/sequential emissions, mutual transitions) +""" + +from simplexity.generative_processes.structures.conditional_transitions import ( + ConditionalTransitions, +) +from simplexity.generative_processes.structures.fully_conditional import ( + FullyConditional, +) +from simplexity.generative_processes.structures.protocol import ( + ConditionalContext, + ConditionalStructure, +) +from simplexity.generative_processes.structures.sequential_conditional import ( + SequentialConditional, +) + +__all__ = [ + "ConditionalContext", + "ConditionalStructure", + "ConditionalTransitions", + "FullyConditional", + "SequentialConditional", +] diff --git a/simplexity/generative_processes/factored_beliefs/topology/conditional_transitions.py b/simplexity/generative_processes/structures/conditional_transitions.py similarity index 97% rename from simplexity/generative_processes/factored_beliefs/topology/conditional_transitions.py rename to simplexity/generative_processes/structures/conditional_transitions.py index d7b62918..9c74a4b0 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/conditional_transitions.py +++ b/simplexity/generative_processes/structures/conditional_transitions.py @@ -9,13 +9,14 @@ """ from __future__ import annotations -from typing import Sequence + +from collections.abc import Sequence import equinox as eqx import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext +from simplexity.generative_processes.structures.protocol import ConditionalContext from simplexity.utils.factoring_utils import compute_obs_dist_for_variant @@ -66,9 +67,7 @@ def __init__( If provided, emission_control_maps[i] should have shape [prod(V_j for j0. """ - self.control_maps_transition = tuple( - jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps_transition - ) + self.control_maps_transition = tuple(jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps_transition) self.emission_variant_indices = jnp.asarray(emission_variant_indices, dtype=jnp.int32) self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) F = len(vocab_sizes) diff --git a/simplexity/generative_processes/factored_beliefs/topology/fully_conditional.py b/simplexity/generative_processes/structures/fully_conditional.py similarity index 98% rename from simplexity/generative_processes/factored_beliefs/topology/fully_conditional.py rename to simplexity/generative_processes/structures/fully_conditional.py index c74a1358..13f0ed38 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/fully_conditional.py +++ b/simplexity/generative_processes/structures/fully_conditional.py @@ -10,7 +10,7 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext +from simplexity.generative_processes.structures.protocol import ConditionalContext from simplexity.utils.factoring_utils import compute_obs_dist_for_variant diff --git a/simplexity/generative_processes/factored_beliefs/topology/topology.py b/simplexity/generative_processes/structures/protocol.py similarity index 100% rename from simplexity/generative_processes/factored_beliefs/topology/topology.py rename to simplexity/generative_processes/structures/protocol.py diff --git a/simplexity/generative_processes/factored_beliefs/topology/sequential_conditional.py b/simplexity/generative_processes/structures/sequential_conditional.py similarity index 94% rename from simplexity/generative_processes/factored_beliefs/topology/sequential_conditional.py rename to simplexity/generative_processes/structures/sequential_conditional.py index f3e51e49..a7e18766 100644 --- a/simplexity/generative_processes/factored_beliefs/topology/sequential_conditional.py +++ b/simplexity/generative_processes/structures/sequential_conditional.py @@ -10,7 +10,7 @@ import jax import jax.numpy as jnp -from simplexity.generative_processes.factored_beliefs.topology.topology import ConditionalContext +from simplexity.generative_processes.structures.protocol import ConditionalContext from simplexity.utils.factoring_utils import compute_obs_dist_for_variant @@ -71,9 +71,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray # Root distribution (factor 0, variant 0) T_root = transition_matrices[0][0] # [V_0, S_0, S_0] norm_root = normalizing_eigenvectors[0][0] if component_types[0] == "ghmm" else None - p_root = compute_obs_dist_for_variant( - component_types[0], states[0], T_root, norm_root - ) # [V_0] + p_root = compute_obs_dist_for_variant(component_types[0], states[0], T_root, norm_root) # [V_0] joint = p_root # Iteratively extend with conditional factors @@ -132,7 +130,7 @@ def select_variants( else: # Select based on parent's observed token parent_token = obs_tuple[i - 1] - k_i = self.control_maps[i][parent_token] # type: ignore + k_i = self.control_maps[i][parent_token] # type: ignore variants.append(k_i) return tuple(variants) diff --git a/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml b/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml index 7e3db3bf..1ef04280 100644 --- a/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml +++ b/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml @@ -2,7 +2,7 @@ # Factor i depends on Factor i-1's emitted token (one-way conditional dependencies) name: unified_chain_3mess3_2tomq -base_vocab_size: 432 # (3^3) * (4^2) = 27 * 16 = 432 +base_vocab_size: ??? # Will auto-infer as (3^3) * (4^2) = 27 * 16 = 432 vocab_size: ??? instance: diff --git a/tests/end_to_end/configs/generative_process/unified_chain_example.yaml b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml index 47e6c56a..c241be52 100644 --- a/tests/end_to_end/configs/generative_process/unified_chain_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml @@ -4,7 +4,7 @@ # Each factor i>0 selects its parameter variant based on factor i-1's token. name: unified_chain_example -base_vocab_size: 12 # 3 * 2 * 2 = 12 +base_vocab_size: ??? # Will auto-infer as 3 * 2 * 2 = 12 vocab_size: ??? instance: diff --git a/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml b/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml index dd594f7d..7cfccae7 100644 --- a/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml @@ -4,7 +4,7 @@ # Uses product-of-experts to compute joint distribution. name: unified_symmetric_example -base_vocab_size: 27 # 3 * 3 * 3 = 27 +base_vocab_size: ??? # Will auto-infer as 3 * 3 * 3 = 27 vocab_size: ??? instance: diff --git a/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml b/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml index 7dc7f3fa..39635ccb 100644 --- a/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml @@ -3,8 +3,8 @@ # - Emissions: Can be independent OR sequential (factor i depends on i-1's token) # - Transitions: Always mutually conditional (factor i's transition depends on all other factors) -name: unified_transition_coupled_example -base_vocab_size: 36 # 3 * 3 * 4 = 24 +name: unified_transition_and_emission_coupled_example +base_vocab_size: ??? # Will auto-infer as 3 * 3 * 4 = 36 vocab_size: ??? instance: diff --git a/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml b/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml index 5b77d0e7..b4613ac4 100644 --- a/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml @@ -4,7 +4,7 @@ # - Transitions: Always mutually conditional (factor i's transition depends on all other factors) name: unified_transition_coupled_example -base_vocab_size: 36 # 3 * 3 * 4 = 24 +base_vocab_size: ??? # Will auto-infer as 3 * 3 * 4 = 36 vocab_size: ??? instance: diff --git a/tests/generative_processes/test_alternating_process.py b/tests/generative_processes/test_alternating_process.py deleted file mode 100644 index ca685580..00000000 --- a/tests/generative_processes/test_alternating_process.py +++ /dev/null @@ -1,341 +0,0 @@ -"""Tests for simple (non-factored) alternating process.""" - -import jax -import jax.numpy as jnp -import pytest - -from simplexity.generative_processes.alternating_process import AlternatingProcess -from simplexity.generative_processes.builder import ( - build_simple_alternating_process, - build_simple_alternating_process_from_spec, -) - - -def test_alternating_process_init(): - """Test AlternatingProcess initialization.""" - n, V, S = 2, 3, 4 - T = jnp.ones((n, V, S, S)) * 0.5 - norm = jnp.ones((n, S)) - initial_state = jnp.ones(S) / S - - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=norm, - initial_state=initial_state, - n_repetitions=1, - ) - - assert process.vocab_size == V - assert process.num_states == S - assert process.n == n - assert process.n_repetitions == 1 - - -def test_alternating_process_invalid_shape(): - """Test that invalid transition matrix shapes are rejected.""" - # Wrong number of dimensions - with pytest.raises(ValueError, match="must have shape"): - AlternatingProcess( - component_type="hmm", - transition_matrices=jnp.ones((2, 3, 4)), # Missing a dimension - normalizing_eigenvectors=jnp.ones((2, 4)), - initial_state=jnp.ones(4) / 4, - ) - - -def test_alternating_process_invalid_state_dim(): - """Test mismatched state dimensions.""" - with pytest.raises(ValueError, match="State dimensions must match"): - AlternatingProcess( - component_type="hmm", - transition_matrices=jnp.ones((2, 3, 4, 5)), # S1=4, S2=5 mismatch - normalizing_eigenvectors=jnp.ones((2, 4)), - initial_state=jnp.ones(4) / 4, - ) - - -def test_alternating_process_invalid_initial_state(): - """Test mismatched initial state shape.""" - with pytest.raises(ValueError, match="initial_state shape"): - AlternatingProcess( - component_type="hmm", - transition_matrices=jnp.ones((2, 3, 4, 4)), - normalizing_eigenvectors=jnp.ones((2, 4)), - initial_state=jnp.ones(5) / 5, # Wrong size - ) - - -def test_alternating_process_invalid_repetitions(): - """Test invalid n_repetitions.""" - with pytest.raises(ValueError, match="n_repetitions must be positive"): - AlternatingProcess( - component_type="hmm", - transition_matrices=jnp.ones((2, 3, 4, 4)), - normalizing_eigenvectors=jnp.ones((2, 4)), - initial_state=jnp.ones(4) / 4, - n_repetitions=0, - ) - - -def test_alternating_process_initial_state(): - """Test initial state structure.""" - n, V, S = 2, 3, 4 - T = jnp.ones((n, V, S, S)) * 0.5 - initial_dist = jnp.ones(S) / S - - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=initial_dist, - ) - - position, belief_state = process.initial_state - assert position == 0 - assert belief_state.shape == (S,) - assert jnp.allclose(belief_state, initial_dist) - - -def test_alternating_process_variant_selection(): - """Test variant selection logic.""" - n, V, S = 3, 2, 2 - n_repetitions = 2 - - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - n_repetitions=n_repetitions, - ) - - # Positions 0,1 -> variant 0 - assert int(process._select_variant(jnp.array(0))) == 0 - assert int(process._select_variant(jnp.array(1))) == 0 - - # Positions 2,3 -> variant 1 - assert int(process._select_variant(jnp.array(2))) == 1 - assert int(process._select_variant(jnp.array(3))) == 1 - - # Positions 4,5 -> variant 2 - assert int(process._select_variant(jnp.array(4))) == 2 - assert int(process._select_variant(jnp.array(5))) == 2 - - # Position 6 -> wraps to variant 0 - assert int(process._select_variant(jnp.array(6))) == 0 - - -def test_alternating_process_observation_distribution(): - """Test observation probability distribution.""" - n, V, S = 2, 3, 2 - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - ) - - state = process.initial_state - dist = process.observation_probability_distribution(state) - - assert dist.shape == (V,) - assert jnp.all(dist >= 0) - - -def test_alternating_process_emission(): - """Test observation emission.""" - n, V, S = 2, 3, 2 - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - ) - - key = jax.random.PRNGKey(42) - state = process.initial_state - obs = process.emit_observation(state, key) - - assert obs.shape == () - assert 0 <= obs < V - - -def test_alternating_process_transition(): - """Test state transition.""" - n, V, S = 2, 3, 2 - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - ) - - state = process.initial_state - position, belief_state = state - - assert position == 0 - - # Transition - new_state = process.transition_states(state, jnp.array(0)) - new_position, new_belief_state = new_state - - assert new_position == 1 - assert new_belief_state.shape == (S,) - - -def test_alternating_process_position_increment(): - """Test position increments through sequence.""" - n, V, S = 2, 2, 2 - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - ) - - state = process.initial_state - - for expected_pos in range(5): - position, _ = state - assert position == expected_pos - state = process.transition_states(state, jnp.array(0)) - - -def test_alternating_process_probability(): - """Test probability computation.""" - n, V, S = 2, 2, 2 - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - ) - - observations = jnp.array([0, 1, 0, 1]) - prob = process.probability(observations) - - assert prob.shape == () - assert 0 <= prob <= 1 - - -def test_alternating_process_log_probability(): - """Test log probability computation.""" - n, V, S = 2, 2, 2 - T = jnp.ones((n, V, S, S)) * 0.5 - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - ) - - observations = jnp.array([0, 1, 0]) - log_prob = process.log_probability(observations) - - assert log_prob.shape == () - assert log_prob <= 0 - - -def test_build_simple_alternating_process(): - """Test builder function.""" - n, V, S = 2, 3, 4 - T = jnp.ones((n, V, S, S)) * 0.5 - - process = build_simple_alternating_process( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.ones(S) / S, - n_repetitions=2, - ) - - assert isinstance(process, AlternatingProcess) - assert process.vocab_size == V - assert process.num_states == S - assert process.n == n - assert process.n_repetitions == 2 - - -def test_build_simple_alternating_process_from_spec(): - """Test spec-based builder.""" - variants = [ - {"process_name": "coin", "p": 0.7}, - {"process_name": "coin", "p": 0.3}, - ] - - process = build_simple_alternating_process_from_spec( - variants=variants, - component_type="hmm", - n_repetitions=3, - ) - - assert isinstance(process, AlternatingProcess) - assert process.n == 2 - assert process.n_repetitions == 3 - assert process.vocab_size == 2 # coin has 2 tokens - - -def test_build_simple_alternating_process_from_spec_empty(): - """Test that empty variants list is rejected.""" - with pytest.raises(ValueError, match="Must provide at least one variant"): - build_simple_alternating_process_from_spec( - variants=[], - component_type="hmm", - ) - - -def test_build_simple_alternating_process_from_spec_mismatched_shapes(): - """Test that mismatched variant shapes are rejected.""" - variants = [ - {"process_name": "coin", "p": 0.7}, # 2 tokens - {"process_name": "mess3", "x": 0.5, "a": 0.6}, # 3 tokens - ] - - with pytest.raises(ValueError, match="All variants must have same shape"): - build_simple_alternating_process_from_spec( - variants=variants, - component_type="hmm", - ) - - -def test_alternating_process_with_repetitions_full_cycle(): - """Test full alternating cycle with repetitions.""" - n = 3 - n_repetitions = 2 - S = 2 - - # Create distinct matrices for each variant (deterministic for testing) - T0 = jnp.array([[[1.0, 0.0], [1.0, 0.0]], [[0.0, 1.0], [0.0, 1.0]]]) - T1 = jnp.array([[[0.0, 1.0], [0.0, 1.0]], [[1.0, 0.0], [1.0, 0.0]]]) - T2 = jnp.array([[[1.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 0.0]]]) - T = jnp.stack([T0, T1, T2]) - - process = AlternatingProcess( - component_type="hmm", - transition_matrices=T, - normalizing_eigenvectors=jnp.ones((n, S)), - initial_state=jnp.array([1.0, 0.0]), - n_repetitions=n_repetitions, - ) - - state = process.initial_state - - # Verify cycling pattern: 0,0,1,1,2,2,0,0,... - expected_variants = [0, 0, 1, 1, 2, 2, 0, 0] - - for i, expected_variant in enumerate(expected_variants): - position, _ = state - assert position == i - actual_variant = int(process._select_variant(position)) - assert actual_variant == expected_variant, f"Position {i} should use variant {expected_variant}" - - # Advance state - if i < len(expected_variants) - 1: - state = process.transition_states(state, jnp.array(0)) diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 757b576d..402159d9 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -1,7 +1,5 @@ """Test the builder module.""" -<<<<<<< HEAD -======= # pylint: disable=all # Temporarily disable all pylint checkers during AST traversal to prevent crash. # The imports checker crashes when resolving simplexity package imports due to a bug @@ -11,7 +9,6 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. ->>>>>>> origin/main import chex import jax.numpy as jnp import pytest @@ -31,13 +28,9 @@ def test_build_transition_matrices(): """Test the build_transition_matrices function.""" -<<<<<<< HEAD - transition_matrices = build_transition_matrices(HMM_MATRIX_FUNCTIONS, "coin", p=0.6) -======= transition_matrices = build_transition_matrices( HMM_MATRIX_FUNCTIONS, process_name="coin", process_params={"p": 0.6} ) ->>>>>>> origin/main assert transition_matrices.shape == (2, 1, 1) expected = jnp.array([[[0.6]], [[0.4]]]) chex.assert_trees_all_close(transition_matrices, expected) @@ -84,11 +77,7 @@ def test_add_begin_of_sequence_token(): def test_build_hidden_markov_model(): """Test the build_hidden_markov_model function.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("even_ones", p=0.5) -======= hmm = build_hidden_markov_model(process_name="even_ones", process_params={"p": 0.5}) ->>>>>>> origin/main assert hmm.vocab_size == 2 with pytest.raises(KeyError): # noqa: PT011 @@ -100,11 +89,7 @@ def test_build_hidden_markov_model(): def test_build_generalized_hidden_markov_model(): """Test the build_generalized_hidden_markov_model function.""" -<<<<<<< HEAD - ghmm = build_generalized_hidden_markov_model("even_ones", p=0.5) -======= ghmm = build_generalized_hidden_markov_model(process_name="even_ones", process_params={"p": 0.5}) ->>>>>>> origin/main assert ghmm.vocab_size == 2 ghmm = build_generalized_hidden_markov_model(process_name="fanizza", process_params={"alpha": 2000, "lamb": 0.49}) @@ -116,13 +101,8 @@ def test_build_generalized_hidden_markov_model(): def test_build_nonergodic_transition_matrices(): """Test the build_nonergodic_transition_matrices function.""" -<<<<<<< HEAD - coin_1 = build_transition_matrices(HMM_MATRIX_FUNCTIONS, "coin", p=0.6) - coin_2 = build_transition_matrices(HMM_MATRIX_FUNCTIONS, "coin", p=0.3) -======= coin_1 = build_transition_matrices(HMM_MATRIX_FUNCTIONS, process_name="coin", process_params={"p": 0.6}) coin_2 = build_transition_matrices(HMM_MATRIX_FUNCTIONS, process_name="coin", process_params={"p": 0.3}) ->>>>>>> origin/main transition_matrices = build_nonergodic_transition_matrices([coin_1, coin_2], [[0, 1], [0, 2]]) assert transition_matrices.shape == (3, 2, 2) expected = jnp.array( diff --git a/tests/generative_processes/test_generalized_hidden_markov_model.py b/tests/generative_processes/test_generalized_hidden_markov_model.py index d08ec45f..be7385f1 100644 --- a/tests/generative_processes/test_generalized_hidden_markov_model.py +++ b/tests/generative_processes/test_generalized_hidden_markov_model.py @@ -9,12 +9,9 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. -<<<<<<< HEAD -======= from typing import cast from unittest.mock import call, create_autospec, patch ->>>>>>> origin/main import chex import equinox as eqx import jax @@ -33,21 +30,13 @@ @pytest.fixture def z1r() -> GeneralizedHiddenMarkovModel: """Return the zero-one random generalized HMM.""" -<<<<<<< HEAD - return build_generalized_hidden_markov_model("zero_one_random", p=0.5) -======= return build_generalized_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main @pytest.fixture def fanizza_model() -> GeneralizedHiddenMarkovModel: """Return the fanizza generalized HMM.""" -<<<<<<< HEAD - return build_generalized_hidden_markov_model("fanizza", alpha=2000, lamb=0.49) -======= return build_generalized_hidden_markov_model(process_name="fanizza", process_params={"alpha": 2000, "lamb": 0.49}) ->>>>>>> origin/main @pytest.mark.parametrize(("model_name", "vocab_size", "num_states"), [("z1r", 2, 3), ("fanizza_model", 2, 4)]) diff --git a/tests/generative_processes/test_generator.py b/tests/generative_processes/test_generator.py index f144bb9b..acdf92ce 100644 --- a/tests/generative_processes/test_generator.py +++ b/tests/generative_processes/test_generator.py @@ -1,7 +1,5 @@ """Test the generator module.""" -<<<<<<< HEAD -======= # pylint: disable=all # Temporarily disable all pylint checkers during AST traversal to prevent crash. # The imports checker crashes when resolving simplexity package imports due to a bug @@ -11,7 +9,6 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. ->>>>>>> origin/main import chex import jax import jax.numpy as jnp @@ -25,11 +22,7 @@ def test_generate_data_batch(): """Test the generate_data_batch function.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("zero_one_random", p=0.5) -======= hmm = build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main batch_size = 10 sequence_len = 10 gen_state: jax.Array = hmm.initial_state @@ -48,11 +41,7 @@ def test_generate_data_batch(): def test_generate_data_batch_with_bos_token(): """Test the generate_data_batch function with a BOS token.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("zero_one_random", p=0.5) -======= hmm = build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main batch_size = 10 sequence_len = 10 gen_state: jax.Array = hmm.initial_state @@ -80,11 +69,7 @@ def test_generate_data_batch_with_bos_token(): def test_generate_data_batch_with_eos_token(): """Test the generate_data_batch function with an EOS token.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("zero_one_random", p=0.5) -======= hmm = build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main batch_size = 10 sequence_len = 10 gen_state: jax.Array = hmm.initial_state diff --git a/tests/generative_processes/test_hidden_markov_model.py b/tests/generative_processes/test_hidden_markov_model.py index 2b2c8125..1426336c 100644 --- a/tests/generative_processes/test_hidden_markov_model.py +++ b/tests/generative_processes/test_hidden_markov_model.py @@ -9,12 +9,9 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. -<<<<<<< HEAD -======= from typing import cast from unittest.mock import call, create_autospec, patch ->>>>>>> origin/main import chex import equinox as eqx import jax @@ -33,11 +30,7 @@ @pytest.fixture def z1r() -> HiddenMarkovModel: """Return the zero-one random HMM.""" -<<<<<<< HEAD - return build_hidden_markov_model("zero_one_random", p=0.5) -======= return build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main def test_properties(z1r: HiddenMarkovModel): diff --git a/tests/generative_processes/test_mixed_state_presentation.py b/tests/generative_processes/test_mixed_state_presentation.py index 7be3422c..09f5bec7 100644 --- a/tests/generative_processes/test_mixed_state_presentation.py +++ b/tests/generative_processes/test_mixed_state_presentation.py @@ -1,7 +1,5 @@ """Tests for mixed-state presentation utilities.""" -<<<<<<< HEAD -======= # pylint: disable=all # Temporarily disable all pylint checkers during AST traversal to prevent crash. # The imports checker crashes when resolving simplexity package imports due to a bug @@ -11,7 +9,6 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. ->>>>>>> origin/main import math from typing import cast @@ -167,22 +164,14 @@ @pytest.fixture def generator() -> MixedStateTreeGenerator: """Return the standard mixed-state tree generator.""" -<<<<<<< HEAD - ghmm = build_generalized_hidden_markov_model("no_consecutive_ones", p=0.5) -======= ghmm = build_generalized_hidden_markov_model(process_name="no_consecutive_ones", process_params={"p": 0.5}) ->>>>>>> origin/main return MixedStateTreeGenerator(ghmm, max_sequence_length=2) @pytest.fixture def log_generator() -> LogMixedStateTreeGenerator: """Return the log-space mixed-state tree generator.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("no_consecutive_ones", p=0.5) -======= hmm = build_hidden_markov_model(process_name="no_consecutive_ones", process_params={"p": 0.5}) ->>>>>>> origin/main return LogMixedStateTreeGenerator(hmm, max_sequence_length=2, prob_threshold=-jnp.inf) diff --git a/tests/generative_processes/test_mixed_state_tree.py b/tests/generative_processes/test_mixed_state_tree.py index a1ef2d71..59deeefc 100644 --- a/tests/generative_processes/test_mixed_state_tree.py +++ b/tests/generative_processes/test_mixed_state_tree.py @@ -1,7 +1,5 @@ """Test the mixed-state tree module.""" -<<<<<<< HEAD -======= # pylint: disable=all # Temporarily disable all pylint checkers during AST traversal to prevent crash. # The imports checker crashes when resolving simplexity package imports due to a bug @@ -11,7 +9,6 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. ->>>>>>> origin/main from pathlib import Path import chex @@ -90,13 +87,8 @@ def load_golden(process_name: str) -> MixedStateTree[TreeData, NodeDictValue]: @pytest.mark.parametrize("process_name", ["mess3", "rrxor", "zero_one_random"]) def test_hmm_mixed_state_tree(process_name: str): """Test generating a mixed-state tree for a hidden Markov model.""" -<<<<<<< HEAD - params = PROCESS_PARAMS[process_name] - model = build_hidden_markov_model(process_name, initial_state=None, **params) -======= process_params = PROCESS_PARAMS[process_name] model = build_hidden_markov_model(process_name=process_name, process_params=process_params, initial_state=None) ->>>>>>> origin/main generator = MixedStateTreeGenerator(model, max_sequence_length=4) tree: MixedStateTree[TreeData, NodeDictValue] = generator.generate() golden = load_golden(process_name) @@ -120,15 +112,10 @@ def test_hmm_mixed_state_tree(process_name: str): ) def test_ghmm_mixed_state_tree(process_name: str): """Test generating a mixed-state tree for a generalized hidden Markov model.""" -<<<<<<< HEAD - params = PROCESS_PARAMS[process_name] - model = build_generalized_hidden_markov_model(process_name, **params) -======= process_params = PROCESS_PARAMS[process_name] model = build_generalized_hidden_markov_model( process_name=process_name, process_params=process_params, initial_state=None ) ->>>>>>> origin/main generator = MixedStateTreeGenerator(model, max_sequence_length=4) tree: MixedStateTree[TreeData, NodeDictValue] = generator.generate() golden = load_golden(process_name) diff --git a/tests/generative_processes/test_torch_generator.py b/tests/generative_processes/test_torch_generator.py index d5582a9a..7052e3c4 100644 --- a/tests/generative_processes/test_torch_generator.py +++ b/tests/generative_processes/test_torch_generator.py @@ -1,7 +1,5 @@ """Test the torch generator module.""" -<<<<<<< HEAD -======= # pylint: disable=all # Temporarily disable all pylint checkers during AST traversal to prevent crash. # The imports checker crashes when resolving simplexity package imports due to a bug @@ -11,7 +9,6 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. ->>>>>>> origin/main import jax import jax.numpy as jnp import torch @@ -25,11 +22,7 @@ def test_generate_data_batch(): """Test generating a batch of data.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("zero_one_random", p=0.5) -======= hmm = build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main batch_size = 10 sequence_len = 10 gen_state: jax.Array = hmm.initial_state @@ -51,11 +44,7 @@ def test_generate_data_batch(): def test_generate_data_batch_with_bos_token(): """Test generating a batch of data with a BOS token.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("zero_one_random", p=0.5) -======= hmm = build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main batch_size = 10 sequence_len = 10 gen_state: jax.Array = hmm.initial_state @@ -86,11 +75,7 @@ def test_generate_data_batch_with_bos_token(): def test_generate_data_batch_with_eos_token(): """Test generating a batch of data with an EOS token.""" -<<<<<<< HEAD - hmm = build_hidden_markov_model("zero_one_random", p=0.5) -======= hmm = build_hidden_markov_model(process_name="zero_one_random", process_params={"p": 0.5}) ->>>>>>> origin/main batch_size = 10 sequence_len = 10 gen_state: jax.Array = hmm.initial_state From 72c114d6399021d03a0e52ba6fa334a424e6e855 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Tue, 2 Dec 2025 16:59:17 -0800 Subject: [PATCH 08/37] test factored representation --- .../generalized_hidden_markov_model.py | 7 +- tests/generative_processes/test_builder.py | 172 ++++++++++++++++++ .../test_factored_generative_process.py | 149 +++++++++++++++ .../test_factored_structures.py | 137 ++++++++++++++ 4 files changed, 460 insertions(+), 5 deletions(-) create mode 100644 tests/generative_processes/test_factored_generative_process.py create mode 100644 tests/generative_processes/test_factored_structures.py diff --git a/simplexity/generative_processes/generalized_hidden_markov_model.py b/simplexity/generative_processes/generalized_hidden_markov_model.py index 5263b2d3..c3c60807 100644 --- a/simplexity/generative_processes/generalized_hidden_markov_model.py +++ b/simplexity/generative_processes/generalized_hidden_markov_model.py @@ -95,13 +95,10 @@ def validate_transition_matrices(self, transition_matrices: jax.Array): if transition_matrices.ndim != 3 or transition_matrices.shape[1] != transition_matrices.shape[2]: raise ValueError("Transition matrices must have shape (vocab_size, num_states, num_states)") - # GHMM-specific validation: matrices should be non-negative - if not jnp.all(transition_matrices >= 0): - raise ValueError("Transition matrices must be non-negative") - # Check that net transition matrix has dominant eigenvalue close to 1 - state_transition_matrix = jnp.sum(transition_matrices, axis=0) + state_transition_matrix = jnp.asarray(jnp.sum(transition_matrices, axis=0)) eigenvalues, _ = jnp.linalg.eig(state_transition_matrix) + eigenvalues = jnp.asarray(eigenvalues) principal_eigenvalue = jnp.max(jnp.abs(eigenvalues)) if not jnp.isclose(principal_eigenvalue, 1.0, rtol=1e-5): diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 402159d9..071d44be 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -15,13 +15,30 @@ from simplexity.generative_processes.builder import ( add_begin_of_sequence_token, + build_chain_from_spec, + build_chain_process, + build_chain_process_from_spec, + build_factored_process, build_generalized_hidden_markov_model, build_hidden_markov_model, + build_matrices_from_spec, build_nonergodic_hidden_markov_model, build_nonergodic_initial_state, build_nonergodic_transition_matrices, + build_symmetric_from_spec, + build_symmetric_process, + build_symmetric_process_from_spec, + build_transition_coupled_from_spec, + build_transition_coupled_process, + build_transition_coupled_process_from_spec, build_transition_matrices, ) +from simplexity.generative_processes.factored_generative_process import FactoredGenerativeProcess +from simplexity.generative_processes.structures import ( + ConditionalTransitions, + FullyConditional, + SequentialConditional, +) from simplexity.generative_processes.transition_matrices import HMM_MATRIX_FUNCTIONS from tests.generative_processes.test_transition_matrices import validate_hmm_transition_matrices @@ -220,3 +237,158 @@ def test_build_nonergodic_hidden_markov_model_bos(): chex.assert_trees_all_close(hmm.transition_matrices, expected_transition_matrices) assert hmm.initial_state.shape == (3,) chex.assert_trees_all_close(hmm.initial_state, jnp.array([0, 0, 1.0])) + + +def _components_spec(): + return [ + { + "component_type": "hmm", + "variants": [{"process_name": "coin", "process_params": {"p": 0.6}}], + }, + { + "component_type": "hmm", + "variants": [ + {"process_name": "coin", "process_params": {"p": 0.25}}, + {"process_name": "coin", "process_params": {"p": 0.75}}, + ], + }, + ] + + +def _chain_spec(): + spec = _components_spec() + spec[1] = {**spec[1], "control_map": [0, 1]} + return spec + + +def _symmetric_control_maps(): + return [[0, 1], [1, 0]] + + +def _transition_coupled_inputs(): + return ( + [[0, 1], [1, 0]], + [0, 1], + [None, [0, 1]], + ) + + +def test_build_matrices_from_spec_returns_consistent_arrays(): + """Factored specs should yield aligned parameter shapes.""" + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + _components_spec() + ) + assert component_types == ["hmm", "hmm"] + assert transition_matrices[0].shape == (1, 2, 1, 1) + assert transition_matrices[1].shape[0] == 2 + assert normalizing_eigenvectors[1].shape == (2, transition_matrices[1].shape[2]) + for state in initial_states: + chex.assert_trees_all_close(jnp.sum(state), jnp.array(1.0)) + + +def test_build_chain_from_spec_returns_control_maps(): + """build_chain_from_spec should return encoded control maps.""" + ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps, + ) = build_chain_from_spec(_chain_spec()) + assert component_types == ["hmm", "hmm"] + assert control_maps[0] is None + chex.assert_trees_all_close(control_maps[1], jnp.array([0, 1], dtype=jnp.int32)) + + +def test_build_chain_from_spec_missing_control_map_raises(): + """Every non-root node in a chain must provide a control map.""" + with pytest.raises(ValueError): + build_chain_from_spec(_components_spec()) + + +def test_build_symmetric_from_spec_validates_lengths(): + """Symmetric control maps must cover every combination of other tokens.""" + ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps, + ) = build_symmetric_from_spec(_components_spec(), _symmetric_control_maps()) + assert component_types == ["hmm", "hmm"] + assert len(control_maps) == 2 + chex.assert_trees_all_close(control_maps[0], jnp.array([0, 1], dtype=jnp.int32)) + with pytest.raises(ValueError): + build_symmetric_from_spec(_components_spec(), [[0], [0, 1]]) + + +def test_build_transition_coupled_from_spec_handles_emission_maps(): + """Transition-coupled specs should surface emission controls when provided.""" + result = build_transition_coupled_from_spec( + _components_spec(), *_transition_coupled_inputs() + ) + ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps_transition, + emission_variant_indices, + emission_control_maps, + ) = result + assert component_types == ["hmm", "hmm"] + assert len(control_maps_transition) == 2 + assert emission_variant_indices.shape == (2,) + assert emission_control_maps is not None + assert emission_control_maps[0] is None + chex.assert_trees_all_close(emission_control_maps[1], jnp.array([0, 1], dtype=jnp.int32)) + + +def test_build_chain_process_from_spec_returns_factored_process(): + """High-level chain builder should return a FactoredGenerativeProcess.""" + process = build_chain_process_from_spec(_chain_spec()) + assert isinstance(process, FactoredGenerativeProcess) + assert isinstance(process.structure, SequentialConditional) + assert process.vocab_size == 4 + + +def test_build_factored_process_dispatches_topologies(): + """build_factored_process should route to the appropriate topology builder.""" + chain_components = build_chain_from_spec(_chain_spec()) + chain_process = build_factored_process( + topology_type="chain", + component_types=chain_components[0], + transition_matrices=chain_components[1], + normalizing_eigenvectors=chain_components[2], + initial_states=chain_components[3], + control_maps=chain_components[4], + ) + assert isinstance(chain_process.structure, SequentialConditional) + + symmetric_components = build_symmetric_from_spec(_components_spec(), _symmetric_control_maps()) + symmetric_process = build_factored_process( + topology_type="symmetric", + component_types=symmetric_components[0], + transition_matrices=symmetric_components[1], + normalizing_eigenvectors=symmetric_components[2], + initial_states=symmetric_components[3], + control_maps=symmetric_components[4], + ) + assert isinstance(symmetric_process.structure, FullyConditional) + + transition_components = build_transition_coupled_from_spec( + _components_spec(), *_transition_coupled_inputs() + ) + transition_process = build_factored_process( + topology_type="transition_coupled", + component_types=transition_components[0], + transition_matrices=transition_components[1], + normalizing_eigenvectors=transition_components[2], + initial_states=transition_components[3], + control_maps_transition=transition_components[4], + emission_variant_indices=transition_components[5], + emission_control_maps=transition_components[6], + ) + assert isinstance(transition_process.structure, ConditionalTransitions) + assert transition_process.vocab_size == 4 + assert transition_process.structure.use_emission_chain is True diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py new file mode 100644 index 00000000..5db8e371 --- /dev/null +++ b/tests/generative_processes/test_factored_generative_process.py @@ -0,0 +1,149 @@ +"""Integration tests for the factored generative process.""" + +import chex +import jax +import jax.numpy as jnp + +from simplexity.generative_processes.factored_generative_process import FactoredGenerativeProcess +from simplexity.generative_processes.structures import SequentialConditional +from simplexity.utils.factoring_utils import transition_with_obs + + +def _tensor_from_probs(variant_probs): + arr = jnp.asarray(variant_probs, dtype=jnp.float32) + return arr[..., None, None] + + +def _build_simple_chain_process(): + component_types = ("hmm", "hmm") + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + normalizing_eigenvectors = ( + jnp.ones((1, 1), dtype=jnp.float32), + jnp.ones((2, 1), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + structure = SequentialConditional( + control_maps=(None, jnp.array([0, 1], dtype=jnp.int32)), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def _build_multistate_chain_process(): + component_types = ("hmm", "hmm") + transition_matrices = ( + jnp.array( + [ + [ + [[0.9, 0.9], [0.1, 0.1]], + [[0.2, 0.2], [0.8, 0.8]], + ] + ], + dtype=jnp.float32, + ), + jnp.array( + [ + [ + [[0.6, 0.6], [0.4, 0.4]], + [[0.3, 0.3], [0.7, 0.7]], + ], + [ + [[0.5, 0.5], [0.5, 0.5]], + [[0.1, 0.1], [0.9, 0.9]], + ], + ], + dtype=jnp.float32, + ), + ) + normalizing_eigenvectors = ( + jnp.ones((1, 2), dtype=jnp.float32), + jnp.ones((2, 2), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([0.7, 0.3], dtype=jnp.float32), + jnp.array([0.4, 0.6], dtype=jnp.float32), + ) + structure = SequentialConditional( + control_maps=(None, jnp.array([0, 1], dtype=jnp.int32)), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def test_factored_process_observation_and_sequence_probability(): + """Observation, log-observation, and sequence probability APIs should agree.""" + process = _build_simple_chain_process() + state = process.initial_state + + dist = process.observation_probability_distribution(state) + expected = jnp.array([0.42, 0.18, 0.08, 0.32], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + chex.assert_trees_all_close(jnp.sum(dist), jnp.array(1.0, dtype=jnp.float32)) + + log_state = tuple(jnp.log(s) for s in state) + log_dist = process.log_observation_probability_distribution(log_state) + chex.assert_trees_all_close(log_dist, jnp.log(dist)) + + observations = jnp.array([0, 3, 1], dtype=jnp.int32) + prob = process.probability(observations) + expected_prob = jnp.array(0.42 * 0.32 * 0.18, dtype=jnp.float32) + chex.assert_trees_all_close(prob, expected_prob) + + log_prob = process.log_probability(observations) + chex.assert_trees_all_close(log_prob, jnp.log(expected_prob)) + + +def test_transition_states_uses_structure_selected_variants(): + """transition_states should respect variant selection from the structure.""" + process = _build_multistate_chain_process() + state = process.initial_state + obs_token = jnp.array(2, dtype=jnp.int32) # (factor0=1, factor1=0) + + new_state = process.transition_states(state, obs_token) + obs_tuple = process.encoder.token_to_tuple(obs_token) + + expected0 = transition_with_obs( + process.component_types[0], + state[0], + process.transition_matrices[0][0], + obs_tuple[0], + None, + ) + context = process._make_context(state) + variant1 = int(process.structure.select_variants(obs_tuple, context)[1]) + expected1 = transition_with_obs( + process.component_types[1], + state[1], + process.transition_matrices[1][variant1], + obs_tuple[1], + None, + ) + + chex.assert_trees_all_close(new_state[0], expected0) + chex.assert_trees_all_close(new_state[1], expected1) + + +def test_emit_observation_respects_vocab_size(): + """Samples from emit_observation should be in the encoded vocabulary.""" + process = _build_simple_chain_process() + token = process.emit_observation(process.initial_state, jax.random.PRNGKey(0)) + assert token.shape == () + assert 0 <= int(token) < process.vocab_size diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py new file mode 100644 index 00000000..6a255989 --- /dev/null +++ b/tests/generative_processes/test_factored_structures.py @@ -0,0 +1,137 @@ +"""Tests for factored generative process conditional structures.""" + +import chex +import jax.numpy as jnp + +from simplexity.generative_processes.factored_generative_process import ComponentType +from simplexity.generative_processes.structures import ( + ConditionalTransitions, + FullyConditional, + SequentialConditional, +) +from simplexity.generative_processes.structures.protocol import ConditionalContext + + +def _tensor_from_probs(variant_probs): + """Convert per-variant emission probabilities into transition tensors.""" + arr = jnp.asarray(variant_probs, dtype=jnp.float32) + return arr[..., None, None] + + +def _make_context(states, transition_matrices): + """Helper to build a ConditionalContext for HMM components.""" + component_types: tuple[ComponentType, ...] = tuple("hmm" for _ in states) + normalizing_eigenvectors = tuple( + jnp.ones((tm.shape[0], tm.shape[-1]), dtype=jnp.float32) for tm in transition_matrices + ) + vocab_sizes = jnp.array([tm.shape[1] for tm in transition_matrices]) + num_variants = tuple(int(tm.shape[0]) for tm in transition_matrices) + return ConditionalContext( + states=states, + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + vocab_sizes=vocab_sizes, + num_variants=num_variants, + ) + + +def test_sequential_conditional_joint_distribution_and_variants(): + """SequentialConditional should respect the chain factorization.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + context = _make_context(states, transition_matrices) + structure = SequentialConditional(control_maps=(None, jnp.array([0, 1], dtype=jnp.int32))) + + dist = structure.compute_joint_distribution(context) + expected = jnp.array([0.42, 0.18, 0.08, 0.32], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + variants = structure.select_variants( + (jnp.array(1, dtype=jnp.int32), jnp.array(0, dtype=jnp.int32)), + context, + ) + chex.assert_trees_all_close(variants[0], jnp.array(0, dtype=jnp.int32)) + chex.assert_trees_all_close(variants[1], jnp.array(1, dtype=jnp.int32)) + + +def test_fully_conditional_product_of_experts(): + """FullyConditional should build a normalized product-of-experts distribution.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.1, 0.9]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + context = _make_context(states, transition_matrices) + structure = FullyConditional( + control_maps=(jnp.array([0, 1], dtype=jnp.int32), jnp.array([1, 0], dtype=jnp.int32)), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + + dist = structure.compute_joint_distribution(context) + expected = jnp.array([0.16, 0.10666667, 0.37333333, 0.36], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + variants = structure.select_variants( + (jnp.array(0, dtype=jnp.int32), jnp.array(1, dtype=jnp.int32)), + context, + ) + chex.assert_trees_all_close(variants[0], jnp.array(1, dtype=jnp.int32)) + chex.assert_trees_all_close(variants[1], jnp.array(1, dtype=jnp.int32)) + + +def test_conditional_transitions_with_independent_emissions(): + """ConditionalTransitions should reduce to independent emissions when no chain is given.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.2, 0.8]]), + _tensor_from_probs([[0.5, 0.5], [0.1, 0.9]]), + ) + context = _make_context(states, transition_matrices) + structure = ConditionalTransitions( + control_maps_transition=( + jnp.array([1, 0], dtype=jnp.int32), + jnp.array([0, 1], dtype=jnp.int32), + ), + emission_variant_indices=jnp.array([1, 0], dtype=jnp.int32), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + + dist = structure.compute_joint_distribution(context) + expected = jnp.array([0.1, 0.1, 0.4, 0.4], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + variants = structure.select_variants( + (jnp.array(1, dtype=jnp.int32), jnp.array(0, dtype=jnp.int32)), + context, + ) + chex.assert_trees_all_close(variants[0], jnp.array(1, dtype=jnp.int32)) + chex.assert_trees_all_close(variants[1], jnp.array(1, dtype=jnp.int32)) + + +def test_conditional_transitions_with_sequential_emissions(): + """ConditionalTransitions should honor sequential emission control maps.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.2, 0.8]]), + _tensor_from_probs([[0.9, 0.1], [0.3, 0.7]]), + ) + context = _make_context(states, transition_matrices) + structure = ConditionalTransitions( + control_maps_transition=( + jnp.array([0, 0], dtype=jnp.int32), + jnp.array([0, 0], dtype=jnp.int32), + ), + emission_variant_indices=jnp.array([0, 0], dtype=jnp.int32), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + emission_control_maps=(None, jnp.array([1, 0], dtype=jnp.int32)), + ) + + assert structure.use_emission_chain is True + + dist = structure.compute_joint_distribution(context) + expected = jnp.array([0.18, 0.42, 0.36, 0.04], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) From 0bd219d10ad07aab2f2e89d523ee81d98eaf1dfe Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 07:19:46 -0800 Subject: [PATCH 09/37] finalise gen-process PR --- simplexity/generative_processes/builder.py | 406 ++++++------------ .../factored_generative_process.py | 77 ++-- .../structures/__init__.py | 6 +- .../structures/independent.py | 73 ++++ .../unified_chain_3mess3_2tomq.yaml | 91 ++-- .../unified_chain_example.yaml | 33 +- .../unified_independent_example.yaml | 53 +++ .../unified_symmetric_example.yaml | 66 +-- ...ansition_and_emission_coupled_example.yaml | 36 +- .../unified_transition_coupled_example.yaml | 36 +- tests/generative_processes/test_builder.py | 256 +++++++++-- .../test_factored_generative_process.py | 324 +++++++++++++- .../test_factored_structures.py | 67 ++- .../test_generative_process_config.py | 147 ++++++- 14 files changed, 1205 insertions(+), 466 deletions(-) create mode 100644 simplexity/generative_processes/structures/independent.py create mode 100644 tests/end_to_end/configs/generative_process/unified_independent_example.yaml diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 7b012e57..6834634d 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -22,6 +22,7 @@ from simplexity.generative_processes.structures import ( ConditionalTransitions, FullyConditional, + IndependentStructure, SequentialConditional, ) from simplexity.generative_processes.transition_matrices import ( @@ -154,120 +155,63 @@ def build_nonergodic_hidden_markov_model( return HiddenMarkovModel(composite_transition_matrix, initial_state) -def build_chain_process( - *, +def build_factored_process( + structure_type: Literal["independent", "chain", "symmetric", "transition_coupled"], component_types: Sequence[ComponentType], transition_matrices: Sequence[jnp.ndarray], normalizing_eigenvectors: Sequence[jnp.ndarray], initial_states: Sequence[jnp.ndarray], - control_maps: Sequence[jnp.ndarray | None], + **structure_kwargs, ) -> FactoredGenerativeProcess: - """Build a chain/conditional factored process. - - Factor i>0 selects its parameter variant based on factor i-1's emitted token. + """Factory function for building factored processes with different conditional structures. Args: + structure_type: Which conditional structure to instantiate component_types: Type of each factor ("hmm" or "ghmm") transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) initial_states: Initial state per factor (shape [S_i]) - control_maps: Control maps for variant selection. control_maps[0] should - be None. control_maps[i] for i>0 should have shape [V_{i-1}]. + **structure_kwargs: Structure-specific keyword arguments: + - For "independent": (none) + - For "chain": control_maps + - For "symmetric": control_maps + - For "transition_coupled": control_maps_transition, + emission_variant_indices, emission_control_maps (optional) Returns: - FactoredGenerativeProcess with sequential conditional structure - """ - # Extract vocab sizes from transition matrices - vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) - - structure = SequentialConditional(control_maps=tuple(control_maps), vocab_sizes=vocab_sizes) - return FactoredGenerativeProcess( - component_types=component_types, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_states=initial_states, - structure=structure, - ) - - -def build_symmetric_process( - *, - component_types: Sequence[ComponentType], - transition_matrices: Sequence[jnp.ndarray], - normalizing_eigenvectors: Sequence[jnp.ndarray], - initial_states: Sequence[jnp.ndarray], - control_maps: Sequence[jnp.ndarray], -) -> FactoredGenerativeProcess: - """Build a symmetric/fully-bidirectional factored process. - - Each factor selects its variant based on all other factors' tokens. + FactoredGenerativeProcess configured with the requested conditional structure - Args: - component_types: Type of each factor ("hmm" or "ghmm") - transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) - normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) - initial_states: Initial state per factor (shape [S_i]) - control_maps: Control maps for variant selection. control_maps[i] should - have shape [prod(V_j for j!=i)]. - - Returns: - FactoredGenerativeProcess with fully conditional structure + Raises: + ValueError: If structure_type is invalid or required kwargs are missing """ - # Extract vocab sizes from transition matrices vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) - structure = FullyConditional( - control_maps=tuple(control_maps), - vocab_sizes=vocab_sizes, - ) - return FactoredGenerativeProcess( - component_types=component_types, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_states=initial_states, - structure=structure, - ) - - -def build_transition_coupled_process( - *, - component_types: Sequence[ComponentType], - transition_matrices: Sequence[jnp.ndarray], - normalizing_eigenvectors: Sequence[jnp.ndarray], - initial_states: Sequence[jnp.ndarray], - control_maps_transition: Sequence[jnp.ndarray], - emission_variant_indices: jnp.ndarray | Sequence[int], - emission_control_maps: Sequence[jnp.ndarray | None] | None = None, -) -> FactoredGenerativeProcess: - """Build a transition-coupled factored process. - - Emissions can be independent or chain-style. Transitions are coupled - based on other factors' tokens. - - Args: - component_types: Type of each factor ("hmm" or "ghmm") - transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) - normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) - initial_states: Initial state per factor (shape [S_i]) - control_maps_transition: Transition control maps. control_maps_transition[i] - should have shape [prod(V_j for j!=i)]. - emission_variant_indices: Fixed emission variant per factor (shape [F]) - emission_control_maps: Optional chain-style emission control maps. - If provided, emission_control_maps[i] should have shape - [prod(V_j for j0. - - Returns: - FactoredGenerativeProcess with conditional transitions structure - """ - # Extract vocab sizes from transition matrices - vocab_sizes = jnp.array([int(T.shape[1]) for T in transition_matrices]) + if structure_type == "independent": + structure = IndependentStructure() + elif structure_type == "chain": + if "control_maps" not in structure_kwargs: + raise ValueError("Missing required argument 'control_maps' for chain structure") + structure = SequentialConditional(control_maps=tuple(structure_kwargs["control_maps"]), vocab_sizes=vocab_sizes) + elif structure_type == "symmetric": + if "control_maps" not in structure_kwargs: + raise ValueError("Missing required argument 'control_maps' for symmetric structure") + structure = FullyConditional(control_maps=tuple(structure_kwargs["control_maps"]), vocab_sizes=vocab_sizes) + elif structure_type == "transition_coupled": + if "control_maps_transition" not in structure_kwargs: + raise ValueError("Missing required argument 'control_maps_transition' for transition_coupled structure") + if "emission_variant_indices" not in structure_kwargs: + raise ValueError("Missing required argument 'emission_variant_indices' for transition_coupled structure") + structure = ConditionalTransitions( + control_maps_transition=tuple(structure_kwargs["control_maps_transition"]), + emission_variant_indices=structure_kwargs["emission_variant_indices"], + vocab_sizes=vocab_sizes, + emission_control_maps=tuple(structure_kwargs["emission_control_maps"]) + if "emission_control_maps" in structure_kwargs and structure_kwargs["emission_control_maps"] is not None + else None, + ) + else: + raise ValueError(f"Unknown structure_type '{structure_type}'") - structure = ConditionalTransitions( - control_maps_transition=tuple(control_maps_transition), - emission_variant_indices=emission_variant_indices, - vocab_sizes=vocab_sizes, - emission_control_maps=tuple(emission_control_maps) if emission_control_maps else None, - ) return FactoredGenerativeProcess( component_types=component_types, transition_matrices=transition_matrices, @@ -277,200 +221,119 @@ def build_transition_coupled_process( ) -def build_factored_process( - topology_type: Literal["chain", "symmetric", "transition_coupled"], - component_types: Sequence[ComponentType], - transition_matrices: Sequence[jnp.ndarray], - normalizing_eigenvectors: Sequence[jnp.ndarray], - initial_states: Sequence[jnp.ndarray], - **topology_kwargs, +def build_factored_process_from_spec( + structure_type: Literal["independent", "chain", "symmetric", "transition_coupled"], + spec: Sequence[dict[str, Any]] | dict[str, Any], + **structure_params, ) -> FactoredGenerativeProcess: - """Factory function for building factored processes with different topologies. + """Unified builder for factored processes from specification. Args: - topology_type: Type of coupling topology to use - component_types: Type of each factor ("hmm" or "ghmm") - transition_matrices: Per-factor transition tensors (shape [K_i, V_i, S_i, S_i]) - normalizing_eigenvectors: Per-factor eigenvectors (shape [K_i, S_i]) - initial_states: Initial state per factor (shape [S_i]) - **topology_kwargs: Topology-specific keyword arguments: - - For "chain": control_maps - - For "symmetric": control_maps - - For "transition_coupled": control_maps_transition, - emission_variant_indices, emission_control_maps (optional) + structure_type: Which conditional structure to use + spec: Component specifications. Format depends on structure_type: + - For "independent": List of component dicts + - For "chain": List of component dicts with control_maps + - For "symmetric": List of component dicts + - For "transition_coupled": List of component dicts + **structure_params: Additional structure-specific parameters: + - For "independent": (none) + - For "chain": (none, uses spec's control_map fields) + - For "symmetric": control_maps (list) + - For "transition_coupled": control_maps_transition, emission_variant_indices, + emission_control_maps (optional) Returns: - FactoredGenerativeProcess with the specified topology + FactoredGenerativeProcess with specified structure - Raises: - ValueError: If topology_type is invalid or required kwargs are missing + Example: + ```python + # Independent + process = build_factored_process_from_spec( + structure_type="independent", + spec=[ + {"component_type": "hmm", "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}]}, + {"component_type": "hmm", "variants": [{"process_name": "mess3", "x": 0.5, "a": 0.6}]}, + ], + ) + + # Symmetric + process = build_factored_process_from_spec( + structure_type="symmetric", + spec=[...], + control_maps=[[0, 1, 0, 1], [1, 0, 1, 0]], + ) + ``` """ - if topology_type == "chain": - return build_chain_process( + if structure_type == "independent": + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec(spec) + return build_factored_process( + structure_type="independent", component_types=component_types, transition_matrices=transition_matrices, normalizing_eigenvectors=normalizing_eigenvectors, initial_states=initial_states, - control_maps=topology_kwargs["control_maps"], ) - elif topology_type == "symmetric": - return build_symmetric_process( + elif structure_type == "chain": + component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps = ( + build_chain_from_spec(spec) + ) + return build_factored_process( + structure_type="chain", component_types=component_types, transition_matrices=transition_matrices, normalizing_eigenvectors=normalizing_eigenvectors, initial_states=initial_states, - control_maps=topology_kwargs["control_maps"], + control_maps=control_maps, ) - elif topology_type == "transition_coupled": - return build_transition_coupled_process( + elif structure_type == "symmetric": + if "control_maps" not in structure_params: + raise ValueError("symmetric structure requires 'control_maps' parameter") + ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps_arrays, + ) = build_symmetric_from_spec(spec, structure_params["control_maps"]) + return build_factored_process( + structure_type="symmetric", component_types=component_types, transition_matrices=transition_matrices, normalizing_eigenvectors=normalizing_eigenvectors, initial_states=initial_states, - control_maps_transition=topology_kwargs["control_maps_transition"], - emission_variant_indices=topology_kwargs["emission_variant_indices"], - emission_control_maps=topology_kwargs.get("emission_control_maps"), + control_maps=control_maps_arrays, ) - - -def build_chain_process_from_spec( - chain: Sequence[dict[str, Any]], -) -> FactoredGenerativeProcess: - """Build chain process directly from specification. - - This is a convenience function that combines spec parsing and process building. - - Args: - chain: List of factor specifications. Each should have: - - component_type: "hmm" or "ghmm" - - variants: List of variant specs with process_name and parameters - - control_map (for i>0): List mapping parent token -> variant index - - Returns: - FactoredGenerativeProcess with chain topology - - Example: - ```python - chain = [ - { - "component_type": "hmm", - "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], - }, - { - "component_type": "hmm", - "variants": [ - {"process_name": "mess3", "x": 0.15, "a": 0.6}, - {"process_name": "mess3", "x": 0.5, "a": 0.6}, - ], - "control_map": [0, 1, 0], - }, - ] - process = build_chain_process_from_spec(chain) - ``` - """ - component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps = ( - build_chain_from_spec(chain) - ) - - return build_chain_process( - component_types=component_types, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_states=initial_states, - control_maps=control_maps, - ) - - -def build_symmetric_process_from_spec( - components: Sequence[dict[str, Any]], - control_maps: Sequence[list[int]], -) -> FactoredGenerativeProcess: - """Build symmetric process directly from specification. - - Args: - components: List of factor specifications - control_maps: Control maps for each factor - - Returns: - FactoredGenerativeProcess with symmetric topology - - Example: - ```python - components = [ - { - "component_type": "hmm", - "variants": [ - {"process_name": "mess3", "x": 0.15, "a": 0.6}, - {"process_name": "mess3", "x": 0.5, "a": 0.6}, - ], - }, - # ... more components - ] - control_maps = [[0, 1, 0, 1], [1, 0, 1, 0]] - process = build_symmetric_process_from_spec(components, control_maps) - ``` - """ - component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps_arrays = ( - build_symmetric_from_spec(components, control_maps) - ) - - return build_symmetric_process( - component_types=component_types, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_states=initial_states, - control_maps=control_maps_arrays, - ) - - -def build_transition_coupled_process_from_spec( - components: Sequence[dict[str, Any]], - control_maps_transition: Sequence[list[int]], - emission_variant_indices: Sequence[int], - emission_control_maps: Sequence[list[int] | None] | None = None, -) -> FactoredGenerativeProcess: - """Build transition-coupled process directly from specification. - - Args: - components: List of factor specifications - control_maps_transition: Transition control maps - emission_variant_indices: Fixed emission variant per factor - emission_control_maps: Optional chain-style emission control maps - - Returns: - FactoredGenerativeProcess with transition-coupled topology - - Example: - ```python - components = [...] - control_maps_transition = [[0, 1, 0, 1], [1, 0, 1, 0]] - emission_variant_indices = [0, 0] - process = build_transition_coupled_process_from_spec( - components, control_maps_transition, emission_variant_indices + elif structure_type == "transition_coupled": + if "control_maps_transition" not in structure_params: + raise ValueError("transition_coupled structure requires 'control_maps_transition' parameter") + if "emission_variant_indices" not in structure_params: + raise ValueError("transition_coupled structure requires 'emission_variant_indices' parameter") + ( + component_types, + transition_matrices, + normalizing_eigenvectors, + initial_states, + control_maps_arrays, + emission_variant_indices_array, + emission_control_maps_arrays, + ) = build_transition_coupled_from_spec( + spec, + structure_params["control_maps_transition"], + structure_params["emission_variant_indices"], + structure_params.get("emission_control_maps"), ) - ``` - """ - ( - component_types, - transition_matrices, - normalizing_eigenvectors, - initial_states, - control_maps_arrays, - emission_variant_indices_array, - emission_control_maps_arrays, - ) = build_transition_coupled_from_spec( - components, control_maps_transition, emission_variant_indices, emission_control_maps - ) - - return build_transition_coupled_process( - component_types=component_types, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_states=initial_states, - control_maps_transition=control_maps_arrays, - emission_variant_indices=emission_variant_indices_array, - emission_control_maps=emission_control_maps_arrays, - ) + return build_factored_process( + structure_type="transition_coupled", + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps_transition=control_maps_arrays, + emission_variant_indices=emission_variant_indices_array, + emission_control_maps=emission_control_maps_arrays, + ) + else: + raise ValueError(f"Unknown structure_type '{structure_type}'") def build_matrices_from_spec( @@ -483,7 +346,7 @@ def build_matrices_from_spec( ]: """Build transition matrices, eigenvectors, and initial states from spec. - This is a generic helper that works for all topologies. Each element of spec + This is a generic helper that works for all conditional structures. Each element of spec should be a dict with: - component_type: "hmm" | "ghmm" - variants: list of dicts, each with "process_name" and process-specific kwargs @@ -535,6 +398,7 @@ def build_matrices_from_spec( for v in variants ] + # Validate dimensions vocab_sizes = [b.vocab_size for b in built] num_states = [b.num_states if hasattr(b, "num_states") else b.transition_matrices.shape[1] for b in built] @@ -574,7 +438,7 @@ def build_chain_from_spec( list[jnp.ndarray], list[jnp.ndarray | None], ]: - """Build all parameters for chain topology from chain specification. + """Build all parameters for chain structure from chain specification. Each element of chain should be a dict with: - component_type: "hmm" | "ghmm" @@ -651,7 +515,7 @@ def build_symmetric_from_spec( list[jnp.ndarray], list[jnp.ndarray], ]: - """Build all parameters for symmetric topology from specification. + """Build all parameters for symmetric structure from specification. Args: components: List of factor specifications (same format as build_matrices_from_spec) @@ -719,7 +583,7 @@ def build_transition_coupled_from_spec( jnp.ndarray, list[jnp.ndarray | None] | None, ]: - """Build all parameters for transition-coupled topology from specification. + """Build all parameters for transition-coupled structure from specification. Args: components: List of factor specifications diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py index 07bddbb6..f35aa49c 100644 --- a/simplexity/generative_processes/factored_generative_process.py +++ b/simplexity/generative_processes/factored_generative_process.py @@ -20,6 +20,36 @@ FactoredState = tuple[jnp.ndarray, ...] +def _move_arrays_to_device( + arrays: Sequence[jnp.ndarray], + device: jax.Device, # type: ignore[valid-type] + name: str, +) -> tuple[jnp.ndarray, ...]: + """Move arrays to specified device with warning if needed. + + Args: + arrays: Sequence of arrays to move + device: Target device + name: Name for warning messages (e.g., "Transition matrices") + + Returns: + Tuple of arrays on target device + """ + result = [] + for i, arr in enumerate(arrays): + if arr.device != device: + SIMPLEXITY_LOGGER.warning( + "%s[%d] on device %s but model is on device %s. Moving to model device.", + name, + i, + arr.device, + device, + ) + arr = jax.device_put(arr, device) + result.append(arr) + return tuple(result) + + class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): """Unified factored generative process with pluggable conditional structures. @@ -78,47 +108,12 @@ def __init__( self.device = resolve_jax_device(device) self.component_types = tuple(component_types) - # Move transition matrices to device - transition_matrices_on_device = [] - for i, T in enumerate(transition_matrices): - if T.device != self.device: - SIMPLEXITY_LOGGER.warning( - "Transition matrices[%d] on device %s but model is on device %s. Moving to model device.", - i, - T.device, - self.device, - ) - T = jax.device_put(T, self.device) - transition_matrices_on_device.append(T) - self.transition_matrices = tuple(transition_matrices_on_device) - - # Move normalizing eigenvectors to device - normalizing_eigenvectors_on_device = [] - for i, norm in enumerate(normalizing_eigenvectors): - if norm.device != self.device: - SIMPLEXITY_LOGGER.warning( - "Normalizing eigenvectors[%d] on device %s but model is on device %s. Moving to model device.", - i, - norm.device, - self.device, - ) - norm = jax.device_put(norm, self.device) - normalizing_eigenvectors_on_device.append(norm) - self.normalizing_eigenvectors = tuple(normalizing_eigenvectors_on_device) - - # Move initial states to device - initial_states_on_device = [] - for i, state in enumerate(initial_states): - if state.device != self.device: - SIMPLEXITY_LOGGER.warning( - "Initial states[%d] on device %s but model is on device %s. Moving to model device.", - i, - state.device, - self.device, - ) - state = jax.device_put(state, self.device) - initial_states_on_device.append(state) - self.initial_states = tuple(initial_states_on_device) + # Move all arrays to device + self.transition_matrices = _move_arrays_to_device(transition_matrices, self.device, "Transition matrices") + self.normalizing_eigenvectors = _move_arrays_to_device( + normalizing_eigenvectors, self.device, "Normalizing eigenvectors" + ) + self.initial_states = _move_arrays_to_device(initial_states, self.device, "Initial states") self.structure = structure diff --git a/simplexity/generative_processes/structures/__init__.py b/simplexity/generative_processes/structures/__init__.py index 5f91c9d9..524c8597 100644 --- a/simplexity/generative_processes/structures/__init__.py +++ b/simplexity/generative_processes/structures/__init__.py @@ -4,7 +4,7 @@ how factors in a factored generative process depend on each other. Available structures: -- Independent: No conditional dependencies between factors +- IndependentStructure: No conditional dependencies between factors - SequentialConditional: One-way chain dependencies (factor i depends on i-1) - FullyConditional: Mutual dependencies between all factors - ConditionalTransitions: Hybrid structure (independent/sequential emissions, mutual transitions) @@ -16,6 +16,9 @@ from simplexity.generative_processes.structures.fully_conditional import ( FullyConditional, ) +from simplexity.generative_processes.structures.independent import ( + IndependentStructure, +) from simplexity.generative_processes.structures.protocol import ( ConditionalContext, ConditionalStructure, @@ -29,5 +32,6 @@ "ConditionalStructure", "ConditionalTransitions", "FullyConditional", + "IndependentStructure", "SequentialConditional", ] diff --git a/simplexity/generative_processes/structures/independent.py b/simplexity/generative_processes/structures/independent.py new file mode 100644 index 00000000..ec16b3bd --- /dev/null +++ b/simplexity/generative_processes/structures/independent.py @@ -0,0 +1,73 @@ +"""Independent structure: no conditional dependencies between factors. + +Each factor operates independently, always using variant 0. +Joint distribution is the product of independent factor distributions. +""" + +from __future__ import annotations + +import equinox as eqx +import jax.numpy as jnp + +from simplexity.generative_processes.structures.protocol import ConditionalContext +from simplexity.utils.factoring_utils import compute_obs_dist_for_variant + + +class IndependentStructure(eqx.Module): + """Independent structure with no conditional dependencies. + + Each factor operates independently: + - All factors always use variant 0 + - No control maps needed + - Joint distribution: P(t0, t1, ..., tF) = P(t0) * P(t1) * ... * P(tF) + + This is the simplest factored structure. + """ + + def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: + """Compute joint distribution as product of independent factors. + + Args: + context: Conditional context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + F = len(context.vocab_sizes) + states = context.states + component_types = context.component_types + transition_matrices = context.transition_matrices + normalizing_eigenvectors = context.normalizing_eigenvectors + + parts = [] + for i in range(F): + T_i = transition_matrices[i][0] + norm_i = normalizing_eigenvectors[i][0] if component_types[i] == "ghmm" else None + p_i = compute_obs_dist_for_variant(component_types[i], states[i], T_i, norm_i) + parts.append(p_i) + + joint = parts[0] + for i in range(1, F): + joint = (joint[..., None] * parts[i]).reshape(*joint.shape, parts[i].shape[0]) + + return joint.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jnp.ndarray, ...], + context: ConditionalContext, + ) -> tuple[jnp.ndarray, ...]: + """Select variants (always 0 for all factors). + + Args: + obs_tuple: Tuple of observed tokens (unused) + context: Conditional context (unused) + + Returns: + Tuple of variant indices (all zeros) + """ + return tuple(jnp.array(0, dtype=jnp.int32) for _ in obs_tuple) + + def get_required_params(self) -> dict[str, type]: + """Return required parameters (none for independent structure).""" + return {} diff --git a/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml b/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml index 1ef04280..d15066cc 100644 --- a/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml +++ b/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml @@ -6,82 +6,101 @@ base_vocab_size: ??? # Will auto-infer as (3^3) * (4^2) = 27 * 16 = 432 vocab_size: ??? instance: - _target_: simplexity.generative_processes.builder.build_chain_process_from_spec + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec - chain: + structure_type: chain + + spec: # Factor 0: mess3 (unconditional, single variant) - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 # Factor 1: mess3 (4 variants based on Factor 0's token) - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.11 - a: 0.79 + process_params: + x: 0.11 + a: 0.79 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.21 + process_params: + x: 0.5 + a: 0.21 control_map: [2, 3, 0] # 3 parent tokens -> 4 variants # Factor 2: mess3 (4 variants based on Factor 1's token) - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.11 - a: 0.79 + process_params: + x: 0.11 + a: 0.79 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.21 + process_params: + x: 0.5 + a: 0.21 control_map: [1, 3, 2] # 3 parent tokens -> 4 variants # Factor 3: tom_quantum (4 variants based on Factor 2's token) - component_type: ghmm variants: - process_name: tom_quantum - alpha: 1.0 - beta: 1.0 + process_params: + alpha: 1.0 + beta: 1.0 - process_name: tom_quantum - alpha: 1.0 - beta: 4.0 + process_params: + alpha: 1.0 + beta: 4.0 - process_name: tom_quantum - alpha: 1.0 - beta: 7.141428 # sqrt(51) + process_params: + alpha: 1.0 + beta: 7.141428 # sqrt(51) - process_name: tom_quantum - alpha: 1.0 - beta: 2.0 + process_params: + alpha: 1.0 + beta: 2.0 control_map: [2, 0, 3] # 3 parent tokens -> 4 variants # Factor 4: tom_quantum (4 variants based on Factor 3's token) - component_type: ghmm variants: - process_name: tom_quantum - alpha: 1.0 - beta: 1.0 + process_params: + alpha: 1.0 + beta: 1.0 - process_name: tom_quantum - alpha: 1.0 - beta: 4.0 + process_params: + alpha: 1.0 + beta: 4.0 - process_name: tom_quantum - alpha: 1.0 - beta: 7.141428 + process_params: + alpha: 1.0 + beta: 7.141428 - process_name: tom_quantum - alpha: 1.0 - beta: 2.0 + process_params: + alpha: 1.0 + beta: 2.0 control_map: [1, 3, 0, 2] # 4 parent tokens -> 4 variants bos_token: ??? diff --git a/tests/end_to_end/configs/generative_process/unified_chain_example.yaml b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml index c241be52..fd1faa78 100644 --- a/tests/end_to_end/configs/generative_process/unified_chain_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml @@ -8,37 +8,44 @@ base_vocab_size: ??? # Will auto-infer as 3 * 2 * 2 = 12 vocab_size: ??? instance: - _target_: simplexity.generative_processes.builder.build_chain_process_from_spec + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec - chain: + structure_type: chain + + spec: # Factor 0: mess3 (root, single variant) - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 # Factor 1: mess3 (depends on Factor 0, 2 variants) - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 control_map: [0, 1, 0] # 3 parent tokens -> 2 variants # Factor 2: mess3 (depends on Factor 1, 2 variants) - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.6 - control_map: [1, 0] # 2 parent tokens -> 2 variants + process_params: + x: 0.5 + a: 0.6 + control_map: [1, 0, 1] # 3 parent tokens -> 2 variants bos_token: ??? eos_token: null diff --git a/tests/end_to_end/configs/generative_process/unified_independent_example.yaml b/tests/end_to_end/configs/generative_process/unified_independent_example.yaml new file mode 100644 index 00000000..8abf8aa2 --- /dev/null +++ b/tests/end_to_end/configs/generative_process/unified_independent_example.yaml @@ -0,0 +1,53 @@ +# Unified API: Independent Structure +# Independent structure has no conditional dependencies: +# Each factor operates independently, always using variant 0. +# Joint distribution is simply the product of independent factor distributions. + +name: unified_independent_example +base_vocab_size: ??? # Will auto-infer as 3 * 3 * 3 = 27 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + # Structure type - determines how factors interact + structure_type: independent + + # Component specifications + # Each factor has exactly one variant (independent factors don't need multiple variants) + spec: + # Factor 0: mess3 process + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + + # Factor 1: mess3 process + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + + # Factor 2: mess3 process + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.2 + a: 0.7 + +bos_token: ??? +eos_token: null +batch_size: 1024 + +# Example interpretation: +# - Each factor operates completely independently +# - Factor 0 always emits from mess3(x=0.15, a=0.6) +# - Factor 1 always emits from mess3(x=0.5, a=0.6) +# - Factor 2 always emits from mess3(x=0.2, a=0.7) +# - Joint distribution: P(t0,t1,t2) = P(t0) * P(t1) * P(t2) +# - No control maps needed - factors don't depend on each other diff --git a/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml b/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml index 7cfccae7..b13fb2f7 100644 --- a/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml @@ -8,54 +8,68 @@ base_vocab_size: ??? # Will auto-infer as 3 * 3 * 3 = 27 vocab_size: ??? instance: - _target_: simplexity.generative_processes.builder.build_symmetric_process_from_spec + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: symmetric # Component specifications - components: + spec: - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.11 - a: 0.79 + process_params: + x: 0.11 + a: 0.79 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.21 + process_params: + x: 0.5 + a: 0.21 - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.11 - a: 0.79 + process_params: + x: 0.11 + a: 0.79 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.21 + process_params: + x: 0.5 + a: 0.21 - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.11 - a: 0.79 + process_params: + x: 0.11 + a: 0.79 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.21 + process_params: + x: 0.5 + a: 0.21 # Control maps: define how other-tokens select variant # control_maps[i] has shape [prod(V_j for j!=i)] diff --git a/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml b/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml index 39635ccb..e171167d 100644 --- a/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml @@ -8,36 +8,44 @@ base_vocab_size: ??? # Will auto-infer as 3 * 3 * 4 = 36 vocab_size: ??? instance: - _target_: simplexity.generative_processes.builder.build_transition_coupled_process_from_spec + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: transition_coupled # Component specifications - components: + spec: - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - component_type: ghmm variants: - process_name: tom_quantum - alpha: 1.0 - beta: 1.0 + process_params: + alpha: 1.0 + beta: 1.0 - process_name: tom_quantum - alpha: 1.0 - beta: 4.0 + process_params: + alpha: 1.0 + beta: 4.0 # Transition control maps: how other-factors' tokens select transition variant # control_maps_transition[i] has shape [prod(V_j for j!=i)] diff --git a/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml b/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml index b4613ac4..ef3302e9 100644 --- a/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml @@ -8,36 +8,44 @@ base_vocab_size: ??? # Will auto-infer as 3 * 3 * 4 = 36 vocab_size: ??? instance: - _target_: simplexity.generative_processes.builder.build_transition_coupled_process_from_spec + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: transition_coupled # Component specifications - components: + spec: - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - component_type: hmm variants: - process_name: mess3 - x: 0.15 - a: 0.6 + process_params: + x: 0.15 + a: 0.6 - process_name: mess3 - x: 0.5 - a: 0.6 + process_params: + x: 0.5 + a: 0.6 - component_type: ghmm variants: - process_name: tom_quantum - alpha: 1.0 - beta: 1.0 + process_params: + alpha: 1.0 + beta: 1.0 - process_name: tom_quantum - alpha: 1.0 - beta: 4.0 + process_params: + alpha: 1.0 + beta: 4.0 # Transition control maps: how other-factors' tokens select transition variant # control_maps_transition[i] has shape [prod(V_j for j!=i)] diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 071d44be..82b638b3 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -16,9 +16,8 @@ from simplexity.generative_processes.builder import ( add_begin_of_sequence_token, build_chain_from_spec, - build_chain_process, - build_chain_process_from_spec, build_factored_process, + build_factored_process_from_spec, build_generalized_hidden_markov_model, build_hidden_markov_model, build_matrices_from_spec, @@ -26,17 +25,14 @@ build_nonergodic_initial_state, build_nonergodic_transition_matrices, build_symmetric_from_spec, - build_symmetric_process, - build_symmetric_process_from_spec, build_transition_coupled_from_spec, - build_transition_coupled_process, - build_transition_coupled_process_from_spec, build_transition_matrices, ) from simplexity.generative_processes.factored_generative_process import FactoredGenerativeProcess from simplexity.generative_processes.structures import ( ConditionalTransitions, FullyConditional, + IndependentStructure, SequentialConditional, ) from simplexity.generative_processes.transition_matrices import HMM_MATRIX_FUNCTIONS @@ -239,7 +235,9 @@ def test_build_nonergodic_hidden_markov_model_bos(): chex.assert_trees_all_close(hmm.initial_state, jnp.array([0, 0, 1.0])) -def _components_spec(): +@pytest.fixture +def components_spec(): + """Base specification for two HMM factors.""" return [ { "component_type": "hmm", @@ -255,17 +253,34 @@ def _components_spec(): ] -def _chain_spec(): - spec = _components_spec() - spec[1] = {**spec[1], "control_map": [0, 1]} - return spec +@pytest.fixture +def chain_spec(): + """Chain specification with control map on the second factor.""" + return [ + { + "component_type": "hmm", + "variants": [{"process_name": "coin", "process_params": {"p": 0.6}}], + }, + { + "component_type": "hmm", + "variants": [ + {"process_name": "coin", "process_params": {"p": 0.25}}, + {"process_name": "coin", "process_params": {"p": 0.75}}, + ], + "control_map": [0, 1], + }, + ] -def _symmetric_control_maps(): +@pytest.fixture +def symmetric_control_maps(): + """Simple symmetric control maps fixture.""" return [[0, 1], [1, 0]] -def _transition_coupled_inputs(): +@pytest.fixture +def transition_coupled_inputs(): + """Control maps and indices for transition-coupled topology.""" return ( [[0, 1], [1, 0]], [0, 1], @@ -273,10 +288,10 @@ def _transition_coupled_inputs(): ) -def test_build_matrices_from_spec_returns_consistent_arrays(): +def test_build_matrices_from_spec_returns_consistent_arrays(components_spec): """Factored specs should yield aligned parameter shapes.""" component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( - _components_spec() + components_spec ) assert component_types == ["hmm", "hmm"] assert transition_matrices[0].shape == (1, 2, 1, 1) @@ -286,7 +301,7 @@ def test_build_matrices_from_spec_returns_consistent_arrays(): chex.assert_trees_all_close(jnp.sum(state), jnp.array(1.0)) -def test_build_chain_from_spec_returns_control_maps(): +def test_build_chain_from_spec_returns_control_maps(chain_spec): """build_chain_from_spec should return encoded control maps.""" ( component_types, @@ -294,19 +309,19 @@ def test_build_chain_from_spec_returns_control_maps(): normalizing_eigenvectors, initial_states, control_maps, - ) = build_chain_from_spec(_chain_spec()) + ) = build_chain_from_spec(chain_spec) assert component_types == ["hmm", "hmm"] assert control_maps[0] is None chex.assert_trees_all_close(control_maps[1], jnp.array([0, 1], dtype=jnp.int32)) -def test_build_chain_from_spec_missing_control_map_raises(): +def test_build_chain_from_spec_missing_control_map_raises(components_spec): """Every non-root node in a chain must provide a control map.""" with pytest.raises(ValueError): - build_chain_from_spec(_components_spec()) + build_chain_from_spec(components_spec) -def test_build_symmetric_from_spec_validates_lengths(): +def test_build_symmetric_from_spec_validates_lengths(components_spec, symmetric_control_maps): """Symmetric control maps must cover every combination of other tokens.""" ( component_types, @@ -314,18 +329,18 @@ def test_build_symmetric_from_spec_validates_lengths(): normalizing_eigenvectors, initial_states, control_maps, - ) = build_symmetric_from_spec(_components_spec(), _symmetric_control_maps()) + ) = build_symmetric_from_spec(components_spec, symmetric_control_maps) assert component_types == ["hmm", "hmm"] assert len(control_maps) == 2 chex.assert_trees_all_close(control_maps[0], jnp.array([0, 1], dtype=jnp.int32)) with pytest.raises(ValueError): - build_symmetric_from_spec(_components_spec(), [[0], [0, 1]]) + build_symmetric_from_spec(components_spec, [[0], [0, 1]]) -def test_build_transition_coupled_from_spec_handles_emission_maps(): +def test_build_transition_coupled_from_spec_handles_emission_maps(components_spec, transition_coupled_inputs): """Transition-coupled specs should surface emission controls when provided.""" result = build_transition_coupled_from_spec( - _components_spec(), *_transition_coupled_inputs() + components_spec, *transition_coupled_inputs ) ( component_types, @@ -344,19 +359,31 @@ def test_build_transition_coupled_from_spec_handles_emission_maps(): chex.assert_trees_all_close(emission_control_maps[1], jnp.array([0, 1], dtype=jnp.int32)) -def test_build_chain_process_from_spec_returns_factored_process(): - """High-level chain builder should return a FactoredGenerativeProcess.""" - process = build_chain_process_from_spec(_chain_spec()) +def test_build_chain_process_from_spec_returns_factored_process(chain_spec): + """Unified builder with chain structure should return a FactoredGenerativeProcess.""" + process = build_factored_process_from_spec(structure_type="chain", spec=chain_spec) assert isinstance(process, FactoredGenerativeProcess) assert isinstance(process.structure, SequentialConditional) assert process.vocab_size == 4 -def test_build_factored_process_dispatches_topologies(): +def test_build_factored_process_dispatches_topologies( + chain_spec, components_spec, symmetric_control_maps, transition_coupled_inputs +): """build_factored_process should route to the appropriate topology builder.""" - chain_components = build_chain_from_spec(_chain_spec()) + independent_components = build_matrices_from_spec(components_spec) + independent_process = build_factored_process( + structure_type="independent", + component_types=independent_components[0], + transition_matrices=independent_components[1], + normalizing_eigenvectors=independent_components[2], + initial_states=independent_components[3], + ) + assert isinstance(independent_process.structure, IndependentStructure) + + chain_components = build_chain_from_spec(chain_spec) chain_process = build_factored_process( - topology_type="chain", + structure_type="chain", component_types=chain_components[0], transition_matrices=chain_components[1], normalizing_eigenvectors=chain_components[2], @@ -365,9 +392,9 @@ def test_build_factored_process_dispatches_topologies(): ) assert isinstance(chain_process.structure, SequentialConditional) - symmetric_components = build_symmetric_from_spec(_components_spec(), _symmetric_control_maps()) + symmetric_components = build_symmetric_from_spec(components_spec, symmetric_control_maps) symmetric_process = build_factored_process( - topology_type="symmetric", + structure_type="symmetric", component_types=symmetric_components[0], transition_matrices=symmetric_components[1], normalizing_eigenvectors=symmetric_components[2], @@ -377,10 +404,10 @@ def test_build_factored_process_dispatches_topologies(): assert isinstance(symmetric_process.structure, FullyConditional) transition_components = build_transition_coupled_from_spec( - _components_spec(), *_transition_coupled_inputs() + components_spec, *transition_coupled_inputs ) transition_process = build_factored_process( - topology_type="transition_coupled", + structure_type="transition_coupled", component_types=transition_components[0], transition_matrices=transition_components[1], normalizing_eigenvectors=transition_components[2], @@ -392,3 +419,164 @@ def test_build_factored_process_dispatches_topologies(): assert isinstance(transition_process.structure, ConditionalTransitions) assert transition_process.vocab_size == 4 assert transition_process.structure.use_emission_chain is True + + +def test_build_factored_process_from_spec_independent(components_spec): + """Test unified builder with independent structure.""" + process = build_factored_process_from_spec( + structure_type="independent", + spec=components_spec, + ) + assert isinstance(process, FactoredGenerativeProcess) + assert isinstance(process.structure, IndependentStructure) + assert process.vocab_size == 4 + + +def test_build_factored_process_from_spec_chain(chain_spec): + """Test unified builder with chain structure.""" + process = build_factored_process_from_spec( + structure_type="chain", + spec=chain_spec, + ) + assert isinstance(process, FactoredGenerativeProcess) + assert isinstance(process.structure, SequentialConditional) + assert process.vocab_size == 4 + + +def test_build_factored_process_from_spec_symmetric(components_spec, symmetric_control_maps): + """Test unified builder with symmetric structure.""" + process = build_factored_process_from_spec( + structure_type="symmetric", + spec=components_spec, + control_maps=symmetric_control_maps, + ) + assert isinstance(process, FactoredGenerativeProcess) + assert isinstance(process.structure, FullyConditional) + assert process.vocab_size == 4 + + +def test_build_factored_process_from_spec_transition_coupled(components_spec, transition_coupled_inputs): + """Test unified builder with transition_coupled structure.""" + control_maps_transition, emission_variant_indices, emission_control_maps = transition_coupled_inputs + process = build_factored_process_from_spec( + structure_type="transition_coupled", + spec=components_spec, + control_maps_transition=control_maps_transition, + emission_variant_indices=emission_variant_indices, + emission_control_maps=emission_control_maps, + ) + assert isinstance(process, FactoredGenerativeProcess) + assert isinstance(process.structure, ConditionalTransitions) + assert process.vocab_size == 4 + + +def test_build_factored_process_from_spec_invalid_structure_type(components_spec): + """Test unified builder rejects invalid structure_type.""" + with pytest.raises(ValueError, match="Unknown structure_type 'invalid'"): + build_factored_process_from_spec( + structure_type="invalid", + spec=components_spec, + ) + + +def test_build_factored_process_from_spec_symmetric_missing_control_maps(components_spec): + """Test unified builder validates required params for symmetric.""" + with pytest.raises(ValueError, match="symmetric structure requires 'control_maps' parameter"): + build_factored_process_from_spec( + structure_type="symmetric", + spec=components_spec, + ) + + +def test_build_factored_process_from_spec_transition_coupled_missing_params(components_spec): + """Test unified builder validates required params for transition_coupled.""" + with pytest.raises(ValueError, match="transition_coupled structure requires 'control_maps_transition' parameter"): + build_factored_process_from_spec( + structure_type="transition_coupled", + spec=components_spec, + ) + + with pytest.raises(ValueError, match="transition_coupled structure requires 'emission_variant_indices' parameter"): + build_factored_process_from_spec( + structure_type="transition_coupled", + spec=components_spec, + control_maps_transition=[[0, 1], [1, 0]], + ) + + +def test_build_factored_process_invalid_structure_type(components_spec): + """Test build_factored_process rejects invalid structure_type.""" + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + components_spec + ) + + with pytest.raises(ValueError, match="Unknown structure_type 'invalid'"): + build_factored_process( + structure_type="invalid", + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + ) + + +def test_build_factored_process_chain_missing_control_maps(components_spec): + """Test build_factored_process chain requires control_maps.""" + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + components_spec + ) + + with pytest.raises(ValueError, match="Missing required argument 'control_maps' for chain structure"): + build_factored_process( + structure_type="chain", + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + ) + + +def test_build_factored_process_symmetric_missing_control_maps(components_spec): + """Test build_factored_process symmetric requires control_maps.""" + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + components_spec + ) + + with pytest.raises(ValueError, match="Missing required argument 'control_maps' for symmetric structure"): + build_factored_process( + structure_type="symmetric", + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + ) + + +def test_build_factored_process_transition_coupled_missing_params(components_spec): + """Test build_factored_process transition_coupled requires all params.""" + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( + components_spec + ) + + with pytest.raises( + ValueError, match="Missing required argument 'control_maps_transition' for transition_coupled structure" + ): + build_factored_process( + structure_type="transition_coupled", + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + ) + + with pytest.raises( + ValueError, match="Missing required argument 'emission_variant_indices' for transition_coupled structure" + ): + build_factored_process( + structure_type="transition_coupled", + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + control_maps_transition=(jnp.array([0, 1]),), + ) diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py index 5db8e371..f40f5007 100644 --- a/tests/generative_processes/test_factored_generative_process.py +++ b/tests/generative_processes/test_factored_generative_process.py @@ -3,9 +3,15 @@ import chex import jax import jax.numpy as jnp +import pytest from simplexity.generative_processes.factored_generative_process import FactoredGenerativeProcess -from simplexity.generative_processes.structures import SequentialConditional +from simplexity.generative_processes.structures import ( + ConditionalTransitions, + FullyConditional, + IndependentStructure, + SequentialConditional, +) from simplexity.utils.factoring_utils import transition_with_obs @@ -14,7 +20,9 @@ def _tensor_from_probs(variant_probs): return arr[..., None, None] -def _build_simple_chain_process(): +@pytest.fixture +def simple_chain_process(): + """Simple two-factor chain with deterministic initial states.""" component_types = ("hmm", "hmm") transition_matrices = ( _tensor_from_probs([[0.6, 0.4]]), @@ -41,7 +49,9 @@ def _build_simple_chain_process(): ) -def _build_multistate_chain_process(): +@pytest.fixture +def multistate_chain_process(): + """Two-factor chain with multi-state beliefs for transition testing.""" component_types = ("hmm", "hmm") transition_matrices = ( jnp.array( @@ -88,9 +98,68 @@ def _build_multistate_chain_process(): ) -def test_factored_process_observation_and_sequence_probability(): +def _build_fully_conditional_process(): + component_types = ("hmm", "hmm") + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.1, 0.9]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + normalizing_eigenvectors = ( + jnp.ones((2, 1), dtype=jnp.float32), + jnp.ones((2, 1), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + structure = FullyConditional( + control_maps=(jnp.array([0, 1], dtype=jnp.int32), jnp.array([1, 0], dtype=jnp.int32)), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def _build_transition_coupled_process(*, emission_control_maps, emission_variant_indices): + component_types = ("hmm", "hmm") + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.2, 0.8]]), + _tensor_from_probs([[0.5, 0.5], [0.1, 0.9]]), + ) + normalizing_eigenvectors = ( + jnp.ones((2, 1), dtype=jnp.float32), + jnp.ones((2, 1), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + structure = ConditionalTransitions( + control_maps_transition=( + jnp.array([0, 0], dtype=jnp.int32), + jnp.array([1, 0], dtype=jnp.int32), + ), + emission_variant_indices=emission_variant_indices, + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + emission_control_maps=emission_control_maps, + ) + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def test_factored_process_observation_and_sequence_probability(simple_chain_process): """Observation, log-observation, and sequence probability APIs should agree.""" - process = _build_simple_chain_process() + process = simple_chain_process state = process.initial_state dist = process.observation_probability_distribution(state) @@ -111,9 +180,9 @@ def test_factored_process_observation_and_sequence_probability(): chex.assert_trees_all_close(log_prob, jnp.log(expected_prob)) -def test_transition_states_uses_structure_selected_variants(): +def test_transition_states_uses_structure_selected_variants(multistate_chain_process): """transition_states should respect variant selection from the structure.""" - process = _build_multistate_chain_process() + process = multistate_chain_process state = process.initial_state obs_token = jnp.array(2, dtype=jnp.int32) # (factor0=1, factor1=0) @@ -141,9 +210,242 @@ def test_transition_states_uses_structure_selected_variants(): chex.assert_trees_all_close(new_state[1], expected1) -def test_emit_observation_respects_vocab_size(): +def test_emit_observation_respects_vocab_size(simple_chain_process): """Samples from emit_observation should be in the encoded vocabulary.""" - process = _build_simple_chain_process() - token = process.emit_observation(process.initial_state, jax.random.PRNGKey(0)) + token = simple_chain_process.emit_observation(simple_chain_process.initial_state, jax.random.PRNGKey(0)) assert token.shape == () - assert 0 <= int(token) < process.vocab_size + assert 0 <= int(token) < simple_chain_process.vocab_size + + +def test_fully_conditional_process_observation_distribution(): + """Fully conditional topology should yield expected product-of-experts distribution.""" + process = _build_fully_conditional_process() + dist = process.observation_probability_distribution(process.initial_state) + expected = jnp.array([0.16, 0.10666667, 0.37333333, 0.36], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + +def test_fully_conditional_transition_variants_follow_control_maps(): + """transition_states should honor fully-conditional variant selection.""" + component_types = ("hmm", "hmm") + transition_matrices = ( + jnp.array( + [ + [ + [[0.8, 0.2], [0.0, 0.0]], + [[0.2, 0.8], [0.0, 0.0]], + ], + [ + [[0.1, 0.9], [0.0, 0.0]], + [[0.9, 0.1], [0.0, 0.0]], + ], + ], + dtype=jnp.float32, + ), + jnp.array( + [ + [ + [[0.3, 0.7], [0.0, 0.0]], + [[0.6, 0.4], [0.0, 0.0]], + ], + [ + [[0.4, 0.6], [0.0, 0.0]], + [[0.7, 0.3], [0.0, 0.0]], + ], + ], + dtype=jnp.float32, + ), + ) + normalizing_eigenvectors = ( + jnp.ones((2, 2), dtype=jnp.float32), + jnp.ones((2, 2), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([0.6, 0.4], dtype=jnp.float32), + jnp.array([0.5, 0.5], dtype=jnp.float32), + ) + structure = FullyConditional( + control_maps=(jnp.array([0, 1], dtype=jnp.int32), jnp.array([1, 0], dtype=jnp.int32)), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + obs_tuple = (jnp.array(0, dtype=jnp.int32), jnp.array(1, dtype=jnp.int32)) + obs_token = process.encoder.tuple_to_token(obs_tuple) + context = process._make_context(initial_states) + variants = process.structure.select_variants(obs_tuple, context) + new_state = process.transition_states(initial_states, obs_token) + expected = ( + transition_with_obs("hmm", initial_states[0], transition_matrices[0][int(variants[0])], obs_tuple[0], None), + transition_with_obs("hmm", initial_states[1], transition_matrices[1][int(variants[1])], obs_tuple[1], None), + ) + chex.assert_trees_all_close(new_state[0], expected[0]) + chex.assert_trees_all_close(new_state[1], expected[1]) + + +def test_transition_coupled_independent_emissions_distribution(): + """ConditionalTransitions with fixed emission variants should factorize emissions.""" + process = _build_transition_coupled_process( + emission_control_maps=None, + emission_variant_indices=jnp.array([1, 0], dtype=jnp.int32), + ) + dist = process.observation_probability_distribution(process.initial_state) + expected = jnp.array([0.1, 0.1, 0.4, 0.4], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + +def test_transition_coupled_sequential_emissions_distribution(): + """ConditionalTransitions with emission control maps should follow sequential chain.""" + process = _build_transition_coupled_process( + emission_control_maps=(None, jnp.array([1, 0], dtype=jnp.int32)), + emission_variant_indices=jnp.array([0, 0], dtype=jnp.int32), + ) + dist = process.observation_probability_distribution(process.initial_state) + expected = jnp.array([0.06, 0.54, 0.20, 0.20], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + +def test_transition_coupled_transition_variants_follow_control_maps(): + """Transition variants should depend on other-factor tokens as configured.""" + component_types = ("hmm", "hmm") + transition_matrices = ( + jnp.array( + [ + [ + [[0.7, 0.3], [0.2, 0.8]], + [[0.5, 0.5], [0.4, 0.6]], + ], + [ + [[0.4, 0.6], [0.3, 0.7]], + [[0.2, 0.8], [0.6, 0.4]], + ], + ], + dtype=jnp.float32, + ), + jnp.array( + [ + [ + [[0.6, 0.4], [0.1, 0.9]], + [[0.3, 0.7], [0.8, 0.2]], + ], + [ + [[0.9, 0.1], [0.2, 0.8]], + [[0.5, 0.5], [0.7, 0.3]], + ], + ], + dtype=jnp.float32, + ), + ) + normalizing_eigenvectors = ( + jnp.ones((2, 2), dtype=jnp.float32), + jnp.ones((2, 2), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([0.8, 0.2], dtype=jnp.float32), + jnp.array([0.3, 0.7], dtype=jnp.float32), + ) + structure = ConditionalTransitions( + control_maps_transition=( + jnp.array([0, 1], dtype=jnp.int32), + jnp.array([1, 0], dtype=jnp.int32), + ), + emission_variant_indices=jnp.array([0, 1], dtype=jnp.int32), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + obs_tuple = (jnp.array(1, dtype=jnp.int32), jnp.array(0, dtype=jnp.int32)) + obs_token = process.encoder.tuple_to_token(obs_tuple) + context = process._make_context(initial_states) + variants = process.structure.select_variants(obs_tuple, context) + new_state = process.transition_states(initial_states, obs_token) + expected = ( + transition_with_obs("hmm", initial_states[0], transition_matrices[0][int(variants[0])], obs_tuple[0], None), + transition_with_obs("hmm", initial_states[1], transition_matrices[1][int(variants[1])], obs_tuple[1], None), + ) + chex.assert_trees_all_close(new_state[0], expected[0]) + chex.assert_trees_all_close(new_state[1], expected[1]) + + + +def test_independent_structure_observation_distribution(): + """IndependentStructure should produce product of independent factor distributions.""" + component_types = ("hmm", "hmm") + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.7, 0.3]]), + ) + normalizing_eigenvectors = ( + jnp.ones((1, 1), dtype=jnp.float32), + jnp.ones((1, 1), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + structure = IndependentStructure() + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + dist = process.observation_probability_distribution(process.initial_state) + expected = jnp.array([0.42, 0.18, 0.28, 0.12], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + +def test_independent_structure_always_uses_variant_zero(): + """IndependentStructure should always select variant 0 for all factors.""" + component_types = ("hmm", "hmm") + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.1, 0.9]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + normalizing_eigenvectors = ( + jnp.ones((2, 1), dtype=jnp.float32), + jnp.ones((2, 1), dtype=jnp.float32), + ) + initial_states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + structure = IndependentStructure() + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + obs_tuple = (jnp.array(0, dtype=jnp.int32), jnp.array(1, dtype=jnp.int32)) + obs_token = process.encoder.tuple_to_token(obs_tuple) + context = process._make_context(initial_states) + variants = process.structure.select_variants(obs_tuple, context) + chex.assert_trees_all_equal(variants, (jnp.array(0), jnp.array(0))) + new_state = process.transition_states(initial_states, obs_token) + expected = ( + transition_with_obs("hmm", initial_states[0], transition_matrices[0][0], obs_tuple[0], None), + transition_with_obs("hmm", initial_states[1], transition_matrices[1][0], obs_tuple[1], None), + ) + chex.assert_trees_all_close(new_state[0], expected[0]) + chex.assert_trees_all_close(new_state[1], expected[1]) + + +def test_independent_structure_get_required_params(): + """IndependentStructure should have no required params.""" + structure = IndependentStructure() + required_params = structure.get_required_params() + assert required_params == {} + diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py index 6a255989..4ef3b52f 100644 --- a/tests/generative_processes/test_factored_structures.py +++ b/tests/generative_processes/test_factored_structures.py @@ -3,13 +3,14 @@ import chex import jax.numpy as jnp -from simplexity.generative_processes.factored_generative_process import ComponentType from simplexity.generative_processes.structures import ( ConditionalTransitions, FullyConditional, + IndependentStructure, SequentialConditional, ) from simplexity.generative_processes.structures.protocol import ConditionalContext +from simplexity.utils.factoring_utils import ComponentType def _tensor_from_probs(variant_probs): @@ -19,7 +20,7 @@ def _tensor_from_probs(variant_probs): def _make_context(states, transition_matrices): - """Helper to build a ConditionalContext for HMM components.""" + """Helper building a ConditionalContext for HMM components.""" component_types: tuple[ComponentType, ...] = tuple("hmm" for _ in states) normalizing_eigenvectors = tuple( jnp.ones((tm.shape[0], tm.shape[-1]), dtype=jnp.float32) for tm in transition_matrices @@ -135,3 +136,65 @@ def test_conditional_transitions_with_sequential_emissions(): dist = structure.compute_joint_distribution(context) expected = jnp.array([0.18, 0.42, 0.36, 0.04], dtype=jnp.float32) chex.assert_trees_all_close(dist, expected) + + +def test_independent_structure_joint_distribution(): + """IndependentStructure should compute product of independent marginals.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.7, 0.3]]), + ) + context = _make_context(states, transition_matrices) + structure = IndependentStructure() + + dist = structure.compute_joint_distribution(context) + expected = jnp.array([0.42, 0.18, 0.28, 0.12], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + +def test_independent_structure_select_variants_always_zero(): + """IndependentStructure should always select variant 0 for all factors.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.1, 0.9]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + context = _make_context(states, transition_matrices) + structure = IndependentStructure() + + variants = structure.select_variants( + (jnp.array(1, dtype=jnp.int32), jnp.array(0, dtype=jnp.int32)), + context, + ) + chex.assert_trees_all_close(variants[0], jnp.array(0, dtype=jnp.int32)) + chex.assert_trees_all_close(variants[1], jnp.array(0, dtype=jnp.int32)) + + +def test_independent_structure_get_required_params(): + """IndependentStructure should have no required params.""" + structure = IndependentStructure() + required_params = structure.get_required_params() + assert required_params == {} + + +def test_independent_structure_with_three_factors(): + """IndependentStructure should handle three or more factors.""" + states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + transition_matrices = ( + _tensor_from_probs([[0.5, 0.5]]), + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.7, 0.3]]), + ) + context = _make_context(states, transition_matrices) + structure = IndependentStructure() + + dist = structure.compute_joint_distribution(context) + # Joint = P(t0) * P(t1) * P(t2) + # For (t0, t1, t2): 0.5 * 0.6 * 0.7 = 0.21 (for 000), etc. + expected = jnp.array([0.21, 0.09, 0.14, 0.06, 0.21, 0.09, 0.14, 0.06], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected, atol=1e-6) diff --git a/tests/structured_configs/test_generative_process_config.py b/tests/structured_configs/test_generative_process_config.py index 64589484..8623a691 100644 --- a/tests/structured_configs/test_generative_process_config.py +++ b/tests/structured_configs/test_generative_process_config.py @@ -247,8 +247,6 @@ def test_generative_process_config(self) -> None: assert cfg.get("eos_token") is None assert cfg.get("vocab_size") == 3 -<<<<<<< HEAD:tests/run_management/test_generative_process_config.py -======= def test_validate_generative_process_config_handles_generalized_builder(self) -> None: cfg = DictConfig( { @@ -328,7 +326,6 @@ def test_validate_generative_process_config_handles_hidden_instance(self) -> Non cfg = _with_missing_tokens(cfg) validate_generative_process_config(cfg) ->>>>>>> origin/main:tests/structured_configs/test_generative_process_config.py def test_is_generative_process_target_valid(self) -> None: """Test is_generative_process_target with valid generative process targets.""" assert is_generative_process_target("simplexity.generative_processes.hidden_markov_model.HiddenMarkovModel") @@ -858,3 +855,147 @@ def test_resolve_generative_process_config_with_invalid_values(self): match=re.escape("GenerativeProcessConfig.vocab_size (4) must be equal to 3"), ): resolve_generative_process_config(cfg, base_vocab_size=3) + + +class TestFactoredProcessBuilders: + """Tests for factored generative process builder configs.""" + + def test_factored_process_from_spec_config_detection(self) -> None: + """Test build_factored_process_from_spec config detection (unified builder).""" + target = "simplexity.generative_processes.builder.build_factored_process_from_spec" + + # Test independent + cfg = DictConfig( + { + "_target_": target, + "structure_type": "independent", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + } + ) + assert is_generative_process_target(target) + assert is_generative_process_config(cfg) + + # Test chain + cfg = DictConfig( + { + "_target_": target, + "structure_type": "chain", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + } + ) + assert is_generative_process_target(target) + assert is_generative_process_config(cfg) + + # Test symmetric + cfg = DictConfig( + { + "_target_": target, + "structure_type": "symmetric", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + "control_maps": [[0]], + } + ) + assert is_generative_process_target(target) + assert is_generative_process_config(cfg) + + # Test transition_coupled + cfg = DictConfig( + { + "_target_": target, + "structure_type": "transition_coupled", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + "control_maps_transition": [[0]], + "emission_variant_indices": [0], + } + ) + assert is_generative_process_target(target) + assert is_generative_process_config(cfg) + + def test_validate_generative_process_config_handles_factored_process_builders(self) -> None: + """Test validate_generative_process_config works with unified factored process builder.""" + # Independent + cfg = DictConfig( + { + "instance": DictConfig( + { + "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", + "structure_type": "independent", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + } + ), + "base_vocab_size": MISSING, + "vocab_size": MISSING, + } + ) + cfg = _with_missing_tokens(cfg) + validate_generative_process_config(cfg) + + # Chain + cfg = DictConfig( + { + "instance": DictConfig( + { + "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", + "structure_type": "chain", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + } + ), + "base_vocab_size": MISSING, + "vocab_size": MISSING, + } + ) + cfg = _with_missing_tokens(cfg) + validate_generative_process_config(cfg) + + # Symmetric + cfg = DictConfig( + { + "instance": DictConfig( + { + "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", + "structure_type": "symmetric", + "spec": [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ], + "control_maps": [[0]], + } + ), + "base_vocab_size": MISSING, + "vocab_size": MISSING, + } + ) + cfg = _with_missing_tokens(cfg) + validate_generative_process_config(cfg) From 46af230c1982ce7010cbdd17089bd2b3db3e2461 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 07:25:35 -0800 Subject: [PATCH 10/37] update after merge --- .../configs/activation_tracker/default.yaml | 20 +++ tests/persistence/test_mlflow_persister.py | 119 ------------------ .../structured_configs/test_mlflow_config.py | 29 ----- .../test_predictive_model_config.py | 40 ------ tests/utils/test_jnp_utils.py | 10 +- 5 files changed, 21 insertions(+), 197 deletions(-) create mode 100644 examples/configs/activation_tracker/default.yaml diff --git a/examples/configs/activation_tracker/default.yaml b/examples/configs/activation_tracker/default.yaml new file mode 100644 index 00000000..5158ba6d --- /dev/null +++ b/examples/configs/activation_tracker/default.yaml @@ -0,0 +1,20 @@ +name: default_tracker +instance: + _target_: simplexity.activations.activation_tracker.ActivationTracker + analyses: + pca_all_tokens: + instance: + _target_: simplexity.activations.activation_analyses.PCAAnalysis + n_components: 20 + last_token_only: false + concat_layers: false + use_probs_as_weights: true + variance_thresholds: [0.80, 0.90, 0.95, 0.99] + + regression: + instance: + _target_: simplexity.activations.activation_analyses.LinearRegressionSVDAnalysis + last_token_only: false + concat_layers: true + use_probs_as_weights: true + rcond_values: [1e-15, 1e-10, 1e-8, 1e-6, 1e-4, 1e-2] \ No newline at end of file diff --git a/tests/persistence/test_mlflow_persister.py b/tests/persistence/test_mlflow_persister.py index 40bf3499..f44d1a2a 100644 --- a/tests/persistence/test_mlflow_persister.py +++ b/tests/persistence/test_mlflow_persister.py @@ -13,10 +13,7 @@ import pytest import torch import yaml -<<<<<<< HEAD -======= from mlflow.models import infer_signature ->>>>>>> origin/main from torch.nn import Linear, Module from simplexity.persistence.mlflow_persister import MLFlowPersister @@ -24,28 +21,6 @@ from simplexity.utils.mlflow_utils import set_mlflow_uris -<<<<<<< HEAD -def get_pytorch_model(seed: int) -> Linear: - """Build a small deterministic PyTorch model for serialization tests.""" - torch.manual_seed(seed) - return Linear(in_features=4, out_features=2) - - -def get_hydra_config_for_pytorch_model() -> dict: - """Get a Hydra config dict for the given PyTorch model.""" - return { - "predictive_model": { - "instance": { - "_target_": "torch.nn.Linear", - "in_features": 4, - "out_features": 2, - } - } - } - - -def pytorch_models_equal(model1: Module, model2: Module) -> bool: -======= def _get_artifacts_root(persister: MLFlowPersister) -> Path: """Get the artifacts root directory for the given persister.""" assert persister.tracking_uri is not None @@ -56,7 +31,6 @@ def _get_artifacts_root(persister: MLFlowPersister) -> Path: def _pytorch_models_equal(model1: Module, model2: Module) -> bool: ->>>>>>> origin/main """Check if two PyTorch models have identical parameters.""" params1 = dict(model1.named_parameters()) params2 = dict(model2.named_parameters()) @@ -168,104 +142,11 @@ def test_round_trip_from_config(persister: MLFlowPersister, framework: ModelFram assert _models_equal(loaded, original) -<<<<<<< HEAD -def test_mlflow_persister_cleanup(tmp_path: Path) -> None: - """Test MLflow persister cleanup.""" - artifact_dir = tmp_path / "mlruns" - artifact_dir.mkdir() - - persister = MLFlowPersister( - experiment_name="cleanup", - run_name="cleanup-run", - tracking_uri=artifact_dir.as_uri(), - artifact_path="models", - ) - - def run_status() -> str: - """Get the status of the run.""" - client = persister.client - run_id = persister.run_id - run = client.get_run(run_id) - return run.info.status - - assert run_status() == "RUNNING" - - model = get_model(0) - persister.save_weights(model, step=0) - local_persister = persister.get_local_persister(model) - assert local_persister.directory.exists() - - persister.cleanup() - assert run_status() == "FINISHED" - assert not local_persister.directory.exists() - - -def test_mlflow_persister_pytorch_round_trip(tmp_path: Path) -> None: - """PyTorch model weights saved via MLflow can be restored back into a new instance.""" - artifact_dir = tmp_path / "mlruns" - artifact_dir.mkdir() - - persister = MLFlowPersister( - experiment_name="pytorch-round-trip", - run_name="pytorch-round-trip-run", - tracking_uri=artifact_dir.as_uri(), - artifact_path="models", - ) - - original = get_pytorch_model(0) - persister.save_weights(original, step=0) - - # MLflow stores artifacts in experiment_id/run_id/artifacts/artifact_path/step/ - experiment_id = persister.experiment_id - run_id = persister.run_id - remote_model_path = artifact_dir / experiment_id / run_id / "artifacts" / "models" / "0" / "model.pt" - assert remote_model_path.exists() - - updated = get_pytorch_model(1) - loaded = persister.load_weights(updated, step=0) - - # Type assertion since we know loaded is a PyTorch model - assert pytorch_models_equal(loaded, original) # type: ignore[arg-type] - - -def test_mlflow_persister_pytorch_round_trip_from_config(tmp_path: Path) -> None: - """PyTorch model weights saved via MLflow can be restored back into a new instance via the config.""" - artifact_dir = tmp_path / "mlruns" - artifact_dir.mkdir() - - persister = MLFlowPersister( - experiment_name="pytorch-round-trip", - run_name="pytorch-round-trip-run", - tracking_uri=artifact_dir.as_uri(), - artifact_path="models", - ) - - original = get_pytorch_model(0) - persister.save_weights(original, step=0) - - # MLflow stores artifacts in experiment_id/run_id/artifacts/artifact_path/step/ - experiment_id = persister.experiment_id - run_id = persister.run_id - remote_model_path = artifact_dir / experiment_id / run_id / "artifacts" / "models" / "0" / "model.pt" - assert remote_model_path.exists() - - # New function expects a config to live at experiment_id/run_id/artifacts/config_path - config_path = artifact_dir / experiment_id / run_id / "artifacts" / "config.yaml" - with open(config_path, "w", encoding="utf-8") as f: - yaml.dump(get_hydra_config_for_pytorch_model(), f) - - loaded = persister.load_model(step=0) - assert pytorch_models_equal(loaded, original) # type: ignore[arg-type] - - -def test_mlflow_persister_pytorch_cleanup(tmp_path: Path) -> None: -======= @pytest.mark.parametrize( "framework", [ModelFramework.PYTORCH, ModelFramework.EQUINOX], ) def test_cleanup(tmp_path: Path, framework: ModelFramework) -> None: ->>>>>>> origin/main """Test PyTorch model cleanup with MLflow persister.""" artifact_dir = tmp_path / "mlruns" artifact_dir.mkdir() diff --git a/tests/structured_configs/test_mlflow_config.py b/tests/structured_configs/test_mlflow_config.py index c5766ff3..1475d8b8 100644 --- a/tests/structured_configs/test_mlflow_config.py +++ b/tests/structured_configs/test_mlflow_config.py @@ -54,35 +54,6 @@ def test_validate_mlflow_config_valid(self) -> None: ) validate_mlflow_config(cfg) -<<<<<<< HEAD:tests/run_management/test_mlflow_config.py - def test_validate_mlflow_config_missing_required_fields(self) -> None: - """Test validate_mlflow_config with missing required fields.""" - # Missing experiment_name - cfg = DictConfig( - { - "run_name": "my_run", - "tracking_uri": "databricks", - "registry_uri": "databricks", - "downgrade_unity_catalog": True, - } - ) - with pytest.raises(ConfigValidationError, match="MLFlowConfig.experiment_name must be a non-empty string"): - validate_mlflow_config(cfg) - - # Missing run_name - cfg = DictConfig( - { - "experiment_name": "my_experiment", - "tracking_uri": "databricks", - "registry_uri": "databricks", - "downgrade_unity_catalog": True, - } - ) - with pytest.raises(ConfigValidationError, match="MLFlowConfig.run_name must be a non-empty string"): - validate_mlflow_config(cfg) - -======= ->>>>>>> origin/main:tests/structured_configs/test_mlflow_config.py def test_validate_mlflow_config_invalid_downgrade_unity_catalog(self) -> None: """Test validate_mlflow_config with invalid downgrade_unity_catalog.""" cfg = DictConfig( diff --git a/tests/structured_configs/test_predictive_model_config.py b/tests/structured_configs/test_predictive_model_config.py index 210dc7ad..e9a95ffa 100644 --- a/tests/structured_configs/test_predictive_model_config.py +++ b/tests/structured_configs/test_predictive_model_config.py @@ -30,15 +30,8 @@ ) -<<<<<<< HEAD:tests/run_management/test_predictive_model_config.py -class TestHookedTransformerConfig: - """Test HookedTransformerConfig.""" - - """Test PredictiveModelConfig.""" -======= class TestHookedTransformerConfig: # pylint: disable=too-many-public-methods """Test HookedTransformerConfig.""" ->>>>>>> origin/main:tests/structured_configs/test_predictive_model_config.py def test_hooked_transformer_config(self) -> None: """Test creating hooked transformer config from dataclass.""" @@ -298,10 +291,7 @@ def test_resolve_hooked_transformer_config_without_kwargs(self) -> None: assert cfg.get("device") == "cpu" def test_resolve_hooked_transformer_config_with_complete_values(self) -> None: -<<<<<<< HEAD:tests/run_management/test_predictive_model_config.py -======= """Test resolve_hooked_transformer_config with complete values.""" ->>>>>>> origin/main:tests/structured_configs/test_predictive_model_config.py cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -329,10 +319,7 @@ def test_resolve_hooked_transformer_config_with_complete_values(self) -> None: assert cfg.get("device") == "cuda" def test_resolve_hooked_transformer_config_with_missing_values(self) -> None: -<<<<<<< HEAD:tests/run_management/test_predictive_model_config.py -======= """Test resolve_hooked_transformer_config with missing values.""" ->>>>>>> origin/main:tests/structured_configs/test_predictive_model_config.py cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -360,10 +347,7 @@ def test_resolve_hooked_transformer_config_with_missing_values(self) -> None: assert cfg.get("device") == "cuda" def test_resolve_hooked_transformer_config_with_invalid_values(self) -> None: -<<<<<<< HEAD:tests/run_management/test_predictive_model_config.py -======= """Test resolve_hooked_transformer_config with invalid values.""" ->>>>>>> origin/main:tests/structured_configs/test_predictive_model_config.py cfg = DictConfig( { "_target_": "transformer_lens.HookedTransformerConfig", @@ -388,28 +372,7 @@ def test_resolve_hooked_transformer_config_with_conflicting_device(self) -> None "n_layers": 2, "d_model": 128, "d_head": 32, -<<<<<<< HEAD:tests/run_management/test_predictive_model_config.py - "n_ctx": 8, - "n_heads": -1, - "d_vocab": MISSING, - } - ) - with pytest.raises( - ConfigValidationError, match=re.escape("HookedTransformerConfig.n_ctx (8) must be equal to 16") - ): - resolve_hooked_transformer_config(cfg, bos_token=3, sequence_len=16) - - def test_resolve_hooked_transformer_config_with_conflicting_device(self) -> None: - cfg = DictConfig( - { - "_target_": "transformer_lens.HookedTransformerConfig", - "n_layers": 2, - "d_model": 128, - "d_head": 32, - "n_ctx": MISSING, -======= "n_ctx": 256, ->>>>>>> origin/main:tests/structured_configs/test_predictive_model_config.py "n_heads": -1, "d_vocab": MISSING, "device": "cuda", @@ -433,8 +396,6 @@ def test_resolve_hooked_transformer_config_with_conflicting_device(self) -> None ) assert cfg.get("device") == "cpu" -<<<<<<< HEAD:tests/run_management/test_predictive_model_config.py -======= def test_resolve_hooked_transformer_config_device_mismatch_updates_cfg(self) -> None: """Test resolve_hooked_transformer_config device mismatch updates config.""" cfg = DictConfig( @@ -479,7 +440,6 @@ def test_resolve_hooked_transformer_config_device_auto(self) -> None: mock_info.assert_any_call("[predictive model] device resolved to: %s", "mps") assert cfg.get("device") == "mps" ->>>>>>> origin/main:tests/structured_configs/test_predictive_model_config.py def test_is_predictive_model_target_valid(self) -> None: """Test is_predictive_model_target with valid model targets.""" assert is_predictive_model_target("transformer_lens.HookedTransformer") diff --git a/tests/utils/test_jnp_utils.py b/tests/utils/test_jnp_utils.py index a424f986..b957e246 100644 --- a/tests/utils/test_jnp_utils.py +++ b/tests/utils/test_jnp_utils.py @@ -1,12 +1,5 @@ """Tests for JAX NumPy utilities.""" -<<<<<<< HEAD -import chex -import jax -import jax.numpy as jnp - -from simplexity.utils.jnp_utils import log_matmul, signed_logsumexp -======= from unittest.mock import create_autospec, patch import chex @@ -126,7 +119,6 @@ def test_resolve_jax_device_invalid_backend(): pytest.raises(DeviceResolutionError, match="Unknown device specification: invalid"), ): resolve_jax_device("invalid") ->>>>>>> origin/main def test_log_matmul(): @@ -168,4 +160,4 @@ def test_signed_logsumexp(): expected_signs = jnp.array([1, -1, 1], dtype=jnp.int32) chex.assert_trees_all_close(actual_log_abs_values, expected_log_abs_values) - chex.assert_trees_all_close(actual_signs, expected_signs) + chex.assert_trees_all_close(actual_signs, expected_signs) \ No newline at end of file From d62fd5dfea25e5113b95fbc05275b7d8d376d7f9 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 07:48:54 -0800 Subject: [PATCH 11/37] static analysis --- simplexity/generative_processes/builder.py | 1 - simplexity/utils/factoring_utils.py | 1 + tests/generative_processes/test_builder.py | 8 +- .../test_factored_generative_process.py | 2 - tests/run_management/test_base_config.py | 83 ------------------- tests/run_management/test_instance_config.py | 49 ----------- .../test_generative_process_config.py | 2 +- tests/utils/test_jnp_utils.py | 2 +- 8 files changed, 5 insertions(+), 143 deletions(-) delete mode 100644 tests/run_management/test_base_config.py delete mode 100644 tests/run_management/test_instance_config.py diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 6834634d..7c7c5a55 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -398,7 +398,6 @@ def build_matrices_from_spec( for v in variants ] - # Validate dimensions vocab_sizes = [b.vocab_size for b in built] num_states = [b.num_states if hasattr(b, "num_states") else b.transition_matrices.shape[1] for b in built] diff --git a/simplexity/utils/factoring_utils.py b/simplexity/utils/factoring_utils.py index 440a7fc0..91cece30 100644 --- a/simplexity/utils/factoring_utils.py +++ b/simplexity/utils/factoring_utils.py @@ -76,6 +76,7 @@ def transition_with_obs( raise ValueError("GHMM requires normalizing_eigenvector") return new_state / (new_state @ normalizing_eigenvector) + """Token encoding utilities for factored observations. Handles conversion between composite tokens and per-factor token tuples diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 82b638b3..eb0c1322 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -339,9 +339,7 @@ def test_build_symmetric_from_spec_validates_lengths(components_spec, symmetric_ def test_build_transition_coupled_from_spec_handles_emission_maps(components_spec, transition_coupled_inputs): """Transition-coupled specs should surface emission controls when provided.""" - result = build_transition_coupled_from_spec( - components_spec, *transition_coupled_inputs - ) + result = build_transition_coupled_from_spec(components_spec, *transition_coupled_inputs) ( component_types, transition_matrices, @@ -403,9 +401,7 @@ def test_build_factored_process_dispatches_topologies( ) assert isinstance(symmetric_process.structure, FullyConditional) - transition_components = build_transition_coupled_from_spec( - components_spec, *transition_coupled_inputs - ) + transition_components = build_transition_coupled_from_spec(components_spec, *transition_coupled_inputs) transition_process = build_factored_process( structure_type="transition_coupled", component_types=transition_components[0], diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py index f40f5007..310d80ec 100644 --- a/tests/generative_processes/test_factored_generative_process.py +++ b/tests/generative_processes/test_factored_generative_process.py @@ -377,7 +377,6 @@ def test_transition_coupled_transition_variants_follow_control_maps(): chex.assert_trees_all_close(new_state[1], expected[1]) - def test_independent_structure_observation_distribution(): """IndependentStructure should produce product of independent factor distributions.""" component_types = ("hmm", "hmm") @@ -448,4 +447,3 @@ def test_independent_structure_get_required_params(): structure = IndependentStructure() required_params = structure.get_required_params() assert required_params == {} - diff --git a/tests/run_management/test_base_config.py b/tests/run_management/test_base_config.py deleted file mode 100644 index c55d90c0..00000000 --- a/tests/run_management/test_base_config.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Tests for BaseConfig validation. - -This module contains tests for the base configuration validation functionality, -including validation of seed, tags, and MLFlow configuration fields. -""" - -# pylint: disable-all -# Temporarily disable all pylint checkers during AST traversal to prevent crash. -# The imports checker crashes when resolving simplexity package imports due to a bug -# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 -# pylint: enable=all -# Re-enable all pylint checkers for the checking phase. This allows other checks -# (code quality, style, undefined names, etc.) to run normally while bypassing -# the problematic imports checker that would crash during AST traversal. - -import pytest -from omegaconf import DictConfig - -from simplexity.exceptions import ConfigValidationError -from simplexity.run_management.structured_configs import validate_base_config - - -class TestBaseConfig: - """Test BaseConfig.""" - - def test_validate_base_config_valid(self) -> None: - """Test validate_base_config with valid configs.""" - cfg = DictConfig({}) - validate_base_config(cfg) - - cfg = DictConfig( - { - "seed": 42, - "tags": DictConfig({"key": "value"}), - "mlflow": DictConfig({"experiment_name": "test", "run_name": "test"}), - } - ) - validate_base_config(cfg) - - def test_validate_base_config_invalid_seed(self) -> None: - """Test validate_base_config with invalid configs.""" - # Non-integer seed - cfg = DictConfig({"seed": "42"}) - with pytest.raises(ConfigValidationError, match="BaseConfig.seed must be an int or None"): - validate_base_config(cfg) - - cfg = DictConfig({"seed": False}) - with pytest.raises(ConfigValidationError, match="BaseConfig.seed must be an int or None"): - validate_base_config(cfg) - - # Negative seed - cfg = DictConfig({"seed": -1}) - with pytest.raises(ConfigValidationError, match="BaseConfig.seed must be non-negative"): - validate_base_config(cfg) - - def test_validate_base_config_invalid_tags(self) -> None: - """Test validate_base_config with invalid tags.""" - # Non-dictionary tags - cfg = DictConfig({"tags": "not a dictionary"}) - with pytest.raises(ConfigValidationError, match="BaseConfig.tags must be a dictionary"): - validate_base_config(cfg) - - # Tags with non-string keys - cfg = DictConfig({"tags": {123: "value"}}) - with pytest.raises(ConfigValidationError, match="BaseConfig.tags keys must be strings"): - validate_base_config(cfg) - - # Tags with non-string values - cfg = DictConfig({"tags": {"key": 123}}) - with pytest.raises(ConfigValidationError, match="BaseConfig.tags values must be strings"): - validate_base_config(cfg) - - def test_validate_base_config_invalid_mlflow(self) -> None: - """Test validate_base_config with invalid mlflow.""" - # Non-MLFlowConfig mlflow - cfg = DictConfig({"mlflow": "not an MLFlowConfig"}) - with pytest.raises(ConfigValidationError, match="BaseConfig.mlflow must be a MLFlowConfig"): - validate_base_config(cfg) - - # MLFlowConfig with missing experiment_name - cfg = DictConfig({"mlflow": DictConfig({"run_name": "test"})}) - with pytest.raises(ConfigValidationError, match="MLFlowConfig.experiment_name must be a non-empty string"): - validate_base_config(cfg) diff --git a/tests/run_management/test_instance_config.py b/tests/run_management/test_instance_config.py deleted file mode 100644 index 65cf2016..00000000 --- a/tests/run_management/test_instance_config.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Tests for InstanceConfig. - -This module contains tests for the InstanceConfig dataclass, including -creation from dataclass and derived config classes. -""" - -# pylint: disable-all -# Temporarily disable all pylint checkers during AST traversal to prevent crash. -# The imports checker crashes when resolving simplexity package imports due to a bug -# in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 -# pylint: enable=all -# Re-enable all pylint checkers for the checking phase. This allows other checks -# (code quality, style, undefined names, etc.) to run normally while bypassing -# the problematic imports checker that would crash during AST traversal. - -from dataclasses import dataclass - -from omegaconf import DictConfig, OmegaConf - -from simplexity.run_management.structured_configs import InstanceConfig - - -class TestInstanceConfig: - """Test InstanceConfig.""" - - def test_instance_config(self) -> None: - """Test creating instance config from dataclass.""" - cfg: DictConfig = OmegaConf.structured(InstanceConfig(_target_="some_target")) - assert cfg.get("_target_") == "some_target" - - def test_instance_derived_config(self) -> None: - """Test creating a child instance config from dataclass.""" - - @dataclass - class SomeInstance(InstanceConfig): - """Some instance config.""" - - other_key: str - default_attribute: int = 42 - - cfg: DictConfig = OmegaConf.structured( - SomeInstance( - _target_="some_target", - other_key="other_value", - ) - ) - assert cfg.get("_target_") == "some_target" - assert cfg.get("other_key") == "other_value" - assert cfg.get("default_attribute") == 42 diff --git a/tests/structured_configs/test_generative_process_config.py b/tests/structured_configs/test_generative_process_config.py index 8623a691..be196e33 100644 --- a/tests/structured_configs/test_generative_process_config.py +++ b/tests/structured_configs/test_generative_process_config.py @@ -863,7 +863,7 @@ class TestFactoredProcessBuilders: def test_factored_process_from_spec_config_detection(self) -> None: """Test build_factored_process_from_spec config detection (unified builder).""" target = "simplexity.generative_processes.builder.build_factored_process_from_spec" - + # Test independent cfg = DictConfig( { diff --git a/tests/utils/test_jnp_utils.py b/tests/utils/test_jnp_utils.py index b957e246..0521213f 100644 --- a/tests/utils/test_jnp_utils.py +++ b/tests/utils/test_jnp_utils.py @@ -160,4 +160,4 @@ def test_signed_logsumexp(): expected_signs = jnp.array([1, -1, 1], dtype=jnp.int32) chex.assert_trees_all_close(actual_log_abs_values, expected_log_abs_values) - chex.assert_trees_all_close(actual_signs, expected_signs) \ No newline at end of file + chex.assert_trees_all_close(actual_signs, expected_signs) From 534b1ff763d4a080f308c4edf6279b7e395a19ab Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 07:55:31 -0800 Subject: [PATCH 12/37] static analysis tweaks --- .../structures/independent.py | 12 ++++----- tests/generative_processes/test_builder.py | 25 ------------------- .../test_factored_generative_process.py | 3 +++ 3 files changed, 9 insertions(+), 31 deletions(-) diff --git a/simplexity/generative_processes/structures/independent.py b/simplexity/generative_processes/structures/independent.py index ec16b3bd..4dac0733 100644 --- a/simplexity/generative_processes/structures/independent.py +++ b/simplexity/generative_processes/structures/independent.py @@ -33,21 +33,21 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray Returns: Flattened joint distribution of shape [prod(V_i)] """ - F = len(context.vocab_sizes) + num_factors = len(context.vocab_sizes) states = context.states component_types = context.component_types transition_matrices = context.transition_matrices normalizing_eigenvectors = context.normalizing_eigenvectors parts = [] - for i in range(F): - T_i = transition_matrices[i][0] + for i in range(num_factors): + T_i = transition_matrices[i][0] # pylint: disable=invalid-name # T_i is standard notation norm_i = normalizing_eigenvectors[i][0] if component_types[i] == "ghmm" else None p_i = compute_obs_dist_for_variant(component_types[i], states[i], T_i, norm_i) parts.append(p_i) joint = parts[0] - for i in range(1, F): + for i in range(1, num_factors): joint = (joint[..., None] * parts[i]).reshape(*joint.shape, parts[i].shape[0]) return joint.reshape(-1) @@ -55,13 +55,13 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, + _: ConditionalContext, ) -> tuple[jnp.ndarray, ...]: """Select variants (always 0 for all factors). Args: obs_tuple: Tuple of observed tokens (unused) - context: Conditional context (unused) + _: Conditional context (unused) Returns: Tuple of variant indices (all zeros) diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index eb0c1322..967c517d 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -466,15 +466,6 @@ def test_build_factored_process_from_spec_transition_coupled(components_spec, tr assert process.vocab_size == 4 -def test_build_factored_process_from_spec_invalid_structure_type(components_spec): - """Test unified builder rejects invalid structure_type.""" - with pytest.raises(ValueError, match="Unknown structure_type 'invalid'"): - build_factored_process_from_spec( - structure_type="invalid", - spec=components_spec, - ) - - def test_build_factored_process_from_spec_symmetric_missing_control_maps(components_spec): """Test unified builder validates required params for symmetric.""" with pytest.raises(ValueError, match="symmetric structure requires 'control_maps' parameter"): @@ -500,22 +491,6 @@ def test_build_factored_process_from_spec_transition_coupled_missing_params(comp ) -def test_build_factored_process_invalid_structure_type(components_spec): - """Test build_factored_process rejects invalid structure_type.""" - component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( - components_spec - ) - - with pytest.raises(ValueError, match="Unknown structure_type 'invalid'"): - build_factored_process( - structure_type="invalid", - component_types=component_types, - transition_matrices=transition_matrices, - normalizing_eigenvectors=normalizing_eigenvectors, - initial_states=initial_states, - ) - - def test_build_factored_process_chain_missing_control_maps(components_spec): """Test build_factored_process chain requires control_maps.""" component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec( diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py index 310d80ec..a9868c3f 100644 --- a/tests/generative_processes/test_factored_generative_process.py +++ b/tests/generative_processes/test_factored_generative_process.py @@ -1,5 +1,8 @@ """Integration tests for the factored generative process.""" +# pylint: disable=protected-access +# Tests need to access protected methods to verify internal implementation + import chex import jax import jax.numpy as jnp From 77076d6cdbd5050bb3114763be12098d18e62bb5 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 08:32:02 -0800 Subject: [PATCH 13/37] arg name --- simplexity/generative_processes/structures/independent.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/simplexity/generative_processes/structures/independent.py b/simplexity/generative_processes/structures/independent.py index 4dac0733..b9cd165a 100644 --- a/simplexity/generative_processes/structures/independent.py +++ b/simplexity/generative_processes/structures/independent.py @@ -55,13 +55,13 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - _: ConditionalContext, + context: ConditionalContext, # pylint: disable=unused-argument # Required by protocol ) -> tuple[jnp.ndarray, ...]: """Select variants (always 0 for all factors). Args: obs_tuple: Tuple of observed tokens (unused) - _: Conditional context (unused) + context: Conditional context (unused) Returns: Tuple of variant indices (all zeros) From 1074e5aa57e6c55518897bf2bcb4a0f27698f45e Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 08:45:23 -0800 Subject: [PATCH 14/37] better test coverage --- tests/generative_processes/test_builder.py | 50 ++++++ .../test_factored_generative_process.py | 3 - .../test_factored_structures.py | 151 ++++++++++++++++++ tests/utils/test_factoring_utils.py | 108 +++++++++++++ 4 files changed, 309 insertions(+), 3 deletions(-) create mode 100644 tests/utils/test_factoring_utils.py diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 967c517d..d0b84453 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -551,3 +551,53 @@ def test_build_factored_process_transition_coupled_missing_params(components_spe initial_states=initial_states, control_maps_transition=(jnp.array([0, 1]),), ) + + +def test_build_matrices_from_spec_empty_spec_raises(): + """Empty spec should raise ValueError.""" + with pytest.raises(ValueError, match="spec must contain at least one factor"): + build_matrices_from_spec([]) + + +def test_build_matrices_from_spec_empty_variants_raises(): + """Factor with empty variants should raise ValueError.""" + spec = [{"component_type": "hmm", "variants": []}] + with pytest.raises(ValueError, match="spec\\[0\\].variants must be non-empty"): + build_matrices_from_spec(spec) + + +def test_build_matrices_from_spec_mismatched_vocab_sizes_raises(): + """Variants with different vocab sizes should raise ValueError.""" + spec = [ + { + "component_type": "hmm", + "variants": [ + {"process_name": "coin", "process_params": {"p": 0.5}}, # vocab=2 + {"process_name": "mess3", "process_params": {"x": 0.5, "a": 0.6}}, # vocab=3 + ], + } + ] + with pytest.raises(ValueError, match="must have same vocab size"): + build_matrices_from_spec(spec) + + +def test_build_matrices_from_spec_with_ghmm_variants(): + """GHMM variants should stack normalizing eigenvectors.""" + spec = [ + { + "component_type": "ghmm", + "variants": [ + {"process_name": "tom_quantum", "process_params": {"alpha": 1.0, "beta": 1.0}}, + {"process_name": "tom_quantum", "process_params": {"alpha": 1.0, "beta": 4.0}}, + ], + } + ] + component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec(spec) + assert component_types == ["ghmm"] + assert normalizing_eigenvectors[0].shape[0] == 2 # 2 variants + + +def test_build_chain_from_spec_empty_chain_raises(): + """Empty chain should raise ValueError.""" + with pytest.raises(ValueError, match="chain must contain at least one node"): + build_chain_from_spec([]) diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py index a9868c3f..310d80ec 100644 --- a/tests/generative_processes/test_factored_generative_process.py +++ b/tests/generative_processes/test_factored_generative_process.py @@ -1,8 +1,5 @@ """Integration tests for the factored generative process.""" -# pylint: disable=protected-access -# Tests need to access protected methods to verify internal implementation - import chex import jax import jax.numpy as jnp diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py index 4ef3b52f..03174a68 100644 --- a/tests/generative_processes/test_factored_structures.py +++ b/tests/generative_processes/test_factored_structures.py @@ -198,3 +198,154 @@ def test_independent_structure_with_three_factors(): # For (t0, t1, t2): 0.5 * 0.6 * 0.7 = 0.21 (for 000), etc. expected = jnp.array([0.21, 0.09, 0.14, 0.06, 0.21, 0.09, 0.14, 0.06], dtype=jnp.float32) chex.assert_trees_all_close(dist, expected, atol=1e-6) + + +def test_sequential_conditional_with_three_factors(): + """SequentialConditional should handle chains with three or more factors.""" + states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.8, 0.2], [0.3, 0.7]]), + _tensor_from_probs([[0.9, 0.1], [0.5, 0.5]]), + ) + context = _make_context(states, transition_matrices) + structure = SequentialConditional( + control_maps=( + None, + jnp.array([0, 1], dtype=jnp.int32), + jnp.array([1, 0], dtype=jnp.int32), + ), + vocab_sizes=jnp.array([2, 2, 2]), + ) + + dist = structure.compute_joint_distribution(context) + assert dist.shape == (8,) + chex.assert_trees_all_close(jnp.sum(dist), 1.0, atol=1e-6) + + +def test_sequential_conditional_without_vocab_sizes(): + """SequentialConditional should work without precomputed vocab_sizes_py.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), + ) + context = _make_context(states, transition_matrices) + # Don't pass vocab_sizes to constructor + structure = SequentialConditional(control_maps=(None, jnp.array([0, 1], dtype=jnp.int32))) + + dist = structure.compute_joint_distribution(context) + expected = jnp.array([0.42, 0.18, 0.08, 0.32], dtype=jnp.float32) + chex.assert_trees_all_close(dist, expected) + + +def test_sequential_conditional_get_required_params(): + """SequentialConditional should return required params.""" + structure = SequentialConditional(control_maps=(None,)) + required_params = structure.get_required_params() + assert required_params == {"control_maps": tuple} + + +def test_fully_conditional_get_required_params(): + """FullyConditional should return required params.""" + structure = FullyConditional( + control_maps=(jnp.array([0], dtype=jnp.int32),), vocab_sizes=jnp.array([2]) + ) + required_params = structure.get_required_params() + assert required_params == {"control_maps": tuple, "vocab_sizes": jnp.ndarray} + + +def test_fully_conditional_with_zero_normalization(): + """FullyConditional should handle zero normalization edge case.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + # Create distributions that will multiply to zero + transition_matrices = ( + _tensor_from_probs([[0.0, 0.0], [1.0, 0.0]]), + _tensor_from_probs([[1.0, 0.0], [0.0, 1.0]]), + ) + context = _make_context(states, transition_matrices) + structure = FullyConditional( + control_maps=(jnp.array([0, 1], dtype=jnp.int32), jnp.array([1, 0], dtype=jnp.int32)), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + + dist = structure.compute_joint_distribution(context) + # Should fall back to uniform distribution when Z=0 + assert dist.shape == (4,) + assert jnp.all(dist >= 0.0) + + +def test_conditional_transitions_get_required_params(): + """ConditionalTransitions should return required params.""" + structure = ConditionalTransitions( + control_maps_transition=(jnp.array([0], dtype=jnp.int32),), + emission_variant_indices=jnp.array([0]), + vocab_sizes=jnp.array([2]), + ) + required_params = structure.get_required_params() + assert "control_maps_transition" in required_params + assert "emission_variant_indices" in required_params + assert "vocab_sizes" in required_params + + +def test_conditional_transitions_with_none_emission_maps(): + """ConditionalTransitions should handle None emission_control_maps.""" + states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4], [0.2, 0.8]]), + _tensor_from_probs([[0.5, 0.5], [0.1, 0.9]]), + ) + context = _make_context(states, transition_matrices) + # Don't pass emission_control_maps at all + structure = ConditionalTransitions( + control_maps_transition=( + jnp.array([1, 0], dtype=jnp.int32), + jnp.array([0, 1], dtype=jnp.int32), + ), + emission_variant_indices=jnp.array([0, 1], dtype=jnp.int32), + vocab_sizes=jnp.array([2, 2], dtype=jnp.int32), + ) + + assert structure.use_emission_chain is False + dist = structure.compute_joint_distribution(context) + assert dist.shape == (4,) + chex.assert_trees_all_close(jnp.sum(dist), 1.0, atol=1e-6) + + +def test_conditional_transitions_sequential_with_none_in_chain(): + """ConditionalTransitions should handle None entries in sequential emission maps.""" + states = ( + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + transition_matrices = ( + _tensor_from_probs([[0.6, 0.4]]), + _tensor_from_probs([[0.8, 0.2], [0.3, 0.7]]), + _tensor_from_probs([[0.9, 0.1], [0.5, 0.5], [0.7, 0.3], [0.4, 0.6]]), + ) + context = _make_context(states, transition_matrices) + # Sequential emissions: factor 0 fixed, factor 1 uses map, factor 2 uses different map + structure = ConditionalTransitions( + control_maps_transition=( + jnp.array([0, 0, 0], dtype=jnp.int32), + jnp.array([0, 0, 0], dtype=jnp.int32), + jnp.array([0, 0, 0], dtype=jnp.int32), + ), + emission_variant_indices=jnp.array([0, 0, 0], dtype=jnp.int32), + vocab_sizes=jnp.array([2, 2, 2], dtype=jnp.int32), + emission_control_maps=( + None, + None, + jnp.array([0, 1, 2, 3], dtype=jnp.int32), + ), + ) + + assert structure.use_emission_chain is True + dist = structure.compute_joint_distribution(context) + assert dist.shape == (8,) + chex.assert_trees_all_close(jnp.sum(dist), 1.0, atol=1e-6) diff --git a/tests/utils/test_factoring_utils.py b/tests/utils/test_factoring_utils.py new file mode 100644 index 00000000..13e3d472 --- /dev/null +++ b/tests/utils/test_factoring_utils.py @@ -0,0 +1,108 @@ +"""Tests for factoring utilities.""" + +import chex +import jax.numpy as jnp +import pytest + +from simplexity.utils.factoring_utils import ( + ComponentType, + TokenEncoder, + compute_obs_dist_for_variant, + transition_with_obs, +) + + +def test_compute_obs_dist_for_variant_ghmm_missing_eigenvector(): + """GHMM without normalizing eigenvector should raise ValueError.""" + state = jnp.array([0.5, 0.5]) + T = jnp.zeros((2, 2, 2)) + + with pytest.raises(ValueError, match="GHMM requires normalizing_eigenvector"): + compute_obs_dist_for_variant("ghmm", state, T, normalizing_eigenvector=None) + + +def test_transition_with_obs_ghmm_missing_eigenvector(): + """GHMM transition without normalizing eigenvector should raise ValueError.""" + state = jnp.array([0.5, 0.5]) + T = jnp.eye(2)[None, :, :] # Shape: [V=1, S=2, S=2] + obs = jnp.array(0) + + with pytest.raises(ValueError, match="GHMM requires normalizing_eigenvector"): + transition_with_obs("ghmm", state, T, obs, normalizing_eigenvector=None) + + +def test_token_encoder_extract_factors_vectorized(): + """TokenEncoder should handle batch decoding.""" + vocab_sizes = jnp.array([2, 3, 4]) + encoder = TokenEncoder(vocab_sizes) + + # Encode multiple tokens + tokens = jnp.array([0, 5, 10, 23]) # Multiple composite tokens + factors = encoder.extract_factors_vectorized(tokens) + + assert factors.shape == (4, 3) # (batch, num_factors) + # Verify each decoding is correct + for i, token in enumerate(tokens): + expected_tuple = encoder.token_to_tuple(token) + expected_array = jnp.array([t.item() for t in expected_tuple]) + chex.assert_trees_all_close(factors[i], expected_array) + + +def test_compute_obs_dist_for_variant_hmm(): + """HMM observation distribution should work without normalizing eigenvector.""" + state = jnp.array([0.6, 0.4]) + # Transition matrix: [V=2, S=2, S=2] + T = jnp.array([ + [[0.8, 0.2], [0.3, 0.7]], # For obs=0 + [[0.1, 0.9], [0.4, 0.6]], # For obs=1 + ]) + + dist = compute_obs_dist_for_variant("hmm", state, T, normalizing_eigenvector=None) + + assert dist.shape == (2,) # V=2 + # P(obs=0) = state @ T[0] @ 1 = [0.6, 0.4] @ [[0.8, 0.2], [0.3, 0.7]] @ [1, 1] + expected_0 = jnp.sum(state @ T[0]) + expected_1 = jnp.sum(state @ T[1]) + chex.assert_trees_all_close(dist, jnp.array([expected_0, expected_1])) + + +def test_transition_with_obs_hmm(): + """HMM transition should work without normalizing eigenvector.""" + state = jnp.array([0.6, 0.4]) + T = jnp.array([ + [[0.8, 0.2], [0.3, 0.7]], # For obs=0 + [[0.1, 0.9], [0.4, 0.6]], # For obs=1 + ]) + obs = jnp.array(0) + + new_state = transition_with_obs("hmm", state, T, obs, normalizing_eigenvector=None) + + assert new_state.shape == (2,) + # New state should be state @ T[0] normalized + unnormalized = state @ T[0] + expected = unnormalized / jnp.sum(unnormalized) + chex.assert_trees_all_close(new_state, expected) + + +def test_token_encoder_tuple_to_token(): + """TokenEncoder should encode tuples to composite tokens.""" + vocab_sizes = jnp.array([2, 3, 4]) + encoder = TokenEncoder(vocab_sizes) + + # Test a few tuples + tuple0 = (jnp.array(0), jnp.array(0), jnp.array(0)) + tuple1 = (jnp.array(1), jnp.array(2), jnp.array(3)) + + token0 = encoder.tuple_to_token(tuple0) + token1 = encoder.tuple_to_token(tuple1) + + assert token0 == 0 # (0, 0, 0) -> 0 + # (1, 2, 3) -> 1 + 2*2 + 3*2*3 = 1 + 4 + 18 = 23 + assert token1 == 23 + + # Verify roundtrip + decoded0 = encoder.token_to_tuple(token0) + decoded1 = encoder.token_to_tuple(token1) + + chex.assert_trees_all_close(decoded0, tuple0) + chex.assert_trees_all_close(decoded1, tuple1) From 7111f30ea877f17c15a900b29776ecf53756918d Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 08:48:24 -0800 Subject: [PATCH 15/37] factor input args --- simplexity/generative_processes/builder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 7c7c5a55..a47fae30 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -223,7 +223,7 @@ def build_factored_process( def build_factored_process_from_spec( structure_type: Literal["independent", "chain", "symmetric", "transition_coupled"], - spec: Sequence[dict[str, Any]] | dict[str, Any], + spec: Sequence[dict[str, Any]], **structure_params, ) -> FactoredGenerativeProcess: """Unified builder for factored processes from specification. From e7d2a929f4800cee0155b9ed38362f54798a74ab Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 08:49:37 -0800 Subject: [PATCH 16/37] ruff --- .../test_factored_structures.py | 4 +--- tests/utils/test_factoring_utils.py | 20 +++++++++++-------- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py index 03174a68..34ffd523 100644 --- a/tests/generative_processes/test_factored_structures.py +++ b/tests/generative_processes/test_factored_structures.py @@ -252,9 +252,7 @@ def test_sequential_conditional_get_required_params(): def test_fully_conditional_get_required_params(): """FullyConditional should return required params.""" - structure = FullyConditional( - control_maps=(jnp.array([0], dtype=jnp.int32),), vocab_sizes=jnp.array([2]) - ) + structure = FullyConditional(control_maps=(jnp.array([0], dtype=jnp.int32),), vocab_sizes=jnp.array([2])) required_params = structure.get_required_params() assert required_params == {"control_maps": tuple, "vocab_sizes": jnp.ndarray} diff --git a/tests/utils/test_factoring_utils.py b/tests/utils/test_factoring_utils.py index 13e3d472..f0b54fc9 100644 --- a/tests/utils/test_factoring_utils.py +++ b/tests/utils/test_factoring_utils.py @@ -52,10 +52,12 @@ def test_compute_obs_dist_for_variant_hmm(): """HMM observation distribution should work without normalizing eigenvector.""" state = jnp.array([0.6, 0.4]) # Transition matrix: [V=2, S=2, S=2] - T = jnp.array([ - [[0.8, 0.2], [0.3, 0.7]], # For obs=0 - [[0.1, 0.9], [0.4, 0.6]], # For obs=1 - ]) + T = jnp.array( + [ + [[0.8, 0.2], [0.3, 0.7]], # For obs=0 + [[0.1, 0.9], [0.4, 0.6]], # For obs=1 + ] + ) dist = compute_obs_dist_for_variant("hmm", state, T, normalizing_eigenvector=None) @@ -69,10 +71,12 @@ def test_compute_obs_dist_for_variant_hmm(): def test_transition_with_obs_hmm(): """HMM transition should work without normalizing eigenvector.""" state = jnp.array([0.6, 0.4]) - T = jnp.array([ - [[0.8, 0.2], [0.3, 0.7]], # For obs=0 - [[0.1, 0.9], [0.4, 0.6]], # For obs=1 - ]) + T = jnp.array( + [ + [[0.8, 0.2], [0.3, 0.7]], # For obs=0 + [[0.1, 0.9], [0.4, 0.6]], # For obs=1 + ] + ) obs = jnp.array(0) new_state = transition_with_obs("hmm", state, T, obs, normalizing_eigenvector=None) From 1babe79cca6263034e484867b8029d630a4b3901 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 09:21:59 -0800 Subject: [PATCH 17/37] better linting --- .../factored_generative_process.py | 21 ++++----- .../structures/conditional_transitions.py | 46 +++++++++---------- .../structures/fully_conditional.py | 38 +++++++-------- .../structures/protocol.py | 3 -- .../structures/sequential_conditional.py | 18 ++++---- .../test_factored_generative_process.py | 3 ++ tests/utils/test_factoring_utils.py | 30 ++++++------ 7 files changed, 78 insertions(+), 81 deletions(-) diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py index f35aa49c..86a6ad71 100644 --- a/simplexity/generative_processes/factored_generative_process.py +++ b/simplexity/generative_processes/factored_generative_process.py @@ -120,15 +120,14 @@ def __init__( # Validate shapes and compute derived sizes vocab_sizes = [] num_variants = [] - for i, T in enumerate(self.transition_matrices): - if T.ndim != 4: - raise ValueError(f"transition_matrices[{i}] must have shape [K, V, S, S], got {T.shape}") - K, V, S1, S2 = T.shape - if S1 != S2: - raise ValueError(f"transition_matrices[{i}] square mismatch: {S1} vs {S2}") - vocab_sizes.append(V) - num_variants.append(K) - + for i, transition_matrix in enumerate(self.transition_matrices): + if transition_matrix.ndim != 4: + raise ValueError(f"transition_matrices[{i}] must have shape [K, V, S, S], got {transition_matrix.shape}") + num_var, vocab_size, state_dim1, state_dim2 = transition_matrix.shape + if state_dim1 != state_dim2: + raise ValueError(f"transition_matrices[{i}] square mismatch: {state_dim1} vs {state_dim2}") + vocab_sizes.append(vocab_size) + num_variants.append(num_var) self.num_variants = tuple(int(k) for k in num_variants) self.encoder = TokenEncoder(jnp.array(vocab_sizes)) @@ -217,9 +216,9 @@ def transition_states(self, state: FactoredState, obs: chex.Array) -> FactoredSt # Update each factor's state new_states: list[jnp.ndarray] = [] for i, (s_i, t_i, k_i) in enumerate(zip(state, obs_tuple, variants, strict=True)): - T_k = self.transition_matrices[i][k_i] + transition_matrix_k = self.transition_matrices[i][k_i] norm_k = self.normalizing_eigenvectors[i][k_i] if self.component_types[i] == "ghmm" else None - new_state_i = transition_with_obs(self.component_types[i], s_i, T_k, t_i, norm_k) + new_state_i = transition_with_obs(self.component_types[i], s_i, transition_matrix_k, t_i, norm_k) new_states.append(new_state_i) return tuple(new_states) diff --git a/simplexity/generative_processes/structures/conditional_transitions.py b/simplexity/generative_processes/structures/conditional_transitions.py index 9c74a4b0..c1dc5254 100644 --- a/simplexity/generative_processes/structures/conditional_transitions.py +++ b/simplexity/generative_processes/structures/conditional_transitions.py @@ -70,7 +70,7 @@ def __init__( self.control_maps_transition = tuple(jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps_transition) self.emission_variant_indices = jnp.asarray(emission_variant_indices, dtype=jnp.int32) self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) - F = len(vocab_sizes) + num_factors = len(vocab_sizes) # Process emission control maps use_chain = False @@ -84,20 +84,20 @@ def __init__( if i > 0: use_chain = True else: - ecm_list = [None] * F + ecm_list = [None] * num_factors self.emission_control_maps = tuple(ecm_list) self.use_emission_chain = bool(use_chain) # Precompute multipliers for other-factor indexing (for transitions) other_multipliers: list[jnp.ndarray] = [] - for i in range(F): + for i in range(num_factors): mult = [] - for j in range(F): + for j in range(num_factors): if j == i: mult.append(0) # Unused else: m = 1 - for k in range(j + 1, F): + for k in range(j + 1, num_factors): if k == i: continue m *= self.vocab_sizes_py[k] @@ -107,9 +107,9 @@ def __init__( # Precompute multipliers for prefix indexing (for sequential emissions) prefix_multipliers: list[jnp.ndarray] = [] - for i in range(F): + for i in range(num_factors): pmult = [] - for j in range(F): + for j in range(num_factors): if j >= i: pmult.append(0) # Unused else: @@ -139,7 +139,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray Returns: Flattened joint distribution of shape [prod(V_i)] """ - F = len(context.vocab_sizes) + num_factors = len(context.vocab_sizes) states = context.states component_types = context.component_types transition_matrices = context.transition_matrices @@ -149,35 +149,35 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray if not self.use_emission_chain: # Independent emissions parts = [] - for i in range(F): + for i in range(num_factors): k_emit = self.emission_variant_indices[i] - T_k = transition_matrices[i][k_emit] + transition_matrix_k = transition_matrices[i][k_emit] norm_k = normalizing_eigenvectors[i][k_emit] if component_types[i] == "ghmm" else None - p_i = compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + p_i = compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) parts.append(p_i) # Product of independent factors - J = parts[0] - for i in range(1, F): - J = (J[..., None] * parts[i]).reshape(*J.shape, parts[i].shape[0]) - return J.reshape(-1) + j_prod = parts[0] + for i in range(1, num_factors): + j_prod = (j_prod[..., None] * parts[i]).reshape(*j_prod.shape, parts[i].shape[0]) + return j_prod.reshape(-1) # Sequential emissions k0 = self.emission_variant_indices[0] - T0 = transition_matrices[0][k0] + transition_matrix0 = transition_matrices[0][k0] norm0 = normalizing_eigenvectors[0][k0] if component_types[0] == "ghmm" else None - joint = compute_obs_dist_for_variant(component_types[0], states[0], T0, norm0) + joint = compute_obs_dist_for_variant(component_types[0], states[0], transition_matrix0, norm0) prev_prod = self.vocab_sizes_py[0] - for i in range(1, F): - K_i = num_variants[i] - ks = jnp.arange(K_i, dtype=jnp.int32) + for i in range(1, num_factors): + variant_k = num_variants[i] + ks = jnp.arange(variant_k, dtype=jnp.int32) # Compute all variant distributions def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: - T_k = transition_matrices[i][k] + transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None - return compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) all_pi = jax.vmap(get_dist_i)(ks) # [K_i, V_i] @@ -201,7 +201,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, + context: ConditionalContext, # pylint: disable=unused-argument ) -> tuple[jnp.ndarray, ...]: """Select transition variants based on other factors' tokens. diff --git a/simplexity/generative_processes/structures/fully_conditional.py b/simplexity/generative_processes/structures/fully_conditional.py index 13f0ed38..ea8f32c8 100644 --- a/simplexity/generative_processes/structures/fully_conditional.py +++ b/simplexity/generative_processes/structures/fully_conditional.py @@ -52,7 +52,7 @@ def __init__( """ self.control_maps = tuple(jnp.asarray(cm, dtype=jnp.int32) for cm in control_maps) self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) - F = len(vocab_sizes) + num_factors = len(vocab_sizes) # Compute joint vocab size jv = 1 @@ -65,15 +65,15 @@ def __init__( other_shapes: list[tuple[int, ...]] = [] perms_py: list[tuple[int, ...]] = [] - for i in range(F): + for i in range(num_factors): # Compute radix multipliers for "other" factors (excluding i) mult = [] - for j in range(F): + for j in range(num_factors): if j == i: mult.append(0) # Unused else: m = 1 - for k in range(j + 1, F): + for k in range(j + 1, num_factors): if k == i: continue m *= self.vocab_sizes_py[k] @@ -84,10 +84,10 @@ def __init__( other_shapes.append(tuple(self.vocab_sizes_py[j] for j in range(F) if j != i)) # Permutation to align [*others, V_i] to [V_0, ..., V_{F-1}] - others = [j for j in range(F) if j != i] + others = [j for j in range(num_factors) if j != i] axis_pos = {j: pos for pos, j in enumerate(others)} perm = [] - for j in range(F): + for j in range(num_factors): if j == i: perm.append(len(others)) # V_i is the last axis else: @@ -124,7 +124,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray Returns: Flattened joint distribution of shape [prod(V_i)] """ - F = len(context.vocab_sizes) + num_factors = len(context.vocab_sizes) states = context.states component_types = context.component_types transition_matrices = context.transition_matrices @@ -133,15 +133,15 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray # Compute per-factor conditionals parts = [] - for i in range(F): - K_i = num_variants[i] - ks = jnp.arange(K_i, dtype=jnp.int32) + for i in range(num_factors): + variant_k = num_variants[i] + ks = jnp.arange(variant_k, dtype=jnp.int32) # Compute all variant distributions for factor i def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: - T_k = transition_matrices[i][k] + transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None - return compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) all_pi = jax.vmap(get_dist_i)(ks) # [K_i, V_i] @@ -157,22 +157,22 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: parts.append(aligned) # Product of experts - J = parts[0] + prod_j = parts[0] for p in parts[1:]: - J = J * p + prod_j = prod_j * p # Normalize - Z = jnp.sum(J) - J_norm = jnp.where(Z > 0, J / Z, jnp.ones_like(J) / self.joint_vocab_size) + sum_j = jnp.sum(prod_j) + norm_j = jnp.where(sum_j > 0, prod_j / sum_j, jnp.ones_like(prod_j) / self.joint_vocab_size) - assert isinstance(J_norm, jnp.ndarray) + assert isinstance(norm_j, jnp.ndarray) - return J_norm.reshape(-1) + return norm_j.reshape(-1) def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, + context: ConditionalContext, # pylint: disable=unused-argument ) -> tuple[jnp.ndarray, ...]: """Select variants based on all other factors' tokens. diff --git a/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py index 372da543..9233dbba 100644 --- a/simplexity/generative_processes/structures/protocol.py +++ b/simplexity/generative_processes/structures/protocol.py @@ -52,7 +52,6 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray Flattened joint distribution of shape [prod(V_i)] Uses radix encoding: token = sum(t_i * prod(V_j for j>i)) """ - ... def select_variants( self, @@ -68,7 +67,6 @@ def select_variants( Returns: Tuple of F variant indices (one per factor) """ - ... def get_required_params(self) -> dict[str, type]: """Return dictionary of structure-specific parameters and their types. @@ -77,4 +75,3 @@ def get_required_params(self) -> dict[str, type]: Dict mapping parameter names to expected types (e.g., {"control_maps": tuple} for sequential conditional) """ - ... diff --git a/simplexity/generative_processes/structures/sequential_conditional.py b/simplexity/generative_processes/structures/sequential_conditional.py index a7e18766..6fdb6279 100644 --- a/simplexity/generative_processes/structures/sequential_conditional.py +++ b/simplexity/generative_processes/structures/sequential_conditional.py @@ -61,7 +61,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray Returns: Flattened joint distribution of shape [prod(V_i)] """ - F = len(context.vocab_sizes) + num_factors = len(context.vocab_sizes) states = context.states component_types = context.component_types transition_matrices = context.transition_matrices @@ -69,22 +69,22 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray num_variants = context.num_variants # Root distribution (factor 0, variant 0) - T_root = transition_matrices[0][0] # [V_0, S_0, S_0] + transition_matrix_root = transition_matrices[0][0] # [V_0, S_0, S_0] norm_root = normalizing_eigenvectors[0][0] if component_types[0] == "ghmm" else None - p_root = compute_obs_dist_for_variant(component_types[0], states[0], T_root, norm_root) # [V_0] + p_root = compute_obs_dist_for_variant(component_types[0], states[0], transition_matrix_root, norm_root) # [V_0] joint = p_root # Iteratively extend with conditional factors - for i in range(1, F): + for i in range(1, num_factors): # Compute distributions for all variants of factor i - K_i = num_variants[i] - ks = jnp.arange(K_i, dtype=jnp.int32) + num_var_i = num_variants[i] + ks = jnp.arange(num_var_i, dtype=jnp.int32) # Vectorize over variants def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: - T_k = transition_matrices[i][k] + transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None - return compute_obs_dist_for_variant(component_types[i], states[i], T_k, norm_k) + return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) all_pi = jax.vmap(get_dist_i)(ks) # [K_i, V_i] @@ -111,7 +111,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, + context: ConditionalContext, # pylint: disable=unused-argument ) -> tuple[jnp.ndarray, ...]: """Select variants based on parent tokens in chain. diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py index 310d80ec..a9868c3f 100644 --- a/tests/generative_processes/test_factored_generative_process.py +++ b/tests/generative_processes/test_factored_generative_process.py @@ -1,5 +1,8 @@ """Integration tests for the factored generative process.""" +# pylint: disable=protected-access +# Tests need to access protected methods to verify internal implementation + import chex import jax import jax.numpy as jnp diff --git a/tests/utils/test_factoring_utils.py b/tests/utils/test_factoring_utils.py index f0b54fc9..51184a62 100644 --- a/tests/utils/test_factoring_utils.py +++ b/tests/utils/test_factoring_utils.py @@ -1,11 +1,11 @@ """Tests for factoring utilities.""" + import chex import jax.numpy as jnp import pytest from simplexity.utils.factoring_utils import ( - ComponentType, TokenEncoder, compute_obs_dist_for_variant, transition_with_obs, @@ -15,21 +15,19 @@ def test_compute_obs_dist_for_variant_ghmm_missing_eigenvector(): """GHMM without normalizing eigenvector should raise ValueError.""" state = jnp.array([0.5, 0.5]) - T = jnp.zeros((2, 2, 2)) + transition_matrix = jnp.zeros((2, 2, 2)) with pytest.raises(ValueError, match="GHMM requires normalizing_eigenvector"): - compute_obs_dist_for_variant("ghmm", state, T, normalizing_eigenvector=None) - + compute_obs_dist_for_variant("ghmm", state, transition_matrix, normalizing_eigenvector=None) def test_transition_with_obs_ghmm_missing_eigenvector(): """GHMM transition without normalizing eigenvector should raise ValueError.""" state = jnp.array([0.5, 0.5]) - T = jnp.eye(2)[None, :, :] # Shape: [V=1, S=2, S=2] + transition_matrix = jnp.eye(2)[None, :, :] # Shape: [V=1, S=2, S=2] obs = jnp.array(0) with pytest.raises(ValueError, match="GHMM requires normalizing_eigenvector"): - transition_with_obs("ghmm", state, T, obs, normalizing_eigenvector=None) - + transition_with_obs("ghmm", state, transition_matrix, obs, normalizing_eigenvector=None) def test_token_encoder_extract_factors_vectorized(): """TokenEncoder should handle batch decoding.""" @@ -52,26 +50,26 @@ def test_compute_obs_dist_for_variant_hmm(): """HMM observation distribution should work without normalizing eigenvector.""" state = jnp.array([0.6, 0.4]) # Transition matrix: [V=2, S=2, S=2] - T = jnp.array( + transition_matrix = jnp.array( [ [[0.8, 0.2], [0.3, 0.7]], # For obs=0 [[0.1, 0.9], [0.4, 0.6]], # For obs=1 ] ) - dist = compute_obs_dist_for_variant("hmm", state, T, normalizing_eigenvector=None) + dist = compute_obs_dist_for_variant("hmm", state, transition_matrix, normalizing_eigenvector=None) assert dist.shape == (2,) # V=2 - # P(obs=0) = state @ T[0] @ 1 = [0.6, 0.4] @ [[0.8, 0.2], [0.3, 0.7]] @ [1, 1] - expected_0 = jnp.sum(state @ T[0]) - expected_1 = jnp.sum(state @ T[1]) + # P(obs=0) = state @ transition_matrix[0] @ 1 = [0.6, 0.4] @ [[0.8, 0.2], [0.3, 0.7]] @ [1, 1] + expected_0 = jnp.sum(state @ transition_matrix[0]) + expected_1 = jnp.sum(state @ transition_matrix[1]) chex.assert_trees_all_close(dist, jnp.array([expected_0, expected_1])) def test_transition_with_obs_hmm(): """HMM transition should work without normalizing eigenvector.""" state = jnp.array([0.6, 0.4]) - T = jnp.array( + transition_matrix = jnp.array( [ [[0.8, 0.2], [0.3, 0.7]], # For obs=0 [[0.1, 0.9], [0.4, 0.6]], # For obs=1 @@ -79,11 +77,11 @@ def test_transition_with_obs_hmm(): ) obs = jnp.array(0) - new_state = transition_with_obs("hmm", state, T, obs, normalizing_eigenvector=None) + new_state = transition_with_obs("hmm", state, transition_matrix, obs, normalizing_eigenvector=None) assert new_state.shape == (2,) - # New state should be state @ T[0] normalized - unnormalized = state @ T[0] + # New state should be state @ transition_matrix[0] normalized + unnormalized = state @ transition_matrix[0] expected = unnormalized / jnp.sum(unnormalized) chex.assert_trees_all_close(new_state, expected) From 04b41102155ac171dd013ded8da57275d073443c Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 09:29:22 -0800 Subject: [PATCH 18/37] bind i --- .../generative_processes/factored_generative_process.py | 4 +++- .../structures/conditional_transitions.py | 4 ++-- .../generative_processes/structures/fully_conditional.py | 6 +++--- .../structures/sequential_conditional.py | 4 ++-- tests/utils/test_factoring_utils.py | 3 ++- 5 files changed, 12 insertions(+), 9 deletions(-) diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py index 86a6ad71..b12e6f9a 100644 --- a/simplexity/generative_processes/factored_generative_process.py +++ b/simplexity/generative_processes/factored_generative_process.py @@ -122,7 +122,9 @@ def __init__( num_variants = [] for i, transition_matrix in enumerate(self.transition_matrices): if transition_matrix.ndim != 4: - raise ValueError(f"transition_matrices[{i}] must have shape [K, V, S, S], got {transition_matrix.shape}") + raise ValueError( + f"transition_matrices[{i}] must have shape [K, V, S, S], got {transition_matrix.shape}" + ) num_var, vocab_size, state_dim1, state_dim2 = transition_matrix.shape if state_dim1 != state_dim2: raise ValueError(f"transition_matrices[{i}] square mismatch: {state_dim1} vs {state_dim2}") diff --git a/simplexity/generative_processes/structures/conditional_transitions.py b/simplexity/generative_processes/structures/conditional_transitions.py index c1dc5254..46dfa1c5 100644 --- a/simplexity/generative_processes/structures/conditional_transitions.py +++ b/simplexity/generative_processes/structures/conditional_transitions.py @@ -174,7 +174,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray ks = jnp.arange(variant_k, dtype=jnp.int32) # Compute all variant distributions - def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: + def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) @@ -201,7 +201,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, # pylint: disable=unused-argument + context: ConditionalContext, # pylint: disable=unused-argument ) -> tuple[jnp.ndarray, ...]: """Select transition variants based on other factors' tokens. diff --git a/simplexity/generative_processes/structures/fully_conditional.py b/simplexity/generative_processes/structures/fully_conditional.py index ea8f32c8..4e011123 100644 --- a/simplexity/generative_processes/structures/fully_conditional.py +++ b/simplexity/generative_processes/structures/fully_conditional.py @@ -81,7 +81,7 @@ def __init__( other_multipliers.append(jnp.array(mult)) # Shape for reshaping conditional [prod_others, V_i] -> [*others, V_i] - other_shapes.append(tuple(self.vocab_sizes_py[j] for j in range(F) if j != i)) + other_shapes.append(tuple(self.vocab_sizes_py[j] for j in range(num_factors) if j != i)) # Permutation to align [*others, V_i] to [V_0, ..., V_{F-1}] others = [j for j in range(num_factors) if j != i] @@ -138,7 +138,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray ks = jnp.arange(variant_k, dtype=jnp.int32) # Compute all variant distributions for factor i - def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: + def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) @@ -172,7 +172,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, # pylint: disable=unused-argument + context: ConditionalContext, # pylint: disable=unused-argument ) -> tuple[jnp.ndarray, ...]: """Select variants based on all other factors' tokens. diff --git a/simplexity/generative_processes/structures/sequential_conditional.py b/simplexity/generative_processes/structures/sequential_conditional.py index 6fdb6279..a427fda0 100644 --- a/simplexity/generative_processes/structures/sequential_conditional.py +++ b/simplexity/generative_processes/structures/sequential_conditional.py @@ -81,7 +81,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray ks = jnp.arange(num_var_i, dtype=jnp.int32) # Vectorize over variants - def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: + def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) @@ -111,7 +111,7 @@ def get_dist_i(k: jnp.ndarray) -> jnp.ndarray: def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], - context: ConditionalContext, # pylint: disable=unused-argument + context: ConditionalContext, # pylint: disable=unused-argument ) -> tuple[jnp.ndarray, ...]: """Select variants based on parent tokens in chain. diff --git a/tests/utils/test_factoring_utils.py b/tests/utils/test_factoring_utils.py index 51184a62..f9fb05b6 100644 --- a/tests/utils/test_factoring_utils.py +++ b/tests/utils/test_factoring_utils.py @@ -1,6 +1,5 @@ """Tests for factoring utilities.""" - import chex import jax.numpy as jnp import pytest @@ -20,6 +19,7 @@ def test_compute_obs_dist_for_variant_ghmm_missing_eigenvector(): with pytest.raises(ValueError, match="GHMM requires normalizing_eigenvector"): compute_obs_dist_for_variant("ghmm", state, transition_matrix, normalizing_eigenvector=None) + def test_transition_with_obs_ghmm_missing_eigenvector(): """GHMM transition without normalizing eigenvector should raise ValueError.""" state = jnp.array([0.5, 0.5]) @@ -29,6 +29,7 @@ def test_transition_with_obs_ghmm_missing_eigenvector(): with pytest.raises(ValueError, match="GHMM requires normalizing_eigenvector"): transition_with_obs("ghmm", state, transition_matrix, obs, normalizing_eigenvector=None) + def test_token_encoder_extract_factors_vectorized(): """TokenEncoder should handle batch decoding.""" vocab_sizes = jnp.array([2, 3, 4]) From 36baf6d5aa01662a02b269a9e2b30ccbf7beda52 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 09:32:09 -0800 Subject: [PATCH 19/37] elipsis to protocol --- simplexity/generative_processes/structures/protocol.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py index 9233dbba..372da543 100644 --- a/simplexity/generative_processes/structures/protocol.py +++ b/simplexity/generative_processes/structures/protocol.py @@ -52,6 +52,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray Flattened joint distribution of shape [prod(V_i)] Uses radix encoding: token = sum(t_i * prod(V_j for j>i)) """ + ... def select_variants( self, @@ -67,6 +68,7 @@ def select_variants( Returns: Tuple of F variant indices (one per factor) """ + ... def get_required_params(self) -> dict[str, type]: """Return dictionary of structure-specific parameters and their types. @@ -75,3 +77,4 @@ def get_required_params(self) -> dict[str, type]: Dict mapping parameter names to expected types (e.g., {"control_maps": tuple} for sequential conditional) """ + ... From 1dd7d44d1634682aacc548367c0b6d5dac12be23 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 09:34:06 -0800 Subject: [PATCH 20/37] simplify protocol --- .../structures/protocol.py | 24 ------------------- 1 file changed, 24 deletions(-) diff --git a/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py index 372da543..d6d7e71f 100644 --- a/simplexity/generative_processes/structures/protocol.py +++ b/simplexity/generative_processes/structures/protocol.py @@ -43,15 +43,6 @@ class ConditionalStructure(Protocol): """ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: - """Compute joint observation distribution across all factors. - - Args: - context: All information needed to compute the joint distribution - - Returns: - Flattened joint distribution of shape [prod(V_i)] - Uses radix encoding: token = sum(t_i * prod(V_j for j>i)) - """ ... def select_variants( @@ -59,22 +50,7 @@ def select_variants( obs_tuple: tuple[jnp.ndarray, ...], context: ConditionalContext, ) -> tuple[jnp.ndarray, ...]: - """Select parameter variant for each factor given observations. - - Args: - obs_tuple: Tuple of F observed tokens (one per factor) - context: Context information - - Returns: - Tuple of F variant indices (one per factor) - """ ... def get_required_params(self) -> dict[str, type]: - """Return dictionary of structure-specific parameters and their types. - - Returns: - Dict mapping parameter names to expected types - (e.g., {"control_maps": tuple} for sequential conditional) - """ ... From dba0782f11af083cf708fefd4692f111c86dcb26 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Wed, 3 Dec 2025 09:35:24 -0800 Subject: [PATCH 21/37] format --- simplexity/generative_processes/structures/protocol.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py index d6d7e71f..24027701 100644 --- a/simplexity/generative_processes/structures/protocol.py +++ b/simplexity/generative_processes/structures/protocol.py @@ -42,15 +42,12 @@ class ConditionalStructure(Protocol): to produce joint observation distributions and how variant selection works. """ - def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: - ... + def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: ... def select_variants( self, obs_tuple: tuple[jnp.ndarray, ...], context: ConditionalContext, - ) -> tuple[jnp.ndarray, ...]: - ... + ) -> tuple[jnp.ndarray, ...]: ... - def get_required_params(self) -> dict[str, type]: - ... + def get_required_params(self) -> dict[str, type]: ... From 4d957f6d3978dcbbe5d064019a35239e9d9dce01 Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 12:57:38 -0800 Subject: [PATCH 22/37] Minor fixes --- .../configs/generative_process/unified_chain_example.yaml | 2 +- tests/structured_configs/test_predictive_model_config.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/end_to_end/configs/generative_process/unified_chain_example.yaml b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml index fd1faa78..7f034cd6 100644 --- a/tests/end_to_end/configs/generative_process/unified_chain_example.yaml +++ b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml @@ -54,4 +54,4 @@ batch_size: 1024 # Example interpretation: # - Factor 0 always uses variant 0 # - Factor 1: parent_token 0->variant 0, token 1->variant 1, token 2->variant 0 -# - Factor 2: parent_token 0->variant 1, token 1->variant 0 \ No newline at end of file +# - Factor 2: parent_token 0->variant 1, token 1->variant 0, token 2->variant 1 \ No newline at end of file diff --git a/tests/structured_configs/test_predictive_model_config.py b/tests/structured_configs/test_predictive_model_config.py index e9a95ffa..ffa6c5e4 100644 --- a/tests/structured_configs/test_predictive_model_config.py +++ b/tests/structured_configs/test_predictive_model_config.py @@ -1,6 +1,6 @@ """Tests for predictive-model structured configs.""" -# pylint: disable-all +# pylint: disable=all # Temporarily disable all pylint checkers during AST traversal to prevent crash. # The imports checker crashes when resolving simplexity package imports due to a bug # in pylint/astroid: https://github.com/pylint-dev/pylint/issues/10185 From da6392c650490eb12b2f938d4735ed8225b82bea Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 12:58:35 -0800 Subject: [PATCH 23/37] Minor fixes --- simplexity/utils/factoring_utils.py | 22 +++++++--------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/simplexity/utils/factoring_utils.py b/simplexity/utils/factoring_utils.py index 91cece30..73578d2a 100644 --- a/simplexity/utils/factoring_utils.py +++ b/simplexity/utils/factoring_utils.py @@ -9,7 +9,6 @@ from typing import Literal import chex -import jax.numpy as jnp import equinox as eqx import jax.numpy as jnp @@ -77,13 +76,6 @@ def transition_with_obs( return new_state / (new_state @ normalizing_eigenvector) -"""Token encoding utilities for factored observations. - -Handles conversion between composite tokens and per-factor token tuples -using radix/base conversion. -""" - - class TokenEncoder(eqx.Module): """Encodes/decodes composite observations from per-factor tokens. @@ -108,11 +100,11 @@ def __init__(self, vocab_sizes: jnp.ndarray): self.vocab_sizes = jnp.asarray(vocab_sizes) # Compute radix multipliers - F = len(vocab_sizes) + f = len(vocab_sizes) multipliers = [] - for i in range(F): + for i in range(f): m = 1 - for j in range(i + 1, F): + for j in range(i + 1, f): m *= int(vocab_sizes[j]) multipliers.append(m) self.radix_multipliers = jnp.array(multipliers) @@ -131,7 +123,7 @@ def tuple_to_token(self, token_tuple: tuple[jnp.ndarray, ...]) -> jnp.ndarray: """Convert per-factor tokens to composite token. Args: - token_tuple: Tuple of F scalar arrays, each in [0, V_i) + token_tuple: Tuple of f scalar arrays, each in [0, V_i) Returns: Scalar array with composite token in [0, prod(V_i)) @@ -150,7 +142,7 @@ def token_to_tuple(self, token: chex.Array) -> tuple[jnp.ndarray, ...]: token: Scalar array with composite token Returns: - Tuple of F scalar arrays with per-factor tokens + Tuple of f scalar arrays with per-factor tokens """ result = [] remaining = jnp.array(token) @@ -165,10 +157,10 @@ def extract_factors_vectorized(self, tokens: jnp.ndarray) -> jnp.ndarray: """Extract per-factor tokens from batch of composite tokens. Args: - tokens: Array of shape [N] with composite tokens + tokens: Array of shape [n] with composite tokens Returns: - Array of shape [N, F] with per-factor tokens + Array of shape [n, f] with per-factor tokens """ tokens = jnp.atleast_1d(tokens) return (tokens[:, None] // self.radix_multipliers[None, :]) % self.vocab_sizes[None, :] From f290aefa3e1d08431b4950d6ce94fa2d64bf863d Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 13:00:25 -0800 Subject: [PATCH 24/37] jnp.ndarray -> jax.Array --- simplexity/generative_processes/builder.py | 48 +++++++++---------- .../factored_generative_process.py | 34 ++++++------- .../structures/conditional_transitions.py | 40 ++++++++-------- .../structures/fully_conditional.py | 24 +++++----- .../structures/independent.py | 6 +-- .../structures/protocol.py | 14 +++--- .../structures/sequential_conditional.py | 14 +++--- simplexity/utils/factoring_utils.py | 31 ++++++------ tests/analysis/test_layerwise_analysis.py | 3 +- .../test_factored_structures.py | 2 +- .../test_transition_matrices.py | 2 +- 11 files changed, 110 insertions(+), 108 deletions(-) diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index a47fae30..efb03877 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -158,9 +158,9 @@ def build_nonergodic_hidden_markov_model( def build_factored_process( structure_type: Literal["independent", "chain", "symmetric", "transition_coupled"], component_types: Sequence[ComponentType], - transition_matrices: Sequence[jnp.ndarray], - normalizing_eigenvectors: Sequence[jnp.ndarray], - initial_states: Sequence[jnp.ndarray], + transition_matrices: Sequence[jax.Array], + normalizing_eigenvectors: Sequence[jax.Array], + initial_states: Sequence[jax.Array], **structure_kwargs, ) -> FactoredGenerativeProcess: """Factory function for building factored processes with different conditional structures. @@ -340,9 +340,9 @@ def build_matrices_from_spec( spec: Sequence[dict[str, Any]], ) -> tuple[ list[ComponentType], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray], + list[jax.Array], + list[jax.Array], + list[jax.Array], ]: """Build transition matrices, eigenvectors, and initial states from spec. @@ -381,9 +381,9 @@ def build_matrices_from_spec( raise ValueError("spec must contain at least one factor") component_types: list[ComponentType] = [] - transition_matrices: list[jnp.ndarray] = [] - normalizing_eigenvectors: list[jnp.ndarray] = [] - initial_states: list[jnp.ndarray] = [] + transition_matrices: list[jax.Array] = [] + normalizing_eigenvectors: list[jax.Array] = [] + initial_states: list[jax.Array] = [] for idx, factor_spec in enumerate(spec): ctype: ComponentType = factor_spec.get("component_type", "ghmm") @@ -432,10 +432,10 @@ def build_chain_from_spec( chain: Sequence[dict[str, Any]], ) -> tuple[ list[ComponentType], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray | None], + list[jax.Array], + list[jax.Array], + list[jax.Array], + list[jax.Array | None], ]: """Build all parameters for chain structure from chain specification. @@ -478,7 +478,7 @@ def build_chain_from_spec( component_types, transition_matrices, normalizing_eigenvectors, initial_states = build_matrices_from_spec(chain) # Extract control maps - control_maps: list[jnp.ndarray | None] = [] + control_maps: list[jax.Array | None] = [] expected_prev_vocab = None for idx, node in enumerate(chain): @@ -509,10 +509,10 @@ def build_symmetric_from_spec( control_maps: Sequence[list[int]], ) -> tuple[ list[ComponentType], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray], + list[jax.Array], + list[jax.Array], + list[jax.Array], + list[jax.Array], ]: """Build all parameters for symmetric structure from specification. @@ -575,12 +575,12 @@ def build_transition_coupled_from_spec( emission_control_maps: Sequence[list[int] | None] | None = None, ) -> tuple[ list[ComponentType], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray], - list[jnp.ndarray], - jnp.ndarray, - list[jnp.ndarray | None] | None, + list[jax.Array], + list[jax.Array], + list[jax.Array], + list[jax.Array], + jax.Array, + list[jax.Array | None] | None, ]: """Build all parameters for transition-coupled structure from specification. diff --git a/simplexity/generative_processes/factored_generative_process.py b/simplexity/generative_processes/factored_generative_process.py index b12e6f9a..2a842b51 100644 --- a/simplexity/generative_processes/factored_generative_process.py +++ b/simplexity/generative_processes/factored_generative_process.py @@ -17,14 +17,14 @@ from simplexity.utils.jnp_utils import resolve_jax_device ComponentType = Literal["hmm", "ghmm"] -FactoredState = tuple[jnp.ndarray, ...] +FactoredState = tuple[jax.Array, ...] def _move_arrays_to_device( - arrays: Sequence[jnp.ndarray], + arrays: Sequence[jax.Array], device: jax.Device, # type: ignore[valid-type] name: str, -) -> tuple[jnp.ndarray, ...]: +) -> tuple[jax.Array, ...]: """Move arrays to specified device with warning if needed. Args: @@ -72,9 +72,9 @@ class FactoredGenerativeProcess(GenerativeProcess[FactoredState]): device: jax.Device # type: ignore[valid-type] # Per-factor parameters - transition_matrices: tuple[jnp.ndarray, ...] - normalizing_eigenvectors: tuple[jnp.ndarray, ...] - initial_states: tuple[jnp.ndarray, ...] + transition_matrices: tuple[jax.Array, ...] + normalizing_eigenvectors: tuple[jax.Array, ...] + initial_states: tuple[jax.Array, ...] # Conditional structure and encoding structure: ConditionalStructure @@ -84,9 +84,9 @@ def __init__( self, *, component_types: Sequence[ComponentType], - transition_matrices: Sequence[jnp.ndarray], - normalizing_eigenvectors: Sequence[jnp.ndarray], - initial_states: Sequence[jnp.ndarray], + transition_matrices: Sequence[jax.Array], + normalizing_eigenvectors: Sequence[jax.Array], + initial_states: Sequence[jax.Array], structure: ConditionalStructure, device: str | None = None, ) -> None: @@ -156,7 +156,7 @@ def initial_state(self) -> FactoredState: return tuple(self.initial_states) @eqx.filter_jit - def observation_probability_distribution(self, state: FactoredState) -> jnp.ndarray: + def observation_probability_distribution(self, state: FactoredState) -> jax.Array: """Compute P(composite_token | state) under the conditional structure. Args: @@ -169,7 +169,7 @@ def observation_probability_distribution(self, state: FactoredState) -> jnp.ndar return self.structure.compute_joint_distribution(context) @eqx.filter_jit - def log_observation_probability_distribution(self, log_belief_state: FactoredState) -> jnp.ndarray: + def log_observation_probability_distribution(self, log_belief_state: FactoredState) -> jax.Array: """Compute log P(composite_token | state). Args: @@ -183,7 +183,7 @@ def log_observation_probability_distribution(self, log_belief_state: FactoredSta return jnp.log(probs) @eqx.filter_jit - def emit_observation(self, state: FactoredState, key: jax.Array) -> jnp.ndarray: + def emit_observation(self, state: FactoredState, key: jax.Array) -> jax.Array: """Sample composite observation from current state. Args: @@ -216,7 +216,7 @@ def transition_states(self, state: FactoredState, obs: chex.Array) -> FactoredSt variants = self.structure.select_variants(obs_tuple, context) # Update each factor's state - new_states: list[jnp.ndarray] = [] + new_states: list[jax.Array] = [] for i, (s_i, t_i, k_i) in enumerate(zip(state, obs_tuple, variants, strict=True)): transition_matrix_k = self.transition_matrices[i][k_i] norm_k = self.normalizing_eigenvectors[i][k_i] if self.component_types[i] == "ghmm" else None @@ -226,7 +226,7 @@ def transition_states(self, state: FactoredState, obs: chex.Array) -> FactoredSt return tuple(new_states) @eqx.filter_jit - def probability(self, observations: jnp.ndarray) -> jnp.ndarray: + def probability(self, observations: jax.Array) -> jax.Array: """Compute P(observations) by scanning through sequence. Args: @@ -236,7 +236,7 @@ def probability(self, observations: jnp.ndarray) -> jnp.ndarray: Scalar probability """ - def step(carry: FactoredState, obs: jnp.ndarray): + def step(carry: FactoredState, obs: jax.Array): state = carry dist = self.observation_probability_distribution(state) p = dist[obs] @@ -247,7 +247,7 @@ def step(carry: FactoredState, obs: jnp.ndarray): return jnp.prod(ps) @eqx.filter_jit - def log_probability(self, observations: jnp.ndarray) -> jnp.ndarray: + def log_probability(self, observations: jax.Array) -> jax.Array: """Compute log P(observations) by scanning through sequence. Args: @@ -257,7 +257,7 @@ def log_probability(self, observations: jnp.ndarray) -> jnp.ndarray: Scalar log-probability """ - def step(carry: FactoredState, obs: jnp.ndarray): + def step(carry: FactoredState, obs: jax.Array): state = carry # Compute distribution directly without converting to log and back dist = self.observation_probability_distribution(state) diff --git a/simplexity/generative_processes/structures/conditional_transitions.py b/simplexity/generative_processes/structures/conditional_transitions.py index 46dfa1c5..9e4db05b 100644 --- a/simplexity/generative_processes/structures/conditional_transitions.py +++ b/simplexity/generative_processes/structures/conditional_transitions.py @@ -41,20 +41,20 @@ class ConditionalTransitions(eqx.Module): vocab_sizes_py: Python int tuple of vocab sizes """ - control_maps_transition: tuple[jnp.ndarray, ...] - emission_variant_indices: jnp.ndarray # shape [F] - emission_control_maps: tuple[jnp.ndarray | None, ...] + control_maps_transition: tuple[jax.Array, ...] + emission_variant_indices: jax.Array # shape [F] + emission_control_maps: tuple[jax.Array | None, ...] use_emission_chain: bool - other_multipliers: tuple[jnp.ndarray, ...] - prefix_multipliers: tuple[jnp.ndarray, ...] + other_multipliers: tuple[jax.Array, ...] + prefix_multipliers: tuple[jax.Array, ...] vocab_sizes_py: tuple[int, ...] def __init__( self, - control_maps_transition: tuple[jnp.ndarray, ...], - emission_variant_indices: jnp.ndarray | Sequence[int], - vocab_sizes: jnp.ndarray, - emission_control_maps: tuple[jnp.ndarray | None, ...] | None = None, + control_maps_transition: tuple[jax.Array, ...], + emission_variant_indices: jax.Array | Sequence[int], + vocab_sizes: jax.Array, + emission_control_maps: tuple[jax.Array | None, ...] | None = None, ): """Initialize conditional transitions structure. @@ -74,7 +74,7 @@ def __init__( # Process emission control maps use_chain = False - ecm_list: list[jnp.ndarray | None] = [] + ecm_list: list[jax.Array | None] = [] if emission_control_maps is not None: for i, cm_i in enumerate(emission_control_maps): if cm_i is None: @@ -89,7 +89,7 @@ def __init__( self.use_emission_chain = bool(use_chain) # Precompute multipliers for other-factor indexing (for transitions) - other_multipliers: list[jnp.ndarray] = [] + other_multipliers: list[jax.Array] = [] for i in range(num_factors): mult = [] for j in range(num_factors): @@ -106,7 +106,7 @@ def __init__( self.other_multipliers = tuple(other_multipliers) # Precompute multipliers for prefix indexing (for sequential emissions) - prefix_multipliers: list[jnp.ndarray] = [] + prefix_multipliers: list[jax.Array] = [] for i in range(num_factors): pmult = [] for j in range(num_factors): @@ -120,17 +120,17 @@ def __init__( prefix_multipliers.append(jnp.array(pmult)) self.prefix_multipliers = tuple(prefix_multipliers) - def _flatten_other_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray: + def _flatten_other_tokens_index(self, tokens: jax.Array, i: int) -> jax.Array: """Flatten other-factor tokens to transition control map index.""" mult = self.other_multipliers[i] return jnp.sum(tokens * mult) - def _flatten_prev_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray: + def _flatten_prev_tokens_index(self, tokens: jax.Array, i: int) -> jax.Array: """Flatten prefix tokens to emission control map index.""" mult = self.prefix_multipliers[i] return jnp.sum(tokens * mult) - def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: """Compute joint distribution based on emission mode. Args: @@ -174,7 +174,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray ks = jnp.arange(variant_k, dtype=jnp.int32) # Compute all variant distributions - def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: + def get_dist_i(k: jax.Array, i: int = i) -> jax.Array: transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) @@ -200,9 +200,9 @@ def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: def select_variants( self, - obs_tuple: tuple[jnp.ndarray, ...], + obs_tuple: tuple[jax.Array, ...], context: ConditionalContext, # pylint: disable=unused-argument - ) -> tuple[jnp.ndarray, ...]: + ) -> tuple[jax.Array, ...]: """Select transition variants based on other factors' tokens. Note: This returns TRANSITION variants, not emission variants. @@ -226,7 +226,7 @@ def get_required_params(self) -> dict[str, type]: """Return required parameters for conditional transitions structure.""" return { "control_maps_transition": tuple, - "emission_variant_indices": jnp.ndarray, - "vocab_sizes": jnp.ndarray, + "emission_variant_indices": jax.Array, + "vocab_sizes": jax.Array, "emission_control_maps": tuple, # optional } diff --git a/simplexity/generative_processes/structures/fully_conditional.py b/simplexity/generative_processes/structures/fully_conditional.py index 4e011123..d8ea5d14 100644 --- a/simplexity/generative_processes/structures/fully_conditional.py +++ b/simplexity/generative_processes/structures/fully_conditional.py @@ -30,8 +30,8 @@ class FullyConditional(eqx.Module): joint_vocab_size: Total vocabulary size (product of all V_i) """ - control_maps: tuple[jnp.ndarray, ...] - other_multipliers: tuple[jnp.ndarray, ...] + control_maps: tuple[jax.Array, ...] + other_multipliers: tuple[jax.Array, ...] other_shapes: tuple[tuple[int, ...], ...] perms_py: tuple[tuple[int, ...], ...] vocab_sizes_py: tuple[int, ...] @@ -39,8 +39,8 @@ class FullyConditional(eqx.Module): def __init__( self, - control_maps: tuple[jnp.ndarray, ...], - vocab_sizes: jnp.ndarray, + control_maps: tuple[jax.Array, ...], + vocab_sizes: jax.Array, ): """Initialize fully conditional structure. @@ -61,7 +61,7 @@ def __init__( self.joint_vocab_size = jv # Precompute indexing helpers for each factor - other_multipliers: list[jnp.ndarray] = [] + other_multipliers: list[jax.Array] = [] other_shapes: list[tuple[int, ...]] = [] perms_py: list[tuple[int, ...]] = [] @@ -98,7 +98,7 @@ def __init__( self.other_shapes = tuple(other_shapes) self.perms_py = tuple(perms_py) - def _flatten_other_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarray: + def _flatten_other_tokens_index(self, tokens: jax.Array, i: int) -> jax.Array: """Flatten other-factor tokens to control map index. Args: @@ -112,7 +112,7 @@ def _flatten_other_tokens_index(self, tokens: jnp.ndarray, i: int) -> jnp.ndarra # Multiply elementwise and sum (mult[i] == 0) return jnp.sum(tokens * mult) - def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: """Compute joint distribution using product-of-experts. For each factor i, computes conditional P(t_i | all other t_j), @@ -138,7 +138,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray ks = jnp.arange(variant_k, dtype=jnp.int32) # Compute all variant distributions for factor i - def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: + def get_dist_i(k: jax.Array, i: int = i) -> jax.Array: transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) @@ -165,15 +165,15 @@ def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: sum_j = jnp.sum(prod_j) norm_j = jnp.where(sum_j > 0, prod_j / sum_j, jnp.ones_like(prod_j) / self.joint_vocab_size) - assert isinstance(norm_j, jnp.ndarray) + assert isinstance(norm_j, jax.Array) return norm_j.reshape(-1) def select_variants( self, - obs_tuple: tuple[jnp.ndarray, ...], + obs_tuple: tuple[jax.Array, ...], context: ConditionalContext, # pylint: disable=unused-argument - ) -> tuple[jnp.ndarray, ...]: + ) -> tuple[jax.Array, ...]: """Select variants based on all other factors' tokens. Args: @@ -193,4 +193,4 @@ def select_variants( def get_required_params(self) -> dict[str, type]: """Return required parameters for fully conditional structure.""" - return {"control_maps": tuple, "vocab_sizes": jnp.ndarray} + return {"control_maps": tuple, "vocab_sizes": jax.Array} diff --git a/simplexity/generative_processes/structures/independent.py b/simplexity/generative_processes/structures/independent.py index b9cd165a..f47f9c6d 100644 --- a/simplexity/generative_processes/structures/independent.py +++ b/simplexity/generative_processes/structures/independent.py @@ -24,7 +24,7 @@ class IndependentStructure(eqx.Module): This is the simplest factored structure. """ - def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: """Compute joint distribution as product of independent factors. Args: @@ -54,9 +54,9 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray def select_variants( self, - obs_tuple: tuple[jnp.ndarray, ...], + obs_tuple: tuple[jax.Array, ...], context: ConditionalContext, # pylint: disable=unused-argument # Required by protocol - ) -> tuple[jnp.ndarray, ...]: + ) -> tuple[jax.Array, ...]: """Select variants (always 0 for all factors). Args: diff --git a/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py index 24027701..dd5023b3 100644 --- a/simplexity/generative_processes/structures/protocol.py +++ b/simplexity/generative_processes/structures/protocol.py @@ -11,7 +11,7 @@ import jax.numpy as jnp ComponentType = Literal["hmm", "ghmm"] -FactoredState = tuple[jnp.ndarray, ...] +FactoredState = tuple[jax.Array, ...] @dataclass @@ -29,9 +29,9 @@ class ConditionalContext: states: FactoredState component_types: tuple[ComponentType, ...] - transition_matrices: tuple[jnp.ndarray, ...] - normalizing_eigenvectors: tuple[jnp.ndarray, ...] - vocab_sizes: jnp.ndarray + transition_matrices: tuple[jax.Array, ...] + normalizing_eigenvectors: tuple[jax.Array, ...] + vocab_sizes: jax.Array num_variants: tuple[int, ...] @@ -42,12 +42,12 @@ class ConditionalStructure(Protocol): to produce joint observation distributions and how variant selection works. """ - def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: ... + def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: ... def select_variants( self, - obs_tuple: tuple[jnp.ndarray, ...], + obs_tuple: tuple[jax.Array, ...], context: ConditionalContext, - ) -> tuple[jnp.ndarray, ...]: ... + ) -> tuple[jax.Array, ...]: ... def get_required_params(self) -> dict[str, type]: ... diff --git a/simplexity/generative_processes/structures/sequential_conditional.py b/simplexity/generative_processes/structures/sequential_conditional.py index a427fda0..fb08b03a 100644 --- a/simplexity/generative_processes/structures/sequential_conditional.py +++ b/simplexity/generative_processes/structures/sequential_conditional.py @@ -29,13 +29,13 @@ class SequentialConditional(eqx.Module): vocab_sizes_py: Python int tuple of vocab sizes (for reshape operations) """ - control_maps: tuple[jnp.ndarray | None, ...] + control_maps: tuple[jax.Array | None, ...] vocab_sizes_py: tuple[int, ...] | None def __init__( self, - control_maps: tuple[jnp.ndarray | None, ...], - vocab_sizes: jnp.ndarray | None = None, + control_maps: tuple[jax.Array | None, ...], + vocab_sizes: jax.Array, ): """Initialize sequential conditional structure. @@ -49,7 +49,7 @@ def __init__( self.control_maps = tuple(control_maps) self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) if vocab_sizes is not None else None - def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray: + def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: """Compute joint distribution using sequential factorization. Builds P(t0, t1, ..., tF) = P(t0) * P(t1|t0) * ... * P(tF|t_{F-1}) @@ -81,7 +81,7 @@ def compute_joint_distribution(self, context: ConditionalContext) -> jnp.ndarray ks = jnp.arange(num_var_i, dtype=jnp.int32) # Vectorize over variants - def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: + def get_dist_i(k: jax.Array, i: int = i) -> jax.Array: transition_matrix_k = transition_matrices[i][k] norm_k = normalizing_eigenvectors[i][k] if component_types[i] == "ghmm" else None return compute_obs_dist_for_variant(component_types[i], states[i], transition_matrix_k, norm_k) @@ -110,9 +110,9 @@ def get_dist_i(k: jnp.ndarray, i: int = i) -> jnp.ndarray: def select_variants( self, - obs_tuple: tuple[jnp.ndarray, ...], + obs_tuple: tuple[jax.Array, ...], context: ConditionalContext, # pylint: disable=unused-argument - ) -> tuple[jnp.ndarray, ...]: + ) -> tuple[jax.Array, ...]: """Select variants based on parent tokens in chain. Args: diff --git a/simplexity/utils/factoring_utils.py b/simplexity/utils/factoring_utils.py index 73578d2a..6da30f8a 100644 --- a/simplexity/utils/factoring_utils.py +++ b/simplexity/utils/factoring_utils.py @@ -10,6 +10,7 @@ import chex import equinox as eqx +import jax import jax.numpy as jnp ComponentType = Literal["hmm", "ghmm"] @@ -17,10 +18,10 @@ def compute_obs_dist_for_variant( component_type: ComponentType, - state: jnp.ndarray, - transition_matrix: jnp.ndarray, - normalizing_eigenvector: jnp.ndarray | None = None, -) -> jnp.ndarray: + state: jax.Array, + transition_matrix: jax.Array, + normalizing_eigenvector: jax.Array | None = None, +) -> jax.Array: """Compute observation distribution for a single factor variant. Args: @@ -47,11 +48,11 @@ def compute_obs_dist_for_variant( def transition_with_obs( component_type: ComponentType, - state: jnp.ndarray, - transition_matrix: jnp.ndarray, - obs: jnp.ndarray, - normalizing_eigenvector: jnp.ndarray | None = None, -) -> jnp.ndarray: + state: jax.Array, + transition_matrix: jax.Array, + obs: jax.Array, + normalizing_eigenvector: jax.Array | None = None, +) -> jax.Array: """Update state after observing a token. Args: @@ -88,10 +89,10 @@ class TokenEncoder(eqx.Module): radix_multipliers: Array of shape [F] with multipliers for encoding """ - vocab_sizes: jnp.ndarray # shape [F] - radix_multipliers: jnp.ndarray # shape [F] + vocab_sizes: jax.Array # shape [F] + radix_multipliers: jax.Array # shape [F] - def __init__(self, vocab_sizes: jnp.ndarray): + def __init__(self, vocab_sizes: jax.Array): """Initialize encoder with vocab sizes. Args: @@ -119,7 +120,7 @@ def composite_vocab_size(self) -> int: """Total vocabulary size of composite observation.""" return int(jnp.prod(self.vocab_sizes)) - def tuple_to_token(self, token_tuple: tuple[jnp.ndarray, ...]) -> jnp.ndarray: + def tuple_to_token(self, token_tuple: tuple[jax.Array, ...]) -> jax.Array: """Convert per-factor tokens to composite token. Args: @@ -135,7 +136,7 @@ def tuple_to_token(self, token_tuple: tuple[jnp.ndarray, ...]) -> jnp.ndarray: multiplier *= self.vocab_sizes[i] return token - def token_to_tuple(self, token: chex.Array) -> tuple[jnp.ndarray, ...]: + def token_to_tuple(self, token: chex.Array) -> tuple[jax.Array, ...]: """Convert composite token to per-factor tokens. Args: @@ -153,7 +154,7 @@ def token_to_tuple(self, token: chex.Array) -> tuple[jnp.ndarray, ...]: remaining = remaining // v return tuple(reversed(result)) - def extract_factors_vectorized(self, tokens: jnp.ndarray) -> jnp.ndarray: + def extract_factors_vectorized(self, tokens: jax.Array) -> jax.Array: """Extract per-factor tokens from batch of composite tokens. Args: diff --git a/tests/analysis/test_layerwise_analysis.py b/tests/analysis/test_layerwise_analysis.py index c0d1a839..e6886d5f 100644 --- a/tests/analysis/test_layerwise_analysis.py +++ b/tests/analysis/test_layerwise_analysis.py @@ -1,5 +1,6 @@ """Tests for the LayerwiseAnalysis orchestrator.""" +import jax import jax.numpy as jnp import pytest @@ -7,7 +8,7 @@ @pytest.fixture -def analysis_inputs() -> tuple[dict[str, jnp.ndarray], jnp.ndarray, jnp.ndarray]: +def analysis_inputs() -> tuple[dict[str, jax.Array], jax.Array, jax.Array]: """Provides sample activations, weights, and belief states for analysis tests.""" activations = { "layer_a": jnp.array([[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]), diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py index 34ffd523..e3a09bca 100644 --- a/tests/generative_processes/test_factored_structures.py +++ b/tests/generative_processes/test_factored_structures.py @@ -254,7 +254,7 @@ def test_fully_conditional_get_required_params(): """FullyConditional should return required params.""" structure = FullyConditional(control_maps=(jnp.array([0], dtype=jnp.int32),), vocab_sizes=jnp.array([2])) required_params = structure.get_required_params() - assert required_params == {"control_maps": tuple, "vocab_sizes": jnp.ndarray} + assert required_params == {"control_maps": tuple, "vocab_sizes": jax.Array} def test_fully_conditional_with_zero_normalization(): diff --git a/tests/generative_processes/test_transition_matrices.py b/tests/generative_processes/test_transition_matrices.py index 2d6f0960..e319834e 100644 --- a/tests/generative_processes/test_transition_matrices.py +++ b/tests/generative_processes/test_transition_matrices.py @@ -50,7 +50,7 @@ def validate_ghmm_transition_matrices(transition_matrices: jax.Array, ergodic: b def validate_hmm_transition_matrices( - transition_matrices: jnp.ndarray, ergodic: bool = True, rtol: float = 1e-6, atol: float = 0 + transition_matrices: jax.Array, ergodic: bool = True, rtol: float = 1e-6, atol: float = 0 ): """Test the validate_hmm_transition_matrices function.""" validate_ghmm_transition_matrices(transition_matrices, ergodic) From 3c1303232aa03573dff8b9381353a68425233d66 Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 13:04:01 -0800 Subject: [PATCH 25/37] Fix JIT compilation issue Previous code extracted values from JAX arrays and convert to Python ints at runtime. This will fail when the function is JIT-compiled because JAX arrays become tracers during compilation, and int() on a tracer raises an error. The vocab_sizes parameter must be provided to __init__ for this method to work with JIT. --- .../structures/sequential_conditional.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/simplexity/generative_processes/structures/sequential_conditional.py b/simplexity/generative_processes/structures/sequential_conditional.py index fb08b03a..87c254ad 100644 --- a/simplexity/generative_processes/structures/sequential_conditional.py +++ b/simplexity/generative_processes/structures/sequential_conditional.py @@ -30,7 +30,7 @@ class SequentialConditional(eqx.Module): """ control_maps: tuple[jax.Array | None, ...] - vocab_sizes_py: tuple[int, ...] | None + vocab_sizes_py: tuple[int, ...] def __init__( self, @@ -43,11 +43,10 @@ def __init__( control_maps: Control maps for variant selection. control_maps[0] should be None (root factor). control_maps[i] for i>0 should have shape [V_{i-1}] mapping parent token to variant index. - vocab_sizes: Optional vocab sizes for shape operations. If provided, - should be array of shape [F]. + vocab_sizes: Vocab sizes for shape operations. Must be array of shape [F]. """ self.control_maps = tuple(control_maps) - self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) if vocab_sizes is not None else None + self.vocab_sizes_py = tuple(int(v) for v in vocab_sizes) def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: """Compute joint distribution using sequential factorization. @@ -96,12 +95,8 @@ def get_dist_i(k: jax.Array, i: int = i) -> jax.Array: # Current joint has shape [..., V_{i-1}] # We want to expand to [..., V_{i-1}, V_i] # Use precomputed Python ints for reshape (JIT-compatible) - if self.vocab_sizes_py is not None: - prev_vocab_size = self.vocab_sizes_py[i - 1] - curr_vocab_size = self.vocab_sizes_py[i] - else: - prev_vocab_size = int(context.vocab_sizes[i - 1]) - curr_vocab_size = int(context.vocab_sizes[i]) + prev_vocab_size = self.vocab_sizes_py[i - 1] + curr_vocab_size = self.vocab_sizes_py[i] left = joint.reshape(-1, prev_vocab_size) # [P, V_{i-1}] extended = left[..., None] * cond[None, ...] # [P, V_{i-1}, V_i] joint = extended.reshape(joint.shape + (curr_vocab_size,)) From b666cc18582710cc88ae980ac321a85baa1e6d4d Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 13:10:29 -0800 Subject: [PATCH 26/37] Refactor generative process config tests to use a helper method for creating factored process configurations. Added parameterized tests for valid and invalid configurations, improving test coverage and maintainability. --- .../test_generative_process_config.py | 142 +++++++++++------- 1 file changed, 86 insertions(+), 56 deletions(-) diff --git a/tests/structured_configs/test_generative_process_config.py b/tests/structured_configs/test_generative_process_config.py index be196e33..fdcda804 100644 --- a/tests/structured_configs/test_generative_process_config.py +++ b/tests/structured_configs/test_generative_process_config.py @@ -931,71 +931,101 @@ def test_factored_process_from_spec_config_detection(self) -> None: assert is_generative_process_target(target) assert is_generative_process_config(cfg) - def test_validate_generative_process_config_handles_factored_process_builders(self) -> None: - """Test validate_generative_process_config works with unified factored process builder.""" - # Independent - cfg = DictConfig( + def _make_factored_process_cfg( + self, structure_type: str, extra_instance_fields: dict[str, Any] | None = None + ) -> DictConfig: + """Helper to create a factored process config for testing.""" + spec = [ { - "instance": DictConfig( - { - "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", - "structure_type": "independent", - "spec": [ - { - "component_type": "hmm", - "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], - } - ], - } - ), - "base_vocab_size": MISSING, - "vocab_size": MISSING, + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], } - ) - cfg = _with_missing_tokens(cfg) - validate_generative_process_config(cfg) + ] + instance_data: dict[str, Any] = { + "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", + "structure_type": structure_type, + "spec": spec, + } + if extra_instance_fields: + instance_data.update(extra_instance_fields) - # Chain cfg = DictConfig( { - "instance": DictConfig( - { - "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", - "structure_type": "chain", - "spec": [ - { - "component_type": "hmm", - "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], - } - ], - } - ), + "instance": DictConfig(instance_data), "base_vocab_size": MISSING, "vocab_size": MISSING, } ) - cfg = _with_missing_tokens(cfg) + return _with_missing_tokens(cfg) + + @pytest.mark.parametrize( + ("structure_type", "extra_instance_fields"), + [ + ("independent", None), + ("chain", None), + ("symmetric", {"control_maps": [[0]]}), + ("transition_coupled", {"control_maps_transition": [[0]], "emission_variant_indices": [0]}), + ], + ) + def test_validate_generative_process_config_handles_factored_process_builders( + self, structure_type: str, extra_instance_fields: dict[str, Any] | None + ) -> None: + """Test validate_generative_process_config works with unified factored process builder.""" + cfg = self._make_factored_process_cfg(structure_type, extra_instance_fields) validate_generative_process_config(cfg) - # Symmetric - cfg = DictConfig( - { - "instance": DictConfig( - { - "_target_": "simplexity.generative_processes.builder.build_factored_process_from_spec", - "structure_type": "symmetric", - "spec": [ - { - "component_type": "hmm", - "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], - } - ], - "control_maps": [[0]], - } - ), - "base_vocab_size": MISSING, - "vocab_size": MISSING, - } - ) - cfg = _with_missing_tokens(cfg) + @pytest.mark.parametrize( + ("structure_type", "extra_instance_fields", "expected_error_pattern"), + [ + # Invalid structure_type values - these pass config validation (target is still valid) + # but would fail when the builder is called + ( + "invalid_structure_type", + None, + None, # Config validation passes, builder will raise ValueError + ), + ( + "", + None, + None, # Empty structure_type passes config validation + ), + # Missing required fields - these pass config validation + # (structure-specific fields are validated by the builder, not config validator) + ( + "symmetric", + None, + None, # Missing control_maps - builder will raise ValueError + ), + ( + "chain", + None, + None, # Missing control_maps - builder will raise ValueError + ), + ( + "transition_coupled", + None, + None, # Missing control_maps_transition - builder will raise ValueError + ), + ( + "transition_coupled", + {"control_maps_transition": [[0]]}, + None, # Missing emission_variant_indices - builder will raise ValueError + ), + ], + ) + def test_validate_generative_process_config_invalid_factored_process_configs( + self, + structure_type: str, + extra_instance_fields: dict[str, Any] | None, + expected_error_pattern: str | None, + ) -> None: + """Test validate_generative_process_config with invalid factored process configs. + + Note: Config validation doesn't check structure-specific required fields (like control_maps + for symmetric/chain or control_maps_transition/emission_variant_indices for transition_coupled) + or invalid structure_type values. These are validated by the builder when the process is + actually constructed. This test documents that config validation passes for these cases. + """ + cfg = self._make_factored_process_cfg(structure_type, extra_instance_fields) + # All these cases pass config validation (builder will validate later) validate_generative_process_config(cfg) From 1d23c0abf7057f9d5ddff3dbc31180e400b847e3 Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 13:13:28 -0800 Subject: [PATCH 27/37] Add docstrings --- .../generative_processes/structures/protocol.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py index dd5023b3..9f82f958 100644 --- a/simplexity/generative_processes/structures/protocol.py +++ b/simplexity/generative_processes/structures/protocol.py @@ -8,7 +8,7 @@ from dataclasses import dataclass from typing import Literal, Protocol -import jax.numpy as jnp +import jax ComponentType = Literal["hmm", "ghmm"] FactoredState = tuple[jax.Array, ...] @@ -42,12 +42,18 @@ class ConditionalStructure(Protocol): to produce joint observation distributions and how variant selection works. """ - def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: ... + def compute_joint_distribution(self, context: ConditionalContext) -> jax.Array: + """Compute the joint distribution of the conditional structure.""" + ... # pylint: disable=unnecessary-ellipsis def select_variants( self, obs_tuple: tuple[jax.Array, ...], context: ConditionalContext, - ) -> tuple[jax.Array, ...]: ... + ) -> tuple[jax.Array, ...]: + """Select the variants of the conditional structure.""" + ... # pylint: disable=unnecessary-ellipsis - def get_required_params(self) -> dict[str, type]: ... + def get_required_params(self) -> dict[str, type]: + """Get the required parameters for the conditional structure.""" + ... # pylint: disable=unnecessary-ellipsis From eac6194b5e5aaa7684c4a12e763c0d95600f4ac6 Mon Sep 17 00:00:00 2001 From: Eric Alt Date: Fri, 5 Dec 2025 13:15:00 -0800 Subject: [PATCH 28/37] Add match strings to value errors in tests --- tests/generative_processes/test_builder.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index d0b84453..bd80643b 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -9,6 +9,8 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. +import re + import chex import jax.numpy as jnp import pytest @@ -317,7 +319,7 @@ def test_build_chain_from_spec_returns_control_maps(chain_spec): def test_build_chain_from_spec_missing_control_map_raises(components_spec): """Every non-root node in a chain must provide a control map.""" - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=re.escape("chain[1].control_map is required for i>0")): build_chain_from_spec(components_spec) @@ -333,7 +335,7 @@ def test_build_symmetric_from_spec_validates_lengths(components_spec, symmetric_ assert component_types == ["hmm", "hmm"] assert len(control_maps) == 2 chex.assert_trees_all_close(control_maps[0], jnp.array([0, 1], dtype=jnp.int32)) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=re.escape("control_maps[0] length 1 must equal prod(V_j for j!=[0]) = 2")): build_symmetric_from_spec(components_spec, [[0], [0, 1]]) From d3be4796ee52635f5983fdd3e8acb1a3b0e06098 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 15:00:30 -0800 Subject: [PATCH 29/37] add better factor handling and allow regression to individual factors --- simplexity/activations/activation_analyses.py | 6 +- simplexity/analysis/linear_regression.py | 46 +++++- simplexity/generative_processes/builder.py | 2 +- simplexity/generative_processes/generator.py | 20 ++- .../generative_processes/torch_generator.py | 12 +- tests/analysis/test_linear_regression.py | 134 ++++++++++++++++++ .../test_factored_structures.py | 17 ++- tests/generative_processes/test_generator.py | 9 +- .../test_torch_generator.py | 7 +- 9 files changed, 226 insertions(+), 27 deletions(-) diff --git a/simplexity/activations/activation_analyses.py b/simplexity/activations/activation_analyses.py index 8e843e13..6888b6ed 100644 --- a/simplexity/activations/activation_analyses.py +++ b/simplexity/activations/activation_analyses.py @@ -76,13 +76,14 @@ def __init__( concat_layers: bool = False, use_probs_as_weights: bool = True, fit_intercept: bool = True, + to_factors: bool = False, ) -> None: super().__init__( analysis_type="linear_regression", last_token_only=last_token_only, concat_layers=concat_layers, use_probs_as_weights=use_probs_as_weights, - analysis_kwargs={"fit_intercept": fit_intercept}, + analysis_kwargs={"fit_intercept": fit_intercept, "to_factors": to_factors}, ) @@ -97,8 +98,9 @@ def __init__( use_probs_as_weights: bool = True, rcond_values: Sequence[float] | None = None, fit_intercept: bool = True, + to_factors: bool = False, ) -> None: - analysis_kwargs: dict[str, Any] = {"fit_intercept": fit_intercept} + analysis_kwargs: dict[str, Any] = {"fit_intercept": fit_intercept, "to_factors": to_factors} if rcond_values is not None: analysis_kwargs["rcond_values"] = tuple(rcond_values) super().__init__( diff --git a/simplexity/analysis/linear_regression.py b/simplexity/analysis/linear_regression.py index 54a8315d..9729022d 100644 --- a/simplexity/analysis/linear_regression.py +++ b/simplexity/analysis/linear_regression.py @@ -139,22 +139,62 @@ def linear_regression_svd( def layer_linear_regression( layer_activations: jax.Array, weights: jax.Array, - belief_states: jax.Array | None, + belief_states: jax.Array | tuple[jax.Array, ...] | None, + to_factors: bool = False, **kwargs: Any, ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: """Layer-wise regression helper that wraps :func:`linear_regression`.""" + if belief_states is None: raise ValueError("linear_regression requires belief_states") - return linear_regression(layer_activations, belief_states, weights, **kwargs) + + if to_factors: + scalars, projections = {}, {} + if not isinstance(belief_states, tuple): + raise ValueError("belief_states must be a tuple when to_factors is True") + for factor_idx, factor in enumerate(belief_states): + if not isinstance(factor, jax.Array): + raise ValueError("Each factor in belief_states must be a jax.Array") + factor_scalars, factor_projections = linear_regression(layer_activations, factor, weights, **kwargs) + for key, value in factor_scalars.items(): + scalars[f"{key}_factor_{factor_idx}"] = value + for key, value in factor_projections.items(): + projections[f"{key}_factor_{factor_idx}"] = value + return scalars, projections + else: + if isinstance(belief_states, tuple): + raise ValueError("belief_states must be a single array when to_factors is False") + return linear_regression(layer_activations, belief_states, weights, **kwargs) + def layer_linear_regression_svd( layer_activations: jax.Array, weights: jax.Array, belief_states: jax.Array | None, + to_factors: bool = False, **kwargs: Any, ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: """Layer-wise regression helper that wraps :func:`linear_regression_svd`.""" if belief_states is None: raise ValueError("linear_regression_svd requires belief_states") - return linear_regression_svd(layer_activations, belief_states, weights, **kwargs) + + if to_factors: + scalars, projections = {}, {} + if not isinstance(belief_states, tuple): + raise ValueError("belief_states must be a tuple when to_factors is True") + for factor_idx, factor in enumerate(belief_states): + if not isinstance(factor, jax.Array): + raise ValueError("Each factor in belief_states must be a jax.Array") + factor_scalars, factor_projections = linear_regression_svd( + layer_activations, factor, weights, **kwargs + ) + for key, value in factor_scalars.items(): + scalars[f"{key}_factor_{factor_idx}"] = value + for key, value in factor_projections.items(): + projections[f"{key}_factor_{factor_idx}"] = value + return scalars, projections + else: + if isinstance(belief_states, tuple): + raise ValueError("belief_states must be a single array when to_factors is False") + return linear_regression_svd(layer_activations, belief_states, weights, **kwargs) diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index efb03877..7b94c346 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -563,7 +563,7 @@ def build_symmetric_from_spec( expected *= vocab_sizes[j] if int(cm.shape[0]) != expected: - raise ValueError(f"control_maps[{i}] length {cm.shape[0]} must equal prod(V_j for j!={i}) = {expected}") + raise ValueError(f"control_maps[{i}] length {cm.shape[0]} must equal prod(V_j for j!=[{i}]) = {expected}") return component_types, transition_matrices, normalizing_eigenvectors, initial_states, control_maps_arrays diff --git a/simplexity/generative_processes/generator.py b/simplexity/generative_processes/generator.py index f9e7fa5d..e7d91392 100644 --- a/simplexity/generative_processes/generator.py +++ b/simplexity/generative_processes/generator.py @@ -49,11 +49,10 @@ def generate_data_batch_with_full_history( key: jax.Array, bos_token: int | None = None, eos_token: int | None = None, -) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]: +) -> dict[str, jax.Array | tuple[jax.Array, ...]]: """Generate sequences plus per-token belief states and prefix probabilities.""" batch_keys = jax.random.split(key, batch_size) belief_states, tokens = data_generator.generate(gen_states, batch_keys, sequence_len, True) - next_states = belief_states[:, -1, :] prefix_probs = _compute_prefix_probabilities(data_generator, gen_states, tokens) @@ -74,7 +73,22 @@ def generate_data_batch_with_full_history( labels = tokens[:, 1:] prefix_probs = prefix_probs[:, : inputs.shape[1]] - return next_states, belief_states, prefix_probs, inputs, labels + result = { + "belief_states": ( + jnp.concatenate(belief_states, axis=-1) + if isinstance(belief_states, tuple) + else belief_states + ), + "prefix_probabilities": prefix_probs, + "inputs": inputs, + "labels": labels, + } + + if isinstance(belief_states, tuple): + result["belief_state_factors"] = belief_states + + return result + def _compute_prefix_probabilities( diff --git a/simplexity/generative_processes/torch_generator.py b/simplexity/generative_processes/torch_generator.py index a260fd92..8cb6fe44 100644 --- a/simplexity/generative_processes/torch_generator.py +++ b/simplexity/generative_processes/torch_generator.py @@ -52,9 +52,9 @@ def generate_data_batch_with_full_history( key: jax.Array, bos_token: int | None = None, eos_token: int | None = None, -) -> tuple[jax.Array, jax.Array, jax.Array, torch.Tensor, torch.Tensor]: +) -> dict[str, jax.Array | torch.Tensor | tuple[jax.Array, ...]]: """Generate data plus full belief/prefix histories.""" - next_states, belief_states, prefix_probs, inputs, labels = generate_jax_data_batch_with_full_history( + result = generate_jax_data_batch_with_full_history( gen_states, data_generator, batch_size, @@ -63,4 +63,10 @@ def generate_data_batch_with_full_history( bos_token, eos_token, ) - return next_states, belief_states, prefix_probs, jax_to_torch(inputs), jax_to_torch(labels) + return { + "belief_states": result["belief_states"], + "prefix_probabilities": result["prefix_probabilities"], + "inputs": jax_to_torch(result["inputs"]), + "labels": jax_to_torch(result["labels"]), + **({k: v for k, v in result.items() if k not in ["belief_states", "prefix_probabilities", "inputs", "labels"]}), + } diff --git a/tests/analysis/test_linear_regression.py b/tests/analysis/test_linear_regression.py index f3413429..17345d64 100644 --- a/tests/analysis/test_linear_regression.py +++ b/tests/analysis/test_linear_regression.py @@ -183,3 +183,137 @@ def test_layer_linear_regression_svd_runs_end_to_end() -> None: assert pytest.approx(1.0, abs=1e-6) == scalars["r2"] chex.assert_trees_all_close(projections["projected"], beliefs) + + +def test_layer_linear_regression_to_factors_basic() -> None: + """Layer regression with to_factors should regress to each factor separately.""" + x = jnp.arange(12.0).reshape(4, 3) # 4 samples, 3 features + weights = jnp.ones(4) / 4.0 + + # Two factors: factor 0 has 2 states, factor 1 has 3 states + factor_0 = jnp.array([[0.3, 0.7], [0.5, 0.5], [0.8, 0.2], [0.1, 0.9]]) # [4, 2] + factor_1 = jnp.array([[0.2, 0.3, 0.5], [0.1, 0.6, 0.3], [0.4, 0.4, 0.2], [0.3, 0.3, 0.4]]) # [4, 3] + factored_beliefs = (factor_0, factor_1) + + scalars, projections = layer_linear_regression( + x, + weights, + factored_beliefs, + to_factors=True, + ) + + # Should have separate metrics for each factor + assert "r2_factor_0" in scalars + assert "r2_factor_1" in scalars + assert "rmse_factor_0" in scalars + assert "rmse_factor_1" in scalars + assert "mae_factor_0" in scalars + assert "mae_factor_1" in scalars + assert "dist_factor_0" in scalars + assert "dist_factor_1" in scalars + + # Should have separate projections for each factor + assert "projected_factor_0" in projections + assert "projected_factor_1" in projections + + # Check shapes + assert projections["projected_factor_0"].shape == factor_0.shape + assert projections["projected_factor_1"].shape == factor_1.shape + + +def test_layer_linear_regression_svd_to_factors_basic() -> None: + """Layer regression SVD with to_factors should regress to each factor separately.""" + x = jnp.arange(12.0).reshape(4, 3) # 4 samples, 3 features + weights = jnp.ones(4) / 4.0 + + # Two factors: factor 0 has 2 states, factor 1 has 3 states + factor_0 = jnp.array([[0.3, 0.7], [0.5, 0.5], [0.8, 0.2], [0.1, 0.9]]) # [4, 2] + factor_1 = jnp.array([[0.2, 0.3, 0.5], [0.1, 0.6, 0.3], [0.4, 0.4, 0.2], [0.3, 0.3, 0.4]]) # [4, 3] + factored_beliefs = (factor_0, factor_1) + + scalars, projections = layer_linear_regression_svd( + x, + weights, + factored_beliefs, + to_factors=True, + rcond_values=[1e-6], + ) + + # Should have separate metrics for each factor including best_rcond + assert "r2_factor_0" in scalars + assert "r2_factor_1" in scalars + assert "best_rcond_factor_0" in scalars + assert "best_rcond_factor_1" in scalars + + # Should have separate projections for each factor + assert "projected_factor_0" in projections + assert "projected_factor_1" in projections + + # Check shapes + assert projections["projected_factor_0"].shape == factor_0.shape + assert projections["projected_factor_1"].shape == factor_1.shape + + +def test_layer_linear_regression_to_factors_single_factor() -> None: + """to_factors=True should work with a single factor tuple.""" + x = jnp.arange(9.0).reshape(3, 3) + weights = jnp.ones(3) / 3.0 + + # Single factor in tuple + factor_0 = jnp.array([[0.3, 0.7], [0.5, 0.5], [0.8, 0.2]]) + factored_beliefs = (factor_0,) + + scalars, projections = layer_linear_regression( + x, + weights, + factored_beliefs, + to_factors=True, + ) + + # Should have metrics for single factor + assert "r2_factor_0" in scalars + assert "projected_factor_0" in projections + assert projections["projected_factor_0"].shape == factor_0.shape + + +def test_layer_linear_regression_to_factors_requires_tuple() -> None: + """to_factors=True requires belief_states to be a tuple.""" + x = jnp.ones((3, 2)) + weights = jnp.ones(3) / 3.0 + beliefs_array = jnp.ones((3, 2)) + + with pytest.raises(ValueError, match="belief_states must be a tuple when to_factors is True"): + layer_linear_regression(x, weights, beliefs_array, to_factors=True) + + with pytest.raises(ValueError, match="belief_states must be a tuple when to_factors is True"): + layer_linear_regression_svd(x, weights, beliefs_array, to_factors=True) + + +def test_layer_linear_regression_to_factors_validates_tuple_contents() -> None: + """to_factors=True requires all elements in tuple to be jax.Arrays.""" + x = jnp.ones((3, 2)) + weights = jnp.ones(3) / 3.0 + + # Invalid: tuple contains non-array + invalid_beliefs = (jnp.ones((3, 2)), "not an array") + + with pytest.raises(ValueError, match="Each factor in belief_states must be a jax.Array"): + layer_linear_regression(x, weights, invalid_beliefs, to_factors=True) + + with pytest.raises(ValueError, match="Each factor in belief_states must be a jax.Array"): + layer_linear_regression_svd(x, weights, invalid_beliefs, to_factors=True) + + +def test_layer_linear_regression_to_factors_false_requires_array() -> None: + """to_factors=False requires belief_states to be a single array, not a tuple.""" + x = jnp.ones((3, 2)) + weights = jnp.ones(3) / 3.0 + + # Invalid: tuple when to_factors=False + factored_beliefs = (jnp.ones((3, 2)), jnp.ones((3, 3))) + + with pytest.raises(ValueError, match="belief_states must be a single array when to_factors is False"): + layer_linear_regression(x, weights, factored_beliefs, to_factors=False) + + with pytest.raises(ValueError, match="belief_states must be a single array when to_factors is False"): + layer_linear_regression_svd(x, weights, factored_beliefs, to_factors=False) diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py index e3a09bca..e45af442 100644 --- a/tests/generative_processes/test_factored_structures.py +++ b/tests/generative_processes/test_factored_structures.py @@ -1,6 +1,7 @@ """Tests for factored generative process conditional structures.""" import chex +import jax import jax.numpy as jnp from simplexity.generative_processes.structures import ( @@ -45,7 +46,9 @@ def test_sequential_conditional_joint_distribution_and_variants(): _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), ) context = _make_context(states, transition_matrices) - structure = SequentialConditional(control_maps=(None, jnp.array([0, 1], dtype=jnp.int32))) + structure = SequentialConditional( + control_maps=(None, jnp.array([0, 1], dtype=jnp.int32)), vocab_sizes=context.vocab_sizes + ) dist = structure.compute_joint_distribution(context) expected = jnp.array([0.42, 0.18, 0.08, 0.32], dtype=jnp.float32) @@ -219,7 +222,7 @@ def test_sequential_conditional_with_three_factors(): jnp.array([0, 1], dtype=jnp.int32), jnp.array([1, 0], dtype=jnp.int32), ), - vocab_sizes=jnp.array([2, 2, 2]), + vocab_sizes=context.vocab_sizes, ) dist = structure.compute_joint_distribution(context) @@ -228,15 +231,17 @@ def test_sequential_conditional_with_three_factors(): def test_sequential_conditional_without_vocab_sizes(): - """SequentialConditional should work without precomputed vocab_sizes_py.""" + """SequentialConditional requires vocab_sizes to be provided.""" states = (jnp.array([1.0], dtype=jnp.float32), jnp.array([1.0], dtype=jnp.float32)) transition_matrices = ( _tensor_from_probs([[0.6, 0.4]]), _tensor_from_probs([[0.7, 0.3], [0.2, 0.8]]), ) context = _make_context(states, transition_matrices) - # Don't pass vocab_sizes to constructor - structure = SequentialConditional(control_maps=(None, jnp.array([0, 1], dtype=jnp.int32))) + # vocab_sizes must be provided + structure = SequentialConditional( + control_maps=(None, jnp.array([0, 1], dtype=jnp.int32)), vocab_sizes=context.vocab_sizes + ) dist = structure.compute_joint_distribution(context) expected = jnp.array([0.42, 0.18, 0.08, 0.32], dtype=jnp.float32) @@ -245,7 +250,7 @@ def test_sequential_conditional_without_vocab_sizes(): def test_sequential_conditional_get_required_params(): """SequentialConditional should return required params.""" - structure = SequentialConditional(control_maps=(None,)) + structure = SequentialConditional(control_maps=(None,), vocab_sizes=jnp.array([2])) required_params = structure.get_required_params() assert required_params == {"control_maps": tuple} diff --git a/tests/generative_processes/test_generator.py b/tests/generative_processes/test_generator.py index acdf92ce..048496c2 100644 --- a/tests/generative_processes/test_generator.py +++ b/tests/generative_processes/test_generator.py @@ -103,14 +103,13 @@ def test_generate_data_batch_with_full_history(): gen_state: jax.Array = hmm.initial_state states = jnp.repeat(gen_state[None, :], batch_size, axis=0) key = jax.random.PRNGKey(0) - next_states, belief_states, prefix_probs, inputs, labels = generate_data_batch_with_full_history( + result = generate_data_batch_with_full_history( states, hmm, batch_size, sequence_len, key, ) - assert belief_states.shape == (batch_size, sequence_len, gen_state.shape[0]) - assert prefix_probs.shape == (batch_size, inputs.shape[1]) - assert next_states.shape == (batch_size, gen_state.shape[0]) - assert labels.shape == inputs.shape + assert result["belief_states"].shape == (batch_size, sequence_len, gen_state.shape[0]) + assert result["prefix_probabilities"].shape == (batch_size, result["inputs"].shape[1]) + assert result["labels"].shape == result["inputs"].shape diff --git a/tests/generative_processes/test_torch_generator.py b/tests/generative_processes/test_torch_generator.py index 7052e3c4..50a0c066 100644 --- a/tests/generative_processes/test_torch_generator.py +++ b/tests/generative_processes/test_torch_generator.py @@ -112,13 +112,12 @@ def test_generate_data_batch_with_full_history(): gen_state: jax.Array = hmm.initial_state states = jnp.repeat(gen_state[None, :], batch_size, axis=0) key = jax.random.PRNGKey(123) - next_states, belief_states, prefix_probs, inputs, _ = generate_data_batch_with_full_history( + result = generate_data_batch_with_full_history( states, hmm, batch_size, sequence_len, key, ) - assert belief_states.shape == (batch_size, sequence_len, gen_state.shape[0]) - assert prefix_probs.shape == (batch_size, inputs.shape[1]) - assert next_states.shape == (batch_size, gen_state.shape[0]) + assert result["belief_states"].shape == (batch_size, sequence_len, gen_state.shape[0]) + assert result["prefix_probabilities"].shape == (batch_size, result["inputs"].shape[1]) From 3fac6ccf0eb9a013b2ece5af9fc5c2c99d600076 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 15:08:23 -0800 Subject: [PATCH 30/37] pass device --- simplexity/generative_processes/builder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/simplexity/generative_processes/builder.py b/simplexity/generative_processes/builder.py index 7b94c346..9f0fc929 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -152,7 +152,7 @@ def build_nonergodic_hidden_markov_model( num_states = composite_transition_matrix.shape[1] initial_state = jnp.zeros((num_states,), dtype=composite_transition_matrix.dtype) initial_state = initial_state.at[num_states - 1].set(1) - return HiddenMarkovModel(composite_transition_matrix, initial_state) + return HiddenMarkovModel(composite_transition_matrix, initial_state, device=device) def build_factored_process( From ea5d28fcebb0e0015d3ea7bc492d5071c939b989 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 15:28:34 -0800 Subject: [PATCH 31/37] static analysis --- simplexity/analysis/linear_regression.py | 10 +++------- simplexity/generative_processes/generator.py | 5 +---- .../structures/independent.py | 1 + .../generative_processes/torch_generator.py | 10 ++++++++-- tests/activations/test_activation_analysis.py | 2 +- tests/analysis/test_linear_regression.py | 6 +++--- tests/generative_processes/test_generator.py | 17 ++++++++++++++--- .../test_torch_generator.py | 13 +++++++++++-- 8 files changed, 42 insertions(+), 22 deletions(-) diff --git a/simplexity/analysis/linear_regression.py b/simplexity/analysis/linear_regression.py index 9729022d..15b278d2 100644 --- a/simplexity/analysis/linear_regression.py +++ b/simplexity/analysis/linear_regression.py @@ -144,7 +144,6 @@ def layer_linear_regression( **kwargs: Any, ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: """Layer-wise regression helper that wraps :func:`linear_regression`.""" - if belief_states is None: raise ValueError("linear_regression requires belief_states") @@ -167,18 +166,17 @@ def layer_linear_regression( return linear_regression(layer_activations, belief_states, weights, **kwargs) - def layer_linear_regression_svd( layer_activations: jax.Array, weights: jax.Array, - belief_states: jax.Array | None, + belief_states: jax.Array | tuple[jax.Array, ...] | None, to_factors: bool = False, **kwargs: Any, ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: """Layer-wise regression helper that wraps :func:`linear_regression_svd`.""" if belief_states is None: raise ValueError("linear_regression_svd requires belief_states") - + if to_factors: scalars, projections = {}, {} if not isinstance(belief_states, tuple): @@ -186,9 +184,7 @@ def layer_linear_regression_svd( for factor_idx, factor in enumerate(belief_states): if not isinstance(factor, jax.Array): raise ValueError("Each factor in belief_states must be a jax.Array") - factor_scalars, factor_projections = linear_regression_svd( - layer_activations, factor, weights, **kwargs - ) + factor_scalars, factor_projections = linear_regression_svd(layer_activations, factor, weights, **kwargs) for key, value in factor_scalars.items(): scalars[f"{key}_factor_{factor_idx}"] = value for key, value in factor_projections.items(): diff --git a/simplexity/generative_processes/generator.py b/simplexity/generative_processes/generator.py index e7d91392..e75b9717 100644 --- a/simplexity/generative_processes/generator.py +++ b/simplexity/generative_processes/generator.py @@ -75,9 +75,7 @@ def generate_data_batch_with_full_history( result = { "belief_states": ( - jnp.concatenate(belief_states, axis=-1) - if isinstance(belief_states, tuple) - else belief_states + jnp.concatenate(belief_states, axis=-1) if isinstance(belief_states, tuple) else belief_states ), "prefix_probabilities": prefix_probs, "inputs": inputs, @@ -90,7 +88,6 @@ def generate_data_batch_with_full_history( return result - def _compute_prefix_probabilities( data_generator: GenerativeProcess, initial_states: jax.Array, diff --git a/simplexity/generative_processes/structures/independent.py b/simplexity/generative_processes/structures/independent.py index f47f9c6d..a7edbbdb 100644 --- a/simplexity/generative_processes/structures/independent.py +++ b/simplexity/generative_processes/structures/independent.py @@ -7,6 +7,7 @@ from __future__ import annotations import equinox as eqx +import jax import jax.numpy as jnp from simplexity.generative_processes.structures.protocol import ConditionalContext diff --git a/simplexity/generative_processes/torch_generator.py b/simplexity/generative_processes/torch_generator.py index 8cb6fe44..16595188 100644 --- a/simplexity/generative_processes/torch_generator.py +++ b/simplexity/generative_processes/torch_generator.py @@ -63,10 +63,16 @@ def generate_data_batch_with_full_history( bos_token, eos_token, ) + # Extract inputs and labels (these are always jax.Arrays) + inputs = result["inputs"] + labels = result["labels"] + assert isinstance(inputs, jax.Array) + assert isinstance(labels, jax.Array) + return { "belief_states": result["belief_states"], "prefix_probabilities": result["prefix_probabilities"], - "inputs": jax_to_torch(result["inputs"]), - "labels": jax_to_torch(result["labels"]), + "inputs": jax_to_torch(inputs), + "labels": jax_to_torch(labels), **({k: v for k, v in result.items() if k not in ["belief_states", "prefix_probabilities", "inputs", "labels"]}), } diff --git a/tests/activations/test_activation_analysis.py b/tests/activations/test_activation_analysis.py index 4bbfc6a8..cea954d9 100644 --- a/tests/activations/test_activation_analysis.py +++ b/tests/activations/test_activation_analysis.py @@ -9,7 +9,7 @@ LinearRegressionSVDAnalysis, PcaAnalysis, ) -from simplexity.activations.activation_tracker import ActivationTracker, prepare_activations, PrepareOptions +from simplexity.activations.activation_tracker import ActivationTracker, PrepareOptions, prepare_activations @pytest.fixture diff --git a/tests/analysis/test_linear_regression.py b/tests/analysis/test_linear_regression.py index 17345d64..ce4ecebd 100644 --- a/tests/analysis/test_linear_regression.py +++ b/tests/analysis/test_linear_regression.py @@ -295,13 +295,13 @@ def test_layer_linear_regression_to_factors_validates_tuple_contents() -> None: weights = jnp.ones(3) / 3.0 # Invalid: tuple contains non-array - invalid_beliefs = (jnp.ones((3, 2)), "not an array") + invalid_beliefs = (jnp.ones((3, 2)), "not an array") # type: ignore with pytest.raises(ValueError, match="Each factor in belief_states must be a jax.Array"): - layer_linear_regression(x, weights, invalid_beliefs, to_factors=True) + layer_linear_regression(x, weights, invalid_beliefs, to_factors=True) # type: ignore with pytest.raises(ValueError, match="Each factor in belief_states must be a jax.Array"): - layer_linear_regression_svd(x, weights, invalid_beliefs, to_factors=True) + layer_linear_regression_svd(x, weights, invalid_beliefs, to_factors=True) # type: ignore def test_layer_linear_regression_to_factors_false_requires_array() -> None: diff --git a/tests/generative_processes/test_generator.py b/tests/generative_processes/test_generator.py index 048496c2..c83c17c6 100644 --- a/tests/generative_processes/test_generator.py +++ b/tests/generative_processes/test_generator.py @@ -110,6 +110,17 @@ def test_generate_data_batch_with_full_history(): sequence_len, key, ) - assert result["belief_states"].shape == (batch_size, sequence_len, gen_state.shape[0]) - assert result["prefix_probabilities"].shape == (batch_size, result["inputs"].shape[1]) - assert result["labels"].shape == result["inputs"].shape + # Extract and type-check all fields + belief_states = result["belief_states"] + prefix_probs = result["prefix_probabilities"] + inputs = result["inputs"] + labels = result["labels"] + + assert isinstance(belief_states, jax.Array) + assert isinstance(prefix_probs, jax.Array) + assert isinstance(inputs, jax.Array) + assert isinstance(labels, jax.Array) + + assert belief_states.shape == (batch_size, sequence_len, gen_state.shape[0]) + assert prefix_probs.shape == (batch_size, inputs.shape[1]) + assert labels.shape == inputs.shape diff --git a/tests/generative_processes/test_torch_generator.py b/tests/generative_processes/test_torch_generator.py index 50a0c066..b532c5cc 100644 --- a/tests/generative_processes/test_torch_generator.py +++ b/tests/generative_processes/test_torch_generator.py @@ -119,5 +119,14 @@ def test_generate_data_batch_with_full_history(): sequence_len, key, ) - assert result["belief_states"].shape == (batch_size, sequence_len, gen_state.shape[0]) - assert result["prefix_probabilities"].shape == (batch_size, result["inputs"].shape[1]) + # Extract and type-check all fields + belief_states = result["belief_states"] + prefix_probs = result["prefix_probabilities"] + inputs = result["inputs"] + + assert isinstance(belief_states, jax.Array) + assert isinstance(prefix_probs, jax.Array) + assert isinstance(inputs, torch.Tensor) + + assert belief_states.shape == (batch_size, sequence_len, gen_state.shape[0]) + assert prefix_probs.shape == (batch_size, inputs.shape[1]) From 3bb6984dafbfb0e31300fd4bf669f7f0ff25c7c3 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 15:38:36 -0800 Subject: [PATCH 32/37] better output format --- simplexity/analysis/linear_regression.py | 8 ++--- tests/analysis/test_linear_regression.py | 46 ++++++++++++------------ 2 files changed, 27 insertions(+), 27 deletions(-) diff --git a/simplexity/analysis/linear_regression.py b/simplexity/analysis/linear_regression.py index 15b278d2..d9d0bf48 100644 --- a/simplexity/analysis/linear_regression.py +++ b/simplexity/analysis/linear_regression.py @@ -156,9 +156,9 @@ def layer_linear_regression( raise ValueError("Each factor in belief_states must be a jax.Array") factor_scalars, factor_projections = linear_regression(layer_activations, factor, weights, **kwargs) for key, value in factor_scalars.items(): - scalars[f"{key}_factor_{factor_idx}"] = value + scalars[f"factor_{factor_idx}/{key}"] = value for key, value in factor_projections.items(): - projections[f"{key}_factor_{factor_idx}"] = value + projections[f"factor_{factor_idx}/{key}"] = value return scalars, projections else: if isinstance(belief_states, tuple): @@ -186,9 +186,9 @@ def layer_linear_regression_svd( raise ValueError("Each factor in belief_states must be a jax.Array") factor_scalars, factor_projections = linear_regression_svd(layer_activations, factor, weights, **kwargs) for key, value in factor_scalars.items(): - scalars[f"{key}_factor_{factor_idx}"] = value + scalars[f"factor_{factor_idx}/{key}"] = value for key, value in factor_projections.items(): - projections[f"{key}_factor_{factor_idx}"] = value + projections[f"factor_{factor_idx}/{key}"] = value return scalars, projections else: if isinstance(belief_states, tuple): diff --git a/tests/analysis/test_linear_regression.py b/tests/analysis/test_linear_regression.py index ce4ecebd..68317982 100644 --- a/tests/analysis/test_linear_regression.py +++ b/tests/analysis/test_linear_regression.py @@ -203,22 +203,22 @@ def test_layer_linear_regression_to_factors_basic() -> None: ) # Should have separate metrics for each factor - assert "r2_factor_0" in scalars - assert "r2_factor_1" in scalars - assert "rmse_factor_0" in scalars - assert "rmse_factor_1" in scalars - assert "mae_factor_0" in scalars - assert "mae_factor_1" in scalars - assert "dist_factor_0" in scalars - assert "dist_factor_1" in scalars + assert "factor_0/r2" in scalars + assert "factor_1/r2" in scalars + assert "factor_0/rmse" in scalars + assert "factor_1/rmse" in scalars + assert "factor_0/mae" in scalars + assert "factor_1/mae" in scalars + assert "factor_0/dist" in scalars + assert "factor_1/dist" in scalars # Should have separate projections for each factor - assert "projected_factor_0" in projections - assert "projected_factor_1" in projections + assert "factor_0/projected" in projections + assert "factor_1/projected" in projections # Check shapes - assert projections["projected_factor_0"].shape == factor_0.shape - assert projections["projected_factor_1"].shape == factor_1.shape + assert projections["factor_0/projected"].shape == factor_0.shape + assert projections["factor_1/projected"].shape == factor_1.shape def test_layer_linear_regression_svd_to_factors_basic() -> None: @@ -240,18 +240,18 @@ def test_layer_linear_regression_svd_to_factors_basic() -> None: ) # Should have separate metrics for each factor including best_rcond - assert "r2_factor_0" in scalars - assert "r2_factor_1" in scalars - assert "best_rcond_factor_0" in scalars - assert "best_rcond_factor_1" in scalars + assert "factor_0/r2" in scalars + assert "factor_1/r2" in scalars + assert "factor_0/best_rcond" in scalars + assert "factor_1/best_rcond" in scalars # Should have separate projections for each factor - assert "projected_factor_0" in projections - assert "projected_factor_1" in projections + assert "factor_0/projected" in projections + assert "factor_1/projected" in projections # Check shapes - assert projections["projected_factor_0"].shape == factor_0.shape - assert projections["projected_factor_1"].shape == factor_1.shape + assert projections["factor_0/projected"].shape == factor_0.shape + assert projections["factor_1/projected"].shape == factor_1.shape def test_layer_linear_regression_to_factors_single_factor() -> None: @@ -271,9 +271,9 @@ def test_layer_linear_regression_to_factors_single_factor() -> None: ) # Should have metrics for single factor - assert "r2_factor_0" in scalars - assert "projected_factor_0" in projections - assert projections["projected_factor_0"].shape == factor_0.shape + assert "factor_0/r2" in scalars + assert "factor_0/projected" in projections + assert projections["factor_0/projected"].shape == factor_0.shape def test_layer_linear_regression_to_factors_requires_tuple() -> None: From b56776f3658dbc4a099b6eecdea91ef24c3adfa0 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 15:44:31 -0800 Subject: [PATCH 33/37] to_factor in validation --- simplexity/activations/activation_analyses.py | 2 +- simplexity/analysis/layerwise_analysis.py | 11 +++++--- tests/analysis/test_layerwise_analysis.py | 27 +++++++++++++++++++ 3 files changed, 35 insertions(+), 5 deletions(-) diff --git a/simplexity/activations/activation_analyses.py b/simplexity/activations/activation_analyses.py index 6888b6ed..37de3e7b 100644 --- a/simplexity/activations/activation_analyses.py +++ b/simplexity/activations/activation_analyses.py @@ -35,7 +35,7 @@ def analyze( self, activations: Mapping[str, jax.Array], weights: jax.Array, - belief_states: jax.Array | None = None, + belief_states: jax.Array | tuple[jax.Array, ...] | None = None, ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: """Analyze activations and return scalar metrics and projections.""" ... diff --git a/simplexity/analysis/layerwise_analysis.py b/simplexity/analysis/layerwise_analysis.py index 94140298..637531cf 100644 --- a/simplexity/analysis/layerwise_analysis.py +++ b/simplexity/analysis/layerwise_analysis.py @@ -34,21 +34,23 @@ class AnalysisRegistration: def _validate_linear_regression_kwargs(kwargs: Mapping[str, Any] | None) -> dict[str, Any]: provided = dict(kwargs or {}) - allowed = {"fit_intercept"} + allowed = {"fit_intercept", "to_factors"} unexpected = set(provided) - allowed if unexpected: raise ValueError(f"Unexpected linear_regression kwargs: {sorted(unexpected)}") fit_intercept = bool(provided.get("fit_intercept", True)) - return {"fit_intercept": fit_intercept} + to_factors = bool(provided.get("to_factors", False)) + return {"fit_intercept": fit_intercept, "to_factors": to_factors} def _validate_linear_regression_svd_kwargs(kwargs: Mapping[str, Any] | None) -> dict[str, Any]: provided = dict(kwargs or {}) - allowed = {"fit_intercept", "rcond_values"} + allowed = {"fit_intercept", "rcond_values", "to_factors"} unexpected = set(provided) - allowed if unexpected: raise ValueError(f"Unexpected linear_regression_svd kwargs: {sorted(unexpected)}") fit_intercept = bool(provided.get("fit_intercept", True)) + to_factors = bool(provided.get("to_factors", False)) rcond_values = provided.get("rcond_values") if rcond_values is not None: if not isinstance(rcond_values, (list, tuple)): @@ -58,6 +60,7 @@ def _validate_linear_regression_svd_kwargs(kwargs: Mapping[str, Any] | None) -> rcond_values = tuple(float(v) for v in rcond_values) return { "fit_intercept": fit_intercept, + "to_factors": to_factors, "rcond_values": rcond_values, } @@ -152,7 +155,7 @@ def analyze( self, activations: Mapping[str, jax.Array], weights: jax.Array, - belief_states: jax.Array | None = None, + belief_states: jax.Array | tuple[jax.Array, ...] | None = None, ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: """Analyze activations and return namespaced scalar metrics and projections.""" if self._requires_belief_states and belief_states is None: diff --git a/tests/analysis/test_layerwise_analysis.py b/tests/analysis/test_layerwise_analysis.py index e6886d5f..7524647d 100644 --- a/tests/analysis/test_layerwise_analysis.py +++ b/tests/analysis/test_layerwise_analysis.py @@ -162,3 +162,30 @@ def test_layerwise_analysis_property_accessors() -> None: assert analysis.concat_layers assert not analysis.use_probs_as_weights assert not analysis.requires_belief_states + + +def test_linear_regression_accepts_to_factors() -> None: + """linear_regression validator should accept to_factors parameter.""" + validator = ANALYSIS_REGISTRY["linear_regression"].validator + params = validator({"fit_intercept": False, "to_factors": True}) + + assert params["fit_intercept"] is False + assert params["to_factors"] is True + + +def test_linear_regression_svd_accepts_to_factors() -> None: + """linear_regression_svd validator should accept to_factors parameter.""" + validator = ANALYSIS_REGISTRY["linear_regression_svd"].validator + params = validator({"fit_intercept": True, "to_factors": True, "rcond_values": [1e-3]}) + + assert params["fit_intercept"] is True + assert params["to_factors"] is True + assert params["rcond_values"] == (0.001,) + + +def test_linear_regression_to_factors_defaults_false() -> None: + """to_factors should default to False when not provided.""" + validator = ANALYSIS_REGISTRY["linear_regression"].validator + params = validator({"fit_intercept": True}) + + assert params["to_factors"] is False From e484396c1b45892f6b013c8b2aa494df0b504fb5 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 16:32:04 -0800 Subject: [PATCH 34/37] update returns and concatenations --- simplexity/analysis/linear_regression.py | 6 ++---- simplexity/generative_processes/generator.py | 7 +------ simplexity/generative_processes/torch_generator.py | 2 -- tests/analysis/test_linear_regression.py | 14 +++++++++----- 4 files changed, 12 insertions(+), 17 deletions(-) diff --git a/simplexity/analysis/linear_regression.py b/simplexity/analysis/linear_regression.py index d9d0bf48..1e3bcf3e 100644 --- a/simplexity/analysis/linear_regression.py +++ b/simplexity/analysis/linear_regression.py @@ -161,8 +161,7 @@ def layer_linear_regression( projections[f"factor_{factor_idx}/{key}"] = value return scalars, projections else: - if isinstance(belief_states, tuple): - raise ValueError("belief_states must be a single array when to_factors is False") + belief_states = jnp.concatenate(belief_states, axis=-1) if isinstance(belief_states, tuple) else belief_states return linear_regression(layer_activations, belief_states, weights, **kwargs) @@ -191,6 +190,5 @@ def layer_linear_regression_svd( projections[f"factor_{factor_idx}/{key}"] = value return scalars, projections else: - if isinstance(belief_states, tuple): - raise ValueError("belief_states must be a single array when to_factors is False") + belief_states = jnp.concatenate(belief_states, axis=-1) if isinstance(belief_states, tuple) else belief_states return linear_regression_svd(layer_activations, belief_states, weights, **kwargs) diff --git a/simplexity/generative_processes/generator.py b/simplexity/generative_processes/generator.py index e75b9717..d8ec2c34 100644 --- a/simplexity/generative_processes/generator.py +++ b/simplexity/generative_processes/generator.py @@ -74,17 +74,12 @@ def generate_data_batch_with_full_history( prefix_probs = prefix_probs[:, : inputs.shape[1]] result = { - "belief_states": ( - jnp.concatenate(belief_states, axis=-1) if isinstance(belief_states, tuple) else belief_states - ), + "belief_states": belief_states, "prefix_probabilities": prefix_probs, "inputs": inputs, "labels": labels, } - if isinstance(belief_states, tuple): - result["belief_state_factors"] = belief_states - return result diff --git a/simplexity/generative_processes/torch_generator.py b/simplexity/generative_processes/torch_generator.py index d3f993a8..53a04980 100644 --- a/simplexity/generative_processes/torch_generator.py +++ b/simplexity/generative_processes/torch_generator.py @@ -87,7 +87,6 @@ def generate_data_batch_with_full_history( - prefix_probabilities: Prefix probabilities (jax.Array) - inputs: Input tokens (torch.Tensor) - labels: Label tokens (torch.Tensor) - - belief_state_factors: Optional factored belief states (tuple[jax.Array, ...]) """ result = generate_jax_data_batch_with_full_history( gen_states, @@ -109,5 +108,4 @@ def generate_data_batch_with_full_history( "prefix_probabilities": result["prefix_probabilities"], "inputs": jax_to_torch(inputs, device), "labels": jax_to_torch(labels, device), - **({k: v for k, v in result.items() if k not in ["belief_states", "prefix_probabilities", "inputs", "labels"]}), } diff --git a/tests/analysis/test_linear_regression.py b/tests/analysis/test_linear_regression.py index 68317982..bca9f6bf 100644 --- a/tests/analysis/test_linear_regression.py +++ b/tests/analysis/test_linear_regression.py @@ -304,7 +304,7 @@ def test_layer_linear_regression_to_factors_validates_tuple_contents() -> None: layer_linear_regression_svd(x, weights, invalid_beliefs, to_factors=True) # type: ignore -def test_layer_linear_regression_to_factors_false_requires_array() -> None: +def test_layer_linear_regression_to_factors_false_works() -> None: """to_factors=False requires belief_states to be a single array, not a tuple.""" x = jnp.ones((3, 2)) weights = jnp.ones(3) / 3.0 @@ -312,8 +312,12 @@ def test_layer_linear_regression_to_factors_false_requires_array() -> None: # Invalid: tuple when to_factors=False factored_beliefs = (jnp.ones((3, 2)), jnp.ones((3, 3))) - with pytest.raises(ValueError, match="belief_states must be a single array when to_factors is False"): - layer_linear_regression(x, weights, factored_beliefs, to_factors=False) + scalars, projections = layer_linear_regression(x, weights, factored_beliefs, to_factors=False) + assert "r2" in scalars + assert "projected" in projections + assert projections["projected"].shape == (3, 5) - with pytest.raises(ValueError, match="belief_states must be a single array when to_factors is False"): - layer_linear_regression_svd(x, weights, factored_beliefs, to_factors=False) + scalars, projections = layer_linear_regression_svd(x, weights, factored_beliefs, to_factors=False) + assert "r2" in scalars + assert "projected" in projections + assert projections["projected"].shape == (3, 5) From 4d567348203049dfc36c7bfbca7f9ca4ea27c8ed Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 17:34:22 -0800 Subject: [PATCH 35/37] tuple handling --- simplexity/activations/activation_tracker.py | 18 +- simplexity/utils/analysis_utils.py | 61 +++- tests/activations/test_activation_analysis.py | 317 ++++++++++++++++++ tests/utils/test_analysis_utils.py | 6 + 4 files changed, 391 insertions(+), 11 deletions(-) diff --git a/simplexity/activations/activation_tracker.py b/simplexity/activations/activation_tracker.py index 03e02253..98f1546e 100644 --- a/simplexity/activations/activation_tracker.py +++ b/simplexity/activations/activation_tracker.py @@ -20,7 +20,7 @@ class PreparedActivations: """Prepared activations with belief states and sample weights.""" activations: Mapping[str, jax.Array] - belief_states: jax.Array | None + belief_states: jax.Array | tuple[jax.Array, ...] | None weights: jax.Array @@ -48,16 +48,26 @@ def _to_jax_array(value: Any) -> jax.Array: return jnp.asarray(value) +def _convert_tuple_to_jax_array(value: tuple[Any, ...]) -> tuple[jax.Array, ...]: + """Convert a tuple of supported tensor types to JAX arrays.""" + return tuple(_to_jax_array(v) for v in value) + + def prepare_activations( inputs: jax.Array | torch.Tensor | np.ndarray, - beliefs: jax.Array | torch.Tensor | np.ndarray, + beliefs: jax.Array + | torch.Tensor + | np.ndarray + | tuple[jax.Array, ...] + | tuple[torch.Tensor, ...] + | tuple[np.ndarray, ...], probs: jax.Array | torch.Tensor | np.ndarray, activations: Mapping[str, jax.Array | torch.Tensor | np.ndarray], prepare_options: PrepareOptions, ) -> PreparedActivations: """Preprocess activations by deduplicating sequences, selecting tokens/layers, and computing weights.""" inputs = _to_jax_array(inputs) - beliefs = _to_jax_array(beliefs) + beliefs = _convert_tuple_to_jax_array(beliefs) if isinstance(beliefs, tuple) else _to_jax_array(beliefs) probs = _to_jax_array(probs) activations = {name: _to_jax_array(layer) for name, layer in activations.items()} @@ -74,7 +84,7 @@ def prepare_activations( weights = ( dataset.probs if prepare_options.use_probs_as_weights - else _get_uniform_weights(belief_states.shape[0], belief_states.dtype) + else _get_uniform_weights(dataset.probs.shape[0], dataset.probs.dtype) ) if prepare_options.concat_layers: diff --git a/simplexity/utils/analysis_utils.py b/simplexity/utils/analysis_utils.py index 2bd76c47..df78c860 100644 --- a/simplexity/utils/analysis_utils.py +++ b/simplexity/utils/analysis_utils.py @@ -38,6 +38,24 @@ def dedup_tensor_first( return jnp.stack(values, axis=0), prefixes +def dedup_tuple_of_tensors_first( + tensors: tuple[jax.Array, ...], + prefix_to_indices: dict[tuple[int, ...], list[tuple[int, int]]], +) -> tuple[tuple[jax.Array, ...], list[tuple[int, ...]]]: + """Deduplicate a tuple of (batch, seq_len, ...) tensors by prefixes, taking the first occurrence in each tuple.""" + combined_values = [] + prefixes = prefix_to_indices.keys() + + for tensor in tensors: + values = [] + for idxs in prefix_to_indices.values(): + seq_idx, pos = idxs[0] + values.append(tensor[seq_idx, pos]) + combined_values.append(jnp.stack(values, axis=0)) + + return tuple(combined_values), list(prefixes) + + def dedup_probs_sum( probs: jax.Array, prefix_to_indices: dict[tuple[int, ...], list[tuple[int, int]]], @@ -127,19 +145,37 @@ def dedup_last_token_probs_sum( return dedup_probs, sequences +def dedup_last_token_tuple_of_tensors_first( + tensors: tuple[jax.Array, ...], + sequence_to_indices: dict[tuple[int, ...], list[int]], +) -> tuple[tuple[jax.Array, ...], list[tuple[int, ...]]]: + """Deduplicate a tuple of (batch, ...) tensors by full sequences, taking the first occurrence in each tuple.""" + combined_values = [] + sequences = list(sequence_to_indices.keys()) + + for tensor in tensors: + values = [] + for idxs in sequence_to_indices.values(): + seq_idx = idxs[0] + values.append(tensor[seq_idx]) + combined_values.append(jnp.stack(values, axis=0)) + + return tuple(combined_values), sequences + + @dataclass class DeduplicatedDataset: """A clean container for last-token-only data.""" sequences: list[tuple[int, ...]] - beliefs: jax.Array + beliefs: jax.Array | tuple[jax.Array, ...] probs: jax.Array activations_by_layer: dict[str, jax.Array] def build_deduplicated_dataset( inputs: jax.Array, - beliefs: jax.Array, + beliefs: jax.Array | tuple[jax.Array, ...], probs: jax.Array, activations_by_layer: dict[str, jax.Array], select_last_token: bool = False, @@ -163,14 +199,18 @@ def build_deduplicated_dataset( def build_prefix_dataset( inputs: jax.Array, - beliefs: jax.Array, + beliefs: jax.Array | tuple[jax.Array, ...], probs: jax.Array, activations_by_layer: dict[str, jax.Array], ) -> DeduplicatedDataset: """Deduplicate everything by prefix.""" prefix_to_indices = make_prefix_groups(inputs) - dedup_beliefs, prefixes = dedup_tensor_first(beliefs, prefix_to_indices) + dedup_beliefs, prefixes = ( + dedup_tensor_first(beliefs, prefix_to_indices) + if isinstance(beliefs, jax.Array) + else dedup_tuple_of_tensors_first(beliefs, prefix_to_indices) + ) dedup_probs, prefixes2 = dedup_probs_sum(probs, prefix_to_indices) if prefixes != prefixes2: @@ -193,18 +233,25 @@ def build_prefix_dataset( def build_last_token_dataset( inputs: jax.Array, - beliefs: jax.Array, + beliefs: jax.Array | tuple[jax.Array, ...], probs: jax.Array, activations_by_layer: dict[str, jax.Array], ) -> DeduplicatedDataset: """Deduplicate everything by full sequence.""" - beliefs = beliefs[:, -1, :] + if isinstance(beliefs, tuple): + beliefs = tuple(b[:, -1, :] for b in beliefs) + else: + beliefs = beliefs[:, -1, :] probs = probs[:, -1] activations_by_layer = {name: acts[:, -1, :] for name, acts in activations_by_layer.items()} sequence_to_indices = make_sequence_groups(inputs) # Dedup beliefs & probs - dedup_beliefs, sequences = dedup_last_token_tensor_first(beliefs, sequence_to_indices) + dedup_beliefs, sequences = ( + dedup_last_token_tensor_first(beliefs, sequence_to_indices) + if isinstance(beliefs, jax.Array) + else dedup_last_token_tuple_of_tensors_first(beliefs, sequence_to_indices) + ) dedup_probs, sequences2 = dedup_last_token_probs_sum(probs, sequence_to_indices) if sequences != sequences2: diff --git a/tests/activations/test_activation_analysis.py b/tests/activations/test_activation_analysis.py index b7dee2aa..3d67e13c 100644 --- a/tests/activations/test_activation_analysis.py +++ b/tests/activations/test_activation_analysis.py @@ -9,6 +9,7 @@ # (code quality, style, undefined names, etc.) to run normally while bypassing # the problematic imports checker that would crash during AST traversal. +import jax import jax.numpy as jnp import numpy as np import pytest @@ -85,6 +86,7 @@ def test_all_tokens_individual(self, synthetic_data): assert "layer_1" in result.activations assert result.belief_states is not None + assert isinstance(result.belief_states, jax.Array) n_prefixes = result.belief_states.shape[0] assert result.activations["layer_0"].shape == (n_prefixes, synthetic_data["d_layer0"]) assert result.activations["layer_1"].shape == (n_prefixes, synthetic_data["d_layer1"]) @@ -110,6 +112,7 @@ def test_all_tokens_concatenated(self, synthetic_data): assert "layer_1" not in result.activations assert result.belief_states is not None + assert isinstance(result.belief_states, jax.Array) n_prefixes = result.belief_states.shape[0] expected_d = synthetic_data["d_layer0"] + synthetic_data["d_layer1"] assert result.activations["concatenated"].shape == (n_prefixes, expected_d) @@ -132,6 +135,7 @@ def test_last_token_individual(self, synthetic_data): assert "layer_1" in result.activations assert result.belief_states is not None + assert isinstance(result.belief_states, jax.Array) batch_size = synthetic_data["batch_size"] assert result.activations["layer_0"].shape == (batch_size, synthetic_data["d_layer0"]) assert result.activations["layer_1"].shape == (batch_size, synthetic_data["d_layer1"]) @@ -237,6 +241,7 @@ def test_basic_regression(self, synthetic_data): assert "layer_1_projected" in projections assert prepared.belief_states is not None + assert isinstance(prepared.belief_states, jax.Array) assert projections["layer_0_projected"].shape == prepared.belief_states.shape assert projections["layer_1_projected"].shape == prepared.belief_states.shape @@ -328,6 +333,7 @@ def test_basic_regression_svd(self, synthetic_data): assert "layer_1_projected" in projections assert prepared.belief_states is not None + assert isinstance(prepared.belief_states, jax.Array) assert projections["layer_0_projected"].shape == prepared.belief_states.shape assert projections["layer_1_projected"].shape == prepared.belief_states.shape @@ -654,3 +660,314 @@ def test_tracker_accepts_torch_inputs(self, synthetic_data): assert "regression/layer_0_r2" in scalars assert "pca/layer_0_pca" in projections + + +class TestTupleBeliefStates: + """Test activation tracker with tuple belief states for factored processes.""" + + @pytest.fixture + def factored_belief_data(self): + """Create synthetic data with factored belief states.""" + batch_size = 4 + seq_len = 5 + d_layer0 = 8 + d_layer1 = 12 + + inputs = jnp.array( + [ + [1, 2, 3, 4, 5], + [1, 2, 3, 6, 7], + [1, 2, 8, 9, 10], + [1, 2, 3, 4, 11], + ] + ) + + # Factored beliefs: 2 factors with dimensions 3 and 2 + factor_0 = jnp.ones((batch_size, seq_len, 3)) * 0.3 + factor_1 = jnp.ones((batch_size, seq_len, 2)) * 0.7 + factored_beliefs = (factor_0, factor_1) + + probs = jnp.ones((batch_size, seq_len)) * 0.1 + + activations = { + "layer_0": jnp.ones((batch_size, seq_len, d_layer0)) * 0.3, + "layer_1": jnp.ones((batch_size, seq_len, d_layer1)) * 0.7, + } + + return { + "inputs": inputs, + "factored_beliefs": factored_beliefs, + "probs": probs, + "activations": activations, + "batch_size": batch_size, + "seq_len": seq_len, + "factor_0_dim": 3, + "factor_1_dim": 2, + "d_layer0": d_layer0, + "d_layer1": d_layer1, + } + + def test_prepare_activations_accepts_tuple_beliefs(self, factored_belief_data): + """prepare_activations should accept and preserve tuple belief states.""" + result = prepare_activations( + factored_belief_data["inputs"], + factored_belief_data["factored_beliefs"], + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + assert result.belief_states is not None + assert isinstance(result.belief_states, tuple) + assert len(result.belief_states) == 2 + + batch_size = factored_belief_data["batch_size"] + assert result.belief_states[0].shape == (batch_size, factored_belief_data["factor_0_dim"]) + assert result.belief_states[1].shape == (batch_size, factored_belief_data["factor_1_dim"]) + + def test_prepare_activations_tuple_beliefs_all_tokens(self, factored_belief_data): + """Tuple beliefs should work with all tokens mode.""" + result = prepare_activations( + factored_belief_data["inputs"], + factored_belief_data["factored_beliefs"], + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=False, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + assert result.belief_states is not None + assert isinstance(result.belief_states, tuple) + assert len(result.belief_states) == 2 + + # With deduplication, we expect fewer samples than batch_size * seq_len + n_prefixes = result.belief_states[0].shape[0] + assert result.belief_states[0].shape == (n_prefixes, factored_belief_data["factor_0_dim"]) + assert result.belief_states[1].shape == (n_prefixes, factored_belief_data["factor_1_dim"]) + assert result.activations["layer_0"].shape[0] == n_prefixes + + def test_prepare_activations_torch_tuple_beliefs(self, factored_belief_data): + """prepare_activations should accept tuple of PyTorch tensors.""" + torch = pytest.importorskip("torch") + + torch_factor_0 = torch.tensor(np.asarray(factored_belief_data["factored_beliefs"][0])) + torch_factor_1 = torch.tensor(np.asarray(factored_belief_data["factored_beliefs"][1])) + torch_beliefs = (torch_factor_0, torch_factor_1) + + result = prepare_activations( + factored_belief_data["inputs"], + torch_beliefs, + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + assert result.belief_states is not None + assert isinstance(result.belief_states, tuple) + assert len(result.belief_states) == 2 + # Should be converted to JAX arrays + assert isinstance(result.belief_states[0], jnp.ndarray) + assert isinstance(result.belief_states[1], jnp.ndarray) + + def test_prepare_activations_numpy_tuple_beliefs(self, factored_belief_data): + """prepare_activations should accept tuple of numpy arrays.""" + np_factor_0 = np.asarray(factored_belief_data["factored_beliefs"][0]) + np_factor_1 = np.asarray(factored_belief_data["factored_beliefs"][1]) + np_beliefs = (np_factor_0, np_factor_1) + + result = prepare_activations( + factored_belief_data["inputs"], + np_beliefs, + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + assert result.belief_states is not None + assert isinstance(result.belief_states, tuple) + assert len(result.belief_states) == 2 + # Should be converted to JAX arrays + assert isinstance(result.belief_states[0], jnp.ndarray) + assert isinstance(result.belief_states[1], jnp.ndarray) + + def test_linear_regression_with_to_factors_true(self, factored_belief_data): + """LinearRegressionAnalysis with to_factors=True should regress to each factor separately.""" + analysis = LinearRegressionAnalysis(to_factors=True) + + prepared = prepare_activations( + factored_belief_data["inputs"], + factored_belief_data["factored_beliefs"], + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + scalars, projections = analysis.analyze( + activations=prepared.activations, + belief_states=prepared.belief_states, + weights=prepared.weights, + ) + + # Should have separate metrics for each factor + # Format is: layer_name_factor_idx/metric_name + assert "layer_0_factor_0/r2" in scalars + assert "layer_0_factor_1/r2" in scalars + assert "layer_0_factor_0/rmse" in scalars + assert "layer_0_factor_1/rmse" in scalars + assert "layer_0_factor_0/mae" in scalars + assert "layer_0_factor_1/mae" in scalars + assert "layer_0_factor_0/dist" in scalars + assert "layer_0_factor_1/dist" in scalars + + assert "layer_1_factor_0/r2" in scalars + assert "layer_1_factor_1/r2" in scalars + + # Should have separate projections for each factor + assert "layer_0_factor_0/projected" in projections + assert "layer_0_factor_1/projected" in projections + assert "layer_1_factor_0/projected" in projections + assert "layer_1_factor_1/projected" in projections + + # Check projection shapes + batch_size = factored_belief_data["batch_size"] + assert projections["layer_0_factor_0/projected"].shape == (batch_size, factored_belief_data["factor_0_dim"]) + assert projections["layer_0_factor_1/projected"].shape == (batch_size, factored_belief_data["factor_1_dim"]) + + def test_linear_regression_svd_with_to_factors_true(self, factored_belief_data): + """LinearRegressionSVDAnalysis with to_factors=True should regress to each factor separately.""" + analysis = LinearRegressionSVDAnalysis(to_factors=True, rcond_values=[1e-10]) + + prepared = prepare_activations( + factored_belief_data["inputs"], + factored_belief_data["factored_beliefs"], + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + scalars, projections = analysis.analyze( + activations=prepared.activations, + belief_states=prepared.belief_states, + weights=prepared.weights, + ) + + # Should have separate metrics for each factor including best_rcond + assert "layer_0_factor_0/r2" in scalars + assert "layer_0_factor_1/r2" in scalars + assert "layer_0_factor_0/best_rcond" in scalars + assert "layer_0_factor_1/best_rcond" in scalars + + # Should have separate projections for each factor + assert "layer_0_factor_0/projected" in projections + assert "layer_0_factor_1/projected" in projections + + def test_tracker_with_factored_beliefs(self, factored_belief_data): + """ActivationTracker should work with tuple belief states.""" + tracker = ActivationTracker( + { + "regression": LinearRegressionAnalysis( + last_token_only=True, + concat_layers=False, + to_factors=True, + ), + "pca": PcaAnalysis( + n_components=2, + last_token_only=True, + concat_layers=False, + ), + } + ) + + scalars, projections = tracker.analyze( + inputs=factored_belief_data["inputs"], + beliefs=factored_belief_data["factored_beliefs"], + probs=factored_belief_data["probs"], + activations=factored_belief_data["activations"], + ) + + # Regression should have per-factor metrics + assert "regression/layer_0_factor_0/r2" in scalars + assert "regression/layer_0_factor_1/r2" in scalars + + # PCA should still work (doesn't use belief states) + assert "pca/layer_0_variance_explained" in scalars + + # Projections should be present + assert "regression/layer_0_factor_0/projected" in projections + assert "regression/layer_0_factor_1/projected" in projections + assert "pca/layer_0_pca" in projections + + def test_single_factor_tuple(self, synthetic_data): + """Test with a single-factor tuple (edge case).""" + # Create single-factor tuple + single_factor = (synthetic_data["beliefs"],) + + result = prepare_activations( + synthetic_data["inputs"], + single_factor, + synthetic_data["probs"], + synthetic_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + assert result.belief_states is not None + assert isinstance(result.belief_states, tuple) + assert len(result.belief_states) == 1 + assert result.belief_states[0].shape == (synthetic_data["batch_size"], synthetic_data["belief_dim"]) + + def test_three_factor_tuple(self, factored_belief_data): + """Test with three factors to ensure generalization.""" + batch_size = factored_belief_data["batch_size"] + seq_len = factored_belief_data["seq_len"] + + # Add a third factor + factor_0 = jnp.ones((batch_size, seq_len, 3)) * 0.3 + factor_1 = jnp.ones((batch_size, seq_len, 2)) * 0.5 + factor_2 = jnp.ones((batch_size, seq_len, 4)) * 0.7 + three_factor_beliefs = (factor_0, factor_1, factor_2) + + result = prepare_activations( + factored_belief_data["inputs"], + three_factor_beliefs, + factored_belief_data["probs"], + factored_belief_data["activations"], + prepare_options=PrepareOptions( + last_token_only=True, + concat_layers=False, + use_probs_as_weights=False, + ), + ) + + assert result.belief_states is not None + assert isinstance(result.belief_states, tuple) + assert len(result.belief_states) == 3 + assert result.belief_states[0].shape == (batch_size, 3) + assert result.belief_states[1].shape == (batch_size, 2) + assert result.belief_states[2].shape == (batch_size, 4) diff --git a/tests/utils/test_analysis_utils.py b/tests/utils/test_analysis_utils.py index 906b4f8e..0320c307 100644 --- a/tests/utils/test_analysis_utils.py +++ b/tests/utils/test_analysis_utils.py @@ -1,5 +1,6 @@ """Tests for analysis utilities.""" +import jax import jax.numpy as jnp import pytest @@ -327,6 +328,8 @@ def test_basic_functionality(self, simple_inputs, simple_beliefs, simple_probs, assert jnp.allclose(jnp.sum(dataset.probs), 1.0) # Check shapes are consistent + + assert isinstance(dataset.beliefs, jax.Array) n_prefixes = dataset.beliefs.shape[0] assert dataset.probs.shape[0] == n_prefixes for layer_acts in dataset.activations_by_layer.values(): @@ -347,6 +350,7 @@ def test_belief_dimensions(self, simple_inputs, simple_beliefs, simple_probs, si dataset = build_prefix_dataset(simple_inputs, simple_beliefs, simple_probs, simple_activations) # Beliefs should have 2 dimensions (from fixture) + assert isinstance(dataset.beliefs, jax.Array) assert dataset.beliefs.shape[1] == 2 @@ -373,6 +377,7 @@ def test_deduplication(self, simple_inputs, simple_beliefs, simple_probs, simple dataset = build_last_token_dataset(simple_inputs, simple_beliefs, simple_probs, simple_activations) # Should have 2 unique sequences + assert isinstance(dataset.beliefs, jax.Array) assert dataset.beliefs.shape[0] == 2 assert dataset.probs.shape[0] == 2 @@ -381,6 +386,7 @@ def test_preserves_dimensions(self, simple_inputs, simple_beliefs, simple_probs, dataset = build_last_token_dataset(simple_inputs, simple_beliefs, simple_probs, simple_activations) # Check belief dimension + assert isinstance(dataset.beliefs, jax.Array) assert dataset.beliefs.shape[1] == 2 # Check layer dimensions From bcee48c7a6247910563f8ad9d8574b030b076327 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 17:36:01 -0800 Subject: [PATCH 36/37] fix typehint --- simplexity/activations/activation_tracker.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/simplexity/activations/activation_tracker.py b/simplexity/activations/activation_tracker.py index 98f1546e..0908e4f9 100644 --- a/simplexity/activations/activation_tracker.py +++ b/simplexity/activations/activation_tracker.py @@ -108,7 +108,12 @@ def __init__(self, analyses: Mapping[str, ActivationAnalysis]): def analyze( self, inputs: jax.Array | torch.Tensor | np.ndarray, - beliefs: jax.Array | torch.Tensor | np.ndarray, + beliefs: jax.Array + | torch.Tensor + | np.ndarray + | tuple[jax.Array, ...] + | tuple[torch.Tensor, ...] + | tuple[np.ndarray, ...], probs: jax.Array | torch.Tensor | np.ndarray, activations: Mapping[str, jax.Array | torch.Tensor | np.ndarray], ) -> tuple[Mapping[str, float], Mapping[str, jax.Array]]: From ebbe21d2f22d2c6fba4fcb27dbe5c65656bd9225 Mon Sep 17 00:00:00 2001 From: Casper Lutzhoft Christensen Date: Mon, 8 Dec 2025 18:13:07 -0800 Subject: [PATCH 37/37] improve test coverage --- .../test_factored_generative_process.py | 177 ++++++++++++++++++ 1 file changed, 177 insertions(+) diff --git a/tests/generative_processes/test_factored_generative_process.py b/tests/generative_processes/test_factored_generative_process.py index a9868c3f..444bd48b 100644 --- a/tests/generative_processes/test_factored_generative_process.py +++ b/tests/generative_processes/test_factored_generative_process.py @@ -450,3 +450,180 @@ def test_independent_structure_get_required_params(): structure = IndependentStructure() required_params = structure.get_required_params() assert required_params == {} + + +def test_factored_process_rejects_empty_components(): + """FactoredGenerativeProcess should reject empty component lists.""" + with pytest.raises(ValueError, match="Must provide at least one component"): + FactoredGenerativeProcess( + component_types=(), + transition_matrices=(), + normalizing_eigenvectors=(), + initial_states=(), + structure=IndependentStructure(), + ) + + +def test_factored_process_validates_transition_matrix_ndim(): + """FactoredGenerativeProcess should reject non-4D transition matrices.""" + component_types = ("hmm",) + # Wrong shape: should be [K, V, S, S] but providing [V, S, S] + transition_matrices = (jnp.ones((2, 3, 3), dtype=jnp.float32),) + normalizing_eigenvectors = (jnp.ones((1, 3), dtype=jnp.float32),) + initial_states = (jnp.ones(3, dtype=jnp.float32),) + structure = IndependentStructure() + + with pytest.raises(ValueError, match=r"transition_matrices\[0\] must have shape \[K, V, S, S\]"): + FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def test_factored_process_validates_transition_matrix_square(): + """FactoredGenerativeProcess should reject non-square transition matrices.""" + component_types = ("hmm",) + # Wrong shape: last two dims should be equal + transition_matrices = (jnp.ones((1, 2, 3, 4), dtype=jnp.float32),) + normalizing_eigenvectors = (jnp.ones((1, 3), dtype=jnp.float32),) + initial_states = (jnp.ones(3, dtype=jnp.float32),) + structure = IndependentStructure() + + with pytest.raises(ValueError, match=r"transition_matrices\[0\] square mismatch"): + FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def test_factored_process_computes_vocab_sizes_from_transitions(): + """FactoredGenerativeProcess should infer vocab sizes from transition matrices.""" + component_types = ("hmm", "hmm") + # Factor 0 has vocab size 2, factor 1 has vocab size 3 + transition_matrices = ( + jnp.ones((1, 2, 4, 4), dtype=jnp.float32), # K=1, V=2, S=4 + jnp.ones((1, 3, 5, 5), dtype=jnp.float32), # K=1, V=3, S=5 + ) + normalizing_eigenvectors = ( + jnp.ones((1, 4), dtype=jnp.float32), + jnp.ones((1, 5), dtype=jnp.float32), + ) + initial_states = ( + jnp.ones(4, dtype=jnp.float32), + jnp.ones(5, dtype=jnp.float32), + ) + structure = IndependentStructure() + + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + assert process.vocab_size == 2 * 3 # Product of vocab sizes + chex.assert_trees_all_equal(process.encoder.vocab_sizes, jnp.array([2, 3])) + + +def test_factored_process_computes_num_variants_from_transitions(): + """FactoredGenerativeProcess should infer num_variants from transition matrices.""" + component_types = ("hmm", "hmm") + # Factor 0 has 2 variants, factor 1 has 3 variants + transition_matrices = ( + jnp.ones((2, 2, 4, 4), dtype=jnp.float32), # K=2 + jnp.ones((3, 2, 5, 5), dtype=jnp.float32), # K=3 + ) + normalizing_eigenvectors = ( + jnp.ones((2, 4), dtype=jnp.float32), + jnp.ones((3, 5), dtype=jnp.float32), + ) + initial_states = ( + jnp.ones(4, dtype=jnp.float32), + jnp.ones(5, dtype=jnp.float32), + ) + structure = IndependentStructure() + + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + assert process.num_variants == (2, 3) + + +def test_factored_process_ghmm_component_uses_normalizing_eigenvector(): + """GHMM components should use normalizing eigenvectors in transitions.""" + component_types = ("ghmm", "hmm") + # Simple transition matrix and eigenvector for GHMM + transition_matrices = ( + jnp.array([[[[0.8, 0.2], [0.3, 0.7]]]], dtype=jnp.float32), # GHMM with K=1, V=1, S=2 + _tensor_from_probs([[0.6, 0.4]]), # HMM + ) + normalizing_eigenvectors = ( + jnp.array([[0.5, 0.5]], dtype=jnp.float32), # For GHMM + jnp.ones((1, 1), dtype=jnp.float32), # For HMM (not used) + ) + initial_states = ( + jnp.array([0.6, 0.4], dtype=jnp.float32), + jnp.array([1.0], dtype=jnp.float32), + ) + structure = SequentialConditional( + control_maps=(None, jnp.array([0], dtype=jnp.int32)), + vocab_sizes=jnp.array([1, 2], dtype=jnp.int32), + ) + + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + # Verify process initializes correctly with GHMM + assert process.component_types[0] == "ghmm" + assert process.component_types[1] == "hmm" + + # Test that transition works (it should use the eigenvector for GHMM) + obs_token = jnp.array(0, dtype=jnp.int32) + new_state = process.transition_states(initial_states, obs_token) + + # Just verify it runs without error and produces valid states + assert new_state[0].shape == initial_states[0].shape + assert new_state[1].shape == initial_states[1].shape + + +def test_factored_process_device_placement(): + """FactoredGenerativeProcess should respect device placement.""" + component_types = ("hmm",) + transition_matrices = (jnp.ones((1, 2, 3, 3), dtype=jnp.float32),) + normalizing_eigenvectors = (jnp.ones((1, 3), dtype=jnp.float32),) + initial_states = (jnp.ones(3, dtype=jnp.float32),) + structure = IndependentStructure() + + # Should work with explicit device specification + process = FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + device="cpu", + ) + + # Device should be set + assert process.device is not None + # Arrays should be on the specified device + assert all(tm.device == process.device for tm in process.transition_matrices) + assert all(ev.device == process.device for ev in process.normalizing_eigenvectors) + assert all(s.device == process.device for s in process.initial_states)