diff --git a/simplexity/activations/activation_analyses.py b/simplexity/activations/activation_analyses.py index 8e843e13..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.""" ... @@ -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/activations/activation_tracker.py b/simplexity/activations/activation_tracker.py index 03e02253..0908e4f9 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: @@ -98,7 +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]]: 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/simplexity/analysis/linear_regression.py b/simplexity/analysis/linear_regression.py index 54a8315d..1e3bcf3e 100644 --- a/simplexity/analysis/linear_regression.py +++ b/simplexity/analysis/linear_regression.py @@ -139,22 +139,56 @@ 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"factor_{factor_idx}/{key}"] = value + for key, value in factor_projections.items(): + projections[f"factor_{factor_idx}/{key}"] = value + return scalars, projections + else: + 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) 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") - 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"factor_{factor_idx}/{key}"] = value + for key, value in factor_projections.items(): + projections[f"factor_{factor_idx}/{key}"] = value + return scalars, projections + else: + 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/builder.py b/simplexity/generative_processes/builder.py index 8731e783..9f0fc929 100644 --- a/simplexity/generative_processes/builder.py +++ b/simplexity/generative_processes/builder.py @@ -11,13 +11,20 @@ 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.structures import ( + ConditionalTransitions, + FullyConditional, + IndependentStructure, + SequentialConditional, +) from simplexity.generative_processes.transition_matrices import ( GHMM_MATRIX_FUNCTIONS, HMM_MATRIX_FUNCTIONS, @@ -146,3 +153,480 @@ 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, device=device) + + +def build_factored_process( + structure_type: Literal["independent", "chain", "symmetric", "transition_coupled"], + component_types: Sequence[ComponentType], + 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. + + 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]) + **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 configured with the requested conditional structure + + Raises: + ValueError: If structure_type is invalid or required kwargs are missing + """ + 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}'") + + return FactoredGenerativeProcess( + component_types=component_types, + transition_matrices=transition_matrices, + normalizing_eigenvectors=normalizing_eigenvectors, + initial_states=initial_states, + structure=structure, + ) + + +def build_factored_process_from_spec( + structure_type: Literal["independent", "chain", "symmetric", "transition_coupled"], + spec: Sequence[dict[str, Any]], + **structure_params, +) -> FactoredGenerativeProcess: + """Unified builder for factored processes from specification. + + Args: + 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 specified structure + + 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 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, + ) + 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=control_maps, + ) + 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=control_maps_arrays, + ) + 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"), + ) + 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( + spec: Sequence[dict[str, Any]], +) -> tuple[ + list[ComponentType], + list[jax.Array], + list[jax.Array], + list[jax.Array], +]: + """Build transition matrices, eigenvectors, and initial states from 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 + + 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[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") + 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}") + + 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[jax.Array], + list[jax.Array], + list[jax.Array], + list[jax.Array | None], +]: + """Build all parameters for chain structure 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[jax.Array | 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 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[jax.Array], + list[jax.Array], + list[jax.Array], + list[jax.Array], +]: + """Build all parameters for symmetric structure 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 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[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. + + 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_generative_process.py b/simplexity/generative_processes/factored_generative_process.py new file mode 100644 index 00000000..2a842b51 --- /dev/null +++ b/simplexity/generative_processes/factored_generative_process.py @@ -0,0 +1,269 @@ +"""Unified factored generative process with pluggable conditional structures.""" + +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.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[jax.Array, ...] + + +def _move_arrays_to_device( + arrays: Sequence[jax.Array], + device: jax.Device, # type: ignore[valid-type] + name: str, +) -> tuple[jax.Array, ...]: + """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. + + This class provides a single implementation of factored generative processes + that supports different conditional dependency patterns via the ConditionalStructure 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 + structure: Conditional structure determining factor interactions + encoder: Token encoder for composite observations + """ + + # Static structure + component_types: tuple[ComponentType, ...] + num_variants: tuple[int, ...] + device: jax.Device # type: ignore[valid-type] + + # Per-factor parameters + transition_matrices: tuple[jax.Array, ...] + normalizing_eigenvectors: tuple[jax.Array, ...] + initial_states: tuple[jax.Array, ...] + + # Conditional structure and encoding + structure: ConditionalStructure + encoder: TokenEncoder + + def __init__( + self, + *, + component_types: Sequence[ComponentType], + transition_matrices: Sequence[jax.Array], + normalizing_eigenvectors: Sequence[jax.Array], + initial_states: Sequence[jax.Array], + structure: ConditionalStructure, + device: str | None = None, + ) -> 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]) + 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) + + # 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 + + # Validate shapes and compute derived sizes + vocab_sizes = [] + 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}" + ) + 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)) + + 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, + 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) -> jax.Array: + """Compute P(composite_token | state) under the conditional structure. + + 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.structure.compute_joint_distribution(context) + + @eqx.filter_jit + def log_observation_probability_distribution(self, log_belief_state: FactoredState) -> jax.Array: + """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) -> jax.Array: + """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 conditional structure + context = self._make_context(state) + variants = self.structure.select_variants(obs_tuple, context) + + # Update each factor's state + 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 + 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) + + @eqx.filter_jit + def probability(self, observations: jax.Array) -> jax.Array: + """Compute P(observations) by scanning through sequence. + + Args: + observations: Array of composite observations + + Returns: + Scalar probability + """ + + def step(carry: FactoredState, obs: jax.Array): + 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: jax.Array) -> jax.Array: + """Compute log P(observations) by scanning through sequence. + + Args: + observations: Array of composite observations + + Returns: + Scalar log-probability + """ + + def step(carry: FactoredState, obs: jax.Array): + state = carry + # 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 + + _, 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 53b70340..c3c60807 100644 --- a/simplexity/generative_processes/generalized_hidden_markov_model.py +++ b/simplexity/generative_processes/generalized_hidden_markov_model.py @@ -87,10 +87,26 @@ def __init__( 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)") + # Check that net transition matrix has dominant eigenvalue close to 1 + 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): + 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/generator.py b/simplexity/generative_processes/generator.py index f9e7fa5d..d8ec2c34 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 +73,14 @@ 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": belief_states, + "prefix_probabilities": prefix_probs, + "inputs": inputs, + "labels": labels, + } + + return result def _compute_prefix_probabilities( diff --git a/simplexity/generative_processes/structures/__init__.py b/simplexity/generative_processes/structures/__init__.py new file mode 100644 index 00000000..524c8597 --- /dev/null +++ b/simplexity/generative_processes/structures/__init__.py @@ -0,0 +1,37 @@ +"""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: +- 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) +""" + +from simplexity.generative_processes.structures.conditional_transitions import ( + ConditionalTransitions, +) +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, +) +from simplexity.generative_processes.structures.sequential_conditional import ( + SequentialConditional, +) + +__all__ = [ + "ConditionalContext", + "ConditionalStructure", + "ConditionalTransitions", + "FullyConditional", + "IndependentStructure", + "SequentialConditional", +] diff --git a/simplexity/generative_processes/structures/conditional_transitions.py b/simplexity/generative_processes/structures/conditional_transitions.py new file mode 100644 index 00000000..9e4db05b --- /dev/null +++ b/simplexity/generative_processes/structures/conditional_transitions.py @@ -0,0 +1,232 @@ +"""Conditional transitions structure: independent/sequential emissions with mutually conditional transitions. + +Emissions can be either: +- Independent: each factor uses a fixed emission variant +- Sequential: factor i selects emission variant based on previous tokens 0..i-1 + +Transitions are always mutually conditional: factor i selects transition variant based on +all other factors' tokens. +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import equinox as eqx +import jax +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 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) + - 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 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 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 + """ + + 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[jax.Array, ...] + prefix_multipliers: tuple[jax.Array, ...] + vocab_sizes_py: tuple[int, ...] + + def __init__( + self, + 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. + + 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 sequential 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) + num_factors = len(vocab_sizes) + + # Process emission control maps + use_chain = False + 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: + 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] * 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[jax.Array] = [] + for i in range(num_factors): + mult = [] + for j in range(num_factors): + if j == i: + mult.append(0) # Unused + else: + m = 1 + for k in range(j + 1, num_factors): + 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 sequential emissions) + prefix_multipliers: list[jax.Array] = [] + for i in range(num_factors): + pmult = [] + for j in range(num_factors): + 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: 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: 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) -> jax.Array: + """Compute joint distribution based on emission mode. + + Args: + context: Conditional context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + num_factors = 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(num_factors): + k_emit = self.emission_variant_indices[i] + 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], transition_matrix_k, norm_k) + parts.append(p_i) + + # Product of independent factors + 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] + 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], transition_matrix0, norm0) + prev_prod = self.vocab_sizes_py[0] + + 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: 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) + + 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 = self.vocab_sizes_py[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[jax.Array, ...], + context: ConditionalContext, # pylint: disable=unused-argument + ) -> tuple[jax.Array, ...]: + """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: Conditional 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 conditional transitions structure.""" + return { + "control_maps_transition": tuple, + "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 new file mode 100644 index 00000000..d8ea5d14 --- /dev/null +++ b/simplexity/generative_processes/structures/fully_conditional.py @@ -0,0 +1,196 @@ +"""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. +""" + +from __future__ import annotations + +import equinox as eqx +import jax +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 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. + + 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[jax.Array, ...] + other_multipliers: tuple[jax.Array, ...] + 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[jax.Array, ...], + vocab_sizes: jax.Array, + ): + """Initialize fully conditional structure. + + 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) + num_factors = 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[jax.Array] = [] + other_shapes: list[tuple[int, ...]] = [] + perms_py: list[tuple[int, ...]] = [] + + for i in range(num_factors): + # Compute radix multipliers for "other" factors (excluding i) + mult = [] + for j in range(num_factors): + if j == i: + mult.append(0) # Unused + else: + m = 1 + for k in range(j + 1, num_factors): + 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(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] + axis_pos = {j: pos for pos, j in enumerate(others)} + perm = [] + for j in range(num_factors): + 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: jax.Array, i: int) -> jax.Array: + """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: ConditionalContext) -> jax.Array: + """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: Conditional context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + num_factors = 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(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: 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) + + 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 + prod_j = parts[0] + for p in parts[1:]: + prod_j = prod_j * p + + # Normalize + 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, jax.Array) + + return norm_j.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jax.Array, ...], + context: ConditionalContext, # pylint: disable=unused-argument + ) -> tuple[jax.Array, ...]: + """Select variants based on all other factors' tokens. + + Args: + obs_tuple: Tuple of observed tokens (one per factor) + context: Conditional context (unused for fully conditional structure) + + 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 fully conditional structure.""" + return {"control_maps": tuple, "vocab_sizes": jax.Array} diff --git a/simplexity/generative_processes/structures/independent.py b/simplexity/generative_processes/structures/independent.py new file mode 100644 index 00000000..a7edbbdb --- /dev/null +++ b/simplexity/generative_processes/structures/independent.py @@ -0,0 +1,74 @@ +"""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 +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) -> jax.Array: + """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)] + """ + 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(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, num_factors): + joint = (joint[..., None] * parts[i]).reshape(*joint.shape, parts[i].shape[0]) + + return joint.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jax.Array, ...], + context: ConditionalContext, # pylint: disable=unused-argument # Required by protocol + ) -> tuple[jax.Array, ...]: + """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/simplexity/generative_processes/structures/protocol.py b/simplexity/generative_processes/structures/protocol.py new file mode 100644 index 00000000..9f82f958 --- /dev/null +++ b/simplexity/generative_processes/structures/protocol.py @@ -0,0 +1,59 @@ +"""Conditional structure protocol for factored generative processes. + +Defines the interface for different conditional dependency structures between factors. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal, Protocol + +import jax + +ComponentType = Literal["hmm", "ghmm"] +FactoredState = tuple[jax.Array, ...] + + +@dataclass +class ConditionalContext: + """Context information needed by conditional structures 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[jax.Array, ...] + normalizing_eigenvectors: tuple[jax.Array, ...] + vocab_sizes: jax.Array + num_variants: tuple[int, ...] + + +class ConditionalStructure(Protocol): + """Protocol for conditional dependency structures between factors. + + 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: 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, ...]: + """Select the variants of the conditional structure.""" + ... # pylint: disable=unnecessary-ellipsis + + def get_required_params(self) -> dict[str, type]: + """Get the required parameters for the conditional structure.""" + ... # pylint: disable=unnecessary-ellipsis diff --git a/simplexity/generative_processes/structures/sequential_conditional.py b/simplexity/generative_processes/structures/sequential_conditional.py new file mode 100644 index 00000000..87c254ad --- /dev/null +++ b/simplexity/generative_processes/structures/sequential_conditional.py @@ -0,0 +1,134 @@ +"""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. +""" + +from __future__ import annotations + +import equinox as eqx +import jax +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 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 + - 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[jax.Array | None, ...] + vocab_sizes_py: tuple[int, ...] + + def __init__( + self, + control_maps: tuple[jax.Array | None, ...], + vocab_sizes: jax.Array, + ): + """Initialize sequential conditional structure. + + 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: 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) + + 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}) + iteratively, then flattens to radix encoding. + + Args: + context: Conditional context with states and parameters + + Returns: + Flattened joint distribution of shape [prod(V_i)] + """ + num_factors = 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) + 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], transition_matrix_root, norm_root) # [V_0] + joint = p_root + + # Iteratively extend with conditional factors + for i in range(1, num_factors): + # Compute distributions for all variants of factor i + num_var_i = num_variants[i] + ks = jnp.arange(num_var_i, dtype=jnp.int32) + + # Vectorize over variants + 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) + + 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) + 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,)) + + return joint.reshape(-1) + + def select_variants( + self, + obs_tuple: tuple[jax.Array, ...], + context: ConditionalContext, # pylint: disable=unused-argument + ) -> tuple[jax.Array, ...]: + """Select variants based on parent tokens in chain. + + Args: + obs_tuple: Tuple of observed tokens (one per factor) + context: Conditional context (unused for sequential conditional) + + 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] # type: ignore + variants.append(k_i) + return tuple(variants) + + def get_required_params(self) -> dict[str, type]: + """Return required parameters for sequential conditional structure.""" + return {"control_maps": tuple} diff --git a/simplexity/generative_processes/torch_generator.py b/simplexity/generative_processes/torch_generator.py index b153911d..53a04980 100644 --- a/simplexity/generative_processes/torch_generator.py +++ b/simplexity/generative_processes/torch_generator.py @@ -68,7 +68,7 @@ def generate_data_batch_with_full_history( bos_token: int | None = None, eos_token: int | None = None, device: str | torch.device | 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. Args: @@ -82,9 +82,13 @@ def generate_data_batch_with_full_history( device: Optional target device for PyTorch tensors Returns: - Tuple of (next states, belief states, prefix probs, inputs, labels) + Dict with keys: + - belief_states: Belief states (jax.Array or tuple[jax.Array, ...]) + - prefix_probabilities: Prefix probabilities (jax.Array) + - inputs: Input tokens (torch.Tensor) + - labels: Label tokens (torch.Tensor) """ - 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, @@ -93,4 +97,15 @@ def generate_data_batch_with_full_history( bos_token, eos_token, ) - return next_states, belief_states, prefix_probs, jax_to_torch(inputs, device), jax_to_torch(labels, device) + # 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(inputs, device), + "labels": jax_to_torch(labels, device), + } 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/simplexity/utils/factoring_utils.py b/simplexity/utils/factoring_utils.py new file mode 100644 index 00000000..6da30f8a --- /dev/null +++ b/simplexity/utils/factoring_utils.py @@ -0,0 +1,167 @@ +"""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 equinox as eqx +import jax +import jax.numpy as jnp + +ComponentType = Literal["hmm", "ghmm"] + + +def compute_obs_dist_for_variant( + component_type: ComponentType, + state: jax.Array, + transition_matrix: jax.Array, + normalizing_eigenvector: jax.Array | None = None, +) -> jax.Array: + """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: jax.Array, + transition_matrix: jax.Array, + obs: jax.Array, + normalizing_eigenvector: jax.Array | None = None, +) -> jax.Array: + """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) + + +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: jax.Array # shape [F] + radix_multipliers: jax.Array # shape [F] + + def __init__(self, vocab_sizes: jax.Array): + """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[jax.Array, ...]) -> jax.Array: + """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[jax.Array, ...]: + """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: jax.Array) -> jax.Array: + """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/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/analysis/test_layerwise_analysis.py b/tests/analysis/test_layerwise_analysis.py index c0d1a839..7524647d 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]]), @@ -161,3 +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 diff --git a/tests/analysis/test_linear_regression.py b/tests/analysis/test_linear_regression.py index f3413429..bca9f6bf 100644 --- a/tests/analysis/test_linear_regression.py +++ b/tests/analysis/test_linear_regression.py @@ -183,3 +183,141 @@ 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 "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 "factor_0/projected" in projections + assert "factor_1/projected" in projections + + # Check shapes + 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: + """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 "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 "factor_0/projected" in projections + assert "factor_1/projected" in projections + + # Check shapes + 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: + """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 "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: + """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") # 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) # 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) # type: ignore + + +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 + + # Invalid: tuple when to_factors=False + factored_beliefs = (jnp.ones((3, 2)), jnp.ones((3, 3))) + + 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) + + 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) diff --git a/tests/end_to_end/configs/activation_tracker/default.yaml b/tests/end_to_end/configs/activation_tracker/default.yaml index 1950e7ee..5158ba6d 100644 --- a/tests/end_to_end/configs/activation_tracker/default.yaml +++ b/tests/end_to_end/configs/activation_tracker/default.yaml @@ -17,4 +17,4 @@ instance: 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] + rcond_values: [1e-15, 1e-10, 1e-8, 1e-6, 1e-4, 1e-2] \ No newline at end of file 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 new file mode 100644 index 00000000..d15066cc --- /dev/null +++ b/tests/end_to_end/configs/generative_process/unified_chain_3mess3_2tomq.yaml @@ -0,0 +1,108 @@ +# 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 +base_vocab_size: ??? # Will auto-infer as (3^3) * (4^2) = 27 * 16 = 432 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: chain + + spec: + # Factor 0: mess3 (unconditional, single variant) + - component_type: hmm + variants: + - process_name: mess3 + 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 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.11 + a: 0.79 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + - process_name: mess3 + 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 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.11 + a: 0.79 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + - process_name: mess3 + 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 + process_params: + alpha: 1.0 + beta: 1.0 + - process_name: tom_quantum + process_params: + alpha: 1.0 + beta: 4.0 + - process_name: tom_quantum + process_params: + alpha: 1.0 + beta: 7.141428 # sqrt(51) + - process_name: tom_quantum + 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 + process_params: + alpha: 1.0 + beta: 1.0 + - process_name: tom_quantum + process_params: + alpha: 1.0 + beta: 4.0 + - process_name: tom_quantum + process_params: + alpha: 1.0 + beta: 7.141428 + - process_name: tom_quantum + process_params: + 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/tests/end_to_end/configs/generative_process/unified_chain_example.yaml b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml new file mode 100644 index 00000000..7f034cd6 --- /dev/null +++ b/tests/end_to_end/configs/generative_process/unified_chain_example.yaml @@ -0,0 +1,57 @@ +# 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. + +name: unified_chain_example +base_vocab_size: ??? # Will auto-infer as 3 * 2 * 2 = 12 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: chain + + spec: + # Factor 0: mess3 (root, single variant) + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + + # Factor 1: mess3 (depends on Factor 0, 2 variants) + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + 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 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + control_map: [1, 0, 1] # 3 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, token 2->variant 1 \ No newline at end of file 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 new file mode 100644 index 00000000..b13fb2f7 --- /dev/null +++ b/tests/end_to_end/configs/generative_process/unified_symmetric_example.yaml @@ -0,0 +1,100 @@ +# 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. + +name: unified_symmetric_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: symmetric + + # Component specifications + spec: + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.11 + a: 0.79 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.21 + + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.11 + a: 0.79 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.21 + + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.11 + a: 0.79 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + - process_name: mess3 + 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)] + # 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/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 new file mode 100644 index 00000000..e171167d --- /dev/null +++ b/tests/end_to_end/configs/generative_process/unified_transition_and_emission_coupled_example.yaml @@ -0,0 +1,93 @@ +# 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_and_emission_coupled_example +base_vocab_size: ??? # Will auto-infer as 3 * 3 * 4 = 36 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: transition_coupled + + # Component specifications + spec: + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + + - 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 + + # 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/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 new file mode 100644 index 00000000..ef3302e9 --- /dev/null +++ b/tests/end_to_end/configs/generative_process/unified_transition_coupled_example.yaml @@ -0,0 +1,93 @@ +# 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: ??? # Will auto-infer as 3 * 3 * 4 = 36 +vocab_size: ??? + +instance: + _target_: simplexity.generative_processes.builder.build_factored_process_from_spec + + structure_type: transition_coupled + + # Component specifications + spec: + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + + - component_type: hmm + variants: + - process_name: mess3 + process_params: + x: 0.15 + a: 0.6 + - process_name: mess3 + process_params: + x: 0.5 + a: 0.6 + + - 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 + + # 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/tests/generative_processes/test_builder.py b/tests/generative_processes/test_builder.py index 402159d9..bd80643b 100644 --- a/tests/generative_processes/test_builder.py +++ b/tests/generative_processes/test_builder.py @@ -9,19 +9,34 @@ # (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 from simplexity.generative_processes.builder import ( add_begin_of_sequence_token, + build_chain_from_spec, + build_factored_process, + build_factored_process_from_spec, 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_transition_coupled_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 from tests.generative_processes.test_transition_matrices import validate_hmm_transition_matrices @@ -220,3 +235,371 @@ 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])) + + +@pytest.fixture +def components_spec(): + """Base specification for two HMM factors.""" + 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}}, + ], + }, + ] + + +@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], + }, + ] + + +@pytest.fixture +def symmetric_control_maps(): + """Simple symmetric control maps fixture.""" + return [[0, 1], [1, 0]] + + +@pytest.fixture +def transition_coupled_inputs(): + """Control maps and indices for transition-coupled topology.""" + return ( + [[0, 1], [1, 0]], + [0, 1], + [None, [0, 1]], + ) + + +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 + ) + 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(chain_spec): + """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(components_spec): + """Every non-root node in a chain must provide a control map.""" + with pytest.raises(ValueError, match=re.escape("chain[1].control_map is required for i>0")): + build_chain_from_spec(components_spec) + + +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, + 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, 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]]) + + +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) + ( + 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(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( + chain_spec, components_spec, symmetric_control_maps, transition_coupled_inputs +): + """build_factored_process should route to the appropriate topology builder.""" + 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( + structure_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( + structure_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( + structure_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 + + +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_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_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]),), + ) + + +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 new file mode 100644 index 00000000..444bd48b --- /dev/null +++ b/tests/generative_processes/test_factored_generative_process.py @@ -0,0 +1,629 @@ +"""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 +import pytest + +from simplexity.generative_processes.factored_generative_process import FactoredGenerativeProcess +from simplexity.generative_processes.structures import ( + ConditionalTransitions, + FullyConditional, + IndependentStructure, + 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] + + +@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]]), + _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, + ) + + +@pytest.fixture +def multistate_chain_process(): + """Two-factor chain with multi-state beliefs for transition testing.""" + 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 _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 = 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(multistate_chain_process): + """transition_states should respect variant selection from the structure.""" + process = 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(simple_chain_process): + """Samples from emit_observation should be in the encoded vocabulary.""" + token = simple_chain_process.emit_observation(simple_chain_process.initial_state, jax.random.PRNGKey(0)) + assert token.shape == () + 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 == {} + + +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) diff --git a/tests/generative_processes/test_factored_structures.py b/tests/generative_processes/test_factored_structures.py new file mode 100644 index 00000000..e45af442 --- /dev/null +++ b/tests/generative_processes/test_factored_structures.py @@ -0,0 +1,354 @@ +"""Tests for factored generative process conditional structures.""" + +import chex +import jax +import jax.numpy as jnp + +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): + """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 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 + ) + 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)), 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) + 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) + + +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) + + +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=context.vocab_sizes, + ) + + 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 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) + # 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) + chex.assert_trees_all_close(dist, expected) + + +def test_sequential_conditional_get_required_params(): + """SequentialConditional should return required params.""" + structure = SequentialConditional(control_maps=(None,), vocab_sizes=jnp.array([2])) + 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": jax.Array} + + +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/generative_processes/test_generator.py b/tests/generative_processes/test_generator.py index acdf92ce..c83c17c6 100644 --- a/tests/generative_processes/test_generator.py +++ b/tests/generative_processes/test_generator.py @@ -103,14 +103,24 @@ 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, ) + # 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 next_states.shape == (batch_size, gen_state.shape[0]) assert labels.shape == inputs.shape diff --git a/tests/generative_processes/test_torch_generator.py b/tests/generative_processes/test_torch_generator.py index 7052e3c4..b532c5cc 100644 --- a/tests/generative_processes/test_torch_generator.py +++ b/tests/generative_processes/test_torch_generator.py @@ -112,13 +112,21 @@ 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, ) + # 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]) - assert next_states.shape == (batch_size, gen_state.shape[0]) 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) diff --git a/tests/structured_configs/test_generative_process_config.py b/tests/structured_configs/test_generative_process_config.py index 898f3df0..fdcda804 100644 --- a/tests/structured_configs/test_generative_process_config.py +++ b/tests/structured_configs/test_generative_process_config.py @@ -855,3 +855,177 @@ 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 _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 = [ + { + "component_type": "hmm", + "variants": [{"process_name": "mess3", "x": 0.15, "a": 0.6}], + } + ] + 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) + + cfg = DictConfig( + { + "instance": DictConfig(instance_data), + "base_vocab_size": MISSING, + "vocab_size": MISSING, + } + ) + 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) + + @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) diff --git a/tests/structured_configs/test_predictive_model_config.py b/tests/structured_configs/test_predictive_model_config.py index 95365ee1..ffa6c5e4 100644 --- a/tests/structured_configs/test_predictive_model_config.py +++ b/tests/structured_configs/test_predictive_model_config.py @@ -1,5 +1,14 @@ """Tests for predictive-model structured configs.""" +# 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 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 diff --git a/tests/utils/test_factoring_utils.py b/tests/utils/test_factoring_utils.py new file mode 100644 index 00000000..f9fb05b6 --- /dev/null +++ b/tests/utils/test_factoring_utils.py @@ -0,0 +1,111 @@ +"""Tests for factoring utilities.""" + +import chex +import jax.numpy as jnp +import pytest + +from simplexity.utils.factoring_utils import ( + 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]) + transition_matrix = jnp.zeros((2, 2, 2)) + + 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]) + 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, 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]) + 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] + 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, transition_matrix, normalizing_eigenvector=None) + + assert dist.shape == (2,) # V=2 + # 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]) + 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 + ] + ) + obs = jnp.array(0) + + new_state = transition_with_obs("hmm", state, transition_matrix, obs, normalizing_eigenvector=None) + + assert new_state.shape == (2,) + # 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) + + +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) diff --git a/uv.lock b/uv.lock 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