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Turns "is this component earning its parameters?" into a one-line config flip for each of the three pluggable network components: - router.type = "none" (NoneRouter, giant/model/routers.py): still builds n_experts expert trunks via RoutedTrunk, but replaces the learned gate with a uniform 1/n_experts weight for every row — no centers/embeddings/classifier. Distinct from router.enabled=false (which drops routing/mixing entirely): this isolates whether the *learned routing signal* specifically is earning its parameters, holding expert count fixed. - stage2_model.autoregressive.history = "none" (NoHistory, giant/model/history.py): ignores feat/has_prev entirely and always returns zeros, ablating whether the AR decoder's history conditioning earns its parameters. Already validated for free by gitea #35's generic HISTORY_REGISTRY membership check. - trunk.type = "linear" (LinearTrunk, giant/model/trunks.py): a bare nn.Linear(in_dim + cond_dim, out_dim) body, no ResBlock stack. Per gitea #33's design, this composes for free with router.enabled=true ("mixture of trivial linear experts"). Both blocking issues (#33 trunk registry, #35 pluggable history encoder) are closed, so this was unblocked.
143 lines
3.2 KiB
Python
143 lines
3.2 KiB
Python
"""Re-export shim.
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`network.py` used to hold every network-related class in one 1742-line file.
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It's now split by concern into `giant/model/{layers,encoders,routers,trunks,
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history,models,_legacy,builders}.py` (issues.md Issue 8); this module just
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re-exports the public surface so every existing `from giant.model.network
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import X` call site keeps working unchanged.
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"""
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from giant.model._legacy import _migrate_legacy_model_config, migrate_legacy_state_dict
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from giant.model.builders import build_critics, build_models
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from giant.model.encoders import ConditionEncoder
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from giant.model.history import (
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HISTORY_REGISTRY,
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AttentionHistory,
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HistoryEncoder,
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MarkovHistory,
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NoHistory,
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_CausalAttnBlock,
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build_history,
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register_history,
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)
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from giant.model.layers import (
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BLOCK_REGISTRY,
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AdaLNResBlock,
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ContextAdapter,
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FilmResBlock,
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ResBlock,
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SinusoidalEmbedding,
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_make_axis_mlp,
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build_block,
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build_mlp_head,
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register_block,
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)
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from giant.model.models import (
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CriticModel,
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Stage1Model,
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Stage2Autoregressive,
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Stage2OneShot,
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StageModel,
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resolve_type_n_classes,
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stage2_trunk_sec_dim,
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stage2_type_dim,
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)
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from giant.model.objectives import (
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OBJECTIVE_REGISTRY,
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DdpmObjective,
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FlowObjective,
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Objective,
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WganObjective,
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build_objective,
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register_objective,
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)
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from giant.model.routers import (
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ROUTER_REGISTRY,
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ComposedRouter,
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EnergyRouter,
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NoneRouter,
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PdgRouter,
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ProcessRouter,
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Router,
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_build_router_from_cfg,
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_check_router_conditioning_compat,
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_parse_composed_axes,
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build_composed_router,
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build_router,
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register_router,
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)
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from giant.model.trunks import (
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TRUNK_REGISTRY,
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ExpertTrunk,
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LinearTrunk,
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RoutedTrunk,
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Trunk,
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_route_forward,
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build_expert_body,
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build_trunk,
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register_trunk,
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)
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__all__ = [
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"AdaLNResBlock",
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"AttentionHistory",
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"BLOCK_REGISTRY",
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"ComposedRouter",
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"ConditionEncoder",
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"ContextAdapter",
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"CriticModel",
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"DdpmObjective",
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"EnergyRouter",
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"ExpertTrunk",
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"FilmResBlock",
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"FlowObjective",
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"HISTORY_REGISTRY",
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"HistoryEncoder",
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"LinearTrunk",
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"MarkovHistory",
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"NoHistory",
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"NoneRouter",
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"OBJECTIVE_REGISTRY",
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"Objective",
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"PdgRouter",
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"ProcessRouter",
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"ROUTER_REGISTRY",
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"ResBlock",
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"RoutedTrunk",
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"Router",
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"SinusoidalEmbedding",
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"Stage1Model",
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"Stage2Autoregressive",
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"Stage2OneShot",
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"StageModel",
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"TRUNK_REGISTRY",
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"Trunk",
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"WganObjective",
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"_CausalAttnBlock",
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"_build_router_from_cfg",
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"_check_router_conditioning_compat",
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"_make_axis_mlp",
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"_migrate_legacy_model_config",
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"_parse_composed_axes",
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"_route_forward",
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"build_block",
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"build_composed_router",
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"build_critics",
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"build_expert_body",
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"build_history",
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"build_mlp_head",
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"build_models",
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"build_objective",
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"build_router",
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"build_trunk",
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"migrate_legacy_state_dict",
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"register_block",
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"register_history",
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"register_objective",
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"register_router",
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"register_trunk",
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"resolve_type_n_classes",
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"stage2_trunk_sec_dim",
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"stage2_type_dim",
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]
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