feat(data): add ParamLog class for structured parameter metadata#73
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johnmarktaylor91 merged 1 commit intomainfrom Mar 1, 2026
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feat(data): add ParamLog class for structured parameter metadata#73johnmarktaylor91 merged 1 commit intomainfrom
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Introduce ParamLog — a dedicated data class for parameter metadata — and ParamAccessor for convenient dict-like access by address, index, or short name. Parameters are now first-class objects with full metadata including trainable/frozen status, module info, linked params, gradient tracking, and optional optimizer tagging. Key additions: - ParamLog class with lazy gradient detection via _param_ref - ParamAccessor on both ModelHistory (mh.params) and TensorLogEntry (entry.params) - Trainable/frozen tallies on MH, TLE, and per-module - Optional optimizer= param on log_forward_pass() - Visualization: param names with bracket convention, trainable/frozen color coding, gradient fills for mixed layers, caption breakdown - 68 new tests covering all functionality Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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… polish fixes Wave 1 — Critical Correctness: - Rewrite safe_copy() to eliminate numpy round-trip, fix bfloat16 overflow, handle sparse/meta/quantized dtypes (#103, #137, #128, #139) - Guard print_override numpy conversion (#140), meta/sparse tensor memory (#24) - Fix validation crash on None parent_values (#150), silent exception pass (#151) - Add buffer duplicate guard in fast path (#116) - Fix postprocess_fast shared reference and unguarded parent_layers (#8, #152) - Add empty graph guard (#153), zombie label cleanup (#75) - Handle defaultdict in _safe_copy_arg (#127) Wave 2 — Exception Safety: - Make module_forward_decorator exception-safe (#122) - Guard _handle_module_entry for untracked tensors (#117) - Clean up output tensors on fast-pass exception (#110) - Wrap activation_postfunc in pause_logging (#96) - Guard validation with save_function_args=False (#131) - Fix CUDA perturbation device consistency (#36) Wave 3 — Fast-Pass State Reset: - Reset timing and stale lookup caches in save_new_activations (#87, #97, #98) - Pass raw label (not final) to gradient hook in fast path (#86) - Raise descriptive error for unexpected missing parents (#111) Wave 4 — Argument Handling: - Deep-copy nested tuples in creation_args (#44) - Slice before cloning in display helper (#73) - Use logged shape in display (#45) Wave 5 — Interface Polish: - Key layer_num_passes by no-pass label (#53) - Use rsplit for colon-split safety (#54) - Add slice indexing support (#78) - Guard to_pandas before pass finished (#124) - List candidates for ambiguous substring (#125) - Add ModuleLog string indexing (#120) - Support int/short-name in ParamAccessor.__contains__ (#84) Wave 6 — Decoration/Model Prep: - Only add mapper entries if setattr succeeded (#31) - Add hasattr guard for identity asymmetry (#39) - Guard buffer setattr (#40) - Store dunder argnames stripped (#82) - Use inspect.Parameter.kind for varargs (#123) Wave 7 — Visualization: - Fix vis_nesting_depth=0 crash (#94) - Use rsplit for colon-split (#104) - Fix collapsed node variable (#100) - Guard None tensor_shape (#118) - Guard LayerLog nesting depth (#138) - Use exact equality instead of substring match (#129) Wave 8 — Control Flow/Loop Detection: - Remove dead sibling_layers iteration (#2) - Prevent shared neighbor double-add (#148) Wave 9 — Cleanup + GC: - Lazy import IPython (#72) - Filter callables from _trim_and_reorder (#134) - Complete cleanup() with internal containers (GC-5, GC-12) - Use weakref in backward hook closure (GC-8) - Clear ModulePassLog forward_args/kwargs after build (GC-11) 585 tests passing across 10 test files, 47 new regression tests. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
ParamLog— a dedicated data class for parameter metadata — replacing scattered fields across TensorLogEntry and ModelHistoryParamAccessorfor convenient dict-like access by address, index, or short name (mh.params['fc1.weight'],mh.params[0],entry.params['weight'])total_params_trainable,total_params_frozen), TensorLogEntry, and per-moduleparam.gradafter backward is called, no timing coordination neededoptimizer=parameter onlog_forward_pass()to tag which params have optimizers()trainable /[]frozen, trainable/frozen color coding with gradient fills for mixed layers, graph caption shows trainable breakdown, collapsed modules show trainable/frozen detailTest plan
tests/test_param_log.pycovering: