Seal AO interface failure and verified isolated postprocessing diagnosis
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Digital Drosophila Archive
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|
||||
"experiment": "/data/digital-drosophila/experiments/phase4ao-postprocess-isolation",
|
||||
"manifest_sha256": "202efff603a05016b3f530df9eb1d3c52e1c4119dbc4372ad7b75c6ca69013a2",
|
||||
"new_brain_runs": 0,
|
||||
"off_compartment_changed_edges": 0,
|
||||
"old_trajectory_rerun": false,
|
||||
"receipt_sha256": "cefe84ac1642285b2c815a1f2a03677be064dd38125024f638eda4892cf477b3",
|
||||
"status": "PASS-ISOLATED-POSTPROCESS-DIAGNOSTIC-ONLY",
|
||||
"weights_installed": false
|
||||
},
|
||||
"executed_biological_ms": 120,
|
||||
"executed_ticks": 1200,
|
||||
"failure_stage": "after neural runtime and immediate seal, before epoch delta output creation",
|
||||
"first_checkpoint_manifest_sha256": "e2ad8068bcc8450840ff4c5fb5fbe6a39a1960cd5869b5e16096fb579df28a3a",
|
||||
"full_conditioning_executed": false,
|
||||
"learned_candidate": false,
|
||||
"learning_success_claimed": false,
|
||||
"next_step": "Separately register a new process-isolated coordinator. Do not overwrite or resume this stopped AO inventory; no result-selected seed or brain parameter change.",
|
||||
"old_integrity_pass": true,
|
||||
"old_integrity_roots": 28,
|
||||
"original_sources_unchanged": true,
|
||||
"planned_brain_calls": 12,
|
||||
"previous_synthetic_test_gap": "Tests covered isolated loader and synthetic overlay fixture, not the mixed actual loader integration.",
|
||||
"prior_AN_AM_seals_preserved": true,
|
||||
"registration_sha256": "9e2ed05879ce279da7dc0b4cac09a44d67665c230b56db94e2ca39c645bdc250",
|
||||
"runtime": {
|
||||
"all_finite": true,
|
||||
"build_or_cache_seconds": 59.22188092600118,
|
||||
"edges": 25582938,
|
||||
"neurons": 166700,
|
||||
"peak_allocated_vram_bytes": 518003712,
|
||||
"simulation_seconds": 6.514109814001131,
|
||||
"spikes": 36539,
|
||||
"wall_seconds": 66.93502944299689
|
||||
},
|
||||
"scientific_learning_assessed": false,
|
||||
"status": "FAIL-POSTPROCESS-MODULE-IDENTITY",
|
||||
"stopped_sha256": "45b096d034cc171039b95de69a97538eaf61a9bf4068a47e4e0a2cb16d0a9736",
|
||||
"unchanged_protection": "No loader cache or frozen source was patched or bypassed."
|
||||
}
|
||||
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"actual_complete_brain_trajectories": 1,
|
||||
"analysis_path": "audit/failure-analysis.json",
|
||||
"analysis_sha256": "ddd996eedf943665e3593180a85a1b65399b7a29646c1ea487a6945076b71e6e",
|
||||
"completed_calls": 0,
|
||||
"diagnostic_manifest_sha256": "202efff603a05016b3f530df9eb1d3c52e1c4119dbc4372ad7b75c6ca69013a2",
|
||||
"finalizer_source_sha256": "a68f25ac6dc9e7b1aff1f8999aad9ee7531576696dd16b6ceb8e847b66dcb6d1",
|
||||
"five_reference_trials": "Sealed AM evidence retained; not rerun by AO or the CPU diagnosis.",
|
||||
"frozen_21_and_previous_draft_20_unchanged": true,
|
||||
"full_conditioning_executed": false,
|
||||
"learned_candidate": false,
|
||||
"learning_success_claimed": false,
|
||||
"model_directory_sealed": false,
|
||||
"old_integrity_pass": true,
|
||||
"old_integrity_roots": 28,
|
||||
"old_trajectory_rerun": false,
|
||||
"original_failure_preserved": true,
|
||||
"planned_calls": 12,
|
||||
"prior_AN_AM_seals_preserved": true,
|
||||
"registered_51_sources_unchanged": true,
|
||||
"registration_sha256": "9e2ed05879ce279da7dc0b4cac09a44d67665c230b56db94e2ca39c645bdc250",
|
||||
"status": "FAIL-POSTPROCESS-MODULE-IDENTITY"
|
||||
}
|
||||
@@ -0,0 +1,47 @@
|
||||
{
|
||||
"schema": "phase4ao-preparation-test-receipt-v1",
|
||||
"code_commit": "4ff241bdc96319823f5fc83f80312468f5208a5c",
|
||||
"remote_commit_verified": true,
|
||||
"final_server_cpu_suite": {
|
||||
"tests": 61,
|
||||
"failures": 0,
|
||||
"errors": 0,
|
||||
"skipped": 0,
|
||||
"unittest_seconds": 29.101,
|
||||
"whole_child_seconds": 29.996908439003164,
|
||||
"python": "3.10.12",
|
||||
"modules": [
|
||||
"test_conditioning_epoch",
|
||||
"test_conditioning_preflight_inputs",
|
||||
"test_conditioning_preflight_compare",
|
||||
"test_conditioning_preflight_prepare"
|
||||
],
|
||||
"CUDA_VISIBLE_DEVICES": "",
|
||||
"brain_runs": 0,
|
||||
"source_sha256_unchanged_before_after": true
|
||||
},
|
||||
"earlier_failure": {
|
||||
"tests": 48,
|
||||
"failures": 0,
|
||||
"errors": 1,
|
||||
"cause": "New synthetic test used only the local source mirror layout when locating pinned phase4l.py on the server.",
|
||||
"fix": "Select between the two observed server/local layouts and enforce the same frozen source SHA; no skip.",
|
||||
"occurred_before_registration_or_real_input_sampling": true,
|
||||
"frozen_sources_modified": false
|
||||
},
|
||||
"previously_delivered_sources_verified": 41,
|
||||
"static_data_schema_check": {
|
||||
"odor_A": "4-ethylphenol",
|
||||
"odor_A_PNs": 26,
|
||||
"odor_B": "3-hexanone",
|
||||
"odor_B_PNs": 50,
|
||||
"KC": 4064,
|
||||
"MBON": 97,
|
||||
"DAN": 340,
|
||||
"selected_PAM15": 4,
|
||||
"unknown_DAN_compartment": 14,
|
||||
"brain_runs": 0
|
||||
},
|
||||
"learning_success": false,
|
||||
"real_GPU_execution_covered_by_this_receipt": false
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"complete": false,
|
||||
"completed_rows": [],
|
||||
"error": "Child failed; preserve partial files and inspect logs",
|
||||
"error_type": "ValueError",
|
||||
"no_automatic_retry": true,
|
||||
"partials_preserved": true,
|
||||
"registration_sha256": "9e2ed05879ce279da7dc0b4cac09a44d67665c230b56db94e2ca39c645bdc250",
|
||||
"traceback": "Traceback (most recent call last):\n File \"/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_preflight_workflow.py\", line 312, in run\n child_process(reg, reg_sha, 'worker', row['row_id'])\n File \"/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_preflight_workflow.py\", line 291, in child_process\n require(result.returncode == 0, 'Child failed; preserve partial files and inspect logs')\n File \"/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/engineering_prepare.py\", line 53, in require\n raise ValueError(message)\nValueError: Child failed; preserve partial files and inspect logs\n"
|
||||
}
|
||||
+130
@@ -0,0 +1,130 @@
|
||||
{
|
||||
"D_and_update_commands_owned_by_epoch_authority": true,
|
||||
"brain_runs": 0,
|
||||
"derived_spec_sha256": "358154926e55e51e3aa56c11ba52a7aee6f7c9388df187701b93e8bf65e144bf",
|
||||
"explicit_field_changes": {
|
||||
"CS_window": {
|
||||
"after": [
|
||||
0,
|
||||
1000
|
||||
],
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"budget_root": {
|
||||
"after": "/data/digital-drosophila/experiments/phase4ao-conditioning-interface",
|
||||
"before": "/data/digital-drosophila/experiments/phase4ak-apl-timing-validation",
|
||||
"present_before": true
|
||||
},
|
||||
"cs_input_seed": {
|
||||
"after": 5717173809506784725,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"cs_input_seed_bits": {
|
||||
"after": 63,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"duration_ms": {
|
||||
"after": 120.0,
|
||||
"before": 100.0,
|
||||
"present_before": true
|
||||
},
|
||||
"expected_CS_requests": {
|
||||
"after": 75.04345290362835,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"max_steps": {
|
||||
"after": 1200,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"name": {
|
||||
"after": "D0--light--epoch-000",
|
||||
"before": "seed_validation-physiological-00-s1073741825",
|
||||
"present_before": true
|
||||
},
|
||||
"odor_id": {
|
||||
"after": "HXDOZKJGKXYMEW-UHFFFAOYSA-N",
|
||||
"before": "OMSUIQOIVADKIM-RXMQYKEDSA-N",
|
||||
"present_before": true
|
||||
},
|
||||
"odor_name": {
|
||||
"after": "4-ethylphenol",
|
||||
"before": "ethyl (R)-(-)-3-hydroxybutanoate",
|
||||
"present_before": true
|
||||
},
|
||||
"outer_arm": {
|
||||
"after": "D0",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"outer_epoch_id": {
|
||||
"after": "physiological__PAM15__reinforce_A__rep01/normal/paired/000",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"outer_protocol": {
|
||||
"after": "conditioning_interface_preflight_not_full_conditioning",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"output_budget_bytes": {
|
||||
"after": 4294967296,
|
||||
"before": 25769803776,
|
||||
"present_before": true
|
||||
},
|
||||
"rate_sum_hz": {
|
||||
"after": 750.4344482421875,
|
||||
"before": 1059.0211181640625,
|
||||
"present_before": true
|
||||
},
|
||||
"seed_scope": {
|
||||
"after": "seed is separate reset control; full cs_input_seed used only by saved-input sampler",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"source_panel_row": {
|
||||
"after": 9,
|
||||
"before": 0,
|
||||
"present_before": true
|
||||
},
|
||||
"source_panel_split": {
|
||||
"after": "heldout",
|
||||
"before": "calibration",
|
||||
"present_before": true
|
||||
},
|
||||
"steps": {
|
||||
"after": 1200,
|
||||
"before": 1000,
|
||||
"present_before": true
|
||||
},
|
||||
"target_count": {
|
||||
"after": 26,
|
||||
"before": 48,
|
||||
"present_before": true
|
||||
},
|
||||
"within_trial_weight_updates": {
|
||||
"after": false,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
}
|
||||
},
|
||||
"full_conditioning_authorized": false,
|
||||
"graph_APL_model_coefficients_and_other_fields_unchanged": true,
|
||||
"independent_brain_reset_seed": 1073741825,
|
||||
"neural_state_between_epochs": "fresh reset; only plastic delta may carry via separate verified authority",
|
||||
"original_spec_sha256": "3e262136531358eaa23439df35966566e30e556b4ead705cf5492a360e758e3a",
|
||||
"outer_learning_pass_claimed": false,
|
||||
"random_draws": 0,
|
||||
"removed_stale_input_metadata": {
|
||||
"expected_external_events": 105.90211181640625,
|
||||
"odor_index": 0,
|
||||
"training": false,
|
||||
"validation_cohort": "seed_validation"
|
||||
},
|
||||
"schema": "phase4an-conditioning-preflight-inputs-v1-unregistered",
|
||||
"semantic_input_seed": 5717173809506784725
|
||||
}
|
||||
+55
@@ -0,0 +1,55 @@
|
||||
{
|
||||
"CS_window": [
|
||||
0,
|
||||
1000
|
||||
],
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4ao-conditioning-interface",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"cs_input_seed": 5717173809506784725,
|
||||
"cs_input_seed_bits": 63,
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 120.0,
|
||||
"expected_CS_requests": 75.04345290362835,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/full",
|
||||
"kind": "fullgraph",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"max_steps": 1200,
|
||||
"mode": "physiological",
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"name": "D0--light--epoch-000",
|
||||
"odor_id": "HXDOZKJGKXYMEW-UHFFFAOYSA-N",
|
||||
"odor_name": "4-ethylphenol",
|
||||
"outer_arm": "D0",
|
||||
"outer_epoch_id": "physiological__PAM15__reinforce_A__rep01/normal/paired/000",
|
||||
"outer_protocol": "conditioning_interface_preflight_not_full_conditioning",
|
||||
"output_budget_bytes": 4294967296,
|
||||
"precision": "float64",
|
||||
"rate_sum_hz": 750.4344482421875,
|
||||
"record_history": false,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"seed": 1073741825,
|
||||
"seed_scope": "seed is separate reset control; full cs_input_seed used only by saved-input sampler",
|
||||
"source_panel_row": 9,
|
||||
"source_panel_split": "heldout",
|
||||
"steps": 1200,
|
||||
"target_count": 26,
|
||||
"within_trial_weight_updates": false
|
||||
}
|
||||
+130
@@ -0,0 +1,130 @@
|
||||
{
|
||||
"D_and_update_commands_owned_by_epoch_authority": true,
|
||||
"brain_runs": 0,
|
||||
"derived_spec_sha256": "4c6cbcf43b3e8f62692052feffe9024a41ceb592fd1661bf7e24c7fd78709ab2",
|
||||
"explicit_field_changes": {
|
||||
"CS_window": {
|
||||
"after": [
|
||||
0,
|
||||
1000
|
||||
],
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"budget_root": {
|
||||
"after": "/data/digital-drosophila/experiments/phase4ao-conditioning-interface",
|
||||
"before": "/data/digital-drosophila/experiments/phase4ak-apl-timing-validation",
|
||||
"present_before": true
|
||||
},
|
||||
"cs_input_seed": {
|
||||
"after": 4342084263790487177,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"cs_input_seed_bits": {
|
||||
"after": 63,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"duration_ms": {
|
||||
"after": 120.0,
|
||||
"before": 100.0,
|
||||
"present_before": true
|
||||
},
|
||||
"expected_CS_requests": {
|
||||
"after": 75.04345290362835,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"max_steps": {
|
||||
"after": 1200,
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"name": {
|
||||
"after": "D0--light--epoch-001",
|
||||
"before": "seed_validation-physiological-00-s1073741825",
|
||||
"present_before": true
|
||||
},
|
||||
"odor_id": {
|
||||
"after": "HXDOZKJGKXYMEW-UHFFFAOYSA-N",
|
||||
"before": "OMSUIQOIVADKIM-RXMQYKEDSA-N",
|
||||
"present_before": true
|
||||
},
|
||||
"odor_name": {
|
||||
"after": "4-ethylphenol",
|
||||
"before": "ethyl (R)-(-)-3-hydroxybutanoate",
|
||||
"present_before": true
|
||||
},
|
||||
"outer_arm": {
|
||||
"after": "D0",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"outer_epoch_id": {
|
||||
"after": "physiological__PAM15__reinforce_A__rep01/normal/paired/001",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"outer_protocol": {
|
||||
"after": "conditioning_interface_preflight_not_full_conditioning",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"output_budget_bytes": {
|
||||
"after": 4294967296,
|
||||
"before": 25769803776,
|
||||
"present_before": true
|
||||
},
|
||||
"rate_sum_hz": {
|
||||
"after": 750.4344482421875,
|
||||
"before": 1059.0211181640625,
|
||||
"present_before": true
|
||||
},
|
||||
"seed_scope": {
|
||||
"after": "seed is separate reset control; full cs_input_seed used only by saved-input sampler",
|
||||
"before": null,
|
||||
"present_before": false
|
||||
},
|
||||
"source_panel_row": {
|
||||
"after": 9,
|
||||
"before": 0,
|
||||
"present_before": true
|
||||
},
|
||||
"source_panel_split": {
|
||||
"after": "heldout",
|
||||
"before": "calibration",
|
||||
"present_before": true
|
||||
},
|
||||
"steps": {
|
||||
"after": 1200,
|
||||
"before": 1000,
|
||||
"present_before": true
|
||||
},
|
||||
"target_count": {
|
||||
"after": 26,
|
||||
"before": 48,
|
||||
"present_before": true
|
||||
},
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"annotation_instance": "PPL103(y2a'1)_R",
|
||||
"annotation_type": "PPL103",
|
||||
"body_id": 14182,
|
||||
"mb_compartment_verbatim": "y2a'1",
|
||||
"neuron_idx": 3888
|
||||
}
|
||||
],
|
||||
"D_equals_measured_dopamine": false,
|
||||
"MBON_members": [
|
||||
{
|
||||
"annotation_instance": "MBON12(y2a'1)_L",
|
||||
"annotation_type": "MBON12",
|
||||
"body_id": 13728,
|
||||
"mb_compartment_verbatim": "y2a'1",
|
||||
"neuron_idx": 3477
|
||||
},
|
||||
{
|
||||
"annotation_instance": "MBON12(y2a'1)_L",
|
||||
"annotation_type": "MBON12",
|
||||
"body_id": 13768,
|
||||
"mb_compartment_verbatim": "y2a'1",
|
||||
"neuron_idx": 3513
|
||||
},
|
||||
{
|
||||
"annotation_instance": "MBON12(y2a'1)_R",
|
||||
"annotation_type": "MBON12",
|
||||
"body_id": 519368,
|
||||
"mb_compartment_verbatim": "y2a'1",
|
||||
"neuron_idx": 130136
|
||||
},
|
||||
{
|
||||
"annotation_instance": "MBON12(y2a'1)_R",
|
||||
"annotation_type": "MBON12",
|
||||
"body_id": 521086,
|
||||
"mb_compartment_verbatim": "y2a'1",
|
||||
"neuron_idx": 131044
|
||||
}
|
||||
],
|
||||
"MBON_type": "MBON12",
|
||||
"artificial_US": true,
|
||||
"candidate_contacts": 22603,
|
||||
"candidate_edges": 4126,
|
||||
"compartment": "y2a'1",
|
||||
"confirmed_DAN_MBON_contacts": 802,
|
||||
"confirmed_DAN_MBON_edge_ids": [
|
||||
1641330,
|
||||
1641331,
|
||||
1643507,
|
||||
1643521,
|
||||
2757348,
|
||||
2757349,
|
||||
2759592,
|
||||
2759607
|
||||
],
|
||||
"confirmed_DAN_MBON_edges": 8,
|
||||
"functional_valence": "cross-dataset type-level hypothesis, not measured MaleCNS body function",
|
||||
"unknown_selected_interface_relations": 0
|
||||
}
|
||||
},
|
||||
"new_identity_inferred": false,
|
||||
"populations": {
|
||||
"DAN": 340,
|
||||
"Kenyon_Cell": 4064,
|
||||
"MBON": 97,
|
||||
"MBON01": 2,
|
||||
"MBON12": 4,
|
||||
"PAM15": 4,
|
||||
"PPL103": 2
|
||||
},
|
||||
"schema": {
|
||||
"DAN": {
|
||||
"annotation_instance": "object",
|
||||
"annotation_type": "object",
|
||||
"body_id": "uint64",
|
||||
"mb_compartment_mapping_status": "object",
|
||||
"mb_compartment_verbatim": "object",
|
||||
"neuron_idx": "int32",
|
||||
"phase4h_population": "object"
|
||||
},
|
||||
"MBON": {
|
||||
"annotation_instance": "object",
|
||||
"annotation_type": "object",
|
||||
"body_id": "uint64",
|
||||
"mb_compartment_mapping_status": "object",
|
||||
"mb_compartment_verbatim": "object",
|
||||
"neuron_idx": "int32",
|
||||
"neurotransmitter": "object",
|
||||
"nt_consensus_nt": "object"
|
||||
},
|
||||
"candidates": {
|
||||
"candidate_basis": "object",
|
||||
"edge_id": "int64",
|
||||
"plasticity_polarity": "int64",
|
||||
"raw_synapse_count": "int64",
|
||||
"source_annotation_instance": "object",
|
||||
"source_annotation_type": "object",
|
||||
"source_body_id": "uint64",
|
||||
"source_compartment_verbatim": "object",
|
||||
"source_neuron_idx": "int32",
|
||||
"target_annotation_class": "object",
|
||||
"target_annotation_instance": "object",
|
||||
"target_annotation_type": "object",
|
||||
"target_body_id": "uint64",
|
||||
"target_compartment_verbatim": "object",
|
||||
"target_neuron_idx": "int32"
|
||||
},
|
||||
"confirmed": {
|
||||
"edge_id": "int64",
|
||||
"exact_compartment_match": "bool",
|
||||
"raw_synapse_count": "int64",
|
||||
"source_annotation_instance": "object",
|
||||
"source_annotation_type": "object",
|
||||
"source_body_id": "uint64",
|
||||
"source_compartment_verbatim": "object",
|
||||
"source_neuron_idx": "int32",
|
||||
"target_annotation_class": "object",
|
||||
"target_annotation_instance": "object",
|
||||
"target_annotation_type": "object",
|
||||
"target_body_id": "uint64",
|
||||
"target_compartment_verbatim": "object",
|
||||
"target_neuron_idx": "int32"
|
||||
},
|
||||
"metadata": {
|
||||
"annotation_assignedOlHex1": "float64",
|
||||
"annotation_assignedOlHex2": "float64",
|
||||
"annotation_birthtime": "object",
|
||||
"annotation_bodyId": "int64",
|
||||
"annotation_class": "object",
|
||||
"annotation_dimorphism": "object",
|
||||
"annotation_entryNerve": "object",
|
||||
"annotation_exitNerve": "object",
|
||||
"annotation_flywireType": "object",
|
||||
"annotation_fruDsx": "object",
|
||||
"annotation_group": "float64",
|
||||
"annotation_hemibrainType": "object",
|
||||
"annotation_instance": "object",
|
||||
"annotation_itoleeHl": "object",
|
||||
"annotation_mancBodyid": "float64",
|
||||
"annotation_mancGroup": "float64",
|
||||
"annotation_mancSerial": "float64",
|
||||
"annotation_mancType": "object",
|
||||
"annotation_matchingNotes": "object",
|
||||
"annotation_mcnsSerial": "float64",
|
||||
"annotation_receptorType": "object",
|
||||
"annotation_rootSide": "object",
|
||||
"annotation_serialMotif": "object",
|
||||
"annotation_somaLocation": "object",
|
||||
"annotation_somaNeuromere": "object",
|
||||
"annotation_somaSide": "object",
|
||||
"annotation_status": "object",
|
||||
"annotation_statusLabel": "category",
|
||||
"annotation_subclass": "object",
|
||||
"annotation_superclass": "object",
|
||||
"annotation_supertype": "object",
|
||||
"annotation_synonyms": "object",
|
||||
"annotation_tosomaLocation": "object",
|
||||
"annotation_trumanHl": "object",
|
||||
"annotation_type": "object",
|
||||
"annotation_vfbId": "object",
|
||||
"body_id": "uint64",
|
||||
"has_swc": "bool",
|
||||
"neuron_class": "object",
|
||||
"neuron_idx": "int32",
|
||||
"neuron_type": "object",
|
||||
"neurotransmitter": "object",
|
||||
"nt_body": "float64",
|
||||
"nt_cell_type": "object",
|
||||
"nt_celltype_predicted_nt": "object",
|
||||
"nt_celltype_predicted_nt_confidence": "float64",
|
||||
"nt_celltype_total_nt_predictions": "float64",
|
||||
"nt_consensus_nt": "object",
|
||||
"nt_ground_truth": "object",
|
||||
"nt_predicted_nt": "object",
|
||||
"nt_predicted_nt_confidence": "float64",
|
||||
"nt_total_nt_predictions": "float64",
|
||||
"side": "object",
|
||||
"swc_path": "object"
|
||||
}
|
||||
},
|
||||
"training": false,
|
||||
"whole_DAN_mapping_unknown": 14,
|
||||
"whole_MBON_mapping_unknown": 0
|
||||
}
|
||||
@@ -0,0 +1,168 @@
|
||||
{
|
||||
"A": {
|
||||
"cue": "A",
|
||||
"glomeruli_order": [
|
||||
"D",
|
||||
"DA1",
|
||||
"DA2",
|
||||
"DA3",
|
||||
"DA4l",
|
||||
"DA4m",
|
||||
"DC1",
|
||||
"DC2",
|
||||
"DC3",
|
||||
"DC4",
|
||||
"DL1",
|
||||
"DL3",
|
||||
"DL4",
|
||||
"DL5",
|
||||
"DM1",
|
||||
"DM2",
|
||||
"DM3",
|
||||
"DM4",
|
||||
"DM5",
|
||||
"DM6",
|
||||
"DP1l",
|
||||
"DP1m",
|
||||
"V",
|
||||
"VA1d",
|
||||
"VA1v",
|
||||
"VA2",
|
||||
"VA3",
|
||||
"VA4",
|
||||
"VA5",
|
||||
"VA6",
|
||||
"VA7l",
|
||||
"VC1",
|
||||
"VC2",
|
||||
"VC3",
|
||||
"VC4",
|
||||
"VC5",
|
||||
"VL1",
|
||||
"VL2a",
|
||||
"VL2p",
|
||||
"VM1",
|
||||
"VM2",
|
||||
"VM3",
|
||||
"VM4",
|
||||
"VM5d",
|
||||
"VM5v",
|
||||
"VM7d",
|
||||
"VM7v"
|
||||
],
|
||||
"identities": {
|
||||
"rates_hz": {
|
||||
"dtype": "<f4",
|
||||
"order": "C-logical-bytes",
|
||||
"sha256": "5a6e7d6668949909ebf03df372ac5205f80b97217d7754e932b7a63994e091bb",
|
||||
"shape": [
|
||||
26
|
||||
]
|
||||
},
|
||||
"targets": {
|
||||
"dtype": "<i4",
|
||||
"order": "C-logical-bytes",
|
||||
"sha256": "e2689126942ce22f39eb17c60f243ca16e2a0aa7e3921a68a6b38dfd15181bc4",
|
||||
"shape": [
|
||||
26
|
||||
]
|
||||
}
|
||||
},
|
||||
"mapped_vector_identity": {
|
||||
"dtype": "<f4",
|
||||
"order": "C-logical-bytes",
|
||||
"sha256": "ff40e4357e37c7d88b4b33e53fcff36fb2aa64c4826246de28762d794cd8a2a1",
|
||||
"shape": [
|
||||
47
|
||||
]
|
||||
},
|
||||
"mode": "physiological",
|
||||
"odor_id": "HXDOZKJGKXYMEW-UHFFFAOYSA-N",
|
||||
"odor_name": "4-ethylphenol",
|
||||
"source_panel_row": 9,
|
||||
"source_panel_split": "heldout"
|
||||
},
|
||||
"B": {
|
||||
"cue": "B",
|
||||
"glomeruli_order": [
|
||||
"D",
|
||||
"DA1",
|
||||
"DA2",
|
||||
"DA3",
|
||||
"DA4l",
|
||||
"DA4m",
|
||||
"DC1",
|
||||
"DC2",
|
||||
"DC3",
|
||||
"DC4",
|
||||
"DL1",
|
||||
"DL3",
|
||||
"DL4",
|
||||
"DL5",
|
||||
"DM1",
|
||||
"DM2",
|
||||
"DM3",
|
||||
"DM4",
|
||||
"DM5",
|
||||
"DM6",
|
||||
"DP1l",
|
||||
"DP1m",
|
||||
"V",
|
||||
"VA1d",
|
||||
"VA1v",
|
||||
"VA2",
|
||||
"VA3",
|
||||
"VA4",
|
||||
"VA5",
|
||||
"VA6",
|
||||
"VA7l",
|
||||
"VC1",
|
||||
"VC2",
|
||||
"VC3",
|
||||
"VC4",
|
||||
"VC5",
|
||||
"VL1",
|
||||
"VL2a",
|
||||
"VL2p",
|
||||
"VM1",
|
||||
"VM2",
|
||||
"VM3",
|
||||
"VM4",
|
||||
"VM5d",
|
||||
"VM5v",
|
||||
"VM7d",
|
||||
"VM7v"
|
||||
],
|
||||
"identities": {
|
||||
"rates_hz": {
|
||||
"dtype": "<f4",
|
||||
"order": "C-logical-bytes",
|
||||
"sha256": "7c38b8bea66cb7b2e10893d04bc8347615601b59ac4c0c1bba4df7b89c0dea33",
|
||||
"shape": [
|
||||
50
|
||||
]
|
||||
},
|
||||
"targets": {
|
||||
"dtype": "<i4",
|
||||
"order": "C-logical-bytes",
|
||||
"sha256": "df54662ef61c39084eeb98007e4323233209a6dbb6042dbe4324e75112f09590",
|
||||
"shape": [
|
||||
50
|
||||
]
|
||||
}
|
||||
},
|
||||
"mapped_vector_identity": {
|
||||
"dtype": "<f4",
|
||||
"order": "C-logical-bytes",
|
||||
"sha256": "4817635c5bf15a5aae980416d8d7c5dd52d12d06eb34cd255b116c02388e8ce7",
|
||||
"shape": [
|
||||
47
|
||||
]
|
||||
},
|
||||
"mode": "physiological",
|
||||
"odor_id": "PFCHFHIRKBAQGU-UHFFFAOYSA-N",
|
||||
"odor_name": "3-hexanone",
|
||||
"source_panel_row": 10,
|
||||
"source_panel_split": "heldout"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"brain_runs": 0,
|
||||
"full_seal_verification_owned_by_coordinator": true,
|
||||
"historical_dynamic_results_read": false,
|
||||
"median_mapped_response_sum": 2.556107997894287,
|
||||
"mode": "physiological",
|
||||
"numpy_version": "2.2.6",
|
||||
"original_function_called_without_PN_reordering": true,
|
||||
"original_pn_drive_AST_sha256": "5dcedb699cc77f5a47ee6d91f52c93138ddbc23e37c9c8b28927e34370d92fd1",
|
||||
"pandas_version": "2.3.3",
|
||||
"random_draws": 0,
|
||||
"schema": "phase4an-conditioning-preflight-inputs-v1-unregistered",
|
||||
"static_sources": {
|
||||
"experiments/phase4l-olfactory-grounding/odor-encoder/frozen-odor-panel.parquet": "907f16a56e024a8e2112b5751e53b0e6037b329fa110098af3d31b119c10a1a0",
|
||||
"experiments/phase4l-olfactory-grounding/odor-encoder/mapped-glomerular-vectors.npz": "f057563152e0fa22060080af13019f319e7f36563a775eff829bbc1929c451b0",
|
||||
"experiments/phase4l-olfactory-grounding/odor-encoder/selection.json": "1b832becf9d08935716fe9ba5e3cd3e619e7d208dfb43c4fa197c969d095f113",
|
||||
"experiments/phase4l-olfactory-grounding/receptor-mapping/olfactory-mapping.parquet": "a01d27000533e9844458a4c559cac084f1c34ca20a1104e97e6a0f72b0cc93b1",
|
||||
"models/male-cns-lif/olfactory-grounding-v1/phase4l.yaml": "9b8bcb7e031bf9063033a1854a439a7cd0bf6730301c2d2d02ca99a76f098d6e",
|
||||
"models/male-cns-lif/phase4l.py": "e97d58d8c1f44920d0e953f8f5831ddc86fa0198784e86460e315de4116ac553"
|
||||
}
|
||||
}
|
||||
+16
@@ -0,0 +1,16 @@
|
||||
Traceback (most recent call last):
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_preflight_workflow.py", line 471, in <module>
|
||||
if args.action == 'worker': worker(args.registration_sha256, args.row_id)
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_preflight_workflow.py", line 254, in worker
|
||||
E.postprocess_epoch(paths['authority'], digest(paths['authority']), paths['update'])
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_epoch.py", line 312, in postprocess_epoch
|
||||
return _coordinate(value, paths, pins, checkpoint, authority_path, out_dir, dependencies, DIMENSIONS)
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/conditioning_epoch.py", line 209, in _coordinate
|
||||
helper, before, checked_meta, _ = r.load_overlay(paths['overlay_before'], paths['overlay_manifest_before'])
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/runtime_light.py", line 160, in load_overlay
|
||||
helper = _load('phase4an_sealed_am_overlay', meta['am_overlay_path'], AM_OVERLAY_SHA)
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/runtime_light.py", line 99, in _load
|
||||
require(_LOADED.get(name) == (path, expected, module), 'Existing unverified module binding: ' + name)
|
||||
File "/data/digital-drosophila/models/male-cns-lif/olfactory-conditioning-lab-v1/runtime_light.py", line 57, in require
|
||||
raise ValueError(message)
|
||||
ValueError: Existing unverified module binding: phase4an_sealed_am_overlay
|
||||
@@ -0,0 +1,765 @@
|
||||
{
|
||||
"files": [
|
||||
{
|
||||
"bytes": 2278,
|
||||
"path": "audit/failure-analysis.json",
|
||||
"sha256": "ddd996eedf943665e3593180a85a1b65399b7a29646c1ea487a6945076b71e6e"
|
||||
},
|
||||
{
|
||||
"bytes": 1073,
|
||||
"path": "audit/final-status.json",
|
||||
"sha256": "f8fff2cb7d4eb0abfc9653c989781c0310b6ad7ec42cce4949bae1a15fd793d8"
|
||||
},
|
||||
{
|
||||
"bytes": 1473,
|
||||
"path": "audit/preparation-tests.json",
|
||||
"sha256": "c07d90c60e2b55a99ae3af8cb42997e012bac3f7a4fa36df97b3a94524604964"
|
||||
},
|
||||
{
|
||||
"bytes": 1025,
|
||||
"path": "audit/stopped.json",
|
||||
"sha256": "45b096d034cc171039b95de69a97538eaf61a9bf4068a47e4e0a2cb16d0a9736"
|
||||
},
|
||||
{
|
||||
"bytes": 3640,
|
||||
"path": "cases/D0--light--epoch-000/derivation.json",
|
||||
"sha256": "eaad7f8b39b236a82346843ed6c510668c30878e641a7179b724e288be34718b"
|
||||
},
|
||||
{
|
||||
"bytes": 714,
|
||||
"path": "cases/D0--light--epoch-000/requests.npz",
|
||||
"sha256": "e3df9625b996a62c38e137a64bbfa7ea2d421ba02303bb9da09850a741e0d85f"
|
||||
},
|
||||
{
|
||||
"bytes": 1960,
|
||||
"path": "cases/D0--light--epoch-000/spec.json",
|
||||
"sha256": "51719e24ec20dec308e6f5d4458597924854c256ab8df8492bfd582d788a69b4"
|
||||
},
|
||||
{
|
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@@ -0,0 +1,32 @@
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@@ -0,0 +1,7 @@
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@@ -0,0 +1,12 @@
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@@ -0,0 +1,57 @@
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"sha256": "887345e497e6fa9c10ffa49c490196711d8cee028c62ec7a73f42db36569be2b"
|
||||
},
|
||||
{
|
||||
"bytes": 5489,
|
||||
"copy": "source-snapshot/test_seed_registry.py",
|
||||
"model_source": "models/male-cns-lif/olfactory-conditioning-lab-v1/test_seed_registry.py",
|
||||
"sealed_source": "protocol/source-snapshot/test_seed_registry.py",
|
||||
"sha256": "7aca38f3c975e3ad4f6337d83b826c09afef336381d124c22ff0eb2eb60a0834"
|
||||
},
|
||||
{
|
||||
"bytes": 9651,
|
||||
"copy": "source-snapshot/test_trial_plan.py",
|
||||
"model_source": "models/male-cns-lif/olfactory-conditioning-lab-v1/test_trial_plan.py",
|
||||
"sealed_source": "protocol/source-snapshot/test_trial_plan.py",
|
||||
"sha256": "927678956ae9959a047b1cd258ee8dee22eb6842a96ff4b2ba2db077310163c9"
|
||||
},
|
||||
{
|
||||
"bytes": 16606,
|
||||
"copy": "source-snapshot/trial_plan.py",
|
||||
"model_source": "models/male-cns-lif/olfactory-conditioning-lab-v1/trial_plan.py",
|
||||
"sealed_source": "protocol/source-snapshot/trial_plan.py",
|
||||
"sha256": "6fed3f494d075339a7e5b001d43b2598640b189e038952fbd4d41200bacac587"
|
||||
}
|
||||
],
|
||||
"git_blobs_reverified_by_publisher": false,
|
||||
"git_code_commit": "4ff241bdc96319823f5fc83f80312468f5208a5c",
|
||||
"model_directory_sealed": false,
|
||||
"original_snapshot_registration_exact": true,
|
||||
"registration_sha256": "9e2ed05879ce279da7dc0b4cac09a44d67665c230b56db94e2ca39c645bdc250",
|
||||
"source_count": 51
|
||||
}
|
||||
+70
@@ -0,0 +1,70 @@
|
||||
# AO — bounded two-epoch conditioning-interface validation
|
||||
|
||||
This independent branch does not replace the frozen reference, runtime or any
|
||||
Phase 1–AN result. It tests the installation interface for the existing frozen
|
||||
eligibility-v2 rule. It is not a new dynamics hypothesis or a learned model.
|
||||
|
||||
## Fixed, pre-outcome selection
|
||||
|
||||
- Master seed: `20260930`; no seed search or outcome-based retries.
|
||||
- First canonical block: physiological / PAM15 / reinforce_A / rep01.
|
||||
- First normal paired epoch requesting a US, and its immediate successor.
|
||||
- Both selected epochs are odor A (4-ethylphenol), not an A/B discrimination
|
||||
test. Exact recorded L glomerular/PN mapping and PN column order are retained.
|
||||
- Each epoch is 120 ms: 100 ms CS plus 20 ms tail, US at tick 900.
|
||||
- D1, D0 and frozen arms receive identical saved CS and DAN requests. The
|
||||
scheduled artificial D scalar is not measured dopamine concentration and is
|
||||
not multiplied by actual DAN request acceptance. Delivery is reported apart.
|
||||
- Two executors (reference and light), two fresh epochs per arm: 12 calls,
|
||||
14,400 timesteps. This does not authorize the full conditioning inventory.
|
||||
|
||||
## Immutable neural semantics and authority
|
||||
|
||||
Each epoch resets V, g, refractory counters, APL state and both delay queues;
|
||||
the zero-initialized eligibility trace is computed from actual sealed spikes.
|
||||
Only the float32-mV plastic delta carries between epochs. No update occurs
|
||||
inside an epoch. Original graph, base weights, transmitter signs, numerical
|
||||
precision, APL parameters, frozen v2 rule and complete 14,232-edge mask remain
|
||||
unchanged. Full graph is 166,700 neurons and 25,582,938 edges.
|
||||
|
||||
Registration pins source snapshots, actual graph/APL/coefficient files, frozen
|
||||
L static resources, H mappings, the complete generated semantic plan and the
|
||||
12 selected call descriptors before CUDA sampling. Sampling receipts bind the
|
||||
two generated request arrays before any brain call. Every input file is shared
|
||||
byte-for-byte between arms and executors. The full 63-bit input seed is separate
|
||||
from the inherited conservative 31-bit neural reset-control value.
|
||||
|
||||
A fixed first-seed CUDA check executes only the four original L sampling AST
|
||||
statements, compares sparse requests, and compares full63-bit versus low32-bit
|
||||
uniform matrices. This is a sampler test, not a neural trial or seed search.
|
||||
Two production and three diagnostic matrices are drawn; each is 1200×PN count,
|
||||
at most 2,000,000 uniform values. No continuous-time Poisson claim is made.
|
||||
|
||||
Each neural output is immediately sealed. Outside that seal, the coordinator
|
||||
binds original inputs, checkpoint events, actual v2 recomputation and the new
|
||||
delta snapshot. `parent_snapshot_sha256` means the previous **overlay NPZ file**,
|
||||
not its manifest or the neural checkpoint. Epoch 2 must install that exact
|
||||
snapshot; D0/frozen deltas must stay positive-zero bitwise.
|
||||
|
||||
## Finite gates and reporting limits
|
||||
|
||||
Required: all 12 processes complete; six reference/light pairs have identical
|
||||
sparse events, counts, final V/g/ref and terminal queues/APL; deltas are exact;
|
||||
D0/frozen identity holds; epoch 2 actually installs epoch 1 delta. D1 must have
|
||||
nonzero actual update coverage or the result is `INCOMPLETE`, never rescued by
|
||||
another input. Nonzero delta is interface coverage, not evidence of memory.
|
||||
|
||||
The light executor does not record intermediate floating states; no all-tick
|
||||
bitwise claim is inferred. This validation has only one training replicate, two
|
||||
A epochs, no independent behavioral evaluation, and no acquisition/reversal/
|
||||
retention success claim. The post-update retention boundary is still unsupported.
|
||||
|
||||
## Resource and failure policy
|
||||
|
||||
Use only RTX4090 UUID GPU-79f3a85d-adce-7833-7003-6488140ca596; admit a child only
|
||||
when all three ComfyUI queues are empty. Never stop or evict services. Cap new
|
||||
output at 4 GiB plus 10 GiB free reserve and each child at 1800 seconds. Preserve
|
||||
partial output and stop on any failure; no automatic brain retry. End by
|
||||
rechecking AN and AM seals and the inherited 28-root integrity inventory. Five
|
||||
old frozen reference results are retained from sealed AM evidence, not claimed
|
||||
to have been rerun by this small interface gate.
|
||||
@@ -0,0 +1,116 @@
|
||||
# Phase 4AN GPU 工程验证门草案
|
||||
|
||||
状态：**未注册 DRAFT；不是执行授权，尚未运行本门。** 日期：2026-09-30。
|
||||
|
||||
本门只验证新的轻记录执行器和连续状态接口是否保存冻结 D18 动力学。它不是 conditioning、retention 学习结论，也不产生 learned candidate。AM 是否已正式完成、其最终状态和 seal SHA，必须在本门预注册时从真实证据核验，本文不代填成功状态或哈希。
|
||||
|
||||
## 1. 固定范围与最小库存
|
||||
|
||||
只使用一个 AM 已准备的 100 ms 全脑输入，分别安装其原有 `zero` 与 `static25` 固定 overlay。选择规则先于本门任何新 GPU 输出：从 AM `protocol/preregistered.json` 的原 `planned` 顺序取第一条 `repeat == 0` 的记录，保存其完整行、序号、case 路径及 SHA；不得按活动强弱、旧 static25 效果或运行快慢挑 case。其原始 case 实际位于 AK `cases/<cohort>/<name>`；AM `run_suite.py` 没有另造一套 case。若预注册者希望改变此规则，应先修订草案并锁定，不得在看新输出后换输入。
|
||||
|
||||
每个 overlay 固定以下库存，所有行均使用同一原始 1000 tick 请求序列或其精确前缀/分段：
|
||||
|
||||
| 行组 | 固定执行内容 | 新执行调用数/overlay |
|
||||
|---|---|---:|
|
||||
| Fresh 对照 | 完整 1000 tick reference r0、light r0、light r1 | 3 |
|
||||
| 连续链 | reference、light 各一条，段长 `[5, 13, 1, 481, 500]` | 10 |
|
||||
| 切点前缀 | 从初态独立运行 reference 至绝对 tick 数 18、19、500 | 3 |
|
||||
|
||||
合计 32 次短执行，11,074 个全脑 tick，即累计 1.1074 生物秒；不是 68,000 个训练 epoch。两条链的第一段均为 fresh 5 tick，其中 reference 第一段兼作第一个切点的前缀参照，不重复运行。切点为累计 tick 数 5、18、19、500，终点为 1000：覆盖短于 18 tick 的父/子段、在绝对 tick 18 的首次合法延迟到达、19-slot 环的相位，以及较晚的活动边界。不得把部分库存完成称作完整门通过。
|
||||
|
||||
固定参数：166,700 neurons、25,582,938 edges、`dt_ms=0.1`、18 tick synaptic delay、19 行 spike ring、18 行 graded-APL ring、LIF float64 SI 状态与旧 APL float32 操作。保留 14,232 mask、5845 fast-APL 出边置零规则及原 APL 全形矩阵。`static25` 是 AM 原 float32-mV 候选基值的固定 `-0.25 * base` overlay，经原 SI bridge 安装；不是声称 float64 原权重每项恰减 25%。两条 overlay 分别与自己的 AM 参照比较，不能要求 zero 与 static25 相等。
|
||||
|
||||
禁止：weight updates、eligibility 推进/应用、eta 改动、DAN/US 新请求、odor/seed 新抽样、动力学调参、训练、额外 tail ticks。这里的连续链只检验工程续接，不检验记忆保留。
|
||||
|
||||
## 2. 当前真实接口与输入派生
|
||||
|
||||
以下是底层 Python API；本门的独立 coordinator 使用它们，不改旧模块 globals：
|
||||
|
||||
```python
|
||||
runtime_light.run_light(case_dir, out_dir, build_root, overlay_file, overlay_manifest)
|
||||
runtime_light.run_reference(case_dir, out_dir, build_root, overlay_file, overlay_manifest)
|
||||
runtime_continuation.seal_checkpoint(checkpoint_dir)
|
||||
runtime_continuation.load_checkpoint(checkpoint_dir, expected_sha)
|
||||
runtime_continuation.run_continued(
|
||||
case_dir, out_dir, build_root, overlay_file, overlay_manifest,
|
||||
checkpoint_dir, mode='light' # 或 'reference'
|
||||
)
|
||||
```
|
||||
|
||||
本门的 comparator、有限库存 coordinator、预注册/执行 receipt 已在独立 engineering 模块实现；必须在服务器通过纯合成测试、完成只读代码审查并锁定全部源码后，才能注册执行。现有 AM comparator 对 AM adapter/provenance 和完整 1000 tick 有硬 schema 约束，不应直接冒充 AN comparator 或修改其旧 globals。
|
||||
|
||||
在新的独占输入目录派生 spec，不改 AK/AM 原文件。完整 fresh case 保留原 `name`、model、APL、图、系数和请求；增加显式 `max_steps=1000`，仅调整新的输出预算根及已列明的非科学标签。要求 `record_history=False`。新旧 spec 的逐字段差异必须列入 receipt，不能用“大致相同”或整类忽略代替。
|
||||
|
||||
前缀保留原请求中 `tick < end` 的行。分段 `[start, stop)` 只保留该区间原请求，局部 tick 精确减 `start`；全局 neuron_idx 不变。每段 `steps=stop-start`、`duration_ms=steps*0.1`、`max_steps=1000`。将各段请求恢复绝对 tick 后拼接，必须与原请求两个 int32 数组逐位相同、顺序相同、无丢失/重复；不能重抽随机输入。NPZ 容器 SHA 可以因新文件而不同，但数组语义必须 exact，并同时保留文件 SHA。
|
||||
|
||||
第一段完成后立即 `seal_checkpoint`；外部 coordinator 把返回的实际 manifest SHA 绑定到下一段新 spec 的 `parent_checkpoint_manifest_sha256`，可同时写准确 `global_tick_offset`。此 SHA 是运行后产出的数据依赖，预注册应锁定派生规则而不是预造值。后续 `run_continued` 自身会产生并封存 checkpoint；父 SHA 仍须外部绑定。fresh seal 是 `state.npz` 的首次新增封存绑定，不能声称它追溯证明 seal 前文件未改：应在同一受控流程中立即比较、封存，保存前后 inventory，期间不允许第三方写入。
|
||||
|
||||
AN overlay manifest 使用现有 `phase4an-overlay-v1` 精确键：`schema, overlay_name, overlay_sha256, mask_path, mask_sha256, base_candidate_path, base_candidate_sha256, aj_model_dir, am_overlay_path, am_overlay_sha256`。复用 AM 原 overlay 数组字节及实际 SHA，仅新建 manifest；不能把 AM 原 manifest 的额外字段直接塞给 AN，也不使用 `learned` 模式。
|
||||
|
||||
## 3. 预注册必须完整绑定的证据
|
||||
|
||||
在任何本门 GPU 执行前，保存不可覆盖的 input/source inventory 与其外部 SHA。至少包括：
|
||||
|
||||
- AM 最终状态、model/experiment seal、`protocol/preregistered.json`、`prepared-hashes.json`、所选原 case spec/requests，以及 `gpu/zero/<name>-r0`、`gpu/static25/<name>-r0` 的完整 `output-hashes.json`、comparison、adapter provenance 与全部待比较 artifact。核验完整 AM seal，而非只相信单个 `pass_all`。
|
||||
- 图的 `nodes/edge_id/src/dst/ptr/signed_raw/weights.npy` 全部文件；APL `input_matrix/output_matrix/kc_globals/apl_globals.npy`；AF 原 `coefficients.json`；AM `protocol/base-candidate.float32-mV.npy`、两个 overlay NPZ/manifest、原 H mask。图和 bundle 的所有 SHA 来自外部锚定的 AM prereg inputs，不是仅由 AN 本次读入后自报。
|
||||
- AN `runtime_light.py`、`runtime_continuation.py`、对应 tests、即将实现的本门 coordinator/comparator/tests，以及本草案最终注册版。固定其实际 SHA 和源 AST 审计；不得在跑到一半后修改源代码继续使用同一 prereg。
|
||||
- `runtime_light.PINS` 的整个依赖链：AJ GPU/timing queue、AG-r2 GPU/legacy APL worker、AE backend/C++/CUDA、AF queue helper，以及 `AM_OVERLAY_SHA` 对应原 `overlay.py`。记录 `runtime_continuation.LIGHT_SHA` 与实际 light 文件一致。运行前后都重验旧来源与静态输入，不依赖时间戳缓存来替代字节哈希。
|
||||
- 新输入每份 spec/requests/manifest 的 SHA，原始记录到前缀/分段的确定性映射，以及库版本、科学 Python 原 venv 入口、GPU UUID/driver/CUDA/Torch/NumPy、编译标志和 extension 来源。Python 用 `-B`；不可通过 resolve venv executable 改变包环境。Torch import 前完成 GPU/CUDA 环境设置；仅记录并核对原 TF32/CuBLAS/确定性配置，不为追求一致或速度偷偷改默认值。
|
||||
|
||||
AM 已完成封存并取得小证据下载包；该下载包不含全部大型轨迹，因此预注册仍必须在服务器完整校验 AM 两个 seal。实际 case 名与 seal SHA 依照上述确定规则写入预注册，不以本文代填。缺失 authority、旧输入变化或 AM 不完整时停止，不进入 GPU 门。
|
||||
|
||||
## 4. Exact 比较，不把摘要布尔当证明
|
||||
|
||||
所有浮点比较按原 dtype 的连续 little-endian raw bits，包括正负零；禁止 tolerance、round、只比均值或把压缩 NPZ 文件字节相等误作数组相等。所有状态要求 finite，事件、计数和请求库存必须完整。
|
||||
|
||||
### Fresh light/reference ↔ 同 overlay AM
|
||||
|
||||
逐数组比 `state.npz` 六键：`step/neuron_idx/counts/ref` 为 int32，`v/g` 为 float64；全部 N 个末态和全部稀疏事件相同，events 自身排序唯一且 bincount 等于 counts。light 与 reference 都保留 `requests.csv`，其五字段 `tick, neuron_idx, attempted, accepted, rejected` 必须与 AM 原输出逐行 exact；不能只比较请求总数。reference 另比每 tick `v_sha256/g_sha256/ref_sha256`、全部 APL drive/state/generated inhibition/applied h float32、pending tail 和每 tick queue。来源标签、新 spec SHA、记录范围和计时等确有差异的字段须显式白名单，不能整份忽略 provenance。
|
||||
|
||||
AN 新 `terminal-queues.npz` 没有对应 AM 原文件时，只从 AM 已记录事实独立重建：1000 tick 最后 19 tick 的全局 sparse events 决定 raw spike ring；末行 `apl-state.float32.npy` 决定 APL state；原 `apl-pending-tail.float32.npy` 决定 chronological pending。比较全部数组及 `next_tick=1000`，不补跑 AM。
|
||||
|
||||
light 没有全 APL trace、逐 tick V/g/ref hash 或 queue.jsonl；不能把这些缺文件当空记录、不能称 light 已做这些逐 tick 对照。但 `ReportingOnly.visit_With` 只移除 queue/hash 的两个 context item，留下 `request_output`，也保留 `writer.writerows` 和请求接受判定的 GPU→CPU 拷贝。因此 light 的门包括完整 events/counts/终态、逐行五字段请求接受记录、APL state、完整两种 pending queue、有限值覆盖、总到达/接受计数及固定权重身份与 reference exact；不能把它描述为完全无逐请求记录或完全无逐 tick CPU 同步。reference 承担完整逐 tick 状态/队列观测。light r1 要与 r0 的科学数组、请求 CSV、终态队列及有限值/总量一致，计时不是 exact 字段。
|
||||
|
||||
### Monolithic ↔ segmented
|
||||
|
||||
每个 overlay、每种模式分别比自己的完整 1000 tick fresh 基线，不跨 overlay 借结果。拼接所有段的 sparse events，并按 receipt offset 恢复绝对 tick；逐段 counts 用 int64 求和、验证范围后与单体 counts exact。末态 V/g/ref、最终 APL state/pending、effective weights raw SHA 必须一致；每个中间切点还与相同绝对长度的 fresh reference 前缀末态对照。切点 5 使用已记录的首段 reference，切点 18/19/500 使用预定前缀，终点使用完整基线。
|
||||
|
||||
spike ring 不能直接比较物理行号：各自按 `terminal.next_tick % 19` 转成“下一写 slot 起”的 19 行顺序后逐位比较；continued 的 `next_tick` 是本段局部相位，绝对时间来自 receipt。APL pending 已按下一次应用顺序保存，直接逐位比较；不得把它当未来脑活动或继续衰减的完整尾迹。短段未覆盖的 spike slots 和未消费 APL prefix 还须精确等于父 carry。
|
||||
|
||||
reference 的全部逐 tick 状态 hashes 和 APL 四种 trace 按全局时间拼接后与 monolithic 对照；不重复/跳过边界 tick。`queue.jsonl` 的 `joint_sha256` 编码了局部 tick，不能直接跨段相等比较：按全局 tick 对齐，精确比较 `count, accepted_count, ordered_edge_sha256, accepted_mask_sha256`；用完整事件、原 CSR、原 ref 规则独立重建原 source-order queue，并分别验证每份文件自己的 local-header joint hash。不能对 queue 重新按 target 排序后才声称原队列相同。**light 和 reference 两条链的请求 CSV 均须**按 offset 还原 tick、保持其余四字段和行序，拼接后与各自 monolithic CSV 逐行 exact；同时与预定请求库存对应，不能漏掉拒收请求。
|
||||
|
||||
上述门可证明 reference 完整记录和 light 已记录状态的一致；**不能据此声称不可观测的 light 每 tick 内部 V/g bits 已被直接逐项比较。** 若正式注册要求这一更强结论，须先单独批准新只读观测器和相应 source pin，而不是运行后扩大结论。
|
||||
|
||||
## 5. 防真空和故障门
|
||||
|
||||
每个 overlay 都必须从当前 reference 实际记录证明：事件非空；generated 与 applied APL h 至少有非零样本；至少一个预定内部切点同时出现非空 spike carry 和非零 APL pending。还须保存至少一个真实跨边界 synaptic witness：源 spike 在边界之前、`arrival_tick=source_spike_tick+18` 位于边界之后且小于 1000；其真实 canonical edge 权重非零、到达被接受，并包含在原队列哈希重建中。记录 boundary、source/target、edge_id、spike/arrival tick、接受标志及权重，不把这个工程 witness 解释为单一生物学原因。
|
||||
|
||||
这些仅是“确实测试过 live carry”的覆盖条件，不是调参阈值。不得强迫所有边界都有活动，也不得用空队列 exact 当作 live-carry PASS。固定 case/切点不满足时，结果为 coverage INCOMPLETE，保存全部输出；不可临时加 pulse、选另一个效果强的 case 或后移边界救场。任何扩展需新草案修订及新 prereg，明确看过既有结果。
|
||||
|
||||
故障检查以纯临时合成测试完成：错误外部 SHA、祖先 state 篡改、局部/绝对时钟错配、未写 ring slot 或未消费 APL prefix 改值、输入数组 dtype/NaN/Inf、重复/遗漏事件、拒收被错算接受、输出/编译目录越入任一祖先或冻结输入、已有目录重用。不要故意破坏 AM 或 GPU 正式输出来造负例。
|
||||
|
||||
## 6. 计时、压缩与磁盘预算
|
||||
|
||||
建议注册一个独立新实验输出根，新增 artifact/build 合计硬上限 **8 GiB**，启动前另保留 **10 GiB** 空闲余量；这是待批准的工程资源上限，不是神经科学门。所有图/APL/旧输出仅只读引用，不复制大型图或全脑 dense history。每次运行前后核实已写字节和剩余空间；不足则停止并保留 partial，不删证据续跑，也不缩短注册 tick 数。
|
||||
|
||||
32 次调用共享新的 extension build cache，但不共享可变神经/APL状态，只有声明的父 checkpoint 传状态。记录每次 wall time、已有 `diagnostic_simulation_seconds`、build/cache 时间、完整校验/压缩在 wall 中的费用及峰值 VRAM。reference r0 先行、light r0/r1 随后；明确首次编译/文件缓存顺序造成的影响，不能把一次差值称为普适性能 benchmark。可报告实测 100 ms trial 的耗时/压缩字节，不以此承诺 conditioning 大库存的总成本。
|
||||
|
||||
压缩格式按实际来源逐项核对：当前固定 SHA 的 AJ `gpu_timing.py::run_gpu` 写 `state.npz` 的语句是 `np.savez_compressed(target, **state)`，不是 `np.savez`；AN 的 light/reference AST 派生没有替换该语句。`terminal-queues.npz` 则由 AN `_Runtime.finish` 明确使用 `np.savez_compressed`。记录各数组 uncompressed `nbytes`、这两种 NPZ 的实际压缩 bytes、全部 JSONL/CSV/NPY 及 build bytes，并在执行 receipt 核验各 NPZ entry 的实际压缩方式；不得仅凭 `.npz` 扩展名判断。不能只按 NPZ 小体积漏掉 reference APL 原始 `.npy`。reference 四种 APL trace 的主体约为每 tick 32,528 bytes；完整 1000 tick 约 32.528 MB，尚未含事件/末态/队列/头部。所有估计与最终实际 inventory 同报，不预称已节省多少磁盘；不改原输出格式或移除证据以追求预算。
|
||||
|
||||
## 7. 执行 receipt 与停止结论
|
||||
|
||||
新 coordinator 须 exclusive 写每行执行 receipt 和全门 inventory：注册 SHA、行 ID/overlay/mode/repeat/区间、case/requests/代码输入 SHA、进程退出码、完整 stdout/stderr、开始/结束记录、实际 completed ticks、原始 output inventory SHA、独立 comparator 结果/输入 SHA、finite coverage、weight identity、父 checkpoint 路径/外部 SHA、全局时钟、计时及磁盘字节。单独的 `summary.complete/pass_all` 不足：它可能先于 wrapper 后置核验失败写出；必须有完整 runtime provenance、无 error/failure marker，并重新验证 input/old integrity。
|
||||
|
||||
checkpoint manifest、continuation receipt 和外部执行 inventory 各自按现有排除自身的规则封存，不能自哈希循环。新比较报告和 coordinator receipt 放在被封存 checkpoint 目录之外，避免为加报告改坏父链。恢复只可跳过经完整库存/输入/独立比较重验的已完成行；存在 partial、故障 marker 或不完整祖先则停止，不覆盖、不自动科学重试。
|
||||
|
||||
全部库存、exact 比较、live-carry 覆盖和前后 integrity 均通过时，最高结论仅为“固定 zero/static25 的 GPU 记录模式与连续状态工程等价门通过”；`learning_pass_claimed=False`、`candidate_generated=False`、`weights_updated_during_run=False`。数值不一致为 FAIL；缺证据、预算中断或 live-carry 未覆盖为 INCOMPLETE；不把未执行项记 PASS。任何结果都不自动授权 conditioning，后续训练协议、独立输入/种子/控制与科学门仍需另行审定。
|
||||
|
||||
## 8. 未来可另行注册的补充，不属于当前库存
|
||||
|
||||
以下来自独立审查者的工程提案，**不自动追加到本门、不授权执行，也不是当前 32 次调用的 PASS 条件**。需要分别固定新输入、代码/比较器、父 checkpoint 来源和资源预算后重新审定；不可把本草案悄然扩为 90 次调用。
|
||||
|
||||
第一项是独立全图 synthetic live-carry 短矩阵。可从冻结 mask 的最低 canonical KC source 及其最低 global edge ID 的真实 MBON target 定义 K、M；从冻结 APL input matrix 的非零列按预定 canonical 规则选 Q，不按新输出选人。拟定 80 tick 的两种请求表：K 在 0/1/22/44/66；必要时 Q 在 0；另一表额外 M 在 18，以覆盖到达接收/不应期拒收、child local tick 0 请求及 tick 84 尚未到期的 spike tail。Q 的实际身份和非零列须静态核验，不能仅凭前缀猜 PN/KC；预期 live/accept/reject 条件也须由实际记录证明。zero/static25 × 两表，各比 reference monolithic 与 reference/light 分段 `[5,12,1,1,18,43]`，提案合计 52 次短调用、960 tick（0.096 生物秒）。它测试人工工程请求，不是新增 odor conditioning；本门固定 AM case 的覆盖不足不能未经新注册就用该矩阵救成 PASS。
|
||||
|
||||
第二项是在上述另注册短矩阵通过并得到明确绑定的 active checkpoint 后，再注册较长的连续 idle 工程门：zero/static25 各比较 light 一段 100,000 tick 与连续 10,000 + 90,000 tick，均零新输入、零权重更新，提案合计 6 次 continuation 调用。比较全部 sparse events、分段 counts 总和、终态 V/g/ref/APL state 及规范化 queues；不记录 10 秒逐 tick CPU hash 时，不能宣称逐 tick 浮点状态已独立对照。1 秒 checkpoint 沿同一 idle 主链续到 10 秒；任何测试 probe 必须另 fork，不能把 probe 插入主轨迹，其调用数和预算另列。所有祖先 manifest 在 fork 前后不变。即使通过，也仅证明较长时钟/状态接口，不是记忆保持或 learning PASS。
|
||||
|
||||
源码依据：本目录 `runtime_light.py` 的 `validate_spec/load_overlay/derive_ast/_Runtime.finish/_run`；`runtime_continuation.py` 的 `rotate_spike_ring/validate_state/load_checkpoint/run_continued/audit_carried_sequence`；封存 AM `prepare.py/run_suite.py/adapter.py/comparison.py`；封存 AJ `gpu_timing.py` 与 AF `oracle_full.py::queue_summary`。本文只读这些来源，未运行 GPU、未注册或修改其内容。
|
||||
@@ -0,0 +1,68 @@
|
||||
# Explicit evaluation policy — draft implementation decision
|
||||
|
||||
Status: **DRAFT / NOT REGISTERED / NO SCIENTIFIC EXECUTION**.
|
||||
|
||||
This is a pre-outcome implementation choice by the coordinator for the existing
|
||||
conditioning proposal. It does not change a frozen brain, eligibility-v2,
|
||||
anatomical mask, odor identity, five-replicate matrix or scientific pass gate.
|
||||
The AN engineering result remains separate; none of its 21 sources is edited.
|
||||
|
||||
## Probe timing and snapshots
|
||||
|
||||
- Every evaluation probe lasts 120 ms, with CS requests only in the half-open
|
||||
interval [0, 100 ms). The remaining 20 ms is a no-CS tail. This matches the
|
||||
proposed normal training CS duration; it is not the old L full-120-ms input.
|
||||
- Normal, delayed and reversal curves use 0, 1, 2, 5, 10, 20 and 50 scheduled
|
||||
reinforcement opportunities. Intermediate snapshots are taken after the
|
||||
**complete epoch containing** that opportunity and its boundary update,
|
||||
not immediately at the US tick.
|
||||
- Terminal checkpoint 50 includes all planned epochs, including trailing
|
||||
neutral or blank epochs. It is not an early snapshot chosen for response.
|
||||
- Each snapshot executes both A and B evaluation inputs. No baseline-output
|
||||
deduplication is assumed. Repeated probes use the same independent evaluation
|
||||
input within a training replicate; they are paired observations, not new
|
||||
training replicates. There are still five independent training replicates
|
||||
per experimental cell and 80 independent A/B evaluation streams overall.
|
||||
- Probe US and weight updates are disabled. A probe cannot update the training
|
||||
or retention trajectory. Acquisition/reversal/delayed start from the common
|
||||
declared reset state; retention forks the actual continuous saved state.
|
||||
|
||||
## Intermediate control alignment
|
||||
|
||||
For normal and delayed experiments, obtain the paired arm's complete ordered
|
||||
CS prefix at the chosen epoch boundary. Match actual cue, occurrence, odor,
|
||||
source random stream and full seed, not just the number of epochs.
|
||||
|
||||
- D=0, frozen, random reward and CS-only use that identical CS prefix.
|
||||
- DAN-only uses the corresponding removed-CS/yoked epoch positions. It has
|
||||
**zero CS exposure**, not equal CS exposure; this is an intended control.
|
||||
- Unpaired uses the earliest epoch boundary that has consumed the same ordered
|
||||
CS prefix, including US-only epochs already inserted before that boundary.
|
||||
Its terminal checkpoint includes the entire 150-epoch inventory.
|
||||
|
||||
Unpaired intermediate US counts and elapsed time can differ from paired.
|
||||
Random-reward intermediate US counts can also differ. These differences must
|
||||
be reported rather than called fully matched exposure. All complete terminal
|
||||
inventories remain fixed by the original proposal. The terminal gate, not the
|
||||
most favorable intermediate point, decides the result.
|
||||
|
||||
Reversal and no-reversal use each arm's own kth reinforcement epoch boundary.
|
||||
Their intermediate CS prefixes may differ because their reinforced cues differ;
|
||||
their complete 100-epoch inventories remain fixed. The signed readout always
|
||||
keeps the original acquisition cue orientation and original pre baseline.
|
||||
|
||||
## Inventory, not approval
|
||||
|
||||
Under these choices the pure planner is expected to enumerate 9,440 probes
|
||||
(1,132.8 biological seconds), in addition to the existing 10,760 seconds of
|
||||
training, controls, reversal and continuous idle. This arithmetic is not a
|
||||
measured wall-time or disk estimate and excludes engineering validation,
|
||||
encoding, output, regressions and reserves. Test fixtures use synthetic masters;
|
||||
none is adopted as a scientific master seed.
|
||||
|
||||
Before any scientific run, complete the actual input/state/delta file bindings,
|
||||
exact event-to-eligibility update coordinator, implemented assay gates and a
|
||||
finite resource plan. Register the protocol and bounded implementation check
|
||||
before observing scientific outcomes. Only after those checks may the staged
|
||||
acquisition suite run. A failed acquisition is sealed as failure; it must not
|
||||
trigger more pairings, parameter changes, odor reselection or control removal.
|
||||
@@ -0,0 +1,84 @@
|
||||
# Phase 4AN：条件化接口的生物学证据与解释边界
|
||||
|
||||
状态：**未注册 DRAFT；不是实验结果、科学 PASS 或学习候选声明。**
|
||||
日期：2026-09-30（Asia/Shanghai）。
|
||||
|
||||
本文件记录为后续协议准备的一手文献核对。未修改冻结的脑、符号策略、4J eligibility-v2、mask、eta 或受体假设；未运行远端实验。文献证据不能代替新实验的完整门禁。
|
||||
|
||||
## 1. 本地身份、文献类型和具体个体必须分层
|
||||
|
||||
冻结 H 的静态身份依据是 `annotation_class`、`annotation_type`、`annotation_instance` 中精确的 compartment token，以及真实 KC→MBON 和 DAN→MBON 边；不是根据 PAM/PPL1 前缀猜测效价或 compartment。可读源码依据为 `../models/male-cns-lif/phase4h.yaml` 的 `audit`/`conditioning` 字段、`phase4h.py` 的 mask/confirmed-DAN 加载，以及 `phase4h_reinforcement_provenance.json`。
|
||||
|
||||
下表本地索引、body ID 和侧别是本轮协调者从冻结 H mask/metadata 的实际静态查询确认的目标集合，**不是文献记录的实验个体**。对应冻结 H 文件为 `/data/digital-drosophila/experiments/phase4h-dopamine-plasticity/plastic-mask/plastic-edge-candidates.parquet`，协调者提供的 SHA256 为 `e5075ee3e5a0edf3a4d197453847163cc295b254f21c67203159bba204b12a76`。本次文献核对没有重新下载该表。正式注册仍须将完整静态映射、来源路径及 SHA 与 mask 一同绑定；不得把本表作为绕过静态核验的硬编码替代。
|
||||
|
||||
| 本地类型与精确 token | 固定本地 neuron_idx | 已提供的本地 body ID | 文献类型对应 |
|
||||
| --- | --- | --- | --- |
|
||||
| MBON01 / `y5B'2a` | `9, 130579` | `9 → 10013`；`130579 → 520151` | MBON-01 = MBON-γ5β′2a |
|
||||
| MBON12 / `y2a'1` | `130136, 131044, 3477, 3513` | `130136 → 519368 (R)`；`131044 → 521086 (R)`；`3477 → 13728 (L)`；`3513 → 13768 (L)` | MBON-12 = MBON-γ2α′1 |
|
||||
|
||||
文献编号与名称的对应来自 [Aso 等，2014，解剖论文 Table 1](https://elifesciences.org/articles/04577/figures)。ASCII token 与希腊字母类型名的对应是本项目跨数据集映射层的一部分，不证明每个 MaleCNS body 的生理效应、性别适用性、行为效价或受体组成。文献不能提供这些本地 neuron_idx/body ID 的身份核验。
|
||||
|
||||
冻结 mask 仍为 14,232 条边、四个精确 compartment；另外的 MBON07/`a1` 四个细胞和 MBON02/`B2B'2a` 两个细胞不会因为是 mask 成员而被自动赋予与下面两组相同的行为功能，也不因本次聚焦而从 mask 删除。
|
||||
|
||||
## 2. MBON01 与 MBON12 的一手功能证据
|
||||
|
||||
### MBON01：avoidance-related 类型，不等于每个本地细胞已验证
|
||||
|
||||
Stahl 等在同一条件化实验框架中测得，食欲条件化后 MBON-γ5β′2a 的 CS+ 相对 CS− 响应降低；论文将该 compartment 的输出联系到 avoidance-promoting MBON。该结果支持“减少这一类型的条件气味输出可能产生 appetitive-like 偏移”，不证明所有条件下它都是单一行为开关。[Stahl 等，2022，Figure 5 及 Figure 5—figure supplement 1](https://pmc.ncbi.nlm.nih.gov/articles/PMC8956283/)
|
||||
|
||||
较早的光遗传行为实验包含表达于多个 M4/M6 MBON 类型的驱动系；不能把多类型联合激活结果重述为每个 MBON01 个体的独立因果验证。[Aso 等，2014，行为论文 Table 2 与 Figures 2–5](https://cdn.elifesciences.org/articles/04580/elife-04580-v1.pdf)
|
||||
|
||||
### MBON12：approach-related 输出与 aversive-learning depression
|
||||
|
||||
Aso 等明确报告激活 MBON-γ2α′1 可引起趋近。Berry 等在气味与电击配对后观察到该 MBON 对 CS+ 的响应特异性抑制，未配对／气味和 US 分离的控制不产生相同结果；同一 DAN 的后续刺激还可恢复已抑制响应。因此这里有类型特异的趋近相关输出和厌恶记忆证据，但不支持“DAN 的任何刺激只会继续 LTD”。[Aso 等，2014，行为论文 pp.26–27](https://cdn.elifesciences.org/articles/04580/elife-04580-v1.pdf)；[Berry 等，2018，Figures 2–4](https://pmc.ncbi.nlm.nih.gov/articles/PMC6239218/)
|
||||
|
||||
Stahl 等还观察到食欲条件化后 MBON-γ2α′1 的相对 CS+ 响应提高，但不能明确全部归因于 CS+ potentiation 或 CS− depression；不能用该结果论证所有相关 plasticity 都是单向 LTD。[Stahl 等，2022](https://pmc.ncbi.nlm.nih.gov/articles/PMC8956283/)
|
||||
|
||||
## 3. 两个人工 US 接口只具有预先声明的 like 解释
|
||||
|
||||
| 固定的源类型与 compartment 接口 | 类型层面的预期 | 不可升级成的结论 |
|
||||
| --- | --- | --- |
|
||||
| PAM15 → `y5B'2a` / MBON01 | 若目标气味的相关 KC 输入发生 LTD，可预期 avoidance-related 输出相对减弱，即 appetitive-like 输出偏移 | PAM15 全体天然奖励、所有 PAM 为正效价、这些本地细胞已经证明奖赏学习 |
|
||||
| PPL103 → `y2a'1` / MBON12 | 若目标气味的相关 KC 输入发生 LTD，可预期 approach-related 输出相对减弱，即 aversive-like 输出偏移 | 所有 PPL1 为负效价、DAN 任意刺激均是惩罚、这些本地细胞已经证明厌恶学习 |
|
||||
|
||||
**PAM15 不是 PAM01 的同义词。** Li 等把 PAM15（γ5β′2a）列为相对既有分类新增的一类；PAM01 对应 γ5，PPL103 对应 γ2α′1。旧文献中 PAM-γ5/PAM01 的全部奖励结果不能不经核对就转记为 PAM15 特异性证据。[Li 等，2020，DAN 分类、Figure 6 与 Figures 35–36](https://pmc.ncbi.nlm.nih.gov/articles/PMC7909955/)；[同文图表页](https://elifesciences.org/articles/62576/figures)
|
||||
|
||||
这里的 D 仍是冻结规则中的人工标量命令，不是测得的 dopamine 浓度，也不是由实际 DAN spike/接受刺激自动推定的生物学门控。命令、刺激是否被接受、实际 DAN events、选定 compartment 与权重更新必须分别记录。两个接口共用既定 polarity = −1；不因为接口名字不同而翻转 D、eta、base sign 或下游权重。
|
||||
|
||||
## 4. 顺序和时间窗证据不等于冻结参数的数值标定
|
||||
|
||||
- Hige 等在 γ1pedc 测得正向配对后的抑制；气味改为在最后一束 DAN 激活光后 0.5 秒开始的 backward protocol 未产生同样变化。这是特定 compartment 和刺激协议的结果，不是所有类型的通用截止时间。[Hige 等，2015，Figure 1](https://pmc.ncbi.nlm.nih.gov/articles/PMC4674068/)
|
||||
- Aso–Rubin 显示，单一 DAN 类型可因相对气味的刺激时序不同而写入、削弱或改变记忆效价；不同 compartment 的学习和更新规则并不相同。[Aso–Rubin，2016](https://pmc.ncbi.nlm.nih.gov/articles/PMC4987137/)
|
||||
- Handler 等在其协议中测得 forward pairing 对应 depression、backward pairing 对应 potentiation，并观察到 PAM/PPL 群均可随时序改变行为偏移方向。这阻止我们把 PAM/PPL 名称当作固定效价开关；本项目不据此加入新受体、LTP 或重设参数。[Handler 等，2019](https://pmc.ncbi.nlm.nih.gov/articles/PMC9012144/)
|
||||
|
||||
出版年复核：该文正式题名为 *Distinct Dopamine Receptor Pathways Underlie the Temporal Sensitivity of Associative Learning*，刊于 **Cell，2019-06-27，178(1):60–75.e19**，online publication 为 2019-06-20，DOI 为 [10.1016/j.cell.2019.05.040](https://doi.org/10.1016/j.cell.2019.05.040)。[PubMed 原文书目记录 PMID 31230716](https://pubmed.ncbi.nlm.nih.gov/31230716/) 明确同时关联 PMC9012144。因此本草稿保留 2019；旧材料中的 2022 不是本论文正式出版年，本次不修改旧冻结材料，也不猜测该旧标注的来源。
|
||||
|
||||
冻结 4J eligibility-v2 的 τ = 100 ms、trace cap = 1、eta = 0.00275 mV、polarity = −1、最多 25% depression 仍是**既定 causal-LTD 工程假说**，不是上述论文为当前全脑提供的定量标定。负时序零更新和 +1000 ms/零延迟比例门只验证冻结规则的一致性；不能据此声称复现了生物学 relief learning、受体机制、bidirectional plasticity 或遗忘。4J 中另列的 bidirectional branch 也不是本接口可偷偷启用的替代规则。
|
||||
|
||||
## 5. 固定 B/S：新的离线分析约定，不是 decoder
|
||||
|
||||
首要输出应保留每个目标细胞的原始 spike/count/rate、训练前后变化，以及两个类型各自的结果。可供正式协议审议的固定类型对比为：
|
||||
|
||||
```text
|
||||
R12(o) = mean of the fixed four MBON12 cells' rates for odor o
|
||||
R01(o) = mean of the fixed two MBON01 cells' rates for odor o
|
||||
B(o) = R12(o) - R01(o)
|
||||
DeltaB(o) = B_post(o) - B_pre(o)
|
||||
S = DeltaB(CS+) - DeltaB(CS-)
|
||||
```
|
||||
|
||||
所有项使用预先锁定的同一评估窗，分别按类型取均值，避免四个 versus 两个细胞的数量差异直接决定符号。CS+/CS− 的角色须事先指定并 counterbalance，不能根据结果挑选气味方向、细胞或 seed。
|
||||
|
||||
这是**新的、需要预注册的离线分析约定**；不是文献给出的定量行为公式，不进入动力学、不拟合权重或阈值、不构建选择策略、classifier 或 decoder，也不替换旧封存指标。它不要求未训练 B = 0，不把 spike rate 等同于真实选择概率。
|
||||
|
||||
两个接口永远使用同一个 B 方向：PAM15 分支的假说是 S > 0，PPL103 分支的假说是 S < 0。不得按接口乘效价符号把所有结果改成“正值即成功”，不得取绝对值掩盖方向错误。若反馈使方向相反，应报告为该假说的反证，不改读出或重命名接口。还须与实际运行的预注册控制比较；单独 S 的方向、MBON separation 增大或权重改变都不构成完整 learning PASS。
|
||||
|
||||
## 6. 正式协议不可省略的解释门
|
||||
|
||||
- 文献类型功能、本地精确身份、人工 US 命令、实际 DAN 激活和实际 KC→MBON 更新须为不同证据字段；任何一层未知都不得用另一层填补。
|
||||
- 冻结脑与 v2 不变；不得新设 receptor sign、改变 eta、扩 mask、选择最有利 compartment、挑训练 checkpoint 或把 direct/static25 诊断作为条件化本身。
|
||||
- paired、unpaired、zero-D、frozen、CS-only、DAN-only 应为真实执行控制，不是 summary 中的标签；独立评估 seeds 和预先锁定的 cue 角色不能在看到结果后改变。
|
||||
- 必须区分 weight update、实际 MBON 响应变化、类型对比方向和下游功能传递。纯 separation 可能在错误方向上增大；“appetitive-like/aversive-like”不能在没有对应证据时升级为行为学习结论。
|
||||
- 本文件不新增 success threshold；完整通过条件应由尚待注册的协议明示，并保留失败、无效应和反向结果。没有训练结果就不生成 learned candidate。
|
||||
|
||||
浏览核对仅采用上列原始研究的期刊、官方 PDF 或 PMC 全文。没有把综述、搜索摘要的二次推断、Google FlyBody 或其他身体运动项目作为本阶段学习机制证据。
|
||||
@@ -0,0 +1,469 @@
|
||||
# DRAFT — fixed-v2 olfactory conditioning in the independent D18 branch
|
||||
|
||||
**Status: DRAFT / NOT REGISTERED / NOT APPROVED FOR EXECUTION.**
|
||||
|
||||
This document is a proposal for review. It does not authorize a run, record a
|
||||
successful gate, freeze a protocol, or generate a learned candidate. It was
|
||||
written from frozen H/J/L source and the declared AM interface contract, not
|
||||
from selecting favorable AM outcomes. The coordinator must approve the final
|
||||
scope, budget, source/input inventory, and exact gate implementation before
|
||||
registration. No training or remote action is performed by this document.
|
||||
|
||||
## 1. Purpose and entry conditions
|
||||
|
||||
The next scientific endpoint should be actual acquisition under the unchanged
|
||||
TemporalEligibility-v2 rule, followed, if its registered acquisition gate passes,
|
||||
by fixed reversal, delayed-reward, and real-time retention assays. File validity,
|
||||
static 25% sensitivity, or a change in delta alone is not learning.
|
||||
|
||||
Entry requires the **complete** AM whole-brain interface gate, including its
|
||||
registered zero-overlay comparisons, nonzero internal/repeat validation, old
|
||||
reference regression, and frozen integrity checks. A partially completed zero
|
||||
suite is not this gate. Continue to use the independent D18 branch; do not edit
|
||||
or overwrite Phase 1–AM artifacts. The original base CSR, anatomical counts,
|
||||
transmitter signs, W_syn, LIF coefficients, APL calibration, D18 timing,
|
||||
14,232-edge candidate mask, and eligibility-v2 remain unchanged.
|
||||
|
||||
New long-run/continuation plumbing must preserve the exact existing timestep
|
||||
operation order. Validate zero-delta and fixed-delta continuation, including a
|
||||
split across pending synaptic/APL arrivals, against an unsplit run before using
|
||||
it for retention. This is an implementation prerequisite, not another tunable
|
||||
scientific endpoint. No game, decoder, RL, visual branch, or brain-gain search
|
||||
is in scope.
|
||||
|
||||
## 2. Fixed population and replicate matrix
|
||||
|
||||
Use the original externally selected 4L odors without re-selection:
|
||||
|
||||
* ODOR_A: **4-ethylphenol**.
|
||||
* ODOR_B: **3-hexanone**.
|
||||
* Both frozen encoder modes: `physiological` and `matched_energy`.
|
||||
* Both reciprocal assignments: A reinforced/B neutral, and B reinforced/A
|
||||
neutral.
|
||||
* Both exact DAN/type-compartment branches below.
|
||||
* Five independent training replicates in each cell.
|
||||
|
||||
Thus there are **2 modes × 2 DAN branches × 2 assignments × 5 replicates =
|
||||
40 blocks**, or eight experimental cells with five training replicates each.
|
||||
|
||||
| Branch identity | Exact raw compartment string | Proposed signed prediction |
|
||||
| --- | --- | --- |
|
||||
| PAM15 | `y5B'2a` | S > 0 |
|
||||
| PPL103 | `y2a'1` | S < 0 |
|
||||
|
||||
These identities are drawn from old H/J configuration, not from a rule that all
|
||||
PAM is appetitive or all PPL1 is aversive. Preparation must materialize and hash
|
||||
the actual DAN body IDs/neuron indices, exact annotation values, confirmed
|
||||
DAN→MBON/compartment mapping, and affected existing plastic edges from the
|
||||
sealed H mapping. Missing, ambiguous, or unmatched mappings remain `unknown`;
|
||||
they cannot be completed by cell-name inference.
|
||||
|
||||
The type-level signed predictions above are **new, falsifiable engineering
|
||||
hypotheses**. Evidence specifically establishing PAM15's proposed functional
|
||||
direction is limited. Correspondence of type labels across fly datasets is not
|
||||
proof that these individual MaleCNS bodies have been functionally validated.
|
||||
Neither branch is labeled a biologically demonstrated reward/aversion channel.
|
||||
|
||||
### Seed inventory
|
||||
|
||||
Before outcomes, generate and save the complete RNG lineage and explicit seed
|
||||
table for replicate, cell, arm, epoch, CS events, US ordering, and evaluation.
|
||||
Use distinct replicate streams, for example an explicitly registered
|
||||
`SeedSequence` hierarchy with a newly audited master seed. Test expanded seed
|
||||
identities for collisions. Merely choosing five different base integers is
|
||||
insufficient: H/J/L used consecutive bases plus the trial number, producing
|
||||
overlapping nominal replicate streams.
|
||||
|
||||
Matched counterfactual arms may deliberately reuse the same input event table;
|
||||
list each such common-random-number alias. They are paired controls, not extra
|
||||
independent replicates. All evaluation RNG streams must be disjoint from all
|
||||
training/US/control RNG streams. Within a replicate use the same registered
|
||||
evaluation events for pre/post/control comparisons. If more than one evaluation
|
||||
stream is used per replicate, report/aggregate it within that replicate;
|
||||
**five evaluation streams never substitute for five independent training
|
||||
replicates**. The minimal budget below uses one independent A/B evaluation
|
||||
stream pair per training replicate, reused for paired contrasts.
|
||||
|
||||
## 3. Frozen update and explicit US interface
|
||||
|
||||
Use the exact frozen J/L v2 implementation and operation order:
|
||||
|
||||
* KC trace float32, tau = 100 ms, cap = 1.
|
||||
* Every timestep: separate multiply, add actual KC spikes, then clamp.
|
||||
* At the registered US tick, sample after that tick's multiply/add/clamp;
|
||||
include its KC spikes. Do not insert an anatomical-delay shift into eligibility.
|
||||
* eta = 0.00275 mV, compartment polarity = −1.
|
||||
* Additive update only on the already-confirmed compartment mask, with the
|
||||
old float32 floor `−0.25 × base_candidate_mV`.
|
||||
* Update once at the end of the training epoch; it affects subsequent epochs,
|
||||
not an earlier part of the same epoch. No recovery, potentiation, or new mask.
|
||||
* Install via the verified AM bridge: immutable base SI float64 plus the
|
||||
float32 delta widened to float64 and converted from mV to V. Preserve all
|
||||
untouched weights and fast-APL handling exactly.
|
||||
|
||||
The first proposal preserves the old **scheduled dimensionless D = 1** artificial
|
||||
unconditioned-signal interface. It is neither measured dopamine concentration
|
||||
nor an actual-DAN-spike count. At the same scheduled tick, request spikes in the
|
||||
fixed matched DAN population. Record requested, accepted, rejected, and actual
|
||||
DAN spikes, including zero-delivery epochs, separately from D. Do not resample,
|
||||
boost, or silently discard failed delivery to obtain a preferred result.
|
||||
|
||||
Multiplying D by actual acceptance, changing D to a spike count, or adding a
|
||||
measured dopamine kinetic model would be a distinct, explicitly registered
|
||||
interface hypothesis; none is hidden in this draft. If neural DAN delivery is
|
||||
absent, any update still demonstrates scheduled artificial gating, not that
|
||||
actual DAN activity caused plasticity. This limitation must remain visible even
|
||||
if an engineering output gate passes.
|
||||
|
||||
## 4. Epochs, timing, and all actual controls
|
||||
|
||||
Training epochs retain the old trial-boundary reset policy: start V/g/ref,
|
||||
spike-delay queues, APL state/queue and KC trace from their declared initial
|
||||
values; carry only plastic_delta between epochs. Log the state policy. This is
|
||||
not continuous natural behavior. Retention deliberately uses the different,
|
||||
explicit continuous-state policy in section 7.
|
||||
|
||||
Normal epochs last 120 ms. CS occupies 0–100 ms, and a paired US is at 90 ms.
|
||||
Delayed epochs last 180 ms; the identical CS occupies 0–100 ms, and US is at
|
||||
150 ms, i.e. **50 ms after CS offset** and 60 ms later than the normal US.
|
||||
All delayed controls use that same 180 ms duration and 150 ms US slot, including
|
||||
the neutral epochs; do not reuse 90 ms controls to attribute delayed effects.
|
||||
|
||||
The frozen L encoder mapping/rates and original A/B identity are retained.
|
||||
However, old `phase4l.py::run_rates` generated PN events throughout its passed
|
||||
120 ms duration. The present 100 ms CS plus a no-CS tail is an explicit new
|
||||
experimental timing choice aligned with validated AK input duration, **not** a
|
||||
claim of bitwise identity to old L's full training input. `matched_energy`
|
||||
matches the frozen encoder's expected total rate, not an artificially edited
|
||||
actual Poisson event count; report actual requests and acceptance by condition.
|
||||
|
||||
Within each arm, present 50 reinforced-cue epochs and 50 neutral-cue epochs in
|
||||
a pre-generated balanced order. The **50 reinforcement opportunities** come
|
||||
from L's fixed formal conditioning count, not from a new magnitude search.
|
||||
|
||||
| Arm | Actual execution in each normal or delayed suite |
|
||||
| --- | --- |
|
||||
| paired | 50 CS+ with its matched DAN/US and 50 CS− without US |
|
||||
| D=0 | Identical input events and US schedule to paired; D=0 in updates |
|
||||
| frozen | Identical input events and US schedule; no delta updates |
|
||||
| random reward | Same 50 A/50 B and 50 US opportunities; exactly 25 A and 25 B get US, selected by a pre-generated random balanced schedule independent of responses |
|
||||
| unpaired | Same 100 CS epochs and same 50 US events in 50 additional separate, no-CS epochs; preserve total event inventory, declare 150 rather than 100 epochs |
|
||||
| CS-only | Same 100 CS epochs, no DAN/US; no scheduled D |
|
||||
| DAN-only | Same 50 US epochs and 50 matched blank epochs, no CS |
|
||||
|
||||
Every control is genuinely executed; neither identity reasoning nor a text label
|
||||
replaces its trajectory. The unpaired arm is specifically **separate-epoch,
|
||||
reset-trace unpairing**, not a claim about a long continuous biological
|
||||
interstimulus interval. Actual DAN-only network activity may recruit KC and
|
||||
produce eligibility; it must be measured, not assumed zero. Blank and US-only
|
||||
epochs retain the corresponding 90/150 ms timing slot and 120/180 ms duration.
|
||||
|
||||
## 5. Offline signed readout — do not discard contrary signs
|
||||
|
||||
Preparation defines complete, exact-annotation MBON12 and MBON01 populations
|
||||
from MaleCNS metadata, with all body/index membership and side information. No
|
||||
best responding neuron, hemisphere, compartment subset, or evaluation pair is
|
||||
selected after outcomes. Missing either population makes this specific readout
|
||||
`NOT-TESTABLE`; do not substitute another type or silently use an empty mean.
|
||||
|
||||
For an evaluation stimulus X and a fixed 0–120 ms probe window, define:
|
||||
|
||||
`B(X) = mean_spike_count(MBON12, X) − mean_spike_count(MBON01, X)`.
|
||||
|
||||
This is **offline analysis only**, not an action decoder, a feedback signal,
|
||||
or proof of approach/avoidance. Also save both population means separately,
|
||||
their sizes and counts, all-MBON output, genuine downstream output, and latency.
|
||||
|
||||
Keep two distinct downstream inventories. The frozen H actual-edge table's
|
||||
complete target union is `legacyDownstream` (11,342 neurons in the verified
|
||||
local data), and contains **all 97 MBONs**. Do not relabel or silently edit that
|
||||
old population. Define `nonMBONDownstream` as the exact set difference of that
|
||||
union and the complete MBON population (11,245 here). Report both. Requiring
|
||||
an effect outside the MBON population is an additional pre-outcome hypothesis,
|
||||
not an already inherited H gate; it prevents a purely MBON-local change from
|
||||
being counted twice as evidence of broader propagation.
|
||||
|
||||
With C denoting that block's reinforced cue and N its neutral cue:
|
||||
|
||||
`S_arm = [B_arm(C) − B_arm(N)] − [B_pre(C) − B_pre(N)]`.
|
||||
|
||||
Always emit the raw signed B and S, pre/post components and control S. For PAM15
|
||||
the proposed relation is **S > 0**; for PPL103 it is **S < 0**. Never replace raw
|
||||
S in the report with `abs(S)`, `polarity × S`, a magnitude-only score, or a
|
||||
renamed “positive learning score.” An opposite sign is a counterexample to the
|
||||
prediction, even if a magnitude or unsigned separation statistic increases.
|
||||
|
||||
The expected-control relation is equally explicit:
|
||||
|
||||
* PAM15: `S_paired > S_control` for each registered control.
|
||||
* PPL103: `S_paired < S_control` for each registered control.
|
||||
|
||||
Use matched evaluation inputs and each arm's matched pre baseline. All probe
|
||||
updates and US inputs are off. Evaluation begins from the same frozen initial
|
||||
state for acquisition/reversal/delayed comparisons; retention uses its saved
|
||||
continuation state and time-matched frozen control as specified below.
|
||||
|
||||
## 6. Acquisition and strict reversal gates
|
||||
|
||||
### 6.1 Acquisition — newly proposed signed gate
|
||||
|
||||
For each of the eight cells, a successful training replicate must satisfy all:
|
||||
|
||||
1. The raw S has the registered direction (PAM15 >0; PPL103 <0).
|
||||
2. It lies in that direction beyond **each of the six actually executed
|
||||
timing-matched controls**, not just the easiest control.
|
||||
3. The inherited L functional requirement holds: conditioned-cue MBON
|
||||
`Delta_g_max_abs` exceeds neutral-cue `Delta_g_max_abs`, and the conditioned
|
||||
cue has a nonzero MBON spike-count or first-latency change versus its own pre
|
||||
probe. Undefined latency is separately reported, not silently zero-filled
|
||||
into proof of a latency effect.
|
||||
4. The inherited H MBON and complete `legacyDownstream` A/B Euclidean
|
||||
separation gains are positive. Independently require positive separation
|
||||
gain in `nonMBONDownstream` as a newly declared specificity/transfer gate.
|
||||
Report all three underlying count vectors and gains, never select whichever
|
||||
downstream definition is more favorable after outcomes.
|
||||
|
||||
Require **at least 3 of the 5 independent training replicates to satisfy the
|
||||
entire conjunction in the same replicate**. Also calculate the inherited H
|
||||
mean-control rule separately. This exact new combined signed gate is a proposal,
|
||||
not a claim that H/L already implemented it.
|
||||
|
||||
Record acquisition at fixed update counts 0, 1, 2, 5, 10, 20 and 50. Only the
|
||||
registered terminal count 50 determines its gate. Do not stop at the best
|
||||
earlier count or add pairings after failure.
|
||||
|
||||
### 6.2 Reversal — new hypothesis, not H's permissive sign test
|
||||
|
||||
After normal acquisition, fork its exact delta into two arms:
|
||||
|
||||
* **Reversal:** 50 pairings of the former neutral cue with the same specific DAN
|
||||
branch, and 50 formerly reinforced cue epochs without US.
|
||||
* **No-reversal continuation:** 50 further original-cue pairings and 50 original
|
||||
neutral epochs, with matched epoch totals and registered independent input
|
||||
streams. This is an additional real control, included in the budget.
|
||||
|
||||
Never reset the learned delta between acquisition and reversal; do not restore
|
||||
depressed old-cue edges. Keep the old epoch-reset neural state policy in both
|
||||
arms and evaluate from common fresh states. The pure-LTD/no-recovery rule may
|
||||
prevent reversal; that is a falsifiable model limitation, not permission to add
|
||||
potentiation, recovery, or more trials.
|
||||
|
||||
For strict reversal, retain the original C−N orientation when calculating
|
||||
`S_acquisition`, `S_reversal`, and `S_continuation` against the **same original
|
||||
pre-training baseline**. At the fixed terminal reversal count 50 require:
|
||||
|
||||
| DAN branch | Acquisition | Reversal | No-reversal continuation |
|
||||
| --- | --- | --- | --- |
|
||||
| PAM15 | `S_acquisition > 0` | `S_reversal < 0` | `S_continuation > 0` |
|
||||
| PPL103 | `S_acquisition < 0` | `S_reversal > 0` | `S_continuation < 0` |
|
||||
|
||||
Additionally require a nonzero change in the former neutral cue's MBON spike
|
||||
count or defined first latency between acquisition and reversal, and a nonzero
|
||||
change in its actual-downstream event/count output. Save both cue vectors;
|
||||
counts alone can conceal a shift confined to the original cue. The same
|
||||
replicate must satisfy all conditions, with a 3/5 majority per cell. These are
|
||||
**new terminal reversal hypotheses**, fixed before the learning results.
|
||||
|
||||
Zero is failure of a strict sign-crossing gate, not reversal. Report H's original
|
||||
`sign(acquisition) != sign(reversal)` separately as a legacy statistic; its
|
||||
nonzero→zero success case does not satisfy this new gate. Do not claim that
|
||||
these engineering sign changes establish reversal of real appetitive/aversive
|
||||
behavior.
|
||||
|
||||
## 7. Delayed reward and genuine retention gates
|
||||
|
||||
### Delayed-reward test — new hypothesis
|
||||
|
||||
Run delayed acquisition from zero delta, using the same fixed 50 reinforcement
|
||||
opportunities and all six controls in the **180 ms / US-at-150 ms** suite. Sample
|
||||
the unchanged trace at 150 ms after that tick's update. Do not extend tau or
|
||||
redefine the trace to force delayed learning.
|
||||
|
||||
At count 50, require the same signed, functional, downstream and control
|
||||
conjunction as section 6.1, with at least 3/5 independent training replicates per
|
||||
cell and raw S direction intact. This explicitly tests whether the unchanged
|
||||
rule supports this particular delayed schedule. H did not supply an independent
|
||||
delayed-reward success threshold; this is a new pre-outcome gate.
|
||||
|
||||
Report delayed and immediate signed S side by side and their per-replicate
|
||||
difference. Do not require a invented retained percentage, impose a monotonic
|
||||
amplitude ratio, pool their seeds, or call an opposite delayed sign a successful
|
||||
larger absolute response. Delayed controls cannot be substituted by normal ones.
|
||||
|
||||
### Retention — new continuous-state implementation and gate
|
||||
|
||||
Fork the terminal normal-acquisition checkpoint and the matched terminal normal
|
||||
frozen-control checkpoint. Each includes V/g/ref, pending spike-delay state,
|
||||
APL state, the complete D18 APL queue, absolute tick, delta, and relevant RNG
|
||||
state. Run real no-input/no-update dynamics continuously to 1 s and 10 s; the 1 s
|
||||
checkpoint is on the same idle trajectory used to reach 10 s. Do not reset,
|
||||
discard pending events, analytically decay only delta, or fabricate elapsed time
|
||||
by changing a seed.
|
||||
|
||||
At 0, 1, 10 s, fork independent A/B probes from each checkpoint; probes do not feed
|
||||
back into the idle continuation. Use the same registered evaluation streams
|
||||
for trained/frozen comparisons at every elapsed time. The probe lasts 120 ms,
|
||||
without US or updates. Keep delta constant throughout idle; the model does not
|
||||
contain molecular consolidation or recovery, and a pass cannot prove either.
|
||||
|
||||
Define signed `S_trained(t)` and `S_frozen(t)` using the original C−N orientation
|
||||
and their registered pre baselines. Require, in the **same replicate at both 1 s
|
||||
and 10 s**:
|
||||
|
||||
* PAM15: `S_trained(t) > 0` and `S_trained(t) > S_frozen(t)`.
|
||||
* PPL103: `S_trained(t) < 0` and `S_trained(t) < S_frozen(t)`.
|
||||
* Conditioned-cue MBON output has a nonzero spike-count or defined first-latency
|
||||
difference from the time-matched frozen probe; actual-downstream event/count
|
||||
output also differs. Save neutral-cue differences and the complete signed
|
||||
specificity contrasts so a generic global response is not presented as memory.
|
||||
|
||||
Require 3/5 independent training replicates per cell, without a fitted amplitude
|
||||
tolerance or an arbitrarily chosen “percent retained.” This is a new functional
|
||||
retention hypothesis, not the old H reset-and-retest result. Report an optional
|
||||
predeclared fresh-state/persistent-delta probe separately as a weight-storage
|
||||
diagnostic; it cannot satisfy the real-time gate.
|
||||
|
||||
## 8. Legacy gates, new hypotheses, and candidate limits
|
||||
|
||||
Retain the following old predicates verbatim as separately labeled audit fields
|
||||
where their data definitions apply:
|
||||
|
||||
* H: minimum seed majority 3; positive MBON and downstream Euclidean separation
|
||||
gains; the smaller mean gain of its two DAN branches exceeds the maximum
|
||||
mean gain among D0/random/frozen controls.
|
||||
* H: legacy reversal sign-inequality and last-retention separation>pre. Disclose
|
||||
the old retention had no actual idle dynamics and is not direct validation of
|
||||
the new real-time protocol.
|
||||
* L: conditioned `Delta_g_max_abs`>neutral plus a functional spike/latency change,
|
||||
in at least 3/5 seeds. Preserve the exact old implementation's legacy result as
|
||||
well as the new explicitly defined handling of missing latencies.
|
||||
|
||||
There was **no completed inherited delayed-reward gate**, no proven signed
|
||||
MBON12−MBON01 valence mapping for these bodies, and no complete L reversal or
|
||||
retention assay. The strict signed gates in sections 6–7 are new proposals, not
|
||||
historical passes and not thresholds obtained from AL/AM responses.
|
||||
|
||||
For the strongest proposed engineering candidate claim, all eight cells must
|
||||
pass their registered acquisition and subsequent assay gates, old required
|
||||
integrity/control gates, and numerical safety checks. This all-cell conjunction
|
||||
is itself an explicit proposed scope of claim; it is not a pre-existing H/L
|
||||
aggregate gate. Emit a complete 8-cell×assay table, including contrary signs,
|
||||
failures, `NOT-RUN`, `NOT-TESTABLE`, and actual delivery limitations. Do not pool
|
||||
one mode/compartment/assignment to rescue another, or silently retain only
|
||||
successful cells as the candidate.
|
||||
|
||||
Suggested staged execution: run normal acquisition and all six normal controls
|
||||
for all 40 blocks first. If the approved overall acquisition gate fails, seal a
|
||||
real acquisition failure and stop, recording subsequent assays as `NOT-RUN`.
|
||||
Otherwise execute the already registered complete delayed, reversal,
|
||||
continuation and retention suites. No gate changes, tuning or new pair choices
|
||||
occur between stages. A scientific failure is not a reason to delete evidence;
|
||||
an integrity/numerical/resource failure stops execution without a scientific
|
||||
success claim.
|
||||
|
||||
Even a full engineering pass would not establish odor preference behavior,
|
||||
motor learning, natural dopamine concentration, appetitive/aversive valence,
|
||||
long-term memory, or readiness for a game. Delta change alone, a file/checksum
|
||||
pass, static-overlay sensitivity, or signed output readout alone is insufficient.
|
||||
|
||||
## 9. Transparent worst-case runtime and data budget
|
||||
|
||||
The original estimate of 38,000 epochs omitted the new no-reversal continuation
|
||||
and a full timing-matched delayed-control suite. They cannot be silently added
|
||||
without revising the budget.
|
||||
|
||||
| Per block | Epochs | Biological seconds |
|
||||
| --- | ---: | ---: |
|
||||
| Normal paired + five 100-epoch controls + 150-epoch unpaired | 750 | 90 |
|
||||
| Delayed paired + five 100-epoch controls + 150-epoch unpaired | 750 | 135 |
|
||||
| Reversal | 100 | 12 |
|
||||
| No-reversal continuation | 100 | 12 |
|
||||
| Total training/control/continuation | 1700 | 249 |
|
||||
|
||||
Across 40 blocks this is **68,000 epochs and 9,960 biological seconds**. Real 10 s
|
||||
idle progression of trained and frozen retention branches adds 800 s, totaling
|
||||
**at least 10,760 s biological time** (107.6 million dt=0.1 ms steps), before
|
||||
evaluation probes, implementation validation, and old regressions. Checkpoints
|
||||
at 1 s are reused within the 10 s trajectory, not separate additional 10 s runs.
|
||||
|
||||
Relative to 38,000: add 4,000 continuation epochs and 26,000 delayed-control epochs.
|
||||
All delayed neutral/control epochs are 180 ms, not shortened to 120 ms. The table
|
||||
includes this duration correction. Any approved reduction of scope must be
|
||||
decided and documented before registration, not after results or a resource
|
||||
overrun.
|
||||
|
||||
Let measured AM walltime per 100 ms be `t100`. A preliminary linear conversion is
|
||||
`107,600 × t100`, plus input generation, updates, evaluation, I/O and validation.
|
||||
If `t100=1 s`, this base is 29.9 h; if 5 s, 149.4 h. These are arithmetic scenarios, not
|
||||
claimed measured performance: learning may change spike load. Measure and
|
||||
reserve a conservative actual budget before registration; enforce a resource
|
||||
stop rather than changing the protocol during a run.
|
||||
|
||||
Full generated+applied APL traces alone use approximately
|
||||
`2 × 4064 × 4 × 10000 = 325,120,000 bytes/biological-second`, hence **more than
|
||||
3.18 TiB** for the above minimum. Do not silently carry the small AM experiment
|
||||
disk budget into this suite. Per-epoch full neuron counts also total about 45 GB
|
||||
at 68,000×166,700×4 bytes, before events or full checkpoints.
|
||||
|
||||
The proposed recording policy to approve before registration is: all spike
|
||||
events, actual input/delivery logs, per-epoch counts, signed metrics, every delta
|
||||
update and full terminal V/g/ref/APL/queue state. Every timestep retains a GPU
|
||||
finite-value latch and first-failure tick, **not a CPU copy/hash of all state**.
|
||||
At epoch boundaries hash the files and bind the complete chain of parent,
|
||||
input, delta-before, events/state/queues, eligibility/D and delta-after. Full
|
||||
APL/per-tick reference recordings are restricted to a fixed pre-outcome
|
||||
validation inventory, not a result-selected subset. That subset's exact
|
||||
comparison must not be described as per-tick regression of all 68,000 epochs.
|
||||
Compute capacity from measured event/log
|
||||
sizes plus a stated safety reserve, and register an exclusive-output storage
|
||||
cap. Do not publish a guessed final compressed size or omit encoder/update/I/O
|
||||
latency from walltime accounting.
|
||||
|
||||
The frozen L/J metric called `MBON_g_max_abs` is the maximum **across neurons
|
||||
of the terminal post-minus-pre g difference**, not a temporal maximum. Save
|
||||
`MBON_final_delta_g_max_abs` with explicit SI units and the legacy alias when
|
||||
needed. Complete terminal g and sparse events suffice to reconstruct that
|
||||
metric; adding a dense time-by-neuron recording is not required by its name.
|
||||
|
||||
Even without full histories, all 68,000 terminal state/ring payloads would use
|
||||
roughly 507 GB uncompressed, before events and evaluation. Use lossless
|
||||
compression and measure real complete-episode outputs before setting the hard
|
||||
storage cap. Do not infer final compressed usage from sparse activity alone.
|
||||
|
||||
## 10. Evidence, provenance, and unresolved biology
|
||||
|
||||
Local source basis (read-only, relative to the project):
|
||||
|
||||
* `23-ai-gpu/source/models/male-cns-lif/phase4h.yaml` and `phase4h.py`:
|
||||
exact DAN configuration, old control/majority predicates, old nominal retention.
|
||||
* `23-ai-gpu/source/models/male-cns-lif/phase4j.yaml` and `phase4j.py`:
|
||||
frozen float32 trace order and `apply_v2`.
|
||||
* `23-ai-gpu/source/models/male-cns-lif/phase4l.yaml` and `phase4l.py`:
|
||||
original odor mapping, 50 formal pairings, input timing, and acquisition gate.
|
||||
* `23-ai-gpu/source/wholebrain-plastic-interface-v1/PROTOCOL.md`:
|
||||
unchanged-base bridge, scheduled-US semantics and later-learning boundary.
|
||||
|
||||
Relevant primary evidence, with narrow use:
|
||||
|
||||
* [Hige et al., Neuron 2015, author institution](https://www.janelia.org/publication/heterosynaptic-plasticity-underlies-aversive-olfactory-learning-drosophila):
|
||||
compartmental KC→MBON plasticity and odor-specific effects motivate local LTD;
|
||||
this does not measure this MaleCNS model's eta, tau or DAN concentration.
|
||||
* [Aso and Rubin, eLife 2016, author institution](https://www.janelia.org/publication/dopaminergic-neurons-write-and-update-memories-cell-type-specific-rules):
|
||||
cell-type-specific DAN learning/update rules oppose a universal cluster-valence
|
||||
assumption. They do not functionally identify every MaleCNS body here.
|
||||
* [Aso et al., eLife 2014](https://elifesciences.org/articles/04580):
|
||||
MBON population outputs motivate population-level readout. The exact
|
||||
MBON12−MBON01 signed hypothesis and the two predictions in this draft remain
|
||||
explicit cross-dataset/model assumptions, not a demonstrated behavioral scale.
|
||||
|
||||
The exact scalar D interface, fixed trace/eta/floor, absence of recovery,
|
||||
trial-boundary resetting, simplified signed readout, and 100/120/180 ms timings
|
||||
are declared model/engineering choices. In particular, the 1.8 ms anatomical
|
||||
synaptic delay is not an eligibility time constant. Physiological applicability,
|
||||
PAM15 functional direction in these bodies, continuous intertrial learning,
|
||||
behavioral valence and long-term retention remain unresolved.
|
||||
|
||||
Before formal registration the coordinator must approve and hash the completed
|
||||
population/seed/input inventory, new readout/gate code, exact continuation
|
||||
semantics, storage policy/cap, resource budget and final candidate wording.
|
||||
Until then this remains **DRAFT — NOT REGISTERED — NO EXECUTION AUTHORITY**.
|
||||
@@ -0,0 +1,118 @@
|
||||
# Olfactory conditioning lab — unregistered preparation
|
||||
|
||||
This directory is an independent, unfinished model-branch implementation.
|
||||
Its 21 engineering sources are registered and sealed by Phase 4AN; those files
|
||||
must not be edited. Phase 4AM and the finite Phase 4AN engineering gate passed.
|
||||
The scientific conditioning proposal and additional planning modules remain
|
||||
unregistered, have not run training, and have not produced a learned candidate.
|
||||
Engineering PASS is not learning PASS.
|
||||
|
||||
Engineering evidence: `registry/phase4an-engineering-validation` in the project
|
||||
repository, commit `aeccb7d0ccdcf6788e0a8de60e7ab96f70dbe734`. Its exact
|
||||
21-source inventory is in `source-identity.json`; this README is not one of
|
||||
those registered sources. The experiment manifest SHA-256 is
|
||||
`e8952a48cd3ea6a93c811a1183a1f7b79e19646299924e7688e5f393737a9aa2`.
|
||||
|
||||
## Existing code
|
||||
|
||||
- `runtime_light.py`: derives a fixed-weight trial runner from the SHA-pinned
|
||||
AJ source. Its full-recording reference retains the entire original tick
|
||||
loop. The light variant removes an explicit diagnostics whitelist while
|
||||
preserving the ordered mathematical AST. It retains sparse events, stimulus
|
||||
acceptance, terminal neural state and delay queues. It supports an explicit
|
||||
longer admission bound, not hidden time truncation or approximate delays.
|
||||
- `seed_registry.py`: explicitly separates training, evaluation and schedule
|
||||
design random streams. Declared same-split aliases can give controls common
|
||||
random inputs; aliases do not count as independent replicates. Duplicate
|
||||
keys, cross-split aliases and seed collisions stop preparation. A full
|
||||
manifest is independently reconstructible.
|
||||
- `runtime_continuation.py`: carries full terminal state, spike/APL delay
|
||||
queues and the absolute clock across fixed-weight segments. It protects
|
||||
every ancestor checkpoint against output/build writes. The explicit
|
||||
arrival-admission change and reporting hooks are AST-audited. The finite AN
|
||||
gate passed 32 actual GPU calls and 74 exact comparisons, including pending
|
||||
arrivals across a split. This does not verify every future 120/180 ms
|
||||
training epoch or the unexecuted 1/10 s retention protocol.
|
||||
- `biological_contract.py`: binds observed canonical identity, exact DAN/MBON
|
||||
compartment tokens and real graph edges without filling unknown relations.
|
||||
- `trial_plan.py`: constructs and independently validates the proposed complete
|
||||
40-block training/control matrix and explicit random-stream aliases. It has
|
||||
not selected a formal master seed or generated scientific brain inputs.
|
||||
Complete-protocol measured storage and runtime budgets remain unfinished.
|
||||
- `evaluation_plan.py`: requires explicit evaluation timing, reversal points
|
||||
and per-control snapshot bindings. It reconstructs the parent inventory and
|
||||
retains 80 independent A/B evaluation streams, paired across snapshots within
|
||||
each training replicate. It does not bind actual state files or authorize a run.
|
||||
- `evaluation_policy.py`: supplies the explicit draft policy in
|
||||
`EVALUATION_POLICY_DRAFT.md`: 100 ms CS, 120 ms probes, seven curve points,
|
||||
actual ordered-CS-prefix control alignment and separate US/time accounting.
|
||||
Its conditional inventory contains 9,440 probes, not executed trials.
|
||||
- `conditioning_inputs.py`: consumes a full 63-bit semantic seed in the original
|
||||
discrete float32 Bernoulli CUDA sampler, with explicit device/draw budget and
|
||||
CS window. CPU/mock tests do not establish CUDA random-sequence equivalence or
|
||||
the influence of high seed bits. It never substitutes a CPU random generator.
|
||||
- `conditioning_update.py`: uses the SHA-pinned AM/J bridge to compute a single
|
||||
epoch-end delta from supplied actual sparse events. It never installs weights;
|
||||
scheduled D, actual DAN activity, attempted increments and actual changed
|
||||
edges remain distinct. Synthetic validation does not establish a trained brain.
|
||||
- `metrics.py`: pure, explicitly draft functional gates; keeps the historical
|
||||
downstream population and the non-MBON difference as distinct readouts.
|
||||
Caller-bound receipts and anatomical membership are still required.
|
||||
- `test_*.py`: CPU synthetic/structural checks. These cannot establish real
|
||||
GPU equivalence, biological validity or successful learning.
|
||||
- `EVIDENCE_DRAFT.md` and `PROTOCOL_DRAFT.md`: evidence and protocol proposals;
|
||||
neither is an approved or frozen experiment.
|
||||
|
||||
## Required before registration or training
|
||||
|
||||
1. Preserve completed AM/AN engineering evidence. AM completed its 144 zero
|
||||
and 144 fixed-overlay runs, five old frozen reference trials, and integrity
|
||||
checks. AN's 32-call continuation gate passed its finite exact comparisons
|
||||
and 28-root integrity checks; it did not rerun the five old reference trials.
|
||||
Neither gate executed conditioning.
|
||||
2. Review and lock the complete experimental matrix, true executed controls,
|
||||
separate training/evaluation stream table, data budget and stop rules.
|
||||
Report any additional gates as new model hypotheses, not inherited facts.
|
||||
The generic seed registry alone does not prove protocol coverage or semantic
|
||||
independence: preparation must require every planned stream and permit
|
||||
aliases only within the same training replicate and input role, for named
|
||||
matched controls/pre-post comparisons. Never alias two training replicates
|
||||
or CS and US roles. Save generator/version, target ordering, rates, timing,
|
||||
consumed stream ID and actual requests hash. Do not truncate a 63-bit seed
|
||||
to 32 bits or derive a new seed by adding the epoch number downstream.
|
||||
3. Bind **all** graph arrays, APL bundle, coefficient JSON, base-candidate
|
||||
vector, full 14,232-edge mask, DAN/MBON identity tables and input files to
|
||||
the registered manifest. A runtime's self-consistent spec or source hash
|
||||
is not sufficient authority for these data.
|
||||
4. Preserve the completed finite real-GPU light/reference comparisons, including exact
|
||||
ordered events, per-neuron counts, terminal V/g/ref, external-request
|
||||
acceptance, APL terminal state and both pending delay queues. No tolerance
|
||||
may excuse a spike divergence. Benchmark encoding, loading, simulation,
|
||||
validation and output time separately and together.
|
||||
5. Validate the actual retention protocol before claiming retention. The
|
||||
separate continuation module passed the finite AN GPU gate. V/g/ref,
|
||||
spike-ring phase, APL state/pending phase and global tick must survive;
|
||||
segmented and monolithic executions must agree. A checkpoint seal binds
|
||||
fresh state bytes when created; it cannot prove pre-seal immutability.
|
||||
6. Verify every learned snapshot's parent chain, input events and exact
|
||||
frozen eligibility-v2 update. The single-trial runtime verifies the
|
||||
bounded overlay and parent-hash declaration, but does not prove the
|
||||
coordinator actually computed that update. Within each neural trial the
|
||||
delta is fixed. The original base is never written.
|
||||
7. Record the realized wall-time/output budget before the full acquisition
|
||||
suite. A failed acquisition gate must not be rescued by tuning eta,
|
||||
thresholds, APL gain, trial count, mask membership or selected odors.
|
||||
|
||||
## Interpretation limits
|
||||
|
||||
PAM15 and PPL103 are specific, statically mapped artificial US interfaces;
|
||||
PAM/PPL cluster names are not general valence labels. The fixed eligibility
|
||||
rule is a single-direction causal-LTD engineering hypothesis, not a complete
|
||||
model of dopamine, bidirectional plasticity, forgetting or consolidation.
|
||||
The proposed fixed MBON type contrast is offline analysis, not a decoder.
|
||||
|
||||
No old sealed artifact, raw connectome, transmitter sign, eligibility-v2 or
|
||||
base weight is modified here. No game, reinforcement-learning policy,
|
||||
classifier, action decoder, visual encoder, web service or 3D interface is
|
||||
introduced. A future learning PASS requires functional stimulus-specific
|
||||
responses and executed controls, never merely a changed delta array.
|
||||
@@ -0,0 +1,205 @@
|
||||
"""Read-only exact-table contracts for the proposed conditioning branch.
|
||||
|
||||
No body identity, compartment or behavioral function is inferred from a name.
|
||||
The specific two interfaces are explicit protocol hypotheses checked against
|
||||
the frozen H maps and actual graph edges. No training or file write occurs.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
INTERFACES = {"PAM15": ("y5B'2a", "MBON01"), "PPL103": ("y2a'1", "MBON12")}
|
||||
N = 166700
|
||||
E = 25582938
|
||||
INPUT_PINS = {
|
||||
"datasets/male-cns-v1.0/processed/neuron-only/neuron-index/unified-metadata.parquet":
|
||||
"922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63",
|
||||
"experiments/phase4h-dopamine-plasticity/plastic-mask/plastic-edge-candidates.parquet":
|
||||
"e5075ee3e5a0edf3a4d197453847163cc295b254f21c67203159bba204b12a76",
|
||||
"experiments/phase4h-dopamine-plasticity/mappings/dan-to-compartment.parquet":
|
||||
"a16f72c3121ed0ec638c1a2b249ace446e6ccee020b49679818eb42589d00662",
|
||||
"experiments/phase4h-dopamine-plasticity/mappings/mbon-to-compartment.parquet":
|
||||
"54f371f7e35b9def44becd1ba9bd2872861f4876b255b2c8ec46feee6473b70c",
|
||||
"experiments/phase4h-dopamine-plasticity/mappings/dan-to-mbon-confirmed.parquet":
|
||||
"730d000f25667b59181d2ee6703c64591229a8beab0856197ed71e68d799d191",
|
||||
}
|
||||
|
||||
|
||||
def require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def digest(path):
|
||||
h = hashlib.sha256()
|
||||
with Path(path).open("rb") as stream:
|
||||
for block in iter(lambda: stream.read(8 << 20), b""):
|
||||
h.update(block)
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def _columns(frame, names, label):
|
||||
require(isinstance(frame, pd.DataFrame) and set(names) <= set(frame.columns),
|
||||
"Missing observed schema columns: " + label)
|
||||
|
||||
|
||||
def _identity(frame, meta, label):
|
||||
_columns(frame, ("neuron_idx", "body_id", "annotation_type", "annotation_instance"), label)
|
||||
require(frame.neuron_idx.dtype == np.int32 and frame.body_id.dtype == np.uint64,
|
||||
"Canonical identity dtypes changed: " + label)
|
||||
ids = frame.neuron_idx.to_numpy()
|
||||
require(len(np.unique(ids)) == len(ids) and np.all((ids >= 0) & (ids < len(meta))),
|
||||
"Ambiguous/out-of-range identity: " + label)
|
||||
for field in ("body_id", "annotation_type", "annotation_instance"):
|
||||
actual = frame[field].reset_index(drop=True)
|
||||
expected = meta.iloc[ids][field].reset_index(drop=True)
|
||||
require(actual.equals(expected), "Identity field mismatch: " + label + "/" + field)
|
||||
|
||||
|
||||
def _edges(frame, meta, src, dst, signed_raw, label):
|
||||
names = ("edge_id", "source_neuron_idx", "source_body_id", "target_neuron_idx", "target_body_id",
|
||||
"raw_synapse_count", "source_annotation_type", "source_annotation_instance",
|
||||
"target_annotation_type", "target_annotation_instance")
|
||||
_columns(frame, names, label)
|
||||
require(frame.edge_id.dtype == np.int64 and frame.edge_id.is_unique, "Invalid edge IDs: " + label)
|
||||
ids = frame.edge_id.to_numpy()
|
||||
require(np.all((ids >= 0) & (ids < len(src))), "Edge outside graph: " + label)
|
||||
require(frame.raw_synapse_count.dtype == np.int64 and (frame.raw_synapse_count > 0).all(),
|
||||
"Invalid anatomical contact count: " + label)
|
||||
require(np.array_equal(np.abs(signed_raw[ids]), frame.raw_synapse_count.to_numpy()),
|
||||
"Anatomical edge weight mismatch: " + label)
|
||||
for side, expected_idx in (("source", src[ids]), ("target", dst[ids])):
|
||||
require(frame[side + "_neuron_idx"].dtype == np.int32 and
|
||||
frame[side + "_body_id"].dtype == np.uint64,
|
||||
"Canonical edge identity dtype mismatch: " + label)
|
||||
require(np.array_equal(frame[side + "_neuron_idx"].to_numpy(), expected_idx),
|
||||
"Actual graph endpoint mismatch: " + label)
|
||||
for field in ("body_id", "annotation_type", "annotation_instance"):
|
||||
actual = frame[side + "_" + field].reset_index(drop=True)
|
||||
expected = meta.iloc[expected_idx][field].reset_index(drop=True)
|
||||
require(actual.equals(expected), "Actual edge identity mismatch: " + label + "/" + side + "/" + field)
|
||||
|
||||
|
||||
def audit_interface_tables(meta, dan, mbon, confirmed, candidates, src, dst, signed_raw):
|
||||
"""Pure table audit; small synthetic graphs are allowed in unit tests only.
|
||||
|
||||
The production loader below separately requires the complete N/E identity.
|
||||
Supplied annotation population membership is never filled from a prefix.
|
||||
"""
|
||||
_columns(meta, ("neuron_idx", "body_id", "annotation_class", "annotation_type", "annotation_instance"), "metadata")
|
||||
require(meta.neuron_idx.dtype == np.int32 and meta.body_id.dtype == np.uint64,
|
||||
"Canonical metadata dtype mismatch")
|
||||
require(np.array_equal(meta.neuron_idx.to_numpy(), np.arange(len(meta), dtype=np.int32)) and meta.body_id.is_unique,
|
||||
"Metadata must have canonical complete ordered identities")
|
||||
require(src.dtype == dst.dtype == np.int32 and signed_raw.dtype == np.int64 and
|
||||
src.ndim == dst.ndim == signed_raw.ndim == 1 and len(src) == len(dst) == len(signed_raw),
|
||||
"Graph endpoint/weight schema mismatch")
|
||||
require(np.all((src >= 0) & (src < len(meta))) and np.all((dst >= 0) & (dst < len(meta))),
|
||||
"Graph endpoint out of range")
|
||||
_identity(dan, meta, "DAN map")
|
||||
_identity(mbon, meta, "MBON map")
|
||||
for table, klass, label in ((dan, "DAN", "DAN"), (mbon, "MBON", "MBON")):
|
||||
require(np.array_equal(np.sort(table.neuron_idx.to_numpy()), np.flatnonzero(meta.annotation_class.eq(klass))),
|
||||
"Mapping must include the complete exact-annotation population: " + label)
|
||||
_columns(table, ("mb_compartment_verbatim", "mb_compartment_mapping_status"), label)
|
||||
_edges(confirmed, meta, src, dst, signed_raw, "confirmed DAN-MBON")
|
||||
_edges(candidates, meta, src, dst, signed_raw, "plastic candidates")
|
||||
_columns(confirmed, ("source_compartment_verbatim", "target_compartment_verbatim", "exact_compartment_match"), "confirmed DAN-MBON")
|
||||
_columns(candidates, ("target_compartment_verbatim", "plasticity_polarity"), "plastic candidates")
|
||||
require(confirmed.exact_compartment_match.dtype == np.bool_ and confirmed.exact_compartment_match.all(),
|
||||
"Confirmed table contains an unknown compartment relation")
|
||||
require(confirmed.source_compartment_verbatim.notna().all() and
|
||||
confirmed.source_compartment_verbatim.equals(confirmed.target_compartment_verbatim),
|
||||
"Confirmed exact compartment relation differs")
|
||||
require(meta.iloc[candidates.source_neuron_idx.to_numpy()].annotation_class.eq("Kenyon_Cell").all() and
|
||||
meta.iloc[candidates.target_neuron_idx.to_numpy()].annotation_class.eq("MBON").all(),
|
||||
"Candidate is not a real KC-MBON edge")
|
||||
require((signed_raw[candidates.edge_id.to_numpy()] > 0).all() and candidates.plasticity_polarity.eq(-1).all(),
|
||||
"Frozen KC base sign/polarity changed")
|
||||
pop = {klass: np.flatnonzero(meta.annotation_class.eq(klass)).astype(np.int32)
|
||||
for klass in ("Kenyon_Cell", "MBON", "DAN")}
|
||||
reports = {}
|
||||
for dan_type, (compartment, mbon_type) in INTERFACES.items():
|
||||
dd = dan[dan.annotation_type.eq(dan_type)].sort_values("neuron_idx")
|
||||
mm = mbon[mbon.annotation_type.eq(mbon_type)].sort_values("neuron_idx")
|
||||
require(len(dd) > 0 and len(mm) > 0, "Missing exact type: " + dan_type)
|
||||
for table in (dd, mm):
|
||||
require(table.mb_compartment_mapping_status.eq("explicit_single_parenthesized_token").all() and
|
||||
table.mb_compartment_verbatim.eq(compartment).all(),
|
||||
"Unknown or mismatched compartment: " + dan_type)
|
||||
edges = confirmed[confirmed.source_neuron_idx.isin(dd.neuron_idx)]
|
||||
require(len(edges) > 0 and edges.source_compartment_verbatim.eq(compartment).all() and
|
||||
edges.target_compartment_verbatim.eq(compartment).all() and
|
||||
edges.target_neuron_idx.isin(mm.neuron_idx).all(),
|
||||
"Specific DAN interface has unknown or mismatched actual target")
|
||||
require(set(edges.source_neuron_idx) == set(dd.neuron_idx) and
|
||||
set(edges.target_neuron_idx) == set(mm.neuron_idx),
|
||||
"Incomplete actual DAN/MBON type coverage")
|
||||
plastic = candidates[candidates.target_compartment_verbatim.eq(compartment)]
|
||||
require(len(plastic) > 0 and plastic.target_neuron_idx.isin(mm.neuron_idx).all() and
|
||||
set(plastic.target_neuron_idx) == set(mm.neuron_idx),
|
||||
"Frozen plastic subset is not the exact mapped target population")
|
||||
pop[dan_type] = dd.neuron_idx.to_numpy(dtype=np.int32)
|
||||
pop[mbon_type] = mm.neuron_idx.to_numpy(dtype=np.int32)
|
||||
def members(frame):
|
||||
return frame[["neuron_idx", "body_id", "annotation_type", "annotation_instance", "mb_compartment_verbatim"]].to_dict(orient="records")
|
||||
reports[dan_type] = dict(compartment=compartment, MBON_type=mbon_type,
|
||||
DAN_members=members(dd), MBON_members=members(mm),
|
||||
confirmed_DAN_MBON_edge_ids=edges.edge_id.tolist(),
|
||||
confirmed_DAN_MBON_edges=len(edges), confirmed_DAN_MBON_contacts=int(edges.raw_synapse_count.sum()),
|
||||
candidate_edges=len(plastic), candidate_contacts=int(plastic.raw_synapse_count.sum()),
|
||||
unknown_selected_interface_relations=0,
|
||||
functional_valence="cross-dataset type-level hypothesis, not measured MaleCNS body function",
|
||||
artificial_US=True, D_equals_measured_dopamine=False)
|
||||
return pop, dict(interfaces=reports, whole_DAN_mapping_unknown=int(dan.mb_compartment_verbatim.isna().sum()),
|
||||
whole_MBON_mapping_unknown=int(mbon.mb_compartment_verbatim.isna().sum()),
|
||||
all_candidate_edges_retained=len(candidates), populations={k: len(v) for k, v in pop.items()},
|
||||
global_unknowns_filled=False, training=False, new_identity_inferred=False)
|
||||
|
||||
|
||||
def _complete_graph_identity(meta, graph, n=N, e=E):
|
||||
"""Exact full-graph dimensions; small explicit sizes are for unit tests."""
|
||||
require(len(meta) == n and graph["nodes"].dtype == np.int32 and
|
||||
np.array_equal(graph["nodes"], np.arange(n, dtype=np.int32)), "Complete canonical neuron set required")
|
||||
require(graph["edge_id"].dtype == np.int64 and
|
||||
np.array_equal(graph["edge_id"], np.arange(e, dtype=np.int64)), "Complete canonical edge set required")
|
||||
require(all(graph[name].shape == (e,) for name in ("src", "dst", "signed_raw")),
|
||||
"Complete graph endpoint/weight lengths required")
|
||||
|
||||
|
||||
def load_bound_tables(root, graph_manifest):
|
||||
"""Require external, registered graph hashes, not self-authorized paths.
|
||||
|
||||
graph_manifest maps the absolute graph paths to their authorized SHA-256.
|
||||
The coordinator must bind this dictionary to the preregistration/parent.
|
||||
"""
|
||||
root = Path(root).resolve()
|
||||
observed = {}
|
||||
tables = []
|
||||
for relative, sha in INPUT_PINS.items():
|
||||
path = root / relative
|
||||
require(digest(path) == sha, "Frozen table SHA mismatch: " + relative)
|
||||
observed[str(path)] = sha
|
||||
tables.append(pd.read_parquet(path))
|
||||
meta, candidates, dan, mbon, confirmed = tables
|
||||
graph_dir = root / "experiments/phase4ag-author-apl-r2/graphs/full"
|
||||
graph = {}
|
||||
for name in ("nodes", "edge_id", "src", "dst", "signed_raw"):
|
||||
path = graph_dir / (name + ".npy")
|
||||
require(str(path) in graph_manifest and digest(path) == graph_manifest[str(path)],
|
||||
"Unbound/changed complete graph input: " + name)
|
||||
observed[str(path)] = graph_manifest[str(path)]
|
||||
graph[name] = np.load(path, mmap_mode="r", allow_pickle=False)
|
||||
_complete_graph_identity(meta, graph)
|
||||
require(len(candidates) == 14232, "Complete frozen mask required")
|
||||
populations, audit = audit_interface_tables(meta, dan, mbon, confirmed, candidates,
|
||||
graph["src"], graph["dst"], graph["signed_raw"])
|
||||
require(all(digest(path) == sha for path, sha in observed.items()), "Input bytes changed during audit")
|
||||
return populations, dict(audit, input_sha256=observed, schema={
|
||||
name: table.dtypes.astype(str).to_dict() for name, table in
|
||||
(("metadata", meta), ("candidates", candidates), ("DAN", dan), ("MBON", mbon), ("confirmed", confirmed))})
|
||||
@@ -0,0 +1,312 @@
|
||||
"""Exclusive postprocessing of one externally authorized sealed fresh epoch.
|
||||
|
||||
No brain execution, source selection, input generation or weight installation.
|
||||
The external caller must derive and pin authority from its registered row and
|
||||
successful process receipt; a SHA supplied to this helper is not self-proving
|
||||
registration authority. Production uses the actual pinned C/R checkpoint and
|
||||
overlay readers. Small synthetic tests exercise only the private core.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
from pathlib import Path
|
||||
import sys
|
||||
from types import ModuleType
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
SCHEMA = 'phase4an-epoch-postprocess-authority-v1'
|
||||
SOURCE_PINS = {
|
||||
'runtime_light_source': '522b8c31ea938890fe15364fccd25ccc5bded54101754909f255532e7ad01aae',
|
||||
'runtime_continuation_source': '0b8e8e0a6a23a079dc62a88355ce355d53b3ae22f6665519a8f6aef2dcfc5aba',
|
||||
'conditioning_update_source': '96efff678cff910654eb08e86f1a17789e4e5589e340345f419c7027a7d547e2',
|
||||
'eligibility_bridge_source': '174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61',
|
||||
'phase4j_source': 'abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e',
|
||||
'am_overlay_source': '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4',
|
||||
}
|
||||
ROLES = frozenset(('spec', 'requests', 'overlay_before', 'overlay_manifest_before', 'state',
|
||||
'kc', 'canonical_kc', 'candidate_map', 'mask', 'base_candidate', *SOURCE_PINS))
|
||||
GRAPH_FILES = ('nodes.npy', 'edge_id.npy', 'src.npy', 'dst.npy', 'signed_raw.npy', 'ptr.npy', 'weights.npy')
|
||||
APL_FILES = ('input_matrix.npy', 'output_matrix.npy', 'kc_globals.npy', 'apl_globals.npy')
|
||||
DIMENSIONS = (166700, 25582938, 4064, 14232)
|
||||
_MODULES = {}
|
||||
|
||||
|
||||
def _require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return type(value) is str and len(value) == 64 and all(c in '0123456789abcdef' for c in value)
|
||||
|
||||
|
||||
def _digest(path):
|
||||
h = hashlib.sha256()
|
||||
with Path(path).open('rb') as stream:
|
||||
for chunk in iter(lambda: stream.read(8 << 20), b''):
|
||||
h.update(chunk)
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def _read(path):
|
||||
def pairs(items):
|
||||
value = {}
|
||||
for key, item in items:
|
||||
_require(key not in value, 'Duplicate JSON key: ' + key)
|
||||
value[key] = item
|
||||
return value
|
||||
return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=pairs,
|
||||
parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value)))
|
||||
|
||||
|
||||
def _save(path, value):
|
||||
with Path(path).open('x', encoding='utf-8') as stream:
|
||||
json.dump(value, stream, sort_keys=True, indent=2, allow_nan=False)
|
||||
stream.write('\n')
|
||||
|
||||
|
||||
def _path(value):
|
||||
_require(type(value) is str and Path(value).is_absolute(), 'Input paths must be explicit and absolute')
|
||||
path = Path(value)
|
||||
_require(path.is_file() and not path.is_symlink(), 'Input must be an existing regular, unlinked file')
|
||||
return path.resolve(strict=True)
|
||||
|
||||
|
||||
def _check_files(pins):
|
||||
for path, expected in pins.items():
|
||||
_require(_sha(expected) and _digest(path) == expected, 'Input/source SHA mismatch: ' + str(path))
|
||||
|
||||
|
||||
def _authority(path, expected):
|
||||
path = _path(str(path))
|
||||
_require(_sha(expected) and _digest(path) == expected, 'Authority differs from external SHA')
|
||||
value = _read(path)
|
||||
keys = {'schema', 'epoch_id', 'diagnostic_mode', 'command', 'checkpoint_dir',
|
||||
'checkpoint_manifest_sha256', 'files', 'frozen_inputs'}
|
||||
_require(type(value) is dict and set(value) == keys and value['schema'] == SCHEMA, 'Invalid authority fields/schema')
|
||||
_require(type(value['epoch_id']) is str and value['epoch_id'] and value['epoch_id'].strip() == value['epoch_id'], 'Invalid epoch identity')
|
||||
_require(value['diagnostic_mode'] in ('light', 'reference'), 'Invalid diagnostic mode')
|
||||
command = value['command']
|
||||
_require(type(command) is dict and set(command) == {'steps', 'compartment', 'us_tick', 'd_command', 'frozen', 'trace_backend'}, 'Invalid command fields')
|
||||
_require(type(command['steps']) is int and command['steps'] > 0, 'Invalid command steps')
|
||||
_require(command['us_tick'] is None or (type(command['us_tick']) is int and 0 <= command['us_tick'] < command['steps']), 'Invalid US tick')
|
||||
_require(type(command['d_command']) in (int, float) and math.isfinite(command['d_command']) and command['d_command'] in (0, 1), 'Invalid scheduled D')
|
||||
_require(command['us_tick'] is not None or command['d_command'] == 0, 'No US requires D=0')
|
||||
_require(type(command['frozen']) is bool and command['trace_backend'] in ('numpy', 'torch'), 'Explicit frozen/backend required')
|
||||
_require(command['compartment'] in ("B2B'2a", 'a1', "y2a'1", "y5B'2a"), 'Explicit frozen compartment required')
|
||||
_require(type(value['files']) is dict and set(value['files']) == ROLES, 'Missing/unknown authority file roles')
|
||||
paths, pins = {}, {path: expected}
|
||||
for role, entry in value['files'].items():
|
||||
_require(type(entry) is dict and set(entry) == {'path', 'sha256'} and _sha(entry['sha256']), 'Invalid file authority: ' + role)
|
||||
location = _path(entry['path'])
|
||||
_require(location not in pins or pins[location] == entry['sha256'], 'Conflicting hashes for the same file')
|
||||
paths[role], pins[location] = location, entry['sha256']
|
||||
_require(type(value['frozen_inputs']) is dict and value['frozen_inputs'], 'Full frozen input pins are required')
|
||||
for name, sha in value['frozen_inputs'].items():
|
||||
location = _path(name)
|
||||
_require(_sha(sha) and (location not in pins or pins[location] == sha), 'Invalid/conflicting frozen input pin')
|
||||
pins[location] = sha
|
||||
checkpoint = Path(value['checkpoint_dir'])
|
||||
_require(checkpoint.is_absolute() and checkpoint.is_dir() and not checkpoint.is_symlink(), 'Explicit existing checkpoint directory required')
|
||||
checkpoint = checkpoint.resolve(strict=True)
|
||||
manifest = checkpoint / 'checkpoint-manifest.json'
|
||||
_require(_sha(value['checkpoint_manifest_sha256']), 'External checkpoint manifest SHA required')
|
||||
_require(manifest not in pins or pins[manifest] == value['checkpoint_manifest_sha256'], 'Conflicting checkpoint manifest pins')
|
||||
pins[manifest] = value['checkpoint_manifest_sha256']
|
||||
_require(paths['state'] == checkpoint / 'state.npz', 'Events must come from this checkpoint state.npz')
|
||||
_check_files(pins)
|
||||
return value, paths, pins, checkpoint, path
|
||||
|
||||
|
||||
def _load_source(path, expected, role):
|
||||
raw = path.read_bytes()
|
||||
_require(hashlib.sha256(raw).hexdigest() == expected, 'Pinned source changed: ' + role)
|
||||
name = '_conditioning_epoch_' + role
|
||||
if name in _MODULES:
|
||||
saved_path, saved_sha, module = _MODULES[name]
|
||||
_require(saved_path == path and saved_sha == expected and sys.modules.get(name) is module,
|
||||
'Source module identity collision: ' + role)
|
||||
return module
|
||||
_require(name not in sys.modules, 'Unverified source module collision: ' + role)
|
||||
module = ModuleType(name)
|
||||
module.__file__ = str(path)
|
||||
sys.modules[name] = module
|
||||
try:
|
||||
exec(compile(raw, str(path), 'exec'), module.__dict__)
|
||||
except BaseException:
|
||||
sys.modules.pop(name, None)
|
||||
raise
|
||||
_MODULES[name] = (path, expected, module)
|
||||
return module
|
||||
|
||||
|
||||
def _production_dependencies(paths, pins):
|
||||
for role, expected in SOURCE_PINS.items():
|
||||
_require(pins[paths[role]] == expected, 'Authority cannot replace frozen source: ' + role)
|
||||
_require(paths['runtime_continuation_source'].parent / 'runtime_light.py' == paths['runtime_light_source'],
|
||||
'Continuation must load its declared adjacent frozen runtime')
|
||||
c = _load_source(paths['runtime_continuation_source'], SOURCE_PINS['runtime_continuation_source'], 'continuation')
|
||||
_require(Path(c.R.__file__).resolve() == paths['runtime_light_source'] and
|
||||
_digest(c.R.__file__) == SOURCE_PINS['runtime_light_source'], 'Loaded actual runtime differs from authority')
|
||||
u = _load_source(paths['conditioning_update_source'], SOURCE_PINS['conditioning_update_source'], 'update')
|
||||
return c, c.R, u
|
||||
|
||||
|
||||
def _npz(path, keys):
|
||||
with np.load(path, allow_pickle=False) as archive:
|
||||
_require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected NPZ schema: ' + str(path))
|
||||
return {key: archive[key].copy() for key in keys}
|
||||
|
||||
|
||||
def _same(a, b):
|
||||
return a.dtype == b.dtype and a.shape == b.shape and a.tobytes() == b.tobytes()
|
||||
|
||||
|
||||
def _vector(a, dtype, count, label):
|
||||
_require(a.dtype == np.dtype(dtype) and a.shape == (count,), 'Invalid dtype/shape: ' + label)
|
||||
|
||||
|
||||
def _coordinate(value, paths, pins, checkpoint, authority_path, out_dir, dependencies, dimensions):
|
||||
"""Private core: explicit tiny-fixture dependencies only in synthetic tests."""
|
||||
c, r, u = dependencies
|
||||
n, edges, kc_count, candidates = dimensions
|
||||
out = Path(out_dir).resolve(strict=False)
|
||||
protected = {checkpoint, *(path.parent for path in pins)}
|
||||
_require(not out.exists() and not any(out.is_relative_to(root) for root in protected),
|
||||
'Output must be new and outside checkpoint/source/data/input parent directories')
|
||||
_require(out.parent.is_dir(), 'Output parent must already exist')
|
||||
source_sha = _digest(__file__)
|
||||
spec = r.validate_spec(_read(paths['spec']), paths['spec'].parent)
|
||||
command = value['command']
|
||||
_require(type(spec['steps']) is int and spec['steps'] == command['steps'], 'Command steps differ from authorized spec')
|
||||
_require(paths['spec'].name == 'spec.json' and paths['requests'] == paths['spec'].parent / 'requests.npz', 'Actual case input filenames differ')
|
||||
graph = Path(spec['graph_dir']).resolve(strict=True)
|
||||
apl = Path(spec['apl_bundle_dir']).resolve(strict=True)
|
||||
required_inputs = [graph / name for name in GRAPH_FILES] + [apl / name for name in APL_FILES] + [Path(spec['coefficients_path']).resolve(strict=True)]
|
||||
_require(all(path in pins for path in required_inputs), 'Full graph/APL/coefficient authority is incomplete')
|
||||
_require(paths['canonical_kc'] == apl / 'kc_globals.npy', 'Canonical KC must be the declared frozen APL/KC membership')
|
||||
old_meta = _read(paths['overlay_manifest_before'])
|
||||
for role, prefix in (('mask', 'mask'), ('base_candidate', 'base_candidate'), ('am_overlay_source', 'am_overlay')):
|
||||
_require(Path(old_meta[prefix + '_path']).resolve(strict=True) == paths[role] and
|
||||
old_meta[prefix + '_sha256'] == pins[paths[role]], 'Overlay authority differs: ' + role)
|
||||
aj = Path(old_meta['aj_model_dir']).resolve(strict=True)
|
||||
for relative, expected in r.PINS.items():
|
||||
path = (aj.parent / relative).resolve(strict=True)
|
||||
_require(pins.get(path) == expected, 'Missing exact frozen dependency pin: ' + relative)
|
||||
record = c.load_checkpoint(checkpoint, value['checkpoint_manifest_sha256'])
|
||||
_require(record['fresh'] is True and record['global_tick_offset'] == 0 and record['steps'] == command['steps'], 'Only complete fresh epochs may produce an update')
|
||||
summary, provenance = record['summary'], record['provenance']
|
||||
_require(summary['nodes'] == n and summary['edges'] == edges and summary['diagnostic_mode'] == value['diagnostic_mode'], 'Runtime population/mode differs from authority')
|
||||
_require(record['manifest']['files_sha256']['state.npz'] == pins[paths['state']], 'State/events are not bound by the expected checkpoint seal')
|
||||
for role in ('spec', 'requests', 'overlay_before', 'overlay_manifest_before'):
|
||||
_require(provenance['input_sha256'].get(str(paths[role])) == pins[paths[role]], 'Runtime did not bind authorized ' + role)
|
||||
_require(_digest(checkpoint / 'overlay-input.npz') == pins[paths['overlay_before']] and
|
||||
_digest(checkpoint / 'overlay-manifest.json') == pins[paths['overlay_manifest_before']], 'Sealed runtime used a different overlay or manifest')
|
||||
helper, before, checked_meta, _ = r.load_overlay(paths['overlay_before'], paths['overlay_manifest_before'])
|
||||
_require(checked_meta == old_meta, 'Overlay manifest changed during validation')
|
||||
mapping = _npz(paths['candidate_map'], ('edge_id', 'source_neuron_idx', 'target_neuron_idx', 'target_compartment_verbatim'))
|
||||
mask = np.load(paths['mask'], allow_pickle=False)
|
||||
kc = np.load(paths['kc'], allow_pickle=False)
|
||||
canonical_kc = np.load(paths['canonical_kc'], allow_pickle=False)
|
||||
base = np.load(paths['base_candidate'], allow_pickle=False)
|
||||
_vector(mask, 'int64', candidates, 'complete mask')
|
||||
_vector(kc, 'int32', kc_count, 'KC')
|
||||
_vector(canonical_kc, 'int32', kc_count, 'canonical KC')
|
||||
_vector(base, 'float32', candidates, 'original base mV')
|
||||
_require(_same(kc, canonical_kc), 'Supplied KC differs from canonical membership bytes')
|
||||
_require(_same(mapping['edge_id'], mask) and _same(before['edge_id'], mask), 'Candidate mapping/overlay differ from exact mask')
|
||||
_require(_same(before['base_candidate_mv'], base), 'Overlay used different original base bytes')
|
||||
for key in ('source_neuron_idx', 'target_neuron_idx'):
|
||||
_vector(mapping[key], 'int32', candidates, key)
|
||||
comps = mapping['target_compartment_verbatim']
|
||||
_require(comps.shape == (candidates,) and comps.dtype.kind == 'U', 'Compartment mapping must be an exact Unicode vector')
|
||||
maps = {}
|
||||
try:
|
||||
for filename, dtype, length in (('nodes.npy', 'int32', n), ('edge_id.npy', 'int64', edges),
|
||||
('src.npy', 'int32', edges), ('dst.npy', 'int32', edges)):
|
||||
array = np.load(graph / filename, allow_pickle=False, mmap_mode='r')
|
||||
maps[filename] = array
|
||||
_vector(array, dtype, length, 'full graph ' + filename)
|
||||
_require(np.array_equal(maps['nodes.npy'], np.arange(n, dtype=np.int32)), 'Global nodes are not canonical')
|
||||
_require(np.all((mask >= 0) & (mask < edges)) and np.all(mask[1:] > mask[:-1]), 'Invalid canonical edge mask')
|
||||
_require(_same(maps['edge_id.npy'][mask], mask), 'Mask is not indexed by original global edge ID')
|
||||
_require(_same(mapping['source_neuron_idx'], maps['src.npy'][mask]) and
|
||||
_same(mapping['target_neuron_idx'], maps['dst.npy'][mask]), 'Candidate endpoints differ from actual graph')
|
||||
finally:
|
||||
for array in maps.values():
|
||||
array._mmap.close()
|
||||
state = _npz(paths['state'], ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref'))
|
||||
_require(all(_same(state[key], record['state'][key]) for key in state), 'Event/state arrays differ from actual sealed reader')
|
||||
_require(record['overlay']['delta_raw_sha256'] == r._raw(before['plastic_delta_mv']) and
|
||||
record['overlay']['effective_weights_raw_sha256'] == summary['effective_weights_raw_sha256'], 'Runtime weight installation is not bound to delta_before')
|
||||
args = dict(step=state['step'], neuron_idx=state['neuron_idx'], kc=kc,
|
||||
candidate_edge_id=mask, candidate_sources=mapping['source_neuron_idx'],
|
||||
candidate_targets=mapping['target_neuron_idx'], candidate_compartments=comps,
|
||||
delta_before=before['plastic_delta_mv'], base_candidate=base, steps=command['steps'], n_neurons=n,
|
||||
compartment=command['compartment'], us_tick=command['us_tick'], d_command=command['d_command'],
|
||||
frozen=command['frozen'], bridge_source=paths['eligibility_bridge_source'],
|
||||
phase4j_source=paths['phase4j_source'], trace_backend=command['trace_backend'])
|
||||
result = u.update_epoch(**args)
|
||||
_check_files(pins)
|
||||
# The directory creation is the exclusive claim. A later failure remains a
|
||||
# partial directory and can never silently be retried or overwritten.
|
||||
out.mkdir()
|
||||
try:
|
||||
with (out / 'delta-after.npz').open('xb') as stream:
|
||||
np.savez_compressed(stream, edge_id=mask, plastic_delta_mv=result.delta_after)
|
||||
metadata = {key: old_meta[key] for key in ('schema', 'mask_path', 'mask_sha256',
|
||||
'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir', 'am_overlay_path', 'am_overlay_sha256')}
|
||||
metadata.update(overlay_name='learned', overlay_sha256=_digest(out / 'delta-after.npz'),
|
||||
parent_snapshot_sha256=pins[paths['overlay_before']], eligibility_v2_source_sha256=SOURCE_PINS['phase4j_source'])
|
||||
_save(out / 'overlay-manifest.json', metadata)
|
||||
_save(out / 'update-receipt.json', result.receipt)
|
||||
_, after, _, _ = r.load_overlay(out / 'delta-after.npz', out / 'overlay-manifest.json')
|
||||
_require(_same(after['plastic_delta_mv'], result.delta_after) and _same(after['edge_id'], mask), 'Written learned snapshot failed actual runtime loader')
|
||||
# Re-read the sealed neural state after writing, without altering its
|
||||
# seal or appending coordinator output inside the runtime directory.
|
||||
c.load_checkpoint(checkpoint, value['checkpoint_manifest_sha256'])
|
||||
_check_files(pins)
|
||||
_require(_digest(__file__) == source_sha, 'Coordinator source changed')
|
||||
output_hashes = {name: _digest(out / name) for name in ('delta-after.npz', 'overlay-manifest.json', 'update-receipt.json')}
|
||||
chain = dict(schema='phase4an-epoch-postprocess-chain-v1', complete=True,
|
||||
epoch_id=value['epoch_id'], authority_file=str(authority_path), authority_sha256=pins[authority_path],
|
||||
coordinator_source_sha256=source_sha, command=dict(command), diagnostic_mode=value['diagnostic_mode'],
|
||||
before_overlay_file_sha256=pins[paths['overlay_before']],
|
||||
before_overlay_manifest_sha256=pins[paths['overlay_manifest_before']],
|
||||
after_overlay_file_sha256=output_hashes['delta-after.npz'],
|
||||
after_overlay_manifest_sha256=output_hashes['overlay-manifest.json'],
|
||||
parent_snapshot_sha256_semantics='delta-before overlay NPZ file SHA256, not neural checkpoint or manifest SHA',
|
||||
source_neural_checkpoint_dir=str(checkpoint),
|
||||
source_neural_checkpoint_manifest_sha256=value['checkpoint_manifest_sha256'],
|
||||
source_state_file_sha256=pins[paths['state']], source_spec_sha256=pins[paths['spec']],
|
||||
source_requests_sha256=pins[paths['requests']],
|
||||
runtime_delta_before_raw_sha256=record['overlay']['delta_raw_sha256'],
|
||||
runtime_effective_weights_raw_sha256=summary['effective_weights_raw_sha256'],
|
||||
input_files_sha256={str(path): sha for path, sha in sorted(pins.items(), key=lambda item: str(item[0]))},
|
||||
output_files_sha256=output_hashes,
|
||||
update_receipt_payload_sha256=result.receipt['payload_sha256'],
|
||||
runtime_learned_manifest_loader_verified=True, input_and_checkpoint_bytes_reverified=True,
|
||||
candidate_endpoints_equal_graph=True, KC_equals_supplied_canonical_membership=True,
|
||||
authority_scope='caller must bind this externally pinned authority to its preregistered row, successful process, anatomical metadata and complete mask audit',
|
||||
registration_authority_independently_proven=False, anatomical_identity_inferred_from_lengths=False,
|
||||
neural_checkpoint_and_plastic_parent_are_distinct=True,
|
||||
weights_installed=False, brain_executed=False, learning_pass_claimed=False, candidate_generated=False)
|
||||
_save(out / 'chain-receipt.json', chain)
|
||||
return chain
|
||||
except BaseException as error:
|
||||
if not (out / 'postprocess-error.json').exists():
|
||||
_save(out / 'postprocess-error.json', dict(complete=False, error_type=type(error).__name__,
|
||||
message=str(error), partial_preserved=True, retry_forbidden=True, learning_pass_claimed=False))
|
||||
raise
|
||||
|
||||
|
||||
def postprocess_epoch(authority_json, expected_authority_sha256, out_dir):
|
||||
"""Production entry: full 166700/25582938/4064/14232, no small-mode flag."""
|
||||
_require(sys.dont_write_bytecode, 'Use python -B; frozen sources must not receive bytecode writes')
|
||||
value, paths, pins, checkpoint, authority_path = _authority(authority_json, expected_authority_sha256)
|
||||
dependencies = _production_dependencies(paths, pins)
|
||||
return _coordinate(value, paths, pins, checkpoint, authority_path, out_dir, dependencies, DIMENSIONS)
|
||||
@@ -0,0 +1,221 @@
|
||||
"""UNREGISTERED input consumer; no I/O, brain execution, or training.
|
||||
|
||||
The frozen L/AK interface is discrete float32 Bernoulli (one request per PN
|
||||
and tick), not a continuous-time Poisson sampler. PN/rate mapping is supplied
|
||||
and externally authenticated by the caller; this module never selects odors,
|
||||
rebuilds mappings, rescales rates, clips probabilities, or resets brain RNGs.
|
||||
|
||||
generate_cs_requests lazily uses the original CUDA Torch random operation.
|
||||
CPU mocks test its contract only, not CUDA sequence equivalence. A full
|
||||
(steps, PN-columns) matrix is drawn once, then the explicit half-open CS
|
||||
window masks requests. A 100 ms CS in a 120 ms epoch is NOT the full old L
|
||||
120 ms input. Different draw shapes need not share identical random prefixes.
|
||||
The caller must register source/rate authority, device/environment, budgets,
|
||||
stream aliases, and a separate bounded CUDA equivalence test before real use.
|
||||
Returned request/source arrays are owned read-only copies, not immutable
|
||||
capabilities; the consumer must recheck their recorded hashes before use.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
N_NEURONS = 166700
|
||||
L_SOURCE_SHA = 'e97d58d8c1f44920d0e953f8f5831ddc86fa0198784e86460e315de4116ac553'
|
||||
AK_ENCODER_SHA = '0061689abb1832e963718bd836bc8a50009ee38b258143a753b4d59e63414925'
|
||||
|
||||
|
||||
def require(value, message):
|
||||
if not value:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def array_identity(value):
|
||||
return dict(dtype=value.dtype.str, shape=list(value.shape), order='C-logical-bytes',
|
||||
sha256=hashlib.sha256(value.tobytes(order='C')).hexdigest())
|
||||
|
||||
|
||||
def _vector(value, dtype, name):
|
||||
require(isinstance(value, np.ndarray) and value.dtype == np.dtype(dtype) and value.ndim == 1,
|
||||
name + ' must be an exact one-dimensional ' + np.dtype(dtype).name + ' array')
|
||||
|
||||
|
||||
def _targets(value):
|
||||
_vector(value, np.int32, 'targets')
|
||||
require(np.all((value >= 0) & (value < N_NEURONS)), 'Target outside canonical global neuron range')
|
||||
require(len(np.unique(value)) == len(value), 'Duplicate PN columns rejected; never merge rates or deduplicate RNG columns')
|
||||
|
||||
|
||||
def _clock(steps, start, stop):
|
||||
require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max, 'steps must be a positive int32-range integer')
|
||||
require(type(start) is type(stop) is int and 0 <= start <= stop <= steps, 'Invalid explicit half-open CS tick window')
|
||||
|
||||
|
||||
def probabilities_f32(targets, rates_hz, *, dt_ms=0.1):
|
||||
"""Preserve the L expression's separate float32 multiply then divide."""
|
||||
_targets(targets); _vector(rates_hz, np.float32, 'rates_hz')
|
||||
require(rates_hz.shape == targets.shape and np.all(np.isfinite(rates_hz)) and np.all(rates_hz >= 0), 'Invalid PN rate vector')
|
||||
require(type(dt_ms) in (int, float) and dt_ms == 0.1, 'Only the frozen dt_ms=0.1 contract is supported')
|
||||
probability = np.divide(np.multiply(rates_hz, np.float32(dt_ms), dtype=np.float32), np.float32(1000.0), dtype=np.float32)
|
||||
original = rates_hz * dt_ms / 1000.0
|
||||
require(original.dtype == np.float32 and original.tobytes() == probability.tobytes(), 'Installed NumPy changed the frozen rate expression')
|
||||
require(np.all(np.isfinite(probability)) and np.all((probability >= 0) & (probability <= 1)), 'Probability outside [0,1]; no clipping or rate adjustment')
|
||||
return probability
|
||||
|
||||
|
||||
def forced_from_uniforms(uniforms, probability):
|
||||
"""Pure arithmetic fixture helper, NOT a replacement CUDA generator."""
|
||||
_vector(probability, np.float32, 'probability')
|
||||
require(isinstance(uniforms, np.ndarray) and uniforms.dtype == np.float32 and uniforms.ndim == 2 and
|
||||
uniforms.shape[1] == len(probability), 'Uniform fixture requires exact float32 [ticks,columns]')
|
||||
require(np.all(np.isfinite(uniforms)) and np.all((uniforms >= 0) & (uniforms < 1)) and
|
||||
np.all(np.isfinite(probability)) and np.all((probability >= 0) & (probability <= 1)), 'Invalid uniform/probability fixture')
|
||||
return uniforms < probability[None, :]
|
||||
|
||||
|
||||
def requests_from_forced(forced, targets, *, cs_start_tick, cs_stop_tick):
|
||||
"""Sort resulting requests only; never reorder the pre-sampling columns."""
|
||||
_targets(targets)
|
||||
require(isinstance(forced, np.ndarray) and forced.dtype == np.bool_ and forced.ndim == 2 and
|
||||
forced.shape[1] == len(targets), 'Forced samples must be Boolean [steps,original PN columns]')
|
||||
steps = forced.shape[0]; _clock(steps, cs_start_tick, cs_stop_tick)
|
||||
ticks, columns = np.nonzero(forced[cs_start_tick:cs_stop_tick])
|
||||
ticks = ticks.astype(np.int32) + np.int32(cs_start_tick)
|
||||
neurons = targets[columns].copy()
|
||||
order = np.lexsort((neurons, ticks))
|
||||
requests = {'tick': ticks[order], 'neuron_idx': neurons[order]}
|
||||
validate_requests(requests, steps)
|
||||
for value in requests.values(): value.setflags(write=False)
|
||||
generated = int(np.count_nonzero(forced)); retained = len(ticks)
|
||||
return dict(requests=requests, audit=dict(steps=steps, CS_window=[cs_start_tick, cs_stop_tick],
|
||||
full_matrix_boolean_hits=generated, retained_CS_requests=retained,
|
||||
outside_CS_boolean_hits_masked=generated - retained,
|
||||
original_PN_column_order_preserved=True, sorted_only_after_sampling=True,
|
||||
duplicate_requests_removed=0, probabilities_clipped=False,
|
||||
returned_requests_read_only=True, consumer_must_recheck_array_hashes=True,
|
||||
forced_identity=array_identity(forced), targets_identity=array_identity(targets),
|
||||
requests_identity={k: array_identity(v) for k, v in requests.items()}))
|
||||
|
||||
|
||||
def _load_torch():
|
||||
import torch
|
||||
return torch
|
||||
|
||||
|
||||
def generate_cs_requests(targets, rates_hz, *, seed, steps, cs_start_tick, cs_stop_tick,
|
||||
device, max_uniform_draws, dt_ms=0.1):
|
||||
"""Future explicit CUDA input generation; no implicit CPU fallback.
|
||||
|
||||
``seed`` is the caller's full semantic seed (Python int in [0,2**63)).
|
||||
The generator is private to this call. No offset/modulo/truncation or
|
||||
global/manual brain seed is used. Aliases must reuse the same complete
|
||||
sampling descriptor, including steps/column order/window, not seed alone.
|
||||
The caller owns authorization, resource admission, and external rate pins.
|
||||
``device`` and ``max_uniform_draws`` must be explicitly supplied. The
|
||||
latter bounds steps * PN columns, not total process/GPU memory. Retaining
|
||||
initial_seed proves only integer delivery, not CUDA high-bit influence.
|
||||
"""
|
||||
require(type(seed) is int and 0 <= seed < 2**63, 'Exact full 63-bit Python integer seed required')
|
||||
_clock(steps, cs_start_tick, cs_stop_tick)
|
||||
probability = probabilities_f32(targets, rates_hz, dt_ms=dt_ms)
|
||||
require(type(device) is str and re.fullmatch('cuda:[0-9]+', device), 'Explicit indexed CUDA device required; no CPU fallback')
|
||||
require(type(max_uniform_draws) is int and max_uniform_draws > 0, 'Explicit positive integer uniform-draw budget required')
|
||||
uniform_draws = steps * len(targets)
|
||||
require(uniform_draws <= max_uniform_draws, 'Full matrix exceeds caller uniform-draw budget; no chunking or clipping')
|
||||
torch = _load_torch()
|
||||
require(torch.cuda.is_available() and torch.get_default_dtype() == torch.float32, 'Original CUDA/default-float32 sampler environment required')
|
||||
actual_device = torch.device(device)
|
||||
generator = torch.Generator(device=actual_device)
|
||||
generator.manual_seed(seed)
|
||||
require(generator.initial_seed() == seed, 'Generator did not retain the complete semantic seed')
|
||||
p_gpu = torch.as_tensor(probability, dtype=torch.float32, device=actual_device)
|
||||
# Exactly one complete draw. No chunking, per-PN loop, or tail-dependent redraw.
|
||||
uniforms = torch.rand((steps, len(targets)), generator=generator, device=actual_device)
|
||||
require(uniforms.dtype == torch.float32 and str(uniforms.device) == str(actual_device), 'Sampler dtype/device changed')
|
||||
forced = (uniforms < p_gpu[None, :]).cpu().numpy()
|
||||
result = requests_from_forced(forced, targets, cs_start_tick=cs_start_tick, cs_stop_tick=cs_stop_tick)
|
||||
# Optional identifiers are recorded only when the installed Torch exposes
|
||||
# them. An indexed device name is not presented as a UUID or external pin.
|
||||
identity = {'uuid': None, 'pci_bus_id': None, 'query_error': None}
|
||||
try:
|
||||
properties = torch.cuda.get_device_properties(actual_device)
|
||||
for key in ('uuid', 'pci_bus_id'):
|
||||
value = getattr(properties, key, None)
|
||||
if value is not None: identity[key] = str(value)
|
||||
except (AttributeError, RuntimeError) as exc:
|
||||
identity['query_error'] = type(exc).__name__ + ': ' + str(exc)
|
||||
result['audit'].update(schema='phase4an-conditioning-input-v1-unregistered',
|
||||
seed=seed, seed_bits=63, seed_hex=f'{seed:016x}', generator_initial_seed=generator.initial_seed(),
|
||||
seed_conversion='exact integer; no truncation/modulo/offset', private_generator=True,
|
||||
sampler='Torch CUDA rand default float32; strict U < float32 probability',
|
||||
generator_type=type(generator).__module__ + '.' + type(generator).__qualname__,
|
||||
numpy_version=str(np.__version__), torch_version=str(torch.__version__), torch_cuda_version=torch.version.cuda,
|
||||
execution_device=str(uniforms.device), device_name=str(torch.cuda.get_device_name(actual_device)),
|
||||
device_capability=list(torch.cuda.get_device_capability(actual_device)),
|
||||
device_identity=identity, device_identity_verified_against_external_pin=False,
|
||||
matrix_draw_calls=1, uniform_matrix_shape=[steps, len(targets)], uniform_scalar_draws=uniform_draws,
|
||||
max_uniform_draws=max_uniform_draws, uniform_matrix_bytes=uniform_draws * np.dtype(np.float32).itemsize,
|
||||
budget_scope='Uniform scalar count only, not total process or GPU memory',
|
||||
sampled_before_window_mask=True, dt_ms=dt_ms, dt_float32_identity=array_identity(np.asarray([dt_ms], np.float32)),
|
||||
rate_units='Hz', probability_identity=array_identity(probability), rates_identity=array_identity(rates_hz),
|
||||
input_mapping_authority_verified_by_this_module=False,
|
||||
reference_L_source_sha256=L_SOURCE_SHA, reference_AK_encoder_source_sha256=AK_ENCODER_SHA,
|
||||
full_old_L_input_identity_claimed=False, CUDA_sequence_equivalence_tested_here=False,
|
||||
CUDA_high_seed_bit_sequence_influence_tested_here=False,
|
||||
timing_scope='Full epoch matrix drawn once, then explicit CS window; old L supplied PN requests over its full duration',
|
||||
RNG_prefix_invariance_across_shapes_claimed=False, continuous_time_Poisson_claimed=False,
|
||||
brain_reset_called=False, brain_executed=False, training_executed=False, protocol_registered=False)
|
||||
return result
|
||||
|
||||
|
||||
def validate_requests(requests, steps):
|
||||
_clock(steps, 0, steps)
|
||||
require(type(requests) is dict and set(requests) == {'tick', 'neuron_idx'}, 'Exact sparse request schema required')
|
||||
for key, value in requests.items(): _vector(value, np.int32, key)
|
||||
tick, neuron = requests['tick'], requests['neuron_idx']
|
||||
require(tick.shape == neuron.shape and np.all((tick >= 0) & (tick < steps)) and
|
||||
np.all((neuron >= 0) & (neuron < N_NEURONS)), 'Requests outside local clock/global neuron range')
|
||||
keys = tick.astype(np.int64) * N_NEURONS + neuron
|
||||
require(len(keys) < 2 or np.all(keys[1:] > keys[:-1]), 'Requests must already be sorted unique; no silent sorting/deduplication')
|
||||
return keys
|
||||
|
||||
|
||||
class RequestConflictError(ValueError):
|
||||
def __init__(self, audit):
|
||||
super().__init__('CS/US request overlap rejected; explicit boolean_union is required to combine it')
|
||||
self.audit = audit
|
||||
|
||||
|
||||
def merge_requests(cs, us, *, steps, conflict_policy='error'):
|
||||
"""Compose two valid streams, recording every cross-source collision.
|
||||
|
||||
boolean_union explicitly reproduces a single Boolean request per neuron /
|
||||
tick, not two deliveries. Separate source attempts are retained in audit
|
||||
and uint8 source_mask (CS=1, US=2, overlap=3). This is not refractory
|
||||
acceptance, actual spiking, an artificial D value, or an update rule.
|
||||
"""
|
||||
require(conflict_policy in ('error', 'boolean_union'), 'Unknown explicit conflict policy')
|
||||
cs_keys, us_keys = validate_requests(cs, steps), validate_requests(us, steps)
|
||||
overlap = np.intersect1d(cs_keys, us_keys, assume_unique=True)
|
||||
conflicts = [dict(tick=int(k // N_NEURONS), neuron_idx=int(k % N_NEURONS), CS_attempts=1, US_attempts=1,
|
||||
combined_boolean_requests=1 if conflict_policy == 'boolean_union' else None) for k in overlap]
|
||||
audit = dict(schema='phase4an-request-composition-v1-unregistered', policy=conflict_policy,
|
||||
CS_attempts=len(cs_keys), US_attempts=len(us_keys), cross_source_conflict_count=len(overlap), conflicts=conflicts,
|
||||
duplicate_within_source_allowed=False, refractory_acceptance_evaluated=False, actual_spikes_evaluated=False,
|
||||
input_identity={name: {k: array_identity(v) for k, v in values.items()} for name, values in (('CS', cs), ('US', us))})
|
||||
if len(overlap) and conflict_policy == 'error': raise RequestConflictError(audit)
|
||||
keys = np.union1d(cs_keys, us_keys)
|
||||
sources = np.zeros(len(keys), np.uint8)
|
||||
sources[np.searchsorted(keys, cs_keys)] |= np.uint8(1)
|
||||
sources[np.searchsorted(keys, us_keys)] |= np.uint8(2)
|
||||
requests = {'tick': (keys // N_NEURONS).astype(np.int32), 'neuron_idx': (keys % N_NEURONS).astype(np.int32)}
|
||||
validate_requests(requests, steps)
|
||||
for value in (*requests.values(), sources): value.setflags(write=False)
|
||||
audit.update(output_requests=len(keys), explicit_boolean_overlap_collapses=len(overlap),
|
||||
requests_identity={k: array_identity(v) for k, v in requests.items()}, source_mask_identity=array_identity(sources),
|
||||
returned_arrays_read_only=True, consumer_must_recheck_array_hashes=True,
|
||||
learning_or_D_command_defined=False, brain_executed=False, protocol_registered=False)
|
||||
return dict(requests=requests, source_mask=sources, audit=audit)
|
||||
+247
@@ -0,0 +1,247 @@
|
||||
"""Pure CPU comparisons for a separately authorized, finite input/update test.
|
||||
|
||||
No files, checkpoint seals, source pins, parent trees or GPU are read here.
|
||||
The coordinator must first verify subprocess receipts, externally pinned
|
||||
checkpoint manifests, registered source/input files and learned-parent chains.
|
||||
It must independently recompute update receipts with conditioning_update's
|
||||
verify_receipt. These functions bind supplied arrays and declared metadata;
|
||||
they cannot replace those external checks or prove functional learning.
|
||||
|
||||
The frozen engineering comparator is reused unchanged. Reference diagnostics
|
||||
must be complete. Light has events, final state and queues, not per-tick V/g
|
||||
or APL histories; missing light observations are never fabricated as exact.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
|
||||
import engineering_compare as C
|
||||
|
||||
|
||||
N, E, N_KC, CANDIDATE_COUNT = 166700, 25582938, 4064, 14232
|
||||
COMPARATOR_SHA256 = "5a2403f11bc08d65a9d242ac2cf9686b850e0c3680d65659f785e9b6b50c0238"
|
||||
PHASE4J_SHA256 = "abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e"
|
||||
ELIGIBILITY_BRIDGE_SHA256 = "174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61"
|
||||
MASK_SHA256 = "8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b"
|
||||
AM_OVERLAY_SHA256 = "7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4"
|
||||
MANIFEST_FIELDS = frozenset(("schema", "overlay_name", "overlay_sha256", "mask_path", "mask_sha256",
|
||||
"base_candidate_path", "base_candidate_sha256", "aj_model_dir", "am_overlay_path", "am_overlay_sha256",
|
||||
"parent_snapshot_sha256", "eligibility_v2_source_sha256"))
|
||||
|
||||
|
||||
def _require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest()
|
||||
|
||||
|
||||
def _valid_sha(value):
|
||||
return type(value) is str and re.fullmatch("[0-9a-f]{64}", value) is not None
|
||||
|
||||
|
||||
def _evidence(value):
|
||||
return dict(dtype=value.dtype.str, shape=list(value.shape), nbytes=int(value.nbytes),
|
||||
raw_c_order_sha256=hashlib.sha256(value.tobytes(order="C")).hexdigest())
|
||||
|
||||
|
||||
def _integer(mapping, key, expected):
|
||||
return type(mapping.get(key)) is int and mapping[key] == expected
|
||||
|
||||
|
||||
def _request_input(value, steps):
|
||||
_require(isinstance(value, np.ndarray) and value.dtype == np.int32 and value.ndim == 2 and value.shape[1] == 2,
|
||||
"Expected input requests must be exact int32 [M,2] (local tick, global neuron)")
|
||||
_require(np.all((value[:, 0] >= 0) & (value[:, 0] < steps)) and
|
||||
np.all((value[:, 1] >= 0) & (value[:, 1] < N)), "Expected input request is out of range")
|
||||
keys = value[:, 0].astype(np.int64) * N + value[:, 1]
|
||||
_require(len(keys) < 2 or np.all(keys[1:] > keys[:-1]), "Expected requests must already be sorted unique")
|
||||
|
||||
|
||||
def compare_epoch_pair(reference_record, light_record, *, expected_requests, expected_steps,
|
||||
expected_effective_weight_sha256):
|
||||
"""Compare fresh reference/light records, not paths or self-asserted seals.
|
||||
|
||||
Inputs are dictionaries returned by frozen engineering_compare.load_run.
|
||||
The coordinator supplies the original registered request array and the
|
||||
independently expected effective-weight raw SHA, not a value copied from
|
||||
the summary being checked. Corrupt array schemas raise ValueError. Valid
|
||||
but different records return numerical_exact/pass_all=False. pass_all is
|
||||
ONLY the supplied-record identity check, not a learning or activity gate.
|
||||
"""
|
||||
_require(type(expected_steps) is int and 0 < expected_steps <= np.iinfo(np.int32).max,
|
||||
"Explicit expected epoch steps required")
|
||||
_require(_valid_sha(expected_effective_weight_sha256), "Expected effective-weight raw SHA is required")
|
||||
_request_input(expected_requests, expected_steps)
|
||||
_require(type(reference_record) is dict and type(light_record) is dict,
|
||||
"Supply already authenticated loaded records, not paths")
|
||||
C.validate_run(reference_record)
|
||||
C.validate_run(light_record)
|
||||
inherited = C.compare_fresh(light_record, reference_record)
|
||||
checks = dict(inherited["checks"])
|
||||
for label, record, mode in (("reference", reference_record, "reference"), ("light", light_record, "light")):
|
||||
summary = record.get("summary")
|
||||
_require(type(summary) is dict, "Missing runtime summary")
|
||||
checks[label + ".full_population"] = len(record["state"]["v"]) == N and _integer(summary, "nodes", N) and _integer(summary, "edges", E)
|
||||
checks[label + ".expected_clock"] = (record["steps"] == expected_steps and record["global_tick_offset"] == 0 and
|
||||
_integer(summary, "steps", expected_steps) and _integer(summary, "completed_ticks", expected_steps) and
|
||||
_integer(summary, "explicit_steps_executed", expected_steps))
|
||||
checks[label + ".fresh_fixed_weight_scope"] = (summary.get("checkpoint_continuation_used") is False and
|
||||
summary.get("within_trial_weight_updates") is False and summary.get("hidden_early_stop") is False and
|
||||
summary.get("entire_neural_trial_executed") is True and summary.get("record_history") is False and
|
||||
summary.get("parent_checkpoint_manifest_sha256") is None)
|
||||
checks[label + ".mode_precision_delay"] = (summary.get("diagnostic_mode") == mode and
|
||||
summary.get("precision") == "float64" and summary.get("apl_precision") == "float32" and
|
||||
_integer(summary, "apl_delay_steps", 18) and summary.get("state_units") == "SI volts")
|
||||
checks[label + ".complete_reported_finite"] = (summary.get("complete") is True and summary.get("pass_all") is True and
|
||||
summary.get("all_finite") is True and _integer(summary, "first_failure_tick", -1))
|
||||
checks[label + ".effective_weight_authority"] = summary.get("effective_weights_raw_sha256") == expected_effective_weight_sha256
|
||||
checks[label + ".input_request_array"] = np.array_equal(record["requests"][:, :2], expected_requests.astype(np.int64))
|
||||
checks[label + ".request_and_spike_accounting"] = (summary.get("counts_match_sparse_events") is True and
|
||||
_integer(summary, "total_spikes", len(record["state"]["step"])) and
|
||||
_integer(summary, "requested", len(expected_requests)) and
|
||||
_integer(summary, "accepted_requests", int(record["requests"][:, 3].sum(dtype=np.int64))) and
|
||||
_integer(summary, "rejected_requests", int(record["requests"][:, 4].sum(dtype=np.int64))))
|
||||
checks[label + ".recorded_full_APL_terminal"] = (record.get("terminal_origin") == "recorded_terminal_queues" and
|
||||
record["terminal"]["apl_pending"].shape == (18, N_KC))
|
||||
full = reference_record
|
||||
full_summary = full["summary"]
|
||||
checks["reference.full_diagnostics"] = (set(full["traces"]) == set(C.TRACE_FILES) and
|
||||
full.get("state_hashes") is not None and full.get("queues") is not None and
|
||||
all(full_summary.get(key) is True for key in
|
||||
("full_APL_history_recorded", "full_tick_state_hashes_recorded", "full_queue_hashes_recorded", "full_sequence_bitwise_audit_performed")))
|
||||
if full.get("queues") is not None:
|
||||
checks["reference.queue_summary_accounting"] = (
|
||||
_integer(full_summary, "arriving_edges", sum(row["count"] for row in full["queues"])) and
|
||||
_integer(full_summary, "accepted_arriving_edges", sum(row["accepted_count"] for row in full["queues"])))
|
||||
else:
|
||||
checks["reference.queue_summary_accounting"] = False
|
||||
light_summary = light_record["summary"]
|
||||
checks["light.declared_recording_scope"] = (not light_record["traces"] and
|
||||
light_record.get("state_hashes") is None and light_record.get("queues") is None and
|
||||
all(light_summary.get(key) is False for key in
|
||||
("full_APL_history_recorded", "full_tick_state_hashes_recorded", "full_queue_hashes_recorded", "full_sequence_bitwise_audit_performed")))
|
||||
for label, summary in (("reference", full_summary), ("light", light_summary)):
|
||||
inputs = summary.get("input_sha256")
|
||||
checks[label + ".input_hash_schema"] = (type(inputs) is dict and
|
||||
set(inputs) == {"base_weights.npy", "coefficients.json", "requests.npz", "spec.json"} and
|
||||
all(_valid_sha(value) for value in inputs.values()))
|
||||
for name in ("base_weights.npy", "coefficients.json"):
|
||||
left = full_summary.get("input_sha256", {}).get(name)
|
||||
right = light_summary.get("input_sha256", {}).get(name)
|
||||
checks["pair.input." + name] = _valid_sha(left) and left == right
|
||||
for name in ("arriving_edges", "accepted_arriving_edges"):
|
||||
checks["pair.summary." + name] = (type(full_summary.get(name)) is int and
|
||||
type(light_summary.get(name)) is int and full_summary[name] == light_summary[name])
|
||||
checks = {key: bool(value) for key, value in checks.items()}
|
||||
exact = all(checks.values())
|
||||
return dict(schema="conditioning-preflight-epoch-comparison-v1", numerical_exact=exact, pass_all=exact,
|
||||
status="EXACT-RECORDED-EPOCH-PAIR-NOT-LEARNING" if exact else "FAIL-EPOCH-COMPARISON",
|
||||
checks=checks, failed_checks=[key for key, ok in checks.items() if not ok],
|
||||
expected_steps=expected_steps, expected_requests=_evidence(expected_requests),
|
||||
expected_effective_weight_raw_sha256=expected_effective_weight_sha256,
|
||||
recorded_pair_availability=inherited["reference_comparisons_available"],
|
||||
activity=inherited["activity"], nonvacuous_activity=inherited["nonvacuous_activity"],
|
||||
activity_is_descriptive_not_a_new_gate=True,
|
||||
full_reference_diagnostics_required=True, light_intermediate_states_observed=False,
|
||||
light_per_tick_V_g_ref_identity_claimed=False, independent_finite_latch_replay_performed=False,
|
||||
independent_CSR_queue_reconstruction_performed=False,
|
||||
independent_wholebrain_CPU_oracle_performed=False,
|
||||
expected_comparator_source_sha256=COMPARATOR_SHA256, source_pin_verified_here=False,
|
||||
external_file_seals_process_and_input_authority_verified_here=False,
|
||||
input_generator_or_parent_delta_chain_verified_here=False,
|
||||
learning_pass_claimed=False, candidate_generated=False,
|
||||
scope="Supplied fresh epoch records and original requests only; prerequisite external checkpoint/source/parent verification belongs to coordinator")
|
||||
|
||||
|
||||
def _delta(value, name):
|
||||
_require(isinstance(value, np.ndarray) and value.dtype == np.float32 and value.shape == (CANDIDATE_COUNT,) and
|
||||
np.isfinite(value).all(), name + " must be the full finite float32 candidate delta vector")
|
||||
|
||||
|
||||
def audit_delta_binding(delta_before, delta_after, installed_delta, *, update_receipt,
|
||||
parent_snapshot_sha256, child_manifest):
|
||||
"""Bind already computed/installed arrays to receipt and learned metadata.
|
||||
|
||||
parent_snapshot_sha256 means the actual PARENT OVERLAY NPZ file hash,
|
||||
externally established by the coordinator, NOT a checkpoint manifest hash
|
||||
and NOT a raw delta-array hash. installed_delta must come from the child
|
||||
overlay subsequently checked by frozen runtime_light.load_overlay.
|
||||
|
||||
This function does not read those files, authenticate anatomical IDs,
|
||||
recompute eligibility-v2, establish clipping bounds, or prove a causal
|
||||
behavioral effect. Call conditioning_update.verify_receipt separately
|
||||
against actual sealed epoch events and complete pinned mappings.
|
||||
"""
|
||||
for name, value in (("delta_before", delta_before), ("delta_after", delta_after), ("installed_delta", installed_delta)):
|
||||
_delta(value, name)
|
||||
_require(_valid_sha(parent_snapshot_sha256), "Externally established parent overlay FILE SHA is required")
|
||||
_require(type(update_receipt) is dict and type(child_manifest) is dict, "Receipt and manifest must be dictionaries")
|
||||
_require(type(update_receipt.get("input_arrays")) is dict and type(update_receipt.get("outputs")) is dict and
|
||||
type(update_receipt.get("source_pins")) is dict and type(update_receipt.get("command")) is dict and
|
||||
type(update_receipt.get("semantics")) is dict and type(update_receipt.get("counts")) is dict,
|
||||
"Incomplete epoch update receipt schema")
|
||||
before, after, installed = (_evidence(value) for value in (delta_before, delta_after, installed_delta))
|
||||
command = update_receipt["command"]
|
||||
counts = update_receipt["counts"]
|
||||
checks = {
|
||||
"receipt.self_hash": update_receipt.get("payload_sha256") == _sha({k: v for k, v in update_receipt.items() if k != "payload_sha256"}),
|
||||
"receipt.schema": update_receipt.get("schema") == "phase4an-conditioning-update-draft-v1" and
|
||||
update_receipt.get("status") == "DRAFT-COMPUTED-EPOCH-UPDATE-NOT-INSTALLED",
|
||||
"receipt.original_source_pins": update_receipt["source_pins"] == {
|
||||
"eligibility_bridge_sha256": ELIGIBILITY_BRIDGE_SHA256, "phase4j_sha256": PHASE4J_SHA256},
|
||||
"receipt.delta_before": _canonical(update_receipt["input_arrays"].get("delta_before")) == _canonical(before),
|
||||
"receipt.delta_after": _canonical(update_receipt["outputs"].get("delta_after")) == _canonical(after),
|
||||
"computed_installed_delta_bitwise": C.bit_equal(delta_after, installed_delta),
|
||||
"delta.nonpositive": bool(np.all(delta_before <= 0) and np.all(delta_after <= 0)),
|
||||
"delta.no_potentiation": bool(np.all(delta_after <= delta_before)),
|
||||
"receipt.candidate_count": _integer(counts, "candidates", CANDIDATE_COUNT),
|
||||
"receipt.changed_bit_count": _integer(counts, "actual_bitwise_changed_edges", int(np.count_nonzero(delta_before.view(np.uint32) != delta_after.view(np.uint32)))),
|
||||
"receipt.changed_value_count": _integer(counts, "actual_value_changed_edges", int(np.count_nonzero(delta_before != delta_after))),
|
||||
"receipt.explicit_scheduled_D": type(command.get("D_scheduled")) is float and command["D_scheduled"] in (0.0, 1.0) and
|
||||
type(command.get("frozen")) is bool,
|
||||
"receipt.epoch_boundary_only": update_receipt["semantics"].get("application") == "epoch_boundary_after_all_recorded_ticks; affects subsequent epoch only" and
|
||||
update_receipt["semantics"].get("delta_dtype") == "float32" and update_receipt["semantics"].get("delta_units") == "mV",
|
||||
"receipt.not_a_DAN_measurement": update_receipt["semantics"].get("D_definition") == "artificial scheduled scalar" and
|
||||
update_receipt["semantics"].get("actual_DAN_delivery_observed") is False and
|
||||
update_receipt["semantics"].get("actual_DAN_delivery_used_to_gate") is False,
|
||||
"manifest.exact_fields": set(child_manifest) == MANIFEST_FIELDS,
|
||||
"manifest.learned_schema": child_manifest.get("schema") == "phase4an-overlay-v1" and child_manifest.get("overlay_name") == "learned",
|
||||
"manifest.parent_overlay_file_sha": child_manifest.get("parent_snapshot_sha256") == parent_snapshot_sha256,
|
||||
"manifest.frozen_rule_mask_helper": child_manifest.get("eligibility_v2_source_sha256") == PHASE4J_SHA256 and
|
||||
child_manifest.get("mask_sha256") == MASK_SHA256 and child_manifest.get("am_overlay_sha256") == AM_OVERLAY_SHA256,
|
||||
"manifest.declared_file_hashes": all(_valid_sha(child_manifest.get(key)) for key in ("overlay_sha256", "base_candidate_sha256")),
|
||||
"manifest.declared_paths": all(type(child_manifest.get(key)) is str and bool(child_manifest[key])
|
||||
for key in ("mask_path", "base_candidate_path", "aj_model_dir", "am_overlay_path")),
|
||||
}
|
||||
no_update = command.get("frozen") is True or command.get("D_scheduled") == 0.0
|
||||
checks["zero_D_or_frozen_identity"] = not no_update or C.bit_equal(delta_before, delta_after)
|
||||
for name in ("weights_installed", "base_modified", "brain_dynamics_executed", "scientific_learning_pass_claimed", "candidate_generated"):
|
||||
checks["receipt.scope." + name] = update_receipt.get(name) is False
|
||||
checks = {key: bool(value) for key, value in checks.items()}
|
||||
exact = all(checks.values())
|
||||
return dict(schema="conditioning-preflight-delta-binding-v1", pass_all=exact,
|
||||
status="EXACT-DELTA-ARRAY-BINDING-NOT-LEARNING" if exact else "FAIL-DELTA-BINDING",
|
||||
checks=checks, failed_checks=[key for key, ok in checks.items() if not ok],
|
||||
delta_before=before, computed_delta_after=after, installed_delta=installed,
|
||||
parent_overlay_file_sha256=parent_snapshot_sha256,
|
||||
declared_child_overlay_file_sha256=child_manifest.get("overlay_sha256"),
|
||||
update_receipt_payload_sha256=update_receipt.get("payload_sha256"),
|
||||
actual_changed_edges=int(np.count_nonzero(delta_before != delta_after)),
|
||||
actual_bitwise_changed_edges=int(np.count_nonzero(delta_before.view(np.uint32) != delta_after.view(np.uint32))),
|
||||
delta_nonzero_or_change_is_not_learning_evidence=True,
|
||||
eligibility_v2_independently_recomputed_here=False, full_mask_identity_verified_here=False,
|
||||
candidate_order_or_compartment_mapping_verified_here=False, floor_bound_independently_verified_here=False,
|
||||
file_bytes_and_parent_snapshot_authority_verified_here=False, installation_in_runtime_verified_here=False,
|
||||
actual_DAN_delivery_verified_here=False, learning_pass_claimed=False, candidate_generated=False,
|
||||
scope="Supplied float32 mV arrays and declared parent/update metadata only; coordinator must verify real files, v2 recomputation and next-epoch installation")
|
||||
+288
@@ -0,0 +1,288 @@
|
||||
"""UNREGISTERED bounded preflight input planning, not a conditioning runner.
|
||||
|
||||
Only the explicitly called generate_epoch_inputs performs random sampling; it
|
||||
delegates to conditioning_inputs' single full-shape CUDA draw. The plan and
|
||||
static rate loader do no sampling, neural execution, plasticity, or I/O writes.
|
||||
The coordinator owns registration, full seals, graph/overlay authority, device
|
||||
UUID admission, and every epoch update/checkpoint receipt.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import copy
|
||||
import hashlib
|
||||
import io
|
||||
import json
|
||||
from pathlib import Path, PurePosixPath
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
|
||||
import conditioning_inputs as inputs
|
||||
import trial_plan
|
||||
|
||||
|
||||
MASTER = 20260930
|
||||
BLOCK = 'physiological__PAM15__reinforce_A__rep01'
|
||||
SCHEMA = 'phase4an-conditioning-preflight-inputs-v1-unregistered'
|
||||
STATIC = 'experiments/phase4l-olfactory-grounding/'
|
||||
SOURCE_REL = 'models/male-cns-lif/phase4l.py'
|
||||
CONFIG_REL = 'models/male-cns-lif/olfactory-grounding-v1/phase4l.yaml'
|
||||
SOURCE_PINS = {
|
||||
SOURCE_REL: 'e97d58d8c1f44920d0e953f8f5831ddc86fa0198784e86460e315de4116ac553',
|
||||
CONFIG_REL: '9b8bcb7e031bf9063033a1854a439a7cd0bf6730301c2d2d02ca99a76f098d6e',
|
||||
STATIC + 'odor-encoder/frozen-odor-panel.parquet': '907f16a56e024a8e2112b5751e53b0e6037b329fa110098af3d31b119c10a1a0',
|
||||
STATIC + 'odor-encoder/mapped-glomerular-vectors.npz': 'f057563152e0fa22060080af13019f319e7f36563a775eff829bbc1929c451b0',
|
||||
STATIC + 'odor-encoder/selection.json': '1b832becf9d08935716fe9ba5e3cd3e619e7d208dfb43c4fa197c969d095f113',
|
||||
STATIC + 'receptor-mapping/olfactory-mapping.parquet': 'a01d27000533e9844458a4c559cac084f1c34ca20a1104e97e6a0f72b0cc93b1',
|
||||
}
|
||||
|
||||
|
||||
def require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(',', ':'), ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return hashlib.sha256(_canonical(value).encode('utf-8')).hexdigest()
|
||||
|
||||
|
||||
def build_preflight_plan():
|
||||
"""Rebuild the whole semantic plan, then select by rule, never outcomes.
|
||||
|
||||
D1/D0/frozen share the two paired input epochs. Every neural epoch starts
|
||||
fresh; only the externally verified plastic delta can cross its boundary.
|
||||
This inventory does not enable the complete 68,000-epoch protocol.
|
||||
"""
|
||||
full = trial_plan.build_trial_plan(MASTER)
|
||||
trial_plan.validate_trial_plan(full)
|
||||
block = full['plan']['blocks'][0]
|
||||
require(block['block_id'] == BLOCK and block['mode'] == 'physiological' and
|
||||
block['dan_type'] == 'PAM15' and block['compartment_verbatim'] == "y5B'2a", 'First fixed block changed')
|
||||
suite = next(s for s in block['suites'] if s['assay'] == 'normal')
|
||||
paired = next(a for a in suite['arms'] if a['arm'] == 'paired')
|
||||
first = next(i for i, epoch in enumerate(paired['epochs']) if epoch['us']['requested'])
|
||||
require(first + 1 < len(paired['epochs']), 'Requested US lacks its following complete epoch')
|
||||
epochs = copy.deepcopy(paired['epochs'][first:first + 2])
|
||||
calls = []
|
||||
for arm in ('D1', 'D0', 'frozen'):
|
||||
for mode in ('reference', 'light'):
|
||||
previous = None
|
||||
for position, epoch in enumerate(epochs):
|
||||
_validate_epoch(epoch)
|
||||
row_id = f'{arm}--{mode}--epoch-{position:03d}'
|
||||
calls.append(dict(row_id=row_id, arm=arm, diagnostic_mode=mode,
|
||||
input_epoch_index=position, epoch_id=epoch['epoch_id'],
|
||||
full_plan_epoch_index=epoch['epoch_index'], steps=1200,
|
||||
d_command=0.0 if arm == 'D0' else epoch['D_command'],
|
||||
frozen=arm == 'frozen', previous_delta_row_id=previous,
|
||||
neural_state='fresh_rest_zero_ref_zero_spike_queue_zero_APL_queue',
|
||||
input_sharing='same saved CS and US arrays across arms and diagnostic modes'))
|
||||
previous = row_id
|
||||
result = dict(schema=SCHEMA, master=MASTER, block_id=BLOCK, mode='physiological',
|
||||
dan_type='PAM15', compartment_verbatim="y5B'2a", full_trial_plan=full,
|
||||
selected_epochs=epochs, calls=calls, total_calls=12, total_ticks=14400,
|
||||
input_generation_calls=2, input_generation_executed=False, brain_runs_executed=0,
|
||||
complete_conditioning_authorized=False, training_result_claimed=False,
|
||||
selection_rule='first normal paired requested US epoch in first fixed block, plus its immediate successor')
|
||||
result['payload_sha256'] = _sha(result)
|
||||
return result
|
||||
|
||||
|
||||
def validate_preflight_plan(value):
|
||||
require(type(value) is dict and _canonical(value) == _canonical(build_preflight_plan()),
|
||||
'Preflight inventory differs from full deterministic reconstruction')
|
||||
return True
|
||||
|
||||
|
||||
def _validate_epoch(epoch):
|
||||
require(type(epoch) is dict and epoch.get('steps') == 1200 and epoch.get('duration_ms') == 120 and
|
||||
epoch.get('initial_neural_state') == 'frozen_reset_including_KC_trace_and_both_delay_queues',
|
||||
'Only complete fresh normal 120 ms epochs are in this preflight')
|
||||
cs, us = epoch.get('cs'), epoch.get('us')
|
||||
require(type(cs) is dict and cs.get('cue') in ('A', 'B') and cs.get('odor') == trial_plan.ODORS[cs['cue']] and
|
||||
type(cs.get('seed')) is int and 0 <= cs['seed'] < 2**63 and cs.get('seed_bits') == 63 and
|
||||
cs.get('start_tick') == 0 and cs.get('stop_tick_exclusive') == 1000, 'CS semantic descriptor changed')
|
||||
require(type(us) is dict and type(us.get('requested')) is bool and us.get('nominal_slot_tick') == 900 and
|
||||
us.get('rng_seed') is None and us.get('rng_draws') == 0, 'US must remain a deterministic scheduled pulse')
|
||||
require((us['requested'] and us.get('tick') == 900 and us.get('request_rule') == 'fixed_single_pulse_all_bound_DAN_targets') or
|
||||
(not us['requested'] and us.get('tick') is None and us.get('request_rule') == 'none'), 'US timing/request rule changed')
|
||||
|
||||
|
||||
def _extract_drive(raw, filename):
|
||||
"""Compile only the SHA-pinned pure pn_drive AST; never import old L."""
|
||||
import pandas as pd
|
||||
require(hashlib.sha256(raw).hexdigest() == SOURCE_PINS[SOURCE_REL], 'Frozen L source SHA mismatch')
|
||||
nodes = [n for n in ast.parse(raw, filename=filename).body if isinstance(n, ast.FunctionDef) and n.name == 'pn_drive']
|
||||
require(len(nodes) == 1 and not nodes[0].decorator_list, 'Unique undecorated pn_drive required')
|
||||
namespace = {'np': np, 'pd': pd, 'Any': Any}
|
||||
exec(compile(ast.Module(body=nodes, type_ignores=[]), filename, 'exec'), namespace)
|
||||
return namespace['pn_drive'], hashlib.sha256(ast.dump(nodes[0], include_attributes=False).encode()).hexdigest()
|
||||
|
||||
|
||||
def _rate_tables(selection, panel, vectors, mapping, config, drive):
|
||||
"""Pure static-table derivation, preserving every original PN column."""
|
||||
require(selection.get('selected_before_KC_simulation') is True and len(panel) == 12 and
|
||||
panel.odor_id.is_unique and panel.split.tolist() == ['calibration'] * 6 + ['heldout'] * 6,
|
||||
'Frozen static odor panel schema changed')
|
||||
gloms = selection['glomeruli_order']
|
||||
require(type(gloms) is list and len(gloms) == len(set(gloms)) and len(gloms) > 0 and
|
||||
set(vectors) == set(panel.odor_id), 'Frozen vector/glomerulus inventory mismatch')
|
||||
encoder = config['odor_encoder']
|
||||
require(encoder['max_total_rate_per_glomerulus_hz'] == 400.0 and
|
||||
encoder['modes'] == ['physiological', 'matched_energy'], 'Frozen rate transform changed')
|
||||
median = float(np.median(panel.mapped_response_sum))
|
||||
require(np.isfinite(median) and median > 0, 'Invalid panel response median')
|
||||
tables = {}
|
||||
for cue, name in trial_plan.ODORS.items():
|
||||
selected = selection['ODOR_' + cue]
|
||||
require(selected['odor_name'] == name, 'Selected frozen A/B odor name changed')
|
||||
odor_id = selected['odor_id']
|
||||
positions = np.flatnonzero(panel.odor_id.to_numpy() == odor_id)
|
||||
require(len(positions) == 1, 'Selected odor ID not unique in frozen panel')
|
||||
panel_row = panel.iloc[int(positions[0])]
|
||||
require(panel_row.odor_name == name and panel_row.formal_label == 'ODOR_' + cue, 'Panel/selection label mismatch')
|
||||
vector = vectors[odor_id]
|
||||
require(isinstance(vector, np.ndarray) and vector.dtype == np.float32 and vector.shape == (len(gloms),) and
|
||||
np.isfinite(vector).all() and np.all(vector >= 0), 'Invalid frozen glomerular vector; never clip')
|
||||
targets, rates, frame = drive(odor_id, vector, mapping, gloms, 'physiological', median, config)
|
||||
inputs.probabilities_f32(targets, rates)
|
||||
require(len(targets) > 0 and np.all(rates > 0), 'Empty or nonpositive selected PN drive')
|
||||
require(frame.neuron_idx.to_numpy(np.int32).tobytes() == targets.tobytes() and
|
||||
frame.rate_hz.to_numpy(np.float32).tobytes() == rates.tobytes(), 'Original frame/array ordering mismatch')
|
||||
targets, rates = targets.copy(), rates.copy()
|
||||
targets.setflags(write=False); rates.setflags(write=False)
|
||||
tables[cue] = dict(cue=cue, odor_id=str(odor_id), odor_name=name, mode='physiological',
|
||||
source_panel_row=int(positions[0]), source_panel_split=str(panel_row.split),
|
||||
targets=targets, rates_hz=rates, identities={
|
||||
'targets': inputs.array_identity(targets), 'rates_hz': inputs.array_identity(rates)},
|
||||
glomeruli_order=copy.deepcopy(gloms), mapped_vector_identity=inputs.array_identity(vector))
|
||||
return tables, median
|
||||
|
||||
|
||||
def load_rate_tables(root):
|
||||
"""Read only the six exact static/source files pinned by frozen AK.
|
||||
|
||||
This does not read any historical neural result or rerun odor selection.
|
||||
Source files are checked before parsing; the parsed bytes are the same
|
||||
checked snapshots. Full prior seal/registration authority remains external.
|
||||
"""
|
||||
import pandas as pd
|
||||
import yaml
|
||||
root = Path(root)
|
||||
raw = {rel: (root / rel).read_bytes() for rel in SOURCE_PINS}
|
||||
require(all(hashlib.sha256(value).hexdigest() == SOURCE_PINS[rel] for rel, value in raw.items()),
|
||||
'Frozen static encoder/source SHA mismatch')
|
||||
source, ast_sha = _extract_drive(raw[SOURCE_REL], str(root / SOURCE_REL))
|
||||
selection = json.loads(raw[STATIC + 'odor-encoder/selection.json'])
|
||||
panel = pd.read_parquet(io.BytesIO(raw[STATIC + 'odor-encoder/frozen-odor-panel.parquet']))
|
||||
mapping = pd.read_parquet(io.BytesIO(raw[STATIC + 'receptor-mapping/olfactory-mapping.parquet']))
|
||||
config = yaml.safe_load(raw[CONFIG_REL])
|
||||
with np.load(io.BytesIO(raw[STATIC + 'odor-encoder/mapped-glomerular-vectors.npz']), allow_pickle=False) as values:
|
||||
require(set(values.files) == set(panel.odor_id) | {'glomeruli'} and
|
||||
values['glomeruli'].tolist() == selection['glomeruli_order'], 'Frozen NPZ inventory/order mismatch')
|
||||
vectors = {str(oid): values[oid].copy() for oid in panel.odor_id}
|
||||
tables, median = _rate_tables(selection, panel, vectors, mapping, config, source)
|
||||
return dict(tables=tables, audit=dict(schema=SCHEMA, static_sources=copy.deepcopy(SOURCE_PINS),
|
||||
original_pn_drive_AST_sha256=ast_sha, historical_dynamic_results_read=False,
|
||||
original_function_called_without_PN_reordering=True, mode='physiological',
|
||||
median_mapped_response_sum=median, full_seal_verification_owned_by_coordinator=True,
|
||||
random_draws=0, brain_runs=0, numpy_version=np.__version__, pandas_version=pd.__version__))
|
||||
|
||||
|
||||
def _validate_table(table, cue):
|
||||
require(type(table) is dict and table.get('cue') == cue and table.get('odor_name') == trial_plan.ODORS[cue] and
|
||||
table.get('mode') == 'physiological' and type(table.get('odor_id')) is str and table['odor_id'], 'Rate-table cue identity mismatch')
|
||||
inputs.probabilities_f32(table['targets'], table['rates_hz'])
|
||||
require(table['identities'] == {key: inputs.array_identity(table[key]) for key in ('targets', 'rates_hz')},
|
||||
'Rate-table arrays changed after authority binding')
|
||||
|
||||
|
||||
def generate_epoch_inputs(epoch, tables, dan_targets, *, device, max_uniform_draws, conflict_policy='error'):
|
||||
"""Future explicit sampler, called once per selected input epoch, not arm.
|
||||
|
||||
No brain reset is called. The scheduled US request is identical for D1,
|
||||
D0, and frozen; D-command and update policy belong to the epoch authority.
|
||||
The caller must preserve and share these returned bytes across all arms.
|
||||
"""
|
||||
_validate_epoch(epoch)
|
||||
cs = epoch['cs']; table = tables[cs['cue']]; _validate_table(table, cs['cue'])
|
||||
require(isinstance(dan_targets, np.ndarray) and dan_targets.dtype == np.int32 and dan_targets.ndim == 1 and
|
||||
len(dan_targets) > 0 and np.all((dan_targets >= 0) & (dan_targets < inputs.N_NEURONS)) and
|
||||
(len(dan_targets) < 2 or np.all(dan_targets[1:] > dan_targets[:-1])), 'Externally bound DAN globals must be sorted unique int32')
|
||||
require(conflict_policy in ('error', 'boolean_union'), 'Unknown explicit request composition policy')
|
||||
cs_result = inputs.generate_cs_requests(table['targets'], table['rates_hz'], seed=cs['seed'], steps=1200,
|
||||
cs_start_tick=0, cs_stop_tick=1000, device=device, max_uniform_draws=max_uniform_draws)
|
||||
nodes = dan_targets.copy() if epoch['us']['requested'] else np.empty(0, np.int32)
|
||||
us = {'tick': np.full(len(nodes), 900, np.int32), 'neuron_idx': nodes}
|
||||
for value in us.values(): value.setflags(write=False)
|
||||
composed = inputs.merge_requests(cs_result['requests'], us, steps=1200, conflict_policy=conflict_policy)
|
||||
return dict(requests=composed['requests'], cs_requests=cs_result['requests'], us_requests=us,
|
||||
source_mask=composed['source_mask'], audit=dict(schema=SCHEMA, epoch_id=epoch['epoch_id'],
|
||||
CS_sampler=cs_result['audit'], composition=composed['audit'],
|
||||
DAN_targets_identity=inputs.array_identity(dan_targets), requested_US=epoch['us']['requested'],
|
||||
scheduled_US_tick=epoch['us']['tick'], CS_shared_across_arms=True, US_shared_across_arms=True,
|
||||
US_rng_draws=0, semantic_seed_never_passed_to_brain_reset=True, brain_reset_called=False,
|
||||
source_authority_and_saved_array_rehash_owned_by_coordinator=True))
|
||||
|
||||
|
||||
def derive_case_spec(original, row, epoch, rate_table, *, brain_reset_seed, budget_root, output_budget_bytes):
|
||||
"""Copy an externally authenticated wholegraph spec with explicit changes.
|
||||
|
||||
``seed`` is an independent conservative 31-bit reset-control value, not a
|
||||
conversion of the semantic input seed. The inherited deterministic AJ
|
||||
runtime consumes saved requests, not this input RNG. No checkpoint carry
|
||||
is admitted: the next epoch carries only its separately verified delta.
|
||||
"""
|
||||
_validate_epoch(epoch); _validate_table(rate_table, epoch['cs']['cue'])
|
||||
require(type(original) is dict and original.get('kind') == 'fullgraph' and original.get('dt_ms') == .1 and
|
||||
original.get('apl_delay_steps') == 18 and original.get('precision') == 'float64' and
|
||||
original.get('apl_precision') == 'float32' and original.get('record_history') is False and
|
||||
original.get('within_trial_weight_updates', False) is False, 'Anchored fresh D18 fullgraph spec required')
|
||||
require(not any('checkpoint' in str(k).lower() or k in ('initial_state', 'resume', 'continuation', 'global_tick_offset') for k in original),
|
||||
'No neural checkpoint continuation in this two-epoch preflight')
|
||||
require(type(brain_reset_seed) is int and 0 <= brain_reset_seed < 2**31, 'Independent conservative 31-bit brain reset seed required')
|
||||
require(type(row) is dict and row.get('arm') in ('D1', 'D0', 'frozen') and row.get('diagnostic_mode') in ('reference', 'light') and
|
||||
row.get('epoch_id') == epoch['epoch_id'] and row.get('steps') == 1200 and
|
||||
row.get('frozen') is (row['arm'] == 'frozen') and
|
||||
row.get('d_command') == (0.0 if row['arm'] == 'D0' else epoch['D_command']), 'Row/epoch command mismatch')
|
||||
require(type(row.get('input_epoch_index')) is int and row['input_epoch_index'] in (0, 1) and
|
||||
row.get('row_id') == f"{row['arm']}--{row['diagnostic_mode']}--epoch-{row['input_epoch_index']:03d}", 'Unsafe row identity')
|
||||
require(type(budget_root) is str and budget_root.startswith('/') and not budget_root.startswith('//') and
|
||||
'..' not in PurePosixPath(budget_root).parts and
|
||||
type(output_budget_bytes) is int and output_budget_bytes > 0, 'Explicit absolute output budget required')
|
||||
old = PurePosixPath(original['budget_root']); new = PurePosixPath(budget_root)
|
||||
require(old.is_absolute() and not new.is_relative_to(old) and not old.is_relative_to(new), 'Budget cannot overlap old experiment')
|
||||
result = copy.deepcopy(original)
|
||||
removed = {key: result.pop(key) for key in ('validation_cohort', 'odor_index', 'expected_external_events', 'training') if key in result}
|
||||
cs = epoch['cs']; rates = rate_table['rates_hz']
|
||||
result.update(name=row['row_id'], steps=1200, duration_ms=120.0, max_steps=1200,
|
||||
budget_root=budget_root, output_budget_bytes=output_budget_bytes, seed=brain_reset_seed,
|
||||
cs_input_seed=cs['seed'], cs_input_seed_bits=63,
|
||||
seed_scope='seed is separate reset control; full cs_input_seed used only by saved-input sampler',
|
||||
mode='physiological', odor_id=rate_table['odor_id'], odor_name=rate_table['odor_name'],
|
||||
source_panel_row=rate_table['source_panel_row'], source_panel_split=rate_table['source_panel_split'],
|
||||
rate_sum_hz=float(rates.sum()), target_count=len(rate_table['targets']),
|
||||
expected_CS_requests=float(np.sum(inputs.probabilities_f32(rate_table['targets'], rates), dtype=np.float64) * 1000),
|
||||
CS_window=[0, 1000], within_trial_weight_updates=False,
|
||||
outer_protocol='conditioning_interface_preflight_not_full_conditioning',
|
||||
outer_epoch_id=epoch['epoch_id'], outer_arm=row['arm'])
|
||||
changes = {k: dict(present_before=k in original, before=copy.deepcopy(original.get(k)), after=copy.deepcopy(v))
|
||||
for k, v in result.items() if k not in original or _canonical(v) != _canonical(original[k])}
|
||||
allowed = {'name', 'steps', 'duration_ms', 'max_steps', 'budget_root', 'output_budget_bytes', 'seed',
|
||||
'cs_input_seed', 'cs_input_seed_bits', 'seed_scope', 'mode', 'odor_id', 'odor_name', 'source_panel_row',
|
||||
'source_panel_split', 'rate_sum_hz', 'target_count', 'expected_CS_requests', 'CS_window',
|
||||
'within_trial_weight_updates', 'outer_protocol', 'outer_epoch_id', 'outer_arm'}
|
||||
require(set(changes) <= allowed and all(_canonical(result[k]) == _canonical(v) for k, v in original.items()
|
||||
if k not in allowed and k not in removed), 'Unexpected frozen spec change')
|
||||
return result, dict(schema=SCHEMA, explicit_field_changes=changes, removed_stale_input_metadata=removed,
|
||||
original_spec_sha256=_sha(original), derived_spec_sha256=_sha(result),
|
||||
graph_APL_model_coefficients_and_other_fields_unchanged=True,
|
||||
independent_brain_reset_seed=brain_reset_seed, semantic_input_seed=cs['seed'],
|
||||
neural_state_between_epochs='fresh reset; only plastic delta may carry via separate verified authority',
|
||||
D_and_update_commands_owned_by_epoch_authority=True, random_draws=0, brain_runs=0,
|
||||
outer_learning_pass_claimed=False, full_conditioning_authorized=False)
|
||||
+197
@@ -0,0 +1,197 @@
|
||||
"""Exclusive CPU preparation for the finite AO conditioning-interface gate.
|
||||
|
||||
This is a two-epoch installation test, not acquisition, retention or a learning
|
||||
candidate. Source authority and the complete 12-call plan precede CUDA sampling.
|
||||
Generated requests and learned snapshots receive separate execution receipts;
|
||||
their not-yet-existing hashes are never invented in the registration.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
import engineering_prepare as P
|
||||
import runtime_light as R
|
||||
import biological_contract as B
|
||||
import conditioning_preflight_inputs as I
|
||||
|
||||
ROOT = Path('/data/digital-drosophila')
|
||||
MODEL = Path(__file__).resolve().parent
|
||||
EXP = ROOT / 'experiments/phase4ao-conditioning-interface'
|
||||
AN = ROOT / 'experiments/phase4an-engineering-validation'
|
||||
AN_REG = '7317441126f191ccc6e6d28438870b3921537ebe282a6acb163716a690057945'
|
||||
AN_MANIFEST = 'e8952a48cd3ea6a93c811a1183a1f7b79e19646299924e7688e5f393737a9aa2'
|
||||
AN_FINAL = '16630d0d666a2467c56b647d021cbdb627a553795f6afcd7f7eb496a2737d815'
|
||||
PREP_COMMIT = 'c187d7d5a6047f183467e3d860e2529575dcda97'
|
||||
DRAFT_INVENTORY_SHA = '2c72d47d09b4ad1949dc6cfdc170f64847ff75f95ded5d20b421fb5a75e0eb84'
|
||||
BUDGET = dict(output_budget_bytes=4 * 1024**3, minimum_free_reserve_bytes=10 * 1024**3,
|
||||
max_uniform_draws=2_000_000, child_timeout_seconds=1800)
|
||||
require, read, save, digest = P.require, P.read, P.save, P.digest
|
||||
|
||||
|
||||
def same(a, b):
|
||||
return json.dumps(a, sort_keys=True, allow_nan=False) == json.dumps(b, sort_keys=True, allow_nan=False)
|
||||
|
||||
|
||||
def verify_pins(pins):
|
||||
for name, sha in pins.items():
|
||||
path = Path(name)
|
||||
require(path.is_absolute() and path.is_file() and not path.is_symlink() and
|
||||
digest(path) == sha, 'Pinned file changed: ' + name)
|
||||
|
||||
|
||||
def save_npz(path, **arrays):
|
||||
with Path(path).open('xb') as stream:
|
||||
np.savez_compressed(stream, **arrays)
|
||||
|
||||
|
||||
def source_inventory():
|
||||
# Explicitly snapshot the current lab's text sources, including this new
|
||||
# coordinator and its tests. Existing 21/20 files are never rewritten.
|
||||
paths = sorted([*MODEL.glob('*.py'), *MODEL.glob('*.md')])
|
||||
required = ('conditioning_epoch.py', 'conditioning_preflight_inputs.py',
|
||||
'conditioning_preflight_compare.py', 'conditioning_preflight_prepare.py',
|
||||
'conditioning_preflight_workflow.py', 'test_conditioning_preflight_prepare.py',
|
||||
'CONDITIONING_INTERFACE_PROTOCOL.md')
|
||||
require(all(MODEL / n in paths for n in required), 'Missing complete coordinator/test/protocol source')
|
||||
require(not any(p.is_symlink() for p in paths), 'Linked source is forbidden')
|
||||
return {str(p): digest(p) for p in paths}
|
||||
|
||||
|
||||
def plan_descriptor(full):
|
||||
return {k: v for k, v in full.items() if k not in ('full_trial_plan', 'payload_sha256')}
|
||||
|
||||
|
||||
def prepare():
|
||||
require(sys.dont_write_bytecode, 'Use scientific Python -B')
|
||||
require(EXP.is_dir() and not any(EXP.iterdir()) and not EXP.is_symlink(),
|
||||
'Preparation requires the explicitly created empty AO directory; never overwrite')
|
||||
require(digest(AN / 'manifest.json') == AN_MANIFEST and digest(AN / 'audit/final-status.json') == AN_FINAL,
|
||||
'Externally anchored AN controls changed')
|
||||
an = P.read_registration(AN_REG)
|
||||
helper = P.load_module('phase4ao_original_seal_helper', ROOT / P.AI_HELPER_REL, P.AI_HELPER_SHA)
|
||||
an_seal = helper.verify_seal(AN)
|
||||
am_seal = P.verify_authority(an['anchors'])
|
||||
old = P.verify_old_integrity(an)
|
||||
full = I.build_preflight_plan()
|
||||
rates = I.load_rate_tables(ROOT)
|
||||
populations, biology = B.load_bound_tables(ROOT, an['input_sha256'])
|
||||
pins = dict(an['input_sha256'])
|
||||
pins.update({str(ROOT / k): v for k, v in I.SOURCE_PINS.items()})
|
||||
pins.update(biology['input_sha256'])
|
||||
pins.update({str(AN / 'manifest.json'): AN_MANIFEST, str(AN / 'audit/final-status.json'): AN_FINAL,
|
||||
str(AN / 'protocol/preregistered.json'): AN_REG})
|
||||
bridge = ROOT / 'models/male-cns-lif/wholebrain-plastic-interface-v1/eligibility_bridge.py'
|
||||
phasej = ROOT / 'models/male-cns-lif/mb-physiology-v1/phase4j.py'
|
||||
pins[str(bridge)] = '174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61'
|
||||
pins[str(phasej)] = 'abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e'
|
||||
original_path = Path(an['selected_case']['original_dir']) / 'spec.json'
|
||||
require(str(original_path) in pins, 'Original graph spec not anchored by AN')
|
||||
original = read(original_path)
|
||||
previous = MODEL / 'draft-source-inventory.json'
|
||||
require(digest(previous) == DRAFT_INVENTORY_SHA, 'Previously delivered 20-file source inventory changed')
|
||||
inventory = read(previous)
|
||||
require(len(inventory['files']) == 20, 'Previous scientific snapshot must contain 20 files')
|
||||
pins[str(previous)] = DRAFT_INVENTORY_SHA
|
||||
for entry in inventory['files']:
|
||||
require(Path(entry['path']).name == entry['path'], 'Nonlocal scientific source inventory entry')
|
||||
pins[str(MODEL / entry['path'])] = entry['sha256']
|
||||
zero = an['overlays']['zero']
|
||||
_, overlay, metadata, _ = R.load_overlay(zero['overlay_file'], zero['overlay_manifest'])
|
||||
require(not np.any(overlay['plastic_delta_mv'].view(np.uint32)), 'Initial delta must be exact positive zero')
|
||||
kc = np.load(Path(original['apl_bundle_dir']) / 'kc_globals.npy', allow_pickle=False)
|
||||
require(np.array_equal(kc, populations['Kenyon_Cell']), 'Canonical metadata and APL KC memberships disagree')
|
||||
import pandas as pd
|
||||
candidate = pd.read_parquet(ROOT / 'experiments/phase4h-dopamine-plasticity/plastic-mask/plastic-edge-candidates.parquet')
|
||||
candidate = candidate.set_index('edge_id').loc[overlay['edge_id']].reset_index()
|
||||
require(candidate.edge_id.to_numpy().tobytes() == overlay['edge_id'].tobytes() and
|
||||
candidate.target_compartment_verbatim.notna().all(), 'Complete mask compartment mapping is not exact')
|
||||
verify_pins(pins)
|
||||
sources = source_inventory()
|
||||
code_commit = subprocess.run(['git', '-C', str(ROOT), 'rev-parse', 'HEAD'], check=True,
|
||||
capture_output=True, text=True).stdout.strip()
|
||||
require(P.sha(code_commit + '0' * 24), 'Invalid Git code version')
|
||||
for path, sha in sources.items():
|
||||
relative = Path(path).relative_to(ROOT).as_posix()
|
||||
blob = subprocess.run(['git', '-C', str(ROOT), 'show', code_commit + ':' + relative],
|
||||
check=True, capture_output=True).stdout
|
||||
require(__import__('hashlib').sha256(blob).hexdigest() == sha, 'Registered source is not the committed Git blob: ' + relative)
|
||||
for name in ('protocol/source-snapshot', 'inputs/static', 'cases', 'runs', 'updates', 'receipts', 'logs', 'audit', 'regression'):
|
||||
(EXP / name).mkdir(parents=True)
|
||||
for path in sources:
|
||||
shutil.copyfile(path, EXP / 'protocol/source-snapshot' / Path(path).name)
|
||||
save(EXP / 'regression/AN-seal-before.json', an_seal)
|
||||
save(EXP / 'regression/AM-seals-before.json', am_seal)
|
||||
save(EXP / 'regression/old-integrity-before.json', old)
|
||||
save(EXP / 'inputs/static/biology-audit.json', biology)
|
||||
save(EXP / 'inputs/static/rates-audit.json', rates['audit'])
|
||||
save_npz(EXP / 'inputs/static/populations.npz', **populations)
|
||||
save_npz(EXP / 'inputs/static/candidate-map.npz', edge_id=candidate.edge_id.to_numpy(np.int64),
|
||||
source_neuron_idx=candidate.source_neuron_idx.to_numpy(np.int32),
|
||||
target_neuron_idx=candidate.target_neuron_idx.to_numpy(np.int32),
|
||||
target_compartment_verbatim=candidate.target_compartment_verbatim.to_numpy(dtype=str))
|
||||
table_metadata = {}
|
||||
for cue, table in rates['tables'].items():
|
||||
save_npz(EXP / f'inputs/static/{cue}-rates.npz', targets=table['targets'], rates_hz=table['rates_hz'])
|
||||
table_metadata[cue] = {k: v for k, v in table.items() if k not in ('targets', 'rates_hz')}
|
||||
save(EXP / 'inputs/static/rate-tables.json', table_metadata)
|
||||
save(EXP / 'protocol/full-trial-plan.json', full['full_trial_plan'])
|
||||
descriptor = plan_descriptor(full)
|
||||
cases = {}
|
||||
reset_seed = original['seed']
|
||||
for row in descriptor['calls']:
|
||||
epoch = descriptor['selected_epochs'][row['input_epoch_index']]
|
||||
case = EXP / 'cases' / row['row_id']
|
||||
case.mkdir()
|
||||
spec, derivation = I.derive_case_spec(original, row, epoch, rates['tables'][epoch['cs']['cue']],
|
||||
brain_reset_seed=reset_seed, budget_root=str(EXP), output_budget_bytes=BUDGET['output_budget_bytes'])
|
||||
R.validate_spec(spec, case)
|
||||
save(case / 'spec.json', spec)
|
||||
save(case / 'derivation.json', derivation)
|
||||
cases[row['row_id']] = str(case)
|
||||
derived = {str(p): digest(p) for parent in ('inputs/static', 'cases', 'protocol/source-snapshot', 'regression')
|
||||
for p in (EXP / parent).rglob('*') if p.is_file()}
|
||||
derived[str(EXP / 'protocol/full-trial-plan.json')] = digest(EXP / 'protocol/full-trial-plan.json')
|
||||
reg = dict(schema='phase4ao-conditioning-interface-registration-v1', plan=descriptor,
|
||||
full_plan_payload_sha256=full['payload_sha256'], full_trial_plan_file_sha256=derived[str(EXP / 'protocol/full-trial-plan.json')],
|
||||
source_sha256=sources, input_sha256=pins, prepared_file_sha256=derived, cases=cases,
|
||||
initial_overlay=zero, original_spec=str(original_path), brain_reset_seed=reset_seed,
|
||||
bridge_source=str(bridge), phase4j_source=str(phasej), budget=BUDGET,
|
||||
git_parent_commit=PREP_COMMIT, git_code_commit=code_commit, committed_source_blobs_verified=len(sources), AN_registration_sha256=AN_REG,
|
||||
generation=dict(device='cuda:0', GPU_UUID='GPU-79f3a85d-adce-7833-7003-6488140ca596',
|
||||
conflict_policy='error', highbit_probe_seed=descriptor['selected_epochs'][0]['cs']['seed'],
|
||||
highbit_comparison='full63bit versus low32bit, one fixed 1200xPN matrix each; never a seed search',
|
||||
original_L_sampling_AST='only rate tensor, private generator and full-shape forced-spike expression',
|
||||
trace_backend='torch', actual_input_files='created only after external registration SHA recorded'),
|
||||
scope=dict(epoch_boundary_updates=True, within_trial_updates=False, full_conditioning=False,
|
||||
candidate_generated=False, learning_success_claimed=False, neural_state_carried=False,
|
||||
delta_carried=True, independent_model_branch=True),
|
||||
success_gate='12 complete calls; exact reference/light events and final state/queues; exact D0/frozen identity; real D1 delta installed in epoch 2; nonzero D1 update coverage required',
|
||||
missing_nonzero_coverage='INCOMPLETE, do not choose another input',
|
||||
failure_policy='preserve all partials; no automatic run retry or result-selected seed',
|
||||
retention_supported=False, future_hashes_fabricated=False, new_brain_runs_during_preparation=0)
|
||||
verify_pins({**pins, **sources, **derived})
|
||||
save(EXP / 'protocol/preregistered.json', reg)
|
||||
return digest(EXP / 'protocol/preregistered.json')
|
||||
|
||||
|
||||
def read_registration(expected):
|
||||
path = EXP / 'protocol/preregistered.json'
|
||||
require(P.sha(expected) and digest(path) == expected, 'Registration differs from external anchor')
|
||||
reg = read(path)
|
||||
require(reg['schema'] == 'phase4ao-conditioning-interface-registration-v1' and
|
||||
reg['AN_registration_sha256'] == AN_REG and reg['budget'] == BUDGET and
|
||||
reg['plan']['total_calls'] == 12 and reg['plan']['total_ticks'] == 14400,
|
||||
'Unexpected finite registration')
|
||||
verify_pins({**reg['source_sha256'], **reg['input_sha256'], **reg['prepared_file_sha256']})
|
||||
return reg
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
require(sys.argv[1:] == ['prepare'], 'Only exclusive prepare is supported')
|
||||
print('REGISTRATION_SHA256', prepare(), flush=True)
|
||||
+474
@@ -0,0 +1,474 @@
|
||||
"""Finite AO runner: exact frozen brain, two fresh epochs, delta-only carry.
|
||||
|
||||
Never starts the full conditioning protocol. Partial rows are preserved. Each
|
||||
runtime is sealed immediately; coordinator evidence lives outside its seal.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import ast
|
||||
from datetime import datetime, timezone
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import traceback
|
||||
from types import SimpleNamespace
|
||||
|
||||
import numpy as np
|
||||
|
||||
import conditioning_preflight_prepare as P
|
||||
import conditioning_preflight_inputs as I
|
||||
import conditioning_inputs as IN
|
||||
import conditioning_update as U
|
||||
import conditioning_epoch as E
|
||||
import conditioning_preflight_compare as Q
|
||||
import engineering_compare as COMP
|
||||
import engineering_workflow as ENV
|
||||
import runtime_light as R
|
||||
import runtime_continuation as C
|
||||
|
||||
EXP, MODEL, ROOT = P.EXP, P.MODEL, P.ROOT
|
||||
require, read, save, digest = P.require, P.read, P.save, P.digest
|
||||
|
||||
|
||||
def npz(path):
|
||||
with np.load(path, allow_pickle=False) as z:
|
||||
return {k: z[k].copy() for k in z.files}
|
||||
|
||||
|
||||
def row_by(reg, name):
|
||||
matches = [r for r in reg['plan']['calls'] if r['row_id'] == name]
|
||||
require(len(matches) == 1, 'Only an exact registered row may execute')
|
||||
return matches[0]
|
||||
|
||||
|
||||
def locations(name):
|
||||
require(type(name) is str and name and all(c.isalnum() or c in '-_' for c in name), 'Unsafe row')
|
||||
return dict(run=EXP / 'runs' / name, update=EXP / 'updates' / name,
|
||||
runtime=EXP / 'receipts' / (name + '.runtime.json'), execution=EXP / 'receipts' / (name + '.process.json'),
|
||||
authority=EXP / 'receipts' / (name + '.authority.json'),
|
||||
stdout=EXP / 'logs' / (name + '.stdout.log'), stderr=EXP / 'logs' / (name + '.stderr.log'))
|
||||
|
||||
|
||||
def budget(reg):
|
||||
files = list(EXP.rglob('*'))
|
||||
require(not any(p.is_symlink() for p in files), 'Linked output is forbidden')
|
||||
used = sum(p.stat().st_size for p in files if p.is_file())
|
||||
free = shutil.disk_usage(EXP).free
|
||||
b = reg['budget']
|
||||
require(used <= b['output_budget_bytes'] and free >= b['minimum_free_reserve_bytes'] + max(0, b['output_budget_bytes'] - used),
|
||||
'Resource cap/reserve exceeded; preserve partials')
|
||||
return dict(used_bytes=used, free_bytes=free, **b)
|
||||
|
||||
|
||||
def tables():
|
||||
metadata = read(EXP / 'inputs/static/rate-tables.json')
|
||||
return {cue: dict(table, **npz(EXP / f'inputs/static/{cue}-rates.npz')) for cue, table in metadata.items()}
|
||||
|
||||
|
||||
def original_sampler_nodes(raw):
|
||||
"""Only the four original L sampling statements, no old brain import."""
|
||||
require(__import__('hashlib').sha256(raw).hexdigest() == I.SOURCE_PINS[I.SOURCE_REL], 'L source changed')
|
||||
methods = [n for n in ast.walk(ast.parse(raw)) if isinstance(n, ast.FunctionDef) and n.name == 'run_rates']
|
||||
require(len(methods) == 1, 'Original sampling method is not unique')
|
||||
chosen = []
|
||||
for node in methods[0].body:
|
||||
if isinstance(node, ast.Assign) and any(isinstance(t, ast.Name) and t.id in ('rg', 'gen', 'forced') for t in node.targets):
|
||||
chosen.append(node)
|
||||
elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call) and ast.unparse(node.value.func) == 'gen.manual_seed':
|
||||
chosen.append(node)
|
||||
require(len(chosen) == 4 and [ast.unparse(n).split('=')[0].strip() for n in chosen[:2]] == ['rg', 'gen'],
|
||||
'Original sampler AST structure changed')
|
||||
return chosen
|
||||
|
||||
|
||||
def sampler_check(reg, table, generated):
|
||||
import torch
|
||||
seed = reg['generation']['highbit_probe_seed']
|
||||
nodes = original_sampler_nodes((ROOT / I.SOURCE_REL).read_bytes())
|
||||
env = dict(torch=torch, targets=table['targets'], rates_hz=table['rates_hz'], seed=seed, steps=1200,
|
||||
self=SimpleNamespace(dt=.1, device=torch.device('cuda:0')))
|
||||
exec(compile(ast.Module(body=nodes, type_ignores=[]), str(ROOT / I.SOURCE_REL), 'exec'), env)
|
||||
original = IN.requests_from_forced(env['forced'].cpu().numpy(), table['targets'], cs_start_tick=0, cs_stop_tick=1000)
|
||||
require(all(COMP.bit_equal(original['requests'][k], generated['cs_requests'][k]) for k in ('tick', 'neuron_idx')),
|
||||
'Actual CUDA original-L sampling expression disagrees')
|
||||
identities = []
|
||||
for value in (seed, seed & 0xffffffff):
|
||||
gen = torch.Generator(device='cuda:0').manual_seed(value)
|
||||
matrix = torch.rand((1200, len(table['targets'])), generator=gen, device='cuda:0')
|
||||
identities.append(IN.array_identity(matrix.cpu().numpy()))
|
||||
require(identities[0] != identities[1], 'Actual CUDA full63bit and low32bit sequence identical; stop, no seed substitution')
|
||||
return dict(original_L_sampler_AST_sha256=__import__('hashlib').sha256(ast.dump(ast.Module(body=nodes, type_ignores=[]), include_attributes=False).encode()).hexdigest(),
|
||||
exact_sparse_CS_requests=True, full_seed=seed, low32_probe_seed=seed & 0xffffffff,
|
||||
uniforms_identities=identities, highbit_influence_observed=True,
|
||||
additional_probe_matrices=3, probe_shape=[1200, len(table['targets'])],
|
||||
new_input_seed_selected=False, old_full_trial_equivalence_claimed=False)
|
||||
|
||||
|
||||
def materialize(reg_sha):
|
||||
reg = P.read_registration(reg_sha)
|
||||
admission()
|
||||
ENV.preflight(); budget(reg)
|
||||
target = EXP / 'inputs/generated'
|
||||
require(not target.exists(), 'Never rerun or overwrite actual input generation')
|
||||
target.mkdir()
|
||||
rate = tables()
|
||||
dan = npz(EXP / 'inputs/static/populations.npz')[reg['plan']['dan_type']]
|
||||
records = []
|
||||
for index, epoch in enumerate(reg['plan']['selected_epochs']):
|
||||
started = time.perf_counter()
|
||||
value = I.generate_epoch_inputs(epoch, rate, dan, device=reg['generation']['device'],
|
||||
max_uniform_draws=reg['budget']['max_uniform_draws'], conflict_policy=reg['generation']['conflict_policy'])
|
||||
directory = target / f'epoch-{index:03d}'
|
||||
directory.mkdir()
|
||||
for kind in ('requests', 'cs_requests', 'us_requests'):
|
||||
P.save_npz(directory / (kind + '.npz'), **value[kind])
|
||||
P.save_npz(directory / 'source-mask.npz', source_mask=value['source_mask'])
|
||||
save(directory / 'sampler-receipt.json', value['audit'])
|
||||
if index == 0:
|
||||
save(target / 'actual-CUDA-sampler-check.json', sampler_check(reg, rate[epoch['cs']['cue']], value))
|
||||
records.append(dict(index=index, epoch_id=epoch['epoch_id'], wall_seconds=time.perf_counter() - started,
|
||||
CS_requests=len(value['cs_requests']['tick']), US_requests=len(value['us_requests']['tick'])))
|
||||
for row in reg['plan']['calls']:
|
||||
src = target / f"epoch-{row['input_epoch_index']:03d}" / 'requests.npz'
|
||||
dst = Path(reg['cases'][row['row_id']]) / 'requests.npz'
|
||||
with src.open('rb') as inp, dst.open('xb') as out:
|
||||
shutil.copyfileobj(inp, out)
|
||||
require(digest(src) == digest(dst), 'Shared input copy differs')
|
||||
pins = {str(p): digest(p) for p in target.rglob('*') if p.is_file()}
|
||||
pins.update({str(Path(path) / 'requests.npz'): digest(Path(path) / 'requests.npz') for path in reg['cases'].values()})
|
||||
P.read_registration(reg_sha)
|
||||
save(EXP / 'receipts/input-generation.json', dict(complete=True, registration_sha256=reg_sha,
|
||||
inputs_sha256=pins, rows=records, GPU_UUID=ENV.GPU, full63bit_delivery_and_influence_checked=True,
|
||||
input_generation_calls=2, brain_runs=0, parameters_changed=False))
|
||||
|
||||
|
||||
def generated_inputs(reg_sha):
|
||||
receipt = read(EXP / 'receipts/input-generation.json')
|
||||
require(receipt.get('complete') is True and receipt.get('registration_sha256') == reg_sha and receipt.get('brain_runs') == 0,
|
||||
'Generated inputs are not bound to registration')
|
||||
process = read(EXP / 'receipts/input-generation.process.json')
|
||||
require(process['exit_code'] == 0 and process['registration_sha256'] == reg_sha and
|
||||
process['receipt_sha256'] == digest(EXP / 'receipts/input-generation.json'), 'Input generation process missing')
|
||||
for k in ('stdout', 'stderr'):
|
||||
require(process[k + '_sha256'] == digest(EXP / 'logs' / ('input-generation.' + k + '.log')), 'Input generation log changed')
|
||||
P.verify_pins(receipt['inputs_sha256'])
|
||||
return receipt
|
||||
|
||||
|
||||
def verify_row(reg, reg_sha, row):
|
||||
paths = locations(row['row_id'])
|
||||
record = read(paths['runtime']); process = read(paths['execution'])
|
||||
require(record['complete'] is True and record['registration_sha256'] == reg_sha and P.same(record['row'], row),
|
||||
'Runtime record differs from registered row')
|
||||
require(process['exit_code'] == 0 and process['registration_sha256'] == reg_sha and
|
||||
process['runtime_sha256'] == digest(paths['runtime']), 'Runtime process did not complete exactly')
|
||||
for key in ('stdout', 'stderr'):
|
||||
require(process[key + '_sha256'] == digest(paths[key]), 'Process log changed')
|
||||
P.verify_pins(record['files_sha256'])
|
||||
checkpoint = C.load_checkpoint(paths['run'], record['checkpoint_manifest_sha256'])
|
||||
require(checkpoint['fresh'] and checkpoint['steps'] == row['steps'] and checkpoint['global_tick_offset'] == 0,
|
||||
'Normal epoch must be fresh, not a continuation')
|
||||
ENV.verify_environment(record['environment'], checkpoint['summary']['apl_component']['environment'])
|
||||
chain = read(paths['update'] / 'chain-receipt.json')
|
||||
require(chain['authority_sha256'] == digest(paths['authority']) and
|
||||
chain['source_neural_checkpoint_manifest_sha256'] == record['checkpoint_manifest_sha256'] and
|
||||
chain['epoch_id'] == row['epoch_id'], 'Delta does not derive from this actual sealed neural epoch')
|
||||
P.verify_pins(chain['input_files_sha256'])
|
||||
for name, sha in chain['output_files_sha256'].items():
|
||||
require(digest(paths['update'] / name) == sha, 'Updated snapshot changed')
|
||||
return record
|
||||
|
||||
|
||||
def before_overlay(reg, reg_sha, row):
|
||||
previous = row['previous_delta_row_id']
|
||||
if previous is None:
|
||||
return reg['initial_overlay']
|
||||
parent = row_by(reg, previous)
|
||||
require(parent['arm'] == row['arm'] and parent['diagnostic_mode'] == row['diagnostic_mode'] and
|
||||
parent['input_epoch_index'] + 1 == row['input_epoch_index'], 'Invalid cross-epoch delta link')
|
||||
verify_row(reg, reg_sha, parent)
|
||||
directory = locations(previous)['update']
|
||||
return dict(overlay_file=str(directory / 'delta-after.npz'), overlay_manifest=str(directory / 'overlay-manifest.json'))
|
||||
|
||||
|
||||
def make_authority(reg, row, checkpoint_sha, overlay):
|
||||
paths = locations(row['row_id']); case = Path(reg['cases'][row['row_id']]); spec = read(case / 'spec.json')
|
||||
metadata = read(overlay['overlay_manifest'])
|
||||
role_paths = dict(spec=case / 'spec.json', requests=case / 'requests.npz',
|
||||
overlay_before=overlay['overlay_file'], overlay_manifest_before=overlay['overlay_manifest'],
|
||||
state=paths['run'] / 'state.npz', kc=Path(spec['apl_bundle_dir']) / 'kc_globals.npy',
|
||||
canonical_kc=Path(spec['apl_bundle_dir']) / 'kc_globals.npy', candidate_map=EXP / 'inputs/static/candidate-map.npz',
|
||||
mask=metadata['mask_path'], base_candidate=metadata['base_candidate_path'],
|
||||
phase4j_source=reg['phase4j_source'], eligibility_bridge_source=reg['bridge_source'],
|
||||
conditioning_update_source=MODEL / 'conditioning_update.py', runtime_light_source=MODEL / 'runtime_light.py',
|
||||
runtime_continuation_source=MODEL / 'runtime_continuation.py', am_overlay_source=metadata['am_overlay_path'])
|
||||
epoch = reg['plan']['selected_epochs'][row['input_epoch_index']]
|
||||
return dict(schema=E.SCHEMA, epoch_id=row['epoch_id'], diagnostic_mode=row['diagnostic_mode'],
|
||||
command=dict(steps=row['steps'], compartment=reg['plan']['compartment_verbatim'],
|
||||
us_tick=epoch['us']['tick'], d_command=row['d_command'], frozen=row['frozen'], trace_backend=reg['generation']['trace_backend']),
|
||||
checkpoint_dir=str(paths['run']), checkpoint_manifest_sha256=checkpoint_sha,
|
||||
files={role: dict(path=str(path), sha256=digest(path)) for role, path in role_paths.items()},
|
||||
frozen_inputs={k: v for k, v in reg['input_sha256'].items()
|
||||
if k not in (str(P.AN / 'protocol/preregistered.json'), str(P.AN / 'manifest.json'), str(P.AN / 'audit/final-status.json'))})
|
||||
|
||||
|
||||
def replay_update(authority, receipt):
|
||||
files = {k: Path(v['path']) for k, v in authority['files'].items()}
|
||||
state = npz(files['state']); mapping = npz(files['candidate_map']); old = npz(files['overlay_before'])
|
||||
command = authority['command']
|
||||
return U.verify_receipt(receipt, step=state['step'], neuron_idx=state['neuron_idx'],
|
||||
kc=np.load(files['kc'], allow_pickle=False), candidate_edge_id=mapping['edge_id'],
|
||||
candidate_sources=mapping['source_neuron_idx'], candidate_targets=mapping['target_neuron_idx'],
|
||||
candidate_compartments=mapping['target_compartment_verbatim'], delta_before=old['plastic_delta_mv'],
|
||||
base_candidate=np.load(files['base_candidate'], allow_pickle=False), steps=command['steps'], n_neurons=166700,
|
||||
compartment=command['compartment'], us_tick=command['us_tick'], d_command=command['d_command'],
|
||||
frozen=command['frozen'], trace_backend=command['trace_backend'],
|
||||
bridge_source=files['eligibility_bridge_source'], phase4j_source=files['phase4j_source'])
|
||||
|
||||
|
||||
def worker(reg_sha, row_id):
|
||||
reg = P.read_registration(reg_sha); generated_inputs(reg_sha)
|
||||
admission()
|
||||
row = row_by(reg, row_id); paths = locations(row_id)
|
||||
require(not any(paths[k].exists() for k in ('run', 'update', 'runtime', 'authority')), 'Partial row cannot be retried')
|
||||
ENV.preflight(); before = budget(reg)
|
||||
overlay = before_overlay(reg, reg_sha, row)
|
||||
case = Path(reg['cases'][row_id])
|
||||
protected = {Path(p).parent for p in (*reg['input_sha256'], *reg['source_sha256'], *reg['prepared_file_sha256'])}
|
||||
protected.update({EXP / 'inputs', EXP / 'updates'})
|
||||
# Build/run siblings must never touch any current or earlier learned input.
|
||||
R.execution_paths(case, paths['run'], EXP / 'extension-build', protected=protected)
|
||||
started = time.perf_counter(); started_utc = datetime.now(timezone.utc).isoformat()
|
||||
execute = R.run_reference if row['diagnostic_mode'] == 'reference' else R.run_light
|
||||
execute(case, paths['run'], EXP / 'extension-build', overlay['overlay_file'], overlay['overlay_manifest'])
|
||||
checkpoint_sha = C.seal_checkpoint(paths['run'])['sha256']
|
||||
runtime_seconds = time.perf_counter() - started
|
||||
authority = make_authority(reg, row, checkpoint_sha, overlay)
|
||||
save(paths['authority'], authority)
|
||||
update_started = time.perf_counter()
|
||||
E.postprocess_epoch(paths['authority'], digest(paths['authority']), paths['update'])
|
||||
receipt = read(paths['update'] / 'update-receipt.json')
|
||||
replay_update(authority, receipt)
|
||||
delta_before = npz(overlay['overlay_file'])['plastic_delta_mv']
|
||||
_, after, _, _ = R.load_overlay(paths['update'] / 'delta-after.npz', paths['update'] / 'overlay-manifest.json')
|
||||
binding = Q.audit_delta_binding(delta_before, after['plastic_delta_mv'], after['plastic_delta_mv'],
|
||||
update_receipt=receipt, parent_snapshot_sha256=digest(overlay['overlay_file']),
|
||||
child_manifest=read(paths['update'] / 'overlay-manifest.json'))
|
||||
require(binding['pass_all'], 'Actual recomputed delta/child binding failed')
|
||||
save(EXP / 'receipts' / (row_id + '.binding.json'), binding)
|
||||
record = dict(complete=True, registration_sha256=reg_sha, row=row,
|
||||
checkpoint_manifest_sha256=checkpoint_sha, environment=ENV.runtime_environment(),
|
||||
runtime_and_immediate_seal_wall_seconds=runtime_seconds,
|
||||
postprocess_and_recompute_wall_seconds=time.perf_counter() - update_started,
|
||||
started_utc=started_utc, budget_before=before, budget_after=budget(reg),
|
||||
files_sha256={str(p): digest(p) for p in (paths['authority'], *paths['update'].iterdir(),
|
||||
EXP / 'receipts' / (row_id + '.binding.json'))},
|
||||
before_overlay_file_sha256=digest(overlay['overlay_file']), epoch_update_recomputed=True,
|
||||
learning_success_claimed=False, full_conditioning_executed=False)
|
||||
ENV.verify_environment(record['environment'], read(paths['run'] / 'summary.json')['apl_component']['environment'])
|
||||
P.read_registration(reg_sha)
|
||||
save(paths['runtime'], record)
|
||||
print('COMPLETE', row_id, receipt['counts']['actual_bitwise_changed_edges'], flush=True)
|
||||
|
||||
|
||||
def child_process(reg, reg_sha, action, row_id=None):
|
||||
if row_id is None:
|
||||
out, err = [EXP / 'logs' / ('input-generation.' + k + '.log') for k in ('stdout', 'stderr')]
|
||||
else:
|
||||
p = locations(row_id); out, err = p['stdout'], p['stderr']
|
||||
require(not out.exists() and not err.exists(), 'Partial child logs exist; never retry')
|
||||
command = [sys.executable, '-B', str(Path(__file__)), action, '--registration-sha256', reg_sha]
|
||||
if row_id is not None: command += ['--row-id', row_id]
|
||||
started = time.perf_counter()
|
||||
with out.open('xb') as stdout, err.open('xb') as stderr:
|
||||
result = subprocess.run(command, cwd=MODEL, stdout=stdout, stderr=stderr,
|
||||
timeout=reg['budget']['child_timeout_seconds'])
|
||||
require(result.returncode == 0, 'Child failed; preserve partial files and inspect logs')
|
||||
receipt = dict(exit_code=result.returncode, registration_sha256=reg_sha,
|
||||
stdout_sha256=digest(out), stderr_sha256=digest(err), wall_seconds=time.perf_counter() - started)
|
||||
if row_id is None:
|
||||
receipt['receipt_sha256'] = digest(EXP / 'receipts/input-generation.json')
|
||||
save(EXP / 'receipts/input-generation.process.json', receipt)
|
||||
else:
|
||||
receipt['runtime_sha256'] = digest(locations(row_id)['runtime'])
|
||||
save(locations(row_id)['execution'], receipt)
|
||||
|
||||
|
||||
def run(reg_sha):
|
||||
reg = P.read_registration(reg_sha)
|
||||
admission()
|
||||
completed = []
|
||||
try:
|
||||
child_process(reg, reg_sha, 'materialize')
|
||||
generated_inputs(reg_sha)
|
||||
for row in reg['plan']['calls']:
|
||||
print('START', row['row_id'], flush=True)
|
||||
budget(reg)
|
||||
child_process(reg, reg_sha, 'worker', row['row_id'])
|
||||
verify_row(reg, reg_sha, row)
|
||||
completed.append(row['row_id'])
|
||||
print('COMPLETE', row['row_id'], flush=True)
|
||||
save(EXP / 'audit/execution-complete.json', dict(registration_sha256=reg_sha, complete=True,
|
||||
completed=completed, calls=12, ticks=14400, learning_success_claimed=False))
|
||||
except Exception as error:
|
||||
save(EXP / 'audit/stopped.json', dict(complete=False, registration_sha256=reg_sha,
|
||||
error_type=type(error).__name__, error=str(error), traceback=traceback.format_exc(),
|
||||
completed_rows=completed, partials_preserved=True, no_automatic_retry=True))
|
||||
raise
|
||||
|
||||
|
||||
def admission():
|
||||
require(not (EXP / 'audit/stopped.json').exists() and not (EXP / 'audit/execution-complete.json').exists() and
|
||||
not (EXP / 'manifest.json').exists(), 'Stopped/completed protocol cannot be automatically restarted')
|
||||
|
||||
|
||||
def expected_weight_hash(reg, reg_sha, row):
|
||||
"""Independent CPU expected installation from authorized base and delta."""
|
||||
overlay = before_overlay(reg, reg_sha, row)
|
||||
helper, plan, _, _ = R.load_overlay(overlay['overlay_file'], overlay['overlay_manifest'])
|
||||
spec = read(Path(reg['cases'][row['row_id']]) / 'spec.json')
|
||||
graph = Path(spec['graph_dir']); arrays = {}
|
||||
try:
|
||||
for name in ('nodes', 'weights', 'edge_id', 'src'):
|
||||
arrays[name] = np.load(graph / (name + '.npy'), mmap_mode='r', allow_pickle=False)
|
||||
require(np.array_equal(arrays['nodes'], np.arange(166700, dtype=np.int32)), 'Global graph node identity changed')
|
||||
apl = np.load(Path(spec['apl_bundle_dir']) / 'apl_globals.npy', allow_pickle=False)
|
||||
mask = np.isin(arrays['src'], apl)
|
||||
require(int(mask.sum()) == 5845, 'Original fast APL mask differs')
|
||||
mapping = helper.map_installation(arrays['weights'], arrays['edge_id'], mask, plan)
|
||||
require(mapping['missing_candidate_count'] == 0, 'Full candidate mask not installed')
|
||||
base_sha, effective_sha = helper.expected_hashes(arrays['weights'], mask, mapping)
|
||||
return dict(base_raw_sha256=base_sha, effective_raw_sha256=effective_sha,
|
||||
expected_from_base_and_overlay_not_run_summary=True)
|
||||
finally:
|
||||
for a in arrays.values(): a._mmap.close()
|
||||
|
||||
|
||||
def analyze(reg_sha):
|
||||
reg = P.read_registration(reg_sha); generated_inputs(reg_sha)
|
||||
require(not (EXP / 'audit/stopped.json').exists(), 'Stopped protocol has no PASS analysis')
|
||||
complete = read(EXP / 'audit/execution-complete.json')
|
||||
require(complete['complete'] and complete['registration_sha256'] == reg_sha and
|
||||
complete['completed'] == [r['row_id'] for r in reg['plan']['calls']], 'Missing finite calls')
|
||||
records = {r['row_id']: verify_row(reg, reg_sha, r) for r in reg['plan']['calls']}
|
||||
pairs, summaries, deltas, cross_arm = [], [], {}, []
|
||||
for row in reg['plan']['calls']:
|
||||
paths = locations(row['row_id']); authority = read(paths['authority'])
|
||||
require(P.same(authority, make_authority(reg, row, records[row['row_id']]['checkpoint_manifest_sha256'],
|
||||
before_overlay(reg, reg_sha, row))), 'Authority differs from registered row and actual parent')
|
||||
update = read(paths['update'] / 'update-receipt.json')
|
||||
replay_update(authority, update)
|
||||
delta = npz(paths['update'] / 'delta-after.npz')['plastic_delta_mv']
|
||||
deltas[row['row_id']] = delta
|
||||
if row['arm'] in ('D0', 'frozen'):
|
||||
require(not np.any(delta.view(np.uint32)), 'D0/frozen must remain exact positive zero')
|
||||
summary = read(paths['run'] / 'summary.json')
|
||||
population = npz(EXP / 'inputs/static/populations.npz')
|
||||
state = npz(paths['run'] / 'state.npz')
|
||||
run_record = COMP.load_run(paths['run'])
|
||||
try:
|
||||
us = npz(EXP / f"inputs/generated/epoch-{row['input_epoch_index']:03d}/us_requests.npz")
|
||||
us_keys = us['tick'].astype(np.int64) * 166700 + us['neuron_idx']
|
||||
request_keys = run_record['requests'][:, 0] * 166700 + run_record['requests'][:, 1]
|
||||
dan_requests = run_record['requests'][np.isin(request_keys, us_keys)]
|
||||
require(len(dan_requests) == len(us_keys), 'Scheduled US requests not fully accounted')
|
||||
delivery = dict(scheduled_D=row['d_command'], requested=int(len(dan_requests)),
|
||||
accepted=int(dan_requests[:, 3].sum()), rejected=int(dan_requests[:, 4].sum()),
|
||||
actual_DAN_spikes=int(state['counts'][population[reg['plan']['dan_type']]].sum()),
|
||||
actual_DAN_spikes_gate_update=False, artificial_US_not_dopamine_concentration=True)
|
||||
finally:
|
||||
COMP.close_run(run_record)
|
||||
summaries.append(dict(row_id=row['row_id'], total_spikes=summary['total_spikes'],
|
||||
active_neurons=int(np.count_nonzero(state['counts'])), all_finite=summary['all_finite'],
|
||||
update_counts=update['counts'], DAN_delivery=delivery,
|
||||
runtime_seconds=records[row['row_id']]['runtime_and_immediate_seal_wall_seconds'],
|
||||
effective_weight_sha256=summary['effective_weights_raw_sha256']))
|
||||
for arm in ('D1', 'D0', 'frozen'):
|
||||
for epoch in range(2):
|
||||
names = [f'{arm}--{mode}--epoch-{epoch:03d}' for mode in ('reference', 'light')]
|
||||
a, b = [COMP.load_run(locations(n)['run']) for n in names]
|
||||
try:
|
||||
requests = npz(EXP / f'inputs/generated/epoch-{epoch:03d}/requests.npz')
|
||||
expected = expected_weight_hash(reg, reg_sha, row_by(reg, names[0]))
|
||||
pair = Q.compare_epoch_pair(a, b, expected_requests=np.column_stack((requests['tick'], requests['neuron_idx'])),
|
||||
expected_steps=1200, expected_effective_weight_sha256=expected['effective_raw_sha256'])
|
||||
require(pair['pass_all'] and COMP.bit_equal(deltas[names[0]], deltas[names[1]]), 'Reference/light exact regression failed')
|
||||
pairs.append(dict(arm=arm, epoch=epoch, exact_delta_after=True, expected_weights=expected, comparison=pair))
|
||||
finally:
|
||||
COMP.close_run(a); COMP.close_run(b)
|
||||
for mode in ('reference', 'light'):
|
||||
for left_arm, right_arm, epoch in (('D0', 'frozen', 0), ('D0', 'frozen', 1), ('D1', 'D0', 0)):
|
||||
left = COMP.load_run(locations(f'{left_arm}--{mode}--epoch-{epoch:03d}')['run'])
|
||||
right = COMP.load_run(locations(f'{right_arm}--{mode}--epoch-{epoch:03d}')['run'])
|
||||
try:
|
||||
comparison = COMP.compare_fresh(left, right)
|
||||
require(comparison['numerical_exact'], 'Identical-input/zero-delta cross-arm neural behavior differs')
|
||||
cross_arm.append(dict(left=left_arm, right=right_arm, mode=mode, epoch=epoch, comparison=comparison))
|
||||
finally:
|
||||
COMP.close_run(left); COMP.close_run(right)
|
||||
d1_rows = [s for s in summaries if s['row_id'].startswith('D1--')]
|
||||
nonzero = all(s['update_counts']['actual_bitwise_changed_edges'] > 0 for s in d1_rows)
|
||||
installed = {}
|
||||
for mode in ('reference', 'light'):
|
||||
first, second = (locations(f'D1--{mode}--epoch-{j:03d}') for j in range(2))
|
||||
raw = R._raw(deltas[f'D1--{mode}--epoch-000'])
|
||||
installation = read(second['run'] / 'overlay-audit.json')
|
||||
require(installation['delta_raw_sha256'] == raw and
|
||||
digest(first['update'] / 'delta-after.npz') == records[f'D1--{mode}--epoch-001']['before_overlay_file_sha256'],
|
||||
'Epoch 2 did not install epoch 1 learned delta')
|
||||
installed[mode] = dict(exact=True, nonzero_delta=int(np.count_nonzero(deltas[f'D1--{mode}--epoch-000'])))
|
||||
status = 'PASS-EXACT-TWO-EPOCH-DELTA-INTERFACE-NOT-LEARNING' if nonzero else 'INCOMPLETE-NO-NONZERO-UPDATE-COVERAGE'
|
||||
return dict(status=status, registration_sha256=reg_sha, calls=12, ticks=14400, pairs=pairs, cross_arm=cross_arm,
|
||||
rows=summaries, epoch2_installations=installed, nonzero_D1_update_coverage=nonzero,
|
||||
D0_and_frozen_exact_zero=True, complete_conditioning_executed=False, learned_candidate=False,
|
||||
learning_success_claimed=False, retention_transition_supported=False,
|
||||
scope='Two selected A epochs, one training replicate, one PAM15 compartment; no acquisition or A/B behavioral test; no general dynamics or learning claim',
|
||||
light_intermediate_states_observed=False)
|
||||
|
||||
|
||||
def finalize(reg_sha):
|
||||
require(not (EXP / 'manifest.json').exists() and not (EXP / 'audit/final-status.json').exists(), 'Do not overwrite final/partial seal')
|
||||
report = analyze(reg_sha)
|
||||
save(EXP / 'audit/interface-analysis.json', report)
|
||||
an = P.P.read_registration(P.AN_REG)
|
||||
helper = P.P.load_module('phase4ao_final_seal_helper', ROOT / P.P.AI_HELPER_REL, P.P.AI_HELPER_SHA)
|
||||
save(EXP / 'regression/AN-seal-after.json', helper.verify_seal(P.AN))
|
||||
save(EXP / 'regression/AM-seals-after.json', P.P.verify_authority(an['anchors']))
|
||||
old = P.P.verify_old_integrity(an)
|
||||
save(EXP / 'regression/old-integrity-after.json', old)
|
||||
P.read_registration(reg_sha)
|
||||
final = dict(status=report['status'], analysis_sha256=digest(EXP / 'audit/interface-analysis.json'),
|
||||
registration_sha256=reg_sha, old_integrity_pass=True, old_roots=len(old),
|
||||
prior_AN_AM_seals_preserved=True, frozen_21_and_previous_draft_20_unchanged=True,
|
||||
five_reference_trials='Previously sealed AM evidence retained, not rerun in this finite interface gate',
|
||||
learned_candidate=False, learning_success_claimed=False, full_conditioning_executed=False,
|
||||
model_directory_sealed=False, new_experiment_only=True, budget=budget(P.read_registration(reg_sha)))
|
||||
save(EXP / 'audit/final-status.json', final)
|
||||
paths = sorted(p for p in EXP.rglob('*') if p.is_file())
|
||||
require(not any(p.is_symlink() for p in EXP.rglob('*')) and not any('__pycache__' in p.parts for p in paths), 'Unsafe seal payload')
|
||||
rows = [dict(path=p.relative_to(EXP).as_posix(), bytes=p.stat().st_size, sha256=digest(p)) for p in paths]
|
||||
save(EXP / 'manifest.json', dict(root=str(EXP), files=rows))
|
||||
with (EXP / 'checksums.sha256').open('x', encoding='utf-8', newline='\n') as out:
|
||||
out.writelines(r['sha256'] + ' ' + r['path'] + '\n' for r in rows)
|
||||
out.write(digest(EXP / 'manifest.json') + ' manifest.json\n')
|
||||
helper.verify_seal(EXP)
|
||||
print(json.dumps(dict(status=report['status'], manifest_sha256=digest(EXP / 'manifest.json'),
|
||||
final_sha256=digest(EXP / 'audit/final-status.json')), sort_keys=True), flush=True)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
require(sys.dont_write_bytecode, 'Use scientific Python -B')
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('action', choices=('materialize', 'worker', 'run', 'finalize'))
|
||||
parser.add_argument('--registration-sha256', required=True)
|
||||
parser.add_argument('--row-id')
|
||||
args = parser.parse_args()
|
||||
if args.action == 'worker': worker(args.registration_sha256, args.row_id)
|
||||
elif args.action == 'materialize': materialize(args.registration_sha256)
|
||||
elif args.action == 'run': run(args.registration_sha256)
|
||||
else: finalize(args.registration_sha256)
|
||||
@@ -0,0 +1,205 @@
|
||||
"""Pure epoch-END adapter to the frozen AM/J eligibility implementation.
|
||||
|
||||
Only the two SHA-pinned source files are read. No event files, brain state,
|
||||
weights, base arrays or artifacts are written. The caller supplies actual sparse
|
||||
events and externally authenticated COMPLETE KC/mask mappings. Hashing supplied
|
||||
arrays identifies their bytes; it does not establish anatomical completeness.
|
||||
The returned overlay is NOT installed. Scheduled D is not actual DAN delivery.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
from pathlib import Path
|
||||
import sys
|
||||
from types import ModuleType
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
SCHEMA = "phase4an-conditioning-update-draft-v1"
|
||||
BRIDGE_SHA256 = "174fb8d14619189e74b095e77a6db18b14c93046dc431c0b55710a5aa2dd2e61"
|
||||
PHASE4J_SHA256 = "abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e"
|
||||
|
||||
|
||||
def _require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest()
|
||||
|
||||
|
||||
def _array(value, dtype, name):
|
||||
_require(isinstance(value, np.ndarray) and value.ndim == 1 and value.dtype == np.dtype(dtype),
|
||||
name + " requires an exact one-dimensional " + str(np.dtype(dtype)) + " array")
|
||||
return value
|
||||
|
||||
|
||||
def _evidence(array):
|
||||
"""Original dtype and logical C-order raw bytes; no cast or rounding."""
|
||||
return dict(dtype=array.dtype.str, shape=list(array.shape), nbytes=int(array.nbytes),
|
||||
raw_c_order_sha256=hashlib.sha256(array.tobytes(order="C")).hexdigest())
|
||||
|
||||
|
||||
def _owned(array):
|
||||
result = np.array(array, copy=True, order="C")
|
||||
result.setflags(write=False)
|
||||
return result
|
||||
|
||||
|
||||
def _pinned_bytes(path, expected, label):
|
||||
raw = Path(path).read_bytes()
|
||||
_require(hashlib.sha256(raw).hexdigest() == expected, label + " source SHA256 mismatch")
|
||||
return raw
|
||||
|
||||
|
||||
def _load_bridge(path):
|
||||
raw = _pinned_bytes(path, BRIDGE_SHA256, "AM eligibility bridge")
|
||||
# Execute precisely the verified bytes, not a second path-based import.
|
||||
# Temporary isolated module registration supports the frozen dataclass;
|
||||
# restore any prior module, rather than colliding with old globals.
|
||||
name = "_conditioning_update_bridge_" + BRIDGE_SHA256
|
||||
module = ModuleType(name)
|
||||
module.__file__ = str(path)
|
||||
previous = sys.modules.get(name)
|
||||
sys.modules[name] = module
|
||||
try:
|
||||
exec(compile(raw, str(path), "exec"), module.__dict__)
|
||||
finally:
|
||||
if previous is None:
|
||||
del sys.modules[name]
|
||||
else:
|
||||
sys.modules[name] = previous
|
||||
_require(module.PHASE4J_SHA256 == PHASE4J_SHA256, "Frozen bridge/J pin disagreement")
|
||||
return module
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class EpochUpdate:
|
||||
delta_after: np.ndarray
|
||||
sampled_trace: np.ndarray
|
||||
final_trace: np.ndarray
|
||||
receipt: dict
|
||||
|
||||
|
||||
def update_epoch(*, step, neuron_idx, kc, candidate_edge_id, candidate_sources,
|
||||
candidate_targets, candidate_compartments, delta_before, base_candidate,
|
||||
steps, n_neurons, compartment, us_tick, d_command, frozen,
|
||||
bridge_source, phase4j_source, trace_backend):
|
||||
"""Return the epoch-boundary update; never apply it to the running brain.
|
||||
|
||||
trace_backend is mandatory: ``torch`` executes the frozen Torch CPU trace
|
||||
expression; ``numpy`` remains its candidate compatibility implementation,
|
||||
without a claim of per-call Torch equivalence. One declared US or no US is
|
||||
supported. A US-less epoch requires D=0. Actual DAN delivery is deliberately
|
||||
absent from this API and must be bound as separate coordinator evidence.
|
||||
"""
|
||||
_require(type(steps) is int and steps > 0, "steps must be a positive integer")
|
||||
_require(type(n_neurons) is int and n_neurons > 0, "n_neurons must be a positive integer")
|
||||
_require(us_tick is None or (type(us_tick) is int and 0 <= us_tick < steps), "us_tick must be None or an integer inside the epoch")
|
||||
_require(type(d_command) in (int, float) and math.isfinite(d_command) and d_command in (0, 1),
|
||||
"D must be the explicit scheduled scalar 0 or 1, not Boolean or measured DAN")
|
||||
_require(us_tick is not None or d_command == 0, "No declared US tick requires D=0")
|
||||
_require(type(frozen) is bool, "frozen must be Boolean")
|
||||
_require(trace_backend in ("numpy", "torch"), "Explicit trace_backend must be numpy or torch")
|
||||
arrays = dict(step=_array(step, "int32", "step"), neuron_idx=_array(neuron_idx, "int32", "neuron_idx"),
|
||||
kc=_array(kc, "int32", "kc"), candidate_edge_id=_array(candidate_edge_id, "int64", "candidate_edge_id"),
|
||||
candidate_sources=_array(candidate_sources, "int32", "candidate_sources"),
|
||||
candidate_targets=_array(candidate_targets, "int32", "candidate_targets"),
|
||||
delta_before=_array(delta_before, "float32", "delta_before"),
|
||||
base_candidate=_array(base_candidate, "float32", "base_candidate"))
|
||||
_require(isinstance(candidate_compartments, np.ndarray) and candidate_compartments.ndim == 1 and
|
||||
candidate_compartments.dtype.kind == "U", "candidate_compartments must be a Unicode vector")
|
||||
arrays["candidate_compartments"] = candidate_compartments
|
||||
size = len(candidate_edge_id)
|
||||
_require(size > 0 and np.all(candidate_edge_id >= 0) and np.all(candidate_edge_id[1:] > candidate_edge_id[:-1]),
|
||||
"Candidate edge IDs must be nonempty, canonical sorted unique int64 IDs")
|
||||
_require(all(len(arrays[name]) == size for name in ("candidate_sources", "candidate_targets",
|
||||
"candidate_compartments", "delta_before", "base_candidate")), "Candidate mapping columns differ in length")
|
||||
_require(np.all((candidate_targets >= 0) & (candidate_targets < n_neurons)), "Candidate target outside supplied graph size")
|
||||
before_evidence = {name: _evidence(value) for name, value in arrays.items()}
|
||||
# Every computation receives owned copies. Caller views/strides are accepted
|
||||
# without changing their values or flags; dtype is never silently repaired.
|
||||
work = {name: np.array(value, copy=True, order="C") for name, value in arrays.items()}
|
||||
bridge = _load_bridge(bridge_source)
|
||||
_pinned_bytes(phase4j_source, PHASE4J_SHA256, "Phase 4J")
|
||||
trace = bridge.trace_from_events(work["step"], work["neuron_idx"], work["kc"], steps,
|
||||
[] if us_tick is None else [us_tick], phase4j_source=phase4j_source,
|
||||
backend=trace_backend, n_neurons=n_neurons)
|
||||
sampled = trace.last_command_trace
|
||||
after, original = bridge.apply_eligibility(sampled, work["kc"], work["candidate_sources"],
|
||||
work["candidate_compartments"], compartment, work["delta_before"], work["base_candidate"],
|
||||
d=d_command, frozen=frozen, phase4j_source=phase4j_source, n_neurons=n_neurons)
|
||||
# Diagnostics only; the update is exclusively the frozen apply_v2 above.
|
||||
selected = work["candidate_compartments"] == compartment
|
||||
local = np.searchsorted(work["kc"], work["candidate_sources"])
|
||||
eligible = (sampled[local] > 0) & selected
|
||||
changed = after.view(np.uint32) != work["delta_before"].view(np.uint32)
|
||||
value_changed = after != work["delta_before"]
|
||||
floor = -bridge.FLOOR_FRACTION * work["base_candidate"]
|
||||
floor_before = work["delta_before"] == floor
|
||||
floor_after = after == floor
|
||||
_require(bridge.bit_equal(after[~selected], work["delta_before"][~selected]), "Off-compartment identity failed")
|
||||
if frozen or d_command == 0:
|
||||
_require(bridge.bit_equal(after, work["delta_before"]), "Zero-D/frozen identity failed")
|
||||
original_report = original["original_report"]
|
||||
attempted = 0 if original_report is None else original_report["updated_edges"]
|
||||
if original_report is not None:
|
||||
_require(original_report["eligible_edges"] == int(eligible.sum()), "Frozen eligible-edge report disagrees with its sampled trace")
|
||||
delta_after, sampled_trace, final_trace = (_owned(a) for a in (after, sampled, trace.final))
|
||||
_require(all(_evidence(value) == before_evidence[name] for name, value in arrays.items()), "Caller input bytes changed")
|
||||
_pinned_bytes(bridge_source, BRIDGE_SHA256, "AM eligibility bridge")
|
||||
_pinned_bytes(phase4j_source, PHASE4J_SHA256, "Phase 4J")
|
||||
receipt = dict(schema=SCHEMA, status="DRAFT-COMPUTED-EPOCH-UPDATE-NOT-INSTALLED",
|
||||
source_pins=dict(eligibility_bridge_sha256=BRIDGE_SHA256, phase4j_sha256=PHASE4J_SHA256),
|
||||
input_arrays=before_evidence,
|
||||
command=dict(steps=steps, n_neurons=n_neurons, compartment=compartment, us_tick=us_tick,
|
||||
D_scheduled=float(d_command), frozen=frozen, trace_backend=trace_backend),
|
||||
semantics=dict(application="epoch_boundary_after_all_recorded_ticks; affects subsequent epoch only",
|
||||
sampling="after_current_tick_spike", trace_initial_state="zero at epoch boundary",
|
||||
no_US_sample="canonical float32 +0, not final trace", D_definition="artificial scheduled scalar",
|
||||
actual_DAN_delivery_observed=False, actual_DAN_delivery_used_to_gate=False,
|
||||
actual_DAN_evidence_authority="separate externally bound epoch record, not an input to this function",
|
||||
original_base_units="mV", delta_units="mV", delta_dtype="float32",
|
||||
candidate_target_role="identity evidence only; update uses original source/compartment mapping"),
|
||||
fixed_rule=dict(dt_ms=bridge.DT_MS, trace_tau_ms=bridge.TAU_MS, trace_cap=bridge.TRACE_CAP,
|
||||
eta_mv=bridge.ETA_MV, polarity=bridge.POLARITY, floor_fraction=bridge.FLOOR_FRACTION),
|
||||
trace_audit=trace.audit, apply_audit=original,
|
||||
counts=dict(events=len(step), actual_KC_events=trace.audit["actual_kc_events"],
|
||||
candidates=size, selected_compartment_edges=int(selected.sum()), sampled_eligible_selected_edges=int(eligible.sum()),
|
||||
original_updated_edges_attempted=int(attempted), original_apply_v2_executed=original["original_apply_v2_executed"],
|
||||
actual_value_changed_edges=int(value_changed.sum()), actual_bitwise_changed_edges=int(changed.sum()),
|
||||
actual_off_compartment_bitwise_changed_edges=int(changed[~selected].sum()),
|
||||
selected_at_floor_before=int((selected & floor_before).sum()),
|
||||
selected_at_floor_after=int((selected & floor_after).sum()),
|
||||
selected_newly_at_floor=int((selected & floor_after & ~floor_before).sum()),
|
||||
sampled_eligible_selected_already_at_floor=int((eligible & floor_before).sum())),
|
||||
count_limitations="original updated_edges counts nonzero attempted increments, including saturation; floor counts do not prove why any edge was unchanged",
|
||||
outputs={"delta_after": _evidence(delta_after), "sampled_trace": _evidence(sampled_trace), "final_trace": _evidence(final_trace)},
|
||||
inputs_exact_unchanged=True, off_compartment_exact_unchanged=True,
|
||||
numpy_torch_bitwise_comparison_performed=False,
|
||||
frozen_Torch_expression_executed=trace_backend == "torch",
|
||||
complete_KC_identity_verified=False, complete_mask_identity_verified=False, full_graph_authority_verified=False,
|
||||
mapping_hash_scope="supplied logical array bytes only; coordinator must bind complete canonical KC and candidate mask",
|
||||
brain_dynamics_executed=False, weights_installed=False, base_modified=False,
|
||||
training_executed_by_this_function=False, scientific_learning_pass_claimed=False, candidate_generated=False)
|
||||
receipt["payload_sha256"] = _sha(receipt)
|
||||
return EpochUpdate(delta_after, sampled_trace, final_trace, receipt)
|
||||
|
||||
|
||||
def verify_receipt(receipt, **epoch_inputs):
|
||||
"""Recompute from supplied arrays/sources; never trust a self-hash alone."""
|
||||
_require(type(receipt) is dict and "payload_sha256" in receipt, "Invalid epoch receipt")
|
||||
_require(receipt["payload_sha256"] == _sha({k: v for k, v in receipt.items() if k != "payload_sha256"}),
|
||||
"Epoch receipt payload SHA mismatch")
|
||||
actual = update_epoch(**epoch_inputs)
|
||||
_require(_canonical(receipt) == _canonical(actual.receipt), "Receipt differs from exact recomputation in this environment")
|
||||
return True
|
||||
@@ -0,0 +1,373 @@
|
||||
"""Deterministic, read-only CPU analysis of the registered 32-call AN gate.
|
||||
|
||||
analyze(reg, reg_sha, verified_records) accepts the actual runtime receipts
|
||||
already verified by engineering_workflow.verify_execution. It does not run
|
||||
that workflow, publish, seal, save, retry or import a GPU runtime. All external
|
||||
AM seals/checkpoint chains/process receipts remain the caller's prior duty.
|
||||
This module rehashes the files it reads, executes every canonical descriptor,
|
||||
and independently reconstructs the reference queues and effective SI weights.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
from pathlib import Path
|
||||
import types
|
||||
|
||||
import numpy as np
|
||||
|
||||
import engineering_compare as compare
|
||||
import engineering_plan as plan_module
|
||||
import engineering_queue_audit as queue_audit
|
||||
import engineering_scopes as scopes
|
||||
|
||||
|
||||
N, E, K = 166700, 25582938, 4064
|
||||
CANDIDATES, FAST_APL_EDGES = 14232, 5845
|
||||
AM_OVERLAY_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4'
|
||||
MASK_SHA = '8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b'
|
||||
SUCCESS = 'PASS-EXACT-LIGHT-CONTINUATION-ENGINEERING-NOT-LEARNING'
|
||||
|
||||
|
||||
def require(value, message):
|
||||
if not value:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(',', ':'), ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _sha_file(path):
|
||||
h = hashlib.sha256()
|
||||
with Path(path).open('rb') as stream:
|
||||
for data in iter(lambda: stream.read(8 << 20), b''):
|
||||
h.update(data)
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
class _Reader:
|
||||
"""Fresh byte hashes, not stat caching; deterministic read manifest."""
|
||||
def __init__(self, registered):
|
||||
self.registered = registered
|
||||
self.reads = {}
|
||||
self.arrays = []
|
||||
|
||||
def bind(self, path, *, registered=True, expected=None):
|
||||
path = Path(path)
|
||||
require(path.is_absolute() and path.is_file() and not path.is_symlink(), 'Missing/linked/relative evidence: ' + str(path))
|
||||
path = path.resolve(strict=True)
|
||||
if registered:
|
||||
require(str(path) in self.registered, 'Unregistered dependency: ' + str(path))
|
||||
require(expected is None or expected == self.registered[str(path)], 'Conflicting external hash authority')
|
||||
expected = self.registered[str(path)]
|
||||
actual = _sha_file(path)
|
||||
require(expected is None or (compare._sha(expected) and actual == expected), 'Evidence bytes changed: ' + str(path))
|
||||
value = dict(path=str(path), bytes=path.stat().st_size, sha256=actual)
|
||||
require(str(path) not in self.reads or self.reads[str(path)] == value, 'File changed during analysis')
|
||||
self.reads[str(path)] = value
|
||||
return path
|
||||
|
||||
def json(self, path, **kwargs):
|
||||
return compare._json(self.bind(path, **kwargs))
|
||||
|
||||
def npy(self, path):
|
||||
value = np.load(self.bind(path), mmap_mode='r', allow_pickle=False)
|
||||
self.arrays.append(value)
|
||||
return value
|
||||
|
||||
def npz(self, path, keys):
|
||||
with np.load(self.bind(path), allow_pickle=False) as archive:
|
||||
require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected input NPZ keys')
|
||||
return {name: archive[name].copy() for name in keys}
|
||||
|
||||
def run(self, directory, *, legacy):
|
||||
directory = Path(directory)
|
||||
names = ['summary.json', 'state.npz', 'apl-timing-audit.json', 'apl-pending-tail.float32.npy', 'requests.csv']
|
||||
if legacy:
|
||||
names.append('adapter-provenance.json')
|
||||
else:
|
||||
names.append('terminal-queues.npz')
|
||||
names += [name for name in [*compare.TRACE_FILES.values(), 'tick-state-hashes.jsonl', 'queue.jsonl']
|
||||
if (directory / name).is_file()]
|
||||
for name in names:
|
||||
self.bind(directory / name, registered=legacy)
|
||||
return compare.load_run(directory, allow_legacy_am=legacy)
|
||||
|
||||
def finish(self):
|
||||
for row in self.reads.values():
|
||||
path = Path(row['path'])
|
||||
require(path.is_file() and path.stat().st_size == row['bytes'] and _sha_file(path) == row['sha256'],
|
||||
'Read evidence changed during analysis: ' + str(path))
|
||||
return [self.reads[key] for key in sorted(self.reads)]
|
||||
|
||||
def close(self):
|
||||
for value in self.arrays:
|
||||
mmap = getattr(value, '_mmap', None)
|
||||
if mmap is not None:
|
||||
mmap.close()
|
||||
|
||||
|
||||
def _registration(reg, reg_sha, verified):
|
||||
require(type(reg) is dict and reg.get('schema') == 'phase4an-engineering-registration-v1' and
|
||||
reg.get('training') is False and compare._sha(reg_sha), 'Invalid engineering registration')
|
||||
plan = plan_module.validate_plan(reg['plan'])
|
||||
ids = [row['row_id'] for row in plan['rows']]
|
||||
require(type(verified) is dict and set(verified) == set(ids) and set(reg['cases']) == set(ids),
|
||||
'Exactly all 32 registered runtime receipts/cases are required')
|
||||
exp = None
|
||||
for row in plan['rows']:
|
||||
identity = row['row_id']; path = Path(reg['cases'][identity]['case_dir'])
|
||||
require(path.is_absolute() and path.name == identity and path.parent.name == 'cases', 'Invalid registered case layout')
|
||||
if exp is None:
|
||||
exp = path.parent.parent
|
||||
require(path == exp / 'cases' / identity, 'Cases span inconsistent experiment roots')
|
||||
receipt = verified[identity]
|
||||
require(receipt.get('schema') == 'phase4an-engineering-runtime-record-v1' and receipt.get('complete') is True and
|
||||
receipt.get('training') is False and receipt.get('registration_sha256') == reg_sha and
|
||||
_canonical(receipt.get('row')) == _canonical(row) and compare._sha(receipt.get('checkpoint_manifest_sha256')),
|
||||
'Runtime receipt is not the exact verified registered row: ' + identity)
|
||||
require(exp.name == 'phase4an-engineering-validation' and exp.parent.name == 'experiments', 'Unexpected fixed engineering output root')
|
||||
require(set(reg['overlays']) == set(reg['selected_case']['AM_outputs']) == {'zero', 'static25'}, 'Incomplete overlay/AM authority')
|
||||
return plan, exp
|
||||
|
||||
|
||||
def _materialize(reader, reg, graph, stage):
|
||||
"""Use the exact pinned pure AM helper; no original globals are patched."""
|
||||
declaration = reg['overlays'][stage]
|
||||
meta = reader.json(declaration['overlay_manifest'])
|
||||
require(meta.get('schema') == 'phase4an-overlay-v1' and meta.get('overlay_name') == stage and
|
||||
meta.get('am_overlay_sha256') == AM_OVERLAY_SHA and meta.get('mask_sha256') == MASK_SHA,
|
||||
'Original fixed overlay authority changed')
|
||||
helper_path = reader.bind(meta['am_overlay_path'], expected=AM_OVERLAY_SHA)
|
||||
module = types.ModuleType('phase4an_analysis_pinned_overlay')
|
||||
module.__file__ = str(helper_path)
|
||||
exec(compile(helper_path.read_bytes(), str(helper_path), 'exec'), module.__dict__)
|
||||
mask = reader.npy(meta['mask_path']); base_candidate = reader.npy(meta['base_candidate_path'])
|
||||
require(reader.reads[str(Path(meta['mask_path']).resolve())]['sha256'] == meta['mask_sha256'] and
|
||||
reader.reads[str(Path(meta['base_candidate_path']).resolve())]['sha256'] == meta['base_candidate_sha256'],
|
||||
'Overlay external mask/base hash mismatch')
|
||||
overlay_file = reader.bind(declaration['overlay_file'], expected=meta['overlay_sha256'])
|
||||
arrays = reader.npz(overlay_file, {'edge_id', 'plastic_delta_mv'})
|
||||
require(mask.dtype == np.int64 and mask.shape == (CANDIDATES,), 'Complete frozen plastic mask required')
|
||||
values = module.validate_arrays(arrays['edge_id'], arrays['plastic_delta_mv'], mask, base_candidate)
|
||||
if stage == 'zero':
|
||||
require(not np.any(values['plastic_delta_mv'] != 0), 'Zero overlay is nonzero')
|
||||
else:
|
||||
require(module.bit_equal(values['plastic_delta_mv'], np.multiply(np.float32(-.25), base_candidate)),
|
||||
'static25 differs from the original float32-mV floor')
|
||||
effective, mapping = module.materialize_numpy(graph['weights'], graph['edge_id'], graph['apl_out_mask'], values)
|
||||
require(mapping['mapped_candidate_count'] == CANDIDATES and mapping['missing_candidate_count'] == 0 and
|
||||
mapping['apl_overlap_count'] == 0, 'Incomplete/overlapping candidate mapping')
|
||||
base_sha, effective_sha = module.expected_hashes(graph['weights'], graph['apl_out_mask'], mapping)
|
||||
require(queue_audit._raw_sha(effective) == effective_sha, 'CPU materialization/hash disagreement')
|
||||
return effective, dict(effective_weights_raw_sha256=effective_sha, base_weights_raw_sha256=base_sha,
|
||||
candidate_count=CANDIDATES, installed_nonzero_count=mapping['installed_nonzero_count'],
|
||||
apl_fast_edges_zeroed=FAST_APL_EDGES, bridge=module.BRIDGE,
|
||||
helper_source_sha256=AM_OVERLAY_SHA, weights_updated_during_run=False)
|
||||
|
||||
|
||||
def _load_context(reg, reg_sha, plan, exp):
|
||||
reader = _Reader(reg['input_sha256'])
|
||||
records, legacy = {}, {}
|
||||
try:
|
||||
actual_reg = reader.json(exp / 'protocol/preregistered.json', registered=False, expected=reg_sha)
|
||||
require(_canonical(actual_reg) == _canonical(reg), 'Supplied registration differs from actual registered bytes')
|
||||
for path, expected in sorted(reg['source_sha256'].items()):
|
||||
reader.bind(path, registered=False, expected=expected)
|
||||
original = Path(reg['selected_case']['original_dir'])
|
||||
spec = reader.json(original / 'spec.json')
|
||||
require(spec.get('kind') == 'fullgraph' and spec.get('steps') == 1000 and spec.get('dt_ms') == .1 and
|
||||
spec.get('apl_delay_steps') == 18, 'Original fixed fullgraph D18 spec changed')
|
||||
requests = reader.npz(original / 'requests.npz', {'tick', 'neuron_idx'})
|
||||
graph_dir, bundle = Path(spec['graph_dir']), Path(spec['apl_bundle_dir'])
|
||||
graph = {key: reader.npy(graph_dir / (key + '.npy')) for key in ('nodes', 'ptr', 'dst', 'edge_id', 'weights')}
|
||||
require(graph['nodes'].dtype == np.int32 and np.array_equal(graph['nodes'], np.arange(N, dtype=np.int32)),
|
||||
'Graph is not the complete canonical neuron set')
|
||||
n, e = queue_audit._graph(graph['ptr'], graph['dst'], graph['edge_id'], graph['weights'])
|
||||
require((n, e) == (N, E), 'Original graph dimensions changed')
|
||||
apl = reader.npy(bundle / 'apl_globals.npy')
|
||||
require(apl.dtype == np.int32 and apl.shape == (2,) and np.all((apl >= 0) & (apl < N)) and
|
||||
apl[0] < apl[1], 'Invalid canonical APL source identity')
|
||||
mask = np.zeros(E, np.bool_)
|
||||
for source in apl:
|
||||
mask[graph['ptr'][source]:graph['ptr'][source + 1]] = True
|
||||
require(int(mask.sum()) == FAST_APL_EDGES, 'Frozen fast-APL source-edge mask changed')
|
||||
graph['apl_out_mask'] = mask
|
||||
storage = {}
|
||||
for row in plan['rows']:
|
||||
identity = row['row_id']; folder = exp / 'runs' / identity
|
||||
require(folder.is_dir() and folder.resolve() == folder and not any(p.is_symlink() for p in folder.rglob('*')),
|
||||
'Missing/linked runtime evidence folder')
|
||||
records[identity] = reader.run(folder, legacy=False)
|
||||
storage[identity] = sum(p.stat().st_size for p in folder.rglob('*') if p.is_file())
|
||||
for stage in ('zero', 'static25'):
|
||||
legacy[stage] = reader.run(reg['selected_case']['AM_outputs'][stage], legacy=True)
|
||||
return dict(reader=reader, records=records, legacy=legacy, graph=graph, requests=requests,
|
||||
storage=storage, input_scope='read-only authenticated selected original inputs and AM outputs')
|
||||
except BaseException:
|
||||
for record in [*records.values(), *legacy.values()]:
|
||||
compare.close_run(record)
|
||||
reader.close()
|
||||
raise
|
||||
|
||||
|
||||
def _pair(candidate, reference):
|
||||
checks, observed = compare._pair_checks(candidate, reference)
|
||||
return dict(numerical_exact=all(checks.values()), checks=checks,
|
||||
failed_checks=[key for key, value in checks.items() if not value],
|
||||
reference_comparisons_available=observed, learning_pass_claimed=False)
|
||||
|
||||
|
||||
def _dispatch(descriptor, records, legacy, overlay):
|
||||
candidates = [records[name] for name in descriptor['candidate_row_ids']]
|
||||
reference = records[descriptor['reference_row_id']] if descriptor['reference_row_id'] is not None else None
|
||||
kind = descriptor['check']
|
||||
if kind == 'am_exact':
|
||||
return compare.compare_fresh(candidates[0], legacy[overlay])
|
||||
if kind == 'fresh_exact':
|
||||
return compare.compare_fresh(candidates[0], reference)
|
||||
if kind in ('prefix_of_reference', 'segment_of_reference'):
|
||||
return scopes.compare_slice(candidates[0], reference)
|
||||
if kind == 'segment_exact':
|
||||
return _pair(candidates[0], reference)
|
||||
if kind == 'checkpoint_exact':
|
||||
return scopes.compare_checkpoint(candidates[0], reference)
|
||||
if kind == 'parent_carry':
|
||||
return scopes.compare_parent_carry(candidates[0], reference)
|
||||
if kind == 'chain_exact':
|
||||
return compare.compare_segments(reference, candidates)
|
||||
raise ValueError('Unimplemented registered comparison: ' + str(kind))
|
||||
|
||||
|
||||
def _number(value, name, integer=False):
|
||||
if value is None:
|
||||
return None
|
||||
require((type(value) is int if integer else type(value) in (int, float)) and math.isfinite(value) and value >= 0,
|
||||
'Malformed observed metric: ' + name)
|
||||
return value
|
||||
|
||||
|
||||
def _run_checks(row, record, expected_hash, reference):
|
||||
compare.validate_run(record)
|
||||
summary = record['summary']; identity = row['row_id']
|
||||
require(len(record['state']['v']) == N and record['terminal']['apl_pending'].shape == (18, K), 'Incomplete full brain state: ' + identity)
|
||||
checks = {key: summary.get(key) is True for key in ('complete', 'pass_all', 'all_finite', 'counts_match_sparse_events')}
|
||||
fixed = dict(nodes=N, edges=E, steps=row['steps'], completed_ticks=row['steps'], apl_delay_steps=18,
|
||||
requested=len(record['requests']), total_spikes=len(record['state']['step']),
|
||||
accepted_requests=int(record['requests'][:, 3].sum(dtype=np.int64)),
|
||||
rejected_requests=int(record['requests'][:, 4].sum(dtype=np.int64)),
|
||||
apl_fast_edges_zeroed=FAST_APL_EDGES, kc_targets_present=K)
|
||||
for key, value in fixed.items():
|
||||
checks[key] = type(summary.get(key)) is int and summary[key] == value
|
||||
checks['registered_clock'] = record['steps'] == row['steps'] and record['global_tick_offset'] == row['start']
|
||||
checks['recording_mode'] = summary.get('diagnostic_mode') == row['mode']
|
||||
checks['effective_weight_bytes'] = summary.get('effective_weights_raw_sha256') == expected_hash
|
||||
checks['within_trial_weights_fixed'] = summary.get('within_trial_weight_updates') is False
|
||||
checks['learning_not_claimed'] = summary.get('learning_pass_claimed') is False
|
||||
expected_queue = reference['queues'][row['start']:row['stop']]
|
||||
for field, queue_key in [('arriving_edges', 'count'), ('accepted_arriving_edges', 'accepted_count')]:
|
||||
expected = sum(q[queue_key] for q in expected_queue)
|
||||
checks[field] = type(summary.get(field)) is int and summary[field] == expected
|
||||
return {name: bool(value) for name, value in checks.items()}
|
||||
|
||||
|
||||
def analyze(reg, reg_sha, verified_records):
|
||||
"""No writes/runs. The caller rechecks registration and receipts afterward."""
|
||||
plan, exp = _registration(reg, reg_sha, verified_records)
|
||||
context = _load_context(reg, reg_sha, plan, exp)
|
||||
records, legacy = context['records'], context['legacy']
|
||||
try:
|
||||
require(set(records) == {r['row_id'] for r in plan['rows']} and set(legacy) == {'zero', 'static25'},
|
||||
'Loaded evidence inventory is incomplete')
|
||||
comparisons = []
|
||||
for row in plan['rows']:
|
||||
for number, descriptor in enumerate(row['expected_comparisons']):
|
||||
try:
|
||||
result = _dispatch(descriptor, records, legacy, row['overlay'])
|
||||
require(type(result.get('numerical_exact')) is bool, 'Comparator lacks an explicit Boolean numerical gate')
|
||||
except (ValueError, KeyError, TypeError, IndexError, OverflowError) as error:
|
||||
result = dict(numerical_exact=False, error_type=type(error).__name__, error=str(error),
|
||||
incomplete_observation_not_fabricated=True)
|
||||
comparisons.append(dict(descriptor_id=row['row_id'] + '/' + str(number),
|
||||
descriptor=descriptor, result=result))
|
||||
expected_descriptors = sum(len(r['expected_comparisons']) for r in plan['rows'])
|
||||
require(len(comparisons) == expected_descriptors, 'Not every registered descriptor was evaluated')
|
||||
overlay_reports, metric_rows, all_row_checks = {}, [], {}
|
||||
for stage in ('zero', 'static25'):
|
||||
effective, installation = _materialize(context['reader'], reg, context['graph'], stage)
|
||||
stage_rows = [r for r in plan['rows'] if r['overlay'] == stage]
|
||||
ref_row = next(r for r in stage_rows if r['kind'] == 'fresh' and r['mode'] == 'reference')
|
||||
reference = records[ref_row['row_id']]
|
||||
reference_chain = [records[r['row_id']] for r in stage_rows if r['kind'] == 'chain' and r['mode'] == 'reference']
|
||||
cuts = [r['start'] for r in stage_rows if r['kind'] == 'chain' and r['mode'] == 'reference' and r['start'] > 0]
|
||||
independent = queue_audit.audit_queues(reference, reference_chain, ptr=context['graph']['ptr'], dst=context['graph']['dst'],
|
||||
edge_id=context['graph']['edge_id'], effective_weights=effective, requests=context['requests'], cuts=cuts)
|
||||
del effective
|
||||
reference_result = next(item['result'] for item in comparisons if item['descriptor']['check'] == 'chain_exact' and
|
||||
item['descriptor']['reference_row_id'] == ref_row['row_id'])
|
||||
light_result = next(item['result'] for item in comparisons if item['descriptor']['check'] == 'chain_exact' and
|
||||
item['descriptor']['reference_row_id'] == plan_module._fresh_id(stage, 'light'))
|
||||
coverage = reference_result.get('coverage', {})
|
||||
apl_values = coverage.get('carried_APL_nonzero_applied_values')
|
||||
simultaneous_carry = any(type(row.get('pending_source_spike_events')) is int and row['pending_source_spike_events'] > 0 and
|
||||
type(row.get('carried_APL_nonzero_pending_values')) is int and row['carried_APL_nonzero_pending_values'] > 0
|
||||
for row in coverage.get('boundaries', []))
|
||||
coverage_pass = (reference_result.get('nonvacuous_temporal_coverage') is True and
|
||||
type(apl_values) is int and apl_values > 0 and simultaneous_carry and
|
||||
independent.get('accepted_nonzero_crossing_witness_verified') is True)
|
||||
legacy_checks = dict(full_dimensions=type(legacy[stage]['summary'].get('nodes')) is int and legacy[stage]['summary']['nodes'] == N and
|
||||
type(legacy[stage]['summary'].get('edges')) is int and legacy[stage]['summary']['edges'] == E,
|
||||
effective_weight_bytes=legacy[stage]['summary'].get('effective_weights_raw_sha256') == installation['effective_weights_raw_sha256'])
|
||||
overlay_reports[stage] = dict(installation=installation, independent_queue_audit=independent,
|
||||
reference_numerical_exact=reference_result.get('numerical_exact') is True,
|
||||
light_numerical_exact=light_result.get('numerical_exact') is True,
|
||||
live_coverage_pass=coverage_pass, legacy_checks=legacy_checks,
|
||||
same_internal_boundary_has_spike_and_APL_carry=simultaneous_carry,
|
||||
carried_APL_nonzero_applied_values=apl_values,
|
||||
light_standalone_coverage=light_result.get('coverage'),
|
||||
light_per_tick_float_state_directly_observed=False,
|
||||
reference_coverage_does_not_fabricate_light_observations=True)
|
||||
for row in stage_rows:
|
||||
identity = row['row_id']; record = records[identity]; summary = record['summary']; receipt = verified_records[identity]
|
||||
checks = _run_checks(row, record, installation['effective_weights_raw_sha256'], reference)
|
||||
all_row_checks[identity] = checks
|
||||
metric_rows.append(dict(row_id=identity, overlay=stage, mode=row['mode'], start=row['start'], stop=row['stop'],
|
||||
biological_time_ms=row['steps'] * .1, runtime_and_immediate_seal_wall_seconds=_number(receipt.get('runtime_and_immediate_seal_wall_seconds'), 'runtime wall'),
|
||||
runtime_reported_wall_seconds=_number(summary.get('wall_seconds'), 'runtime reported wall'),
|
||||
diagnostic_simulation_seconds=_number(summary.get('diagnostic_simulation_seconds'), 'simulation'),
|
||||
build_or_cache_seconds=_number(summary.get('build_or_cache_seconds'), 'build/cache'),
|
||||
peak_allocated_vram_bytes=_number(summary.get('peak_allocated_vram_bytes'), 'allocated VRAM', True),
|
||||
peak_reserved_vram_bytes=_number(summary.get('peak_reserved_vram_bytes'), 'reserved VRAM', True),
|
||||
sealed_output_file_bytes=context['storage'][identity], compressed_storage=receipt.get('storage'),
|
||||
gpu_utilization=None, cpu_utilization=None, unobserved_metrics=['gpu_utilization', 'cpu_utilization'],
|
||||
checkpoint_manifest_sha256=receipt['checkpoint_manifest_sha256'],
|
||||
verified_runtime_receipt_canonical_sha256=hashlib.sha256(_canonical(receipt).encode('utf-8')).hexdigest()))
|
||||
numerical = (all(item['result']['numerical_exact'] for item in comparisons) and
|
||||
all(all(checks.values()) for checks in all_row_checks.values()) and
|
||||
all(all(r['legacy_checks'].values()) and r['independent_queue_audit']['queue_ref_request_exact'] for r in overlay_reports.values()))
|
||||
covered = all(row['live_coverage_pass'] for row in overlay_reports.values())
|
||||
read_manifest = context['reader'].finish()
|
||||
report = dict(schema='phase4an-engineering-analysis-v1', registration_sha256=reg_sha,
|
||||
status='FAIL-NUMERICAL' if not numerical else (SUCCESS if covered else 'INCOMPLETE-COVERAGE'),
|
||||
pass_all=bool(numerical and covered), numerical_exact=bool(numerical), nonvacuous_temporal_coverage=bool(covered),
|
||||
expected_calls=32, analyzed_calls=len(records), executed_ticks=plan['total_ticks'],
|
||||
expected_comparisons=expected_descriptors, evaluated_comparisons=len(comparisons), comparisons=comparisons,
|
||||
run_checks=all_row_checks, failed_run_checks={identity: [k for k, ok in checks.items() if not ok] for identity, checks in all_row_checks.items() if not all(checks.values())},
|
||||
overlay_reports=overlay_reports, metrics=metric_rows, read_manifest=read_manifest,
|
||||
source_sha256=reg['source_sha256'], process_receipts_and_checkpoint_chains_verified_by_caller=True,
|
||||
external_AM_full_seals_reverified_here=False, no_new_brain_runs_during_analysis=True,
|
||||
GPU_used=False, training=False, learning_pass_claimed=False, candidate_generated=False,
|
||||
physiology_or_memory_success_claimed=False,
|
||||
limitations=['Fixed zero/static25 engineering trajectories only; not conditioning or learned memory.',
|
||||
'Light intermediate V/g/ref, APL histories and per-tick queue arrays were not directly recorded.',
|
||||
'Independent queue/ref reconstruction is conditional on recorded spikes; V/g dynamics are not independently re-simulated.',
|
||||
'Missing performance metrics are null, not zero. No universal performance claim is made.',
|
||||
'Caller verifies all process receipts, parent chains, registration authority and full AM/frozen seals.'])
|
||||
_canonical(report) # Fail rather than return non-JSON/nonfinite output.
|
||||
return report
|
||||
finally:
|
||||
for record in [*records.values(), *legacy.values()]:
|
||||
compare.close_run(record)
|
||||
context['reader'].close()
|
||||
@@ -0,0 +1,413 @@
|
||||
"""Read-only CPU comparisons for the UNREGISTERED AN engineering gate.
|
||||
|
||||
load_run(path, allow_legacy_am=False) reads actual AJ/AM/AN artifact schemas.
|
||||
Call close_run(record) after using explicitly loaded memory-mapped traces.
|
||||
compare_fresh(left, right, legacy_am=None) and compare_segments(monolithic,
|
||||
segments) accept loaded dictionaries or paths and return JSON-safe reports.
|
||||
No report is written, no GPU is imported, no training/biological PASS is claimed.
|
||||
The caller owns external source/input/root SHA pins and case selection. These
|
||||
comparisons do not replace seal verification or prove unseen inputs unchanged.
|
||||
|
||||
AM did not save its raw spike ring: its fresh terminal ring is explicitly
|
||||
reconstructed from sparse events, never described as independently recorded.
|
||||
Reference queue joint hashes include LOCAL tick headers; cross-segment checks
|
||||
compare the separate ordered-edge/accepted-mask hashes and counts instead.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import csv
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
STATE_KEYS = ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')
|
||||
REQUEST_FIELDS = ('tick', 'neuron_idx', 'attempted', 'accepted', 'rejected')
|
||||
TRACE_FILES = {'drive': 'apl-drive.float32.npy', 'state': 'apl-state.float32.npy',
|
||||
'generated': 'apl-inhibition.float32.npy', 'applied': 'apl-applied.float32.npy'}
|
||||
HASH_FIELDS = ('v_sha256', 'g_sha256', 'ref_sha256')
|
||||
QUEUE_FIELDS = ('count', 'accepted_count', 'ordered_edge_sha256', 'accepted_mask_sha256')
|
||||
|
||||
|
||||
def require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def bit_equal(a, b):
|
||||
return (isinstance(a, np.ndarray) and isinstance(b, np.ndarray) and a.dtype == b.dtype and a.shape == b.shape and
|
||||
np.array_equal(np.ascontiguousarray(a).view(np.uint8), np.ascontiguousarray(b).view(np.uint8)))
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return isinstance(value, str) and re.fullmatch('[0-9a-f]{64}', value) is not None
|
||||
|
||||
|
||||
def _json(path):
|
||||
def pairs(values):
|
||||
result = {}
|
||||
for key, value in values:
|
||||
require(key not in result, 'Duplicate JSON key: ' + key)
|
||||
result[key] = value
|
||||
return result
|
||||
return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=pairs,
|
||||
parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value)))
|
||||
|
||||
|
||||
def _jsonl(path):
|
||||
with Path(path).open(encoding='utf-8') as stream:
|
||||
return [json.loads(line) for line in stream if line.strip()]
|
||||
|
||||
|
||||
def _npz(path, keys):
|
||||
with np.load(path, allow_pickle=False) as archive:
|
||||
require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected array keys: ' + str(path))
|
||||
return {key: archive[key].copy() for key in keys}
|
||||
|
||||
|
||||
def reconstructed_fresh_ring(state, steps, neurons):
|
||||
ring = np.zeros((19, neurons), np.uint8)
|
||||
for tick in range(max(0, steps - 19), steps):
|
||||
first, last = np.searchsorted(state['step'], (tick, tick + 1))
|
||||
ring[tick % 19, state['neuron_idx'][first:last]] = 1
|
||||
return ring
|
||||
|
||||
|
||||
def normalized_ring(terminal):
|
||||
ring, phase = terminal['spike_ring'], int(terminal['next_tick'])
|
||||
return ring[(np.arange(19) + phase % 19) % 19]
|
||||
|
||||
|
||||
def _requests(path):
|
||||
with Path(path).open(encoding='utf-8', newline='') as stream:
|
||||
reader = csv.DictReader(stream)
|
||||
require(tuple(reader.fieldnames or ()) == REQUEST_FIELDS, 'requests.csv must use the exact five AJ fields in order')
|
||||
rows = []
|
||||
for row in reader:
|
||||
require(set(row) == set(REQUEST_FIELDS), 'Extra/missing request CSV field')
|
||||
rows.append([int(row[key]) for key in REQUEST_FIELDS])
|
||||
return np.asarray(rows, dtype=np.int64).reshape(-1, 5)
|
||||
|
||||
|
||||
def load_run(directory, *, allow_legacy_am=False):
|
||||
directory = Path(directory).resolve(strict=True)
|
||||
require(directory.is_dir(), 'Output is not a directory')
|
||||
require(not any('error' in p.name.lower() or 'failure' in p.name.lower() for p in directory.iterdir()),
|
||||
'Run contains error/failure evidence')
|
||||
summary = _json(directory / 'summary.json')
|
||||
require(summary.get('complete') is True and summary.get('pass_all') is True and summary.get('all_finite') is True and
|
||||
summary.get('steps') == summary.get('completed_ticks'), 'Run is incomplete or failed')
|
||||
state = _npz(directory / 'state.npz', STATE_KEYS)
|
||||
steps, neurons = summary['steps'], len(state['v'])
|
||||
require(summary.get('nodes') == neurons and summary.get('precision') == 'float64' and
|
||||
summary.get('apl_precision') == 'float32' and summary.get('apl_delay_steps') == 18, 'Unexpected AN/AM numerical schema')
|
||||
timing = _json(directory / 'apl-timing-audit.json')
|
||||
pending = np.load(directory / 'apl-pending-tail.float32.npy', mmap_mode='r', allow_pickle=False)
|
||||
terminal_path = directory / 'terminal-queues.npz'
|
||||
if terminal_path.is_file():
|
||||
terminal = _npz(terminal_path, ('spike_ring', 'apl_state', 'apl_pending', 'next_tick'))
|
||||
origin = 'recorded_terminal_queues'
|
||||
else:
|
||||
require(allow_legacy_am and (directory / 'adapter-provenance.json').is_file(), 'Recorded terminal queues are missing')
|
||||
bits = timing.get('final_apl_state_u32_bits')
|
||||
require(isinstance(bits, list) and len(bits) == 2 and all(type(v) is int and 0 <= v < 2**32 for v in bits),
|
||||
'AM has no exact terminal APL bits')
|
||||
terminal = {'spike_ring': reconstructed_fresh_ring(state, steps, neurons),
|
||||
'apl_state': np.asarray(bits, dtype=np.uint32).view(np.float32), 'apl_pending': np.asarray(pending),
|
||||
'next_tick': np.asarray(steps, np.int64)}
|
||||
origin = 'AM_spike_ring_derived_from_fresh_events_APL_bits_from_timing'
|
||||
require(bit_equal(terminal['apl_pending'], pending), 'Terminal/pending file mismatch')
|
||||
require(timing.get('final_apl_state_u32_bits') == terminal['apl_state'].view(np.uint32).tolist(), 'Terminal APL bit metadata mismatch')
|
||||
continued = summary.get('checkpoint_continuation_used', False)
|
||||
require(type(continued) is bool, 'Invalid continuation scope flag')
|
||||
offset = summary.get('global_tick_offset', 0)
|
||||
require(not continued or (type(offset) is int and offset > 0 and summary.get('global_next_tick') == offset + steps),
|
||||
'Continued output lacks an explicit global clock')
|
||||
require(continued or offset == 0, 'Fresh output has a nonzero global offset')
|
||||
traces = {name: np.load(directory / file, mmap_mode='r', allow_pickle=False)
|
||||
for name, file in TRACE_FILES.items() if (directory / file).is_file()}
|
||||
reference = summary.get('diagnostic_mode') == 'reference' or origin.startswith('AM_')
|
||||
if reference or summary.get('full_APL_history_recorded') is True:
|
||||
require(len(traces) == 4, 'Declared reference APL history is incomplete')
|
||||
hashes = _jsonl(directory / 'tick-state-hashes.jsonl') if (directory / 'tick-state-hashes.jsonl').is_file() else None
|
||||
queues = _jsonl(directory / 'queue.jsonl') if (directory / 'queue.jsonl').is_file() else None
|
||||
if reference or summary.get('full_tick_state_hashes_recorded') is True:
|
||||
require(hashes is not None, 'Declared state hashes are missing')
|
||||
if reference or summary.get('full_queue_hashes_recorded') is True:
|
||||
require(queues is not None, 'Declared queue hashes are missing')
|
||||
result = {'source': str(directory), 'state': state, 'terminal': terminal, 'requests': _requests(directory / 'requests.csv'),
|
||||
'steps': steps, 'global_tick_offset': offset, 'summary': summary, 'traces': traces,
|
||||
'state_hashes': hashes, 'queues': queues, 'terminal_origin': origin}
|
||||
validate_run(result)
|
||||
return result
|
||||
|
||||
|
||||
def close_run(run):
|
||||
"""Release read-only mappings opened by load_run; no files are modified."""
|
||||
arrays = [*run.get('traces', {}).values(), *run.get('terminal', {}).values()]
|
||||
closed = set()
|
||||
for array in arrays:
|
||||
current = array
|
||||
while current is not None:
|
||||
mapping = getattr(current, '_mmap', None)
|
||||
if mapping is not None and id(mapping) not in closed:
|
||||
mapping.close(); closed.add(id(mapping))
|
||||
current = getattr(current, 'base', None)
|
||||
|
||||
|
||||
def validate_run(run):
|
||||
"""Array-level contract, also usable by synthetic tests without files."""
|
||||
require(isinstance(run, dict), 'Run must be a dictionary')
|
||||
steps, offset = run['steps'], run['global_tick_offset']
|
||||
require(type(steps) is int and steps > 0 and type(offset) is int and offset >= 0, 'Invalid segment clock')
|
||||
state, terminal = run['state'], run['terminal']
|
||||
require(set(state) == set(STATE_KEYS), 'Expected exactly the six state keys')
|
||||
n = len(state['v'])
|
||||
require(n > 0, 'Empty neuron population')
|
||||
for name, dtype in (('v', np.float64), ('g', np.float64), ('ref', np.int32), ('counts', np.int32)):
|
||||
value = state[name]
|
||||
require(value.dtype == dtype and value.shape == (n,) and np.isfinite(value).all(), 'Invalid state ' + name)
|
||||
require(np.all((state['ref'] >= 0) & (state['ref'] <= 22)) and np.all(state['counts'] >= 0), 'Invalid ref/count values')
|
||||
tick, node = state['step'], state['neuron_idx']
|
||||
require(tick.dtype == node.dtype == np.int32 and tick.ndim == node.ndim == 1 and tick.shape == node.shape and
|
||||
np.all((tick >= 0) & (tick < steps)) and np.all((node >= 0) & (node < n)), 'Invalid events')
|
||||
keys = tick.astype(np.int64) * n + node
|
||||
require(len(keys) < 2 or np.all(keys[1:] > keys[:-1]), 'Events must be sorted unique')
|
||||
require(np.array_equal(np.bincount(node, minlength=n), state['counts']), 'Counts disagree with segment events')
|
||||
request = run['requests']
|
||||
require(isinstance(request, np.ndarray) and request.dtype == np.int64 and request.ndim == 2 and request.shape[1] == 5,
|
||||
'Requests must be int64 rows in the five AJ fields')
|
||||
require(np.all((request[:, 0] >= 0) & (request[:, 0] < steps)) and np.all((request[:, 1] >= 0) & (request[:, 1] < n)) and
|
||||
np.all(request[:, 2] == 1) and np.all((request[:, 3] == 0) | (request[:, 3] == 1)) and
|
||||
np.all(request[:, 4] == 1 - request[:, 3]), 'Invalid request values')
|
||||
request_keys = request[:, 0] * n + request[:, 1]
|
||||
require(len(request_keys) < 2 or np.all(request_keys[1:] > request_keys[:-1]), 'Requests must be sorted unique')
|
||||
require(set(terminal) == {'spike_ring', 'apl_state', 'apl_pending', 'next_tick'}, 'Invalid terminal keys')
|
||||
ring = terminal['spike_ring']
|
||||
require(ring.dtype == np.uint8 and ring.shape == (19, n) and np.all(ring <= 1), 'Invalid spike ring')
|
||||
phase = terminal['next_tick']
|
||||
require(isinstance(phase, np.ndarray) and phase.dtype == np.int64 and phase.shape == () and int(phase) == steps,
|
||||
'Terminal phase must be local segment length')
|
||||
pending, apl_state = terminal['apl_pending'], terminal['apl_state']
|
||||
require(pending.dtype == np.float32 and pending.ndim == 2 and pending.shape[0] == 18 and pending.shape[1] > 0 and
|
||||
apl_state.dtype == np.float32 and apl_state.shape == (2,) and np.isfinite(pending).all() and np.isfinite(apl_state).all(),
|
||||
'Invalid APL terminal state')
|
||||
for t in range(max(0, steps - 19), steps):
|
||||
expected = np.zeros(n, np.uint8)
|
||||
first, last = np.searchsorted(tick, (t, t + 1)); expected[node[first:last]] = 1
|
||||
require(bit_equal(ring[t % 19], expected), 'Written ring slot differs from sparse events')
|
||||
if offset == 0 and steps < 19:
|
||||
require(not np.any(ring[steps:]), 'Fresh uninitialized ring slots are not zero')
|
||||
traces = run.get('traces', {})
|
||||
require(not traces or set(traces) == set(TRACE_FILES), 'Partial APL trace set')
|
||||
for key, trace in traces.items():
|
||||
width = 2 if key in ('drive', 'state') else pending.shape[1]
|
||||
require(trace.dtype == np.float32 and trace.shape == (steps, width) and np.isfinite(trace).all(), 'Invalid APL history: ' + key)
|
||||
if traces:
|
||||
require(bit_equal(traces['state'][-1], apl_state), 'Final APL history/state mismatch')
|
||||
kept = min(steps, 18)
|
||||
require(bit_equal(pending[-kept:], traces['generated'][-kept:]), 'Terminal APL queue does not retain generated suffix')
|
||||
if steps > 18:
|
||||
require(bit_equal(traces['applied'][18:], traces['generated'][:-18]), 'Within-segment APL delay differs from 18')
|
||||
if offset == 0:
|
||||
require(np.all(traces['applied'][:min(18, steps)].view(np.uint32) == 0), 'Fresh APL application padding is not positive zero')
|
||||
hashes, queues = run.get('state_hashes'), run.get('queues')
|
||||
if hashes is not None:
|
||||
require(len(hashes) == steps, 'State hash coverage incomplete')
|
||||
for t, row in enumerate(hashes):
|
||||
require(set(row) == {'tick', *HASH_FIELDS} and type(row['tick']) is int and row['tick'] == t and
|
||||
all(_sha(row[key]) for key in HASH_FIELDS), 'Invalid state hash row')
|
||||
for name in ('v', 'g', 'ref'):
|
||||
raw = np.ascontiguousarray(state[name], dtype=state[name].dtype.newbyteorder('<')).tobytes()
|
||||
require(hashes[-1][name + '_sha256'] == hashlib.sha256(raw).hexdigest(), 'Last state hash differs from terminal ' + name)
|
||||
if queues is not None:
|
||||
require(len(queues) == steps, 'Queue coverage incomplete')
|
||||
for t, row in enumerate(queues):
|
||||
require(set(row) == {'tick', *QUEUE_FIELDS, 'joint_sha256'} and type(row['tick']) is int and row['tick'] == t and
|
||||
type(row['count']) is int and type(row['accepted_count']) is int and 0 <= row['accepted_count'] <= row['count'] and
|
||||
all(_sha(row[key]) for key in ('ordered_edge_sha256', 'accepted_mask_sha256', 'joint_sha256')), 'Invalid queue row')
|
||||
return run
|
||||
|
||||
|
||||
def _run(value, legacy=False):
|
||||
return load_run(value, allow_legacy_am=legacy) if isinstance(value, (str, Path)) else validate_run(value)
|
||||
|
||||
|
||||
def _activity(run):
|
||||
terminal = run['terminal']
|
||||
result = {'spikes': len(run['state']['step']), 'requested': len(run['requests']),
|
||||
'accepted_requests': int(run['requests'][:, 3].sum(dtype=np.int64)),
|
||||
'terminal_apl_state_nonzero_values': int(np.count_nonzero(terminal['apl_state'])),
|
||||
'terminal_pending_nonzero_values': int(np.count_nonzero(terminal['apl_pending']))}
|
||||
traces = run.get('traces', {})
|
||||
result['generated_h_nonzero_values'] = int(np.count_nonzero(traces['generated'])) if traces else None
|
||||
result['applied_h_nonzero_values'] = int(np.count_nonzero(traces['applied'])) if traces else None
|
||||
result['apl_nonzero_observed'] = bool(result['terminal_apl_state_nonzero_values'] or result['terminal_pending_nonzero_values'] or
|
||||
(result['generated_h_nonzero_values'] or 0) or (result['applied_h_nonzero_values'] or 0))
|
||||
return result
|
||||
|
||||
|
||||
def _pair_checks(left, right, prefix='pair'):
|
||||
checks = {prefix + '.steps': left['steps'] == right['steps'], prefix + '.offset': left['global_tick_offset'] == right['global_tick_offset']}
|
||||
for name in STATE_KEYS:
|
||||
checks[prefix + '.state.' + name] = bit_equal(left['state'][name], right['state'][name])
|
||||
checks[prefix + '.requests_five_fields'] = bit_equal(left['requests'], right['requests'])
|
||||
for name in ('spike_ring', 'apl_state', 'apl_pending', 'next_tick'):
|
||||
checks[prefix + '.terminal.' + name] = bit_equal(left['terminal'][name], right['terminal'][name])
|
||||
for field in ('effective_weights_raw_sha256',):
|
||||
a, b = left.get('summary', {}).get(field), right.get('summary', {}).get(field)
|
||||
if a is not None or b is not None:
|
||||
checks[prefix + '.' + field] = _sha(a) and a == b
|
||||
availability = {'APL_traces': bool(left.get('traces')) and bool(right.get('traces')),
|
||||
'state_hashes': left.get('state_hashes') is not None and right.get('state_hashes') is not None,
|
||||
'queue_hashes': left.get('queues') is not None and right.get('queues') is not None}
|
||||
if availability['APL_traces']:
|
||||
for name in TRACE_FILES:
|
||||
checks[prefix + '.trace.' + name] = bit_equal(left['traces'][name], right['traces'][name])
|
||||
if availability['state_hashes']:
|
||||
checks[prefix + '.state_hashes'] = left['state_hashes'] == right['state_hashes']
|
||||
if availability['queue_hashes']:
|
||||
checks[prefix + '.queues'] = left['queues'] == right['queues']
|
||||
return checks, availability
|
||||
|
||||
|
||||
def compare_fresh(left, right, legacy_am=None):
|
||||
left, right = _run(left), _run(right)
|
||||
require(left['global_tick_offset'] == right['global_tick_offset'] == 0, 'Fresh comparison requires fresh runs')
|
||||
checks, available = _pair_checks(left, right)
|
||||
origins = [left.get('terminal_origin', 'caller_arrays'), right.get('terminal_origin', 'caller_arrays')]
|
||||
if legacy_am is not None:
|
||||
legacy = _run(legacy_am, legacy=True)
|
||||
require(legacy['global_tick_offset'] == 0, 'AM control must be fresh')
|
||||
extra, legacy_available = _pair_checks(left, legacy, 'AM')
|
||||
checks.update(extra); available['AM'] = legacy_available
|
||||
# The left run may be light. Do not thereby skip the independent full
|
||||
# diagnostic comparison between the right reference and sealed AM.
|
||||
extra, reference_available = _pair_checks(right, legacy, 'reference_vs_AM')
|
||||
checks.update(extra); available['reference_vs_AM'] = reference_available
|
||||
origins.append(legacy.get('terminal_origin', 'caller_arrays'))
|
||||
activity = _activity(left)
|
||||
exact = bool(all(checks.values()))
|
||||
nonvacuous = activity['spikes'] > 0 and activity['apl_nonzero_observed']
|
||||
return {'schema': 'phase4an-fresh-engineering-comparison-v1', 'numerical_exact': exact,
|
||||
'nonvacuous_activity': nonvacuous, 'pass_all': exact and nonvacuous,
|
||||
'status': 'FAIL-NUMERICAL' if not exact else ('PASS-FRESH-SCOPE' if nonvacuous else 'INCOMPLETE-COVERAGE'),
|
||||
'checks': checks, 'failed_checks': [key for key, value in checks.items() if not value], 'activity': activity,
|
||||
'reference_comparisons_available': available, 'terminal_origins': origins,
|
||||
'cross_segment_validation_performed': False, 'temporal_validation_complete': False,
|
||||
'scope': 'Fresh event/state/request/terminal identity only; no continuation, learning, physiology or performance PASS',
|
||||
'independent_CSR_queue_reconstruction_performed': False,
|
||||
'accepted_nonzero_weight_edge_witness_verified': False,
|
||||
'external_seals_and_input_authority_verified_here': False}
|
||||
|
||||
|
||||
def compare_segments(monolithic, segments):
|
||||
mono = _run(monolithic)
|
||||
parts = [_run(value) for value in segments]
|
||||
require(len(parts) >= 2, 'Need a monolithic run and at least two segments')
|
||||
origin = mono['global_tick_offset']
|
||||
if origin:
|
||||
parent = mono.get('summary', {}).get('parent_checkpoint_manifest_sha256')
|
||||
require(_sha(parent) and parts[0].get('summary', {}).get('parent_checkpoint_manifest_sha256') == parent,
|
||||
'Nonzero-origin comparisons must bind the same starting parent checkpoint')
|
||||
cursor = origin
|
||||
for part in parts:
|
||||
require(part['global_tick_offset'] == cursor, 'Segment clock gap/overlap/order error')
|
||||
require(len(part['state']['v']) == len(mono['state']['v']), 'Segment population changed')
|
||||
cursor += part['steps']
|
||||
require(cursor == origin + mono['steps'], 'Segments do not cover the monolithic duration')
|
||||
checks = {}
|
||||
step = np.concatenate([part['state']['step'].astype(np.int64) + part['global_tick_offset'] for part in parts])
|
||||
neuron = np.concatenate([part['state']['neuron_idx'] for part in parts])
|
||||
checks['concatenated_global_events'] = np.array_equal(step, mono['state']['step'].astype(np.int64) + origin) and bit_equal(neuron, mono['state']['neuron_idx'])
|
||||
counts = np.zeros_like(mono['state']['counts'], dtype=np.int64)
|
||||
for part in parts:
|
||||
counts += part['state']['counts'].astype(np.int64)
|
||||
checks['sum_segment_counts'] = np.array_equal(counts, mono['state']['counts'].astype(np.int64))
|
||||
requests = []
|
||||
for part in parts:
|
||||
rows = part['requests'].copy(); rows[:, 0] += part['global_tick_offset']; requests.append(rows)
|
||||
mono_requests = mono['requests'].copy(); mono_requests[:, 0] += origin
|
||||
checks['concatenated_requests_five_fields'] = bit_equal(np.concatenate(requests), mono_requests)
|
||||
last = parts[-1]
|
||||
for name in ('v', 'g', 'ref'):
|
||||
checks['terminal.' + name] = bit_equal(last['state'][name], mono['state'][name])
|
||||
checks['terminal.normalized_spike_ring'] = bit_equal(normalized_ring(last['terminal']), normalized_ring(mono['terminal']))
|
||||
for name in ('apl_state', 'apl_pending'):
|
||||
checks['terminal.' + name] = bit_equal(last['terminal'][name], mono['terminal'][name])
|
||||
expected_weight = mono.get('summary', {}).get('effective_weights_raw_sha256')
|
||||
if expected_weight is not None:
|
||||
checks['unchanged_effective_weights'] = _sha(expected_weight) and all(part.get('summary', {}).get('effective_weights_raw_sha256') == expected_weight for part in parts)
|
||||
trace_available = bool(mono.get('traces')) and all(part.get('traces') for part in parts)
|
||||
hash_available = mono.get('state_hashes') is not None and all(part.get('state_hashes') is not None for part in parts)
|
||||
queue_available = mono.get('queues') is not None and all(part.get('queues') is not None for part in parts)
|
||||
if trace_available:
|
||||
for name in TRACE_FILES:
|
||||
checks['concatenated_APL.' + name] = all(bit_equal(part['traces'][name], mono['traces'][name][part['global_tick_offset'] - origin:part['global_tick_offset'] - origin + part['steps']]) for part in parts)
|
||||
if hash_available:
|
||||
checks['concatenated_per_tick_state_hashes'] = all(all(row[name] == mono['state_hashes'][part['global_tick_offset'] - origin + tick][name] for name in HASH_FIELDS)
|
||||
for part in parts for tick, row in enumerate(part['state_hashes']))
|
||||
if queue_available:
|
||||
checks['concatenated_queue_order_and_acceptance'] = all(all(row[name] == mono['queues'][part['global_tick_offset'] - origin + tick][name] for name in QUEUE_FIELDS)
|
||||
for part in parts for tick, row in enumerate(part['queues']))
|
||||
boundary_rows = []
|
||||
events = mono['state']['step'].astype(np.int64) + origin
|
||||
for before, part in zip(parts, parts[1:]):
|
||||
cut, stop = part['global_tick_offset'], part['global_tick_offset'] + part['steps']
|
||||
emitted_before = (events < cut) & (events + 18 >= cut)
|
||||
arriving_this_segment = emitted_before & (events + 18 < stop)
|
||||
pending = before['terminal']['apl_pending']
|
||||
consume = min(part['steps'], 18)
|
||||
spike_carry = normalized_ring(before['terminal'])
|
||||
# Future local arrival tick j consumes normalized slot j+1. This also
|
||||
# covers spikes inherited from before a nonzero monolithic origin,
|
||||
# which are legitimately absent from that run's local event log.
|
||||
pending_spikes = int(spike_carry[1:].sum(dtype=np.int64))
|
||||
arriving_spikes = int(spike_carry[1:consume + 1].sum(dtype=np.int64))
|
||||
if cut - origin >= 18:
|
||||
checks['boundary.' + str(cut) + '.ring_pending_event_count'] = pending_spikes == int(emitted_before.sum())
|
||||
# Slots not overwritten by this short segment must remain bit-identical.
|
||||
if part['steps'] < 19:
|
||||
checks['boundary.' + str(cut) + '.unwritten_spike_slots'] = bit_equal(part['terminal']['spike_ring'][part['steps']:], spike_carry[part['steps']:])
|
||||
if part['steps'] < 18:
|
||||
checks['boundary.' + str(cut) + '.unconsumed_APL'] = bit_equal(part['terminal']['apl_pending'][:18 - part['steps']], pending[part['steps']:])
|
||||
if part.get('traces'):
|
||||
checks['boundary.' + str(cut) + '.applied_parent_APL'] = bit_equal(part['traces']['applied'][:consume], pending[:consume])
|
||||
queue_rows = (part['queues'][:consume] if part.get('queues') is not None else
|
||||
mono['queues'][cut - origin:cut - origin + consume] if mono.get('queues') is not None else None)
|
||||
boundary_rows.append({'global_tick': cut, 'next_segment_steps': part['steps'],
|
||||
'pending_source_spike_events': pending_spikes, 'source_spikes_arriving_in_next_segment': arriving_spikes,
|
||||
'visible_local_log_source_spikes_arriving_in_next_segment': int(arriving_this_segment.sum()),
|
||||
'pending_source_spike_origin': 'recorded normalized ring slots; includes pre-origin carry',
|
||||
'observed_carried_arriving_edges': sum(row['count'] for row in queue_rows) if queue_rows is not None else None,
|
||||
'observed_carried_accepted_edges': sum(row['accepted_count'] for row in queue_rows) if queue_rows is not None else None,
|
||||
'carried_APL_nonzero_pending_values': int(np.count_nonzero(pending)),
|
||||
'carried_APL_nonzero_values_applied_in_next_segment': int(np.count_nonzero(pending[:consume])),
|
||||
'carried_APL_nonzero_rows_applied_in_next_segment': int(np.count_nonzero(np.any(pending[:consume] != 0, axis=1)))})
|
||||
source_arrivals = sum(row['source_spikes_arriving_in_next_segment'] for row in boundary_rows)
|
||||
edge_counts = [row['observed_carried_arriving_edges'] for row in boundary_rows]
|
||||
observed_edges = sum(value for value in edge_counts if value is not None) if any(value is not None for value in edge_counts) else None
|
||||
applied_values = sum(row['carried_APL_nonzero_values_applied_in_next_segment'] for row in boundary_rows)
|
||||
activity = _activity(mono)
|
||||
coverage = {'boundaries': boundary_rows, 'source_spikes_crossing_boundaries_and_due_in_receiver': source_arrivals,
|
||||
'observed_carried_arriving_edges': observed_edges, 'carried_APL_nonzero_applied_values': applied_values,
|
||||
'boundary_pending_event_counts_may_overlap_across_short_segments': True,
|
||||
'actual_edge_arrival_coverage_known': observed_edges is not None}
|
||||
nonvacuous = bool(activity['spikes'] > 0 and activity['apl_nonzero_observed'] and source_arrivals > 0 and
|
||||
observed_edges is not None and observed_edges > 0 and applied_values > 0)
|
||||
exact = bool(all(checks.values()))
|
||||
return {'schema': 'phase4an-segment-engineering-comparison-v1', 'numerical_exact': exact,
|
||||
'nonvacuous_temporal_coverage': nonvacuous, 'temporal_validation_complete': exact and nonvacuous,
|
||||
'pass_all': exact and nonvacuous,
|
||||
'status': 'FAIL-NUMERICAL' if not exact else ('PASS-SEGMENT-SCOPE' if nonvacuous else 'INCOMPLETE-COVERAGE'),
|
||||
'checks': checks, 'failed_checks': [key for key, value in checks.items() if not value],
|
||||
'activity': activity, 'coverage': coverage, 'segments': len(parts), 'steps': mono['steps'], 'global_tick_offset': origin,
|
||||
'reference_comparisons_available': {'APL_traces': trace_available, 'state_hashes': hash_available, 'queue_hashes': queue_available},
|
||||
'queue_joint_hash_compared': False, 'queue_joint_hash_exclusion_reason': 'local tick header differs; ordered-edge/mask hashes and counts compared when available',
|
||||
'scope': 'Explicit finite engineering trajectory/terminal/carry checks only; no learning, physiology, or performance PASS',
|
||||
'independent_CSR_queue_reconstruction_performed': False,
|
||||
'accepted_nonzero_weight_edge_witness_verified': False,
|
||||
'external_seals_and_input_authority_verified_here': False}
|
||||
@@ -0,0 +1,152 @@
|
||||
"""Pure exact input derivations for a future preregistered engineering gate.
|
||||
|
||||
No I/O, random draws, scientific execution or registration. The coordinator
|
||||
must bind the original case/overlay and every static dependency to the sealed
|
||||
AM manifest before using these helpers. A self-consistent dictionary is not
|
||||
independent input authority. Derivation outputs are copies; validate_requests
|
||||
only checks and returns its original, unmodified input.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import copy
|
||||
import re
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
N = 166700
|
||||
AM_HELPER_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4'
|
||||
MASK_SHA = '8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b'
|
||||
|
||||
|
||||
def require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _positive_int(value, name):
|
||||
require(type(value) is int and 0 < value <= np.iinfo(np.int32).max, 'Invalid ' + name)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return type(value) is str and re.fullmatch('[0-9a-f]{64}', value) is not None
|
||||
|
||||
|
||||
def _absolute(value):
|
||||
return (type(value) is str and value.startswith('/') and not value.startswith('//') and
|
||||
PurePosixPath(value).is_absolute() and
|
||||
'..' not in PurePosixPath(value).parts)
|
||||
|
||||
|
||||
def validate_requests(requests, steps):
|
||||
_positive_int(steps, 'steps')
|
||||
require(type(requests) is dict and set(requests) == {'tick', 'neuron_idx'}, 'Exact sparse request schema required')
|
||||
for key, value in requests.items():
|
||||
require(isinstance(value, np.ndarray) and value.dtype == np.int32 and value.ndim == 1,
|
||||
'Request requires int32 vector: ' + key)
|
||||
tick, neuron = requests['tick'], requests['neuron_idx']
|
||||
require(tick.shape == neuron.shape, 'Request shape mismatch')
|
||||
require(np.all((tick >= 0) & (tick < steps)) and np.all((neuron >= 0) & (neuron < N)), 'Request outside fixed trial/canonical set')
|
||||
key = tick.astype(np.int64) * N + neuron
|
||||
require(len(key) < 2 or np.all(key[1:] > key[:-1]), 'Requests must be sorted and unique; no implicit deduplication')
|
||||
return requests
|
||||
|
||||
|
||||
def slice_requests(requests, steps, start, stop):
|
||||
"""Half-open exact slice; translate ticks only, never redraw or reorder."""
|
||||
validate_requests(requests, steps)
|
||||
require(type(start) is int and type(stop) is int and 0 <= start < stop <= steps, 'Invalid half-open input interval')
|
||||
lo, hi = np.searchsorted(requests['tick'], (start, stop))
|
||||
result = {'tick': (requests['tick'][lo:hi] - np.int32(start)).copy(),
|
||||
'neuron_idx': requests['neuron_idx'][lo:hi].copy()}
|
||||
validate_requests(result, stop - start)
|
||||
return result
|
||||
|
||||
|
||||
def verify_partition(original, steps, intervals, parts):
|
||||
"""Verify complete coverage plus exact request reconstruction, even blanks."""
|
||||
validate_requests(original, steps)
|
||||
require(type(intervals) in (list, tuple) and type(parts) in (list, tuple) and
|
||||
len(intervals) == len(parts) > 0, 'Partition needs all intervals and parts')
|
||||
end, reconstructed = 0, {'tick': [], 'neuron_idx': []}
|
||||
for interval, part in zip(intervals, parts):
|
||||
require(type(interval) in (list, tuple) and len(interval) == 2, 'Invalid interval schema')
|
||||
start, stop = interval
|
||||
require(type(start) is int and type(stop) is int and start == end and start < stop <= steps,
|
||||
'Partition gap/overlap/nonpositive interval')
|
||||
validate_requests(part, stop - start)
|
||||
expected = slice_requests(original, steps, start, stop)
|
||||
require(all(part[k].tobytes() == expected[k].tobytes() for k in part), 'Partition changes actual requests')
|
||||
reconstructed['tick'].append(part['tick'].astype(np.int64) + start)
|
||||
reconstructed['neuron_idx'].append(part['neuron_idx'])
|
||||
end = stop
|
||||
require(end == steps, 'Partition omits the terminal interval')
|
||||
require(np.array_equal(np.concatenate(reconstructed['tick']), original['tick']) and
|
||||
np.array_equal(np.concatenate(reconstructed['neuron_idx']), original['neuron_idx']), 'Reconstructed input mismatch')
|
||||
return {'complete': True, 'intervals': [list(x) for x in intervals], 'steps': steps,
|
||||
'requests': len(original['tick']), 'random_draws': 0, 'input_reselected': False}
|
||||
|
||||
|
||||
def derive_spec(original, *, start, stop, budget_root, output_budget_bytes, parent_sha=None):
|
||||
"""Explicit new case admission/budget labels; frozen model fields unchanged.
|
||||
|
||||
New budget_root must be outside the OLD budget tree. Full source/data/
|
||||
ancestor containment checks remain the runtime/coordinator's responsibility.
|
||||
A nonzero interval requires the actual parent manifest's external SHA;
|
||||
future parent hashes must not be fabricated during preregistration.
|
||||
"""
|
||||
require(type(original) is dict and original.get('kind') == 'fullgraph', 'Original fullgraph spec required')
|
||||
steps = original.get('steps')
|
||||
_positive_int(steps, 'original steps')
|
||||
require(original.get('dt_ms') == .1 and original.get('duration_ms') == steps * .1 and
|
||||
type(original.get('apl_delay_steps')) is int and original['apl_delay_steps'] == 18 and
|
||||
original.get('record_history') is False and original.get('training') is False and
|
||||
original.get('within_trial_weight_updates', False) is False, 'Original fixed D18 trial contract required')
|
||||
require(not any('checkpoint' in k.lower() or k in ('global_tick_offset', 'resume', 'continuation', 'initial_state')
|
||||
for k in original), 'Original must be a fresh case')
|
||||
require(type(start) is int and type(stop) is int and 0 <= start < stop <= steps, 'Invalid case interval')
|
||||
require(_absolute(budget_root) and _absolute(original.get('budget_root')), 'Explicit absolute budget roots required')
|
||||
new, old = PurePosixPath(budget_root), PurePosixPath(original['budget_root'])
|
||||
require(not new.is_relative_to(old) and not old.is_relative_to(new), 'New output budget must not overlap old experiment tree')
|
||||
require(type(output_budget_bytes) is int and output_budget_bytes > 0, 'Explicit positive byte budget required')
|
||||
require((start == 0 and parent_sha is None) or (start > 0 and _sha(parent_sha)), 'Only a continued interval needs a real parent SHA')
|
||||
result = copy.deepcopy(original)
|
||||
result.update(steps=stop - start, duration_ms=(stop - start) * .1, max_steps=steps,
|
||||
budget_root=budget_root, output_budget_bytes=output_budget_bytes,
|
||||
within_trial_weight_updates=False,
|
||||
outer_protocol='phase4an-engineering-validation-no-training')
|
||||
if start:
|
||||
result.update(global_tick_offset=start, parent_checkpoint_manifest_sha256=parent_sha)
|
||||
changes = {key: {'present_before': key in original, 'before': copy.deepcopy(original.get(key)),
|
||||
'after': copy.deepcopy(value)} for key, value in result.items()
|
||||
if key not in original or value != original[key]}
|
||||
allowed = {'steps', 'duration_ms', 'max_steps', 'budget_root', 'output_budget_bytes',
|
||||
'within_trial_weight_updates', 'outer_protocol', 'global_tick_offset',
|
||||
'parent_checkpoint_manifest_sha256'}
|
||||
require(set(changes) <= allowed and all(result[k] == v for k, v in original.items() if k not in allowed),
|
||||
'Unexpected scientific spec edit')
|
||||
return result, {'input_interval': [start, stop], 'explicit_field_changes': changes,
|
||||
'all_other_fields_unchanged': True, 'random_draws': 0, 'training': False}
|
||||
|
||||
|
||||
def adapt_overlay_manifest(original, am_helper_path):
|
||||
"""Translate observed AM manifest fields; preserve original overlay bytes.
|
||||
|
||||
This function validates declarations only. Callers independently rehash
|
||||
every referenced file against external AM/source authority before use.
|
||||
"""
|
||||
require(type(original) is dict and original.get('schema') == 'phase4am-overlay-v1' and
|
||||
original.get('overlay_name') in ('zero', 'static25') and
|
||||
original.get('training') is False and original.get('fixed_for_whole_trial') is True,
|
||||
'Only the fixed AM zero/static25 overlays are in this engineering gate')
|
||||
require(original.get('mask_sha256') == MASK_SHA and _absolute(am_helper_path), 'Original mask/helper identity required')
|
||||
names = ('overlay_name', 'overlay_sha256', 'mask_path', 'mask_sha256',
|
||||
'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir')
|
||||
require(all(k in original for k in names), 'Missing observed AM manifest field')
|
||||
require(all(_sha(original[k]) for k in ('overlay_sha256', 'mask_sha256', 'base_candidate_sha256')),
|
||||
'Invalid external overlay/base SHA')
|
||||
require(all(_absolute(original[k]) for k in ('mask_path', 'base_candidate_path', 'aj_model_dir')),
|
||||
'Invalid external overlay/base path')
|
||||
return dict(schema='phase4an-overlay-v1', **{k: copy.deepcopy(original[k]) for k in names},
|
||||
am_overlay_path=am_helper_path, am_overlay_sha256=AM_HELPER_SHA)
|
||||
@@ -0,0 +1,153 @@
|
||||
"""Pure fixed 32-call engineering inventory; no I/O, GPU or input preparation.
|
||||
|
||||
Rows describe required executions and comparisons, not evidence they occurred.
|
||||
The external AM reference is a symbolic role: a coordinator must bind its actual
|
||||
case, overlay, sealed outputs and hashes before execution. A validated plan does
|
||||
not prove input authority, comparison success, conditioning or learning.
|
||||
All public results are independently constructed mutable copies.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
SCHEMA = 'phase4an-engineering-plan-draft-v1'
|
||||
OVERLAYS = ('zero', 'static25')
|
||||
MODES = ('reference', 'light')
|
||||
CHAIN_LENGTHS = (5, 13, 1, 481, 500)
|
||||
PREFIX_ENDS = (18, 19, 500)
|
||||
|
||||
|
||||
def _fresh_id(overlay, mode, repeat=0):
|
||||
return f'{overlay}--fresh--{mode}-r{repeat}'
|
||||
|
||||
|
||||
def _prefix_id(overlay, end):
|
||||
return f'{overlay}--prefix--reference-t{end:04d}'
|
||||
|
||||
|
||||
def _chain_id(overlay, mode, index):
|
||||
return f'{overlay}--chain--{mode}-s{index:02d}'
|
||||
|
||||
|
||||
def _comparison(check, row, *, reference=None, external=None, scope, candidates=None):
|
||||
"""Exact descriptors; start/stop are always absolute, half-open ticks."""
|
||||
return dict(check=check, reference_row_id=reference, external_reference=external,
|
||||
candidate_row_ids=[row['row_id']] if candidates is None else list(candidates),
|
||||
start=row['start'], stop=row['stop'], scope=scope)
|
||||
|
||||
|
||||
def _row(identity, overlay, mode, kind, start, stop, parent=None):
|
||||
return dict(row_id=identity, overlay=overlay, mode=mode, kind=kind,
|
||||
start=start, stop=stop, steps=stop - start, parent_row_id=parent,
|
||||
expected_comparisons=[])
|
||||
|
||||
|
||||
def _terminal_reference(overlay, stop):
|
||||
if stop == 5:
|
||||
return _chain_id(overlay, 'reference', 0)
|
||||
if stop == 1000:
|
||||
return _fresh_id(overlay, 'reference')
|
||||
return _prefix_id(overlay, stop)
|
||||
|
||||
|
||||
def build_plan():
|
||||
"""Return the sole current proposal: 32 calls / 11,074 full-graph ticks.
|
||||
|
||||
kind='chain' with parent_row_id=None is its fresh first segment, NOT a
|
||||
continuation from an assumed zero checkpoint. Later segments explicitly
|
||||
reference their predecessor. A chain's local request/event coordinates
|
||||
are translated using start; terminal.next_tick remains its local phase.
|
||||
|
||||
All fresh/prefix rows globally precede chains. Reference chains precede
|
||||
light chains, allowing the reference first segment to supply prefix 5.
|
||||
No extra prefix-5 execution, synthetic 52-call matrix, long idle run,
|
||||
input selection, training, parameter update or executor is implemented.
|
||||
|
||||
Comparison scopes:
|
||||
reference_full/reference_prefix/reference_slice: include reference
|
||||
tick-state hashes, APL traces, queues, requests, events and states;
|
||||
light_recorded: requests.csv, events/counts, final state, APL state,
|
||||
normalized pending queues and finite/weight/arrival accounting;
|
||||
terminal_state_and_normalized_queues: no comparison of segment counts
|
||||
against cumulative prefix counts; counts are handled by chain_exact;
|
||||
parent_state_and_pending_queues: bound parent restoration/carry checks.
|
||||
A comparator must implement these scopes from artifacts, not this plan's
|
||||
existence. Shift local queue headers before comparing absolute timelines.
|
||||
"""
|
||||
rows = []
|
||||
for overlay in OVERLAYS:
|
||||
for mode, repeat in (('reference', 0), ('light', 0), ('light', 1)):
|
||||
row = _row(_fresh_id(overlay, mode, repeat), overlay, mode, 'fresh', 0, 1000)
|
||||
scope = 'reference_full' if mode == 'reference' else 'light_recorded'
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'am_exact', row, external='sealed_AM_selected_case_same_overlay_r0', scope=scope))
|
||||
if mode == 'light':
|
||||
reference = _fresh_id(overlay, 'reference' if repeat == 0 else 'light')
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'fresh_exact', row, reference=reference, scope='light_recorded'))
|
||||
rows.append(row)
|
||||
for end in PREFIX_ENDS:
|
||||
row = _row(_prefix_id(overlay, end), overlay, 'reference', 'prefix', 0, end)
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'prefix_of_reference', row, reference=_fresh_id(overlay, 'reference'), scope='reference_prefix'))
|
||||
rows.append(row)
|
||||
|
||||
for overlay in OVERLAYS:
|
||||
for mode in MODES:
|
||||
start, parent, members = 0, None, []
|
||||
for index, length in enumerate(CHAIN_LENGTHS):
|
||||
row = _row(_chain_id(overlay, mode, index), overlay, mode, 'chain', start, start + length, parent)
|
||||
members.append(row['row_id'])
|
||||
if mode == 'reference':
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'segment_of_reference', row, reference=_fresh_id(overlay, 'reference'), scope='reference_slice'))
|
||||
else:
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'segment_exact', row, reference=_chain_id(overlay, 'reference', index), scope='light_recorded'))
|
||||
terminal = _terminal_reference(overlay, row['stop'])
|
||||
if terminal != row['row_id']:
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'checkpoint_exact', row, reference=terminal, scope='terminal_state_and_normalized_queues'))
|
||||
if parent is not None:
|
||||
row['expected_comparisons'].append(_comparison(
|
||||
'parent_carry', row, reference=parent, scope='parent_state_and_pending_queues'))
|
||||
if index == len(CHAIN_LENGTHS) - 1:
|
||||
comparison = _comparison('chain_exact', row, reference=_fresh_id(overlay, mode),
|
||||
scope='reference_full' if mode == 'reference' else 'light_recorded', candidates=members)
|
||||
comparison['start'] = 0
|
||||
row['expected_comparisons'].append(comparison)
|
||||
rows.append(row)
|
||||
start, parent = row['stop'], row['row_id']
|
||||
|
||||
return dict(schema=SCHEMA, overlays=list(OVERLAYS), chain_lengths=list(CHAIN_LENGTHS),
|
||||
rows=rows, total_calls=32, total_ticks=11074,
|
||||
training=False, candidate_generated=False)
|
||||
|
||||
|
||||
def _exact(actual, expected, path):
|
||||
"""JSON-shaped values with exact Python types; True is not integer 1."""
|
||||
if type(actual) is not type(expected):
|
||||
raise ValueError(f'Plan type mismatch at {path}')
|
||||
if type(expected) is dict:
|
||||
if any(type(key) is not str for key in actual) or set(actual) != set(expected):
|
||||
raise ValueError(f'Plan fields mismatch at {path}')
|
||||
for key in expected:
|
||||
_exact(actual[key], expected[key], path + '.' + key)
|
||||
elif type(expected) is list:
|
||||
if len(actual) != len(expected):
|
||||
raise ValueError(f'Plan inventory length mismatch at {path}')
|
||||
for index, (left, right) in enumerate(zip(actual, expected)):
|
||||
_exact(left, right, f'{path}[{index}]')
|
||||
elif actual != expected:
|
||||
raise ValueError(f'Plan value mismatch at {path}')
|
||||
|
||||
|
||||
def validate_plan(plan):
|
||||
"""Reject any noncanonical plan; return a new copy, never a PASS receipt.
|
||||
|
||||
Exact canonical reconstruction rejects unknown fields, order changes,
|
||||
omitted/extra overlays or calls, interval gaps, false parent links and
|
||||
weakening/removal of required comparisons. No input files are read.
|
||||
"""
|
||||
canonical = build_plan()
|
||||
_exact(plan, canonical, 'plan')
|
||||
return canonical
|
||||
@@ -0,0 +1,532 @@
|
||||
"""Read-only AM authority verification and exclusive AN engineering preparation.
|
||||
|
||||
Public prepare(model_manifest_sha, experiment_manifest_sha, final_sha) requires
|
||||
three externally recorded, actual AM evidence hashes. It verifies both complete
|
||||
AM seals once, checks their declared gates, then fixes the first planned r0 case.
|
||||
It creates only templates/requests/overlay descriptors and a preregistration;
|
||||
never a GPU trajectory or fictitious future checkpoint hash. The caller records
|
||||
the returned preregistration SHA outside this experiment before execution.
|
||||
|
||||
read_registration(expected_sha) rehashes the exact used source/input inventory
|
||||
and control anchors without rerunning either scientific gate or full AM seal.
|
||||
Future spec.json/checkpoint receipts are runtime artifacts, not preregistered
|
||||
bytes. No frozen model module globals are modified; no Torch is imported.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import importlib.util
|
||||
import json
|
||||
from pathlib import Path, PurePosixPath
|
||||
import re
|
||||
import shutil
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
ROOT = Path('/data/digital-drosophila')
|
||||
MODEL = Path(__file__).resolve().parent
|
||||
EXP = ROOT / 'experiments/phase4an-engineering-validation'
|
||||
AM_MODEL_REL = Path('models/male-cns-lif/wholebrain-plastic-interface-v1')
|
||||
AM_EXP_REL = Path('experiments/phase4am-wholebrain-plastic-interface')
|
||||
AK_REL = Path('experiments/phase4ak-apl-timing-validation')
|
||||
AI_HELPER_REL = Path('models/male-cns-lif/pn-recruitment-audit-v1/publication.py')
|
||||
AI_HELPER_SHA = '7a79c499098b74163e7bb16adabcbc54e7069dafa204d0b99c7f4c03d79f8016'
|
||||
AM_PREREG_SHA = '465886b78ada08092c2c83de278844707499718977fb6dc6a7608391da0e5304'
|
||||
AM_OVERLAY_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4'
|
||||
FINAL_STATUS = 'PASS-EXACT-WHOLEBRAIN-PLASTIC-INTERFACE-NOT-LEARNING'
|
||||
REG_SCHEMA = 'phase4an-engineering-registration-v1'
|
||||
BUDGET = {'output_budget_bytes': 8 * 1024**3, 'minimum_free_reserve_bytes': 10 * 1024**3}
|
||||
MODULES = ('runtime_light', 'runtime_continuation', 'engineering_inputs', 'engineering_plan',
|
||||
'engineering_compare', 'engineering_queue_audit', 'engineering_prepare', 'engineering_workflow',
|
||||
'engineering_scopes', 'engineering_analysis')
|
||||
SOURCE_FILES = tuple(sorted([name + '.py' for name in MODULES] + ['test_' + name + '.py' for name in MODULES] + ['ENGINEERING_GATE_DRAFT.md']))
|
||||
REFERENCES = ('auditory-10hz', 'auditory-100hz', 'olfactory-50hz', 'mechanosensory-50hz', 'gustatory-50hz')
|
||||
GRAPH_FILES = ('nodes.npy', 'edge_id.npy', 'src.npy', 'dst.npy', 'signed_raw.npy', 'ptr.npy', 'weights.npy')
|
||||
APL_FILES = ('input_matrix.npy', 'output_matrix.npy', 'kc_globals.npy', 'apl_globals.npy')
|
||||
_MODULE_CACHE = {}
|
||||
|
||||
|
||||
def require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def digest(path):
|
||||
value = hashlib.sha256()
|
||||
with Path(path).open('rb') as stream:
|
||||
for block in iter(lambda: stream.read(8 << 20), b''):
|
||||
value.update(block)
|
||||
return value.hexdigest()
|
||||
|
||||
|
||||
def sha(value):
|
||||
return type(value) is str and re.fullmatch('[0-9a-f]{64}', value) is not None
|
||||
|
||||
|
||||
def read(path):
|
||||
def unique(pairs):
|
||||
result = {}
|
||||
for key, value in pairs:
|
||||
require(key not in result, 'Duplicate JSON key: ' + key)
|
||||
result[key] = value
|
||||
return result
|
||||
return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=unique,
|
||||
parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value)))
|
||||
|
||||
|
||||
def save(path, value):
|
||||
path = Path(path)
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with path.open('x', encoding='utf-8') as stream:
|
||||
json.dump(value, stream, sort_keys=True, indent=2, ensure_ascii=False, allow_nan=False)
|
||||
stream.write('\n')
|
||||
|
||||
|
||||
def load_module(name, path, expected):
|
||||
path = Path(path).resolve(strict=True)
|
||||
raw = path.read_bytes()
|
||||
require(hashlib.sha256(raw).hexdigest() == expected, 'Source pin mismatch: ' + str(path))
|
||||
identity = str(path), expected
|
||||
if name in sys.modules:
|
||||
require(_MODULE_CACHE.get(name) == (identity, sys.modules[name]), 'Unverified module namespace collision')
|
||||
return sys.modules[name]
|
||||
spec = importlib.util.spec_from_file_location(name, path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
sys.modules[name] = module
|
||||
try:
|
||||
exec(compile(raw, str(path), 'exec'), module.__dict__)
|
||||
except BaseException:
|
||||
sys.modules.pop(name, None)
|
||||
raise
|
||||
_MODULE_CACHE[name] = identity, module
|
||||
return module
|
||||
|
||||
|
||||
def source_inventory(model):
|
||||
model = Path(model).resolve(strict=True)
|
||||
result = {}
|
||||
for name in SOURCE_FILES:
|
||||
path = model / name
|
||||
require(path.is_file() and not path.is_symlink() and path.resolve().parent == model, 'Required engineering source missing/linked: ' + name)
|
||||
result[str(path)] = digest(path)
|
||||
return result
|
||||
|
||||
|
||||
def _relative(value):
|
||||
require(type(value) is str and value and '\\' not in value, 'Invalid relative inventory path')
|
||||
path = PurePosixPath(value)
|
||||
require(not path.is_absolute() and str(path) == value and all(part not in ('', '.', '..') and ':' not in part for part in path.parts),
|
||||
'Noncanonical relative inventory path')
|
||||
return path
|
||||
|
||||
|
||||
def _safe_file(root, relative):
|
||||
relative = _relative(relative)
|
||||
root = Path(root).resolve(strict=True)
|
||||
path = root
|
||||
for part in relative.parts:
|
||||
path = path / part
|
||||
require(not path.is_symlink(), 'Linked authority/input file')
|
||||
require(path.is_file() and path.resolve().is_relative_to(root), 'Missing or escaping input file')
|
||||
return path
|
||||
|
||||
|
||||
def _index(rows):
|
||||
require(type(rows) is list and rows, 'Missing sealed inventory rows')
|
||||
result = {}
|
||||
for row in rows:
|
||||
require(type(row) is dict and set(row) == {'path', 'bytes', 'sha256'} and
|
||||
type(row['bytes']) is int and row['bytes'] >= 0 and sha(row['sha256']), 'Invalid sealed inventory row')
|
||||
_relative(row['path'])
|
||||
require(row['path'] not in result, 'Duplicate sealed inventory row')
|
||||
result[row['path']] = row
|
||||
return result
|
||||
|
||||
|
||||
def _bind(inputs, path, expected=None):
|
||||
path = Path(path).resolve(strict=True)
|
||||
actual = digest(path)
|
||||
require(expected is None or actual == expected, 'Authority/input SHA mismatch: ' + str(path))
|
||||
require(str(path) not in inputs or inputs[str(path)] == actual, 'Conflicting authority for input')
|
||||
inputs[str(path)] = actual
|
||||
return path
|
||||
|
||||
|
||||
def _sealed_json(experiment, index, relative):
|
||||
require(relative in index, 'Required authority absent from AM seal: ' + relative)
|
||||
path = _safe_file(experiment, relative)
|
||||
require(digest(path) == index[relative]['sha256'], 'Sealed JSON changed: ' + relative)
|
||||
return read(path)
|
||||
|
||||
|
||||
def validate_final_status(final, scientific, before, after, refs):
|
||||
"""Check actual AM final/summary/report schemas, never infer scientific PASS."""
|
||||
require(final.get('status') == scientific.get('status') == FINAL_STATUS, 'AM final status is not the required completed interface gate')
|
||||
for key in ('zero_overlay_exact', 'static25_repeat_exact', 'old_integrity_pass', 'frozen_five_reference_pass',
|
||||
'frozen_eligibility_and_mask_unchanged', 'base_weights_unchanged'):
|
||||
require(final.get(key) is True, 'AM final gate failed/missing: ' + key)
|
||||
require(final.get('primary_trials_per_stage') == 120 and final.get('repeats_per_stage') == 24 and
|
||||
scientific.get('primary_trials_per_stage') == 120 and scientific.get('technical_repeats_per_stage') == 24,
|
||||
'AM complete coverage counts changed')
|
||||
for value in (final, scientific):
|
||||
require(value.get('training') is False and value.get('learned_candidate') is False, 'AM interface must not claim training/learning')
|
||||
require(scientific.get('zero_overlay_exact') is True and scientific.get('static25_repeat_exact') is True, 'Scientific summary gate mismatch')
|
||||
require(type(before) is dict and type(after) is dict and len(before) == len(after) == 28 and set(before) == set(after),
|
||||
'AM must retain the same complete 28-root old-integrity inventory')
|
||||
for name in before:
|
||||
for report in (before[name], after[name]):
|
||||
require(type(report) is dict and report.get('pass') is True, 'Old-integrity gate failed: ' + name)
|
||||
if 'mismatches' in report:
|
||||
require(report['mismatches'] == [], 'Old-integrity mismatch list is nonempty')
|
||||
for key in ('manifest_sha256', 'checksums_sha256'):
|
||||
if key in before[name] or key in after[name]:
|
||||
require(sha(before[name].get(key)) and before[name][key] == after[name].get(key), 'Old-root control changed: ' + name)
|
||||
require(refs.get('pass_all') is True and type(refs.get('trials')) is list and
|
||||
[row.get('trial') for row in refs['trials']] == list(REFERENCES), 'Five old deterministic references are incomplete or reordered')
|
||||
for row in refs['trials']:
|
||||
require(type(row.get('exact')) is dict and set(row['exact']) == {'step', 'neuron_idx', 'counts', 'v', 'g'} and
|
||||
all(value is True for value in row['exact'].values()), 'Old reference arrays are not exact')
|
||||
|
||||
|
||||
def validate_planned(planned):
|
||||
require(type(planned) is list and len(planned) == 144, 'AM planned coverage must be 144')
|
||||
keys = []
|
||||
for row in planned:
|
||||
require(type(row) is dict and type(row.get('name')) is str and re.fullmatch('[A-Za-z0-9_-]+', row['name']) and
|
||||
row.get('cohort') in ('seed_validation', 'novel_to_aj') and type(row.get('repeat')) is int and row['repeat'] in (0, 1),
|
||||
'Invalid AM planned row identity')
|
||||
keys.append((row['name'], row['repeat']))
|
||||
require(len(set(keys)) == 144 and sum(row['repeat'] == 0 for row in planned) == 120 and sum(row['repeat'] == 1 for row in planned) == 24,
|
||||
'AM primary/repeat set incomplete')
|
||||
require(all((name, 0) in set(keys) for name, repeat in keys if repeat == 1), 'AM repeat lacks primary')
|
||||
return next(dict(row) for row in planned if row['repeat'] == 0)
|
||||
|
||||
|
||||
def validate_stage(experiment, index, stage, planned, root):
|
||||
"""Validate all 144 declared results against the already verified AM seal."""
|
||||
receipt = _sealed_json(experiment, index, 'audit/' + stage + '-complete.json')
|
||||
require(receipt.get('stage') == stage and receipt.get('pass_all') is True and receipt.get('trials') == 144 and
|
||||
receipt.get('primary_trials') == 120 and receipt.get('technical_repeats') == 24 and receipt.get('training') is False,
|
||||
'AM stage is incomplete: ' + stage)
|
||||
completed = receipt.get('completed')
|
||||
require(type(completed) is list and len(completed) == 144, 'AM stage completion inventory truncated')
|
||||
for row, done in zip(planned, completed):
|
||||
require(done.get('name') == row['name'] and done.get('repeat') == row['repeat'], 'AM stage completion order/identity mismatch')
|
||||
prefix = 'gpu/' + stage + '/' + row['name'] + '-r' + str(row['repeat']) + '/'
|
||||
expected = [dict(path=key[len(prefix):], bytes=value['bytes'], sha256=value['sha256']) for key, value in sorted(index.items())
|
||||
if key.startswith(prefix) and key != prefix + 'output-hashes.json']
|
||||
inventory = _sealed_json(experiment, index, prefix + 'output-hashes.json')
|
||||
require(inventory == expected and inventory, 'AM completed output inventory differs from sealed payload')
|
||||
require(done.get('output_hashes_sha256') == index[prefix + 'output-hashes.json']['sha256'] and
|
||||
done.get('comparison_sha256') == index[prefix + 'comparison.json']['sha256'], 'AM stage receipt SHA mismatch')
|
||||
summary = _sealed_json(experiment, index, prefix + 'summary.json')
|
||||
compare = _sealed_json(experiment, index, prefix + 'comparison.json')
|
||||
folder, case = experiment / prefix.rstrip('/'), root / AK_REL / 'cases' / row['cohort'] / row['name']
|
||||
require(summary.get('case') == row['name'] and summary.get('complete') is True and summary.get('pass_all') is True and
|
||||
summary.get('all_finite') is True and summary.get('steps') == summary.get('completed_ticks') == 1000,
|
||||
'AM selected stage contains a failed/truncated trial')
|
||||
require(compare.get('pass_all') is True and compare.get('overlay_name') == stage and compare.get('case_dir') == str(case) and
|
||||
compare.get('right') == str(folder), 'AM comparison identity/gate mismatch')
|
||||
require(type(compare.get('checks')) is dict and compare['checks'] and all(value is True for value in compare['checks'].values()) and
|
||||
compare.get('failed_checks') == [] and 'error' not in compare and 'read_manifest_error' not in compare and
|
||||
compare.get('full_queue_reconstructed') is True, 'AM comparison has a hidden negative/missing numerical gate')
|
||||
mode = 'zero' if stage == 'zero' else ('repeat' if row['repeat'] else 'self')
|
||||
left = root / AK_REL / 'gpu' / row['cohort'] / (row['name'] + '-r' + str(row['repeat'])) if stage == 'zero' else (
|
||||
experiment / 'gpu' / stage / (row['name'] + '-r0') if row['repeat'] else folder)
|
||||
require(compare.get('mode') == mode and compare.get('left') == str(left), 'AM comparison reference/mode mismatch')
|
||||
return receipt
|
||||
|
||||
|
||||
def _layout(root, model, experiment):
|
||||
root, model = Path(root).resolve(strict=True), Path(model).resolve(strict=True)
|
||||
experiment = Path(experiment).resolve(strict=False)
|
||||
require(experiment == root / 'experiments/phase4an-engineering-validation' and model.is_relative_to(root / 'models/male-cns-lif'),
|
||||
'Unexpected project/output layout')
|
||||
require(not experiment.exists() or (experiment.is_dir() and not any(experiment.iterdir())), 'Refusing populated experiment; preserve all partials')
|
||||
return root, model, experiment
|
||||
|
||||
|
||||
def _budget(experiment):
|
||||
experiment = Path(experiment)
|
||||
used = sum(path.stat().st_size for path in experiment.rglob('*') if path.is_file()) if experiment.exists() else 0
|
||||
require(used < BUDGET['output_budget_bytes'], '8 GiB engineering budget exhausted; preserve partials')
|
||||
free = shutil.disk_usage(experiment.parent).free
|
||||
require(free >= BUDGET['minimum_free_reserve_bytes'] + BUDGET['output_budget_bytes'] - used,
|
||||
'Insufficient capacity for remaining 8 GiB budget plus 10 GiB free reserve')
|
||||
return dict(BUDGET, used_bytes=used, free_bytes=free)
|
||||
|
||||
|
||||
def budget():
|
||||
return _budget(EXP)
|
||||
|
||||
|
||||
def verify_authority(anchors):
|
||||
"""Full read-only AM seal recheck for the finalizer; no gate rerun."""
|
||||
require(sys.dont_write_bytecode, 'Use python -B for immutable authority verification')
|
||||
root = ROOT.resolve(strict=True)
|
||||
values = [(root / AM_MODEL_REL, 'am_model_manifest_sha256'), (root / AM_EXP_REL, 'am_experiment_manifest_sha256')]
|
||||
for directory, key in values:
|
||||
require(sha(anchors.get(key)) and digest(directory / 'manifest.json') == anchors[key], 'External AM manifest anchor mismatch')
|
||||
require(sha(anchors.get('am_final_sha256')) and digest(root / AM_EXP_REL / 'audit/final-status.json') == anchors['am_final_sha256'],
|
||||
'External AM final anchor mismatch')
|
||||
require(anchors.get('am_preregistration_sha256') == AM_PREREG_SHA and
|
||||
digest(root / AM_EXP_REL / 'protocol/preregistered.json') == AM_PREREG_SHA, 'AM preregistration anchor mismatch')
|
||||
require(anchors.get('AI_publication_helper_sha256') == AI_HELPER_SHA, 'Publication helper authority changed')
|
||||
publication = load_module('phase4an_pinned_AI_publication', root / AI_HELPER_REL, AI_HELPER_SHA)
|
||||
result = {'model': publication.verify_seal(values[0][0]), 'experiment': publication.verify_seal(values[1][0])}
|
||||
require(result['model']['manifest_sha256'] == anchors['am_model_manifest_sha256'] and
|
||||
result['experiment']['manifest_sha256'] == anchors['am_experiment_manifest_sha256'], 'AM seal changed during verification')
|
||||
return result
|
||||
|
||||
|
||||
def _old_integrity(root, context_sha, expected):
|
||||
"""Reuse the pinned AM read-only integrity chain, not a brain runtime."""
|
||||
context_path = root / AM_MODEL_REL / 'context.py'
|
||||
previous_path = list(sys.path)
|
||||
torch_before = 'torch' in sys.modules
|
||||
try:
|
||||
context = load_module('phase4an_sealed_AM_integrity', context_path, context_sha)
|
||||
require(context.ROOT.resolve() == root.resolve(), 'AM integrity helper belongs to another root')
|
||||
result = context.integrity()
|
||||
finally:
|
||||
sys.path[:] = previous_path
|
||||
require(torch_before or 'torch' not in sys.modules, 'Integrity unexpectedly imported Torch')
|
||||
require(type(result) is dict and len(result) == 28 and set(result) == set(expected) and
|
||||
all(type(row) is dict and row.get('pass') is True for row in result.values()), 'Current 28-root old-integrity gate failed')
|
||||
for name, row in result.items():
|
||||
if 'mismatches' in row:
|
||||
require(row['mismatches'] == [], 'Current old-integrity mismatch list is nonempty')
|
||||
for key in ('manifest_sha256', 'checksums_sha256', 'reference_sha256'):
|
||||
if key in expected[name] or key in row:
|
||||
require(sha(row.get(key)) and row[key] == expected[name].get(key), 'Old integrity authority changed: ' + name)
|
||||
return result
|
||||
|
||||
|
||||
def verify_old_integrity(registration):
|
||||
"""Rehash the pinned 28 old roots/snapshot via original CPU-only code."""
|
||||
require(sys.dont_write_bytecode, 'Use python -B for old-integrity verification')
|
||||
path = ROOT / AM_MODEL_REL / 'context.py'
|
||||
expected_sha = registration['input_sha256'].get(str(path.resolve(strict=True)))
|
||||
require(sha(expected_sha) and digest(path) == expected_sha, 'AM integrity source is not a registered input')
|
||||
before_path = EXP / 'regression/old-integrity-before.json'
|
||||
require(digest(before_path) == registration['input_sha256'].get(str(before_path.resolve(strict=True))), 'Before-integrity evidence changed')
|
||||
return _old_integrity(ROOT, expected_sha, read(before_path))
|
||||
|
||||
|
||||
def _prepare(root, model, experiment, am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha):
|
||||
require(sys.dont_write_bytecode, 'Preparation requires python -B; old sources must remain byte-for-byte read-only')
|
||||
require(all(sha(value) for value in (am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha)), 'Three actual external AM SHA-256 anchors are mandatory')
|
||||
root, model, experiment = _layout(root, model, experiment)
|
||||
_budget(experiment)
|
||||
sources = source_inventory(model)
|
||||
inputs_module = load_module('phase4an_prepare_inputs', model / 'engineering_inputs.py', sources[str(model / 'engineering_inputs.py')])
|
||||
plan_module = load_module('phase4an_prepare_plan', model / 'engineering_plan.py', sources[str(model / 'engineering_plan.py')])
|
||||
runtime = load_module('phase4an_prepare_light', model / 'runtime_light.py', sources[str(model / 'runtime_light.py')])
|
||||
publication = load_module('phase4an_pinned_AI_publication', root / AI_HELPER_REL, AI_HELPER_SHA)
|
||||
am_model, am = root / AM_MODEL_REL, root / AM_EXP_REL
|
||||
require(digest(am_model / 'manifest.json') == am_model_manifest_sha and digest(am / 'manifest.json') == am_experiment_manifest_sha and
|
||||
digest(am / 'audit/final-status.json') == am_final_sha, 'External AM authority mismatch')
|
||||
verified = {'model': publication.verify_seal(am_model), 'experiment': publication.verify_seal(am)}
|
||||
require(verified['model']['manifest_sha256'] == am_model_manifest_sha and verified['experiment']['manifest_sha256'] == am_experiment_manifest_sha,
|
||||
'AM controls changed during full seal verification')
|
||||
idx = _index(verified['experiment']['files']); midx = _index(verified['model']['files'])
|
||||
reg = _sealed_json(am, idx, 'protocol/preregistered.json')
|
||||
require(idx['protocol/preregistered.json']['sha256'] == AM_PREREG_SHA, 'AM preregistration authority differs from the fixed anchor')
|
||||
planned = reg.get('planned'); selected = validate_planned(planned)
|
||||
final = _sealed_json(am, idx, 'audit/final-status.json')
|
||||
scientific = _sealed_json(am, idx, 'audit/scientific-summary.json')
|
||||
require(final.get('scientific_summary_sha256') == idx['audit/scientific-summary.json']['sha256'], 'AM final does not bind scientific summary')
|
||||
before = _sealed_json(am, idx, 'regression/old-integrity-before.json')
|
||||
after = _sealed_json(am, idx, 'regression/old-integrity-after.json')
|
||||
refs = _sealed_json(am, idx, 'regression/frozen-five.json')
|
||||
validate_final_status(final, scientific, before, after, refs)
|
||||
for stage in ('zero', 'static25'):
|
||||
validate_stage(am, idx, stage, planned, root)
|
||||
current_old = _old_integrity(root, midx['context.py']['sha256'], after)
|
||||
inputs = {}
|
||||
anchors = {'am_model_manifest_sha256': am_model_manifest_sha, 'am_experiment_manifest_sha256': am_experiment_manifest_sha,
|
||||
'am_final_sha256': am_final_sha, 'am_preregistration_sha256': AM_PREREG_SHA, 'AI_publication_helper_sha256': AI_HELPER_SHA}
|
||||
for path, expected in ((am_model / 'manifest.json', am_model_manifest_sha), (am / 'manifest.json', am_experiment_manifest_sha),
|
||||
(am / 'audit/final-status.json', am_final_sha), (root / AI_HELPER_REL, AI_HELPER_SHA),
|
||||
(am_model / 'overlay.py', AM_OVERLAY_SHA)):
|
||||
_bind(inputs, path, expected)
|
||||
require(midx.get('overlay.py', {}).get('sha256') == AM_OVERLAY_SHA, 'AM model seal does not contain the pinned overlay helper')
|
||||
for name in ('context.py', 'config.json'):
|
||||
_bind(inputs, am_model / name, midx[name]['sha256'])
|
||||
for path in (am_model / 'checksums.sha256', am / 'checksums.sha256'):
|
||||
_bind(inputs, path)
|
||||
for relative in ('protocol/preregistered.json', 'protocol/prepared-hashes.json', 'audit/scientific-summary.json', 'audit/zero-complete.json', 'audit/static25-complete.json',
|
||||
'regression/old-integrity-before.json', 'regression/old-integrity-after.json', 'regression/frozen-five.json'):
|
||||
_bind(inputs, am / relative, idx[relative]['sha256'])
|
||||
am_inputs = {}
|
||||
for row in reg['inputs']:
|
||||
require(set(row) == {'path', 'sha256'} and sha(row['sha256']), 'Invalid AM registered input row')
|
||||
_relative(row['path']); require(row['path'] not in am_inputs, 'Duplicate AM registered input')
|
||||
am_inputs[row['path']] = row['sha256']
|
||||
def authority(path):
|
||||
path = Path(path).resolve(strict=True)
|
||||
require(path.is_relative_to(root), 'AM input leaves project root')
|
||||
relative = path.relative_to(root).as_posix()
|
||||
if path.is_relative_to(am):
|
||||
expected = idx[path.relative_to(am).as_posix()]['sha256']
|
||||
else:
|
||||
require(relative in am_inputs, 'Dependency absent from AM preregistration: ' + relative)
|
||||
expected = am_inputs[relative]
|
||||
return _bind(inputs, path, expected)
|
||||
original_dir = root / AK_REL / 'cases' / selected['cohort'] / selected['name']
|
||||
authority(original_dir / 'spec.json'); authority(original_dir / 'requests.npz')
|
||||
original = read(original_dir / 'spec.json')
|
||||
require(original['steps'] == 1000 and original['kind'] == 'fullgraph' and original['name'] == selected['name'], 'Fixed selected AM case changed')
|
||||
with np.load(original_dir / 'requests.npz', allow_pickle=False) as archive:
|
||||
require(set(archive.files) == {'tick', 'neuron_idx'} and len(archive.files) == 2, 'Unexpected original request arrays')
|
||||
requests = {key: archive[key].copy() for key in archive.files}
|
||||
inputs_module.validate_requests(requests, 1000)
|
||||
for name in GRAPH_FILES:
|
||||
authority(Path(original['graph_dir']) / name)
|
||||
for name in APL_FILES:
|
||||
authority(Path(original['apl_bundle_dir']) / name)
|
||||
authority(original['coefficients_path'])
|
||||
selected_outputs = {}
|
||||
for stage in ('zero', 'static25'):
|
||||
prefix = 'gpu/' + stage + '/' + selected['name'] + '-r0/'
|
||||
selected_outputs[stage] = str(am / prefix.rstrip('/'))
|
||||
for relative, row in idx.items():
|
||||
if relative.startswith(prefix):
|
||||
_bind(inputs, am / relative, row['sha256'])
|
||||
adapted = {}
|
||||
for stage in ('zero', 'static25'):
|
||||
old_manifest = authority(am / 'overlays' / (stage + '.json'))
|
||||
overlay_file = authority(am / 'overlays' / (stage + '.npz'))
|
||||
declaration = read(old_manifest)
|
||||
require(declaration['overlay_name'] == stage and declaration['overlay_sha256'] == digest(overlay_file), 'AM overlay declaration mismatch')
|
||||
for label in ('mask', 'base_candidate'):
|
||||
require(digest(authority(declaration[label + '_path'])) == declaration[label + '_sha256'], 'Overlay external array authority mismatch')
|
||||
adapted[stage] = inputs_module.adapt_overlay_manifest(declaration, str(am_model / 'overlay.py'))
|
||||
require(adapted[stage]['am_overlay_sha256'] == AM_OVERLAY_SHA, 'Adapted helper pin differs')
|
||||
for relative, expected in runtime.PINS.items():
|
||||
_bind(inputs, model.parent / relative, expected)
|
||||
plan = plan_module.validate_plan(plan_module.build_plan())
|
||||
require(plan['total_calls'] == 32 and plan['total_ticks'] == 11074, 'Engineering plan matrix changed')
|
||||
# No writes have occurred before all authority/source/space checks above.
|
||||
experiment.mkdir(parents=True, exist_ok=True)
|
||||
try:
|
||||
for folder in ('protocol', 'cases', 'overlays', 'audit', 'runs', 'receipts', 'logs', 'regression', 'extension-build'):
|
||||
(experiment / folder).mkdir(exist_ok=False)
|
||||
save(experiment / 'regression/old-integrity-before.json', current_old)
|
||||
_bind(inputs, experiment / 'regression/old-integrity-before.json')
|
||||
for source, expected in sources.items():
|
||||
target = experiment / 'protocol/source-snapshot' / Path(source).name
|
||||
publication._copy_verified(Path(source), target, Path(source).stat().st_size, expected)
|
||||
_bind(inputs, target, expected)
|
||||
save(experiment / 'protocol/AM-seal-verification.json', verified)
|
||||
save(experiment / 'protocol/AM-gate-authority.json', dict(anchors=anchors, all_144_per_stage_declared_gates_pass=True,
|
||||
old_roots=28, old_references=list(REFERENCES), verification_scope='Full AM seals and finalized declared gate evidence; no new scientific execution'))
|
||||
for name in ('AM-seal-verification.json', 'AM-gate-authority.json'):
|
||||
_bind(inputs, experiment / 'protocol' / name)
|
||||
overlays, cases = {}, {}
|
||||
for stage, value in adapted.items():
|
||||
manifest_path = experiment / 'overlays' / (stage + '.json')
|
||||
save(manifest_path, value)
|
||||
_bind(inputs, manifest_path)
|
||||
overlays[stage] = {'overlay_file': str(am / 'overlays' / (stage + '.npz')), 'overlay_manifest': str(manifest_path)}
|
||||
for row in plan['rows']:
|
||||
case = experiment / 'cases' / row['row_id']; case.mkdir(exist_ok=False)
|
||||
template = {'schema': 'phase4an-engineering-case-template-v1', 'row_id': row['row_id'], 'original_spec': original,
|
||||
'start': row['start'], 'stop': row['stop'], 'budget_root': str(experiment), 'output_budget_bytes': BUDGET['output_budget_bytes']}
|
||||
save(case / 'template-spec.json', template)
|
||||
part = inputs_module.slice_requests(requests, 1000, row['start'], row['stop'])
|
||||
with (case / 'requests.npz').open('xb') as stream:
|
||||
np.savez_compressed(stream, **part)
|
||||
_bind(inputs, case / 'template-spec.json'); _bind(inputs, case / 'requests.npz')
|
||||
cases[row['row_id']] = {'case_dir': str(case), 'template_spec_sha256': digest(case / 'template-spec.json'),
|
||||
'requests_sha256': digest(case / 'requests.npz')}
|
||||
require(source_inventory(model) == sources and all(digest(path) == expected for path, expected in inputs.items()), 'Source/input changed during preparation')
|
||||
# Repeat the full AM verification after producing only small new inputs.
|
||||
after_verified = {'model': publication.verify_seal(am_model), 'experiment': publication.verify_seal(am)}
|
||||
require(after_verified == verified, 'AM sealed payload changed during preparation')
|
||||
_budget(experiment)
|
||||
registration = {'schema': REG_SCHEMA, 'plan': plan, 'selected_case': {'row': selected, 'original_dir': str(original_dir), 'AM_outputs': selected_outputs},
|
||||
'source_sha256': sources, 'input_sha256': inputs, 'cases': cases, 'overlays': overlays, 'budget': dict(BUDGET), 'anchors': anchors,
|
||||
'training': False, 'new_brain_runs_during_preparation': 0, 'future_checkpoint_hashes_fabricated': False,
|
||||
'runtime_spec_derivation': 'engineering_inputs.derive_spec(template.original_spec, interval, budget, actual verified parent SHA or None)',
|
||||
'scope': 'Fixed 32-call engineering gate only; not conditioning or learning registration'}
|
||||
save(experiment / 'protocol/preregistered.json', registration)
|
||||
return {'prepared': True, 'registration_path': str(experiment / 'protocol/preregistered.json'),
|
||||
'registration_sha256': digest(experiment / 'protocol/preregistered.json'), 'calls': 32, 'brain_runs': 0}
|
||||
except Exception as error:
|
||||
failure = experiment / 'audit/preparation-failure.json'
|
||||
if not failure.exists():
|
||||
save(failure, {'prepared': False, 'type': type(error).__name__, 'message': str(error), 'partial_outputs_preserved': True})
|
||||
raise
|
||||
|
||||
|
||||
def prepare(am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha):
|
||||
return _prepare(ROOT, MODEL, EXP, am_model_manifest_sha, am_experiment_manifest_sha, am_final_sha)
|
||||
|
||||
|
||||
def row_path(registration, row_id):
|
||||
require(type(row_id) is str and row_id in {row['row_id'] for row in registration['plan']['rows']}, 'Unknown engineering row ID')
|
||||
expected = EXP.resolve(strict=True) / 'cases' / row_id
|
||||
actual = Path(registration['cases'][row_id]['case_dir'])
|
||||
require(actual == expected and actual.resolve(strict=True) == expected and actual.is_dir(), 'Row case path is not its fixed safe mapping')
|
||||
return actual
|
||||
|
||||
|
||||
def read_registration(expected_sha):
|
||||
require(sys.dont_write_bytecode and sha(expected_sha), 'Require python -B and an external preregistration SHA')
|
||||
path = EXP / 'protocol/preregistered.json'
|
||||
require(digest(path) == expected_sha, 'Preregistration differs from external authority')
|
||||
require(not (EXP / 'audit/preparation-failure.json').exists(), 'Preparation failed; preserve partials')
|
||||
registration = read(path)
|
||||
require(registration.get('schema') == REG_SCHEMA and registration.get('training') is False and registration.get('budget') == BUDGET,
|
||||
'Unexpected registration schema/scope/budget')
|
||||
require(registration['source_sha256'] == source_inventory(MODEL), 'Registered engineering sources changed or inventory incomplete')
|
||||
for name, expected in registration['input_sha256'].items():
|
||||
require(Path(name).is_absolute() and sha(expected) and digest(name) == expected, 'Registered input changed: ' + name)
|
||||
plan_module = load_module('phase4an_read_plan', MODEL / 'engineering_plan.py', registration['source_sha256'][str(MODEL / 'engineering_plan.py')])
|
||||
inputs_module = load_module('phase4an_read_inputs', MODEL / 'engineering_inputs.py', registration['source_sha256'][str(MODEL / 'engineering_inputs.py')])
|
||||
plan_module.validate_plan(registration['plan'])
|
||||
selected = validate_planned(read(ROOT / AM_EXP_REL / 'protocol/preregistered.json')['planned'])
|
||||
original_dir = ROOT / AK_REL / 'cases' / selected['cohort'] / selected['name']
|
||||
require(registration['selected_case']['row'] == selected and registration['selected_case']['original_dir'] == str(original_dir),
|
||||
'Registered case is not the fixed first AM planned primary')
|
||||
original = read(original_dir / 'spec.json')
|
||||
with np.load(original_dir / 'requests.npz', allow_pickle=False) as archive:
|
||||
require(set(archive.files) == {'tick', 'neuron_idx'} and len(archive.files) == 2, 'Unexpected original sparse request schema')
|
||||
requests = {key: archive[key].copy() for key in archive.files}
|
||||
inputs_module.validate_requests(requests, 1000)
|
||||
require(set(registration['cases']) == {row['row_id'] for row in registration['plan']['rows']}, 'Registered case inventory differs from plan')
|
||||
for row in registration['plan']['rows']:
|
||||
case = row_path(registration, row['row_id']); entry = registration['cases'][row['row_id']]
|
||||
require(entry['template_spec_sha256'] == digest(case / 'template-spec.json') and entry['requests_sha256'] == digest(case / 'requests.npz'),
|
||||
'Template/request metadata does not bind registered bytes')
|
||||
template = read(case / 'template-spec.json')
|
||||
require(set(template) == {'schema', 'row_id', 'original_spec', 'start', 'stop', 'budget_root', 'output_budget_bytes'} and
|
||||
template['schema'] == 'phase4an-engineering-case-template-v1' and template['original_spec'] == original and
|
||||
template['row_id'] == row['row_id'] and template['start'] == row['start'] and template['stop'] == row['stop'] and
|
||||
template['budget_root'] == str(EXP) and template['output_budget_bytes'] == BUDGET['output_budget_bytes'], 'Template rule differs from canonical row')
|
||||
expected = inputs_module.slice_requests(requests, 1000, row['start'], row['stop'])
|
||||
with np.load(case / 'requests.npz', allow_pickle=False) as archive:
|
||||
require(len(archive.files) == 2 and set(archive.files) == set(expected) and
|
||||
all(archive[key].dtype == expected[key].dtype and archive[key].shape == expected[key].shape and
|
||||
archive[key].tobytes() == expected[key].tobytes() for key in expected), 'Registered request slice is not exact')
|
||||
require(set(registration['overlays']) == {'zero', 'static25'}, 'Registered overlay inventory changed')
|
||||
for stage in ('zero', 'static25'):
|
||||
entry = registration['overlays'][stage]
|
||||
expected_file = ROOT / AM_EXP_REL / 'overlays' / (stage + '.npz')
|
||||
expected_manifest = EXP / 'overlays' / (stage + '.json')
|
||||
require(entry == {'overlay_file': str(expected_file), 'overlay_manifest': str(expected_manifest)}, 'Overlay path mapping changed')
|
||||
original_overlay = read(ROOT / AM_EXP_REL / 'overlays' / (stage + '.json'))
|
||||
require(read(expected_manifest) == inputs_module.adapt_overlay_manifest(original_overlay, str(ROOT / AM_MODEL_REL / 'overlay.py')),
|
||||
'Adapted overlay manifest is not the exact declared transformation')
|
||||
anchors = registration['anchors']
|
||||
for name, relative in (('am_model_manifest_sha256', AM_MODEL_REL / 'manifest.json'),
|
||||
('am_experiment_manifest_sha256', AM_EXP_REL / 'manifest.json'),
|
||||
('am_final_sha256', AM_EXP_REL / 'audit/final-status.json')):
|
||||
require(sha(anchors[name]) and digest(ROOT / relative) == anchors[name], 'AM external anchor changed')
|
||||
require(anchors['am_preregistration_sha256'] == AM_PREREG_SHA and digest(ROOT / AM_EXP_REL / 'protocol/preregistered.json') == AM_PREREG_SHA and
|
||||
anchors['AI_publication_helper_sha256'] == AI_HELPER_SHA and digest(ROOT / AI_HELPER_REL) == AI_HELPER_SHA, 'Fixed AM/helper authority changed')
|
||||
require(digest(path) == expected_sha, 'Registration changed during verification')
|
||||
return registration
|
||||
@@ -0,0 +1,243 @@
|
||||
"""Independent, read-only CPU queue/ref audit for the draft AN 32-call gate.
|
||||
|
||||
The caller supplies externally authenticated records and CSR arrays. No file
|
||||
I/O, runtime import, GPU, membrane integration, weight update or learning is
|
||||
performed. Spikes are observed facts, not independently predicted here.
|
||||
|
||||
queue digest bytes independently implement frozen AF oracle_full.queue_summary
|
||||
(source SHA below): header, little-endian signed int64 local tick/count, all
|
||||
global edge IDs in source/CSR order, then uint8 post-threshold acceptance bits.
|
||||
The function never target-sorts or drops zero-weight edges from the queue.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import struct
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
AF_QUEUE_SOURCE_SHA256 = 'd72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961'
|
||||
QUEUE_HEADER = b'phase4af-queue-v1\0'
|
||||
DELAY, REFRACTORY = 18, 22
|
||||
CHUNK = 1 << 18
|
||||
QUEUE_KEYS = {'tick', 'count', 'accepted_count', 'ordered_edge_sha256', 'accepted_mask_sha256', 'joint_sha256'}
|
||||
|
||||
|
||||
def require(value, message):
|
||||
if not value:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _array(value, dtype, shape, name):
|
||||
require(isinstance(value, np.ndarray) and value.dtype == np.dtype(dtype) and value.shape == shape,
|
||||
'Invalid dtype/shape: ' + name)
|
||||
|
||||
|
||||
def _raw_sha(value):
|
||||
result = hashlib.sha256()
|
||||
for start in range(0, len(value), CHUNK):
|
||||
result.update(np.ascontiguousarray(value[start:start + CHUNK], dtype=value.dtype.newbyteorder('<')).tobytes())
|
||||
return result.hexdigest()
|
||||
|
||||
|
||||
def _event_schema(tick, node, steps, n, label):
|
||||
require(isinstance(tick, np.ndarray) and tick.ndim == 1, 'Invalid event vector: ' + label)
|
||||
_array(tick, np.int32, tick.shape, label + '.tick')
|
||||
_array(node, np.int32, tick.shape, label + '.neuron_idx')
|
||||
require(np.all((tick >= 0) & (tick < steps)) and np.all((node >= 0) & (node < n)),
|
||||
'Out-of-range event: ' + label)
|
||||
key = tick.astype(np.int64) * n + node
|
||||
require(len(key) < 2 or np.all(key[1:] > key[:-1]), 'Events must be sorted/unique: ' + label)
|
||||
|
||||
|
||||
def _record_schema(record, n, label):
|
||||
require(isinstance(record, dict), 'Record must be a dictionary: ' + label)
|
||||
steps, offset = record['steps'], record['global_tick_offset']
|
||||
require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max and
|
||||
type(offset) is int and offset >= 0, 'Invalid record clock: ' + label)
|
||||
state = record['state']
|
||||
require(set(state) == {'step', 'neuron_idx', 'counts', 'v', 'g', 'ref'}, 'Invalid state keys: ' + label)
|
||||
_event_schema(state['step'], state['neuron_idx'], steps, n, label)
|
||||
for name, dtype in [('counts', np.int32), ('ref', np.int32), ('v', np.float64), ('g', np.float64)]:
|
||||
_array(state[name], dtype, (n,), label + '.' + name)
|
||||
require(np.isfinite(state[name]).all(), 'Nonfinite state: ' + label + '.' + name)
|
||||
require(np.all(state['counts'] >= 0) and np.all((state['ref'] >= 0) & (state['ref'] <= REFRACTORY)),
|
||||
'Invalid state count/ref: ' + label)
|
||||
rows = record['requests']
|
||||
require(isinstance(rows, np.ndarray) and rows.ndim == 2 and rows.shape[1] == 5,
|
||||
'Invalid request table: ' + label)
|
||||
_array(rows, np.int64, rows.shape, label + '.requests')
|
||||
queues, hashes = record.get('queues'), record.get('state_hashes')
|
||||
require(isinstance(queues, list) and isinstance(hashes, list) and len(queues) == len(hashes) == steps,
|
||||
'Complete reference queue and state hashes are required: ' + label)
|
||||
for t, (queue, state_hash) in enumerate(zip(queues, hashes)):
|
||||
require(isinstance(queue, dict) and set(queue) == QUEUE_KEYS and type(queue['tick']) is int and queue['tick'] == t,
|
||||
'Invalid local queue row: ' + label)
|
||||
require(isinstance(state_hash, dict) and set(state_hash) == {'tick', 'v_sha256', 'g_sha256', 'ref_sha256'} and
|
||||
type(state_hash['tick']) is int and state_hash['tick'] == t,
|
||||
'Invalid local state-hash row: ' + label)
|
||||
return record
|
||||
|
||||
|
||||
def _graph(ptr, dst, edge_id, effective_weights):
|
||||
require(isinstance(ptr, np.ndarray) and ptr.ndim == 1 and len(ptr) > 1, 'Invalid CSR ptr')
|
||||
n, e = len(ptr) - 1, len(dst)
|
||||
_array(ptr, np.int64, (n + 1,), 'ptr')
|
||||
_array(dst, np.int32, (e,), 'dst')
|
||||
_array(edge_id, np.int64, (e,), 'edge_id')
|
||||
_array(effective_weights, np.float64, (e,), 'effective_weights_SI')
|
||||
require(ptr[0] == 0 and ptr[-1] == e and np.all(ptr[1:] >= ptr[:-1]), 'Invalid CSR pointers')
|
||||
for start in range(0, e, CHUNK):
|
||||
stop = min(start + CHUNK, e)
|
||||
require(np.all((dst[start:stop] >= 0) & (dst[start:stop] < n)), 'CSR destination out of range')
|
||||
require(np.all(edge_id[start:stop] >= 0) and
|
||||
np.all(np.diff(edge_id[max(0, start - 1):stop]) > 0), 'Global edge IDs must preserve canonical increasing CSR order')
|
||||
require(np.isfinite(effective_weights[start:stop]).all(), 'Nonfinite effective SI weight')
|
||||
return n, e
|
||||
|
||||
|
||||
def _queue(tick, local_tick, sources, ref, ptr, dst, edge_id, weights, boundaries):
|
||||
"""Two headers, shared source-ordered payload; memory bounded by one chunk."""
|
||||
count = sum(int(ptr[int(s) + 1] - ptr[int(s)]) for s in sources)
|
||||
joints = [hashlib.sha256(QUEUE_HEADER + struct.pack('<qq', t, count)) for t in (tick, local_tick)]
|
||||
edges_hash, accepted_hash = hashlib.sha256(), hashlib.sha256()
|
||||
for source in sources:
|
||||
for start in range(int(ptr[source]), int(ptr[source + 1]), CHUNK):
|
||||
stop = min(start + CHUNK, int(ptr[source + 1]))
|
||||
data = np.ascontiguousarray(edge_id[start:stop], dtype='<i8').tobytes()
|
||||
edges_hash.update(data)
|
||||
for joint in joints:
|
||||
joint.update(data)
|
||||
accepted_count, nonzero_count, first = 0, 0, None
|
||||
for source in sources:
|
||||
for start in range(int(ptr[source]), int(ptr[source + 1]), CHUNK):
|
||||
stop = min(start + CHUNK, int(ptr[source + 1]))
|
||||
mask = np.ascontiguousarray(ref[dst[start:stop]] == 0, dtype=np.uint8)
|
||||
accepted_count += int(mask.sum(dtype=np.int64))
|
||||
data = mask.tobytes(); accepted_hash.update(data)
|
||||
for joint in joints:
|
||||
joint.update(data)
|
||||
if boundaries:
|
||||
live = np.flatnonzero((mask != 0) & (weights[start:stop] != 0))
|
||||
nonzero_count += len(live)
|
||||
if first is None and len(live):
|
||||
index = start + int(live[0])
|
||||
first = {'source_neuron_idx': int(source), 'target_neuron_idx': int(dst[index]),
|
||||
'global_edge_id': int(edge_id[index]), 'csr_edge_index': index,
|
||||
'source_spike_tick': tick - DELAY, 'arrival_tick': tick,
|
||||
'effective_weight_SI_volt': float(weights[index]),
|
||||
'effective_weight_float64_le_hex': np.asarray(weights[index], dtype='<f8').tobytes().hex(),
|
||||
'accepted': True, 'post_threshold_target_ref': 0,
|
||||
'weight_is_nonzero': True, 'evidence_is_unique_cause_or_necessity': False}
|
||||
rows = [{'tick': t, 'count': count, 'accepted_count': accepted_count,
|
||||
'ordered_edge_sha256': edges_hash.hexdigest(), 'accepted_mask_sha256': accepted_hash.hexdigest(),
|
||||
'joint_sha256': joint.hexdigest()} for t, joint in zip((tick, local_tick), joints)]
|
||||
return rows, first, nonzero_count
|
||||
|
||||
|
||||
def audit_queues(monolithic, segments, *, ptr, dst, edge_id, effective_weights, requests, cuts):
|
||||
"""Audit a fresh reference plus its complete reference segmentation.
|
||||
|
||||
All inputs are read-only. Return PASS/FAIL/INCOMPLETE JSON-safe evidence.
|
||||
Structural input damage raises ValueError; valid but differing artifacts
|
||||
return FAIL. ``cuts`` must be the exact internal segment boundaries.
|
||||
No initial state is inferred for arbitrary nonzero-origin experiments.
|
||||
"""
|
||||
n, e = _graph(ptr, dst, edge_id, effective_weights)
|
||||
mono = _record_schema(monolithic, n, 'monolithic')
|
||||
require(mono['global_tick_offset'] == 0, 'This audit requires the registered fresh monolithic origin')
|
||||
require(isinstance(segments, (list, tuple)) and len(segments) >= 2, 'At least two reference segments required')
|
||||
parts = [_record_schema(p, n, 'segment-' + str(i)) for i, p in enumerate(segments)]
|
||||
cursor, actual_cuts = 0, []
|
||||
for i, part in enumerate(parts):
|
||||
require(part['global_tick_offset'] == cursor, 'Segment clock gap/overlap')
|
||||
if i:
|
||||
actual_cuts.append(cursor)
|
||||
cursor += part['steps']
|
||||
require(cursor == mono['steps'] and type(cuts) in (list, tuple) and all(type(t) is int for t in cuts) and
|
||||
list(cuts) == actual_cuts, 'Cuts/segments must cover the exact complete monolithic interval')
|
||||
require(isinstance(requests, dict) and set(requests) == {'tick', 'neuron_idx'}, 'Exact original request arrays required')
|
||||
_event_schema(requests['tick'], requests['neuron_idx'], mono['steps'], n, 'original_requests')
|
||||
records = [('monolithic', mono), *[('segment-' + str(i), p) for i, p in enumerate(parts)]]
|
||||
differences, difference_count = [], 0
|
||||
|
||||
def check(value, field, **location):
|
||||
nonlocal difference_count
|
||||
if not value:
|
||||
difference_count += 1
|
||||
if len(differences) < 100:
|
||||
differences.append({'field': field, **location})
|
||||
|
||||
tick, node = mono['state']['step'], mono['state']['neuron_idx']
|
||||
for label, record in records:
|
||||
start, stop = record['global_tick_offset'], record['global_tick_offset'] + record['steps']
|
||||
lo, hi = np.searchsorted(tick, (start, stop))
|
||||
check(np.array_equal(record['state']['step'].astype(np.int64) + start, tick[lo:hi]), 'events.tick', record=label)
|
||||
check(np.array_equal(record['state']['neuron_idx'], node[lo:hi]), 'events.neuron_idx', record=label)
|
||||
check(np.array_equal(record['state']['counts'], np.bincount(record['state']['neuron_idx'], minlength=n)), 'counts', record=label)
|
||||
expected_requests = np.empty((len(requests['tick']), 5), np.int64)
|
||||
if len(expected_requests):
|
||||
expected_requests[:, 0] = requests['tick']; expected_requests[:, 1] = requests['neuron_idx']; expected_requests[:, 2] = 1
|
||||
ref = np.zeros(n, np.int32)
|
||||
boundary_report = {t: {'boundary_tick': t, 'accepted_nonzero_crossing_edge_events': 0, 'first_witness': None} for t in cuts}
|
||||
due_total, accepted_total, segment_index = 0, 0, 0
|
||||
for t in range(mono['steps']):
|
||||
while t >= parts[segment_index]['global_tick_offset'] + parts[segment_index]['steps']:
|
||||
segment_index += 1
|
||||
part = parts[segment_index]; local = t - part['global_tick_offset']
|
||||
rlo, rhi = np.searchsorted(requests['tick'], (t, t + 1))
|
||||
requested = requests['neuron_idx'][rlo:rhi]
|
||||
accepted_requests = ref[requested] <= 1
|
||||
expected_requests[rlo:rhi, 3] = accepted_requests
|
||||
expected_requests[rlo:rhi, 4] = ~accepted_requests
|
||||
np.maximum(ref - np.int32(1), np.int32(0), out=ref)
|
||||
slo, shi = np.searchsorted(tick, (t, t + 1)); fired = node[slo:shi]
|
||||
check(np.all(ref[fired] == 0), 'spike_during_refractory', global_tick=t)
|
||||
check(np.isin(requested[accepted_requests], fired, assume_unique=True).all(), 'accepted_request_without_spike', global_tick=t)
|
||||
ref[fired] = REFRACTORY
|
||||
sources = node[0:0]
|
||||
if t >= DELAY:
|
||||
dlo, dhi = np.searchsorted(tick, (t - DELAY, t - DELAY + 1)); sources = node[dlo:dhi]
|
||||
crossing = [cut for cut in cuts if t - DELAY < cut <= t]
|
||||
rows, witness, live_count = _queue(t, local, sources, ref, ptr, dst, edge_id, effective_weights, crossing)
|
||||
due_total += rows[0]['count']; accepted_total += rows[0]['accepted_count']
|
||||
ref_sha = _raw_sha(ref)
|
||||
for label, record, at, expected in [('monolithic', mono, t, rows[0]), ('segment-' + str(segment_index), part, local, rows[1])]:
|
||||
for key in sorted(QUEUE_KEYS):
|
||||
check(type(record['queues'][at][key]) is type(expected[key]) and record['queues'][at][key] == expected[key],
|
||||
'queue.' + key, record=label, local_tick=at, global_tick=t)
|
||||
check(record['state_hashes'][at]['ref_sha256'] == ref_sha, 'state_hash.ref_sha256', record=label, local_tick=at, global_tick=t)
|
||||
if at == record['steps'] - 1:
|
||||
check(np.array_equal(record['state']['ref'], ref), 'terminal.ref', record=label)
|
||||
for cut in crossing:
|
||||
boundary_report[cut]['accepted_nonzero_crossing_edge_events'] += live_count
|
||||
if witness is not None and boundary_report[cut]['first_witness'] is None:
|
||||
boundary_report[cut]['first_witness'] = {'boundary_tick': cut, **witness}
|
||||
for label, record in records:
|
||||
start, stop = record['global_tick_offset'], record['global_tick_offset'] + record['steps']
|
||||
lo, hi = np.searchsorted(requests['tick'], (start, stop))
|
||||
expected = expected_requests[lo:hi].copy(); expected[:, 0] -= start
|
||||
check(np.array_equal(record['requests'], expected), 'requests.five_fields', record=label)
|
||||
first_witness = min((row['first_witness'] for row in boundary_report.values() if row['first_witness'] is not None),
|
||||
key=lambda row: (row['arrival_tick'], row['source_neuron_idx'], row['csr_edge_index'], row['boundary_tick']), default=None)
|
||||
exact, live = difference_count == 0, first_witness is not None
|
||||
return {'schema': 'phase4an-independent-queue-audit-v1', 'pass_all': exact and live,
|
||||
'status': 'FAIL-QUEUE-OR-REF' if not exact else ('PASS-QUEUE-WITNESS-SCOPE' if live else 'INCOMPLETE-NO-ACCEPTED-NONZERO-CROSSING'),
|
||||
'queue_ref_request_exact': exact, 'accepted_nonzero_crossing_witness_verified': exact and live,
|
||||
'difference_count': difference_count, 'differences': differences, 'differences_truncated': difference_count > len(differences),
|
||||
'neurons': n, 'edges': e, 'steps': mono['steps'], 'segments': len(parts), 'cuts': list(cuts),
|
||||
'arriving_edges': due_total, 'accepted_arriving_edges': accepted_total, 'rejected_arriving_edges': due_total - accepted_total,
|
||||
'requests': len(expected_requests), 'accepted_requests': int(expected_requests[:, 3].sum(dtype=np.int64)),
|
||||
'rejected_requests': int(expected_requests[:, 4].sum(dtype=np.int64)),
|
||||
'first_witness': first_witness, 'boundaries': list(boundary_report.values()),
|
||||
'same_edge_event_may_cross_multiple_boundaries': True,
|
||||
'input_array_sha256': {name: _raw_sha(value) for name, value in [('ptr', ptr), ('dst', dst), ('edge_id', edge_id),
|
||||
('effective_weights_float64_SI', effective_weights), ('request_tick', requests['tick']), ('request_neuron_idx', requests['neuron_idx'])]},
|
||||
'digest_definition': {'AF_source_sha256': AF_QUEUE_SOURCE_SHA256, 'header_hex': QUEUE_HEADER.hex(),
|
||||
'payload_order': 'local tick/count <qq; all canonical source/CSR edge IDs <i8; all post-threshold accepted u1'},
|
||||
'delay_steps': DELAY, 'refractory_steps': REFRACTORY,
|
||||
'scope': 'Conditional on recorded spikes: exact ref, original requests, source-ordered real-edge arrival queues and finite-cut witness only',
|
||||
'membrane_dynamics_independently_replayed': False, 'v_g_hashes_independently_verified': False,
|
||||
'unique_causal_or_necessity_claim': False, 'external_seals_and_input_authority_verified_here': False,
|
||||
'training': False, 'GPU_used': False}
|
||||
@@ -0,0 +1,219 @@
|
||||
"""Pure CPU comparisons of already-loaded engineering_compare run records.
|
||||
|
||||
No paths are accepted, no artifacts are opened, no GPU is imported. Structural
|
||||
or missing-required-observation errors raise ValueError. Valid but different
|
||||
records return numerical_exact=False. External seals, parent SHA authority and
|
||||
independent CSR/local-header queue reconstruction remain coordinator duties.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
|
||||
import numpy as np
|
||||
|
||||
import engineering_compare as ec
|
||||
|
||||
|
||||
SCHEMA = 'phase4an-engineering-scopes-v1'
|
||||
|
||||
|
||||
def _require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _raw_sha(value):
|
||||
return hashlib.sha256(np.ascontiguousarray(value, dtype=value.dtype.newbyteorder('<')).tobytes()).hexdigest()
|
||||
|
||||
|
||||
def _record(value, label):
|
||||
_require(type(value) is dict, label + ' must be an already-loaded run dictionary, not a path')
|
||||
try:
|
||||
ec.validate_run(value)
|
||||
summary = value['summary']
|
||||
_require(type(summary) is dict, label + ' requires summary metadata')
|
||||
steps, offset = value['steps'], value['global_tick_offset']
|
||||
_require(steps <= np.iinfo(np.int32).max, label + ' steps exceed int32 event capacity')
|
||||
for key in ('complete', 'pass_all', 'all_finite'):
|
||||
_require(summary.get(key) is True, label + ' is incomplete or failed: ' + key)
|
||||
for key in ('steps', 'completed_ticks'):
|
||||
_require(type(summary.get(key)) is int and summary[key] == steps, label + ' summary clock mismatch')
|
||||
_require(type(summary.get('nodes')) is int and summary['nodes'] == len(value['state']['v']),
|
||||
label + ' summary node count mismatch')
|
||||
_require(summary.get('precision') == 'float64' and summary.get('apl_precision') == 'float32' and
|
||||
type(summary.get('apl_delay_steps')) is int and summary['apl_delay_steps'] == 18,
|
||||
label + ' numerical metadata mismatch')
|
||||
continued = summary.get('checkpoint_continuation_used', False)
|
||||
_require(type(continued) is bool and continued == (offset > 0), label + ' continuation scope mismatch')
|
||||
declared_offset = summary.get('global_tick_offset', 0)
|
||||
_require(type(declared_offset) is int and declared_offset == offset, label + ' summary offset mismatch')
|
||||
if continued or 'global_next_tick' in summary:
|
||||
_require(type(summary.get('global_next_tick')) is int and summary['global_next_tick'] == offset + steps,
|
||||
label + ' summary global end mismatch')
|
||||
_require(ec._sha(summary.get('effective_weights_raw_sha256')), label + ' effective weight SHA is missing')
|
||||
mode = summary.get('diagnostic_mode')
|
||||
legacy = str(value.get('terminal_origin', '')).startswith('AM_')
|
||||
_require(mode in ('light', 'reference') or (mode is None and legacy), label + ' recording mode is not declared')
|
||||
observations = {'apl_traces': bool(value.get('traces')), 'state_hashes': value.get('state_hashes') is not None,
|
||||
'queues': value.get('queues') is not None}
|
||||
for name, field in (('apl_traces', 'full_APL_history_recorded'),
|
||||
('state_hashes', 'full_tick_state_hashes_recorded'), ('queues', 'full_queue_hashes_recorded')):
|
||||
if field in summary:
|
||||
_require(type(summary[field]) is bool, label + ' observation flag must be Boolean')
|
||||
if mode == 'reference' or legacy or summary.get(field) is True:
|
||||
_require(observations[name], label + ' declared reference observation missing: ' + name)
|
||||
return value, observations
|
||||
except (KeyError, TypeError, AttributeError, IndexError, OverflowError) as error:
|
||||
raise ValueError(label + ' has an invalid run-record schema') from error
|
||||
|
||||
|
||||
def _common(left, right):
|
||||
return {'same_neuron_count': len(left['state']['v']) == len(right['state']['v']),
|
||||
'same_APL_width': left['terminal']['apl_pending'].shape[1] == right['terminal']['apl_pending'].shape[1],
|
||||
'same_effective_weights': left['summary']['effective_weights_raw_sha256'] ==
|
||||
right['summary']['effective_weights_raw_sha256']}
|
||||
|
||||
|
||||
def _report(comparison, checks, observed, limitations):
|
||||
# All predicates are JSON booleans, not array scalars or success claims.
|
||||
checks = {key: bool(value) for key, value in checks.items()}
|
||||
return dict(schema=SCHEMA, comparison=comparison, numerical_exact=all(checks.values()),
|
||||
checks=checks, failed_checks=[key for key, value in checks.items() if not value],
|
||||
observed_scope=observed, limitations=list(limitations), learning_pass_claimed=False,
|
||||
complete_dynamical_equivalence_proven=False, external_authority_verified_here=False,
|
||||
independent_CSR_queue_reconstruction_performed=False)
|
||||
|
||||
|
||||
def compare_slice(candidate, monolithic):
|
||||
"""Compare candidate's absolute [start,stop) with an origin-zero full reference.
|
||||
|
||||
The final candidate V/g/ref bytes must match the reference's tick stop-1
|
||||
hashes even for light candidates. Missing light intermediate observations
|
||||
are disclosed, never replaced by fabricated zero arrays or inferred hashes.
|
||||
"""
|
||||
candidate, observed = _record(candidate, 'candidate')
|
||||
monolithic, full = _record(monolithic, 'monolithic')
|
||||
_require(monolithic['global_tick_offset'] == 0, 'Monolithic slice reference must have origin zero')
|
||||
_require(monolithic['summary'].get('diagnostic_mode') == 'reference' or
|
||||
str(monolithic.get('terminal_origin', '')).startswith('AM_'),
|
||||
'Monolithic slice source must be declared reference or loaded legacy AM')
|
||||
_require(all(full.values()), 'Monolithic slice reference needs all four APL traces, state hashes and queues')
|
||||
start, steps = candidate['global_tick_offset'], candidate['steps']
|
||||
stop = start + steps
|
||||
_require(stop <= monolithic['steps'], 'Candidate slice extends beyond monolithic reference')
|
||||
checks = _common(candidate, monolithic)
|
||||
state, whole = candidate['state'], monolithic['state']
|
||||
lo, hi = np.searchsorted(whole['step'], (start, stop))
|
||||
expected_ticks = (whole['step'][lo:hi].astype(np.int64) - start).astype(np.int32)
|
||||
expected_nodes = whole['neuron_idx'][lo:hi]
|
||||
checks['slice.event_steps'] = ec.bit_equal(state['step'], expected_ticks)
|
||||
checks['slice.event_neurons'] = ec.bit_equal(state['neuron_idx'], expected_nodes)
|
||||
counts = np.bincount(expected_nodes, minlength=len(whole['v'])).astype(np.int32)
|
||||
checks['slice.segment_counts'] = ec.bit_equal(state['counts'], counts)
|
||||
requests = monolithic['requests']
|
||||
lo, hi = np.searchsorted(requests[:, 0], (start, stop))
|
||||
expected_requests = requests[lo:hi].copy()
|
||||
expected_requests[:, 0] -= start
|
||||
checks['slice.requests_five_fields'] = ec.bit_equal(candidate['requests'], expected_requests)
|
||||
for name in ('v', 'g', 'ref'):
|
||||
checks['slice.final_' + name + '_raw_hash'] = _raw_sha(state[name]) == monolithic['state_hashes'][stop - 1][name + '_sha256']
|
||||
raw_ring = ec.reconstructed_fresh_ring(whole, stop, len(whole['v']))
|
||||
expected_ring = raw_ring[(np.arange(19) + stop % 19) % 19]
|
||||
checks['slice.normalized_spike_ring'] = ec.bit_equal(ec.normalized_ring(candidate['terminal']), expected_ring)
|
||||
traces = monolithic['traces']
|
||||
checks['slice.APL_state'] = ec.bit_equal(candidate['terminal']['apl_state'], traces['state'][stop - 1])
|
||||
pending = np.zeros((18, traces['generated'].shape[1]), dtype=np.float32)
|
||||
kept = min(stop, 18)
|
||||
pending[-kept:] = traces['generated'][stop - kept:stop]
|
||||
checks['slice.APL_pending'] = ec.bit_equal(candidate['terminal']['apl_pending'], pending)
|
||||
if observed['apl_traces']:
|
||||
for name in ec.TRACE_FILES:
|
||||
checks['slice.APL_trace.' + name] = ec.bit_equal(candidate['traces'][name], traces[name][start:stop])
|
||||
if observed['state_hashes']:
|
||||
checks['slice.per_tick_state_hashes'] = all(
|
||||
all(left[key] == right[key] for key in ec.HASH_FIELDS)
|
||||
for left, right in zip(candidate['state_hashes'], monolithic['state_hashes'][start:stop]))
|
||||
if observed['queues']:
|
||||
checks['slice.queue_order_and_acceptance'] = all(
|
||||
all(left[key] == right[key] for key in ec.QUEUE_FIELDS)
|
||||
for left, right in zip(candidate['queues'], monolithic['queues'][start:stop]))
|
||||
limitations = ['External input/seal/case/parent authority is not established by these records.',
|
||||
'Queue joint hashes contain local tick headers; their independent CSR reconstruction is not performed here.',
|
||||
'Numerical equality of this observed slice is not a learning, live-coverage or complete dynamics proof.']
|
||||
for key, label in (('apl_traces', 'APL histories'), ('state_hashes', 'intermediate V/g/ref state hashes'),
|
||||
('queues', 'per-tick queue order/acceptance')):
|
||||
if not observed[key]:
|
||||
limitations.append('Candidate ' + label + ' are unobserved; no direct comparison is claimed.')
|
||||
return _report('slice', checks, dict(start=start, stop=stop, segment_steps=steps,
|
||||
events=True, segment_counts=True, requests_five_fields=True, final_state_against_reference_tick_hash=True,
|
||||
normalized_spike_ring_from_reference_prefix_events=True, terminal_APL_state_and_pending=True,
|
||||
candidate_APL_histories=observed['apl_traces'], candidate_per_tick_state_hashes=observed['state_hashes'],
|
||||
candidate_per_tick_queue_fields=observed['queues'], queue_joint_hashes_compared=False), limitations)
|
||||
|
||||
|
||||
def compare_checkpoint(candidate, prefix):
|
||||
"""Compare equal absolute endpoints, not local phases or segment counts."""
|
||||
candidate, _ = _record(candidate, 'candidate')
|
||||
prefix, _ = _record(prefix, 'prefix')
|
||||
checks = _common(candidate, prefix)
|
||||
end = candidate['global_tick_offset'] + candidate['steps']
|
||||
reference_end = prefix['global_tick_offset'] + prefix['steps']
|
||||
checks['checkpoint.same_absolute_end'] = end == reference_end
|
||||
for name in ('v', 'g', 'ref'):
|
||||
checks['checkpoint.state.' + name] = ec.bit_equal(candidate['state'][name], prefix['state'][name])
|
||||
checks['checkpoint.normalized_spike_ring'] = ec.bit_equal(
|
||||
ec.normalized_ring(candidate['terminal']), ec.normalized_ring(prefix['terminal']))
|
||||
for name in ('apl_state', 'apl_pending'):
|
||||
checks['checkpoint.' + name] = ec.bit_equal(candidate['terminal'][name], prefix['terminal'][name])
|
||||
return _report('checkpoint', checks, dict(candidate_absolute_end=end, reference_absolute_end=reference_end,
|
||||
candidate_local_steps=candidate['steps'], reference_local_steps=prefix['steps'], terminal_v_g_ref=True,
|
||||
normalized_spike_ring=True, terminal_APL_state_and_pending=True, counts_compared=False,
|
||||
event_histories_compared=False, per_tick_state_histories_compared=False), [
|
||||
'Segment counts and cumulative prefix counts are deliberately not compared in this endpoint scope.',
|
||||
'Matching terminal records do not prove matching intermediate states or complete event histories.',
|
||||
'External input/seal/parent authority and live-carry coverage remain separate gates.'])
|
||||
|
||||
|
||||
def compare_parent_carry(candidate, parent):
|
||||
"""Check recorded short-segment carry invariants, not unobserved restoration.
|
||||
|
||||
The parent ends where the candidate starts. Slots already overwritten by
|
||||
this segment cannot be checked as initial carry from terminal data alone.
|
||||
When present, the child's applied trace directly tests consumed APL rows.
|
||||
"""
|
||||
candidate, observed = _record(candidate, 'candidate')
|
||||
parent, _ = _record(parent, 'parent')
|
||||
_require(candidate['global_tick_offset'] > 0, 'Parent-carry candidate must be a continued segment')
|
||||
checks = _common(candidate, parent)
|
||||
start, steps = candidate['global_tick_offset'], candidate['steps']
|
||||
parent_end = parent['global_tick_offset'] + parent['steps']
|
||||
checks['parent.contiguous_absolute_clock'] = parent_end == start
|
||||
restored_ring = ec.normalized_ring(parent['terminal'])
|
||||
unconsumed_spikes, unconsumed_apl = steps < 19, steps < 18
|
||||
if unconsumed_spikes:
|
||||
checks['parent.unwritten_spike_slots'] = ec.bit_equal(
|
||||
candidate['terminal']['spike_ring'][steps:], restored_ring[steps:])
|
||||
pending = parent['terminal']['apl_pending']
|
||||
if unconsumed_apl:
|
||||
checks['parent.unconsumed_APL_prefix'] = ec.bit_equal(
|
||||
candidate['terminal']['apl_pending'][:18 - steps], pending[steps:])
|
||||
consume = min(18, steps)
|
||||
if observed['apl_traces']:
|
||||
checks['parent.applied_APL_prefix'] = ec.bit_equal(candidate['traces']['applied'][:consume], pending[:consume])
|
||||
limitations = ['Initial V/g/ref/APL-state restoration is not directly observed by this terminal-only carry scope.',
|
||||
'Overwritten spike slots and consumed pending slots require the separate full-reference slice/endpoint gates.',
|
||||
'Parent manifest hashes and ancestor provenance must be independently bound by the coordinator.',
|
||||
'No queue arrival/acceptance reconstruction, live-carry or learning success is claimed here.']
|
||||
if not observed['apl_traces']:
|
||||
limitations.append('Candidate applied APL history is unobserved; consumed parent APL values are not directly compared.')
|
||||
carry_observed = unconsumed_spikes or unconsumed_apl or observed['apl_traces']
|
||||
if not carry_observed:
|
||||
limitations.append('No carry array remains directly observable in this long light segment; this scope checks clock/shape/weight identity only.')
|
||||
return _report('parent_carry', checks, dict(parent_absolute_start=parent['global_tick_offset'],
|
||||
parent_absolute_end=parent_end, candidate_absolute_start=start, candidate_absolute_end=start + steps,
|
||||
candidate_local_steps=steps, unwritten_spike_slots_checked=unconsumed_spikes,
|
||||
unwritten_spike_slot_count=max(19 - steps, 0), unconsumed_APL_prefix_checked=unconsumed_apl,
|
||||
unconsumed_APL_row_count=max(18 - steps, 0), applied_parent_APL_prefix_checked=observed['apl_traces'],
|
||||
applied_parent_APL_rows_compared=consume if observed['apl_traces'] else None,
|
||||
any_carry_values_directly_compared=carry_observed,
|
||||
initial_neural_state_restoration_directly_observed=False), limitations)
|
||||
@@ -0,0 +1,421 @@
|
||||
"""Finite, no-training execution of the separately registered 32-call gate.
|
||||
|
||||
All files are exclusive creations. A partial/failed row is preserved and is
|
||||
not retried. Runtime outputs are sealed before external reports are written.
|
||||
The preparation module owns source/input authority; no summary Boolean alone
|
||||
is accepted as proof that a subprocess or a complete engineering gate passed.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
from datetime import datetime, timezone
|
||||
import importlib
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import traceback
|
||||
import urllib.request
|
||||
import zipfile
|
||||
|
||||
import numpy as np
|
||||
|
||||
import engineering_inputs as I
|
||||
import engineering_plan as PLAN
|
||||
import runtime_light as R
|
||||
import runtime_continuation as C
|
||||
|
||||
|
||||
GPU = 'GPU-79f3a85d-adce-7833-7003-6488140ca596'
|
||||
ROOT = Path('/data/digital-drosophila')
|
||||
MODEL = Path(__file__).resolve().parent
|
||||
EXP = ROOT / 'experiments/phase4an-engineering-validation'
|
||||
require = R.require
|
||||
|
||||
|
||||
def canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(',', ':'), allow_nan=False)
|
||||
|
||||
|
||||
def same_json(left, right):
|
||||
return canonical(left) == canonical(right)
|
||||
|
||||
|
||||
def preparation():
|
||||
return importlib.import_module('engineering_prepare')
|
||||
|
||||
|
||||
def row_from(registration, row_id):
|
||||
plan = PLAN.validate_plan(registration['plan'])
|
||||
rows = [row for row in plan['rows'] if row['row_id'] == row_id]
|
||||
require(len(rows) == 1, 'Only an exact registered row may execute')
|
||||
return rows[0]
|
||||
|
||||
|
||||
def locations(row_id):
|
||||
require(type(row_id) is str and row_id and all(ch.isalnum() or ch in '-_' for ch in row_id),
|
||||
'Unsafe row identity')
|
||||
return dict(output=EXP / 'runs' / row_id,
|
||||
runtime=EXP / 'receipts' / (row_id + '.runtime.json'),
|
||||
execution=EXP / 'receipts' / (row_id + '.json'),
|
||||
derivation=EXP / 'receipts' / (row_id + '.derivation.json'),
|
||||
stdout=EXP / 'logs' / (row_id + '.stdout.log'),
|
||||
stderr=EXP / 'logs' / (row_id + '.stderr.log'))
|
||||
|
||||
|
||||
def disk_budget(registration):
|
||||
limit = registration['budget']['output_budget_bytes']
|
||||
reserve = registration['budget']['minimum_free_reserve_bytes']
|
||||
require(type(limit) is type(reserve) is int and limit > 0 and reserve > 0, 'Invalid registered budget')
|
||||
files = [p for p in EXP.rglob('*') if p.is_file()]
|
||||
require(not any(p.is_symlink() for p in EXP.rglob('*')), 'Experiment contains a linked output')
|
||||
used = sum(p.stat().st_size for p in files)
|
||||
free = shutil.disk_usage(EXP).free
|
||||
require(used <= limit and free >= reserve + max(0, limit - used), 'Engineering budget/reserve exceeded; preserve partials')
|
||||
return dict(used_bytes=used, free_bytes=free, hard_cap_bytes=limit, reserve_bytes=reserve)
|
||||
|
||||
|
||||
def preflight():
|
||||
"""Never evict an existing service; configure only this child process."""
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = GPU
|
||||
os.environ['CUDA_HOME'] = '/usr/local/cuda'
|
||||
os.environ['PATH'] = str(ROOT / 'models/male-cns-lif/.venv/bin') + ':/usr/local/cuda/bin:' + os.environ['PATH']
|
||||
os.environ['MAX_JOBS'] = '2'
|
||||
os.environ['TORCH_CUDA_ARCH_LIST'] = '8.9'
|
||||
for port in (8188, 8189, 8190):
|
||||
with urllib.request.urlopen(f'http://192.168.10.51:{port}/queue', timeout=5) as response:
|
||||
queue = json.load(response)
|
||||
require(queue['queue_running'] == [] and queue['queue_pending'] == [],
|
||||
'ComfyUI busy: defer rather than evict')
|
||||
|
||||
|
||||
def derive_case(template, row, parent_sha):
|
||||
require(type(template) is dict and set(template) == {'schema', 'row_id', 'original_spec', 'start', 'stop',
|
||||
'budget_root', 'output_budget_bytes'}, 'Unexpected registered case template fields')
|
||||
require(template['schema'] == 'phase4an-engineering-case-template-v1' and template['row_id'] == row['row_id'] and
|
||||
type(template['start']) is int and type(template['stop']) is int and
|
||||
(template['start'], template['stop']) == (row['start'], row['stop']), 'Template differs from exact plan')
|
||||
require((row['parent_row_id'] is None) == (parent_sha is None), 'Parent linkage differs from registered plan')
|
||||
return I.derive_spec(template['original_spec'], start=row['start'], stop=row['stop'],
|
||||
budget_root=template['budget_root'], output_budget_bytes=template['output_budget_bytes'], parent_sha=parent_sha)
|
||||
|
||||
|
||||
def npz_storage(path):
|
||||
with zipfile.ZipFile(path) as archive:
|
||||
rows = archive.infolist()
|
||||
require(rows and all(r.compress_type == zipfile.ZIP_DEFLATED and r.filename.endswith('.npy') for r in rows),
|
||||
'Declared lossless compressed state format differs from actual archive')
|
||||
return dict(file_bytes=Path(path).stat().st_size,
|
||||
uncompressed_entry_bytes=sum(r.file_size for r in rows),
|
||||
compressed_entry_bytes=sum(r.compress_size for r in rows),
|
||||
entries=[dict(name=r.filename, bytes=r.file_size, compressed_bytes=r.compress_size,
|
||||
compression=r.compress_type) for r in rows])
|
||||
|
||||
|
||||
def runtime_environment():
|
||||
"""Read settings after the runtime imported Torch; change none of them."""
|
||||
torch = sys.modules.get('torch')
|
||||
require(torch is not None and os.environ.get('CUDA_VISIBLE_DEVICES') == GPU and
|
||||
torch.cuda.device_count() == 1 and '4090' in torch.cuda.get_device_name(0),
|
||||
'Actual runtime is not the registered single RTX4090')
|
||||
properties = torch.cuda.get_device_properties(0)
|
||||
inventory = subprocess.run(['nvidia-smi', '-i', GPU,
|
||||
'--query-gpu=uuid,name,memory.total,driver_version', '--format=csv,noheader'],
|
||||
check=True, capture_output=True, text=True, timeout=20).stdout.strip()
|
||||
require(inventory.startswith(GPU + ','), 'GPU hardware inventory does not match the registered UUID')
|
||||
return dict(python_executable=sys.executable, python_version=sys.version, numpy_version=np.__version__,
|
||||
torch_version=str(torch.__version__), torch_cuda_version=torch.version.cuda,
|
||||
device_name=properties.name, total_memory_bytes=int(properties.total_memory),
|
||||
gpu_uuid=GPU, nvidia_smi_inventory=inventory,
|
||||
matmul_allow_tf32=bool(torch.backends.cuda.matmul.allow_tf32),
|
||||
cudnn_allow_tf32=bool(torch.backends.cudnn.allow_tf32),
|
||||
deterministic_algorithms=bool(torch.are_deterministic_algorithms_enabled()),
|
||||
cudnn_deterministic=bool(torch.backends.cudnn.deterministic),
|
||||
cudnn_benchmark=bool(torch.backends.cudnn.benchmark),
|
||||
float32_matmul_precision=torch.get_float32_matmul_precision(),
|
||||
CUBLAS_WORKSPACE_CONFIG=os.environ.get('CUBLAS_WORKSPACE_CONFIG'),
|
||||
TORCH_CUDA_ARCH_LIST=os.environ.get('TORCH_CUDA_ARCH_LIST'),
|
||||
settings_changed_by_environment_recorder=False,
|
||||
environment_recorder_adds_no_timing=True,
|
||||
build_timing_source='Use inherited summary.build_or_cache_seconds; build/cache is also included in runtime wall time')
|
||||
|
||||
|
||||
def verify_environment(recorded, component):
|
||||
require(type(recorded) is type(component) is dict and
|
||||
recorded.get('gpu_uuid') == GPU and recorded.get('settings_changed_by_environment_recorder') is False,
|
||||
'Missing or invalid actual environment receipt')
|
||||
require(component.get('cuda_matmul_allow_tf32') is False and
|
||||
component.get('float32_matmul_precision') == 'highest' and
|
||||
component.get('precision_configuration_changed_by_component') is False,
|
||||
'Frozen AM precision configuration changed; never correct it automatically')
|
||||
pairs = {'matmul_allow_tf32': 'cuda_matmul_allow_tf32', 'cudnn_allow_tf32': 'cudnn_allow_tf32',
|
||||
'deterministic_algorithms': 'deterministic_algorithms', 'cudnn_deterministic': 'cudnn_deterministic',
|
||||
'cudnn_benchmark': 'cudnn_benchmark', 'float32_matmul_precision': 'float32_matmul_precision',
|
||||
'CUBLAS_WORKSPACE_CONFIG': 'CUBLAS_WORKSPACE_CONFIG', 'device_name': 'device_name',
|
||||
'torch_version': 'torch', 'torch_cuda_version': 'cuda', 'numpy_version': 'numpy'}
|
||||
for left, right in pairs.items():
|
||||
require(left in recorded and right in component and same_json(recorded[left], component[right]),
|
||||
'Actual runtime/APL environment disagreement: ' + left)
|
||||
require(type(recorded['device_name']) is str and '4090' in recorded['device_name'], 'Wrong physical device class')
|
||||
|
||||
|
||||
def check_runtime_record(reg, reg_sha, row, record):
|
||||
"""Recheck actual bytes, runtime completion and parent chain, not intent."""
|
||||
paths = locations(row['row_id'])
|
||||
require(record.get('complete') is True and record.get('registration_sha256') == reg_sha and
|
||||
same_json(record.get('row'), row) and record.get('training') is False,
|
||||
'Runtime record is not bound to this registered row')
|
||||
case = Path(reg['cases'][row['row_id']]['case_dir'])
|
||||
checkpoint = C.load_checkpoint(paths['output'], record['checkpoint_manifest_sha256'])
|
||||
summary = checkpoint['summary']
|
||||
verify_environment(record['environment'], summary['apl_component']['environment'])
|
||||
require(checkpoint['steps'] == row['steps'] and checkpoint['global_tick_offset'] == row['start'] and
|
||||
checkpoint['global_next_tick'] == row['stop'] and summary['nodes'] == 166700 and summary['edges'] == 25582938 and
|
||||
summary['diagnostic_mode'] == row['mode'], 'Runtime clock/population/mode differs from plan')
|
||||
require(record['case_sha256'] == R.digest(case / 'spec.json') and
|
||||
record['requests_sha256'] == R.digest(case / 'requests.npz') and
|
||||
record['derivation_sha256'] == R.digest(paths['derivation']), 'Derived input/receipt bytes changed')
|
||||
template = R._read_json(case / 'template-spec.json')
|
||||
parent_pin = None
|
||||
if row['parent_row_id'] is not None:
|
||||
parent = verify_execution(reg, reg_sha, row_from(reg, row['parent_row_id']))
|
||||
parent_pin = parent['checkpoint_manifest_sha256']
|
||||
expected, derivation = derive_case(template, row, parent_pin)
|
||||
require(same_json(R._read_json(case / 'spec.json'), expected) and
|
||||
same_json(R._read_json(paths['derivation']), derivation), 'Case was not generated by the registered exact rule')
|
||||
require(record['parent_checkpoint_manifest_sha256'] == parent_pin and
|
||||
summary.get('parent_checkpoint_manifest_sha256') == parent_pin, 'Execution parent SHA mismatch')
|
||||
require(record['checkpoint_inventory_sha256'] == R.digest(paths['output'] / C.MANIFEST), 'Checkpoint manifest changed')
|
||||
for name in ('state.npz', 'terminal-queues.npz'):
|
||||
require(same_json(record['storage'][name], npz_storage(paths['output'] / name)), 'Storage measurement changed')
|
||||
return record
|
||||
|
||||
|
||||
def verify_execution(reg, reg_sha, row):
|
||||
paths = locations(row['row_id'])
|
||||
receipt = R._read_json(paths['execution'])
|
||||
require(receipt.get('complete') is True and type(receipt.get('process_exit_code')) is int and receipt['process_exit_code'] == 0 and
|
||||
receipt.get('registration_sha256') == reg_sha and receipt.get('row_id') == row['row_id'],
|
||||
'Missing completed process receipt')
|
||||
for field, key in (('runtime_record_sha256', 'runtime'), ('stdout_sha256', 'stdout'), ('stderr_sha256', 'stderr')):
|
||||
require(receipt[field] == R.digest(paths[key]), 'Execution log/runtime receipt changed')
|
||||
require(not (EXP / 'audit/stopped.json').exists(), 'A failed/deferred run requires explicit review, not automatic retry')
|
||||
record = check_runtime_record(reg, reg_sha, row, R._read_json(paths['runtime']))
|
||||
require(receipt['checkpoint_manifest_sha256'] == record['checkpoint_manifest_sha256'], 'Execution/checkpoint linkage mismatch')
|
||||
return record
|
||||
|
||||
|
||||
def worker(reg_sha, row_id):
|
||||
require(sys.dont_write_bytecode, 'Use the scientific venv with -B')
|
||||
reg = preparation().read_registration(reg_sha)
|
||||
row = row_from(reg, row_id)
|
||||
paths = locations(row_id)
|
||||
require(not paths['output'].exists() and not paths['runtime'].exists() and not paths['derivation'].exists(),
|
||||
'Never overwrite/retry a partial row')
|
||||
budget_before = disk_budget(reg)
|
||||
preflight()
|
||||
parent_sha = None
|
||||
parent_dir = None
|
||||
if row['parent_row_id'] is not None:
|
||||
parent_record = verify_execution(reg, reg_sha, row_from(reg, row['parent_row_id']))
|
||||
parent_sha = parent_record['checkpoint_manifest_sha256']
|
||||
parent_dir = locations(row['parent_row_id'])['output']
|
||||
case = Path(reg['cases'][row_id]['case_dir'])
|
||||
template = R._read_json(case / 'template-spec.json')
|
||||
spec, derivation = derive_case(template, row, parent_sha)
|
||||
require(not (case / 'spec.json').exists(), 'Derived spec already exists; preserve partial instead of rerun')
|
||||
R._save(case / 'spec.json', spec)
|
||||
R._save(paths['derivation'], derivation)
|
||||
overlay = reg['overlays'][row['overlay']]
|
||||
start_utc = datetime.now(timezone.utc).isoformat()
|
||||
start = time.perf_counter()
|
||||
if parent_dir is None:
|
||||
function = R.run_reference if row['mode'] == 'reference' else R.run_light
|
||||
function(case, paths['output'], EXP / 'extension-build', overlay['overlay_file'], overlay['overlay_manifest'])
|
||||
pin = C.seal_checkpoint(paths['output'])['sha256']
|
||||
else:
|
||||
result = C.run_continued(case, paths['output'], EXP / 'extension-build',
|
||||
overlay['overlay_file'], overlay['overlay_manifest'], parent_dir, mode=row['mode'])
|
||||
pin = result['checkpoint_manifest_sha256']
|
||||
elapsed = time.perf_counter() - start
|
||||
budget_after = disk_budget(reg)
|
||||
record = dict(schema='phase4an-engineering-runtime-record-v1', complete=True, training=False,
|
||||
registration_sha256=reg_sha, row=row, case_sha256=R.digest(case / 'spec.json'),
|
||||
requests_sha256=R.digest(case / 'requests.npz'), derivation_sha256=R.digest(paths['derivation']),
|
||||
checkpoint_manifest_sha256=pin, checkpoint_inventory_sha256=R.digest(paths['output'] / C.MANIFEST),
|
||||
parent_checkpoint_manifest_sha256=parent_sha, runtime_and_immediate_seal_wall_seconds=elapsed,
|
||||
started_utc=start_utc, completed_utc=datetime.now(timezone.utc).isoformat(),
|
||||
environment=runtime_environment(),
|
||||
budget_before=budget_before, budget_after=budget_after,
|
||||
storage={name: npz_storage(paths['output'] / name) for name in ('state.npz', 'terminal-queues.npz')},
|
||||
no_result_selected_retry=True, learning_pass_claimed=False)
|
||||
# Do not write this or any later report INSIDE the sealed checkpoint.
|
||||
check_runtime_record(reg, reg_sha, row, record)
|
||||
preparation().read_registration(reg_sha)
|
||||
R._save(paths['runtime'], record)
|
||||
print('COMPLETED ENGINEERING ROW', row_id, flush=True)
|
||||
|
||||
|
||||
def run_registered(reg_sha):
|
||||
require(sys.dont_write_bytecode, 'Use python -B')
|
||||
reg = preparation().read_registration(reg_sha)
|
||||
require(not (EXP / 'audit/stopped.json').exists() and not (EXP / 'audit/execution-complete.json').exists(),
|
||||
'Stopped/completed gate must not be restarted')
|
||||
started = time.perf_counter()
|
||||
completed = []
|
||||
try:
|
||||
for row in reg['plan']['rows']:
|
||||
paths = locations(row['row_id'])
|
||||
if paths['execution'].exists():
|
||||
verify_execution(reg, reg_sha, row)
|
||||
completed.append(dict(row_id=row['row_id'], execution_sha256=R.digest(paths['execution']),
|
||||
reverified_existing=True))
|
||||
continue
|
||||
require(not any(paths[k].exists() for k in ('output', 'runtime', 'stdout', 'stderr', 'derivation')),
|
||||
'Partial row present; no automatic retry')
|
||||
disk_budget(reg)
|
||||
preflight()
|
||||
began = time.perf_counter()
|
||||
print('START', row['row_id'], flush=True)
|
||||
with paths['stdout'].open('xb') as out, paths['stderr'].open('xb') as err:
|
||||
result = subprocess.run([sys.executable, '-B', str(Path(__file__)), 'worker',
|
||||
'--registration-sha256', reg_sha, '--row-id', row['row_id']], cwd=MODEL, stdout=out, stderr=err)
|
||||
require(result.returncode == 0, 'Worker failed; preserve its stdout/stderr and partial output')
|
||||
record = check_runtime_record(reg, reg_sha, row, R._read_json(paths['runtime']))
|
||||
receipt = dict(schema='phase4an-engineering-execution-receipt-v1', complete=True,
|
||||
process_exit_code=result.returncode, row_id=row['row_id'], registration_sha256=reg_sha,
|
||||
runtime_record_sha256=R.digest(paths['runtime']), stdout_sha256=R.digest(paths['stdout']),
|
||||
stderr_sha256=R.digest(paths['stderr']), process_wall_seconds=time.perf_counter() - began,
|
||||
checkpoint_manifest_sha256=record['checkpoint_manifest_sha256'], scientific_pass_claimed=False)
|
||||
R._save(paths['execution'], receipt)
|
||||
verify_execution(reg, reg_sha, row)
|
||||
completed.append(dict(row_id=row['row_id'], execution_sha256=R.digest(paths['execution']),
|
||||
reverified_existing=False))
|
||||
print('COMPLETE', row['row_id'], flush=True)
|
||||
require(len(completed) == 32, 'Incomplete registered inventory')
|
||||
preparation().read_registration(reg_sha)
|
||||
R._save(EXP / 'audit/execution-complete.json', dict(complete=True, registration_sha256=reg_sha,
|
||||
completed=completed, expected_calls=32, executed_ticks=11074, wall_seconds=time.perf_counter() - started,
|
||||
numerical_gate_evaluated=False, training=False, learning_pass_claimed=False))
|
||||
except Exception as error:
|
||||
R._save(EXP / 'audit/stopped.json', dict(complete=False, type=type(error).__name__, message=str(error),
|
||||
traceback=traceback.format_exc(), completed_before_error=completed, partial_outputs_preserved=True,
|
||||
no_automatic_retry=True, scientific_failure_not_inferred_from_engineering_failure=True))
|
||||
raise
|
||||
|
||||
|
||||
def verified_inventory(reg, reg_sha):
|
||||
"""A completed child and every planned descriptor must remain auditable."""
|
||||
plan = PLAN.validate_plan(reg['plan'])
|
||||
receipt = R._read_json(EXP / 'audit/execution-complete.json')
|
||||
require(receipt.get('complete') is True and receipt.get('registration_sha256') == reg_sha and
|
||||
type(receipt.get('expected_calls')) is int and receipt['expected_calls'] == plan['total_calls'] and
|
||||
type(receipt.get('executed_ticks')) is int and receipt['executed_ticks'] == plan['total_ticks'] and
|
||||
receipt.get('training') is False and receipt.get('learning_pass_claimed') is False,
|
||||
'Execution inventory is not a complete no-training registered gate')
|
||||
require(type(receipt.get('completed')) is list and len(receipt['completed']) == plan['total_calls'],
|
||||
'Execution inventory truncated')
|
||||
records = {}
|
||||
for row, entry in zip(plan['rows'], receipt['completed']):
|
||||
require(entry.get('row_id') == row['row_id'] and
|
||||
entry.get('execution_sha256') == R.digest(locations(row['row_id'])['execution']),
|
||||
'Execution completion inventory reordered or receipt changed')
|
||||
records[row['row_id']] = verify_execution(reg, reg_sha, row)
|
||||
return records
|
||||
|
||||
|
||||
def evaluate(reg_sha):
|
||||
reg = preparation().read_registration(reg_sha)
|
||||
records = verified_inventory(reg, reg_sha)
|
||||
report = importlib.import_module('engineering_analysis').analyze(reg, reg_sha, records)
|
||||
require(type(report) is dict and report.get('training') is False and report.get('learning_pass_claimed') is False,
|
||||
'Engineering analysis must not claim training or learning')
|
||||
preparation().read_registration(reg_sha)
|
||||
return reg, report
|
||||
|
||||
|
||||
def analyze(reg_sha):
|
||||
require(sys.dont_write_bytecode, 'Use python -B')
|
||||
target = EXP / 'audit/engineering-analysis.json'
|
||||
require(not target.exists() and not (EXP / 'manifest.json').exists(), 'Analysis/seal already exists; do not overwrite')
|
||||
began = time.perf_counter()
|
||||
_, report = evaluate(reg_sha)
|
||||
R._save(target, report)
|
||||
R._save(EXP / 'audit/analysis-execution.json', dict(registration_sha256=reg_sha,
|
||||
analysis_sha256=R.digest(target), wall_seconds=time.perf_counter() - began, GPU_used=False,
|
||||
training=False, learning_pass_claimed=False))
|
||||
print(report['status'], flush=True)
|
||||
|
||||
|
||||
def seal_experiment():
|
||||
"""Seal only the new engineering experiment, not the unfinished lab model."""
|
||||
controls = ('manifest.json', 'checksums.sha256')
|
||||
require(not any((EXP / name).exists() for name in controls), 'Do not replace an existing or partial seal')
|
||||
paths = sorted(EXP.rglob('*'))
|
||||
require(not any(path.is_symlink() for path in paths), 'Cannot seal linked artifacts')
|
||||
require(not any('__pycache__' in path.relative_to(EXP).parts for path in paths), 'Unexpected bytecode cache in new experiment')
|
||||
rows = [dict(path=path.relative_to(EXP).as_posix(), bytes=path.stat().st_size, sha256=R.digest(path))
|
||||
for path in paths if path.is_file()]
|
||||
require(rows, 'Cannot seal an empty experiment')
|
||||
# Only ROOT controls are excluded from their own hash. Nested checkpoint
|
||||
# manifests and source snapshots are ordinary payload and remain included.
|
||||
R._save(EXP / controls[0], dict(root=str(EXP), files=rows))
|
||||
with (EXP / controls[1]).open('x', encoding='utf-8', newline='\n') as stream:
|
||||
stream.writelines(row['sha256'] + ' ' + row['path'] + '\n' for row in rows)
|
||||
stream.write(R.digest(EXP / controls[0]) + ' manifest.json\n')
|
||||
return dict(manifest_sha256=R.digest(EXP / controls[0]), checksums_sha256=R.digest(EXP / controls[1]))
|
||||
|
||||
|
||||
def finalize(reg_sha):
|
||||
require(sys.dont_write_bytecode, 'Use python -B')
|
||||
require(not (EXP / 'manifest.json').exists() and not (EXP / 'audit/final-status.json').exists(),
|
||||
'Final/partial seal already exists; preserve it')
|
||||
reg, actual = evaluate(reg_sha)
|
||||
target = EXP / 'audit/engineering-analysis.json'
|
||||
require(same_json(actual, R._read_json(target)), 'Recomputed analysis differs from saved evidence')
|
||||
execution = R._read_json(EXP / 'audit/analysis-execution.json')
|
||||
require(execution.get('registration_sha256') == reg_sha and execution.get('analysis_sha256') == R.digest(target),
|
||||
'Saved analysis is not bound to its execution receipt')
|
||||
authority = preparation().verify_authority(reg['anchors'])
|
||||
old = preparation().verify_old_integrity(reg)
|
||||
R._save(EXP / 'regression/AM-seals-after.json', authority)
|
||||
R._save(EXP / 'regression/old-integrity-after.json', old)
|
||||
preparation().read_registration(reg_sha)
|
||||
final = dict(schema='phase4an-engineering-final-v1', status=actual['status'],
|
||||
registration_sha256=reg_sha, analysis_sha256=R.digest(target),
|
||||
analysis_recomputed_exactly=True, completed_calls=32, executed_ticks=11074,
|
||||
old_integrity_pass=True, old_integrity_roots=len(old), AM_seals_preserved=True,
|
||||
old_five_reference_evidence='Sealed AM five-trial evidence retained; not rerun by this engineering gate',
|
||||
training=False, learning_pass_claimed=False, learned_candidate=False,
|
||||
model_directory_sealed=False, used_engineering_source_snapshots_sealed=True,
|
||||
budget=disk_budget(reg),
|
||||
scope='Finite registered 100 ms input and fixed continuation cuts only; no universal determinism, learning or physiology claim')
|
||||
R._save(EXP / 'audit/final-status.json', final)
|
||||
pins = seal_experiment()
|
||||
helper = preparation().load_module('phase4an_pinned_AI_publication',
|
||||
ROOT / preparation().AI_HELPER_REL, preparation().AI_HELPER_SHA)
|
||||
verified = helper.verify_seal(EXP)
|
||||
require(verified['manifest_sha256'] == pins['manifest_sha256'] and
|
||||
verified['checksums_sha256'] == pins['checksums_sha256'], 'Final experiment seal verification failed')
|
||||
print(canonical(dict(status=final['status'], **pins)), flush=True)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument('action', choices=('run', 'worker', 'analyze', 'finalize'))
|
||||
parser.add_argument('--registration-sha256', required=True)
|
||||
parser.add_argument('--row-id')
|
||||
args = parser.parse_args()
|
||||
if args.action == 'worker':
|
||||
require(args.row_id is not None, 'Worker requires a row ID')
|
||||
worker(args.registration_sha256, args.row_id)
|
||||
else:
|
||||
require(args.row_id is None, 'Full inventory run has no selected row')
|
||||
{'run': run_registered, 'analyze': analyze, 'finalize': finalize}[args.action](args.registration_sha256)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,260 @@
|
||||
"""Pure, UNREGISTERED evaluation inventory; no files, RNG draws or execution.
|
||||
|
||||
build_evaluation_plan(trial_payload, config) requires the complete existing
|
||||
trial_plan payload, including its caller-chosen master and seed manifest. It
|
||||
reuses that payload's 80 evaluation inputs; repeated probes are paired
|
||||
observations, never extra independent training replicates. No defaults select
|
||||
CS offset, reversal curve checkpoints, control snapshot boundaries, or baseline
|
||||
execution policy. See required_choices(). A complete inventory is not approval,
|
||||
an executed snapshot, an externally verified source pin, or a scientific gate.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import Counter
|
||||
import hashlib
|
||||
import json
|
||||
|
||||
import seed_registry
|
||||
import trial_plan
|
||||
|
||||
|
||||
SCHEMA = "phase4an-evaluation-plan-draft-v1"
|
||||
CONFIG_SCHEMA = "phase4an-evaluation-config-draft-v1"
|
||||
CONFIG_FIELDS = frozenset(("schema", "probe_steps", "cs_start_tick",
|
||||
"cs_stop_tick_exclusive", "reversal_checkpoints", "baseline_execution",
|
||||
"snapshot_binding_policy", "snapshot_bindings"))
|
||||
BINDING_FIELDS = frozenset(("block_id", "assay", "arm", "checkpoint", "completed_epochs"))
|
||||
|
||||
|
||||
def _require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(",", ":"),
|
||||
ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest()
|
||||
|
||||
|
||||
def required_choices():
|
||||
"""Describe mandatory choices, without filling or approving any of them."""
|
||||
return {
|
||||
"probe_steps": "Explicit integer 1200: the draft's fixed 120 ms metric window.",
|
||||
"cs_start_tick": "Explicit integer 0: stimulus onset defines probe latency.",
|
||||
"cs_stop_tick_exclusive": "Explicit integer in [1,1200]; draft does not settle evaluation CS offset.",
|
||||
"reversal_checkpoints": "Explicit sorted subset of [0,1,2,5,10,20,50], including 0 and 50.",
|
||||
"baseline_execution": "Only execute_each_snapshot is implemented; declare it explicitly. No output deduplication.",
|
||||
"snapshot_binding_policy": "Explicit completed_epochs; nominal control labels are not actual update counts.",
|
||||
"snapshot_bindings": "Every block/assay/arm/checkpoint, in trial-plan order, with explicit completed_epochs. No control alignment is guessed.",
|
||||
"authority": "Provide the complete existing trial payload; no master seed is chosen here. Source/input pins remain coordinator responsibilities.",
|
||||
}
|
||||
|
||||
|
||||
def _configuration(config):
|
||||
_require(type(config) is dict, "Explicit evaluation config is required; see required_choices()")
|
||||
_require(set(config) == CONFIG_FIELDS,
|
||||
"Evaluation config fields: missing=" + str(sorted(CONFIG_FIELDS - set(config))) +
|
||||
"; unknown=" + str(sorted(set(config) - CONFIG_FIELDS)))
|
||||
_require(config["schema"] == CONFIG_SCHEMA, "Wrong evaluation config schema")
|
||||
_require(type(config["probe_steps"]) is int and config["probe_steps"] == 1200,
|
||||
"Explicit probe_steps must be integer 1200")
|
||||
_require(type(config["cs_start_tick"]) is int and config["cs_start_tick"] == 0,
|
||||
"Explicit CS onset must be integer tick 0")
|
||||
stop = config["cs_stop_tick_exclusive"]
|
||||
_require(type(stop) is int and 0 < stop <= 1200,
|
||||
"Evaluation CS stop tick is required; no training-CS default is inferred")
|
||||
checks = config["reversal_checkpoints"]
|
||||
_require(type(checks) is list and len(checks) >= 2 and
|
||||
all(type(v) is int and v in trial_plan.PROBE_CHECKPOINTS for v in checks) and
|
||||
checks == sorted(set(checks)) and checks[0] == 0 and checks[-1] == 50,
|
||||
"Explicit reversal checkpoints must include 0/50, sorted, unique, and within the draft curve")
|
||||
_require(config["baseline_execution"] == "execute_each_snapshot",
|
||||
"Declare execute_each_snapshot explicitly; baseline reuse is not implemented or inferred")
|
||||
_require(config["snapshot_binding_policy"] == "explicit_completed_epochs",
|
||||
"Explicit control snapshot epoch bindings are mandatory")
|
||||
_require(type(config["snapshot_bindings"]) is list, "snapshot_bindings must be an explicit ordered list")
|
||||
return json.loads(_canonical(config))
|
||||
|
||||
|
||||
def _binding_inventory(plan, config):
|
||||
expected, specs = [], []
|
||||
for block in plan["blocks"]:
|
||||
for suite in block["suites"]:
|
||||
assay = suite["assay"]
|
||||
points = config["reversal_checkpoints"] if assay == "reversal" else block["evaluation_checkpoints"]
|
||||
for arm in suite["arms"]:
|
||||
for point in points:
|
||||
expected.append((block["block_id"], assay, arm["arm"], point))
|
||||
specs.append((block, suite, arm))
|
||||
bindings = config["snapshot_bindings"]
|
||||
_require(len(bindings) == len(expected), "Missing/extra snapshot bindings: require every block, arm and checkpoint")
|
||||
previous = {}
|
||||
result = []
|
||||
for binding, identity, spec in zip(bindings, expected, specs):
|
||||
_require(type(binding) is dict and set(binding) == BINDING_FIELDS, "Invalid snapshot binding fields")
|
||||
actual = tuple(binding[key] for key in ("block_id", "assay", "arm", "checkpoint"))
|
||||
_require(all(type(v) is str for v in actual[:3]) and type(actual[3]) is int and actual == identity,
|
||||
"Snapshot bindings must match exact canonical coverage/order/types")
|
||||
block, suite, arm = spec
|
||||
count = binding["completed_epochs"]
|
||||
_require(type(count) is int and 0 <= count <= len(arm["epochs"]), "Invalid completed_epochs")
|
||||
point = identity[-1]
|
||||
_require((count == 0) == (point == 0), "Checkpoint zero is before the arm's first epoch")
|
||||
_require(point != 50 or count == len(arm["epochs"]), "Terminal checkpoint must include the entire arm, including neutral/blank epochs")
|
||||
key = identity[:3]
|
||||
_require(key not in previous or count > previous[key], "Snapshot epochs must advance strictly")
|
||||
previous[key] = count
|
||||
us = sum(e["us"]["requested"] for e in arm["epochs"][:count])
|
||||
updates = sum(e["update_enabled"] and e["D_command"] != 0 for e in arm["epochs"][:count])
|
||||
if arm["arm"] in ("paired", "reversal", "no_reversal"):
|
||||
_require(us == point and updates == point,
|
||||
"Paired/reversal checkpoint must contain exactly its declared reinforcement opportunities")
|
||||
result.append((binding, block, suite, arm, us, updates))
|
||||
return result
|
||||
|
||||
|
||||
def _streams(trial):
|
||||
"""Reconstruct evaluation semantic keys, not just plausible seed strings."""
|
||||
manifest = trial["seed_manifest"]
|
||||
master = trial["plan"]["master"]
|
||||
independent = {(r["split"], tuple(r["key"])): r for r in manifest["independent_streams"]}
|
||||
non_eval = {r["seed"] for r in manifest["independent_streams"] if r["split"] != "evaluation"}
|
||||
used, result = set(), {}
|
||||
for block in trial["plan"]["blocks"]:
|
||||
_require([v["cue"] for v in block["evaluation_streams"]] == ["A", "B"], "Both original evaluation cues are required")
|
||||
for row in block["evaluation_streams"]:
|
||||
cue = row["cue"]
|
||||
key = (block["block_id"], "evaluation", "CS", cue, "00", "base-probe")
|
||||
seed, derivation = seed_registry.seed_for(master, "evaluation", key)
|
||||
registered = independent.get(("evaluation", key))
|
||||
_require(registered == {"split": "evaluation", "key": list(key), "seed": seed,
|
||||
"derivation_sha256": derivation}, "Missing exact evaluation registry key")
|
||||
stream_id = "evaluation:" + _sha([trial_plan.SCHEMA, "stream-id", master, "evaluation", list(key)])
|
||||
_require(type(row["seed"]) is int and row["seed"] == seed and row["seed_bits"] == 63 and
|
||||
row["stream_id"] == stream_id and row["odor"] == trial_plan.ODORS[cue] and row["role"] == "CS",
|
||||
"Evaluation seed/key/odor lineage mismatch")
|
||||
_require(seed not in used and seed not in non_eval, "Evaluation seed overlaps another independent stream")
|
||||
used.add(seed)
|
||||
result[(block["block_id"], cue)] = dict(stream_id=stream_id, split="evaluation",
|
||||
semantic_key=list(key), seed=seed, seed_bits=63, derivation_sha256=derivation,
|
||||
input_id=block["block_id"] + "/evaluation/input-" + cue,
|
||||
reuse="paired repeated probes within this training replicate; not additional independent seeds")
|
||||
_require(len(result) == manifest["split_counts"]["evaluation"] == 80, "Exactly the existing 80 evaluation streams are required")
|
||||
return result
|
||||
|
||||
|
||||
def _snapshot_id(block_id, assay, arm, checkpoint):
|
||||
return f"{block_id}/{assay}/{arm}/checkpoint-{checkpoint:02d}"
|
||||
|
||||
|
||||
def _assemble(trial, config):
|
||||
"""Internal assembly; public entry points first validate full trial authority."""
|
||||
config = _configuration(config)
|
||||
records = _binding_inventory(trial["plan"], config)
|
||||
streams = _streams(trial)
|
||||
snapshots, probes = [], []
|
||||
|
||||
def emit(block, snapshot):
|
||||
common = dict(block_id=block["block_id"], mode=block["mode"], dan_type=block["dan_type"],
|
||||
compartment_verbatim=block["compartment_verbatim"], training_replicate=block["replicate"],
|
||||
original_reinforced_cue=block["initial_reinforced_cue"], original_neutral_cue=block["initial_neutral_cue"],
|
||||
original_S_orientation="original reinforced minus original neutral, never flipped for reversal")
|
||||
snapshot = dict(common, **snapshot)
|
||||
snapshots.append(snapshot)
|
||||
for cue in trial_plan.CUES:
|
||||
probes.append(dict(common, probe_id=snapshot["snapshot_id"] + "/probe-" + cue,
|
||||
snapshot_id=snapshot["snapshot_id"], baseline_snapshot_id=snapshot["baseline_snapshot_id"],
|
||||
assay=snapshot["assay"], arm=snapshot["arm"], checkpoint=snapshot["checkpoint"],
|
||||
elapsed_idle_ticks=snapshot["elapsed_idle_ticks"], cue=cue, odor=trial_plan.ODORS[cue],
|
||||
steps=config["probe_steps"], dt_ms=0.1, cs_start_tick=config["cs_start_tick"],
|
||||
cs_stop_tick_exclusive=config["cs_stop_tick_exclusive"],
|
||||
input=streams[(block["block_id"], cue)], execution="independent fork; discard probe terminal state",
|
||||
US_requested=False, D_command=0.0, weight_updates_enabled=False,
|
||||
feedback_to_training_or_idle=False, adds_training_replicate=False))
|
||||
|
||||
for binding, block, suite, arm, us, updates in records:
|
||||
block_id, assay, name = block["block_id"], suite["assay"], arm["arm"]
|
||||
point, count = binding["checkpoint"], binding["completed_epochs"]
|
||||
last = None if count == 0 else arm["epochs"][count - 1]["epoch_id"]
|
||||
delta_source = (dict(arm["delta_start"]) if count == 0 else
|
||||
{"kind": "epoch_delta_after", "source": last + "/delta-after"})
|
||||
baseline = _snapshot_id(block_id, "normal" if assay == "reversal" else assay,
|
||||
"paired" if assay == "reversal" else name, 0)
|
||||
emit(block, dict(snapshot_id=_snapshot_id(block_id, assay, name, point), assay=assay, arm=name,
|
||||
checkpoint=point, checkpoint_semantics="nominal reinforcement progress; control epoch boundary explicitly supplied",
|
||||
completed_epochs=count, last_completed_epoch_id=last,
|
||||
actual_US_requests_completed=us, enabled_nonzero_D_update_opportunities_completed=updates,
|
||||
arm_reinforced_cue=arm["reinforced_cue"], delta_source=delta_source,
|
||||
neural_state_source={"kind": "common_frozen_reset", "from_training_terminal_state": False},
|
||||
baseline_snapshot_id=baseline, elapsed_idle_ticks=None,
|
||||
probe_count=2, snapshot_mutated_by_planning=False))
|
||||
|
||||
for block in trial["plan"]["blocks"]:
|
||||
block_id = block["block_id"]
|
||||
normal = {a["arm"]: a for a in block["suites"][0]["arms"]}
|
||||
for branch in block["retention"]["branches"]:
|
||||
name = branch.split("/")[1]
|
||||
terminal_epoch = normal[name]["epochs"][-1]["epoch_id"]
|
||||
lineage = block_id + "/retention/" + name + "/idle-10s"
|
||||
for seconds in block["retention"]["checkpoint_seconds"]:
|
||||
ticks = seconds * 10000
|
||||
emit(block, dict(snapshot_id=f"{block_id}/retention/{name}/idle-{seconds:02d}s",
|
||||
assay="retention", arm=name, checkpoint=None, completed_epochs=len(normal[name]["epochs"]),
|
||||
last_completed_epoch_id=terminal_epoch, delta_source={"kind": "epoch_delta_after", "source": terminal_epoch + "/delta-after"},
|
||||
neural_state_source={"kind": "actual_terminal_checkpoint" if seconds == 0 else "continuous_idle_checkpoint",
|
||||
"terminal_epoch_id": terminal_epoch, "idle_lineage_id": lineage,
|
||||
"elapsed_ticks": ticks, "total_idle_ticks": 100000,
|
||||
"one_second_is_prefix_of_same_ten_second_idle": True,
|
||||
"carry": ["v", "g", "ref", "spike_ring", "apl_state", "apl_pending", "absolute_tick", "delta", "relevant_rng_state"],
|
||||
"reset_or_analytic_time_advance": False},
|
||||
baseline_snapshot_id=_snapshot_id(block_id, "normal", name, 0),
|
||||
elapsed_idle_ticks=ticks, probe_count=2, snapshot_mutated_by_planning=False))
|
||||
|
||||
ids = {row["snapshot_id"] for row in snapshots}
|
||||
_require(len(ids) == len(snapshots) and all(p["baseline_snapshot_id"] in ids for p in probes),
|
||||
"Duplicate snapshot or missing original pre baseline")
|
||||
by_assay = Counter(p["assay"] for p in probes)
|
||||
steps = len(probes) * config["probe_steps"]
|
||||
result = dict(schema=SCHEMA, status="DRAFT-NOT-REGISTERED", config=config,
|
||||
trial_payload_sha256=trial["payload_sha256"], seed_manifest_sha256=trial["seed_manifest"]["payload_sha256"],
|
||||
config_sha256=_sha(config), snapshots=snapshots, probes=probes,
|
||||
budget=dict(snapshot_count=len(snapshots), evaluation_probes=len(probes),
|
||||
probes_by_assay=dict(sorted(by_assay.items())), evaluation_steps=steps,
|
||||
evaluation_biological_seconds=steps / 10000,
|
||||
preexisting_training_control_reversal_epochs=trial["budget"]["training_control_reversal_epochs"],
|
||||
preexisting_minimum_steps_excluding_evaluation=trial["budget"]["minimum_steps_excluding_evaluation"],
|
||||
planned_scientific_steps_including_evaluation=trial["budget"]["minimum_steps_excluding_evaluation"] + steps,
|
||||
planned_scientific_biological_seconds=(trial["budget"]["minimum_steps_excluding_evaluation"] + steps) / 10000,
|
||||
independent_evaluation_inputs=80, independent_training_blocks=40,
|
||||
evaluation_probes_are_training_replicates=False, baseline_output_deduplication=False,
|
||||
scope="conditional full draft inventory, not an executed count or wall-time/storage estimate",
|
||||
excludes=["implementation validation", "frozen regressions", "encoding", "I/O", "resource reserve"]),
|
||||
evaluation_inventory_structurally_complete=True, scientific_gate_added=False,
|
||||
protocol_approval_claimed=False, execution_authorized=False, training_executed=False,
|
||||
inputs_generated=False, input_generator_implementation_verified=False, candidate_generated=False,
|
||||
learning_pass_claimed=False, external_source_and_snapshot_authority_verified=False,
|
||||
subsequent_assays_condition="not authorized here; full planned inventory remains subject to the draft's acquisition gate")
|
||||
result["payload_sha256"] = _sha(result)
|
||||
# No returned nested object aliases caller-owned trial/config structures.
|
||||
return json.loads(_canonical(result))
|
||||
|
||||
|
||||
def build_evaluation_plan(trial_payload, config):
|
||||
"""Build a complete draft inventory, with mandatory explicit choices."""
|
||||
_configuration(config) # Report missing choices before expensive reconstruction.
|
||||
trial_plan.validate_trial_plan(trial_payload)
|
||||
return _assemble(trial_payload, config)
|
||||
|
||||
|
||||
def validate_evaluation_plan(payload, trial_payload, config):
|
||||
"""Reconstruct every probe/binding; an updated self-hash cannot excuse drift."""
|
||||
_require(type(payload) is dict and "payload_sha256" in payload, "Invalid evaluation payload")
|
||||
_require(payload["payload_sha256"] == _sha({k: v for k, v in payload.items() if k != "payload_sha256"}),
|
||||
"Evaluation payload SHA mismatch")
|
||||
expected = build_evaluation_plan(trial_payload, config)
|
||||
_require(_canonical(payload) == _canonical(expected), "Evaluation coverage, binding, type, seed, or order differs from exact reconstruction")
|
||||
return True
|
||||
@@ -0,0 +1,294 @@
|
||||
"""Pure pre-outcome DRAFT snapshot policy; no I/O, inputs, execution or RNG.
|
||||
|
||||
The caller must supply the complete existing trial_plan payload. This module
|
||||
does not select a master seed. Its fixed policy choices are newly proposed
|
||||
planning choices, not inherited biological measurements or scientific gates.
|
||||
|
||||
build_evaluation_policy(trial_payload) returns a config directly consumable by
|
||||
evaluation_plan.build_evaluation_plan, plus a separate ordered prefix audit.
|
||||
Every snapshot boundary is AFTER the complete selected epoch and its scheduled
|
||||
delta update, not immediately after the US tick. All returned timing and counts
|
||||
describe planned exposures, never observed or accepted DAN spikes.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import Counter
|
||||
import hashlib
|
||||
import json
|
||||
|
||||
import evaluation_plan
|
||||
import trial_plan
|
||||
|
||||
|
||||
SCHEMA = "phase4an-evaluation-policy-draft-v1"
|
||||
ALGORITHM = "fixed-ordered-CS-prefix-completed-epoch-v1"
|
||||
POINTS = (0, 1, 2, 5, 10, 20, 50)
|
||||
CS_FIELDS = frozenset(("cue", "odor", "occurrence", "stream_id", "source_stream_id",
|
||||
"seed", "seed_bits", "seed_conversion", "role", "start_tick", "stop_tick_exclusive"))
|
||||
IDENTITY_FIELDS = tuple(sorted(CS_FIELDS - {"stream_id"}))
|
||||
SAME_POSITION_ARMS = frozenset(("d_zero", "frozen", "random_reward", "cs_only"))
|
||||
|
||||
|
||||
def _require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(",", ":"),
|
||||
ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return hashlib.sha256(_canonical(value).encode("utf-8")).hexdigest()
|
||||
|
||||
|
||||
def _registry_index(trial):
|
||||
manifest = trial["seed_manifest"]
|
||||
independent = {(r["split"], tuple(r["key"])): r for r in manifest["independent_streams"]}
|
||||
aliases = {(r["split"], tuple(r["key"])): r for r in manifest["aliases"]}
|
||||
_require(len(independent) == len(manifest["independent_streams"]) and
|
||||
len(aliases) == len(manifest["aliases"]) and not (independent.keys() & aliases.keys()),
|
||||
"Duplicate or overlapping seed registry keys")
|
||||
return independent, aliases
|
||||
|
||||
|
||||
def _stream_id(master, key):
|
||||
return "training:" + _sha([trial_plan.SCHEMA, "stream-id", master, "training", list(key)])
|
||||
|
||||
|
||||
def _cs_identity(cs, *, block_id, assay, arm, base_arm, master, registry):
|
||||
"""Verify actual alias source/seed; omit only the control's own alias ID."""
|
||||
_require(type(cs) is dict and set(cs) == CS_FIELDS, "CS binding schema differs")
|
||||
_require(cs["cue"] in trial_plan.CUES and cs["odor"] == trial_plan.ODORS[cs["cue"]] and
|
||||
type(cs["occurrence"]) is int and 0 <= cs["occurrence"] < 50 and
|
||||
type(cs["seed"]) is int and 0 <= cs["seed"] < 2**63 and
|
||||
type(cs["seed_bits"]) is int and cs["seed_bits"] == 63 and cs["role"] == "CS" and
|
||||
type(cs["start_tick"]) is int and cs["start_tick"] == 0 and
|
||||
type(cs["stop_tick_exclusive"]) is int and cs["stop_tick_exclusive"] == 1000 and
|
||||
cs["seed_conversion"] == "exact integer; no truncation or addition",
|
||||
"CS cue/occurrence/full seed/role/timing differs")
|
||||
key = (block_id, assay, "CS", cs["cue"], f'{cs["occurrence"]:02d}', arm)
|
||||
source_key = key[:-1] + (base_arm,)
|
||||
independent, aliases = registry
|
||||
source = independent.get(("training", source_key))
|
||||
_require(source is not None and source["seed"] == cs["seed"] and
|
||||
cs["source_stream_id"] == _stream_id(master, source_key) and
|
||||
cs["stream_id"] == _stream_id(master, key), "CS seed/source stream authority differs")
|
||||
if arm == base_arm:
|
||||
_require(("training", key) not in aliases and cs["stream_id"] == cs["source_stream_id"],
|
||||
"Original CS must be its own independent source")
|
||||
else:
|
||||
alias = aliases.get(("training", key))
|
||||
_require(alias is not None and alias["source_split"] == "training" and
|
||||
alias["source_key"] == list(source_key) and alias["seed"] == cs["seed"] and
|
||||
alias["independent_replicate"] is False,
|
||||
"CS alias is not registered to this block/assay/cue/occurrence source")
|
||||
return {key: cs[key] for key in IDENTITY_FIELDS}
|
||||
|
||||
|
||||
def _prefixes(arm, *, block_id, assay, base_arm, master, registry):
|
||||
"""Return verified ordered CS records indexed by actual completed epochs."""
|
||||
identities = []
|
||||
prefixes = [[]]
|
||||
for epoch in arm["epochs"]:
|
||||
if epoch["cs"] is not None:
|
||||
identities.append(_cs_identity(epoch["cs"], block_id=block_id, assay=assay,
|
||||
arm=arm["arm"], base_arm=base_arm, master=master, registry=registry))
|
||||
prefixes.append(list(identities))
|
||||
if arm["arm"] == "dan_only":
|
||||
_require(not identities, "DAN-only must contain no delivered CS")
|
||||
else:
|
||||
_require(len(identities) == 100, "Every CS-present arm must retain all 100 CS epochs")
|
||||
counters = Counter()
|
||||
for row in identities:
|
||||
_require(row["occurrence"] == counters[row["cue"]], "CS occurrence order is not canonical")
|
||||
counters[row["cue"]] += 1
|
||||
_require(dict(counters) == {"A": 50, "B": 50}, "Both original odors require 50 CS occurrences")
|
||||
return prefixes
|
||||
|
||||
|
||||
def _reinforcement_boundary(arm, point):
|
||||
"""The whole epoch containing the kth requested US; terminal includes all."""
|
||||
if point == 0:
|
||||
return 0
|
||||
epochs = arm["epochs"]
|
||||
us_indices = [i for i, e in enumerate(epochs) if e["us"]["requested"]]
|
||||
_require(len(us_indices) == 50, "Reference arm must have exactly 50 requested US epochs")
|
||||
return len(epochs) if point == 50 else us_indices[point - 1] + 1
|
||||
|
||||
|
||||
def _exposure(arm, count, cs_prefix):
|
||||
epochs = arm["epochs"][:count]
|
||||
return dict(completed_epochs=count, CS_epochs=len(cs_prefix),
|
||||
CS_counts_by_cue={cue: sum(c["cue"] == cue for c in cs_prefix) for cue in trial_plan.CUES},
|
||||
ordered_CS_source_identity_sha256=_sha(cs_prefix),
|
||||
requested_US_epochs=sum(e["us"]["requested"] for e in epochs),
|
||||
enabled_nonzero_D_update_opportunities=sum(e["update_enabled"] and e["D_command"] != 0 for e in epochs),
|
||||
elapsed_training_ticks=sum(e["steps"] for e in epochs),
|
||||
actual_accepted_US_or_DAN_spikes_known=False)
|
||||
|
||||
|
||||
def _suite_policy(block, suite, master, registry):
|
||||
"""Independent prefix/accounting construction, separate from evaluation_plan."""
|
||||
assay, block_id = suite["assay"], block["block_id"]
|
||||
_require(assay in ("normal", "delayed", "reversal"), "Unexpected assay")
|
||||
expected_arms = trial_plan.REVERSAL_ARMS if assay == "reversal" else trial_plan.ARMS
|
||||
_require([a["arm"] for a in suite["arms"]] == list(expected_arms), "Missing, reordered or duplicate control arms")
|
||||
arms = {a["arm"]: a for a in suite["arms"]}
|
||||
base_name = "reversal" if assay == "reversal" else "paired"
|
||||
base = arms[base_name]
|
||||
_require(len(base["epochs"]) == 100, "Base arm must contain all 100 epochs")
|
||||
prefixes = {name: _prefixes(arm, block_id=block_id, assay=assay, base_arm=base_name,
|
||||
master=master, registry=registry) for name, arm in arms.items()}
|
||||
base_full = prefixes[base_name][-1]
|
||||
for name, arm in arms.items():
|
||||
_require(len(arm["epochs"]) == (150 if name == "unpaired" else 100), "Unexpected epoch coverage")
|
||||
if name == "dan_only":
|
||||
for i, epoch in enumerate(arm["epochs"]):
|
||||
original = base["epochs"][i]
|
||||
_require(epoch["cs"] is None and
|
||||
epoch["yoked_cue_removed"] == original["cs"]["cue"] and
|
||||
type(epoch["yoked_occurrence_removed"]) is int and
|
||||
epoch["yoked_occurrence_removed"] == original["cs"]["occurrence"] and
|
||||
_canonical(epoch["us"]) == _canonical(original["us"]) and
|
||||
epoch["steps"] == original["steps"],
|
||||
"DAN-only omitted CS/yoked US/epoch position differs from paired")
|
||||
else:
|
||||
_require(_canonical(prefixes[name][-1]) == _canonical(base_full),
|
||||
"Actual ordered CS cue/occurrence/seed/source sequence differs")
|
||||
if name != "unpaired":
|
||||
_require(all(_canonical(a) == _canonical(b)
|
||||
for a, b in zip(prefixes[name], prefixes[base_name])),
|
||||
"CS sequence differs at a paired epoch position")
|
||||
|
||||
bindings, audits = [], []
|
||||
for name in expected_arms:
|
||||
arm = arms[name]
|
||||
previous = -1
|
||||
for point in POINTS:
|
||||
base_count = _reinforcement_boundary(base, point)
|
||||
expected_prefix = prefixes[base_name][base_count]
|
||||
if assay == "reversal":
|
||||
count = _reinforcement_boundary(arm, point)
|
||||
alignment = "own reinforced-US ordinal; intermediate CS prefixes need not match across arms"
|
||||
elif name == "unpaired":
|
||||
if point == 0:
|
||||
count = 0
|
||||
elif point == 50:
|
||||
count = len(arm["epochs"])
|
||||
else:
|
||||
matches = [i for i, prefix in enumerate(prefixes[name]) if len(prefix) == len(expected_prefix)]
|
||||
_require(bool(matches), "Unpaired arm never reaches the required CS prefix")
|
||||
count = matches[0]
|
||||
alignment = "earliest ordered-CS-prefix boundary; terminal includes all 150 epochs"
|
||||
else:
|
||||
count = base_count
|
||||
alignment = ("paired yoked epoch positions; zero delivered CS, not equal CS exposure"
|
||||
if name == "dan_only" else "exact paired completed-epoch and ordered-CS prefix")
|
||||
_require(count > previous, "Policy snapshot epochs must advance strictly")
|
||||
previous = count
|
||||
own_prefix = prefixes[name][count]
|
||||
same_cs = _canonical(own_prefix) == _canonical(expected_prefix)
|
||||
if assay != "reversal" and name != "dan_only":
|
||||
_require(same_cs, "Ordered CS prefix mismatch at a control checkpoint")
|
||||
if name == "unpaired" and point not in (0, 50):
|
||||
_require(count > 0 and len(prefixes[name][count - 1]) < len(expected_prefix),
|
||||
"Unpaired snapshot is not the earliest matching completed-epoch boundary")
|
||||
actual = _exposure(arm, count, own_prefix)
|
||||
reference = _exposure(base, base_count, expected_prefix)
|
||||
binding = dict(block_id=block_id, assay=assay, arm=name,
|
||||
checkpoint=point, completed_epochs=count)
|
||||
bindings.append(binding)
|
||||
audits.append(dict(binding=binding, alignment=alignment,
|
||||
reference_arm=base_name, reference_completed_epochs=base_count,
|
||||
boundary="after complete epoch and scheduled delta update; not at the US tick",
|
||||
terminal_includes_all_epochs=point == 50,
|
||||
actual=actual, reference=reference,
|
||||
ordered_CS_prefix_exact=same_cs if name != "dan_only" else None,
|
||||
CS_exposure_equal=same_cs if name != "dan_only" else False,
|
||||
DAN_only_yoke_verified=name == "dan_only",
|
||||
requested_US_count_equal_to_reference=actual["requested_US_epochs"] == reference["requested_US_epochs"],
|
||||
requested_US_count_difference=actual["requested_US_epochs"] - reference["requested_US_epochs"],
|
||||
elapsed_ticks_equal_to_reference=actual["elapsed_training_ticks"] == reference["elapsed_training_ticks"],
|
||||
elapsed_ticks_difference=actual["elapsed_training_ticks"] - reference["elapsed_training_ticks"],
|
||||
nonzero_D_update_opportunity_difference=actual["enabled_nonzero_D_update_opportunities"] - reference["enabled_nonzero_D_update_opportunities"],
|
||||
all_US_timing_or_total_exposure_matched_claimed=False))
|
||||
return bindings, audits
|
||||
|
||||
|
||||
def _assemble(trial):
|
||||
registry = _registry_index(trial)
|
||||
bindings, audits = [], []
|
||||
blocks = trial["plan"]["blocks"]
|
||||
_require(len(blocks) == 40, "Exactly 40 original training blocks are required")
|
||||
for block in blocks:
|
||||
_require(block["evaluation_checkpoints"] == list(POINTS) and
|
||||
[s["assay"] for s in block["suites"]] == ["normal", "delayed", "reversal"],
|
||||
"Original assay/checkpoint coverage differs")
|
||||
for suite in block["suites"]:
|
||||
suite_bindings, suite_audits = _suite_policy(block, suite, trial["plan"]["master"], registry)
|
||||
bindings.extend(suite_bindings)
|
||||
audits.extend(suite_audits)
|
||||
config = dict(schema=evaluation_plan.CONFIG_SCHEMA, probe_steps=1200,
|
||||
cs_start_tick=0, cs_stop_tick_exclusive=1000, reversal_checkpoints=list(POINTS),
|
||||
baseline_execution="execute_each_snapshot", snapshot_binding_policy="explicit_completed_epochs",
|
||||
snapshot_bindings=bindings)
|
||||
# Check the unchanged consumer's declared schema/coverage as well as our
|
||||
# independent ordered-prefix construction. Do not generate its probes here.
|
||||
evaluation_plan._configuration(config)
|
||||
evaluation_plan._binding_inventory(trial["plan"], config)
|
||||
_require(len(bindings) == 4480 and len({_canonical(v) for v in bindings}) == 4480,
|
||||
"Incomplete or duplicate snapshot coverage")
|
||||
counts = Counter(row["assay"] for row in bindings)
|
||||
result = dict(schema=SCHEMA, status="DRAFT-NOT-REGISTERED", algorithm=ALGORITHM,
|
||||
trial_payload_sha256=trial["payload_sha256"],
|
||||
seed_manifest_sha256=trial["seed_manifest"]["payload_sha256"],
|
||||
config_sha256=_sha(config), config=config,
|
||||
audit=dict(bindings=audits,
|
||||
coverage=dict(training_blocks=40, training_replicates_per_cell=5,
|
||||
nonretention_snapshots=4480, snapshots_by_assay=dict(sorted(counts.items())),
|
||||
original_full_CS_alias_sequences_verified=True,
|
||||
new_random_streams=0, new_master_seed_selected=False),
|
||||
exposure_limits=[
|
||||
"CS-present normal/delayed controls match actual ordered CS prefixes, not just counts.",
|
||||
"US counts, nonzero-D opportunities and elapsed time are separately reported, not assumed matched.",
|
||||
"Unpaired boundaries include earlier inserted US; terminal includes all 150 epochs and trailing US.",
|
||||
"DAN-only has zero delivered CS; yoked positions are not equal CS exposure.",
|
||||
"Reversal and continuation use their own kth US; intermediate CS prefixes can differ.",
|
||||
"All snapshots are after a full epoch plus its delta update, not the US tick.",
|
||||
"Requested US is not accepted DAN activity, dopamine concentration or learning evidence."],
|
||||
unchanged_retention_scope=dict(arms=["normal/paired", "normal/frozen"],
|
||||
idle_checkpoints_seconds=[0, 1, 10],
|
||||
same_continuous_ten_second_lineage=True, probes_fork_and_do_not_advance_idle=True,
|
||||
retained_snapshot_execution_or_lineage_verified=False),
|
||||
conditional_evaluation_inventory=dict(nonretention_probes=8960,
|
||||
retention_snapshots=240, retention_probes=480, all_snapshots=4720,
|
||||
all_probes=9440, probe_steps=1200, evaluation_steps=11328000,
|
||||
includes_full_seven_point_reversal=True,
|
||||
baseline_output_deduplication=False,
|
||||
actual_execution_count=0)),
|
||||
policy_structural_checks_complete=True, choices_fixed_before_outcomes=True,
|
||||
choices_are_new_draft_proposals_not_inherited_gates=True,
|
||||
protocol_approval_claimed=False, execution_authorized=False, training_executed=False,
|
||||
inputs_generated=False, input_generator_implementation_verified=False,
|
||||
source_and_snapshot_authority_verified=False, candidate_generated=False,
|
||||
learning_pass_claimed=False, scientific_gate_added=False)
|
||||
result["payload_sha256"] = _sha(result)
|
||||
return json.loads(_canonical(result))
|
||||
|
||||
|
||||
def build_evaluation_policy(trial_payload):
|
||||
"""Return fixed DRAFT config/audit; caller provides, we never choose, master."""
|
||||
trial_plan.validate_trial_plan(trial_payload)
|
||||
return _assemble(trial_payload)
|
||||
|
||||
|
||||
def validate_evaluation_policy(payload, trial_payload):
|
||||
"""Exact reconstruction, including JSON types; a replaced self-hash is not authority."""
|
||||
_require(type(payload) is dict and "payload_sha256" in payload, "Invalid policy payload")
|
||||
_require(payload["payload_sha256"] == _sha({k: v for k, v in payload.items() if k != "payload_sha256"}),
|
||||
"Actual evaluation policy SHA mismatch")
|
||||
expected = build_evaluation_policy(trial_payload)
|
||||
_require(_canonical(payload) == _canonical(expected), "Policy config/audit/coverage differs from exact reconstruction")
|
||||
return True
|
||||
@@ -0,0 +1,494 @@
|
||||
"""Pure Phase 4AN proposal metrics; no file I/O, simulation or fitted decoder.
|
||||
|
||||
The caller must bind complete anatomical population membership (not a plastic
|
||||
mask subset), finalized execution receipts, input identities and seed lineages
|
||||
to its preregistration. These functions validate arrays/declared coverage; they
|
||||
cannot establish provenance by trusting a caller's Boolean or SHA string.
|
||||
All gates below are NEW DRAFT hypotheses, not a historical learning result.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
from collections.abc import Mapping
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
SCHEMA = 'phase4an-metrics-draft-v1'
|
||||
CUES = ('4-ethylphenol', '3-hexanone')
|
||||
MODES = ('physiological', 'matched_energy')
|
||||
DAN_TYPES = ('PAM15', 'PPL103')
|
||||
CONTROLS = ('D=0', 'frozen', 'random reward', 'unpaired', 'CS-only', 'DAN-only')
|
||||
ARMS = ('paired',) + CONTROLS
|
||||
POPULATIONS = ('MBON', 'legacyDownstream', 'nonMBONDownstream', 'MBON01', 'MBON12')
|
||||
SEPARATION_POPULATIONS = ('MBON', 'legacyDownstream', 'nonMBONDownstream')
|
||||
POPULATION_SEMANTICS = {
|
||||
'MBON': 'complete caller-bound anatomical MBON population',
|
||||
'legacyDownstream': 'complete frozen H downstream membership, including MBONs; never mask-filtered',
|
||||
'nonMBONDownstream': 'exact legacyDownstream minus MBON; NEW hypothesis readout, not the old H population',
|
||||
'MBON01': 'complete caller-bound MBON01 type, not plastic-mask targets only',
|
||||
'MBON12': 'complete caller-bound MBON12 type, not plastic-mask targets only',
|
||||
}
|
||||
STATE_KEYS = frozenset(('step', 'neuron_idx', 'counts', 'v', 'g', 'ref'))
|
||||
|
||||
|
||||
def require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _hash(value):
|
||||
return hashlib.sha256(np.ascontiguousarray(value, dtype=value.dtype.newbyteorder('<')).tobytes()).hexdigest()
|
||||
|
||||
|
||||
def _json_hash(value):
|
||||
raw = json.dumps(value, sort_keys=True, separators=(',', ':'), allow_nan=False).encode('utf-8')
|
||||
return hashlib.sha256(raw).hexdigest()
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return type(value) is str and len(value) == 64 and all(c in '0123456789abcdef' for c in value)
|
||||
|
||||
|
||||
def _token(value, name):
|
||||
require(type(value) is str and bool(value) and value.strip() == value, 'Invalid ' + name)
|
||||
return value
|
||||
|
||||
|
||||
def _vector(value, dtype, name, size=None):
|
||||
require(isinstance(value, np.ndarray) and value.dtype == np.dtype(dtype) and value.ndim == 1,
|
||||
name + ' requires an exact ' + dtype + ' vector')
|
||||
require(size is None or value.shape == (size,), name + ' shape mismatch')
|
||||
return value
|
||||
|
||||
|
||||
def _ids(value, name):
|
||||
_vector(value, 'int32', name)
|
||||
require(len(value) > 0 and np.all((value >= 0) & (value < 166700)) and np.all(value[1:] > value[:-1]),
|
||||
name + ' must be nonempty sorted unique global neuron indices')
|
||||
return value
|
||||
|
||||
|
||||
def _positions(nodes, values, name):
|
||||
position = np.searchsorted(nodes, values)
|
||||
require(np.all(position < len(nodes)), name + ' contains absent neurons')
|
||||
require(np.array_equal(nodes[position], values), name + ' contains absent neurons')
|
||||
return position
|
||||
|
||||
|
||||
def validate_populations(nodes, populations):
|
||||
"""Validate full caller-bound type lists; never derive them from an edge mask."""
|
||||
_ids(nodes, 'nodes')
|
||||
require(isinstance(populations, Mapping) and set(populations) == set(POPULATIONS),
|
||||
'Require exactly MBON/legacyDownstream/nonMBONDownstream/MBON01/MBON12 memberships')
|
||||
for name in POPULATIONS:
|
||||
_positions(nodes, _ids(populations[name], name), name)
|
||||
for name in ('MBON01', 'MBON12'):
|
||||
_positions(populations['MBON'], populations[name], name + ' outside MBON')
|
||||
require(not len(np.intersect1d(populations['MBON01'], populations['MBON12'])), 'Type populations overlap')
|
||||
_positions(populations['legacyDownstream'], populations['MBON'], 'MBON outside legacyDownstream')
|
||||
require(np.array_equal(populations['nonMBONDownstream'],
|
||||
np.setdiff1d(populations['legacyDownstream'], populations['MBON'])),
|
||||
'nonMBONDownstream must equal the complete legacyDownstream minus MBON')
|
||||
payload = {name: populations[name].tolist() for name in POPULATIONS}
|
||||
return dict(indices=payload, membership_sha256=_json_hash(payload),
|
||||
population_semantics=dict(POPULATION_SEMANTICS),
|
||||
complete_anatomical_membership_authority='caller preregistration; never inferred from mask')
|
||||
|
||||
|
||||
def validate_state(state, *, nodes, steps, dt_ms=.1):
|
||||
"""Check every original state element and all sparse events without mutation."""
|
||||
_ids(nodes, 'nodes')
|
||||
require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max, 'Invalid steps')
|
||||
require(type(dt_ms) in (int, float) and math.isfinite(dt_ms) and dt_ms > 0, 'Invalid dt_ms')
|
||||
require(isinstance(state, Mapping) and set(state) == STATE_KEYS, 'State must contain exactly six runtime keys')
|
||||
n = len(nodes)
|
||||
for key, dtype in (('counts', 'int32'), ('ref', 'int32'), ('v', 'float64'), ('g', 'float64')):
|
||||
_vector(state[key], dtype, key, n)
|
||||
require(np.isfinite(state[key]).all(), 'Nonfinite state: ' + key)
|
||||
require(np.all((state['counts'] >= 0) & (state['counts'] <= steps)), 'Invalid counts')
|
||||
require(np.all((state['ref'] >= 0) & (state['ref'] <= 22)), 'Invalid refractory state')
|
||||
ticks = _vector(state['step'], 'int32', 'step')
|
||||
events = _vector(state['neuron_idx'], 'int32', 'neuron_idx', len(ticks))
|
||||
require(np.all((ticks >= 0) & (ticks < steps)), 'Event tick outside trial')
|
||||
require(len(ticks) < 2 or np.all((ticks[1:] > ticks[:-1]) |
|
||||
((ticks[1:] == ticks[:-1]) & (events[1:] > events[:-1]))),
|
||||
'Events must be sorted unique by tick/global neuron')
|
||||
local = _positions(nodes, events, 'events')
|
||||
require(np.array_equal(np.bincount(local, minlength=n), state['counts']), 'Sparse events/counts mismatch')
|
||||
first = np.full(n, -1, dtype=np.int32)
|
||||
if len(ticks):
|
||||
first.fill(steps)
|
||||
np.minimum.at(first, local, ticks)
|
||||
first[first == steps] = -1
|
||||
return dict(first_tick=first, local_events=local,
|
||||
hashes={key: _hash(state[key]) for key in sorted(STATE_KEYS)})
|
||||
|
||||
|
||||
def _first_ms(first, dt_ms):
|
||||
present = first[first >= 0]
|
||||
return float(int(present.min()) * dt_ms) if len(present) else None
|
||||
|
||||
|
||||
def _latencies(first, dt_ms):
|
||||
return [None if int(tick) < 0 else float(int(tick) * dt_ms) for tick in first]
|
||||
|
||||
|
||||
def _population_change(pre, post, checked_pre, checked_post, nodes, local, dt_ms, vectors):
|
||||
cp, cq = pre['counts'][local].astype(np.int64), post['counts'][local].astype(np.int64)
|
||||
fp, fq = checked_pre['first_tick'][local], checked_post['first_tick'][local]
|
||||
lp, lq = _first_ms(fp, dt_ms), _first_ms(fq, dt_ms)
|
||||
both = (fp >= 0) & (fq >= 0)
|
||||
ep = np.isin(checked_pre['local_events'], local)
|
||||
eq = np.isin(checked_post['local_events'], local)
|
||||
events_same = (np.array_equal(pre['step'][ep], post['step'][eq]) and
|
||||
np.array_equal(pre['neuron_idx'][ep], post['neuron_idx'][eq]))
|
||||
try:
|
||||
with np.errstate(over='raise', invalid='raise'):
|
||||
dv, dg = post['v'][local] - pre['v'][local], post['g'][local] - pre['g'][local]
|
||||
vmax, gmax = float(np.max(np.abs(dv))), float(np.max(np.abs(dg)))
|
||||
except FloatingPointError as error:
|
||||
raise ValueError('Nonfinite derived final-state difference') from error
|
||||
result = dict(neurons=len(local), pre_total_spikes=int(cp.sum()), post_total_spikes=int(cq.sum()),
|
||||
spike_count_l1=int(np.abs(cq - cp).sum()), spike_count_changed_neurons=int(np.count_nonzero(cp != cq)),
|
||||
events_changed=not events_same, pre_first_latency_ms=lp, post_first_latency_ms=lq,
|
||||
first_latency_delta_ms=None if lp is None or lq is None else float(lq - lp),
|
||||
defined_first_latency_changed=bool(lp is not None and lq is not None and lp != lq),
|
||||
first_latency_both_defined_neurons=int(both.sum()),
|
||||
first_latency_changed_among_both_defined_neurons=int(np.count_nonzero(both & (fp != fq))),
|
||||
first_latency_became_defined_neurons=int(np.count_nonzero((fp < 0) & (fq >= 0))),
|
||||
first_latency_became_undefined_neurons=int(np.count_nonzero((fp >= 0) & (fq < 0))),
|
||||
final_delta_v_max_abs=vmax, final_delta_g_max_abs=gmax,
|
||||
final_ref_changed_neurons=int(np.count_nonzero(pre['ref'][local] != post['ref'][local])),
|
||||
final_state_units='SI volts; refractory integer ticks',
|
||||
max_abs_scope='maximum across population neurons at terminal tick; NOT a temporal peak')
|
||||
if vectors:
|
||||
result.update(neuron_idx=nodes[local].tolist(), pre_counts=cp.tolist(), post_counts=cq.tolist(),
|
||||
pre_first_latency_ms_per_neuron=_latencies(fp, dt_ms),
|
||||
post_first_latency_ms_per_neuron=_latencies(fq, dt_ms))
|
||||
for key in ('v', 'g', 'ref'):
|
||||
result['pre_final_' + key] = pre[key][local].tolist()
|
||||
result['post_final_' + key] = post[key][local].tolist()
|
||||
return result
|
||||
|
||||
|
||||
def functional_summary(pre, post, *, nodes, populations, steps, dt_ms=.1):
|
||||
"""Full-state validation plus wholebrain and anatomical-group differences.
|
||||
|
||||
The population's earliest event defines the legacy-style first latency.
|
||||
Per-neuron first events and missingness transitions are additional diagnostics.
|
||||
A missing latency is None, never zero or evidence of a defined latency shift.
|
||||
"""
|
||||
membership = validate_populations(nodes, populations)
|
||||
a = validate_state(pre, nodes=nodes, steps=steps, dt_ms=dt_ms)
|
||||
b = validate_state(post, nodes=nodes, steps=steps, dt_ms=dt_ms)
|
||||
groups = {name: _population_change(pre, post, a, b, nodes,
|
||||
_positions(nodes, populations[name], name), dt_ms, True) for name in POPULATIONS}
|
||||
return dict(schema=SCHEMA, full_six_key_state_validated=True, steps=steps, dt_ms=float(dt_ms),
|
||||
nodes=len(nodes), nodes_raw_sha256=_hash(nodes), membership=membership,
|
||||
pre_state_raw_sha256=a['hashes'], post_state_raw_sha256=b['hashes'],
|
||||
wholebrain=_population_change(pre, post, a, b, nodes, np.arange(len(nodes)), dt_ms, False),
|
||||
populations=groups, MBON_final_delta_g_max_abs=groups['MBON']['final_delta_g_max_abs'],
|
||||
MBON_final_delta_g_units='SI volts', MBON_final_delta_g_scope='terminal across-neuron maximum, not time peak')
|
||||
|
||||
|
||||
def arm_metrics(pre_by_cue, post_by_cue, *, conditioned_cue, nodes, populations, steps=1200, dt_ms=.1):
|
||||
"""New fixed, unscaled type-count contrast; neither decoder nor feedback."""
|
||||
require(conditioned_cue in CUES, 'Unknown conditioned odor label')
|
||||
require(isinstance(pre_by_cue, Mapping) and isinstance(post_by_cue, Mapping) and
|
||||
set(pre_by_cue) == set(post_by_cue) == set(CUES), 'Both original odor labels are required')
|
||||
neutral = next(cue for cue in CUES if cue != conditioned_cue)
|
||||
summaries = {cue: functional_summary(pre_by_cue[cue], post_by_cue[cue], nodes=nodes,
|
||||
populations=populations, steps=steps, dt_ms=dt_ms) for cue in CUES}
|
||||
b, means = {}, {}
|
||||
for phase in ('pre', 'post'):
|
||||
b[phase], means[phase] = {}, {}
|
||||
for cue in CUES:
|
||||
groups = summaries[cue]['populations']
|
||||
means[phase][cue] = {name: float(np.mean(groups[name][phase + '_counts'])) for name in ('MBON01', 'MBON12')}
|
||||
b[phase][cue] = means[phase][cue]['MBON12'] - means[phase][cue]['MBON01']
|
||||
raw_s = (b['post'][conditioned_cue] - b['post'][neutral]) - (b['pre'][conditioned_cue] - b['pre'][neutral])
|
||||
separation = {}
|
||||
for name in SEPARATION_POPULATIONS:
|
||||
values = {}
|
||||
for phase in ('pre', 'post'):
|
||||
first = np.asarray(summaries[CUES[0]]['populations'][name][phase + '_counts'], dtype=np.float64)
|
||||
second = np.asarray(summaries[CUES[1]]['populations'][name][phase + '_counts'], dtype=np.float64)
|
||||
values[phase] = float(np.linalg.norm(first - second))
|
||||
separation[name] = dict(values, gain=values['post'] - values['pre'],
|
||||
vector='all bound member spike counts', population_semantics=POPULATION_SEMANTICS[name])
|
||||
return dict(schema=SCHEMA, analysis_status='DRAFT_NEW_HYPOTHESIS', conditioned_cue=conditioned_cue,
|
||||
neutral_cue=neutral, steps=steps, dt_ms=float(dt_ms), rawB=b, rawS=float(raw_s),
|
||||
type_mean_counts=means, B_definition='mean full MBON12 counts - mean full MBON01 counts',
|
||||
B_is_decoder=False, S_orientation='conditioned minus neutral; post minus own pre',
|
||||
separation=separation, cues=summaries,
|
||||
historical_implementation_equivalence_claimed=False, learning_pass_claimed=False)
|
||||
|
||||
|
||||
def _finite_tree(value):
|
||||
if isinstance(value, Mapping):
|
||||
for item in value.values():
|
||||
_finite_tree(item)
|
||||
elif isinstance(value, (list, tuple)):
|
||||
for item in value:
|
||||
_finite_tree(item)
|
||||
elif isinstance(value, (float, np.floating)):
|
||||
require(math.isfinite(float(value)), 'Nonfinite metric value')
|
||||
|
||||
|
||||
def _bound_metric(metric, cue):
|
||||
require(isinstance(metric, Mapping) and metric.get('schema') == SCHEMA and
|
||||
metric.get('analysis_status') == 'DRAFT_NEW_HYPOTHESIS', 'Unexpected metric schema')
|
||||
_finite_tree(metric)
|
||||
require(metric['conditioned_cue'] == cue and metric['neutral_cue'] == next(c for c in CUES if c != cue),
|
||||
'Cue role mismatch')
|
||||
require(metric['steps'] == 1200 and metric['dt_ms'] == .1, 'Evaluation must use the fixed 120 ms probe')
|
||||
# Derive gate quantities from recorded raw vectors, not a supplied gate Boolean.
|
||||
for odor in CUES:
|
||||
summary = metric['cues'][odor]
|
||||
require(summary.get('full_six_key_state_validated') is True and summary['steps'] == 1200 and
|
||||
summary['dt_ms'] == .1 and _sha(summary['nodes_raw_sha256']), 'Unvalidated cue state')
|
||||
membership = summary['membership']['indices']
|
||||
require(set(membership) == set(POPULATIONS) and summary['membership']['membership_sha256'] == _json_hash(membership),
|
||||
'Population membership digest mismatch')
|
||||
require(summary['membership'].get('population_semantics') == POPULATION_SEMANTICS,
|
||||
'Population semantics must distinguish old H membership from the new non-MBON readout')
|
||||
for key in ('pre_state_raw_sha256', 'post_state_raw_sha256'):
|
||||
require(set(summary[key]) == STATE_KEYS and all(_sha(v) for v in summary[key].values()), 'Missing state digest')
|
||||
for name in POPULATIONS:
|
||||
group = summary['populations'][name]
|
||||
ids, count = group['neuron_idx'], group['neurons']
|
||||
require(type(count) is int and count > 0 and len(ids) == count and ids == membership[name] and
|
||||
all(type(v) is int and 0 <= v < 166700 for v in ids) and ids == sorted(set(ids)),
|
||||
'Empty or mismatched anatomical population')
|
||||
for phase in ('pre', 'post'):
|
||||
counts = group[phase + '_counts']
|
||||
latency = group[phase + '_first_latency_ms_per_neuron']
|
||||
require(len(counts) == len(latency) == count and
|
||||
all(type(v) is int and 0 <= v <= 1200 for v in counts), 'Invalid count/latency vector')
|
||||
for c, value in zip(counts, latency):
|
||||
require((c == 0 and value is None) or (c > 0 and type(value) in (int, float) and
|
||||
0 <= value < 120 and value == round(value / .1) * .1), 'Missing/nonphysical first latency')
|
||||
present = [v for v in latency if v is not None]
|
||||
require(group[phase + '_first_latency_ms'] == (min(present) if present else None),
|
||||
'Population first latency differs from raw cells')
|
||||
for key in ('v', 'g', 'ref'):
|
||||
values = group[phase + '_final_' + key]
|
||||
require(len(values) == count and all(type(v) in (int, float) and math.isfinite(v) for v in values),
|
||||
'Invalid final-state vector')
|
||||
if key == 'ref':
|
||||
require(all(type(v) is int and 0 <= v <= 22 for v in values), 'Invalid final ref')
|
||||
l1 = sum(abs(a - b) for a, b in zip(group['pre_counts'], group['post_counts']))
|
||||
lp, lq = group['pre_first_latency_ms'], group['post_first_latency_ms']
|
||||
changed = lp is not None and lq is not None and lp != lq
|
||||
require(type(group['spike_count_l1']) is int and group['spike_count_l1'] == l1 and
|
||||
group['defined_first_latency_changed'] is changed and
|
||||
group['first_latency_delta_ms'] == (None if lp is None or lq is None else lq - lp),
|
||||
'Functional predicate differs from raw counts/defined latency')
|
||||
try:
|
||||
with np.errstate(over='raise', invalid='raise'):
|
||||
for key in ('v', 'g'):
|
||||
difference = np.asarray(group['post_final_' + key]) - np.asarray(group['pre_final_' + key])
|
||||
require(group['final_delta_' + key + '_max_abs'] == float(np.max(np.abs(difference))),
|
||||
'Final-state maximum differs from raw terminal vectors')
|
||||
except FloatingPointError as error:
|
||||
raise ValueError('Nonfinite derived terminal metric') from error
|
||||
require(not set(membership['MBON01']).intersection(membership['MBON12']), 'Type population overlap')
|
||||
require(set(membership['MBON']) <= set(membership['legacyDownstream']),
|
||||
'Legacy downstream must retain the complete MBON population')
|
||||
require(membership['nonMBONDownstream'] == sorted(set(membership['legacyDownstream']) - set(membership['MBON'])),
|
||||
'nonMBONDownstream differs from exact legacyDownstream minus MBON')
|
||||
for name, parent in (('MBON01', 'MBON'), ('MBON12', 'MBON'),
|
||||
('MBON', 'legacyDownstream'), ('nonMBONDownstream', 'legacyDownstream')):
|
||||
require(set(membership[name]) <= set(membership[parent]), 'Population is not a subset of its bound parent')
|
||||
lookup = {v: i for i, v in enumerate(membership[parent])}
|
||||
for phase in ('pre', 'post'):
|
||||
for field in ('counts', 'first_latency_ms_per_neuron', 'final_v', 'final_g', 'final_ref'):
|
||||
key = phase + '_' + field
|
||||
require(summary['populations'][name][key] == [
|
||||
summary['populations'][parent][key][lookup[v]] for v in membership[name]],
|
||||
'Overlapping population vectors disagree with the bound parent')
|
||||
for phase in ('pre', 'post'):
|
||||
for odor in CUES:
|
||||
groups = metric['cues'][odor]['populations']
|
||||
means = []
|
||||
for name in ('MBON12', 'MBON01'):
|
||||
counts = groups[name][phase + '_counts']
|
||||
require(len(counts) > 0 and all(type(v) is int and 0 <= v <= 1200 for v in counts),
|
||||
'Empty or invalid type count vector')
|
||||
means.append(sum(counts) / len(counts))
|
||||
require(metric['type_mean_counts'][phase][odor][name] == means[-1], 'Type mean differs from raw counts')
|
||||
require(metric['rawB'][phase][odor] == means[0] - means[1], 'Raw B disagrees with full type vectors')
|
||||
neutral = metric['neutral_cue']
|
||||
calculated = (metric['rawB']['post'][cue] - metric['rawB']['post'][neutral]) - (
|
||||
metric['rawB']['pre'][cue] - metric['rawB']['pre'][neutral])
|
||||
require(type(metric['rawS']) in (int, float) and metric['rawS'] == calculated, 'Raw S disagrees with signed components')
|
||||
for name in SEPARATION_POPULATIONS:
|
||||
calculated = {}
|
||||
for phase in ('pre', 'post'):
|
||||
a = metric['cues'][CUES[0]]['populations'][name][phase + '_counts']
|
||||
b = metric['cues'][CUES[1]]['populations'][name][phase + '_counts']
|
||||
require(metric['cues'][CUES[0]]['membership'] == metric['cues'][CUES[1]]['membership'],
|
||||
'A/B population mismatch')
|
||||
calculated[phase] = float(np.linalg.norm(np.asarray(a, dtype=np.float64) - np.asarray(b, dtype=np.float64)))
|
||||
require(metric['separation'][name]['pre'] == calculated['pre'] and
|
||||
metric['separation'][name]['post'] == calculated['post'] and
|
||||
metric['separation'][name]['gain'] == calculated['post'] - calculated['pre'],
|
||||
'Separation gain differs from raw count vectors')
|
||||
require(metric['separation'][name].get('population_semantics') == POPULATION_SEMANTICS[name],
|
||||
'Separation population semantics mismatch')
|
||||
return metric
|
||||
|
||||
|
||||
def _execution(arm, name, assay, used_receipts):
|
||||
require(isinstance(arm, Mapping) and set(arm) == {'metrics', 'execution'}, 'Arm needs metrics and actual execution evidence')
|
||||
e = arm['execution']
|
||||
required = {'complete', 'recording_verified', 'receipt_sha256', 'assay', 'epoch_duration_ms',
|
||||
'cs_duration_ms', 'us_tick', 'completed_epochs', 'reinforcement_opportunities', 'terminal_pairings'}
|
||||
require(isinstance(e, Mapping) and set(e) == required, 'Incomplete execution evidence')
|
||||
require(e['complete'] is True and e['recording_verified'] is True and _sha(e['receipt_sha256']),
|
||||
'All arms require an actual verified completed execution receipt')
|
||||
require(e['receipt_sha256'] not in used_receipts, 'Execution receipt reused across arms/replicates')
|
||||
used_receipts.add(e['receipt_sha256'])
|
||||
require(e['assay'] == assay and e['epoch_duration_ms'] == (120. if assay == 'normal' else 180.) and
|
||||
e['cs_duration_ms'] == 100. and type(e['us_tick']) is int and
|
||||
e['us_tick'] == (900 if assay == 'normal' else 1500), 'Normal/delayed control timing mismatch')
|
||||
require(type(e['completed_epochs']) is int and e['completed_epochs'] == (150 if name == 'unpaired' else 100),
|
||||
'Incomplete arm epoch inventory')
|
||||
require(type(e['reinforcement_opportunities']) is int and e['reinforcement_opportunities'] == (0 if name == 'CS-only' else 50),
|
||||
'Wrong arm US opportunity inventory')
|
||||
require(type(e['terminal_pairings']) is int and e['terminal_pairings'] == 50, 'Only fixed terminal count 50 may gate')
|
||||
|
||||
|
||||
def evaluate_acquisition(replicates, *, assay, mode, dan_type, conditioned_cue, registered_training_streams):
|
||||
"""Strict new normal/delayed 3-of-5 SAME-training-replicate conjunction.
|
||||
|
||||
Each row has replicate_id, replicate_unit='independent_training',
|
||||
training_stream_ids (all independent source IDs, not aliases),
|
||||
evaluation_stream_ids, assay/mode/dan_type/conditioned_cue, and seven arms.
|
||||
Arm = {metrics: arm_metrics(...), execution: complete receipt declaration}.
|
||||
registered_training_streams maps exactly five predeclared replicate IDs to
|
||||
their source-ID lists. Aliased controls stay within a row; evaluation cannot
|
||||
become new training replication. This pure function does NOT verify a file
|
||||
receipt/seed derivation; the coordinator must independently do so.
|
||||
"""
|
||||
require(assay in ('normal', 'delayed') and mode in MODES and dan_type in DAN_TYPES and conditioned_cue in CUES,
|
||||
'Unknown raw assay/mode/DAN/odor label')
|
||||
require(isinstance(registered_training_streams, Mapping) and len(registered_training_streams) == 5,
|
||||
'Require five predeclared independent training replicates')
|
||||
registered, training_all = {}, set()
|
||||
for identity, sources in registered_training_streams.items():
|
||||
_token(identity, 'training replicate identity')
|
||||
require(isinstance(sources, (tuple, list)) and len(sources) > 0, 'Missing actual training source identities')
|
||||
values = tuple(_token(v, 'training source identity') for v in sources)
|
||||
require(len(set(values)) == len(values) and not training_all.intersection(values),
|
||||
'Training source reused across independent replicates')
|
||||
training_all.update(values)
|
||||
registered[identity] = values
|
||||
require(isinstance(replicates, (tuple, list)) and len(replicates) == 5, 'Exactly five training rows required; do not count evaluation draws')
|
||||
used_ids, used_eval, receipts, reports = set(), set(), set(), []
|
||||
for row in replicates:
|
||||
keys = {'replicate_id', 'replicate_unit', 'training_stream_ids', 'evaluation_stream_ids',
|
||||
'assay', 'mode', 'dan_type', 'conditioned_cue', 'arms'}
|
||||
require(isinstance(row, Mapping) and set(row) == keys, 'Invalid replicate schema')
|
||||
identity = row['replicate_id']
|
||||
require(identity in registered and identity not in used_ids and row['replicate_unit'] == 'independent_training',
|
||||
'Unknown/duplicate training identity or evaluation pseudo-replication')
|
||||
used_ids.add(identity)
|
||||
require(isinstance(row['training_stream_ids'], (list, tuple)) and
|
||||
tuple(row['training_stream_ids']) == registered[identity], 'Training lineage does not match preregistration')
|
||||
require((row['assay'], row['mode'], row['dan_type'], row['conditioned_cue']) == (assay, mode, dan_type, conditioned_cue),
|
||||
'Cannot pool modes, DAN branches, cue assignments or timing suites')
|
||||
ev = row['evaluation_stream_ids']
|
||||
require(isinstance(ev, (tuple, list)) and len(ev) > 0, 'Missing independent evaluation identity')
|
||||
ev = tuple(_token(v, 'evaluation identity') for v in ev)
|
||||
require(len(set(ev)) == len(ev) and not training_all.intersection(ev) and not used_eval.intersection(ev),
|
||||
'Evaluation sources overlap training or another training replicate')
|
||||
used_eval.update(ev)
|
||||
require(isinstance(row['arms'], Mapping) and set(row['arms']) == set(ARMS), 'All six actual controls and paired arm are mandatory')
|
||||
metrics = {}
|
||||
for name in ARMS:
|
||||
_execution(row['arms'][name], name, assay, receipts)
|
||||
metrics[name] = _bound_metric(row['arms'][name]['metrics'], conditioned_cue)
|
||||
baseline = metrics['paired']
|
||||
# State baseline and populations must really be matched for each control.
|
||||
for metric in metrics.values():
|
||||
for cue in CUES:
|
||||
a, b = baseline['cues'][cue], metric['cues'][cue]
|
||||
require(a['membership'] == b['membership'] and a['nodes_raw_sha256'] == b['nodes_raw_sha256'] and
|
||||
a['pre_state_raw_sha256'] == b['pre_state_raw_sha256'], 'Control population or pre-baseline mismatch')
|
||||
s = baseline['rawS']
|
||||
control_s = {name: metrics[name]['rawS'] for name in CONTROLS}
|
||||
direction = s > 0 if dan_type == 'PAM15' else s < 0
|
||||
beyond = {name: (s > value if dan_type == 'PAM15' else s < value) for name, value in control_s.items()}
|
||||
neutral = baseline['neutral_cue']
|
||||
c = baseline['cues'][conditioned_cue]['populations']['MBON']
|
||||
n = baseline['cues'][neutral]['populations']['MBON']
|
||||
functional = c['spike_count_l1'] > 0 or c['defined_first_latency_changed'] is True
|
||||
predicates = dict(raw_signed_direction=bool(direction), beyond_each_actual_control=all(beyond.values()),
|
||||
conditioned_final_g_exceeds_neutral=c['final_delta_g_max_abs'] > n['final_delta_g_max_abs'],
|
||||
conditioned_count_or_defined_population_first_latency_change=bool(functional),
|
||||
all_MBON_AB_euclidean_gain_positive=baseline['separation']['MBON']['gain'] > 0,
|
||||
legacyDownstream_AB_euclidean_gain_positive=baseline['separation']['legacyDownstream']['gain'] > 0,
|
||||
nonMBONDownstream_AB_euclidean_gain_positive=baseline['separation']['nonMBONDownstream']['gain'] > 0)
|
||||
reports.append(dict(replicate_id=identity, rawS=s, raw_control_S=control_s,
|
||||
population_separation_gains={name: baseline['separation'][name]['gain'] for name in SEPARATION_POPULATIONS},
|
||||
beyond_controls= beyond, predicates=predicates, conjunction_pass=all(predicates.values()),
|
||||
legacy_audit=dict(L_like_functional_with_explicit_missing_latency=bool(
|
||||
predicates['conditioned_final_g_exceeds_neutral'] and functional),
|
||||
H_like_MBON_positive_gain=predicates['all_MBON_AB_euclidean_gain_positive'],
|
||||
H_like_downstream_population='legacyDownstream',
|
||||
H_like_downstream_positive_gain=predicates['legacyDownstream_AB_euclidean_gain_positive'],
|
||||
original_implementation_equivalence_claimed=False),
|
||||
new_hypothesis_audit=dict(nonMBONDownstream_positive_gain=
|
||||
predicates['nonMBONDownstream_AB_euclidean_gain_positive'],
|
||||
inherited_from_old_H=False, either_downstream_may_rescue_other=False)))
|
||||
reports.sort(key=lambda r: r['replicate_id'])
|
||||
successful = sum(r['conjunction_pass'] for r in reports)
|
||||
return dict(schema=SCHEMA, status='DRAFT-PROPOSAL-GATE-PASS' if successful >= 3 else 'DRAFT-PROPOSAL-GATE-FAIL',
|
||||
proposal_gate_pass=successful >= 3, scientific_learning_pass_claimed=False, candidate_generated=False,
|
||||
assay=assay, mode=mode, dan_type=dan_type, conditioned_cue=conditioned_cue,
|
||||
required_training_replicates=5, successful_same_replicate_conjunctions=successful, required_majority=3,
|
||||
actual_control_receipts=len(receipts) - 5, evaluation_draws_count_as_replicates=False,
|
||||
execution_receipt_authority='caller must verify finalized receipts and seed registry; pure metrics do not read files',
|
||||
historical_full_H_mean_control_rule_computed=False, replicates=reports)
|
||||
|
||||
|
||||
def legacy_h_mean_control_audit(protocol_mbon_gains):
|
||||
"""Old H scalar predicate only, kept separate from the new signed gate.
|
||||
|
||||
Input uses the ORIGINAL five protocol labels, each with five already-bound
|
||||
training-replicate MBON Euclidean gains. The caller must explicitly define
|
||||
any AN-to-old-protocol mapping; this function never picks a DAN/control or
|
||||
pools AN cells. It is not the entire old H gate or a reproduced old result.
|
||||
"""
|
||||
names = ('appetitive-conditioning', 'aversive-conditioning', 'd-zero-control',
|
||||
'random-reward-control', 'frozen-brain-control')
|
||||
require(isinstance(protocol_mbon_gains, Mapping) and set(protocol_mbon_gains) == set(names),
|
||||
'Legacy audit requires exactly the five original H protocol labels')
|
||||
means = {}
|
||||
for name in names:
|
||||
values = protocol_mbon_gains[name]
|
||||
require(isinstance(values, (list, tuple)) and len(values) == 5 and
|
||||
all(type(v) in (int, float) and math.isfinite(v) for v in values),
|
||||
'Legacy audit needs five finite, separately bound training-replicate gains')
|
||||
means[name] = float(np.mean(values))
|
||||
require(math.isfinite(means[name]), 'Nonfinite legacy mean')
|
||||
floor, ceiling = min(means[names[0]], means[names[1]]), max(means[name] for name in names[2:])
|
||||
return dict(schema=SCHEMA, audit='legacy_H_mean_control_predicate_only', protocol_means=means,
|
||||
condition_floor=floor, control_ceiling=ceiling, predicate_pass=floor > ceiling,
|
||||
source_reference='frozen phase4h.py:412-414', full_old_H_gate_computed=False,
|
||||
scientific_learning_pass_claimed=False, AN_to_legacy_protocol_mapping_authority='caller; no automatic pooling')
|
||||
|
||||
|
||||
def unavailable_assay(assay):
|
||||
"""Do not turn a terminal delta or fresh reset into a retention/reversal PASS."""
|
||||
require(assay in ('reversal', 'retention'), 'Unknown unavailable assay')
|
||||
missing = {'reversal': ['bound acquisition/reversal/no-reversal lineage',
|
||||
'original pre baseline and original cue orientation', 'terminal reversal count and both cue event outputs'],
|
||||
'retention': ['validated continuous-state checkpoint/queue/RNG schema',
|
||||
'bound same idle trajectory at 1 s and 10 s', 'matched frozen continuation and non-feedback probes']}
|
||||
return dict(schema=SCHEMA, assay=assay, status='NOT-IMPLEMENTED', proposal_gate_pass=None,
|
||||
scientific_learning_pass_claimed=False, candidate_generated=False, missing=missing[assay])
|
||||
@@ -0,0 +1,478 @@
|
||||
"""Unregistered fixed-weight continuation of the reporting-only AN executor.
|
||||
|
||||
run_continued(case, out, build, overlay, manifest, checkpoint_dir, mode='light')
|
||||
also accepts mode='reference'. Inputs/events/counts use segment-local ticks;
|
||||
summary and receipts expose global_tick_offset/global_next_tick. No training,
|
||||
weight update, early stopping, or implicit extra tail is performed.
|
||||
|
||||
The caller must externally pin spec.parent_checkpoint_manifest_sha256. A fresh
|
||||
parent's original runtime receipt did NOT bind state.npz: seal_checkpoint adds
|
||||
that binding, after checking the available original receipts and current bytes.
|
||||
It cannot retroactively prove that an unsealed state file was never modified.
|
||||
Full graph/APL/coefficient/preregistered-input authority remains the coordinator's
|
||||
responsibility, as in runtime_light. No executable/venv path is resolved here.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import copy
|
||||
import hashlib
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
LIGHT_SHA = '522b8c31ea938890fe15364fccd25ccc5bded54101754909f255532e7ad01aae'
|
||||
N = 166700
|
||||
E = 25582938
|
||||
MANIFEST = 'checkpoint-manifest.json'
|
||||
RECEIPT = 'continuation-receipt.json'
|
||||
RESTORE = '_continuation.restore(v, g, ref, counts, ring, apl_ring, apl, upload)'
|
||||
CLOCK_OLD = 'tick >= 18'
|
||||
CLOCK_NEW = 'tick + _continuation.global_offset >= 18'
|
||||
|
||||
|
||||
def _light():
|
||||
path = Path(__file__).resolve().parent / 'runtime_light.py'
|
||||
raw = path.read_bytes()
|
||||
if hashlib.sha256(raw).hexdigest() != LIGHT_SHA:
|
||||
raise ValueError('Fresh runtime source pin changed')
|
||||
name = 'phase4an_continuation_pinned_light'
|
||||
existing = sys.modules.get(name)
|
||||
if existing is not None:
|
||||
if getattr(existing, '__continuation_pin__', None) != LIGHT_SHA or Path(existing.__file__).resolve() != path:
|
||||
raise ValueError('Unverified runtime module binding')
|
||||
return existing
|
||||
spec = importlib.util.spec_from_file_location(name, path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
sys.modules[name] = module
|
||||
try:
|
||||
exec(compile(raw, str(path), 'exec'), module.__dict__)
|
||||
except BaseException:
|
||||
sys.modules.pop(name, None)
|
||||
raise
|
||||
module.__continuation_pin__ = LIGHT_SHA
|
||||
return module
|
||||
|
||||
|
||||
R = _light() # Pure NumPy/stdlib; this does not import Torch or run a brain.
|
||||
|
||||
|
||||
def rotate_spike_ring(raw_ring, phase_tick):
|
||||
"""Parent's local next-write slot becomes the child's local slot zero."""
|
||||
R.require(isinstance(raw_ring, np.ndarray) and raw_ring.dtype == np.uint8 and
|
||||
raw_ring.ndim == 2 and raw_ring.shape[0] == 19, 'Invalid spike ring dtype/shape')
|
||||
R.require(type(phase_tick) is int and phase_tick >= 0 and np.all(raw_ring <= 1), 'Invalid ring phase/content')
|
||||
return raw_ring[(np.arange(19) + phase_tick % 19) % 19].copy()
|
||||
|
||||
|
||||
def _tree_hashes(directory):
|
||||
"""Exact regular-file inventory, no symlink/reparse escapes, no self-hash."""
|
||||
directory = Path(directory).resolve(strict=True)
|
||||
R.require(directory.is_dir(), 'Checkpoint must be a directory')
|
||||
result = {}
|
||||
for path in sorted(directory.rglob('*')):
|
||||
R.require(not path.is_symlink() and path.resolve().is_relative_to(directory), 'Checkpoint contains a linked/escaping path')
|
||||
if path.is_dir():
|
||||
continue
|
||||
R.require(path.is_file(), 'Checkpoint contains a nonregular entry')
|
||||
relative = path.relative_to(directory).as_posix()
|
||||
if relative == MANIFEST:
|
||||
continue
|
||||
R.require('error' not in path.name.lower() and 'failure' not in path.name.lower(), 'Parent has failure/error evidence')
|
||||
result[relative] = R.digest(path)
|
||||
return result
|
||||
|
||||
|
||||
def _npz(path, keys):
|
||||
with np.load(path, allow_pickle=False) as archive:
|
||||
R.require(len(archive.files) == len(keys) and set(archive.files) == set(keys), 'Unexpected checkpoint array keys: ' + str(path))
|
||||
return {name: archive[name].copy() for name in keys}
|
||||
|
||||
|
||||
def validate_state(state, terminal, steps, *, fresh):
|
||||
"""Validate local events/counts and the slots written by this segment."""
|
||||
R.require(type(steps) is int and 0 < steps <= np.iinfo(np.int32).max, 'Invalid segment length')
|
||||
for name in ('v', 'g'):
|
||||
value = state[name]
|
||||
R.require(value.dtype == np.float64 and value.shape == (N,) and np.isfinite(value).all(), 'Invalid parent ' + name)
|
||||
for name in ('ref', 'counts'):
|
||||
value = state[name]
|
||||
R.require(value.dtype == np.int32 and value.shape == (N,) and np.all(value >= 0), 'Invalid parent ' + name)
|
||||
R.require(np.all(state['ref'] <= 22), 'Parent refractory counter exceeds frozen 22-step contract')
|
||||
tick, neuron = state['step'], state['neuron_idx']
|
||||
R.require(tick.dtype == neuron.dtype == np.int32 and tick.ndim == neuron.ndim == 1 and tick.shape == neuron.shape,
|
||||
'Invalid sparse event dtype/shape')
|
||||
R.require(np.all((tick >= 0) & (tick < steps)) and np.all((neuron >= 0) & (neuron < N)), 'Parent event outside segment')
|
||||
key = tick.astype(np.int64) * N + neuron
|
||||
R.require(len(key) < 2 or np.all(key[1:] > key[:-1]), 'Parent events must be sorted and unique')
|
||||
R.require(np.array_equal(np.bincount(neuron, minlength=N), state['counts']), 'Parent counts are not segment-local event counts')
|
||||
ring = terminal['spike_ring']
|
||||
R.require(ring.dtype == np.uint8 and ring.shape == (19, N) and np.all(ring <= 1), 'Invalid parent spike ring')
|
||||
for name, shape in (('apl_state', (2,)), ('apl_pending', (18, 4064))):
|
||||
value = terminal[name]
|
||||
R.require(value.dtype == np.float32 and value.shape == shape and np.isfinite(value).all(), 'Invalid terminal ' + name)
|
||||
phase = terminal['next_tick']
|
||||
R.require(phase.dtype == np.int64 and phase.shape == () and phase.item() == steps, 'Terminal local phase does not match segment length')
|
||||
for local_tick in range(max(0, steps - 19), steps):
|
||||
start, stop = np.searchsorted(tick, (local_tick, local_tick + 1))
|
||||
expected = np.zeros(N, dtype=np.uint8)
|
||||
expected[neuron[start:stop]] = 1
|
||||
R.require(np.array_equal(ring[local_tick % 19], expected), 'Spike ring does not match emitted segment events')
|
||||
if fresh and steps < 19:
|
||||
R.require(not np.any(ring[steps:]), 'Fresh parent has nonzero uninitialized spike slots')
|
||||
return state, terminal
|
||||
|
||||
|
||||
def _verify_receipt_hashes(directory, receipt):
|
||||
for key, name in (('summary_sha256', 'summary.json'), ('finite_audit_sha256', 'finite-audit.json'),
|
||||
('terminal_queues_sha256', 'terminal-queues.npz'), ('overlay_audit_sha256', 'overlay-audit.json')):
|
||||
R.require(receipt.get(key) == R.digest(directory / name), 'Parent receipt hash mismatch: ' + name)
|
||||
inputs = receipt.get('input_sha256')
|
||||
R.require(isinstance(inputs, dict) and inputs, 'Parent receipt lacks immutable input bindings')
|
||||
for name, sha in inputs.items():
|
||||
R.require(Path(name).is_absolute() and R._sha(sha) and R.digest(name) == sha, 'Parent input/source bytes changed: ' + name)
|
||||
|
||||
|
||||
def _validate_record(directory, seen):
|
||||
summary = R._read_json(directory / 'summary.json')
|
||||
finite = R._read_json(directory / 'finite-audit.json')
|
||||
provenance = R._read_json(directory / 'runtime-provenance.json')
|
||||
steps = summary.get('steps')
|
||||
R.require(summary.get('complete') is True and summary.get('pass_all') is True and summary.get('all_finite') is True and
|
||||
summary.get('completed_ticks') == steps and summary.get('nodes') == N and summary.get('edges') == E and
|
||||
summary.get('precision') == 'float64' and summary.get('apl_precision') == 'float32' and
|
||||
summary.get('within_trial_weight_updates') is False, 'Parent is not a complete fixed-weight fullgraph trial')
|
||||
R.require(finite.get('all_finite') is True and finite.get('first_failure_tick') == -1 and
|
||||
finite.get('finite_observed_ticks') == steps, 'Parent finite coverage failed')
|
||||
R.require(provenance.get('complete') is True and provenance.get('weights_updated_during_run') is False and
|
||||
provenance.get('old_globals_patched') is False, 'Invalid parent runtime receipt')
|
||||
_verify_receipt_hashes(directory, provenance)
|
||||
fresh = provenance.get('schema') == 'phase4an-reporting-adapter-v1'
|
||||
if fresh:
|
||||
R.require(provenance.get('source_sha256') == LIGHT_SHA and R.digest(provenance['source_file']) == LIGHT_SHA and
|
||||
summary.get('checkpoint_continuation_used') is False, 'Fresh parent source/continuation scope mismatch')
|
||||
offset = 0
|
||||
else:
|
||||
R.require(provenance.get('schema') == 'phase4an-continuation-runtime-v1' and
|
||||
provenance.get('source_sha256') == R.digest(__file__) and
|
||||
R.digest(provenance['source_file']) == R.digest(__file__) and
|
||||
summary.get('checkpoint_continuation_used') is True, 'Unrecognized continued parent source')
|
||||
receipt = R._read_json(directory / RECEIPT)
|
||||
R.require(receipt.get('schema') == 'phase4an-continuation-receipt-v1' and receipt.get('complete') is True,
|
||||
'Invalid continuation receipt')
|
||||
R.require(receipt.get('weights_updated_during_run') is False and receipt.get('segment_steps') == steps and
|
||||
receipt.get('counts_scope') == 'segment_only_not_lifetime' and
|
||||
provenance.get('state_sha256') == R.digest(directory / 'state.npz'), 'Continued state/scope receipt mismatch')
|
||||
expected_payload = _tree_hashes(directory)
|
||||
expected_payload.pop(RECEIPT, None)
|
||||
R.require(receipt.get('payload_sha256') == expected_payload, 'Continuation receipt does not bind the exact payload')
|
||||
parent_pin = receipt.get('parent_checkpoint_manifest_sha256')
|
||||
case_spec = _parent_spec({'summary': summary, 'provenance': provenance})
|
||||
R.require(R._sha(parent_pin) and provenance.get('parent_checkpoint_manifest_sha256') == parent_pin and
|
||||
summary.get('parent_checkpoint_manifest_sha256') == parent_pin and
|
||||
case_spec.get('parent_checkpoint_manifest_sha256') == parent_pin,
|
||||
'Parent checkpoint pin disagrees across case/summary/provenance/continuation receipts')
|
||||
R.require(Path(receipt['parent_checkpoint_dir']).is_absolute(), 'Parent checkpoint directory is not absolute')
|
||||
parent = load_checkpoint(receipt['parent_checkpoint_dir'], parent_pin, _seen=seen)
|
||||
offset = parent['global_next_tick']
|
||||
R.require(receipt.get('global_tick_offset') == offset and receipt.get('global_next_tick') == offset + steps and
|
||||
summary.get('global_tick_offset') == offset and summary.get('global_next_tick') == offset + steps and
|
||||
provenance.get('global_tick_offset') == offset and provenance.get('global_next_tick') == offset + steps,
|
||||
'Continuation global clock chain mismatch')
|
||||
R.require(summary.get('effective_weights_raw_sha256') == parent['summary'].get('effective_weights_raw_sha256'),
|
||||
'Retention effective weights changed between parent and child')
|
||||
state = _npz(directory / 'state.npz', ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref'))
|
||||
terminal = _npz(directory / 'terminal-queues.npz', ('spike_ring', 'apl_state', 'apl_pending', 'next_tick'))
|
||||
validate_state(state, terminal, steps, fresh=fresh)
|
||||
initial_padding = max(18 - (offset + steps), 0)
|
||||
R.require(np.all(terminal['apl_pending'][:initial_padding].view(np.uint32) == 0),
|
||||
'Unconsumed original APL padding is not positive zero')
|
||||
if not fresh:
|
||||
if steps < 19:
|
||||
restored = rotate_spike_ring(parent['terminal']['spike_ring'], int(parent['terminal']['next_tick']))
|
||||
R.require(np.array_equal(terminal['spike_ring'][steps:], restored[steps:]), 'Unwritten carried spike slots changed')
|
||||
if steps < 18:
|
||||
carried = parent['terminal']['apl_pending'][steps:]
|
||||
R.require(np.array_equal(terminal['apl_pending'][:18 - steps].view(np.uint32), carried.view(np.uint32)),
|
||||
'Unconsumed carried APL slots changed')
|
||||
overlay = R._read_json(directory / 'overlay-audit.json')
|
||||
R.require(overlay.get('complete') is True and overlay.get('weights_updated_during_run') is False and
|
||||
overlay.get('effective_weights_raw_sha256') == summary.get('effective_weights_raw_sha256') and
|
||||
overlay.get('mask_sha256') == R.MASK_SHA and overlay.get('am_helper_sha256') == R.AM_OVERLAY_SHA,
|
||||
'Parent effective overlay receipt mismatch')
|
||||
overlay_manifest = R._read_json(directory / 'overlay-manifest.json')
|
||||
R.require(R.digest(directory / 'overlay-input.npz') == overlay_manifest.get('overlay_sha256') and
|
||||
R.digest(directory / 'overlay-manifest.json') in provenance['input_sha256'].values() and
|
||||
overlay_manifest.get('mask_sha256') == R.MASK_SHA and
|
||||
overlay_manifest.get('base_candidate_sha256') == overlay.get('base_candidate_sha256'),
|
||||
'Parent copied overlay inputs do not match their original receipt')
|
||||
timing = R._read_json(directory / 'apl-timing-audit.json')
|
||||
with (directory / 'apl-pending-tail.float32.npy').open('rb') as stream:
|
||||
pending = np.load(stream, allow_pickle=False)
|
||||
R.require(pending.dtype == np.float32 and pending.shape == (18, 4064) and
|
||||
pending.tobytes() == terminal['apl_pending'].tobytes(), 'Parent terminal/pending output mismatch')
|
||||
R.require(timing.get('pending_raw_sha256') == R._raw(pending) and timing.get('apl_delay_steps') == 18 and
|
||||
timing.get('brain_tail_advanced') is False and
|
||||
timing.get('final_apl_state_u32_bits') == terminal['apl_state'].view(np.uint32).tolist(), 'Parent APL terminal audit mismatch')
|
||||
return {'directory': directory, 'summary': summary, 'provenance': provenance, 'overlay': overlay,
|
||||
'state': state, 'terminal': terminal, 'steps': steps, 'global_tick_offset': offset,
|
||||
'global_next_tick': offset + steps, 'fresh': fresh,
|
||||
'ancestry_directories': [directory] if fresh else [directory, *parent['ancestry_directories']]}
|
||||
|
||||
|
||||
def seal_checkpoint(checkpoint_dir):
|
||||
"""Exclusive file-hash seal, not a brain run or proof of pre-seal integrity.
|
||||
|
||||
The returned manifest SHA must be externally pinned into the next case spec.
|
||||
No existing receipt is overwritten. Fresh state.npz is newly bound here.
|
||||
"""
|
||||
directory = Path(checkpoint_dir).resolve(strict=True)
|
||||
R.require(not (directory / MANIFEST).exists(), 'Checkpoint is already sealed')
|
||||
before = _tree_hashes(directory)
|
||||
record = _validate_record(directory, {directory})
|
||||
R.require(before == _tree_hashes(directory), 'Checkpoint changed while sealing')
|
||||
value = {'schema': 'phase4an-checkpoint-v1', 'complete': True, 'files_sha256': before,
|
||||
'segment_steps': record['steps'], 'global_tick_offset': record['global_tick_offset'],
|
||||
'global_next_tick': record['global_next_tick'], 'counts_scope': 'segment_only',
|
||||
'state_binding_origin': 'checkpoint seal; fresh runtime receipt did not hash state.npz',
|
||||
'pre_seal_state_integrity_independently_proven': False}
|
||||
R._save(directory / MANIFEST, value)
|
||||
return {'path': str(directory / MANIFEST), 'sha256': R.digest(directory / MANIFEST), **value}
|
||||
|
||||
|
||||
def load_checkpoint(checkpoint_dir, expected_sha, *, _seen=None):
|
||||
directory = Path(checkpoint_dir).resolve(strict=True)
|
||||
R.require(R._sha(expected_sha) and R.digest(directory / MANIFEST) == expected_sha, 'Parent checkpoint manifest differs from external pin')
|
||||
seen = set() if _seen is None else set(_seen)
|
||||
R.require(directory not in seen, 'Checkpoint ancestry cycle')
|
||||
seen.add(directory)
|
||||
manifest = R._read_json(directory / MANIFEST)
|
||||
R.require(manifest.get('schema') == 'phase4an-checkpoint-v1' and manifest.get('complete') is True and
|
||||
manifest.get('counts_scope') == 'segment_only' and manifest.get('files_sha256') == _tree_hashes(directory),
|
||||
'Checkpoint file inventory/hash mismatch')
|
||||
record = _validate_record(directory, seen)
|
||||
R.require(manifest.get('segment_steps') == record['steps'] and manifest.get('global_tick_offset') == record['global_tick_offset'] and
|
||||
manifest.get('global_next_tick') == record['global_next_tick'], 'Checkpoint clock metadata mismatch')
|
||||
R.require(manifest['files_sha256'] == _tree_hashes(directory) and R.digest(directory / MANIFEST) == expected_sha,
|
||||
'Checkpoint changed while loading')
|
||||
record.update(manifest=manifest, manifest_sha256=expected_sha)
|
||||
return record
|
||||
|
||||
|
||||
def derive_continued(source_bytes, mode):
|
||||
"""One explicit arrival-admission edit plus one pre-loop restore hook."""
|
||||
tree, inherited = R.derive_ast(source_bytes, mode)
|
||||
original = copy.deepcopy(tree)
|
||||
function = tree.body[-1]
|
||||
body = next(node for node in function.body if isinstance(node, ast.Try)).body
|
||||
candidates = [i for i, node in enumerate(body) if isinstance(node, ast.Assign) and
|
||||
len(node.targets) == 1 and isinstance(node.targets[0], ast.Name) and node.targets[0].id == 'apl_ring']
|
||||
R.require(len(candidates) == 1, 'Expected one APL ring initialization')
|
||||
body.insert(candidates[0] + 1, ast.parse(RESTORE).body[0])
|
||||
clocks = [node for node in ast.walk(function) if isinstance(node, ast.If) and ast.unparse(node.test) == CLOCK_OLD]
|
||||
R.require(len(clocks) == 1, 'Expected exact original local arrival-admission condition')
|
||||
clocks[0].test = ast.parse(CLOCK_NEW, mode='eval').body
|
||||
tree = ast.fix_missing_locations(tree)
|
||||
# Independently reverse exactly the two edits, then require the ENTIRE AST.
|
||||
rollback = copy.deepcopy(tree)
|
||||
reverse_body = next(node for node in rollback.body[-1].body if isinstance(node, ast.Try)).body
|
||||
restores = [i for i, node in enumerate(reverse_body) if ast.unparse(node) == RESTORE]
|
||||
reverse_clocks = [node for node in ast.walk(rollback) if isinstance(node, ast.If) and ast.unparse(node.test) == CLOCK_NEW]
|
||||
R.require(len(restores) == len(reverse_clocks) == 1, 'Ambiguous continuation edit rollback')
|
||||
del reverse_body[restores[0]]
|
||||
reverse_clocks[0].test = ast.parse(CLOCK_OLD, mode='eval').body
|
||||
R.require(R._dump(rollback) == R._dump(original), 'Additional unlisted continuation AST edit')
|
||||
audit = {'base': inherited, 'restore_hook': RESTORE, 'clock_admission_old': CLOCK_OLD,
|
||||
'clock_admission_new': CLOCK_NEW, 'full_AST_rollback_identical': True,
|
||||
'kernels_sorting_and_local_ring_math_unchanged': True,
|
||||
'derived_module_ast_sha256': hashlib.sha256(R._dump(tree).encode()).hexdigest()}
|
||||
return tree, audit
|
||||
|
||||
|
||||
def audit_carried_sequence(generated, applied, pending, initial_pending):
|
||||
"""Bitwise FIFO accounting with a carried queue, never a new brain step."""
|
||||
for name, value in (('generated', generated), ('applied', applied), ('pending', pending), ('initial', initial_pending)):
|
||||
R.require(isinstance(value, np.ndarray) and value.dtype == np.float32 and value.ndim == 2 and
|
||||
np.isfinite(value).all() and np.all(value >= 0), 'Invalid continued APL trace: ' + name)
|
||||
R.require(generated.shape == applied.shape and pending.shape == initial_pending.shape == (18, generated.shape[1]),
|
||||
'Continued APL sequence shapes differ')
|
||||
steps = len(generated)
|
||||
for tick in range(steps + 18):
|
||||
actual = applied[tick] if tick < steps else pending[tick - steps]
|
||||
expected = initial_pending[tick] if tick < 18 else generated[tick - 18]
|
||||
R.require(np.array_equal(actual.view(np.uint32), expected.view(np.uint32)),
|
||||
'Carried FIFO bitwise accounting failed at local application tick ' + str(tick))
|
||||
return {'pass_all': True, 'apl_delay_steps': 18, 'steps': steps, 'sample_bits_preserved': True,
|
||||
'initial_queue_from_parent': True, 'initial_queue_raw_sha256': R._raw(initial_pending),
|
||||
'full_sequence_bitwise_audit_performed': True, 'brain_tail_advanced': False}
|
||||
|
||||
|
||||
class ContinuedRuntime(R._Runtime):
|
||||
def __init__(self, *args, parent, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.parent = parent
|
||||
self.global_offset = parent['global_next_tick']
|
||||
self.restorations = 0
|
||||
|
||||
def install(self, *args, **kwargs):
|
||||
super().install(*args, **kwargs)
|
||||
R.require(self.effective_sha == self.parent['summary']['effective_weights_raw_sha256'], 'Retention changed effective weights')
|
||||
|
||||
def restore(self, v, g, ref, counts, ring, apl_ring, apl, upload):
|
||||
R.require(self.installations == 1 and self.restorations == 0, 'Restore must occur once after fixed-weight installation')
|
||||
R.require(not bool(counts.any().item()), 'Segment counts must start at zero')
|
||||
state, terminal = self.parent['state'], self.parent['terminal']
|
||||
for target, value, dtype in ((v, state['v'], self.torch.float64), (g, state['g'], self.torch.float64),
|
||||
(ref, state['ref'], self.torch.int32),
|
||||
(ring, rotate_spike_ring(terminal['spike_ring'], int(terminal['next_tick'])), self.torch.uint8),
|
||||
(apl_ring, terminal['apl_pending'], self.torch.float32),
|
||||
(apl.state, terminal['apl_state'], self.torch.float32)):
|
||||
target.copy_(upload(value, dtype))
|
||||
self.restorations += 1
|
||||
|
||||
def finish(self, *args, **kwargs):
|
||||
R.require(self.restorations == 1, 'Continuation was not restored')
|
||||
return super().finish(*args, **kwargs)
|
||||
|
||||
def audit_sequence(self, generated, applied, pending, delay):
|
||||
R.require(delay == 18, 'Continuation changed D18')
|
||||
return audit_carried_sequence(generated, applied, pending, self.parent['terminal']['apl_pending'])
|
||||
|
||||
def _timing_metadata(self, value):
|
||||
result = dict(value)
|
||||
next_tick, steps = self.global_offset + self.spec['steps'], self.spec['steps']
|
||||
result.pop('initial_padding_positive_zero', None)
|
||||
result.update(initial_queue_from_parent=True, initial_queue_raw_sha256=R._raw(self.parent['terminal']['apl_pending']),
|
||||
global_tick_offset=self.global_offset, global_next_tick=next_tick, tick_coordinate='global',
|
||||
segment_steps=steps, segment_generated_rows_applied=max(steps - 18, 0),
|
||||
applied_parent_pending_rows=min(steps, 18),
|
||||
applied_initial_zero_rows=min(steps, max(18 - self.global_offset, 0)),
|
||||
applied_parent_generated_rows=min(steps, 18) - min(steps, max(18 - self.global_offset, 0)),
|
||||
pending_segment_generated_rows=min(steps, 18),
|
||||
pending_parent_generated_rows=max(18 - steps, 0) - max(18 - next_tick, 0),
|
||||
pending_generated_count=min(next_tick, 18), pending_initial_zero_prefix=max(18 - next_tick, 0),
|
||||
pending_application_tick_start=next_tick, pending_application_tick_stop_exclusive=next_tick + 18,
|
||||
pending_generated_tick_start=max(0, next_tick - 18), pending_generated_tick_stop_exclusive=next_tick,
|
||||
brain_tail_advanced_scope='no extra brain ticks beyond this explicit segment')
|
||||
if self.mode == 'light':
|
||||
result.update(generated_trace=None, applied_trace=None, full_sequence_bitwise_audit_performed=False,
|
||||
scope='Original D18 ring math retained with restored queue; histories not recorded')
|
||||
return result
|
||||
|
||||
def timing(self, steps, pending):
|
||||
return self._timing_metadata(super().timing(steps, pending))
|
||||
|
||||
def summary(self, value):
|
||||
result = super().summary(value)
|
||||
result.update(checkpoint_continuation_used=True, global_tick_offset=self.global_offset,
|
||||
global_next_tick=self.global_offset + self.spec['steps'], event_tick_scope='segment_local',
|
||||
counts_scope='segment_only_not_lifetime', parent_checkpoint_manifest_sha256=self.parent['manifest_sha256'])
|
||||
result.update(pending_generated_count=min(result['global_next_tick'], 18),
|
||||
pending_initial_zero_prefix=max(18 - result['global_next_tick'], 0))
|
||||
if 'apl_timing' in result:
|
||||
result['apl_timing'] = self._timing_metadata(result['apl_timing'])
|
||||
return result
|
||||
|
||||
def write_json(self, path, value):
|
||||
if Path(path).name == 'apl-timing-audit.json':
|
||||
value = self._timing_metadata(value)
|
||||
return super().write_json(path, value)
|
||||
|
||||
|
||||
def compile_continued(aj, runtime, mode):
|
||||
tree, audit = derive_continued(Path(aj.__file__).read_bytes(), mode)
|
||||
namespace = dict(aj.__dict__)
|
||||
namespace.update(__name__=__name__ + '.derived_' + mode, __file__=str(Path(__file__).resolve()),
|
||||
_runtime=runtime, _continuation=runtime, write_json=runtime.write_json,
|
||||
audit_sequence=runtime.audit_sequence)
|
||||
audit['diagnostic_namespace_binding'] = 'audit_sequence checks parent_pending + generated, not an assumed zero-initial queue'
|
||||
exec(compile(tree, str(Path(__file__).resolve()) + '::continued-AJ-AST-' + mode, 'exec'), namespace)
|
||||
return namespace['run_gpu'], audit
|
||||
|
||||
|
||||
def _parent_spec(parent):
|
||||
expected = parent['summary']['input_sha256']['spec.json']
|
||||
paths = [Path(path) for path, sha in parent['provenance']['input_sha256'].items()
|
||||
if Path(path).name == 'spec.json' and sha == expected]
|
||||
R.require(len(paths) == 1, 'Ambiguous original parent case spec')
|
||||
return R._read_json(paths[0])
|
||||
|
||||
|
||||
def run_continued(case_dir, out_dir, build_root, overlay_file, overlay_manifest, checkpoint_dir, *, mode='light'):
|
||||
R.require(sys.dont_write_bytecode, 'Use python -B; frozen sources must not receive bytecode writes')
|
||||
R.require(mode in ('light', 'reference'), 'Invalid diagnostic mode')
|
||||
case_dir, out_dir, build_root = R.execution_paths(case_dir, out_dir, build_root, (checkpoint_dir,))
|
||||
spec = R._read_json(case_dir / 'spec.json')
|
||||
parent_sha = spec.get('parent_checkpoint_manifest_sha256')
|
||||
fresh_admission = dict(spec)
|
||||
fresh_admission.pop('parent_checkpoint_manifest_sha256', None)
|
||||
R.validate_spec(fresh_admission, case_dir)
|
||||
parent = load_checkpoint(checkpoint_dir, parent_sha)
|
||||
# A continuation may never create output or a build cache inside ANY
|
||||
# immutable ancestor, not only its immediate checkpoint parent. This check
|
||||
# precedes overlay imports, Torch imports, compilation and output creation.
|
||||
case_dir, out_dir, build_root = R.execution_paths(case_dir, out_dir, build_root, parent['ancestry_directories'])
|
||||
if 'global_tick_offset' in spec:
|
||||
R.require(type(spec['global_tick_offset']) is int and spec['global_tick_offset'] == parent['global_next_tick'],
|
||||
'Declared global offset disagrees with checkpoint chain')
|
||||
R.require(parent['global_next_tick'] + spec['steps'] <= np.iinfo(np.int64).max, 'Absolute tick overflow')
|
||||
helper, plan, meta, inputs = R.load_overlay(overlay_file, overlay_manifest)
|
||||
R.require(R.digest(overlay_file) == R.digest(parent['directory'] / 'overlay-input.npz') and
|
||||
R.digest(overlay_manifest) == R.digest(parent['directory'] / 'overlay-manifest.json'), 'Retention overlay snapshot/manifest changed')
|
||||
previous = _parent_spec(parent)
|
||||
for key in ('dt_ms', 'model', 'apl', 'apl_delay_steps'):
|
||||
R.require(spec[key] == previous[key], 'Continuation changes frozen dynamics: ' + key)
|
||||
for key in ('graph_dir', 'apl_bundle_dir'):
|
||||
R.require(Path(spec[key]).resolve(strict=True) == Path(previous[key]).resolve(strict=True), 'Continuation changes input directory: ' + key)
|
||||
R.require(R.digest(spec['coefficients_path']) == parent['summary']['input_sha256']['coefficients.json'], 'Continuation coefficients changed')
|
||||
R.require(R.digest(Path(spec['graph_dir']) / 'weights.npy') == parent['summary']['input_sha256']['base_weights.npy'], 'Continuation base weights changed')
|
||||
aj_dir = Path(meta['aj_model_dir']).resolve(strict=True)
|
||||
protected = (aj_dir.parent, Path(meta['am_overlay_path']).resolve().parent, spec['graph_dir'], spec['apl_bundle_dir'],
|
||||
*parent['ancestry_directories'],
|
||||
*(Path(path).resolve(strict=True).parent for path in
|
||||
(overlay_file, overlay_manifest, meta['mask_path'], meta['base_candidate_path'], spec['coefficients_path'])))
|
||||
case_dir, out_dir, build_root = R.execution_paths(case_dir, out_dir, build_root, protected)
|
||||
for path in (case_dir / 'spec.json', case_dir / 'requests.npz', Path(spec['graph_dir']) / 'weights.npy',
|
||||
Path(spec['coefficients_path']), Path(checkpoint_dir).resolve() / MANIFEST, Path(R.__file__).resolve()):
|
||||
inputs[str(path)] = R.digest(path)
|
||||
source_sha = R.digest(__file__)
|
||||
R._load('timing_queue', aj_dir / 'timing_queue.py', R.PINS['apl-timing-lab-v1/timing_queue.py'])
|
||||
aj = R._load('phase4an_continued_sealed_aj_gpu', aj_dir / 'gpu_timing.py', R.AJ_SHA)
|
||||
runtime = ContinuedRuntime(mode, spec, helper, plan, meta, inputs, aj.torch, overlay_file, overlay_manifest, parent=parent)
|
||||
function, ast_audit = compile_continued(aj, runtime, mode)
|
||||
try:
|
||||
inherited = function(case_dir, out_dir, build_root)
|
||||
R.require(runtime.finished and runtime.restorations == 1 and runtime.installations == 1 and inherited['complete'], 'Incomplete continuation')
|
||||
R.require(inherited['effective_weights_raw_sha256'] == runtime.effective_sha, 'Effective weight identity changed')
|
||||
R.require(source_sha == R.digest(__file__) and all(R.digest(path) == sha for path, sha in inputs.items()), 'Source/input changed during continuation')
|
||||
load_checkpoint(checkpoint_dir, parent_sha)
|
||||
summary = R._read_json(out_dir / 'summary.json')
|
||||
R.require(summary == runtime.summary(inherited), 'Stored continuation summary mismatch')
|
||||
provenance = {'schema': 'phase4an-continuation-runtime-v1', 'complete': True, 'mode': mode,
|
||||
'source_file': str(Path(__file__).resolve()), 'source_sha256': source_sha, 'fresh_runtime_sha256': LIGHT_SHA,
|
||||
'ast': ast_audit, 'input_sha256': inputs, 'old_globals_patched': False, 'weights_updated_during_run': False,
|
||||
'learning_pass_claimed': False, 'global_tick_offset': runtime.global_offset,
|
||||
'global_next_tick': runtime.global_offset + spec['steps'],
|
||||
'parent_checkpoint_manifest_sha256': parent_sha}
|
||||
for key, name in (('summary_sha256', 'summary.json'), ('finite_audit_sha256', 'finite-audit.json'),
|
||||
('terminal_queues_sha256', 'terminal-queues.npz'), ('overlay_audit_sha256', 'overlay-audit.json'),
|
||||
('state_sha256', 'state.npz')):
|
||||
provenance[key] = R.digest(out_dir / name)
|
||||
R._save(out_dir / 'runtime-provenance.json', provenance)
|
||||
receipt = {'schema': 'phase4an-continuation-receipt-v1', 'complete': True,
|
||||
'parent_checkpoint_dir': str(parent['directory']), 'parent_checkpoint_manifest_sha256': parent_sha,
|
||||
'global_tick_offset': runtime.global_offset, 'global_next_tick': runtime.global_offset + spec['steps'],
|
||||
'segment_steps': spec['steps'], 'counts_scope': 'segment_only_not_lifetime',
|
||||
'payload_sha256': _tree_hashes(out_dir), 'weights_updated_during_run': False,
|
||||
'clock_admission_edit': CLOCK_NEW, 'checkpoint_continuation_verified_by_GPU_comparison': False}
|
||||
R._save(out_dir / RECEIPT, receipt)
|
||||
sealed = seal_checkpoint(out_dir)
|
||||
return dict(summary, checkpoint_manifest_sha256=sealed['sha256'])
|
||||
except Exception as error:
|
||||
if out_dir.is_dir() and not (out_dir / 'continuation-error.json').exists():
|
||||
R._save(out_dir / 'continuation-error.json', {'complete': False, 'type': type(error).__name__,
|
||||
'message': str(error), 'learning_update_forbidden': True})
|
||||
raise
|
||||
@@ -0,0 +1,559 @@
|
||||
"""Engineering-only, fixed-weight trial execution derived from frozen AJ AST.
|
||||
|
||||
Public APIs run_light(case_dir,out_dir,build_root,overlay_file,overlay_manifest)
|
||||
and run_reference(...) have identical arguments. Neither trains nor carries a
|
||||
previous trial's neural state. The reference retains AJ's eager diagnostics;
|
||||
the light function removes only an explicit diagnostic whitelist. Both accept
|
||||
an explicit spec.max_steps instead of AJ's 1000-step admission bound.
|
||||
|
||||
The parent protocol owns conditioning, cue/tail schedules, frozen input/mask
|
||||
authority and success criteria. A completed trial is not a learning PASS.
|
||||
Before any scientific execution, the coordinator must verify externally fixed
|
||||
hashes for the entire graph, APL bundle, coefficients/model, and preregistered
|
||||
case spec (including max_steps). Inherited AJ consistency checks and this
|
||||
adapter's input hashes are not a replacement for those external authorities.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import copy
|
||||
import hashlib
|
||||
import importlib.util
|
||||
import json
|
||||
from pathlib import Path
|
||||
import re
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
AJ_SHA = 'af1975ba5e5e700208c0a598d3cbe648a3cf10885c6231239b6e4b28f76d173b'
|
||||
AM_OVERLAY_SHA = '7f891d0ba79b393c96e75fd2dee2eddc51e57d8213eaac2779703bc2c18712b4'
|
||||
MASK_SHA = '8b0fb6eb2e6c31da4d17dc20d890aa3f367e514f05642de5bad6b42a1611c05b'
|
||||
V2_SHA = 'abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e'
|
||||
PINS = {
|
||||
'apl-timing-lab-v1/gpu_timing.py': AJ_SHA,
|
||||
'apl-timing-lab-v1/timing_queue.py': '54df6d4818e2a0c26e96fb0aaf5d9b71cd964981706da937562d48af79aca14e',
|
||||
'author-apl-lab-v2/gpu_apl.py': '69f5d893f47dafff4ef7c3186c3df0b9014666b32c8f4e943b57016bec92b18a',
|
||||
'author-apl-lab-v2/legacy_apl_worker.py': '21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291',
|
||||
'author-runtime-lab-v1/gpu_backend.py': '03758d07572f4b44d272059d9308fac534268ad201b57bdd77d69fee95e67278',
|
||||
'author-runtime-lab-v1/author_ext.cpp': '208a937d17bbb041b9bf6b25b89264ee566830b60609e58491b44583d8541f15',
|
||||
'author-runtime-lab-v1/author_ext.cu': '669f166e18ce9a3f63fc723db8b0a8b74f7fa4757194b1bd65b4cf6008a7787e',
|
||||
'author-fullgraph-lab-v1/oracle_full.py': 'd72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961',
|
||||
}
|
||||
_LOADED = {}
|
||||
_INSTALL = '_runtime.install(weights, graph, apl_out_mask, upload, out_dir, source_provenance, device)'
|
||||
_FINISH = '_runtime.finish(ring, apl_ring, apl, steps, out_dir)'
|
||||
_MATH_TARGETS = frozenset(('indptr', 'dst', 'global_nodes_gpu', 'base_weights', 'weights',
|
||||
'local_apl', 'apl_out_mask', 'common_kc', 'local_kc', 'full_kc', 'kc_local_gpu', 'kc_full_gpu',
|
||||
'v', 'g', 'ref', 'counts', 'requests', 'ring', 'apl_delay', 'apl_ring', 'requested_local_gpu',
|
||||
'spikes', 'due', 'sources', 'degrees', 'offsets', 'edge_count', 'expanded', 'destination',
|
||||
'order', 'ordered_edges', 'ordered_targets', 'boundary', 'starts', 'targets', 'full_spikes',
|
||||
'drive', 'apl_state', 'h', 'slot', 'applied_h', 'delta_si'))
|
||||
|
||||
|
||||
def require(ok, message):
|
||||
if not ok:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def digest(path):
|
||||
h = hashlib.sha256()
|
||||
with Path(path).open('rb') as stream:
|
||||
for block in iter(lambda: stream.read(8 << 20), b''):
|
||||
h.update(block)
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def _read_json(path):
|
||||
def pairs(items):
|
||||
out = {}
|
||||
for key, value in items:
|
||||
require(key not in out, 'Duplicate JSON key: ' + key)
|
||||
out[key] = value
|
||||
return out
|
||||
return json.loads(Path(path).read_text(encoding='utf-8'), object_pairs_hook=pairs,
|
||||
parse_constant=lambda value: (_ for _ in ()).throw(ValueError('Nonfinite JSON: ' + value)))
|
||||
|
||||
|
||||
def _save(path, value):
|
||||
with Path(path).open('x', encoding='utf-8') as stream:
|
||||
json.dump(value, stream, sort_keys=True, indent=2, allow_nan=False)
|
||||
stream.write('\n')
|
||||
|
||||
|
||||
def _raw(value):
|
||||
return hashlib.sha256(np.ascontiguousarray(value, dtype=value.dtype.newbyteorder('<')).tobytes()).hexdigest()
|
||||
|
||||
|
||||
def _sha(value):
|
||||
return isinstance(value, str) and re.fullmatch('[0-9a-f]{64}', value) is not None
|
||||
|
||||
|
||||
def _load(name, path, expected):
|
||||
path = Path(path).absolute()
|
||||
raw = path.read_bytes()
|
||||
require(hashlib.sha256(raw).hexdigest() == expected, 'Unpinned source: ' + str(path))
|
||||
if name in sys.modules:
|
||||
module = sys.modules[name]
|
||||
require(_LOADED.get(name) == (path, expected, module), 'Existing unverified module binding: ' + name)
|
||||
return module
|
||||
spec = importlib.util.spec_from_file_location(name, path)
|
||||
require(spec is not None and spec.loader is not None, 'Missing module loader')
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
sys.modules[name] = module
|
||||
try:
|
||||
exec(compile(raw, str(path), 'exec'), module.__dict__)
|
||||
except BaseException:
|
||||
sys.modules.pop(name, None)
|
||||
raise
|
||||
_LOADED[name] = (path, expected, module)
|
||||
return module
|
||||
|
||||
|
||||
def validate_spec(spec, case_dir):
|
||||
"""Admission policy only. No implicit early stopping or checkpoint support."""
|
||||
require(type(spec.get('max_steps')) is int and 0 < spec['max_steps'] <= np.iinfo(np.int32).max,
|
||||
'An explicit positive int32-range max_steps is mandatory')
|
||||
require(type(spec.get('steps')) is int and 0 < spec['steps'] <= spec['max_steps'], 'steps exceeds its declared bound')
|
||||
require(spec.get('dt_ms') == .1 and spec.get('duration_ms') == spec['steps'] * .1 and
|
||||
type(spec.get('apl_delay_steps')) is int and spec['apl_delay_steps'] == 18,
|
||||
'Frozen clock/duration/D18 contract changed')
|
||||
require(spec.get('record_history') is False, 'Only wholebrain fresh-state trials are supported')
|
||||
require(not (Path(case_dir) / 'arrays.npz').exists(), 'Initial arrays/checkpoint continuation are not supported in this version')
|
||||
require(not any('checkpoint' in str(key).lower() or str(key) in ('initial_state', 'resume', 'continuation') for key in spec),
|
||||
'Checkpoint/continuation configuration is unsupported')
|
||||
require(spec.get('within_trial_weight_updates', False) is False, 'No within-trial plasticity is supported')
|
||||
return spec
|
||||
|
||||
|
||||
def load_overlay(overlay_file, manifest_file):
|
||||
"""New manifest policy, frozen AM array validators; no old-global mutation.
|
||||
|
||||
phase4an-overlay-v1 requires overlay_name zero/static25/learned,
|
||||
overlay_sha256, original mask_path/mask_sha256, external positive f32-mV
|
||||
base_candidate_path/base_candidate_sha256, aj_model_dir,
|
||||
am_overlay_path/am_overlay_sha256. Learned snapshots additionally require
|
||||
parent_snapshot_sha256 and eligibility_v2_source_sha256=the frozen 4J pin.
|
||||
These hashes bind provenance; the coordinator must verify the parent chain.
|
||||
NPZ contains ONLY edge_id:int64[14232], plastic_delta_mv:float32[14232].
|
||||
"""
|
||||
overlay_file, manifest_file = Path(overlay_file).absolute(), Path(manifest_file).absolute()
|
||||
manifest_sha = digest(manifest_file)
|
||||
meta = _read_json(manifest_file)
|
||||
keys = {'schema', 'overlay_name', 'overlay_sha256', 'mask_path', 'mask_sha256',
|
||||
'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir', 'am_overlay_path', 'am_overlay_sha256'}
|
||||
require(isinstance(meta, dict) and keys <= meta.keys() and meta['schema'] == 'phase4an-overlay-v1', 'Invalid overlay manifest schema')
|
||||
require(meta['overlay_name'] in ('zero', 'static25', 'learned'), 'Unknown overlay snapshot type')
|
||||
learned_keys = {'parent_snapshot_sha256', 'eligibility_v2_source_sha256'}
|
||||
allowed = keys | (learned_keys if meta['overlay_name'] == 'learned' else set())
|
||||
require(not (meta.keys() - allowed), 'Unknown or inapplicable overlay manifest fields')
|
||||
require(meta['mask_sha256'] == MASK_SHA and meta['am_overlay_sha256'] == AM_OVERLAY_SHA,
|
||||
'Original mask or frozen AM helper authority changed')
|
||||
require(_sha(meta['overlay_sha256']) and digest(overlay_file) == meta['overlay_sha256'], 'Overlay SHA mismatch')
|
||||
inputs = {str(overlay_file): meta['overlay_sha256'], str(manifest_file): manifest_sha}
|
||||
for label in ('mask', 'base_candidate', 'am_overlay'):
|
||||
path = Path(meta[label + '_path'])
|
||||
require(path.is_absolute() and _sha(meta[label + '_sha256']) and digest(path) == meta[label + '_sha256'],
|
||||
'Missing/mismatched anchored input: ' + label)
|
||||
inputs[str(path)] = meta[label + '_sha256']
|
||||
helper = _load('phase4an_sealed_am_overlay', meta['am_overlay_path'], AM_OVERLAY_SHA)
|
||||
mask = np.load(meta['mask_path'], allow_pickle=False)
|
||||
base = np.load(meta['base_candidate_path'], allow_pickle=False)
|
||||
require(mask.dtype == np.int64 and mask.shape == (helper.CANDIDATE_COUNT,), 'Original complete 14,232 mask is required')
|
||||
with np.load(overlay_file, allow_pickle=False) as archive:
|
||||
require(len(archive.files) == 2 and set(archive.files) == {'edge_id', 'plastic_delta_mv'}, 'No embedded base or additional overlay arrays allowed')
|
||||
plan = helper.validate_arrays(archive['edge_id'], archive['plastic_delta_mv'], mask, base)
|
||||
delta = plan['plastic_delta_mv']
|
||||
if meta['overlay_name'] == 'zero':
|
||||
require(not np.any(delta != 0), 'Zero snapshot contains a nonzero delta')
|
||||
elif meta['overlay_name'] == 'static25':
|
||||
require(helper.bit_equal(delta, np.multiply(np.float32(-.25), base)), 'static25 must equal the original f32 floor')
|
||||
else:
|
||||
require(_sha(meta.get('parent_snapshot_sha256')) and meta.get('eligibility_v2_source_sha256') == V2_SHA,
|
||||
'Learned snapshot needs parent hash and explicit frozen eligibility-v2 authority')
|
||||
aj_dir = Path(meta['aj_model_dir'])
|
||||
require(aj_dir.is_absolute() and aj_dir.name == 'apl-timing-lab-v1', 'AJ must use its real original directory')
|
||||
for relative, expected in PINS.items():
|
||||
path = aj_dir.parent / relative
|
||||
require(digest(path) == expected, 'Sealed dependency changed: ' + relative)
|
||||
inputs[str(path)] = expected
|
||||
require(all(digest(path) == expected for path, expected in inputs.items()), 'Input changed while loading')
|
||||
return helper, plan, meta, inputs
|
||||
|
||||
|
||||
def _dump(node):
|
||||
return ast.dump(node, annotate_fields=True, include_attributes=False)
|
||||
|
||||
|
||||
def _target_roots(node):
|
||||
if isinstance(node, ast.Name):
|
||||
return {node.id}
|
||||
if isinstance(node, (ast.Tuple, ast.List)):
|
||||
return set().union(*(_target_roots(value) for value in node.elts))
|
||||
if isinstance(node, (ast.Subscript, ast.Attribute)):
|
||||
return _target_roots(node.value)
|
||||
return set()
|
||||
|
||||
|
||||
def math_projection(function):
|
||||
"""Ordered/control-structured frozen math statements, excluding diagnostics.
|
||||
|
||||
Whitelist explicitly covers state/weight initialization, request mutation,
|
||||
expansion/stable grouping, original CUDA calls, LegacyAPL and ring/g write.
|
||||
Tests also require the entire reference loop AST to equal the original.
|
||||
"""
|
||||
def block(statements):
|
||||
result = []
|
||||
for node in statements:
|
||||
if isinstance(node, ast.FunctionDef) and node.name == 'upload':
|
||||
result.append(copy.deepcopy(node))
|
||||
elif isinstance(node, ast.Assign) and set().union(*(_target_roots(t) for t in node.targets)) & _MATH_TARGETS:
|
||||
result.append(copy.deepcopy(node))
|
||||
elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call):
|
||||
text = ast.unparse(node.value.func)
|
||||
if text.startswith('ext.') or text in ('requests.zero_', 'apl.spikes.zero_', 'apl_ring[slot].copy_'):
|
||||
result.append(copy.deepcopy(node))
|
||||
elif isinstance(node, (ast.With, ast.Try)):
|
||||
result.extend(block(node.body))
|
||||
elif isinstance(node, (ast.If, ast.For)):
|
||||
body, other = block(node.body), block(node.orelse)
|
||||
if body or other:
|
||||
keep = copy.deepcopy(node)
|
||||
keep.body, keep.orelse = body, other
|
||||
result.append(keep)
|
||||
return result
|
||||
return _dump(ast.Module(body=block(function.body), type_ignores=[]))
|
||||
|
||||
|
||||
def derive_ast(source_bytes, mode):
|
||||
"""Derive only from exact AJ bytes; return ASTs plus an explicit edit receipt."""
|
||||
require(mode in ('light', 'reference'), 'Unknown diagnostic mode')
|
||||
require(hashlib.sha256(source_bytes).hexdigest() == AJ_SHA, 'AJ source pin mismatch')
|
||||
source = ast.parse(source_bytes)
|
||||
original = next(node for node in source.body if isinstance(node, ast.FunctionDef) and node.name == 'run_gpu')
|
||||
loader = copy.deepcopy(next(node for node in source.body if isinstance(node, ast.FunctionDef) and node.name == 'load_inputs'))
|
||||
bounds = [node for node in ast.walk(loader) if isinstance(node, ast.Compare) and
|
||||
ast.unparse(node) == '0 < steps <= 1000']
|
||||
require(len(bounds) == 1, 'Expected one explicit step-admission bound')
|
||||
bounds[0].comparators[-1] = ast.parse("spec['max_steps']", mode='eval').body
|
||||
function = copy.deepcopy(original)
|
||||
body = next(node for node in function.body if isinstance(node, ast.Try)).body
|
||||
anchor = next(i for i, node in enumerate(body) if ast.unparse(node) == 'effective_hash = hashlib.sha256()')
|
||||
require(ast.unparse(body[anchor - 1]) == 'if bool(np.any(apl_out_mask)):\n weights[upload(apl_out_mask, torch.bool)] = 0.0',
|
||||
'Installation no longer follows APL fast-edge zeroing')
|
||||
body.insert(anchor, ast.parse(_INSTALL).body[0])
|
||||
sync = [i for i, node in enumerate(body) if ast.unparse(node) == 'torch.cuda.synchronize(device)']
|
||||
require(len(sync) == 1, 'Expected one post-loop synchronization')
|
||||
body.insert(sync[0] + 1, ast.parse(_FINISH).body[0])
|
||||
removed, replacements = [], []
|
||||
|
||||
class ReportingOnly(ast.NodeTransformer):
|
||||
def note(self, node):
|
||||
removed.append(_dump(node))
|
||||
return None
|
||||
|
||||
def visit_Assign(self, node):
|
||||
names = set().union(*(_target_roots(t) for t in node.targets))
|
||||
if names & {'expanded_global', 'accepted_cpu', 'queue', 'apl_cpu', 'actual', 'finite', 'row'}:
|
||||
return self.note(node)
|
||||
if names == {'maps'} and isinstance(node.targets[0], ast.Subscript):
|
||||
return self.note(node)
|
||||
if names == {'timing'}:
|
||||
require(ast.unparse(node.value.func) == 'audit_sequence', 'Unexpected timing assignment')
|
||||
replacements.append(_dump(node))
|
||||
return ast.parse('timing = _runtime.timing(steps, pending)').body[0]
|
||||
if names == {'destination'}:
|
||||
return [node, ast.parse('_runtime.arrivals(expanded, destination, ref)').body[0]]
|
||||
if ast.unparse(node) == 'g[kc_local_gpu] = g[kc_local_gpu] - delta_si':
|
||||
return [node, ast.parse('_runtime.after_tick(tick, v, g, ref, drive, apl_state, h, applied_h)').body[0]]
|
||||
return node
|
||||
|
||||
def visit_AugAssign(self, node):
|
||||
if _target_roots(node.target) & {'due_total', 'accepted_due_total'}:
|
||||
return self.note(node)
|
||||
return node
|
||||
|
||||
def visit_For(self, node):
|
||||
text = ast.unparse(node.iter)
|
||||
if text in ("(('drive', 2), ('state', 2), ('inhibition', 4064))", 'apl_cpu.items()', 'actual.items()'):
|
||||
return self.note(node)
|
||||
return self.generic_visit(node)
|
||||
|
||||
def visit_If(self, node):
|
||||
if ast.unparse(node.test) in ("spec['record_history']", 'not finite'):
|
||||
return self.note(node)
|
||||
return self.generic_visit(node)
|
||||
|
||||
def visit_Expr(self, node):
|
||||
if isinstance(node.value, ast.Call) and ast.unparse(node.value.func) in ('queue_output.write', 'hash_output.write'):
|
||||
return self.note(node)
|
||||
return node
|
||||
|
||||
def visit_With(self, node):
|
||||
targets = [getattr(item.optional_vars, 'id', None) for item in node.items]
|
||||
if targets == ['queue_output', 'hash_output', 'request_output']:
|
||||
replacements.append(_dump(node.items[0])); replacements.append(_dump(node.items[1]))
|
||||
node.items = [node.items[-1]]
|
||||
return self.generic_visit(node)
|
||||
|
||||
if mode == 'light':
|
||||
function = ReportingOnly().visit(function)
|
||||
require(len(removed) == 20 and len(replacements) == 3, 'Reporting whitelist changed; stop instead of broad rewriting')
|
||||
loops = lambda f: [node for node in ast.walk(f) if isinstance(node, ast.For) and
|
||||
isinstance(node.target, ast.Name) and node.target.id == 'tick']
|
||||
require(len(loops(original)) == len(loops(function)) == 1, 'Missing/duplicated trial loop')
|
||||
if mode == 'reference':
|
||||
require(_dump(loops(original)[0]) == _dump(loops(function)[0]), 'Reference loop must stay entirely unchanged')
|
||||
original_math, derived_math = math_projection(original), math_projection(function)
|
||||
require(original_math == derived_math, 'Ordered mathematical AST changed')
|
||||
extra_calls = [ast.unparse(node) for node in ast.walk(function) if isinstance(node, ast.Call) and
|
||||
ast.unparse(node.func).startswith('_runtime.')]
|
||||
expected_calls = [_INSTALL, _FINISH]
|
||||
if mode == 'light':
|
||||
expected_calls += ['_runtime.arrivals(expanded, destination, ref)',
|
||||
'_runtime.after_tick(tick, v, g, ref, drive, apl_state, h, applied_h)',
|
||||
'_runtime.timing(steps, pending)']
|
||||
require(sorted(extra_calls) == sorted(expected_calls), 'Unlisted runtime callback added')
|
||||
tree = ast.fix_missing_locations(ast.Module(body=[loader, function], type_ignores=[]))
|
||||
sha = lambda value: hashlib.sha256(value.encode('utf-8')).hexdigest()
|
||||
receipt = {'mode': mode, 'aj_source_sha256': AJ_SHA, 'math_ast_sha256': sha(original_math),
|
||||
'derived_math_ast_sha256': sha(derived_math), 'mathematical_ast_identical': True,
|
||||
'reference_entire_tick_loop_identical': mode == 'reference',
|
||||
'admission_edit': '0 < steps <= 1000 -> 0 < steps <= spec.max_steps',
|
||||
'pre_loop_installation': _INSTALL, 'post_loop_terminal_recording': _FINISH,
|
||||
'added_runtime_callbacks': extra_calls,
|
||||
'removed_diagnostic_statements': removed, 'replaced_diagnostic_nodes': replacements,
|
||||
'derived_module_ast_sha256': sha(_dump(tree)), 'old_globals_patched': False}
|
||||
return tree, receipt
|
||||
|
||||
|
||||
class FiniteObserver:
|
||||
"""GPU scalar diagnostics only; never writes a neuronal/APL input tensor."""
|
||||
def __init__(self, torch, device):
|
||||
self.torch = torch
|
||||
self.ok = torch.ones((), device=device, dtype=torch.bool)
|
||||
self.first = torch.full((), -1, device=device, dtype=torch.int64)
|
||||
self.accepted = torch.zeros((), device=device, dtype=torch.int64)
|
||||
self.due = 0
|
||||
self.ticks = 0
|
||||
|
||||
def arrivals(self, expanded, destination, ref):
|
||||
self.due += expanded.numel()
|
||||
self.accepted.add_((ref[destination.long()] == 0).to(self.torch.int64).sum())
|
||||
|
||||
def observe(self, tick, *values):
|
||||
require(tick == self.ticks, 'Finite observer tick coverage changed')
|
||||
finite = self.torch.ones_like(self.ok)
|
||||
for value in values:
|
||||
finite.logical_and_(self.torch.isfinite(value).all())
|
||||
self.first = self.torch.where((self.first < 0) & (~finite), self.torch.full_like(self.first, tick), self.first)
|
||||
self.ok.logical_and_(finite)
|
||||
self.ticks += 1
|
||||
|
||||
def result(self):
|
||||
return {'all_finite': bool(self.ok.item()), 'first_failure_tick': int(self.first.item()),
|
||||
'finite_observed_ticks': self.ticks, 'arriving_edges': self.due,
|
||||
'accepted_arriving_edges': int(self.accepted.item())}
|
||||
|
||||
|
||||
def _copy_input(source, target, expected):
|
||||
with Path(source).open('rb') as src, Path(target).open('xb') as dst:
|
||||
for block in iter(lambda: src.read(8 << 20), b''):
|
||||
dst.write(block)
|
||||
require(digest(target) == expected, 'Copied input differs')
|
||||
|
||||
|
||||
class _Runtime:
|
||||
def __init__(self, mode, spec, helper, plan, meta, inputs, torch, overlay_file, manifest_file):
|
||||
self.mode, self.spec, self.helper, self.plan, self.meta, self.inputs = mode, spec, helper, plan, meta, inputs
|
||||
self.torch, self.overlay_file, self.manifest_file = torch, Path(overlay_file).absolute(), Path(manifest_file).absolute()
|
||||
self.observer = None
|
||||
self.installations = 0
|
||||
self.finished = False
|
||||
self.result = None
|
||||
|
||||
def install(self, weights, graph, apl_mask, upload, out_dir, inherited, device):
|
||||
require(self.installations == 0, 'Repeated installation is forbidden')
|
||||
require(len(graph['nodes']) == 166700 and len(graph['edge_id']) == self.helper.EDGE_COUNT,
|
||||
'Only the intact full graph is permitted')
|
||||
require(int(apl_mask.sum()) == 5845, 'Original fast APL mask changed')
|
||||
mapping = self.helper.map_installation(graph['weights'], graph['edge_id'], apl_mask, self.plan)
|
||||
require(mapping['missing_candidate_count'] == 0, 'All original candidates must be present')
|
||||
rows = mapping['rows']
|
||||
if len(rows):
|
||||
index = upload(rows, self.torch.int64)
|
||||
nonzero = self.plan['plastic_delta_mv'] != np.float32(0)
|
||||
change = upload(self.plan['plastic_delta_mv'][nonzero], self.torch.float64) * 0.001
|
||||
weights[index] = weights[index] + change
|
||||
require(self.helper.bit_equal(weights[index].cpu().numpy(), mapping['expected_si']), 'Independent SI installation mismatch')
|
||||
raw_base, expected = self.helper.expected_hashes(graph['weights'], apl_mask, mapping)
|
||||
actual = hashlib.sha256()
|
||||
for start in range(0, weights.numel(), 1 << 20):
|
||||
actual.update(weights[start:start + (1 << 20)].cpu().numpy().astype('<f8', copy=False).tobytes())
|
||||
require(actual.hexdigest() == expected, 'Effective weight bytes differ from declared overlay')
|
||||
self.effective_sha = expected
|
||||
self.observer = FiniteObserver(self.torch, device)
|
||||
self.installations += 1
|
||||
_copy_input(self.overlay_file, out_dir / 'overlay-input.npz', self.meta['overlay_sha256'])
|
||||
_copy_input(self.manifest_file, out_dir / 'overlay-manifest.json', self.inputs[str(self.manifest_file)])
|
||||
_save(out_dir / 'overlay-audit.json', {'schema': 'phase4an-overlay-install-v1', 'complete': True,
|
||||
'overlay_name': self.meta['overlay_name'], 'candidate_count': len(self.plan['edge_id']),
|
||||
'nonzero_delta_count': len(rows), 'am_helper_sha256': AM_OVERLAY_SHA, 'mask_sha256': MASK_SHA,
|
||||
'base_candidate_sha256': self.meta['base_candidate_sha256'], 'delta_raw_sha256': _raw(self.plan['plastic_delta_mv']),
|
||||
'base_weights_raw_sha256': raw_base, 'effective_weights_raw_sha256': expected,
|
||||
'delta_dtype': 'float32', 'delta_units': 'mV', 'state_dtype': 'float64', 'bridge': self.helper.BRIDGE,
|
||||
'zero_rows_untouched': True, 'weights_updated_during_run': False, 'apl_fast_edges_zeroed': int(apl_mask.sum()),
|
||||
'parent_snapshot_sha256': self.meta.get('parent_snapshot_sha256'),
|
||||
'eligibility_v2_source_sha256': self.meta.get('eligibility_v2_source_sha256'),
|
||||
'parent_chain_verified_by_runtime': False, 'inherited_AJ_provenance_only': inherited})
|
||||
|
||||
def arrivals(self, expanded, destination, ref):
|
||||
self.observer.arrivals(expanded, destination, ref)
|
||||
|
||||
def after_tick(self, tick, v, g, ref, drive, state, h, applied):
|
||||
self.observer.observe(tick, v, g, ref, drive, state, h, applied)
|
||||
|
||||
def finish(self, ring, apl_ring, apl, steps, out_dir):
|
||||
require(not self.finished and self.installations == 1, 'Invalid terminal recording sequence')
|
||||
if self.mode == 'light':
|
||||
self.result = self.observer.result()
|
||||
require(self.result['finite_observed_ticks'] == steps, 'Missing finite checks')
|
||||
else:
|
||||
# Reaching this point means AJ's original per-tick finite check passed.
|
||||
self.result = {'all_finite': True, 'first_failure_tick': -1, 'finite_observed_ticks': steps}
|
||||
_save(out_dir / 'finite-audit.json', dict(self.result, mode=self.mode,
|
||||
scope='every completed tick; no neuronal or APL tensor mutated by observer'))
|
||||
raw_ring, raw_apl = ring.cpu().numpy().copy(), apl_ring.cpu().numpy().copy()
|
||||
head = steps % len(raw_apl)
|
||||
pending = np.concatenate((raw_apl[head:], raw_apl[:head]), axis=0)
|
||||
apl_state = apl.state.cpu().numpy().copy()
|
||||
require(raw_ring.dtype == np.uint8 and raw_ring.shape == (19, 166700) and
|
||||
apl_state.dtype == pending.dtype == np.float32 and apl_state.shape == (2,) and pending.shape == (18, 4064),
|
||||
'Terminal queue dtype/shape changed')
|
||||
with (out_dir / 'terminal-queues.npz').open('xb') as target:
|
||||
np.savez_compressed(target, spike_ring=raw_ring, apl_state=apl_state, apl_pending=pending,
|
||||
next_tick=np.asarray(steps, dtype=np.int64))
|
||||
self.finished = True
|
||||
require(self.result['all_finite'], 'Nonfinite value observed; trial is invalid and must not produce a learning update')
|
||||
|
||||
def timing(self, steps, pending):
|
||||
return {'apl_delay_steps': 18, 'steps': steps, 'pending_generated_count': min(steps, 18),
|
||||
'pending_initial_zero_prefix': max(18 - steps, 0), 'brain_tail_advanced': False,
|
||||
'full_sequence_bitwise_audit_performed': False,
|
||||
'scope': 'Original D18 ring AST retained; generated/applied histories not recorded'}
|
||||
|
||||
def summary(self, value):
|
||||
result = dict(value)
|
||||
result.pop('learning', None)
|
||||
result.update(diagnostic_mode=self.mode, within_trial_weight_updates=False,
|
||||
outer_protocol=self.spec.get('outer_protocol'), outer_learning_status='not evaluated by this single-trial executor',
|
||||
learning_pass_claimed=False, checkpoint_continuation_used=False,
|
||||
max_steps=self.spec['max_steps'], explicit_steps_executed=self.spec['steps'],
|
||||
entire_neural_trial_executed=True, hidden_early_stop=False,
|
||||
full_APL_history_recorded=self.mode == 'reference', full_tick_state_hashes_recorded=self.mode == 'reference',
|
||||
full_queue_hashes_recorded=self.mode == 'reference',
|
||||
full_sequence_bitwise_audit_performed=self.mode == 'reference',
|
||||
first_failure_tick=self.result['first_failure_tick'],
|
||||
pass_all_scope='single fixed-weight trial finite/recording integrity only; no learning or equivalence claim')
|
||||
if self.mode == 'light':
|
||||
result.update(arriving_edges=self.result['arriving_edges'], accepted_arriving_edges=self.result['accepted_arriving_edges'])
|
||||
if 'apl_timing' in result:
|
||||
result['apl_timing'] = dict(result['apl_timing'], generated_trace=None, applied_trace=None,
|
||||
full_sequence_bitwise_audit_performed=False)
|
||||
return result
|
||||
|
||||
def write_json(self, path, value):
|
||||
path = Path(path)
|
||||
if path.name == 'summary.json':
|
||||
value = self.summary(value)
|
||||
elif path.name == 'apl-timing-audit.json' and self.mode == 'light':
|
||||
value = dict(value)
|
||||
value.update(generated_trace=None, applied_trace=None, full_sequence_bitwise_audit_performed=False)
|
||||
_save(path, value)
|
||||
|
||||
|
||||
def compile_runtime(aj_module, runtime, mode):
|
||||
"""Fresh namespace; __file__ is this real adapter, not a disguised old file."""
|
||||
tree, audit = derive_ast(Path(aj_module.__file__).read_bytes(), mode)
|
||||
namespace = dict(aj_module.__dict__)
|
||||
namespace.update(__name__=__name__ + '.derived_' + mode, __file__=str(Path(__file__).absolute()),
|
||||
_runtime=runtime, write_json=runtime.write_json)
|
||||
exec(compile(tree, str(Path(__file__).absolute()) + '::AJ-AST-' + mode, 'exec'), namespace)
|
||||
audit.update(derived_namespace_file=namespace['__file__'], original_module_file=str(Path(aj_module.__file__).absolute()),
|
||||
output_metadata_binding='new write_json only labels recording/within-trial scope; no old global mutated')
|
||||
return namespace['run_gpu'], audit
|
||||
|
||||
|
||||
def execution_paths(case_dir, out_dir, build_root, protected=()):
|
||||
"""Resolve directory aliases before containment checks, without any writes.
|
||||
|
||||
Only data/source/output directories are canonicalized here. This helper
|
||||
must not be used for a Python executable: resolving a venv entry changes
|
||||
interpreter semantics. The coordinator must keep input paths stable during
|
||||
execution; this is not a defense against concurrent filesystem mutation.
|
||||
"""
|
||||
case_dir = Path(case_dir).resolve(strict=True)
|
||||
require(case_dir.is_dir(), 'Input case must be an existing directory')
|
||||
out_dir, build_root = (Path(path).resolve(strict=False) for path in (out_dir, build_root))
|
||||
require(not out_dir.exists(), 'Output must be a new directory')
|
||||
require(not build_root.exists() or build_root.is_dir(), 'Build cache must be a directory')
|
||||
roots = [case_dir]
|
||||
for path in protected:
|
||||
root = Path(path).resolve(strict=True)
|
||||
require(root.is_dir(), 'Protected input/source root must be a directory')
|
||||
roots.append(root)
|
||||
require(not any(out_dir.is_relative_to(root) or build_root.is_relative_to(root) for root in roots),
|
||||
'Output/build paths cannot write into frozen source or input directories')
|
||||
return case_dir, out_dir, build_root
|
||||
|
||||
|
||||
def _run(mode, case_dir, out_dir, build_root, overlay_file, overlay_manifest):
|
||||
require(sys.dont_write_bytecode, 'Use python -B; frozen dependencies must not receive bytecode writes')
|
||||
case_dir, out_dir, build_root = execution_paths(case_dir, out_dir, build_root)
|
||||
spec = validate_spec(_read_json(case_dir / 'spec.json'), case_dir)
|
||||
helper, plan, meta, inputs = load_overlay(overlay_file, overlay_manifest)
|
||||
own_sha = digest(__file__)
|
||||
inputs[str(case_dir / 'spec.json')] = digest(case_dir / 'spec.json')
|
||||
inputs[str(case_dir / 'requests.npz')] = digest(case_dir / 'requests.npz')
|
||||
inputs[str(Path(spec['graph_dir']) / 'weights.npy')] = digest(Path(spec['graph_dir']) / 'weights.npy')
|
||||
aj_dir = Path(meta['aj_model_dir']).resolve(strict=True)
|
||||
protected = (aj_dir.parent, Path(meta['am_overlay_path']).resolve(strict=True).parent,
|
||||
Path(spec['graph_dir']), Path(spec['apl_bundle_dir']),
|
||||
*(Path(path).resolve(strict=True).parent for path in
|
||||
(overlay_file, overlay_manifest, meta['mask_path'], meta['base_candidate_path'], spec['coefficients_path'])))
|
||||
case_dir, out_dir, build_root = execution_paths(case_dir, out_dir, build_root, protected)
|
||||
_load('timing_queue', aj_dir / 'timing_queue.py', PINS['apl-timing-lab-v1/timing_queue.py'])
|
||||
aj = _load('phase4an_sealed_aj_gpu', aj_dir / 'gpu_timing.py', AJ_SHA)
|
||||
runtime = _Runtime(mode, spec, helper, plan, meta, inputs, aj.torch, overlay_file, overlay_manifest)
|
||||
function, ast_audit = compile_runtime(aj, runtime, mode)
|
||||
try:
|
||||
inherited = function(case_dir, out_dir, build_root)
|
||||
require(runtime.finished and runtime.installations == 1 and inherited['complete'], 'Incomplete single-trial execution')
|
||||
require(inherited['effective_weights_raw_sha256'] == runtime.effective_sha, 'Effective weight identity changed during trial')
|
||||
require(digest(__file__) == own_sha and all(digest(path) == expected for path, expected in inputs.items()),
|
||||
'Source or input changed during execution')
|
||||
summary = _read_json(out_dir / 'summary.json')
|
||||
require(summary == runtime.summary(inherited), 'Stored summary does not bind actual trial')
|
||||
_save(out_dir / 'runtime-provenance.json', {'schema': 'phase4an-reporting-adapter-v1', 'complete': True,
|
||||
'source_file': str(Path(__file__).absolute()), 'source_sha256': own_sha, 'mode': mode, 'ast': ast_audit,
|
||||
'input_sha256': inputs, 'old_globals_patched': False, 'math_changed': False,
|
||||
'admission_bound_changed_explicitly': True, 'weights_updated_during_run': False,
|
||||
'learning_pass_claimed': False, 'parent_chain_verification_owned_by_coordinator': True,
|
||||
'summary_sha256': digest(out_dir / 'summary.json'), 'finite_audit_sha256': digest(out_dir / 'finite-audit.json'),
|
||||
'terminal_queues_sha256': digest(out_dir / 'terminal-queues.npz'),
|
||||
'overlay_audit_sha256': digest(out_dir / 'overlay-audit.json')})
|
||||
return summary
|
||||
except Exception as error:
|
||||
if out_dir.is_dir() and not (out_dir / 'runtime-error.json').exists():
|
||||
_save(out_dir / 'runtime-error.json', {'complete': False, 'type': type(error).__name__, 'message': str(error),
|
||||
'learning_update_forbidden': True})
|
||||
raise
|
||||
|
||||
|
||||
def run_light(case_dir, out_dir, build_root, overlay_file, overlay_manifest):
|
||||
return _run('light', case_dir, out_dir, build_root, overlay_file, overlay_manifest)
|
||||
|
||||
|
||||
def run_reference(case_dir, out_dir, build_root, overlay_file, overlay_manifest):
|
||||
return _run('reference', case_dir, out_dir, build_root, overlay_file, overlay_manifest)
|
||||
@@ -0,0 +1,118 @@
|
||||
"""Explicit, order-independent random streams for an unregistered protocol.
|
||||
|
||||
This module does not select odors, generate neural input, or run experiments.
|
||||
Every independent stream has a semantic key and a SHA-derived 63-bit seed.
|
||||
Intentional common-random-number controls must be declared as aliases of a
|
||||
same-split stream. Aliases are never counted as independent replicates.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
|
||||
|
||||
SCHEMA = "phase4an-seed-registry-v1"
|
||||
SPLITS = frozenset(("training", "evaluation", "design"))
|
||||
|
||||
|
||||
def _require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def _canonical(value):
|
||||
return json.dumps(value, sort_keys=True, separators=(",", ":"),
|
||||
ensure_ascii=False, allow_nan=False)
|
||||
|
||||
|
||||
def _key(key):
|
||||
_require(isinstance(key, (tuple, list)) and len(key) > 0,
|
||||
"A nonempty list/tuple semantic key is required")
|
||||
_require(all(type(v) is str and v and v.strip() == v for v in key),
|
||||
"Semantic key tokens must be nonempty, unpadded strings")
|
||||
return tuple(key)
|
||||
|
||||
|
||||
def seed_for(master, split, key):
|
||||
_require(type(master) is int and 0 <= master < 2**63, "master must be a 63-bit integer")
|
||||
_require(type(split) is str and split in SPLITS, "Unknown stream split")
|
||||
key = _key(key)
|
||||
message = _canonical([SCHEMA, master, split, list(key)]).encode("utf-8")
|
||||
full_sha = hashlib.sha256(message).hexdigest()
|
||||
return int(full_sha[:16], 16) & ((1 << 63) - 1), full_sha
|
||||
|
||||
|
||||
class SeedRegistry:
|
||||
"""Fail closed on duplicate keys, collisions or cross-split aliasing.
|
||||
|
||||
No collision rehashing or seed search is performed: such a failure must be
|
||||
resolved before registration, never after observing a neural response.
|
||||
"""
|
||||
|
||||
def __init__(self, master):
|
||||
seed_for(master, "design", ("validate-master",))
|
||||
self.master = master
|
||||
self._streams = {}
|
||||
self._aliases = {}
|
||||
self._seed_to_key = {}
|
||||
|
||||
def add(self, split, key):
|
||||
key = _key(key)
|
||||
identity = (split, key)
|
||||
_require(identity not in self._streams and identity not in self._aliases,
|
||||
"Duplicate semantic stream key")
|
||||
seed, full_sha = seed_for(self.master, split, key)
|
||||
_require(seed not in self._seed_to_key, "Seed collision: stop before registration")
|
||||
row = dict(split=split, key=list(key), seed=seed, derivation_sha256=full_sha)
|
||||
self._streams[identity] = row
|
||||
self._seed_to_key[seed] = identity
|
||||
return seed
|
||||
|
||||
def alias(self, split, key, source_split, source_key, *, reason):
|
||||
"""Declare paired controls, never silently create/reuse a stream."""
|
||||
key, source_key = _key(key), _key(source_key)
|
||||
identity, source = (split, key), (source_split, source_key)
|
||||
_require(split == source_split and split in SPLITS,
|
||||
"Cross-split random-stream sharing is forbidden")
|
||||
_require(identity not in self._streams and identity not in self._aliases,
|
||||
"Duplicate semantic stream key")
|
||||
_require(source in self._streams, "Alias must target an independent stream, not another alias")
|
||||
_require(type(reason) is str and bool(reason.strip()), "Intentional sharing requires a reason")
|
||||
seed = self._streams[source]["seed"]
|
||||
self._aliases[identity] = dict(split=split, key=list(key), seed=seed,
|
||||
source_split=source_split, source_key=list(source_key), reason=reason,
|
||||
independent_replicate=False)
|
||||
return seed
|
||||
|
||||
def manifest(self):
|
||||
streams = [dict(self._streams[k]) for k in sorted(self._streams)]
|
||||
aliases = [dict(self._aliases[k]) for k in sorted(self._aliases)]
|
||||
split_counts = {split: sum(r["split"] == split for r in streams) for split in sorted(SPLITS)}
|
||||
result = dict(schema=SCHEMA, master=self.master,
|
||||
algorithm="SHA-256 canonical JSON [schema,master,split,key]; first64 bits masked to63",
|
||||
independent_streams=streams, aliases=aliases,
|
||||
independent_count=len(streams), alias_count=len(aliases), split_counts=split_counts,
|
||||
all_independent_seeds_unique=True, training_evaluation_seed_overlap=0,
|
||||
aliases_count_as_independent_replicates=False,
|
||||
training_executed=False, protocol_approval_claimed=False)
|
||||
result["payload_sha256"] = hashlib.sha256(_canonical(result).encode("utf-8")).hexdigest()
|
||||
# JSON round-trip makes nested data independent of the mutable registry.
|
||||
return json.loads(_canonical(result))
|
||||
|
||||
|
||||
def validate_manifest(manifest):
|
||||
"""Reconstruct every seed/alias and require the complete canonical payload."""
|
||||
_require(isinstance(manifest, dict) and manifest.get("schema") == SCHEMA, "Invalid seed manifest")
|
||||
payload = {key: value for key, value in manifest.items() if key != "payload_sha256"}
|
||||
actual_sha = hashlib.sha256(_canonical(payload).encode("utf-8")).hexdigest()
|
||||
_require(manifest.get("payload_sha256") == actual_sha, "Actual manifest payload SHA mismatch")
|
||||
rebuilt = SeedRegistry(manifest["master"])
|
||||
for row in manifest["independent_streams"]:
|
||||
rebuilt.add(row["split"], row["key"])
|
||||
for row in manifest["aliases"]:
|
||||
rebuilt.alias(row["split"], row["key"], row["source_split"], row["source_key"], reason=row["reason"])
|
||||
# Python dict equality conflates False/0 and 1/1.0; the serialized contract
|
||||
# must retain JSON Boolean versus integer versus floating-point identity.
|
||||
_require(_canonical(manifest) == _canonical(rebuilt.manifest()),
|
||||
"Seed manifest differs from full independent reconstruction")
|
||||
return True
|
||||
@@ -0,0 +1,153 @@
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
import biological_contract as b
|
||||
|
||||
|
||||
def fixture():
|
||||
types = ["KC-a", "KC-b", "PAM15", "PPL103", "MBON01", "MBON12", "other-DAN"]
|
||||
classes = ["Kenyon_Cell", "Kenyon_Cell", "DAN", "DAN", "MBON", "MBON", "DAN"]
|
||||
instances = ["KC-a_L", "KC-b_R", "PAM15(y5B'2a)_L", "PPL103(y2a'1)_R",
|
||||
"MBON01(y5B'2a)_L", "MBON12(y2a'1)_R", "other-DAN"]
|
||||
meta = pd.DataFrame(dict(neuron_idx=np.arange(7, dtype=np.int32),
|
||||
body_id=np.arange(7, dtype=np.uint64) + np.uint64(2**63),
|
||||
annotation_type=pd.Series(types, dtype=object), annotation_class=pd.Series(classes, dtype=object),
|
||||
annotation_instance=pd.Series(instances, dtype=object)))
|
||||
dan = meta.iloc[[2, 3, 6]].drop(columns="annotation_class").copy()
|
||||
mbon = meta.iloc[[4, 5]].drop(columns="annotation_class").copy()
|
||||
dan["mb_compartment_verbatim"] = ["y5B'2a", "y2a'1", None]
|
||||
dan["mb_compartment_mapping_status"] = ["explicit_single_parenthesized_token"] * 2 + ["no_parenthesized_token"]
|
||||
mbon["mb_compartment_verbatim"] = ["y5B'2a", "y2a'1"]
|
||||
mbon["mb_compartment_mapping_status"] = "explicit_single_parenthesized_token"
|
||||
src = np.array([0, 0, 1, 1, 2, 3], np.int32)
|
||||
dst = np.array([4, 5, 4, 5, 4, 5], np.int32)
|
||||
raw = np.array([4, 8, 12, 16, 20, 24], np.int64)
|
||||
def table(ids):
|
||||
rows = []
|
||||
for i in ids:
|
||||
row = dict(edge_id=i, raw_synapse_count=int(raw[i]))
|
||||
for label, node in (("source", src[i]), ("target", dst[i])):
|
||||
for key in ("neuron_idx", "body_id", "annotation_type", "annotation_instance"):
|
||||
row[label + "_" + key] = meta.iloc[node][key]
|
||||
row["target_compartment_verbatim"] = "y5B'2a" if dst[i] == 4 else "y2a'1"
|
||||
rows.append(row)
|
||||
out = pd.DataFrame(rows)
|
||||
for key in ("source_neuron_idx", "target_neuron_idx"):
|
||||
out[key] = out[key].astype(np.int32)
|
||||
for key in ("source_body_id", "target_body_id"):
|
||||
out[key] = out[key].astype(np.uint64)
|
||||
# Match the observed frozen parquet object dtype; local pandas 3 may
|
||||
# infer its new string extension dtype from constructor literals.
|
||||
for prefix in ("source", "target"):
|
||||
for suffix in ("annotation_type", "annotation_instance"):
|
||||
out[prefix + "_" + suffix] = out[prefix + "_" + suffix].astype(object)
|
||||
out["edge_id"] = out.edge_id.astype(np.int64)
|
||||
out["raw_synapse_count"] = out.raw_synapse_count.astype(np.int64)
|
||||
return out
|
||||
candidates = table(range(4))
|
||||
candidates["plasticity_polarity"] = np.int64(-1)
|
||||
confirmed = table([4, 5])
|
||||
confirmed["source_compartment_verbatim"] = confirmed.target_compartment_verbatim
|
||||
confirmed["exact_compartment_match"] = True
|
||||
return meta, dan, mbon, confirmed, candidates, src, dst, raw
|
||||
|
||||
|
||||
class Biology(unittest.TestCase):
|
||||
def test_complete_graph_binds_every_edge_array_length(self):
|
||||
meta, *_, src, dst, raw = fixture()
|
||||
graph = dict(nodes=np.arange(7, dtype=np.int32), edge_id=np.arange(6, dtype=np.int64),
|
||||
src=src, dst=dst, signed_raw=raw)
|
||||
b._complete_graph_identity(meta, graph, n=7, e=6)
|
||||
for names in (("src",), ("dst",), ("signed_raw",), ("src", "dst", "signed_raw")):
|
||||
changed = dict(graph)
|
||||
for name in names:
|
||||
changed[name] = changed[name][:-1]
|
||||
with self.subTest(names=names), self.assertRaisesRegex(ValueError, "endpoint/weight lengths"):
|
||||
b._complete_graph_identity(meta, changed, n=7, e=6)
|
||||
for name in ("nodes", "edge_id"):
|
||||
changed = dict(graph)
|
||||
changed[name] = changed[name].reshape(1, -1)
|
||||
with self.subTest(name=name), self.assertRaises(ValueError):
|
||||
b._complete_graph_identity(meta, changed, n=7, e=6)
|
||||
|
||||
def test_exact_id_compartment_and_real_edge_contract(self):
|
||||
values = fixture()
|
||||
populations, audit = b.audit_interface_tables(*values)
|
||||
self.assertEqual(populations["PAM15"].tolist(), [2])
|
||||
self.assertEqual(populations["MBON12"].tolist(), [5])
|
||||
self.assertEqual(audit["whole_DAN_mapping_unknown"], 1)
|
||||
self.assertFalse(audit["global_unknowns_filled"])
|
||||
self.assertEqual(audit["all_candidate_edges_retained"], 4)
|
||||
self.assertEqual(audit["interfaces"]["PAM15"]["candidate_contacts"], 16)
|
||||
self.assertEqual(audit["interfaces"]["PPL103"]["confirmed_DAN_MBON_contacts"], 24)
|
||||
self.assertGreater(audit["interfaces"]["PAM15"]["DAN_members"][0]["body_id"], 2**63)
|
||||
self.assertTrue(pd.isna(values[1].iloc[-1].mb_compartment_verbatim))
|
||||
|
||||
def test_unknown_selected_mapping_is_not_filled_from_type(self):
|
||||
for field, new_value in (("mb_compartment_verbatim", None),
|
||||
("mb_compartment_mapping_status", "unknown")):
|
||||
values = fixture()
|
||||
values[1].loc[2, field] = new_value
|
||||
with self.assertRaisesRegex(ValueError, "Unknown or mismatched"):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_changed_body_id_or_type_or_instance_rejected(self):
|
||||
for field, new_value in (("body_id", np.uint64(5)), ("annotation_type", "PAM_OTHER"),
|
||||
("annotation_instance", "inferred")):
|
||||
values = fixture()
|
||||
values[1].loc[2, field] = new_value
|
||||
with self.assertRaisesRegex(ValueError, "Identity field mismatch"):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_unmapped_dan_cannot_be_silently_removed(self):
|
||||
values = list(fixture())
|
||||
values[1] = values[1].iloc[:2].copy()
|
||||
with self.assertRaisesRegex(ValueError, "complete exact-annotation"):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_missing_actual_edge_fails_not_guessed(self):
|
||||
values = list(fixture())
|
||||
values[3] = values[3].iloc[:1].copy()
|
||||
with self.assertRaisesRegex(ValueError, "unknown or mismatched actual target"):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_edge_identity_and_contacts_and_polarity(self):
|
||||
changes = ((3, "source_body_id", np.uint64(8)),
|
||||
(3, "target_annotation_type", "MBON_OTHER"),
|
||||
(4, "raw_synapse_count", np.int64(1)),
|
||||
(4, "plasticity_polarity", np.int64(1)))
|
||||
for which, field, value in changes:
|
||||
values = fixture()
|
||||
values[which].loc[values[which].index[0], field] = value
|
||||
with self.subTest(field=field), self.assertRaises(ValueError):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_false_or_nonboolean_confirmed_flag_rejected(self):
|
||||
for value in (False, "True"):
|
||||
values = fixture()
|
||||
values[3]["exact_compartment_match"] = value
|
||||
with self.assertRaisesRegex(ValueError, "unknown compartment"):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_graph_endpoint_and_sign_change_rejected(self):
|
||||
for where in (5, 6, 7):
|
||||
values = fixture()
|
||||
values[where][0] = -1 if where == 7 else 6
|
||||
with self.assertRaises(ValueError):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
def test_schema_and_duplicate_identity_rejected(self):
|
||||
values = list(fixture())
|
||||
values[0] = values[0].drop(columns="annotation_class")
|
||||
with self.assertRaises(ValueError):
|
||||
b.audit_interface_tables(*values)
|
||||
values = fixture()
|
||||
values[0].loc[1, "body_id"] = values[0].loc[0, "body_id"]
|
||||
with self.assertRaises(ValueError):
|
||||
b.audit_interface_tables(*values)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -0,0 +1,298 @@
|
||||
"""Tiny temporary CPU fixtures, never scientific events or a real brain run."""
|
||||
import copy
|
||||
import json
|
||||
from pathlib import Path
|
||||
import tempfile
|
||||
from types import SimpleNamespace
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
|
||||
import conditioning_epoch as m
|
||||
import conditioning_update as update
|
||||
from test_conditioning_update import BRIDGE, SOURCE
|
||||
|
||||
|
||||
def save(path, value):
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(value, sort_keys=True, allow_nan=False), encoding='utf-8')
|
||||
|
||||
|
||||
class TinyFixture:
|
||||
def __init__(self, root):
|
||||
self.root = root
|
||||
self.inputs = root / 'inputs'; self.inputs.mkdir()
|
||||
self.case = self.inputs / 'case'; self.case.mkdir()
|
||||
self.graph = self.inputs / 'graph'; self.graph.mkdir()
|
||||
self.apl = self.inputs / 'apl'; self.apl.mkdir()
|
||||
self.checkpoint = root / 'runtime'; self.checkpoint.mkdir()
|
||||
self.authority_dir = root / 'authority'; self.authority_dir.mkdir()
|
||||
self.output_parent = root / 'outputs'; self.output_parent.mkdir()
|
||||
self.authority_path = self.authority_dir / 'epoch.json'
|
||||
self.mask = np.array([1, 3], np.int64)
|
||||
self.kc = np.array([1, 3], np.int32)
|
||||
self.base = np.array([.275, .55], np.float32)
|
||||
self.before = np.zeros(2, np.float32)
|
||||
graph = dict(nodes=np.arange(5, dtype=np.int32), edge_id=np.arange(6, dtype=np.int64),
|
||||
src=np.array([0, 1, 1, 3, 3, 4], np.int32), dst=np.array([1, 2, 4, 2, 4, 0], np.int32),
|
||||
signed_raw=np.ones(6, np.int64), ptr=np.array([0, 1, 3, 3, 5, 6], np.int64), weights=np.full(6, .000275))
|
||||
for name, array in graph.items():
|
||||
np.save(self.graph / (name + '.npy'), array)
|
||||
for name, array in dict(input_matrix=np.zeros((2, 5), np.float32),
|
||||
output_matrix=np.zeros((2, 2), np.float32), kc_globals=self.kc, apl_globals=np.array([0, 4], np.int32)).items():
|
||||
np.save(self.apl / (name + '.npy'), array)
|
||||
np.save(self.inputs / 'kc.npy', self.kc)
|
||||
np.save(self.inputs / 'mask.npy', self.mask)
|
||||
np.save(self.inputs / 'base.npy', self.base)
|
||||
np.savez_compressed(self.inputs / 'mapping.npz', edge_id=self.mask,
|
||||
source_neuron_idx=graph['src'][self.mask], target_neuron_idx=graph['dst'][self.mask],
|
||||
target_compartment_verbatim=np.array(["y5B'2a", 'a1']))
|
||||
np.savez_compressed(self.inputs / 'before.npz', edge_id=self.mask, plastic_delta_mv=self.before)
|
||||
save(self.inputs / 'coeff.json', {'synthetic': True})
|
||||
source_root = Path(m.__file__).resolve().parent
|
||||
source_files = dict(runtime_light_source=source_root / 'runtime_light.py',
|
||||
runtime_continuation_source=source_root / 'runtime_continuation.py',
|
||||
conditioning_update_source=source_root / 'conditioning_update.py',
|
||||
eligibility_bridge_source=BRIDGE.resolve(), phase4j_source=SOURCE.resolve(),
|
||||
am_overlay_source=source_root.parent / 'wholebrain-plastic-interface-v1/overlay.py')
|
||||
metadata = dict(schema='phase4an-overlay-v1', overlay_name='zero',
|
||||
overlay_sha256=m._digest(self.inputs / 'before.npz'), mask_path=str(self.inputs / 'mask.npy'),
|
||||
mask_sha256=m._digest(self.inputs / 'mask.npy'), base_candidate_path=str(self.inputs / 'base.npy'),
|
||||
base_candidate_sha256=m._digest(self.inputs / 'base.npy'), aj_model_dir=str(self.graph),
|
||||
am_overlay_path=str(source_files['am_overlay_source']), am_overlay_sha256=m._digest(source_files['am_overlay_source']))
|
||||
save(self.inputs / 'before.json', metadata)
|
||||
spec = dict(name='synthetic', steps=4, max_steps=4, dt_ms=.1, duration_ms=.4,
|
||||
apl_delay_steps=18, record_history=False, within_trial_weight_updates=False,
|
||||
graph_dir=str(self.graph), apl_bundle_dir=str(self.apl), coefficients_path=str(self.inputs / 'coeff.json'))
|
||||
save(self.case / 'spec.json', spec)
|
||||
np.savez_compressed(self.case / 'requests.npz', tick=np.array([0], np.int32), neuron_idx=np.array([1], np.int32))
|
||||
state = dict(step=np.array([0], np.int32), neuron_idx=np.array([1], np.int32),
|
||||
counts=np.array([0, 1, 0, 0, 0], np.int32), v=np.full(5, -.052), g=np.zeros(5), ref=np.zeros(5, np.int32))
|
||||
np.savez_compressed(self.checkpoint / 'state.npz', **state)
|
||||
(self.checkpoint / 'overlay-input.npz').write_bytes((self.inputs / 'before.npz').read_bytes())
|
||||
(self.checkpoint / 'overlay-manifest.json').write_bytes((self.inputs / 'before.json').read_bytes())
|
||||
summary = dict(nodes=5, edges=6, diagnostic_mode='light', effective_weights_raw_sha256='e' * 64)
|
||||
save(self.checkpoint / 'summary.json', summary)
|
||||
provenance = dict(input_sha256={str(path): m._digest(path) for path in
|
||||
(self.case / 'spec.json', self.case / 'requests.npz', self.inputs / 'before.npz', self.inputs / 'before.json')})
|
||||
save(self.checkpoint / 'runtime-provenance.json', provenance)
|
||||
save(self.checkpoint / 'overlay-audit.json', dict(delta_raw_sha256=self.raw(self.before), effective_weights_raw_sha256='e' * 64))
|
||||
paths = dict(spec=self.case / 'spec.json', requests=self.case / 'requests.npz',
|
||||
overlay_before=self.inputs / 'before.npz', overlay_manifest_before=self.inputs / 'before.json',
|
||||
state=self.checkpoint / 'state.npz', kc=self.inputs / 'kc.npy', canonical_kc=self.apl / 'kc_globals.npy',
|
||||
candidate_map=self.inputs / 'mapping.npz', mask=self.inputs / 'mask.npy', base_candidate=self.inputs / 'base.npy', **source_files)
|
||||
self.value = dict(schema=m.SCHEMA, epoch_id='synthetic/epoch-0', diagnostic_mode='light',
|
||||
command=dict(steps=4, compartment="y5B'2a", us_tick=1, d_command=1., frozen=False, trace_backend='numpy'),
|
||||
checkpoint_dir=str(self.checkpoint), checkpoint_manifest_sha256=None,
|
||||
files={role: dict(path=str(path), sha256=m._digest(path)) for role, path in paths.items()},
|
||||
frozen_inputs={str(path): m._digest(path) for path in [
|
||||
*(self.graph / name for name in m.GRAPH_FILES), *(self.apl / name for name in m.APL_FILES), self.inputs / 'coeff.json']})
|
||||
self.reseal()
|
||||
|
||||
@staticmethod
|
||||
def raw(array):
|
||||
return __import__('hashlib').sha256(array.tobytes()).hexdigest()
|
||||
|
||||
def reseal(self):
|
||||
files = {path.name: m._digest(path) for path in self.checkpoint.iterdir() if path.name != 'checkpoint-manifest.json'}
|
||||
save(self.checkpoint / 'checkpoint-manifest.json', dict(files_sha256=files))
|
||||
self.value['checkpoint_manifest_sha256'] = m._digest(self.checkpoint / 'checkpoint-manifest.json')
|
||||
for entry in self.value['files'].values():
|
||||
entry['sha256'] = m._digest(entry['path'])
|
||||
for path in self.value['frozen_inputs']:
|
||||
self.value['frozen_inputs'][path] = m._digest(path)
|
||||
save(self.authority_path, self.value)
|
||||
|
||||
def load_checkpoint(self, directory, pin):
|
||||
self.asserted_loads += 1
|
||||
directory = Path(directory)
|
||||
m._require(m._digest(directory / 'checkpoint-manifest.json') == pin, 'synthetic seal pin mismatch')
|
||||
manifest = m._read(directory / 'checkpoint-manifest.json')
|
||||
actual = {path.name: m._digest(path) for path in directory.iterdir() if path.name != 'checkpoint-manifest.json'}
|
||||
m._require(manifest['files_sha256'] == actual, 'synthetic sealed inventory mismatch')
|
||||
return dict(fresh=True, global_tick_offset=0, steps=4, summary=m._read(directory / 'summary.json'),
|
||||
provenance=m._read(directory / 'runtime-provenance.json'), manifest=manifest,
|
||||
state=m._npz(directory / 'state.npz', ('step', 'neuron_idx', 'counts', 'v', 'g', 'ref')),
|
||||
overlay=m._read(directory / 'overlay-audit.json'))
|
||||
|
||||
def load_overlay(self, file, manifest):
|
||||
meta = m._read(manifest)
|
||||
m._require(meta['overlay_sha256'] == m._digest(file), 'synthetic overlay SHA mismatch')
|
||||
value = m._npz(file, ('edge_id', 'plastic_delta_mv'))
|
||||
value['base_candidate_mv'] = np.load(meta['base_candidate_path'], allow_pickle=False)
|
||||
if meta['overlay_name'] == 'learned':
|
||||
expected = {'schema', 'overlay_name', 'overlay_sha256', 'mask_path', 'mask_sha256',
|
||||
'base_candidate_path', 'base_candidate_sha256', 'aj_model_dir', 'am_overlay_path', 'am_overlay_sha256',
|
||||
'parent_snapshot_sha256', 'eligibility_v2_source_sha256'}
|
||||
m._require(set(meta) == expected, 'learned manifest fields differ from real runtime contract')
|
||||
return None, value, meta, {}
|
||||
|
||||
def run(self, out=None):
|
||||
self.asserted_loads = 0
|
||||
parsed = m._authority(self.authority_path, m._digest(self.authority_path))
|
||||
c = SimpleNamespace(load_checkpoint=self.load_checkpoint)
|
||||
r = SimpleNamespace(PINS={}, validate_spec=lambda value, _: value,
|
||||
load_overlay=self.load_overlay, _raw=self.raw)
|
||||
return m._coordinate(*parsed, self.output_parent / 'result' if out is None else out,
|
||||
(c, r, update), (5, 6, 2, 2))
|
||||
|
||||
|
||||
class EpochPostprocessing(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.tmp = tempfile.TemporaryDirectory()
|
||||
self.f = TinyFixture(Path(self.tmp.name).resolve())
|
||||
|
||||
def tearDown(self):
|
||||
self.tmp.cleanup()
|
||||
|
||||
def test_exclusive_complete_output_and_distinct_parent_hashes(self):
|
||||
chain = self.f.run()
|
||||
output = self.f.output_parent / 'result'
|
||||
self.assertEqual({p.name for p in output.iterdir()}, {'delta-after.npz', 'overlay-manifest.json', 'update-receipt.json', 'chain-receipt.json'})
|
||||
meta = m._read(output / 'overlay-manifest.json')
|
||||
self.assertEqual(meta['parent_snapshot_sha256'], self.f.value['files']['overlay_before']['sha256'])
|
||||
self.assertNotEqual(meta['parent_snapshot_sha256'], self.f.value['checkpoint_manifest_sha256'])
|
||||
self.assertEqual(chain['before_overlay_manifest_sha256'], self.f.value['files']['overlay_manifest_before']['sha256'])
|
||||
self.assertEqual(chain['source_neural_checkpoint_manifest_sha256'], self.f.value['checkpoint_manifest_sha256'])
|
||||
self.assertEqual(self.f.asserted_loads, 2)
|
||||
self.assertFalse(chain['weights_installed'])
|
||||
self.assertFalse(chain['registration_authority_independently_proven'])
|
||||
after = m._npz(output / 'delta-after.npz', ('edge_id', 'plastic_delta_mv'))
|
||||
self.assertLess(after['plastic_delta_mv'][0], 0)
|
||||
self.assertEqual(after['plastic_delta_mv'][1].view(np.uint32), 0)
|
||||
with self.assertRaisesRegex(ValueError, 'Output must be new'):
|
||||
self.f.run()
|
||||
|
||||
def test_every_external_sha_before_processing(self):
|
||||
with self.assertRaisesRegex(ValueError, 'Authority differs'):
|
||||
m._authority(self.f.authority_path, '0' * 64)
|
||||
with (self.f.inputs / 'mapping.npz').open('ab') as stream:
|
||||
stream.write(b'changed')
|
||||
with self.assertRaisesRegex(ValueError, 'SHA mismatch'):
|
||||
self.f.run()
|
||||
self.assertFalse((self.f.output_parent / 'result').exists())
|
||||
|
||||
def test_sealed_event_substitution_and_unbound_case_are_rejected(self):
|
||||
path = self.f.checkpoint / 'runtime-provenance.json'
|
||||
provenance = m._read(path)
|
||||
provenance['input_sha256'][str(self.f.case / 'requests.npz')] = 'f' * 64
|
||||
save(path, provenance)
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'did not bind authorized requests'):
|
||||
self.f.run()
|
||||
|
||||
def test_changed_runtime_overlay_cannot_match_only_its_own_seal(self):
|
||||
(self.f.checkpoint / 'overlay-input.npz').write_bytes(b'different')
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'used a different overlay'):
|
||||
self.f.run()
|
||||
|
||||
def test_candidate_endpoint_and_canonical_kc_must_really_match(self):
|
||||
path = self.f.inputs / 'mapping.npz'
|
||||
mapping = m._npz(path, ('edge_id', 'source_neuron_idx', 'target_neuron_idx', 'target_compartment_verbatim'))
|
||||
mapping['target_neuron_idx'][0] = 4
|
||||
np.savez_compressed(path, **mapping)
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'endpoints differ'):
|
||||
self.f.run()
|
||||
mapping['target_neuron_idx'][0] = 2
|
||||
np.savez_compressed(path, **mapping)
|
||||
np.save(self.f.inputs / 'kc.npy', np.array([1, 4], np.int32))
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'KC differs'):
|
||||
self.f.run()
|
||||
|
||||
def test_command_steps_and_full_frozen_inventory_required(self):
|
||||
self.f.value['command']['steps'] = 5
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'Command steps differ'):
|
||||
self.f.run()
|
||||
self.f.value['command']['steps'] = 4
|
||||
del self.f.value['frozen_inputs'][str(self.f.graph / 'signed_raw.npy')]
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'authority is incomplete'):
|
||||
self.f.run()
|
||||
|
||||
def test_protected_directories_and_existing_output_fail_before_write(self):
|
||||
for path in (self.f.checkpoint / 'new', self.f.inputs / 'new', self.f.graph / 'new',
|
||||
self.f.authority_dir / 'new', self.f.checkpoint):
|
||||
with self.subTest(path=path), self.assertRaisesRegex(ValueError, 'Output must be new'):
|
||||
self.f.run(path)
|
||||
|
||||
def test_partial_is_preserved_and_not_retryable(self):
|
||||
with patch.object(self.f, 'load_overlay', side_effect=[
|
||||
self.f.load_overlay(self.f.inputs / 'before.npz', self.f.inputs / 'before.json'),
|
||||
ValueError('synthetic output validation failed')]):
|
||||
with self.assertRaisesRegex(ValueError, 'output validation'):
|
||||
self.f.run()
|
||||
output = self.f.output_parent / 'result'
|
||||
self.assertTrue((output / 'postprocess-error.json').is_file())
|
||||
self.assertTrue((output / 'delta-after.npz').is_file())
|
||||
self.assertFalse((output / 'chain-receipt.json').exists())
|
||||
with self.assertRaisesRegex(ValueError, 'Output must be new'):
|
||||
self.f.run()
|
||||
|
||||
def test_actual_pinned_dependencies_load_without_gpu(self):
|
||||
_, paths, pins, _, _ = m._authority(self.f.authority_path, m._digest(self.f.authority_path))
|
||||
c, r, u = m._production_dependencies(paths, pins)
|
||||
self.assertEqual(Path(r.__file__).resolve(), paths['runtime_light_source'])
|
||||
self.assertEqual(c.N, 166700)
|
||||
self.assertEqual(u.BRIDGE_SHA256, update.BRIDGE_SHA256)
|
||||
for role in m.SOURCE_PINS:
|
||||
self.assertEqual(m._digest(paths[role]), m.SOURCE_PINS[role])
|
||||
|
||||
def test_production_dimensions_cannot_be_set_by_authority(self):
|
||||
self.assertEqual(m.DIMENSIONS, (166700, 25582938, 4064, 14232))
|
||||
self.f.value['small_test_mode'] = True
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'fields/schema'):
|
||||
m.postprocess_epoch(self.f.authority_path, m._digest(self.f.authority_path), self.f.output_parent / 'result')
|
||||
|
||||
def test_public_entry_rejects_tiny_population_even_with_valid_fixture_seal(self):
|
||||
self.f.asserted_loads = 0
|
||||
c = SimpleNamespace(load_checkpoint=self.f.load_checkpoint)
|
||||
r = SimpleNamespace(PINS={}, validate_spec=lambda value, _: value,
|
||||
load_overlay=self.f.load_overlay, _raw=self.f.raw)
|
||||
with patch.object(m, '_production_dependencies', return_value=(c, r, update)):
|
||||
with self.assertRaisesRegex(ValueError, 'Runtime population/mode'):
|
||||
m.postprocess_epoch(self.f.authority_path, m._digest(self.f.authority_path), self.f.output_parent / 'result')
|
||||
self.assertFalse((self.f.output_parent / 'result').exists())
|
||||
|
||||
def test_authorized_event_file_cannot_be_an_unsealed_copy(self):
|
||||
other = self.f.inputs / 'state-copy.npz'
|
||||
other.write_bytes((self.f.checkpoint / 'state.npz').read_bytes())
|
||||
self.f.value['files']['state'] = dict(path=str(other), sha256=m._digest(other))
|
||||
self.f.reseal()
|
||||
with self.assertRaisesRegex(ValueError, 'Events must come from this checkpoint'):
|
||||
self.f.run()
|
||||
|
||||
def test_late_input_change_preserves_partial_but_never_complete_chain(self):
|
||||
original = self.f.load_checkpoint
|
||||
calls = 0
|
||||
def late_change(directory, pin):
|
||||
nonlocal calls
|
||||
calls += 1
|
||||
result = original(directory, pin)
|
||||
if calls == 2:
|
||||
with (self.f.graph / 'weights.npy').open('ab') as stream:
|
||||
stream.write(b'changed after outputs were written')
|
||||
return result
|
||||
with patch.object(self.f, 'load_checkpoint', side_effect=late_change):
|
||||
with self.assertRaisesRegex(ValueError, 'SHA mismatch'):
|
||||
self.f.run()
|
||||
output = self.f.output_parent / 'result'
|
||||
self.assertTrue((output / 'postprocess-error.json').is_file())
|
||||
self.assertFalse((output / 'chain-receipt.json').exists())
|
||||
|
||||
def test_zero_d_and_frozen_return_identity_without_installation(self):
|
||||
for index, (d, frozen) in enumerate(((0., False), (1., True))):
|
||||
self.f.value['command'].update(d_command=d, frozen=frozen)
|
||||
self.f.reseal()
|
||||
self.f.run(self.f.output_parent / str(index))
|
||||
value = m._npz(self.f.output_parent / str(index) / 'delta-after.npz', ('edge_id', 'plastic_delta_mv'))
|
||||
self.assertEqual(value['plastic_delta_mv'].tobytes(), self.f.before.tobytes())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -0,0 +1,258 @@
|
||||
"""Synthetic CPU/mocked-CUDA contract tests; never run a real input generator."""
|
||||
import ast
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
import types
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
|
||||
import conditioning_inputs as c
|
||||
|
||||
|
||||
def req(rows):
|
||||
values = np.asarray(rows, np.int32).reshape(-1, 2)
|
||||
return {'tick': values[:, 0].copy(), 'neuron_idx': values[:, 1].copy()}
|
||||
|
||||
|
||||
class FakeTensor:
|
||||
def __init__(self, values, device):
|
||||
self.values = np.array(values, copy=True)
|
||||
self.device, self.dtype = device, self.values.dtype
|
||||
|
||||
def __getitem__(self, key): return FakeTensor(self.values[key], self.device)
|
||||
def __lt__(self, other): return FakeTensor(self.values < other.values, self.device)
|
||||
def cpu(self): return self
|
||||
def numpy(self): return self.values.copy()
|
||||
|
||||
|
||||
class FakeTorch:
|
||||
"""Observable adapter, not a CUDA or random-number emulation."""
|
||||
float32 = np.dtype(np.float32)
|
||||
__version__ = 'SYNTHETIC-CONTRACT-MOCK-NOT-TORCH'
|
||||
version = types.SimpleNamespace(cuda=None)
|
||||
|
||||
def __init__(self, uniforms, truncate=False):
|
||||
self.uniforms = uniforms
|
||||
self.calls, self.seeds, self.truncate = [], [], truncate
|
||||
self.cuda = types.SimpleNamespace(is_available=lambda: True,
|
||||
get_device_name=lambda device: 'SYNTHETIC-NOT-A-REAL-GPU', get_device_capability=lambda device: (0, 0),
|
||||
get_device_properties=lambda device: types.SimpleNamespace())
|
||||
|
||||
def get_default_dtype(self): return self.float32
|
||||
def device(self, name): return name
|
||||
|
||||
def Generator(self, device):
|
||||
owner = self
|
||||
class Generator:
|
||||
def manual_seed(self, value):
|
||||
owner.seeds.append(value); self.seed = value & 0xffffffff if owner.truncate else value
|
||||
def initial_seed(self): return self.seed
|
||||
self.calls.append(('generator', device)); return Generator()
|
||||
|
||||
def as_tensor(self, values, dtype, device):
|
||||
self.calls.append(('probability', np.array(values, copy=True), dtype, device))
|
||||
return FakeTensor(values, device)
|
||||
|
||||
def rand(self, shape, generator, device):
|
||||
self.calls.append(('rand', shape, generator.initial_seed(), device))
|
||||
if shape != self.uniforms.shape: raise AssertionError('Unexpected draw shape')
|
||||
return FakeTensor(self.uniforms, device)
|
||||
|
||||
|
||||
class InputsTests(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.targets = np.array([19, 3], np.int32)
|
||||
self.rates = np.array([5000, 10000], np.float32)
|
||||
|
||||
def test_frozen_probability_dtype_order_signed_zero_and_no_mutation(self):
|
||||
targets = np.arange(6, dtype=np.int32)
|
||||
rates = np.array([0., -0., np.nextafter(np.float32(0), np.float32(1)), .1, 123.456, 10000.], np.float32)
|
||||
before = rates.tobytes(); rates.setflags(write=False)
|
||||
actual = c.probabilities_f32(targets, rates)
|
||||
self.assertEqual(actual.dtype, np.float32)
|
||||
self.assertEqual(actual.tobytes(), (rates * .1 / 1000.).tobytes())
|
||||
self.assertEqual(rates.tobytes(), before)
|
||||
|
||||
def test_uniform_threshold_is_strict_and_bernoulli_not_poisson_counts(self):
|
||||
p = np.array([0, .5, 1], np.float32)
|
||||
u = np.array([[0, .5, np.nextafter(np.float32(1), np.float32(0))], [0, .499, 0]], np.float32)
|
||||
forced = c.forced_from_uniforms(u, p)
|
||||
np.testing.assert_array_equal(forced, [[False, False, True], [False, True, True]])
|
||||
self.assertEqual(forced.dtype, np.bool_)
|
||||
|
||||
def test_original_PN_column_order_then_sparse_tick_global_sort_only(self):
|
||||
forced = np.array([[True, True], [True, False], [False, True]], np.bool_)
|
||||
before = forced.copy(); self.targets.setflags(write=False)
|
||||
result = c.requests_from_forced(forced, self.targets, cs_start_tick=0, cs_stop_tick=3)
|
||||
np.testing.assert_array_equal(result['requests']['tick'], [0, 0, 1, 2])
|
||||
np.testing.assert_array_equal(result['requests']['neuron_idx'], [3, 19, 19, 3])
|
||||
np.testing.assert_array_equal(forced, before)
|
||||
self.assertEqual(result['audit']['duplicate_requests_removed'], 0)
|
||||
|
||||
def test_half_open_window_masks_full_matrix_including_empty_window(self):
|
||||
forced = np.ones((5, 2), np.bool_)
|
||||
result = c.requests_from_forced(forced, self.targets, cs_start_tick=1, cs_stop_tick=4)
|
||||
self.assertEqual(result['audit']['full_matrix_boolean_hits'], 10)
|
||||
self.assertEqual(result['audit']['outside_CS_boolean_hits_masked'], 4)
|
||||
self.assertEqual(set(result['requests']['tick']), {1, 2, 3})
|
||||
empty = c.requests_from_forced(forced, self.targets, cs_start_tick=2, cs_stop_tick=2)
|
||||
self.assertEqual(len(empty['requests']['tick']), 0)
|
||||
self.assertEqual(empty['audit']['outside_CS_boolean_hits_masked'], 10)
|
||||
|
||||
def test_full_63bit_seed_single_full_draw_and_mock_scope_receipt(self):
|
||||
seed = (1 << 62) + (1 << 32) + 19
|
||||
fake = FakeTorch(np.array([[.2, .6], [.7, .7], [.1, .2], [.8, .1], [.1, .1]], np.float32))
|
||||
with patch.object(c, '_load_torch', return_value=fake):
|
||||
result = c.generate_cs_requests(self.targets, self.rates, seed=seed, steps=5, cs_start_tick=1, cs_stop_tick=4,
|
||||
device='cuda:0', max_uniform_draws=10)
|
||||
self.assertEqual(fake.seeds, [seed]); draws = [x for x in fake.calls if x[0] == 'rand']
|
||||
self.assertEqual(draws, [('rand', (5, 2), seed, 'cuda:0')])
|
||||
a = result['audit']; self.assertEqual(a['seed_hex'], f'{seed:016x}')
|
||||
self.assertEqual(a['numpy_version'], np.__version__)
|
||||
self.assertEqual(a['torch_version'], fake.__version__)
|
||||
self.assertEqual(a['device_name'], 'SYNTHETIC-NOT-A-REAL-GPU')
|
||||
self.assertFalse(a['CUDA_sequence_equivalence_tested_here'])
|
||||
self.assertFalse(a['CUDA_high_seed_bit_sequence_influence_tested_here'])
|
||||
self.assertFalse(a['full_old_L_input_identity_claimed'])
|
||||
self.assertEqual(a['uniform_scalar_draws'], 10)
|
||||
self.assertEqual((a['max_uniform_draws'], a['uniform_matrix_bytes']), (10, 40))
|
||||
self.assertEqual(a['device_identity'], {'uuid': None, 'pci_bus_id': None, 'query_error': None})
|
||||
np.testing.assert_array_equal(result['requests']['tick'], [1, 2, 2, 3])
|
||||
np.testing.assert_array_equal(result['requests']['neuron_idx'], [3, 3, 19, 3])
|
||||
json.dumps(a, allow_nan=False)
|
||||
|
||||
def test_high_seed_bits_preserved_and_bad_consumer_detected_before_draw(self):
|
||||
for seed in (0, 17, (1 << 32) + 17, (1 << 63) - 1):
|
||||
fake = FakeTorch(np.zeros((1, 2), np.float32))
|
||||
with patch.object(c, '_load_torch', return_value=fake):
|
||||
result = c.generate_cs_requests(self.targets, self.rates, seed=seed, steps=1, cs_start_tick=0, cs_stop_tick=1,
|
||||
device='cuda:0', max_uniform_draws=2)
|
||||
self.assertEqual(result['audit']['generator_initial_seed'], seed)
|
||||
self.assertEqual(fake.seeds, [seed])
|
||||
fake = FakeTorch(np.zeros((1, 2), np.float32), truncate=True)
|
||||
with patch.object(c, '_load_torch', return_value=fake), self.assertRaisesRegex(ValueError, 'complete semantic seed'):
|
||||
c.generate_cs_requests(self.targets, self.rates, seed=(1 << 32) + 17, steps=1, cs_start_tick=0, cs_stop_tick=1,
|
||||
device='cuda:0', max_uniform_draws=2)
|
||||
self.assertFalse(any(x[0] == 'rand' for x in fake.calls))
|
||||
|
||||
def test_invalid_seed_clock_or_rate_never_imports_torch(self):
|
||||
for seed in (-1, 2**63, True, np.int64(3), 3.):
|
||||
with patch.object(c, '_load_torch') as loader, self.assertRaises(ValueError):
|
||||
c.generate_cs_requests(self.targets, self.rates, seed=seed, steps=5, cs_start_tick=0, cs_stop_tick=5,
|
||||
device='cuda:0', max_uniform_draws=10)
|
||||
loader.assert_not_called()
|
||||
for start, stop in ((-1, 1), (2, 1), (0, 6), (False, 1)):
|
||||
with patch.object(c, '_load_torch') as loader, self.assertRaises(ValueError):
|
||||
c.generate_cs_requests(self.targets, self.rates, seed=3, steps=5, cs_start_tick=start, cs_stop_tick=stop,
|
||||
device='cuda:0', max_uniform_draws=10)
|
||||
loader.assert_not_called()
|
||||
|
||||
def test_invalid_arrays_and_no_clipping_or_duplicate_merging(self):
|
||||
for rates in (np.array([-1, 2], np.float32), np.array([np.nan, 2], np.float32), np.array([np.inf, 2], np.float32),
|
||||
np.array([10001, 2], np.float32), self.rates.astype(np.float64), self.rates[:1]):
|
||||
with self.assertRaises(ValueError): c.probabilities_f32(self.targets, rates)
|
||||
for targets in (np.array([3, 3], np.int32), np.array([-1, 3], np.int32), np.array([166700, 3], np.int32), self.targets.astype(np.int64)):
|
||||
with self.assertRaises(ValueError): c.probabilities_f32(targets, self.rates)
|
||||
with self.assertRaises(ValueError): c.probabilities_f32(self.targets, self.rates, dt_ms=.2)
|
||||
with self.assertRaises(ValueError): c.requests_from_forced(np.ones((2, 2), np.uint8), self.targets, cs_start_tick=0, cs_stop_tick=2)
|
||||
|
||||
def test_no_cpu_fallback_or_changed_default_float_dtype(self):
|
||||
for variant in ('cpu', 'unavailable', 'float64'):
|
||||
fake = FakeTorch(np.zeros((1, 2), np.float32)); device = 'cuda:0'
|
||||
if variant == 'cpu': device = 'cpu'
|
||||
if variant == 'unavailable': fake.cuda.is_available = lambda: False
|
||||
if variant == 'float64': fake.get_default_dtype = lambda: np.dtype(np.float64)
|
||||
with patch.object(c, '_load_torch', return_value=fake), self.assertRaises(ValueError):
|
||||
c.generate_cs_requests(self.targets, self.rates, seed=3, steps=1, cs_start_tick=0, cs_stop_tick=1,
|
||||
device=device, max_uniform_draws=2)
|
||||
self.assertFalse(any(x[0] == 'rand' for x in fake.calls))
|
||||
|
||||
def test_explicit_device_and_budget_required_and_budget_checked_before_torch(self):
|
||||
common = dict(seed=3, steps=5, cs_start_tick=0, cs_stop_tick=1)
|
||||
for missing in ({}, {'device': 'cuda:0'}, {'max_uniform_draws': 10}):
|
||||
with patch.object(c, '_load_torch') as loader, self.assertRaises(TypeError):
|
||||
c.generate_cs_requests(self.targets, self.rates, **common, **missing)
|
||||
loader.assert_not_called()
|
||||
for budget in (0, -1, 9, True, 10., np.int64(10)):
|
||||
with patch.object(c, '_load_torch') as loader, self.assertRaises(ValueError):
|
||||
c.generate_cs_requests(self.targets, self.rates, **common, device='cuda:0', max_uniform_draws=budget)
|
||||
loader.assert_not_called()
|
||||
# Full 5x2 matrix admission, not merely the 1-tick active CS window.
|
||||
fake = FakeTorch(np.zeros((5, 2), np.float32))
|
||||
with patch.object(c, '_load_torch', return_value=fake):
|
||||
result = c.generate_cs_requests(self.targets, self.rates, **common, device='cuda:1', max_uniform_draws=10)
|
||||
self.assertEqual(result['audit']['execution_device'], 'cuda:1')
|
||||
self.assertEqual(result['audit']['uniform_scalar_draws'], 10)
|
||||
|
||||
def test_optional_device_uuid_recorded_as_reported_or_explicitly_unavailable(self):
|
||||
fake = FakeTorch(np.zeros((1, 2), np.float32))
|
||||
fake.cuda.get_device_properties = lambda device: types.SimpleNamespace(uuid='SYNTHETIC-UUID', pci_bus_id='SYNTHETIC-PCI')
|
||||
args = dict(seed=3, steps=1, cs_start_tick=0, cs_stop_tick=1, device='cuda:7', max_uniform_draws=2)
|
||||
with patch.object(c, '_load_torch', return_value=fake):
|
||||
audit = c.generate_cs_requests(self.targets, self.rates, **args)['audit']
|
||||
self.assertEqual(audit['device_identity'], {'uuid': 'SYNTHETIC-UUID', 'pci_bus_id': 'SYNTHETIC-PCI', 'query_error': None})
|
||||
self.assertFalse(audit['device_identity_verified_against_external_pin'])
|
||||
def unavailable(device): raise RuntimeError('Synthetic property unavailable')
|
||||
fake.cuda.get_device_properties = unavailable
|
||||
with patch.object(c, '_load_torch', return_value=fake):
|
||||
audit = c.generate_cs_requests(self.targets, self.rates, **args)['audit']
|
||||
self.assertIsNone(audit['device_identity']['uuid'])
|
||||
self.assertEqual(audit['device_identity']['query_error'], 'RuntimeError: Synthetic property unavailable')
|
||||
|
||||
def test_returned_audited_arrays_are_owned_readonly_and_inputs_not_frozen(self):
|
||||
forced = np.ones((2, 2), np.bool_)
|
||||
generated = c.requests_from_forced(forced, self.targets, cs_start_tick=0, cs_stop_tick=2)
|
||||
merged = c.merge_requests(req([[0, 9]]), req([[1, 8]]), steps=2)
|
||||
for value in (*generated['requests'].values(), *merged['requests'].values(), merged['source_mask']):
|
||||
self.assertTrue(value.flags.owndata)
|
||||
self.assertFalse(value.flags.writeable)
|
||||
with self.assertRaises(ValueError): value[0] = 0
|
||||
self.assertTrue(forced.flags.writeable)
|
||||
self.assertTrue(self.targets.flags.writeable)
|
||||
|
||||
def test_empty_targets_and_p0_p1_are_explicit_valid_limits(self):
|
||||
result = c.requests_from_forced(np.zeros((3, 0), np.bool_), np.empty(0, np.int32), cs_start_tick=0, cs_stop_tick=3)
|
||||
self.assertEqual(result['requests']['tick'].dtype, np.int32); self.assertEqual(len(result['requests']['tick']), 0)
|
||||
np.testing.assert_array_equal(c.probabilities_f32(self.targets, np.array([0, 10000], np.float32)), [0, 1])
|
||||
|
||||
def test_merging_disjoint_sources_is_sorted_without_mutating_inputs(self):
|
||||
cs, us = req([[0, 9], [3, 4]]), req([[0, 2], [2, 5]])
|
||||
for value in [*cs.values(), *us.values()]: value.setflags(write=False)
|
||||
result = c.merge_requests(cs, us, steps=4)
|
||||
np.testing.assert_array_equal(result['requests']['tick'], [0, 0, 2, 3])
|
||||
np.testing.assert_array_equal(result['requests']['neuron_idx'], [2, 9, 5, 4])
|
||||
np.testing.assert_array_equal(result['source_mask'], [2, 1, 2, 1])
|
||||
self.assertEqual(result['audit']['explicit_boolean_overlap_collapses'], 0)
|
||||
|
||||
def test_collision_rejected_by_default_or_explicitly_audited_boolean_union(self):
|
||||
cs, us = req([[0, 9], [3, 4]]), req([[0, 9], [2, 5], [3, 4]])
|
||||
with self.assertRaises(c.RequestConflictError) as error: c.merge_requests(cs, us, steps=4)
|
||||
self.assertEqual(error.exception.audit['cross_source_conflict_count'], 2)
|
||||
result = c.merge_requests(cs, us, steps=4, conflict_policy='boolean_union')
|
||||
np.testing.assert_array_equal(result['source_mask'], [3, 2, 3])
|
||||
a = result['audit']; self.assertEqual((a['CS_attempts'], a['US_attempts'], a['output_requests']), (2, 3, 3))
|
||||
self.assertEqual(a['explicit_boolean_overlap_collapses'], 2)
|
||||
self.assertEqual([x['combined_boolean_requests'] for x in a['conflicts']], [1, 1])
|
||||
json.dumps(a, allow_nan=False)
|
||||
|
||||
def test_within_source_duplicate_unsorted_and_bad_range_never_deduped(self):
|
||||
for rows in ([[0, 3], [0, 3]], [[1, 3], [0, 3]], [[4, 3]], [[0, -1]]):
|
||||
with self.assertRaises(ValueError): c.merge_requests(req(rows), req([]), steps=4, conflict_policy='boolean_union')
|
||||
with self.assertRaises(ValueError): c.merge_requests(req([]), req([]), steps=4, conflict_policy='clip')
|
||||
|
||||
def test_frozen_AK_pure_conversion_AST_matches_for_full_window(self):
|
||||
local = Path(__file__).resolve().parent.parent / 'apl-timing-validation-v1/encoder_inputs.py'
|
||||
raw = local.read_bytes(); self.assertEqual(hashlib.sha256(raw).hexdigest(), c.AK_ENCODER_SHA)
|
||||
tree = ast.parse(raw); node = next(n for n in tree.body if isinstance(n, ast.FunctionDef) and n.name == 'requests_from_forced')
|
||||
namespace = {'np': np}; exec(compile(ast.Module(body=[node], type_ignores=[]), str(local), 'exec'), namespace)
|
||||
forced = np.array([[True, True], [False, True], [True, False]], np.bool_)
|
||||
expected = namespace['requests_from_forced'](forced, self.targets)
|
||||
actual = c.requests_from_forced(forced, self.targets, cs_start_tick=0, cs_stop_tick=3)['requests']
|
||||
np.testing.assert_array_equal(expected, np.column_stack([actual['tick'], actual['neuron_idx']]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
+308
@@ -0,0 +1,308 @@
|
||||
"""Synthetic CPU array/receipt mutation tests; no files or neural execution."""
|
||||
import copy
|
||||
import hashlib
|
||||
import json
|
||||
import unittest
|
||||
from unittest import mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
import conditioning_preflight_compare as P
|
||||
from test_engineering_compare import synthetic_parts
|
||||
|
||||
|
||||
def rehash(receipt):
|
||||
receipt["payload_sha256"] = P._sha({k: v for k, v in receipt.items() if k != "payload_sha256"})
|
||||
|
||||
|
||||
def pair(*, active=True):
|
||||
reference = synthetic_parts([64], active=active)[0]
|
||||
light = synthetic_parts([64], active=active, traces=False, queues=False)[0]
|
||||
for record, mode in ((reference, "reference"), (light, "light")):
|
||||
summary = record["summary"]
|
||||
summary.update(edges=6, explicit_steps_executed=64, within_trial_weight_updates=False,
|
||||
hidden_early_stop=False, entire_neural_trial_executed=True, record_history=False,
|
||||
state_units="SI volts", first_failure_tick=-1, counts_match_sparse_events=True,
|
||||
total_spikes=len(record["state"]["step"]), requested=len(record["requests"]),
|
||||
accepted_requests=int(record["requests"][:, 3].sum()), rejected_requests=int(record["requests"][:, 4].sum()),
|
||||
full_APL_history_recorded=mode == "reference", full_tick_state_hashes_recorded=mode == "reference",
|
||||
full_queue_hashes_recorded=mode == "reference", full_sequence_bitwise_audit_performed=mode == "reference",
|
||||
arriving_edges=sum(row["count"] for row in reference["queues"]),
|
||||
accepted_arriving_edges=sum(row["accepted_count"] for row in reference["queues"]),
|
||||
input_sha256={"base_weights.npy": "a" * 64, "coefficients.json": "b" * 64,
|
||||
"requests.npz": "c" * 64, "spec.json": "d" * 64})
|
||||
record["terminal_origin"] = "recorded_terminal_queues"
|
||||
return reference, light, reference["requests"][:, :2].astype(np.int32)
|
||||
|
||||
|
||||
def delta_fixture():
|
||||
before = np.zeros(P.CANDIDATE_COUNT, np.float32)
|
||||
after = before.copy()
|
||||
after[1] = np.float32(-.001)
|
||||
receipt = dict(schema="phase4an-conditioning-update-draft-v1",
|
||||
status="DRAFT-COMPUTED-EPOCH-UPDATE-NOT-INSTALLED",
|
||||
source_pins={"eligibility_bridge_sha256": P.ELIGIBILITY_BRIDGE_SHA256, "phase4j_sha256": P.PHASE4J_SHA256},
|
||||
input_arrays={"delta_before": P._evidence(before)}, outputs={"delta_after": P._evidence(after)},
|
||||
command={"D_scheduled": 1.0, "frozen": False},
|
||||
semantics={"application": "epoch_boundary_after_all_recorded_ticks; affects subsequent epoch only",
|
||||
"delta_dtype": "float32", "delta_units": "mV", "D_definition": "artificial scheduled scalar",
|
||||
"actual_DAN_delivery_observed": False, "actual_DAN_delivery_used_to_gate": False},
|
||||
counts={"candidates": P.CANDIDATE_COUNT, "actual_bitwise_changed_edges": 1, "actual_value_changed_edges": 1},
|
||||
weights_installed=False, base_modified=False, brain_dynamics_executed=False,
|
||||
scientific_learning_pass_claimed=False, candidate_generated=False)
|
||||
rehash(receipt)
|
||||
manifest = dict(schema="phase4an-overlay-v1", overlay_name="learned", overlay_sha256="1" * 64,
|
||||
mask_path="/synthetic/mask.npy", mask_sha256=P.MASK_SHA256,
|
||||
base_candidate_path="/synthetic/base.npy", base_candidate_sha256="2" * 64,
|
||||
aj_model_dir="/synthetic/apl-timing-lab-v1", am_overlay_path="/synthetic/overlay.py",
|
||||
am_overlay_sha256=P.AM_OVERLAY_SHA256, parent_snapshot_sha256="3" * 64,
|
||||
eligibility_v2_source_sha256=P.PHASE4J_SHA256)
|
||||
return before, after, after.copy(), receipt, manifest
|
||||
|
||||
|
||||
class EpochPairTests(unittest.TestCase):
|
||||
def setUp(self):
|
||||
# Pure three-neuron fixtures; production constants remain full MaleCNS.
|
||||
self.patches = [mock.patch.object(P, "N", 3), mock.patch.object(P, "E", 6), mock.patch.object(P, "N_KC", 2)]
|
||||
for patch in self.patches:
|
||||
patch.start()
|
||||
self.addCleanup(patch.stop)
|
||||
|
||||
def compare(self, reference, light, requests):
|
||||
return P.compare_epoch_pair(reference, light, expected_requests=requests,
|
||||
expected_steps=64, expected_effective_weight_sha256="e" * 64)
|
||||
|
||||
def test_exact_actual_arrays_with_explicit_light_limits(self):
|
||||
reference, light, requests = pair()
|
||||
report = self.compare(reference, light, requests)
|
||||
self.assertTrue(report["pass_all"], report["failed_checks"])
|
||||
self.assertTrue(report["numerical_exact"])
|
||||
self.assertTrue(report["nonvacuous_activity"])
|
||||
self.assertFalse(report["light_intermediate_states_observed"])
|
||||
self.assertFalse(report["light_per_tick_V_g_ref_identity_claimed"])
|
||||
self.assertEqual(report["recorded_pair_availability"],
|
||||
{"APL_traces": False, "state_hashes": False, "queue_hashes": False})
|
||||
self.assertFalse(report["external_file_seals_process_and_input_authority_verified_here"])
|
||||
self.assertFalse(report["learning_pass_claimed"])
|
||||
self.assertFalse(report["candidate_generated"])
|
||||
json.dumps(report, allow_nan=False)
|
||||
|
||||
def test_equal_quiet_records_are_not_new_activity_or_learning_pass(self):
|
||||
reference, light, requests = pair(active=False)
|
||||
report = self.compare(reference, light, requests)
|
||||
self.assertTrue(report["numerical_exact"])
|
||||
self.assertFalse(report["nonvacuous_activity"])
|
||||
self.assertTrue(report["activity_is_descriptive_not_a_new_gate"])
|
||||
self.assertFalse(report["learning_pass_claimed"])
|
||||
|
||||
def test_signed_zero_final_voltage_difference_fails(self):
|
||||
reference, light, requests = pair()
|
||||
light["state"]["g"][0] = np.float64(0.)
|
||||
report = self.compare(reference, light, requests)
|
||||
self.assertFalse(report["pass_all"])
|
||||
self.assertIn("pair.state.g", report["failed_checks"])
|
||||
|
||||
def test_equal_but_wrong_original_requests_fail(self):
|
||||
reference, light, requests = pair()
|
||||
wrong = requests.copy()
|
||||
wrong[0, 1] = 1
|
||||
report = self.compare(reference, light, wrong)
|
||||
self.assertFalse(report["pass_all"])
|
||||
self.assertIn("reference.input_request_array", report["failed_checks"])
|
||||
self.assertIn("light.input_request_array", report["failed_checks"])
|
||||
|
||||
def test_reference_missing_diagnostics_cannot_be_excused_by_light(self):
|
||||
for key in ("traces", "state_hashes", "queues"):
|
||||
with self.subTest(key=key):
|
||||
reference, light, requests = pair()
|
||||
reference[key] = {} if key == "traces" else None
|
||||
report = self.compare(reference, light, requests)
|
||||
self.assertFalse(report["pass_all"])
|
||||
self.assertIn("reference.full_diagnostics", report["failed_checks"])
|
||||
|
||||
def test_partial_or_corrupt_array_schema_fails_closed(self):
|
||||
reference, light, requests = pair()
|
||||
del reference["traces"]["drive"]
|
||||
with self.assertRaises(ValueError):
|
||||
self.compare(reference, light, requests)
|
||||
reference, light, requests = pair()
|
||||
light["state"]["v"][0] = np.nan
|
||||
with self.assertRaises(ValueError):
|
||||
self.compare(reference, light, requests)
|
||||
|
||||
def test_reported_modes_population_delay_and_scope_fail_closed(self):
|
||||
mutations = (("nodes", 2), ("edges", 7), ("apl_delay_steps", 17),
|
||||
("diagnostic_mode", "reference"), ("first_failure_tick", 0),
|
||||
("within_trial_weight_updates", True), ("explicit_steps_executed", 63),
|
||||
("all_finite", 1), ("parent_checkpoint_manifest_sha256", "f" * 64))
|
||||
for key, value in mutations:
|
||||
with self.subTest(key=key):
|
||||
reference, light, requests = pair()
|
||||
light["summary"][key] = value
|
||||
self.assertFalse(self.compare(reference, light, requests)["pass_all"])
|
||||
|
||||
def test_effective_weight_and_base_coefficient_hash_binding(self):
|
||||
for mutation in ("effective", "base_weights.npy", "coefficients.json"):
|
||||
with self.subTest(mutation=mutation):
|
||||
reference, light, requests = pair()
|
||||
if mutation == "effective":
|
||||
for record in (reference, light):
|
||||
record["summary"]["effective_weights_raw_sha256"] = "f" * 64
|
||||
else:
|
||||
light["summary"]["input_sha256"][mutation] = "f" * 64
|
||||
self.assertFalse(self.compare(reference, light, requests)["pass_all"])
|
||||
|
||||
def test_performance_is_not_compared_and_path_only_spec_identity_is_external(self):
|
||||
reference, light, requests = pair()
|
||||
reference["summary"].update(wall_seconds=1., peak_allocated_vram_bytes=1)
|
||||
light["summary"].update(wall_seconds=99., peak_allocated_vram_bytes=999)
|
||||
light["summary"]["input_sha256"]["spec.json"] = "f" * 64
|
||||
report = self.compare(reference, light, requests)
|
||||
self.assertTrue(report["pass_all"])
|
||||
self.assertFalse(report["external_file_seals_process_and_input_authority_verified_here"])
|
||||
|
||||
def test_declared_light_full_diagnostics_or_derived_terminal_is_rejected(self):
|
||||
for mutation in ("recorded", "origin"):
|
||||
reference, light, requests = pair()
|
||||
if mutation == "recorded":
|
||||
light["summary"]["full_tick_state_hashes_recorded"] = True
|
||||
else:
|
||||
light["terminal_origin"] = "AM_spike_ring_derived_from_fresh_events_APL_bits_from_timing"
|
||||
self.assertFalse(self.compare(reference, light, requests)["pass_all"])
|
||||
|
||||
def test_actual_summary_accounting_cannot_be_self_reported_wrong(self):
|
||||
for key in ("requested", "accepted_requests", "rejected_requests", "total_spikes", "arriving_edges", "accepted_arriving_edges"):
|
||||
with self.subTest(key=key):
|
||||
reference, light, requests = pair()
|
||||
light["summary"][key] += 1
|
||||
self.assertFalse(self.compare(reference, light, requests)["pass_all"])
|
||||
|
||||
def test_invalid_planned_request_seed_arrays_are_not_silently_coerced(self):
|
||||
reference, light, requests = pair()
|
||||
for bad in (requests.astype(np.int64), requests[::-1], np.concatenate((requests[:1], requests))):
|
||||
with self.assertRaises(ValueError):
|
||||
self.compare(reference, light, bad)
|
||||
with self.assertRaises(ValueError):
|
||||
P.compare_epoch_pair(reference, light, expected_requests=requests, expected_steps=True,
|
||||
expected_effective_weight_sha256="e" * 64)
|
||||
|
||||
|
||||
class DeltaBindingTests(unittest.TestCase):
|
||||
def audit(self, values):
|
||||
before, after, installed, receipt, manifest = values
|
||||
return P.audit_delta_binding(before, after, installed, update_receipt=receipt,
|
||||
parent_snapshot_sha256="3" * 64, child_manifest=manifest)
|
||||
|
||||
def test_exact_array_binding_preserves_explicit_external_authority_limits(self):
|
||||
values = delta_fixture()
|
||||
for value in values[:3]:
|
||||
value.setflags(write=False)
|
||||
original = [value.tobytes() for value in values[:3]]
|
||||
result = self.audit(values)
|
||||
self.assertTrue(result["pass_all"], result["failed_checks"])
|
||||
self.assertEqual(result["actual_changed_edges"], 1)
|
||||
for name in ("eligibility_v2_independently_recomputed_here", "full_mask_identity_verified_here",
|
||||
"file_bytes_and_parent_snapshot_authority_verified_here", "installation_in_runtime_verified_here",
|
||||
"actual_DAN_delivery_verified_here", "learning_pass_claimed"):
|
||||
self.assertFalse(result[name])
|
||||
self.assertEqual([value.tobytes() for value in values[:3]], original)
|
||||
json.dumps(result, allow_nan=False)
|
||||
|
||||
def test_parent_overlay_file_sha_is_not_raw_delta_or_checkpoint_sha(self):
|
||||
values = delta_fixture()
|
||||
self.assertNotEqual(values[4]["overlay_sha256"], P._evidence(values[1])["raw_c_order_sha256"])
|
||||
self.assertTrue(self.audit(values)["pass_all"])
|
||||
for false_parent in (P._evidence(values[0])["raw_c_order_sha256"], "4" * 64):
|
||||
values[4]["parent_snapshot_sha256"] = false_parent
|
||||
result = self.audit(values)
|
||||
self.assertFalse(result["pass_all"])
|
||||
self.assertIn("manifest.parent_overlay_file_sha", result["failed_checks"])
|
||||
|
||||
def test_installed_delta_signed_zero_and_row_mutation_rejected(self):
|
||||
for mutation in ("zero", "row"):
|
||||
values = delta_fixture()
|
||||
if mutation == "zero":
|
||||
values[2][0] = np.float32(-0.)
|
||||
else:
|
||||
values[2][1], values[2][2] = values[2][2], values[2][1]
|
||||
result = self.audit(values)
|
||||
self.assertFalse(result["pass_all"])
|
||||
self.assertIn("computed_installed_delta_bitwise", result["failed_checks"])
|
||||
|
||||
def test_rehashed_receipt_must_still_bind_supplied_arrays(self):
|
||||
for location, key in (("input_arrays", "delta_before"), ("outputs", "delta_after")):
|
||||
values = delta_fixture()
|
||||
values[3][location][key]["raw_c_order_sha256"] = "f" * 64
|
||||
rehash(values[3])
|
||||
result = self.audit(values)
|
||||
self.assertFalse(result["pass_all"])
|
||||
self.assertIn("receipt." + key, result["failed_checks"])
|
||||
|
||||
def test_rehashed_false_count_and_boolean_integer_ambiguity_rejected(self):
|
||||
for field, value in (("actual_bitwise_changed_edges", 2), ("actual_value_changed_edges", True), ("candidates", float(P.CANDIDATE_COUNT))):
|
||||
values = delta_fixture()
|
||||
values[3]["counts"][field] = value
|
||||
rehash(values[3])
|
||||
self.assertFalse(self.audit(values)["pass_all"])
|
||||
|
||||
def test_stale_receipt_or_changed_frozen_rule_manifest_rejected(self):
|
||||
for mutation in ("hash", "v2", "mask", "AMhelper", "extra", "wrong_name"):
|
||||
values = delta_fixture()
|
||||
if mutation == "hash":
|
||||
values[3]["payload_sha256"] = "0" * 64
|
||||
elif mutation == "extra":
|
||||
values[4]["unregistered_override"] = True
|
||||
elif mutation == "wrong_name":
|
||||
values[4]["overlay_name"] = "static25"
|
||||
else:
|
||||
key = {"v2": "eligibility_v2_source_sha256", "mask": "mask_sha256", "AMhelper": "am_overlay_sha256"}[mutation]
|
||||
values[4][key] = "0" * 64
|
||||
self.assertFalse(self.audit(values)["pass_all"])
|
||||
|
||||
def test_zero_D_and_frozen_require_exact_no_update_including_signed_zero(self):
|
||||
for mode in ("D0", "frozen"):
|
||||
values = delta_fixture()
|
||||
values[3]["command"]["D_scheduled" if mode == "D0" else "frozen"] = 0.0 if mode == "D0" else True
|
||||
rehash(values[3])
|
||||
result = self.audit(values)
|
||||
self.assertFalse(result["pass_all"])
|
||||
self.assertIn("zero_D_or_frozen_identity", result["failed_checks"])
|
||||
values[1][:] = values[0]
|
||||
values[2][:] = values[0]
|
||||
values[3]["outputs"]["delta_after"] = P._evidence(values[1])
|
||||
values[3]["counts"].update(actual_value_changed_edges=0, actual_bitwise_changed_edges=0)
|
||||
rehash(values[3])
|
||||
self.assertTrue(self.audit(values)["pass_all"])
|
||||
|
||||
def test_actual_DAN_or_immediate_US_update_claim_does_not_match_interface(self):
|
||||
for field, value in (("actual_DAN_delivery_used_to_gate", True), ("actual_DAN_delivery_observed", True),
|
||||
("application", "at US tick"), ("delta_units", "V")):
|
||||
values = delta_fixture()
|
||||
values[3]["semantics"][field] = value
|
||||
rehash(values[3])
|
||||
self.assertFalse(self.audit(values)["pass_all"])
|
||||
|
||||
def test_wrong_dtype_shape_nonfinite_or_positive_delta_rejected(self):
|
||||
values = delta_fixture()
|
||||
with self.assertRaises(ValueError):
|
||||
P.audit_delta_binding(values[0].astype(np.float64), values[1], values[2], update_receipt=values[3],
|
||||
parent_snapshot_sha256="3" * 64, child_manifest=values[4])
|
||||
values = list(delta_fixture())
|
||||
values[0] = values[0][:-1]
|
||||
with self.assertRaises(ValueError):
|
||||
self.audit(values)
|
||||
values = delta_fixture()
|
||||
values[0][0] = np.nan
|
||||
with self.assertRaises(ValueError):
|
||||
self.audit(values)
|
||||
values = delta_fixture()
|
||||
values[1][1] = values[2][1] = np.float32(.001)
|
||||
values[3]["outputs"]["delta_after"] = P._evidence(values[1])
|
||||
rehash(values[3])
|
||||
result = self.audit(values)
|
||||
self.assertFalse(result["pass_all"])
|
||||
self.assertIn("delta.nonpositive", result["failed_checks"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
+229
@@ -0,0 +1,229 @@
|
||||
"""Pure/synthetic preflight tests: no actual rates, CUDA draws, or brain run."""
|
||||
import copy
|
||||
import hashlib
|
||||
import io
|
||||
import json
|
||||
from pathlib import Path
|
||||
import tempfile
|
||||
import types
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
import conditioning_preflight_inputs as p
|
||||
|
||||
|
||||
def synthetic_tables():
|
||||
names = ['synthetic-' + str(i) for i in range(12)]
|
||||
names[6], names[7] = '4-ethylphenol', '3-hexanone'
|
||||
ids = ['synthetic-id-' + str(i) for i in range(12)]
|
||||
panel = pd.DataFrame(dict(odor_id=ids, odor_name=names,
|
||||
split=['calibration'] * 6 + ['heldout'] * 6, mapped_response_sum=np.arange(1, 13, dtype=float),
|
||||
formal_label=[''] * 6 + ['ODOR_A', 'ODOR_B'] + [''] * 4))
|
||||
selection = dict(selected_before_KC_simulation=True, glomeruli_order=['G2', 'G1'],
|
||||
ODOR_A=dict(odor_id=ids[6], odor_name=names[6]), ODOR_B=dict(odor_id=ids[7], odor_name=names[7]))
|
||||
vectors = {oid: np.array([.5, .25], np.float32) for oid in ids}
|
||||
mapping = pd.DataFrame(dict(glomerulus=['G2', 'G2', 'G2', 'G1', 'G1'],
|
||||
mapping_confidence=['exact', 'alias-normalized', 'exact', 'exact', 'unmatched'],
|
||||
neuron_idx=[19., 3., 19., 8., 100.]))
|
||||
config = dict(odor_encoder=dict(max_total_rate_per_glomerulus_hz=400., modes=['physiological', 'matched_energy']))
|
||||
return selection, panel, vectors, mapping, config
|
||||
|
||||
|
||||
def sparse(rows):
|
||||
values = np.asarray(rows, np.int32).reshape(-1, 2)
|
||||
return {'tick': values[:, 0].copy(), 'neuron_idx': values[:, 1].copy()}
|
||||
|
||||
|
||||
class PreflightTests(unittest.TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.plan = p.build_preflight_plan()
|
||||
cls.epoch = cls.plan['selected_epochs'][0]
|
||||
source_parent = Path(__file__).resolve().parent.parent
|
||||
candidates = (source_parent / 'phase4l.py', source_parent / 'models/male-cns-lif/phase4l.py')
|
||||
path = next((candidate for candidate in candidates if candidate.is_file()), None)
|
||||
if path is None:
|
||||
raise FileNotFoundError('Pinned L source missing at both known server/local paths: ' + repr(candidates))
|
||||
cls.source_raw = path.read_bytes()
|
||||
if hashlib.sha256(cls.source_raw).hexdigest() != p.SOURCE_PINS[p.SOURCE_REL]:
|
||||
raise ValueError('Selected server/local L source differs from the frozen SHA')
|
||||
cls.drive, cls.ast_sha = p._extract_drive(cls.source_raw, str(path))
|
||||
cls.tables, _ = p._rate_tables(*synthetic_tables(), cls.drive)
|
||||
|
||||
def test_fixed_full_plan_master_first_two_real_epochs_and_12_calls(self):
|
||||
full = self.plan['full_trial_plan']
|
||||
self.assertEqual(full['plan']['master'], 20260930)
|
||||
self.assertEqual(full['payload_sha256'], 'a8e8620875e8213f3cc3b4f153fc5c06e1351bf1d91c7edaf4f18c1e2cdef62e')
|
||||
self.assertEqual([e['epoch_index'] for e in self.plan['selected_epochs']], [0, 1])
|
||||
self.assertEqual([e['cs']['cue'] for e in self.plan['selected_epochs']], ['A', 'A'])
|
||||
self.assertEqual([e['cs']['seed'] for e in self.plan['selected_epochs']], [5717173809506784725, 4342084263790487177])
|
||||
self.assertTrue(all(e['us']['requested'] for e in self.plan['selected_epochs']))
|
||||
self.assertEqual(self.plan['total_calls'], 12)
|
||||
self.assertEqual(sum(r['steps'] for r in self.plan['calls']), 14400)
|
||||
self.assertEqual(len({r['row_id'] for r in self.plan['calls']}), 12)
|
||||
self.assertFalse(self.plan['complete_conditioning_authorized'])
|
||||
|
||||
def test_plan_rows_share_inputs_but_keep_D_and_freeze_distinct(self):
|
||||
for row in self.plan['calls']:
|
||||
self.assertEqual(row['epoch_id'], self.plan['selected_epochs'][row['input_epoch_index']]['epoch_id'])
|
||||
self.assertEqual(row['d_command'], 0. if row['arm'] == 'D0' else 1.)
|
||||
self.assertEqual(row['frozen'], row['arm'] == 'frozen')
|
||||
self.assertEqual(row['previous_delta_row_id'], None if row['input_epoch_index'] == 0 else
|
||||
f"{row['arm']}--{row['diagnostic_mode']}--epoch-000")
|
||||
self.assertEqual(self.plan['input_generation_calls'], 2)
|
||||
|
||||
def test_whole_plan_validator_rejects_wrong_master_and_row_mutation(self):
|
||||
# Reuse an already independently rebuilt expected fixture here; the
|
||||
# production validator calls its full builder, not a count-only gate.
|
||||
with patch.object(p, 'build_preflight_plan', return_value=self.plan) as rebuild:
|
||||
self.assertTrue(p.validate_preflight_plan(copy.deepcopy(self.plan)))
|
||||
bad = copy.deepcopy(self.plan); bad['master'] = 461
|
||||
with self.assertRaises(ValueError): p.validate_preflight_plan(bad)
|
||||
bad = copy.deepcopy(self.plan); bad['calls'][0]['d_command'] = 0.
|
||||
with self.assertRaises(ValueError): p.validate_preflight_plan(bad)
|
||||
self.assertEqual(rebuild.call_count, 3)
|
||||
|
||||
def test_actual_frozen_function_AST_preserves_mapping_order_rates_and_dtype(self):
|
||||
table = self.tables['A']
|
||||
np.testing.assert_array_equal(table['targets'], [19, 3, 8])
|
||||
self.assertEqual(table['targets'].dtype, np.int32)
|
||||
np.testing.assert_array_equal(table['rates_hz'], np.array([100, 100, 100], np.float32))
|
||||
self.assertEqual(table['source_panel_row'], 6)
|
||||
self.assertEqual(table['source_panel_split'], 'heldout') # original static label only
|
||||
self.assertFalse(table['targets'].flags.writeable)
|
||||
self.assertFalse(table['rates_hz'].flags.writeable)
|
||||
self.assertEqual(hashlib.sha256(self.source_raw).hexdigest(), p.SOURCE_PINS[p.SOURCE_REL])
|
||||
self.assertEqual(len(self.ast_sha), 64)
|
||||
|
||||
def test_static_mapping_fails_closed_on_selection_order_schema_or_duplicate_PN(self):
|
||||
for kind in ('name', 'split', 'vector', 'duplicate'):
|
||||
args = list(synthetic_tables())
|
||||
if kind == 'name': args[0]['ODOR_A']['odor_name'] = 'different'
|
||||
if kind == 'split': args[1].loc[0, 'split'] = 'heldout'
|
||||
if kind == 'vector': args[2][args[0]['ODOR_A']['odor_id']] = np.array([np.nan, .25], np.float32)
|
||||
if kind == 'duplicate': args[3].loc[3, 'neuron_idx'] = 19.
|
||||
with self.assertRaises(ValueError): p._rate_tables(*args, type(self).drive)
|
||||
|
||||
def test_loader_checks_all_static_bytes_then_uses_original_AST_no_old_import(self):
|
||||
selection, panel, vectors, mapping, config = synthetic_tables()
|
||||
buffer = io.BytesIO(); np.savez(buffer, glomeruli=np.array(selection['glomeruli_order']), **vectors)
|
||||
payload = {
|
||||
p.SOURCE_REL: self.source_raw, p.CONFIG_REL: json.dumps(config).encode(),
|
||||
p.STATIC + 'odor-encoder/frozen-odor-panel.parquet': b'SYNTHETIC-PANEL',
|
||||
p.STATIC + 'odor-encoder/mapped-glomerular-vectors.npz': buffer.getvalue(),
|
||||
p.STATIC + 'odor-encoder/selection.json': json.dumps(selection).encode(),
|
||||
p.STATIC + 'receptor-mapping/olfactory-mapping.parquet': b'SYNTHETIC-MAPPING',
|
||||
}
|
||||
pins = {k: hashlib.sha256(v).hexdigest() for k, v in payload.items()}
|
||||
with tempfile.TemporaryDirectory() as td:
|
||||
for name, raw in payload.items():
|
||||
target = Path(td) / name; target.parent.mkdir(parents=True, exist_ok=True); target.write_bytes(raw)
|
||||
# Synthetic config is JSON (a YAML subset); local runtime lacks
|
||||
# PyYAML. This mocks parsing only, not any scientific input/RNG.
|
||||
yaml_mock = types.SimpleNamespace(safe_load=json.loads)
|
||||
with patch.object(p, 'SOURCE_PINS', pins), patch.object(pd, 'read_parquet', side_effect=[panel, mapping]), \
|
||||
patch.dict('sys.modules', {'yaml': yaml_mock}):
|
||||
result = p.load_rate_tables(td)
|
||||
self.assertEqual(result['audit']['static_sources'], pins)
|
||||
self.assertFalse(result['audit']['historical_dynamic_results_read'])
|
||||
self.assertEqual(result['audit']['random_draws'], 0)
|
||||
self.assertEqual(result['tables']['B']['odor_name'], '3-hexanone')
|
||||
(Path(td) / p.CONFIG_REL).write_bytes(b'CHANGED')
|
||||
with patch.object(p, 'SOURCE_PINS', pins), patch.object(pd, 'read_parquet') as parse, \
|
||||
patch.dict('sys.modules', {'yaml': yaml_mock}), self.assertRaises(ValueError):
|
||||
p.load_rate_tables(td)
|
||||
parse.assert_not_called()
|
||||
|
||||
def test_generate_delegates_full_shape_full_seed_and_shared_US_without_real_draw(self):
|
||||
fake = dict(requests=sparse([[2, 19], [999, 8]]), audit={'synthetic_sampler': True})
|
||||
dan = np.array([80, 90], np.int32)
|
||||
with patch.object(p.inputs, 'generate_cs_requests', return_value=fake) as sampler:
|
||||
result = p.generate_epoch_inputs(self.epoch, self.tables, dan, device='cuda:1', max_uniform_draws=3600)
|
||||
args, kwargs = sampler.call_args
|
||||
self.assertIs(args[0], self.tables['A']['targets'])
|
||||
self.assertEqual(kwargs, dict(seed=5717173809506784725, steps=1200, cs_start_tick=0, cs_stop_tick=1000,
|
||||
device='cuda:1', max_uniform_draws=3600))
|
||||
np.testing.assert_array_equal(result['requests']['tick'], [2, 900, 900, 999])
|
||||
np.testing.assert_array_equal(result['requests']['neuron_idx'], [19, 80, 90, 8])
|
||||
self.assertTrue(result['audit']['US_shared_across_arms'])
|
||||
self.assertFalse(result['audit']['brain_reset_called'])
|
||||
|
||||
def test_explicit_conflict_union_and_default_error_preserve_US_attempts(self):
|
||||
fake = dict(requests=sparse([[900, 19]]), audit={'synthetic_sampler': True})
|
||||
with patch.object(p.inputs, 'generate_cs_requests', return_value=fake):
|
||||
with self.assertRaises(p.inputs.RequestConflictError):
|
||||
p.generate_epoch_inputs(self.epoch, self.tables, np.array([19], np.int32), device='cuda:0', max_uniform_draws=3600)
|
||||
value = p.generate_epoch_inputs(self.epoch, self.tables, np.array([19], np.int32),
|
||||
device='cuda:0', max_uniform_draws=3600, conflict_policy='boolean_union')
|
||||
self.assertEqual(value['audit']['composition']['cross_source_conflict_count'], 1)
|
||||
np.testing.assert_array_equal(value['source_mask'], [3])
|
||||
|
||||
def test_no_US_epoch_still_draws_CS_but_no_DAN_pulse(self):
|
||||
epoch = copy.deepcopy(self.epoch)
|
||||
epoch['us'].update(requested=False, tick=None, request_rule='none'); epoch['D_command'] = 0.
|
||||
with patch.object(p.inputs, 'generate_cs_requests', return_value=dict(requests=sparse([]), audit={})):
|
||||
value = p.generate_epoch_inputs(epoch, self.tables, np.array([80], np.int32), device='cuda:0', max_uniform_draws=3600)
|
||||
self.assertEqual(len(value['us_requests']['tick']), 0)
|
||||
|
||||
def test_invalid_targets_and_mutated_rates_rejected_before_sampling(self):
|
||||
with patch.object(p.inputs, 'generate_cs_requests') as sampler:
|
||||
for dan in (np.array([2, 2], np.int32), np.array([9, 2], np.int32), np.array([], np.int32), np.array([2], np.int64)):
|
||||
with self.assertRaises(ValueError):
|
||||
p.generate_epoch_inputs(self.epoch, self.tables, dan, device='cuda:0', max_uniform_draws=3600)
|
||||
tables = copy.deepcopy(self.tables); tables['A']['rates_hz'][0] = 3.
|
||||
with self.assertRaises(ValueError):
|
||||
p.generate_epoch_inputs(self.epoch, tables, np.array([80], np.int32), device='cuda:0', max_uniform_draws=3600)
|
||||
sampler.assert_not_called()
|
||||
|
||||
def original_spec(self):
|
||||
return dict(name='old-case', kind='fullgraph', steps=1000, dt_ms=.1, duration_ms=100.,
|
||||
model={'immutable': 'synthetic model'}, graph_dir='/old/graph', apl_bundle_dir='/old/apl',
|
||||
coefficients_path='/old/coeff.json', science_python='/old/venv/bin/python',
|
||||
apl_delay_steps=18, precision='float64', apl_precision='float32', record_history=False,
|
||||
training=False, validation_cohort='old_cohort', odor_index=0, expected_external_events=123.,
|
||||
odor_id='old-id', odor_name='old-name', source_panel_row=0, source_panel_split='calibration',
|
||||
rate_sum_hz=999., target_count=100, seed=22, budget_root='/old/experiment', output_budget_bytes=100)
|
||||
|
||||
def test_derive_spec_changes_only_explicit_input_scope_and_clock_budget_fields(self):
|
||||
old = self.original_spec(); before = copy.deepcopy(old)
|
||||
value, audit = p.derive_case_spec(old, self.plan['calls'][0], self.epoch, self.tables['A'],
|
||||
brain_reset_seed=20260930, budget_root='/new/preflight', output_budget_bytes=10000)
|
||||
self.assertEqual(old, before)
|
||||
self.assertEqual((value['steps'], value['duration_ms'], value['max_steps']), (1200, 120., 1200))
|
||||
self.assertEqual(value['seed'], 20260930)
|
||||
self.assertEqual(value['cs_input_seed'], 5717173809506784725)
|
||||
for key in ('model', 'graph_dir', 'apl_bundle_dir', 'coefficients_path', 'science_python'):
|
||||
self.assertEqual(value[key], old[key])
|
||||
self.assertFalse(value['within_trial_weight_updates'])
|
||||
self.assertEqual(value['odor_name'], '4-ethylphenol')
|
||||
self.assertNotIn('validation_cohort', value); self.assertNotIn('training', value)
|
||||
self.assertEqual(set(audit['removed_stale_input_metadata']), {'validation_cohort', 'training', 'odor_index', 'expected_external_events'})
|
||||
self.assertFalse(audit['full_conditioning_authorized'])
|
||||
json.dumps(audit, allow_nan=False)
|
||||
|
||||
def test_seed_or_neural_continuation_or_budget_alias_invalid(self):
|
||||
args = dict(brain_reset_seed=3, budget_root='/new/preflight', output_budget_bytes=10000)
|
||||
for seed in (-1, 2**31, self.epoch['cs']['seed'], True, np.int32(3)):
|
||||
with self.assertRaises(ValueError):
|
||||
p.derive_case_spec(self.original_spec(), self.plan['calls'][0], self.epoch, self.tables['A'], **{**args, 'brain_reset_seed': seed})
|
||||
for root in ('/old/experiment/new', '/old', '/new/../old', 'relative'):
|
||||
with self.assertRaises(ValueError):
|
||||
p.derive_case_spec(self.original_spec(), self.plan['calls'][0], self.epoch, self.tables['A'], **{**args, 'budget_root': root})
|
||||
old = self.original_spec(); old['parent_checkpoint_manifest_sha256'] = 'a' * 64
|
||||
with self.assertRaises(ValueError): p.derive_case_spec(old, self.plan['calls'][0], self.epoch, self.tables['A'], **args)
|
||||
|
||||
def test_row_command_mismatch_and_rate_label_mismatch_fail_closed(self):
|
||||
row = copy.deepcopy(self.plan['calls'][0]); row['d_command'] = 0.
|
||||
with self.assertRaises(ValueError):
|
||||
p.derive_case_spec(self.original_spec(), row, self.epoch, self.tables['A'],
|
||||
brain_reset_seed=3, budget_root='/new/preflight', output_budget_bytes=10000)
|
||||
with self.assertRaises(ValueError):
|
||||
p.derive_case_spec(self.original_spec(), self.plan['calls'][0], self.epoch, self.tables['B'],
|
||||
brain_reset_seed=3, budget_root='/new/preflight', output_budget_bytes=10000)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
+109
@@ -0,0 +1,109 @@
|
||||
"""CPU-only coordinator contract tests; no real brain/input sampling."""
|
||||
import ast
|
||||
from pathlib import Path
|
||||
import tempfile
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
|
||||
import conditioning_preflight_prepare as P
|
||||
import conditioning_preflight_workflow as W
|
||||
|
||||
|
||||
class CoordinatorTests(unittest.TestCase):
|
||||
def test_npz_exclusive_roundtrip(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
path = Path(d) / 'x.npz'
|
||||
a = np.asarray([0, 3], np.int32)
|
||||
P.save_npz(path, tick=a)
|
||||
self.assertEqual(W.npz(path)['tick'].tobytes(), a.tobytes())
|
||||
with self.assertRaises(FileExistsError): P.save_npz(path, tick=a)
|
||||
|
||||
def test_exact_pins_and_mutation(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
p = Path(d) / 'input'
|
||||
p.write_bytes(b'original')
|
||||
pins = {str(p): P.digest(p)}
|
||||
P.verify_pins(pins)
|
||||
p.write_bytes(b'changed')
|
||||
with self.assertRaisesRegex(ValueError, 'Pinned file'): P.verify_pins(pins)
|
||||
|
||||
def test_rows_exact_unique(self):
|
||||
row = dict(row_id='D1--reference--epoch-000')
|
||||
self.assertEqual(W.row_by({'plan': {'calls': [row]}}, row['row_id']), row)
|
||||
for rows in ([], [row, row]):
|
||||
with self.assertRaises(ValueError): W.row_by({'plan': {'calls': rows}}, row['row_id'])
|
||||
|
||||
def test_locations_reject_path_traversal(self):
|
||||
for value in ('../x', '/x', 'a/b', 'a\\b', '', None):
|
||||
with self.assertRaises(ValueError): W.locations(value)
|
||||
|
||||
def test_plan_descriptor_avoids_huge_payload_duplication(self):
|
||||
value = dict(full_trial_plan={'large': [1]}, payload_sha256='x', calls=[{'row_id': 'a'}])
|
||||
self.assertEqual(P.plan_descriptor(value), {'calls': [{'row_id': 'a'}]})
|
||||
self.assertIn('full_trial_plan', value)
|
||||
|
||||
def test_original_AST_rejects_unanchored_bytes(self):
|
||||
with self.assertRaisesRegex(ValueError, 'L source changed'):
|
||||
W.original_sampler_nodes(b'def run_rates(): pass')
|
||||
|
||||
def test_original_AST_if_local_frozen_source_available(self):
|
||||
# Both repository checkout and local source mirror are supported; this
|
||||
# test never reads historical dynamics or executes the extracted code.
|
||||
candidates = [Path(__file__).resolve().parents[1] / 'phase4l.py',
|
||||
Path(__file__).resolve().parents[1] / 'models/male-cns-lif/phase4l.py']
|
||||
source = next((p for p in candidates if p.is_file()), None)
|
||||
self.assertIsNotNone(source, 'Original L source required for actual AST extraction test')
|
||||
nodes = W.original_sampler_nodes(source.read_bytes())
|
||||
self.assertEqual(len(nodes), 4)
|
||||
self.assertNotIn('reset', ast.unparse(ast.Module(body=nodes, type_ignores=[])))
|
||||
|
||||
def test_registration_external_hash_rejection_before_parse(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
exp = Path(d); (exp / 'protocol').mkdir()
|
||||
P.save(exp / 'protocol/preregistered.json', {'fake': True})
|
||||
with patch.object(P, 'EXP', exp):
|
||||
with self.assertRaisesRegex(ValueError, 'external anchor'):
|
||||
P.read_registration('0' * 64)
|
||||
|
||||
def test_initial_overlay_has_no_parent_process_lookup(self):
|
||||
expected = dict(overlay_file='/immutable/zero.npz', overlay_manifest='/immutable/zero.json')
|
||||
self.assertIs(W.before_overlay({'initial_overlay': expected}, 'unused', {'previous_delta_row_id': None}), expected)
|
||||
|
||||
def test_wrong_parent_arm_rejected_before_runtime_read(self):
|
||||
parent = dict(row_id='parent', arm='D0', diagnostic_mode='light', input_epoch_index=0)
|
||||
child = dict(previous_delta_row_id='parent', arm='D1', diagnostic_mode='light', input_epoch_index=1)
|
||||
with self.assertRaisesRegex(ValueError, 'cross-epoch'):
|
||||
W.before_overlay({'plan': {'calls': [parent]}}, 'unused', child)
|
||||
|
||||
def test_budget_refuses_overrun_without_delete(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
exp = Path(d); p = exp / 'keep'
|
||||
p.write_bytes(b'1234')
|
||||
with patch.object(W, 'EXP', exp):
|
||||
with self.assertRaisesRegex(ValueError, 'Resource cap'):
|
||||
W.budget({'budget': {'output_budget_bytes': 1, 'minimum_free_reserve_bytes': 1}})
|
||||
self.assertEqual(p.read_bytes(), b'1234')
|
||||
|
||||
def test_partial_log_is_not_retried(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
exp = Path(d); (exp / 'logs').mkdir()
|
||||
(exp / 'logs/input-generation.stdout.log').write_bytes(b'partial')
|
||||
with patch.object(W, 'EXP', exp), patch.object(W.subprocess, 'run') as run:
|
||||
with self.assertRaisesRegex(ValueError, 'never retry'): W.child_process({}, 'sha', 'materialize')
|
||||
run.assert_not_called()
|
||||
|
||||
def test_admission_blocks_stopped_complete_or_sealed(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
exp = Path(d); (exp / 'audit').mkdir()
|
||||
with patch.object(W, 'EXP', exp):
|
||||
W.admission()
|
||||
for name in ('audit/stopped.json', 'audit/execution-complete.json', 'manifest.json'):
|
||||
p = exp / name; p.write_bytes(b'{}')
|
||||
with self.assertRaisesRegex(ValueError, 'Stopped/completed'): W.admission()
|
||||
p.unlink()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user