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Current d5 retained; publication score covers all130conditions with equal d3/d5 weights.", + "choices": { + "incumbent": 0.2397442157451923, + "fresh-homogeneous-ema": 0.24002779447115385, + "fresh-mixed-raw": 0.2400165264423077, + "fresh-mixed-ema": 0.23996394230769227, + "independent-homogeneous-ema": 0.24005784254807694, + "independent-mixed-raw": 0.24025315504807693, + "independent-mixed-ema": 0.2401630108173077, + "shared-homogeneous-ema": 0.24015925480769232, + "shared-mixed-raw": 0.24014610877403844, + "shared-mixed-ema": 0.24009915865384615 + }, + "selected": "incumbent" + } + ] + } + ] + }, + { + "name": "d3_conditional_edge_brier1", + "expected_P": 0.7677480844350961, + "failure_by_distance": { + "d3": 0.2397442157451923, + "d5": 0.22475961538461536 + }, + "completion_record": { + "complete": true, + "published": false, + "expected_P": 0.7677480844350961, + "performance": null + }, + "selection_records": [ + { + "component": "neural_d5", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23212139423076927, + "0": 0.23353365384615382, + "blend-0": 0.23306790865384613, + "512": 0.23380408653846152, + "blend-512": 0.23366887019230773, + "1024": 0.23197115384615388, + "blend-1024": 0.23299278846153842, + "1536": 0.23206129807692305, + "blend-1536": 0.2329326923076923 + }, + "selected": "1024" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0": 0.22103365384615387, + "blend-0": 0.2211388221153846, + "512": 0.22092848557692304, + "blend-512": 0.22094350961538464, + "1024": 0.2204326923076923, + "blend-1024": 0.22046274038461539, + "1536": 0.22086838942307688, + "blend-1536": 0.22059795673076926 + }, + "selected": "incumbent" + } + ] + }, + { + "component": "neural_probability_ensemble", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.2319861778846154, + "0-physical-0": 0.23278245192307695, + "0-physical-0.25": 0.23291766826923077, + "0-physical-0.5": 0.23315805288461539, + "512-physical-0": 0.2319110576923077, + "512-physical-0.25": 0.23314302884615387, + "512-physical-0.5": 0.2329477163461538, + "1024-physical-0": 0.2325721153846154, + "1024-physical-0.25": 0.23218149038461536, + "1024-physical-0.5": 0.2329627403846154, + "1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "scope": "Only raw2048/4096/8192 plus then-currentincumbent. OneGLOBALd3choice acrossall104conditions8groups13durations. No subgroup/blending/EMA/calibration/additionalchoices.", + "choices": { + "incumbent": 0.2397442157451923, + "2048": 0.24002779447115388, + "4096": 0.24027944711538465, + "8192": 0.24046724759615384 + }, + "selected": "incumbent" + } + ] + } + ] + }, + { + "name": "d3_conditional_edge_projection1", + "expected_P": 0.7677480844350961, + "failure_by_distance": { + "d3": 0.2397442157451923, + "d5": 0.22475961538461536 + }, + "completion_record": { + "complete": true, + "published": false, + "expected_P": 0.7677480844350961, + "performance": null + }, + "selection_records": [ + { + "component": "neural_d5", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23212139423076927, + "0": 0.23353365384615382, + "blend-0": 0.23306790865384613, + "512": 0.23380408653846152, + "blend-512": 0.23366887019230773, + "1024": 0.23197115384615388, + "blend-1024": 0.23299278846153842, + "1536": 0.23206129807692305, + "blend-1536": 0.2329326923076923 + }, + "selected": "1024" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0": 0.22103365384615387, + "blend-0": 0.2211388221153846, + "512": 0.22092848557692304, + "blend-512": 0.22094350961538464, + "1024": 0.2204326923076923, + "blend-1024": 0.22046274038461539, + "1536": 0.22086838942307688, + "blend-1536": 0.22059795673076926 + }, + "selected": "incumbent" + } + ] + }, + { + "component": "neural_probability_ensemble", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.2319861778846154, + "0-physical-0": 0.23278245192307695, + "0-physical-0.25": 0.23291766826923077, + "0-physical-0.5": 0.23315805288461539, + "512-physical-0": 0.2319110576923077, + "512-physical-0.25": 0.23314302884615387, + "512-physical-0.5": 0.2329477163461538, + "1024-physical-0": 0.2325721153846154, + "1024-physical-0.25": 0.23218149038461536, + "1024-physical-0.5": 0.2329627403846154, + "1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "scope": "Only2048/4096/8192 raw projection states plus then-currentincumbent. One GLOBAL d3choice acrossall104conditions,8groups,13durations. No subgroup,duration,blending,EMA,calibration,intermediate validation screens or additional checkpoint choices.", + "choices": { + "incumbent": 0.2397442157451923, + "2048": 0.24034517728365382, + "4096": 0.24031512920673076, + "8192": 0.24051419771634613 + }, + "selected": "incumbent" + } + ] + } + ] + }, + { + "name": "d3_coupled_pretraining1", + "expected_P": 0.7677480844350961, + "failure_by_distance": { + "d3": 0.2397442157451923, + "d5": 0.22475961538461536 + }, + "completion_record": { + "complete": true, + "published": false, + "expected_P": 0.7677480844350961, + "performance": null + }, + "selection_records": [ + { + "component": "neural_d5", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23212139423076927, + "0": 0.23353365384615382, + "blend-0": 0.23306790865384613, + "512": 0.23380408653846152, + "blend-512": 0.23366887019230773, + "1024": 0.23197115384615388, + "blend-1024": 0.23299278846153842, + "1536": 0.23206129807692305, + "blend-1536": 0.2329326923076923 + }, + "selected": "1024" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0": 0.22103365384615387, + "blend-0": 0.2211388221153846, + "512": 0.22092848557692304, + "blend-512": 0.22094350961538464, + "1024": 0.2204326923076923, + "blend-1024": 0.22046274038461539, + "1536": 0.22086838942307688, + "blend-1536": 0.22059795673076926 + }, + "selected": "incumbent" + } + ] + }, + { + "component": "neural_probability_ensemble", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.2319861778846154, + "0-physical-0": 0.23278245192307695, + "0-physical-0.25": 0.23291766826923077, + "0-physical-0.5": 0.23315805288461539, + "512-physical-0": 0.2319110576923077, + "512-physical-0.25": 0.23314302884615387, + "512-physical-0.5": 0.2329477163461538, + "1024-physical-0": 0.2325721153846154, + "1024-physical-0.25": 0.23218149038461536, + "1024-physical-0.5": 0.2329627403846154, + "1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "scope": "One GLOBAL d3 choice across 3 arms x 4 checkpoints and incumbent; all 8 groups and 13 durations equal weight. No per-group, duration, blend or calibration selection. Retain current d5 and score all 130 conditions.", + "choices": { + "incumbent": 0.2397442157451923, + "fresh-1024": 0.2400259164663462, + "fresh-2048": 0.24021371694711538, + "fresh-4096": 0.24039400540865385, + "fresh-8192": 0.24004469651442306, + "independent-1024": 0.2400766225961538, + "independent-2048": 0.24041466346153847, + "independent-4096": 0.2404015174278846, + "independent-8192": 0.24015174278846155, + "shared-1024": 0.24007286658653845, + "shared-2048": 0.24045410156250002, + "shared-4096": 0.24063814603365385, + "shared-8192": 0.24024188701923077 + }, + "selected": "incumbent" + } + ] + } + ] + }, + { + "name": "d3_latent_readout1", + "expected_P": 0.7677537184495192, + "failure_by_distance": { + "d3": 0.23973294771634618, + "d5": 0.22475961538461536 + }, + "publication_status": "Verified and selected for this atomic publication", + "full_validation": { + "status": "valid", + "performance": 0.7677537184495192, + "failure_by_distance": { + "d3": 0.23973294771634615, + "d5": 0.22475961538461536 + } + }, + "selection_records": [ + { + "component": "neural_d5", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23212139423076927, + "0": 0.23353365384615382, + "blend-0": 0.23306790865384613, + "512": 0.23380408653846152, + "blend-512": 0.23366887019230773, + "1024": 0.23197115384615388, + "blend-1024": 0.23299278846153842, + "1536": 0.23206129807692305, + "blend-1536": 0.2329326923076923 + }, + "selected": "1024" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0": 0.22103365384615387, + "blend-0": 0.2211388221153846, + "512": 0.22092848557692304, + "blend-512": 0.22094350961538464, + "1024": 0.2204326923076923, + "blend-1024": 0.22046274038461539, + "1536": 0.22086838942307688, + "blend-1536": 0.22059795673076926 + }, + "selected": "incumbent" + } + ] + }, + { + "component": "neural_probability_ensemble", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.2319861778846154, + "0-physical-0": 0.23278245192307695, + "0-physical-0.25": 0.23291766826923077, + "0-physical-0.5": 0.23315805288461539, + "512-physical-0": 0.2319110576923077, + "512-physical-0.25": 0.23314302884615387, + "512-physical-0.5": 0.2329477163461538, + "1024-physical-0": 0.2325721153846154, + "1024-physical-0.25": 0.23218149038461536, + "1024-physical-0.5": 0.2329627403846154, + "1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "scope": "Only one GLOBAL d3 choice among fixed2048/4096/8192checkpoints and then-current incumbent. No per-group/per-duration blends or extra checkpoints. All104d3 conditions evaluated, d5 unchanged; actual assembled replay and all130trusted score decide publication.", + "choices": { + "incumbent": 0.2397442157451923, + "2048": 0.23984374999999997, + "4096": 0.2397742638221154, + "8192": 0.23973294771634618 + }, + "selected": "8192", + "zero_amplitude_eligible": false + } + ] + } + ] + }, + { + "name": "d3_masked_head1", + "expected_P": 0.7675189678485577, + "failure_by_distance": { + "d3": 0.2397442157451923, + "d5": 0.22521784855769234 + }, + "completion_record": { + "complete": true, + "published": false, + "expected_P": 0.7675189678485577, + "performance": null + }, + "selection_records": [ + { + "component": "neural_d5", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23212139423076927, + "0": 0.23353365384615382, + "blend-0": 0.23306790865384613, + "512": 0.23380408653846152, + "blend-512": 0.23366887019230773, + "1024": 0.23197115384615388, + "blend-1024": 0.23299278846153842, + "1536": 0.23206129807692305, + "blend-1536": 0.2329326923076923 + }, + "selected": "1024" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0": 0.22103365384615387, + "blend-0": 0.2211388221153846, + "512": 0.22092848557692304, + "blend-512": 0.22094350961538464, + "1024": 0.2204326923076923, + "blend-1024": 0.22046274038461539, + "1536": 0.22086838942307688, + "blend-1536": 0.22059795673076926 + }, + "selected": "incumbent" + } + ] + }, + { + "component": "neural_probability_ensemble", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.2319861778846154, + "0-physical-0": 0.23278245192307695, + "0-physical-0.25": 0.23291766826923077, + "0-physical-0.5": 0.23315805288461539, + "512-physical-0": 0.2319110576923077, + "512-physical-0.25": 0.23314302884615387, + "512-physical-0.5": 0.2329477163461538, + "1024-physical-0": 0.2325721153846154, + "1024-physical-0.25": 0.23218149038461536, + "1024-physical-0.5": 0.2329627403846154, + "1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "scope": "One global d3 choice, all104conditions; no per-group or blend selection", + "choices": { + "incumbent": 0.2397442157451923, + "head-1024": 0.24104942908653848, + "head-2048": 0.24033766526442307, + "head-4096": 0.23999399038461539 + }, + "selected": "incumbent" + } + ] + } + ] + }, + { + "name": "d3_masked_pretraining1", + "expected_P": 0.7677480844350961, + "failure_by_distance": { + "d3": 0.2397442157451923, + "d5": 0.22475961538461536 + }, + "completion_record": { + "complete": true, + "published": false, + "expected_P": 0.7677480844350961, + "performance": null + }, + "selection_records": [ + { + "component": "neural_d5", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23212139423076927, + "0": 0.23353365384615382, + "blend-0": 0.23306790865384613, + "512": 0.23380408653846152, + "blend-512": 0.23366887019230773, + "1024": 0.23197115384615388, + "blend-1024": 0.23299278846153842, + "1536": 0.23206129807692305, + "blend-1536": 0.2329326923076923 + }, + "selected": "1024" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0": 0.22103365384615387, + "blend-0": 0.2211388221153846, + "512": 0.22092848557692304, + "blend-512": 0.22094350961538464, + "1024": 0.2204326923076923, + "blend-1024": 0.22046274038461539, + "1536": 0.22086838942307688, + "blend-1536": 0.22059795673076926 + }, + "selected": "incumbent" + } + ] + }, + { + "component": "neural_probability_ensemble", + "history": [ + { + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.2319861778846154, + "0-physical-0": 0.23278245192307695, + "0-physical-0.25": 0.23291766826923077, + "0-physical-0.5": 0.23315805288461539, + "512-physical-0": 0.2319110576923077, + "512-physical-0.25": 0.23314302884615387, + "512-physical-0.5": 0.2329477163461538, + "1024-physical-0": 0.2325721153846154, + "1024-physical-0.25": 0.23218149038461536, + "1024-physical-0.5": 0.2329627403846154, + "1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "scope": "Only final8192 raw and EMA weights from each hardware branch are eligible: four new GLOBAL d3 choices plus then-current incumbent, all104conditions/8groups/13durations. No intermediate, subgroup, duration, prediction-blend or calibration choices. 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"1536-physical-0": 0.23186598557692306, + "1536-physical-0.25": 0.23234675480769235, + "1536-physical-0.5": 0.2330829326923077 + }, + "selected": "1536-physical-0", + "original_checkpoint": "full3_swa/mean3" + }, + { + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21953124999999998, + "0-physical-0": 0.22029747596153845, + "0-physical-0.25": 0.22067307692307694, + "0-physical-0.5": 0.22079326923076922, + "512-physical-0": 0.22034254807692305, + "512-physical-0.25": 0.22050781249999998, + "512-physical-0.5": 0.2203725961538462, + "1024-physical-0": 0.21998197115384613, + "1024-physical-0.25": 0.22049278846153844, + "1024-physical-0.5": 0.22031250000000002, + "1536-physical-0": 0.2204026442307692, + "1536-physical-0.25": 0.2201923076923077, + "1536-physical-0.5": 0.22061298076923078 + }, + "selected": "incumbent", + "original_checkpoint": "full3_1/1536" + } + ] + }, + { + "component": "candidate", + "history": [ + { + "basis": "X", + "group": "d5_bX_center_5_5", + "candidates": { + "incumbent": 0.23158052884615385, + "three-0": 0.23170072115384616, + "three-512": 0.23213641826923076, + "three-1024": 0.2317157451923077, + "three-2048": 0.23203124999999997 + }, + "selected": "incumbent" + }, + { + "basis": "Z", + "group": "d5_bZ_center_5_5", + "candidates": { + "incumbent": 0.21915564903846152, + "three-0": 0.2205679086538461, + "three-512": 0.22085336538461536, + "three-1024": 0.22142427884615387, + "three-2048": 0.22017728365384615 + }, + "selected": "incumbent" + } + ] + } + ] + }, + { + "name": "turbo1", + "expected_P": 0.7646089993990385, + "failure_by_distance": { + "d3": 0.2413649338942308, + "d5": 0.2294170673076923 + }, + "publication_status": "No finished.json record; historical candidate status not inferred", + "full_validation": { + "status": "valid", + "performance": 0.7646089993990384, + "failure_by_distance": { + "d3": 0.2413649338942308, + "d5": 0.22941706730769232 + } + }, + "selection_records": [] + } + ] +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/short_syndrome_model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/short_syndrome_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3bd23ad7ad4caa7b03269e10741553b87d9b5a87 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/short_syndrome_model.py @@ -0,0 +1,8 @@ +"""Categorical logical decoder indexed only by an8-bit detection syndrome.""" +import numpy as np +from scipy.special import expit + +def predict(packed,state): + assert packed.dtype==np.uint8 and packed.ndim==2 and packed.shape[1]==1 + key=packed[:,0] + return expit(state['physical_logits'][key]+state['residual_logits'][key]).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_base_contextual.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_base_contextual.py new file mode 100644 index 0000000000000000000000000000000000000000..ab264d20ec2fdc03753a65a3bb24c5e0a3fd7ead --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_base_contextual.py @@ -0,0 +1,32 @@ +"""Portable temporal contextual exact decoder with shot-local features.""" +from pathlib import Path +import numpy as np + + +class TemporalContextualDecoder: + def __init__(self): + import jax + from temporal_model import probabilities + from exact import make_decoder + exact=make_decoder(10) + @jax.jit + def infer(weights,ds,context,static,base_logits,valid,masks,measured,grid,measurement_indices,counts): + p=probabilities(weights,ds,context,static,base_logits,valid,grid,measurement_indices,counts) + return exact(ds,p,masks,measured) + self.infer=infer;self.weights={} + + def predict(self,root,record,group_record,packed,num_detectors,batch=1024): + import jax.numpy as jnp + path=group_record['weights'] + if path not in self.weights: + self.weights[path]={k:jnp.asarray(v) for k,v in dict(np.load(root/path,allow_pickle=False)).items()} + plan=dict(np.load(root/record['rich_plan'],allow_pickle=False)) + ds=np.unpackbits(packed,axis=1,count=num_detectors,bitorder='little') + ds=np.pad(ds,((0,0),(0,201-num_detectors))) + args=[plan[k] for k in ['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts']] + outputs=[] + for begin in range(0,len(ds),batch): + selected=ds[begin:begin+batch];n=len(selected) + selected=np.pad(selected,((0,batch-n),(0,0))) + outputs.append(np.asarray(self.infer(self.weights[path],selected,*args))[:n]) + return np.concatenate(outputs).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_contextual.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_contextual.py new file mode 100644 index 0000000000000000000000000000000000000000..83709b9863c43c8ddcabb241d44a5fcf7acd8849 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_contextual.py @@ -0,0 +1,42 @@ +"""Portable one-quality-chain route; original surrounding routes remain callable.""" +import numpy as np +from d3_pre_latent_temporal import TemporalContextualDecoder as ParentDecoder +from d3_lr_compiled import PersistentCompiled +from d3_lr_core import make_kernels + +class TemporalContextualDecoder(ParentDecoder): + def __init__(self): + import jax + super().__init__();self.latent_weights={};self.latent_groups={};self.latent_plans={} + self.latent_infer=PersistentCompiled(make_kernels(interpret=jax.default_backend()=='cpu')[0],max_executables=32) + def predict(self,root,record,group_record,packed,num_detectors,batch=None): + settings=record.get('d3_latent_readout') + if settings is None: + if batch is None:return super().predict(root,record,group_record,packed,num_detectors) + return super().predict(root,record,group_record,packed,num_detectors,batch) + if batch is None:batch=256 + import jax.numpy as jnp + key=(settings['weights'],settings['group']) + if key not in self.latent_weights: + with np.load(root/settings['weights'],allow_pickle=False) as f:self.latent_weights[key]=jnp.asarray(f[settings['group']]) + group=settings['group'] + if group not in self.latent_groups: + weights=[];coefficients=[] + for member in group_record.get('members',[group_record]): + with np.load(root/member['weights'],allow_pickle=False) as f:weights.append({k:jnp.asarray(f[k]) for k in f.files}) + coefficients.append(member.get('coefficient',1.)) + coefficients=np.asarray(coefficients,np.float32);coefficients/=coefficients.sum() + self.latent_groups[group]=({k:jnp.stack([w[k] for w in weights]) for k in weights[0]},coefficients) + gp=settings['geometry'] + if gp not in self.latent_plans: + with np.load(root/record['rich_plan'],allow_pickle=False) as f:plan={k:f[k] for k in f.files} + with np.load(root/gp,allow_pickle=False) as f:geometry={k:f[k] for k in f.files} + self.latent_plans[gp]=(plan,geometry) + plan,geometry=self.latent_plans[gp];frozen,coefficients=self.latent_groups[group] + ds=np.pad(np.unpackbits(packed,axis=1,count=num_detectors,bitorder='little'),((0,0),(0,201-num_detectors)));values=[] + for pos in range(0,len(ds),batch): + n=min(batch,len(ds)-pos);dets=np.pad(ds[pos:pos+batch],((0,batch-n),(0,0))) + q=self.latent_infer(self.latent_weights[key],frozen,dets,coefficients,np.ones(batch,np.float32),np.zeros(batch,np.float32), + *[plan[k] for k in ['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts']],geometry['quality_times'],geometry['transition_gaps']) + values.append(np.asarray(q)[:n]) + return np.concatenate(values).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_model.py new file mode 100644 index 0000000000000000000000000000000000000000..28c0a97b4469d5f6172b5c011b7e3788cd0e60a0 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/temporal_model.py @@ -0,0 +1,44 @@ +"""Long temporal context for contextual exact d3 decoding.""" +import numpy as np +import jax +import jax.numpy as jnp + + +def initialize_from_rich(path,seed=20261104,channels=32): + parameters={k:jnp.asarray(v) for k,v in dict(np.load(path,allow_pickle=False)).items()} + keys=jax.random.split(jax.random.key(seed),6) + parameters['w1']=jnp.concatenate([parameters['w1'],jax.random.normal(keys[0],(channels,parameters['w1'].shape[1]))*.001],axis=0) + parameters['temporal_in']=jax.random.normal(keys[1],(24,channels))*jnp.sqrt(2/24) + parameters['temporal_bias']=jnp.zeros(channels) + for i in range(4): + parameters[f'temporal_conv_{i}']=jax.random.normal(keys[i+2],(3,channels,channels))*jnp.sqrt(1/(3*channels)) + parameters[f'temporal_conv_bias_{i}']=jnp.zeros(channels) + return parameters + + +def probabilities(parameters,dets,context_indices,static,base_logits,valid,grid,measurement_indices,counts): + event_grid=dets[:,grid] + measurement_grid=(jnp.cumsum(event_grid.astype(jnp.int32),axis=1)&1).astype(jnp.float32) + observed=(grid!=200).astype(jnp.float32) + frames=jnp.concatenate([event_grid.astype(jnp.float32),measurement_grid*observed[None,:,:], + jnp.broadcast_to(observed[None,:,:],event_grid.shape)],axis=-1) + temporal=jax.nn.silu(frames@parameters['temporal_in']+parameters['temporal_bias']) + for i,dilation in enumerate([1,2,4,8]): + mean=jnp.mean(temporal,axis=-1,keepdims=True) + normalized=(temporal-mean)*jax.lax.rsqrt(jnp.mean((temporal-mean)**2,axis=-1,keepdims=True)+1e-5) + value=jax.lax.conv_general_dilated(normalized,parameters[f'temporal_conv_{i}'],(1,),padding='SAME', + rhs_dilation=(dilation,),dimension_numbers=('NWC','WIO','NWC')) + temporal=temporal+jax.nn.silu(value+parameters[f'temporal_conv_bias_{i}']) + times=jnp.clip(jnp.rint(static[:,43]*25).astype(jnp.int32),0,25) + temporal_context=temporal[:,times,:] + measured=jnp.pad(measurement_grid.reshape(len(dets),208),((0,0),(0,1))) + events=dets[:,context_indices].astype(jnp.float32) + measurements=measured[:,measurement_indices[context_indices]].astype(jnp.float32) + density=jnp.sum(event_grid,axis=1)/counts[None,:] + features=jnp.concatenate([events,measurements, + jnp.broadcast_to(density[:,None,:],(len(dets),len(context_indices),8)), + jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape)),temporal_context],axis=-1) + h=jax.nn.silu(features@parameters['w1']+parameters['b1']) + h=jax.nn.silu(h@parameters['w2']+parameters['b2']) + delta=4*jnp.tanh((h@parameters['w3']+parameters['b3'])[...,0]) + return (jax.nn.sigmoid(base_logits[None,:]+delta)*valid[None,:]).T diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_code.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_code.py new file mode 100644 index 0000000000000000000000000000000000000000..1b8f0218efec38726337f184c4d8ba7e241a9a3f --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_code.py @@ -0,0 +1,108 @@ +"""Fit matching ensemble from random coefficients using the hardware training split.""" +import os +os.environ['OPENBLAS_NUM_THREADS'] = '1' +os.environ['OMP_NUM_THREADS'] = '1' +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +import json, time, hashlib, shutil, importlib.metadata +import numpy as np +import stim +from scipy.optimize import minimize +from scipy.special import expit +from decoder import match_features, predict_features + +ROOT = Path(__file__).resolve().parent +DATA = Path('/workspace/data') + + +def features(row): + path = ROOT/'cache'/f"{row['name']}.npz" + if path.exists(): + return row['name'] + dem = stim.DetectorErrorModel.from_file(DATA/'train'/row['noise']) + content = {} + for split in ('train', 'validation'): + with np.load(DATA/split/row['arrays'], allow_pickle=False) as a: + content[split+'_x'] = match_features(dem, a['detectors'], row['rounds']) + content[split+'_y'] = a['labels'] + np.savez(path, **content) + return row['name'] + + +def main(): + start = time.time() + rows = json.loads((DATA/'train/manifest.json').read_text())['conditions'] + (ROOT/'cache').mkdir(exist_ok=True) + with ProcessPoolExecutor(max_workers=16) as pool: + futures = [pool.submit(features, row) for row in rows] + for i, future in enumerate(as_completed(futures)): + print(json.dumps({'features_done': i+1, 'condition': future.result(), 'seconds': time.time()-start}), flush=True) + pkg = ROOT/'package' + pkg.mkdir(exist_ok=True) + (pkg/'weights').mkdir(exist_ok=True) + (pkg/'dems').mkdir(exist_ok=True) + config = {'architecture': 'per-group logistic ensemble of MWPM, correlated MWPM, and two constrained logical cosets', + 'feature_version': 1, 'groups': {}, 'conditions': {}} + history = [] + for group in sorted({r['group'] for r in rows}): + members = [r for r in rows if r['group'] == group] + cache = [np.load(ROOT/'cache'/f"{r['name']}.npz") for r in members] + x = np.concatenate([a['train_x'] for a in cache]).astype(np.float64) + y = np.concatenate([a['train_y'] for a in cache]).astype(np.float64) + vx = np.concatenate([a['validation_x'] for a in cache]).astype(np.float64) + vy = np.concatenate([a['validation_y'] for a in cache]) + scale = np.maximum(np.sqrt(np.mean(x*x, axis=0)), 0.01) + x /= scale + rng = np.random.default_rng(20261021) + initial = rng.normal(0, 0.01, x.shape[1]) + def objective(w): + z = x @ w + loss = np.mean(np.logaddexp(0, z)-y*z) + 1e-5*np.sum(w*w) + grad = x.T @ (expit(z)-y)/len(y) + 2e-5*w + return loss, grad + fit = minimize(objective, initial, jac=True, method='L-BFGS-B', options={'maxiter': 300, 'ftol': 1e-12}) + state = {'coef': fit.x.astype(np.float32), 'scale': scale.astype(np.float32)} + p = predict_features(vx, state) + metrics = {'group': group, 'train_loss': float(fit.fun), 'iterations': int(fit.nit), + 'failure': float(np.mean((p>=.5)!=vy)), + 'plain_failure': float(np.mean((vx[:,2]>0)!=vy)), + 'correlated_failure': float(np.mean((vx[:,3]>0)!=vy)), + 'coset_failure': float(np.mean((vx[:,1]>0)!=vy)), + 'optimizer_success': bool(fit.success)} + history.append(metrics) + print(json.dumps(metrics), flush=True) + np.savez(pkg/'weights'/f'{group}.npz', **state) + config['groups'][group] = f'weights/{group}.npz' + for row in rows: + target = f"dems/{row['name']}.dem" + shutil.copyfile(DATA/'train'/row['noise'], pkg/target) + config['conditions'][row['name']] = {'dem': target, 'circuit_sha256': row['circuit_sha256']} + shutil.copyfile(ROOT/'decoder.py', pkg/'decoder.py') + shutil.copyfile(ROOT/'train.py', pkg/'training_code.py') + (pkg/'infer.sh').write_text('#!/bin/sh\nset -eu\nexport OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1\nROOT="$(CDPATH= cd -- "$(dirname -- "$0")" && pwd)"\nexec python "$ROOT/decoder.py" "$1" "$2"\n') + (pkg/'infer.sh').chmod(0o755) + (pkg/'README.md').write_text('# Learned matching ensemble\n\nRun `bash infer.sh INPUT_ROOT OUTPUT_ROOT`. Ten logistic specialists are trained from random initialization on all supplied hardware training shots. Features combine matching decisions, constrained logical-coset weight gaps, event density and duration. No IDs enter the model. Fitted DEMs are included for all 130 conditions. Training and validation history is in training_provenance.json.\n') + deps = ['numpy', 'scipy', 'stim', 'pymatching'] + (pkg/'requirements.lock').write_text('\n'.join(f'{d}=={importlib.metadata.version(d)}' for d in deps)+'\n') + sha = lambda p: hashlib.sha256(p.read_bytes()).hexdigest() + provenance = {'initialization': {'distribution': 'Normal(0,0.01)', 'seed': 20261021, 'pretrained_weights': False}, + 'training_data': 'all 19880 supplied training shots per condition; all 130 conditions', + 'train_manifest_sha256': sha(DATA/'train/manifest.json'), + 'validation_manifest_sha256': sha(DATA/'validation/manifest.json'), + 'calibration_exception': 'Supplied even-fitted DEMs include validation syndromes but no logical labels or test data.', + 'optimizer': 'L-BFGS-B binary cross entropy plus 1e-5 L2, maximum 300 iterations', + 'selection': 'First trained candidate; validation reported without parameter fitting or hyperparameter selection', + 'test_data_used': False, 'released_per_shot_predictions_used': False, + 'history': history, 'elapsed_seconds': time.time()-start} + for name, obj in [('model_config.json', config), ('training_provenance.json', provenance)]: + (pkg/name).write_text(json.dumps(obj, indent=2)+'\n') + checkpoints = [{'path': str(p.relative_to(pkg)), 'sha256': sha(p), 'format': 'npz' if p.suffix=='.npz' else 'stim-dem'} + for p in sorted((pkg/'weights').glob('*'))+sorted((pkg/'dems').glob('*'))] + (pkg/'model_manifest.json').write_text(json.dumps({'schema': 'surface-code-decoding-model/v1', 'checkpoints': checkpoints, + 'model_config': 'model_config.json', 'training_provenance': 'training_provenance.json'}, indent=2)+'\n') + p = 1-np.mean([np.mean([h['failure'] for h in history if h['group'].startswith(d)]) for d in ['d3', 'd5']]) + print(json.dumps({'P': p, 'package': str(pkg), 'seconds': time.time()-start}), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/data.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3974b7ca215031282e475306b185666b8a15c7 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/data.py @@ -0,0 +1,51 @@ +"""Authorized data and frozen, session-trained parent probabilities.""" +from pathlib import Path +import hashlib,json +import numpy as np + +ROOT=Path(__file__).resolve().parent +RESEARCH=ROOT.parent + +def digest(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + +def reference(): + info=json.loads((ROOT/'reference.json').read_text());package=Path(info['package']) + for name,sha in info['sha256'].items():assert digest(package/name)==sha,name + return info,package,json.loads((package/'model_config.json').read_text()) + +def aligned(path,ids): + with np.load(path,allow_pickle=False) as saved: + assert set(saved.files)=={'sample_ids','probabilities'} + source_ids=saved['sample_ids'];q=saved['probabilities'] + assert source_ids.dtype==np.uint64 and len(np.unique(source_ids))==len(source_ids) + order=np.argsort(source_ids);locations=np.searchsorted(source_ids[order],ids) + assert np.all(locations=0)&(q<=1)).all() + return q + +def load_all(splits=('train','validation'),cache=None): + cache=ROOT/'cache1' if cache is None else Path(cache) + info,package,config=reference();items=[] + manifests={s:json.loads((Path('/workspace/data')/s/'manifest.json').read_text()) for s in splits} + rows=sorted([r for r in json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'] if r['distance']==5],key=lambda r:r['name']) + for row in rows: + path=package/config['conditions'][row['name']]['neural_d5_plan'] + with np.load(path,allow_pickle=False) as plan:grid=plan['grid'].copy() + assert grid.shape==(26,24) and np.array_equal(np.sort(grid[grid!=600]),np.arange(row['num_detectors'])) + item={'row':row,'grid':grid,'feature_plan_sha256':digest(path)} + for split in splits: + record=next(r for r in manifests[split]['conditions'] if r['name']==row['name']) + with np.load(Path('/workspace/data')/split/record['arrays'],allow_pickle=False) as saved: + ids=saved['sample_ids'];labels=saved['labels'];packed=saved['detectors'] + source=(cache if split=='train' else Path(info['predictions']))/(row['name']+'.npz') + q=aligned(source,ids) + detectors=np.unpackbits(packed,axis=1,count=row['num_detectors'],bitorder='little') + item[split+'_dets']=np.pad(detectors,((0,0),(0,601-row['num_detectors']))) + item[split+'_parent']=q;item[split+'_labels']=labels;item[split+'_sample_ids']=ids + item[split+'_reference']={'file':str(source),'file_sha256':digest(source),'input_arrays_sha256':record['arrays_sha256'], + 'sample_ids_sha256':hashlib.sha256(ids.tobytes()).hexdigest(),'alignment':'Opaque sample ID equality join, with uniqueness and membership assertions'} + items.append(item) + assert len(items)==26 + return items diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/model.py new file mode 100644 index 0000000000000000000000000000000000000000..7a94456304309825a9fd457e6780b97aa0502431 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/model.py @@ -0,0 +1,89 @@ +"""Untrained axial-attention residual over a frozen session-trained decoder.""" +import jax +import jax.numpy as jnp + +DEFAULT={'width':64,'heads':4,'depth':3,'expansion':2,'dropout':.1,'correction_bound':4.} + +def initialize(seed,config=None): + c=DEFAULT if config is None else config;d=c['width'];params={};key=jax.random.key(seed) + def random(shape,scale): + nonlocal key + key,sub=jax.random.split(key);return jax.random.normal(sub,shape)*scale + params['input_w']=random((8,d),jnp.sqrt(1/8));params['input_b']=jnp.zeros(d) + params['space_embedding']=random((24,d),.02);params['time_embedding']=random((26,d),.02) + for i in range(c['depth']): + for axis in ['time','space']: + prefix=f'b{i}_{axis}' + params[prefix+'_scale']=jnp.ones(d);params[prefix+'_bias']=jnp.zeros(d) + params[prefix+'_qkv']=random((d,3*d),jnp.sqrt(1/d));params[prefix+'_qkv_b']=jnp.zeros(3*d) + params[prefix+'_out']=random((d,d),jnp.sqrt(1/d));params[prefix+'_out_b']=jnp.zeros(d) + prefix=f'b{i}_ff';hidden=d*c['expansion'] + params[prefix+'_scale']=jnp.ones(d);params[prefix+'_bias']=jnp.zeros(d) + params[prefix+'_w1']=random((d,hidden),jnp.sqrt(1/d));params[prefix+'_b1']=jnp.zeros(hidden) + params[prefix+'_w2']=random((hidden,d),jnp.sqrt(1/hidden));params[prefix+'_b2']=jnp.zeros(d) + params['readout_scale']=jnp.ones(d);params['readout_bias']=jnp.zeros(d) + params['readout_w1']=random((d+2,d),jnp.sqrt(1/(d+2)));params['readout_b1']=jnp.zeros(d) + params['readout_w2']=jnp.zeros((d,1));params['readout_b2']=jnp.zeros(1) + return params + +def parent_logit(probability): + p=jnp.clip(probability,1e-7,1-1e-7) + return jnp.log(p)-jnp.log1p(-p) + +def token_features(dets,grid,basis,rounds,parent): + valid=grid!=600;events=dets[:,grid].astype(jnp.float32)*valid[None] + measured=(jnp.cumsum(events.astype(jnp.int32),axis=1)&1).astype(jnp.float32) + shape=events.shape + broadcast=lambda v:jnp.broadcast_to(v,shape) + z=jnp.clip(parent_logit(parent),-8.,8.)/8 + return jnp.stack([events,measured,broadcast(valid[None]),broadcast(jnp.arange(26)[None,:,None]/25), + broadcast(jnp.asarray(basis,jnp.float32)),broadcast(jnp.asarray(rounds,jnp.float32)/25), + broadcast(z[:,None,None]),broadcast(jnp.abs(z)[:,None,None])],axis=-1),valid + +def layer_norm(x,scale,bias): + mean=x.mean(axis=-1,keepdims=True);variance=jnp.mean((x-mean)**2,axis=-1,keepdims=True) + return (x-mean)*jax.lax.rsqrt(variance+1e-5)*scale+bias + +def dropout(x,key,rate,training): + if not training or rate==0:return x + keep=1-rate;return jnp.where(jax.random.bernoulli(key,keep,x.shape),x/keep,0.) + +def attention(parameters,prefix,x,valid,axis,heads): + # Reshape each temporal/spatial line into its own attention group. + if axis=='time':x=jnp.swapaxes(x,1,2);valid=valid.T + batch,groups,length,width=x.shape;head_width=width//heads + qkv=x@parameters[prefix+'_qkv']+parameters[prefix+'_qkv_b'] + q,k,v=[part.reshape(batch,groups,length,heads,head_width) for part in jnp.split(qkv,3,axis=-1)] + score=jnp.einsum('bgihd,bgjhd->bghij',q,k)/jnp.sqrt(jnp.float32(head_width)) + score=jnp.where(valid[None,:,None,None,:],score,-1e9) + weights=jax.nn.softmax(score,axis=-1) + value=jnp.einsum('bghij,bgjhd->bgihd',weights,v).reshape(batch,groups,length,width) + value=(value@parameters[prefix+'_out']+parameters[prefix+'_out_b'])*valid[None,:,:,None] + return jnp.swapaxes(value,1,2) if axis=='time' else value + +def logits(parameters,dets,grid,basis,rounds,parent,*,training=False,key=None,config=None): + c=DEFAULT if config is None else config + features,valid=token_features(dets,grid,basis,rounds,parent);mask=valid[None,:,:,None] + x=(features@parameters['input_w']+parameters['input_b']+parameters['space_embedding'][None,None]+parameters['time_embedding'][None,:,None])*mask + if key is None:key=jax.random.key(0) + keys=jax.random.split(key,3*c['depth']) + for i in range(c['depth']): + for rank,axis in enumerate(['time','space']): + prefix=f'b{i}_{axis}';normalized=layer_norm(x,parameters[prefix+'_scale'],parameters[prefix+'_bias']) + update=attention(parameters,prefix,normalized,valid,axis,c['heads']) + x=(x+dropout(update,keys[3*i+rank],c['dropout'],training))*mask + prefix=f'b{i}_ff';normalized=layer_norm(x,parameters[prefix+'_scale'],parameters[prefix+'_bias']) + update=jax.nn.gelu(normalized@parameters[prefix+'_w1']+parameters[prefix+'_b1'])@parameters[prefix+'_w2']+parameters[prefix+'_b2'] + x=(x+dropout(update,keys[3*i+2],c['dropout'],training))*mask + x=layer_norm(x,parameters['readout_scale'],parameters['readout_bias'])*mask + pooled=x.sum(axis=(1,2))/jnp.maximum(valid.sum(),1) + base=parent_logit(parent);z=jnp.clip(base,-8,8)/8 + hidden=jax.nn.silu(jnp.concatenate([pooled,z[:,None],jnp.abs(z)[:,None]],axis=1)@parameters['readout_w1']+parameters['readout_b1']) + correction=c['correction_bound']*jnp.tanh((hidden@parameters['readout_w2']+parameters['readout_b2'])[:,0]) + return base+correction + +def predict(parameters,dets,grid,basis,rounds,parent,config=None): + return jax.nn.sigmoid(logits(parameters,dets,grid,basis,rounds,parent,config=config)) + +def binary_loss(logit,labels): + return jnp.mean(jax.nn.softplus(logit)-labels*logit) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/state.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/state.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc1450fa59e3b8b6afb452b78254878d00411cf --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/state.py @@ -0,0 +1,34 @@ +"""Numeric-only checkpoint state for a newly initialized residual model.""" +import numpy as np +import jax +import jax.numpy as jnp +import optax + +def save_state(path,state): + np.savez(path,**{f'arr_{i}':np.asarray(v) for i,v in enumerate(jax.tree_util.tree_leaves(state))}) + +def load_state(path,template): + leaves,tree=jax.tree_util.tree_flatten(template);values=[] + with np.load(path,allow_pickle=False) as saved: + assert set(saved.files)=={f'arr_{i}' for i in range(len(leaves))} + for i,v in enumerate(leaves): + a=saved[f'arr_{i}'];assert a.shape==v.shape and a.dtype==v.dtype + values.append(jnp.asarray(a)) + return jax.tree_util.tree_unflatten(tree,values) + +def optimizer(learning_rate,total_steps=4096): + schedule=optax.warmup_cosine_decay_schedule(learning_rate/128,learning_rate,128,total_steps,end_value=learning_rate*.1) + return optax.chain(optax.clip_by_global_norm(1.),optax.adamw(schedule,weight_decay=1e-4)) + +def make_step(architecture,optim,dropout_seed): + from model import logits,binary_loss + def objective(p,dets,grid,basis,rounds,parent,y,step): + key=jax.random.fold_in(jax.random.key(dropout_seed),step) + z=logits(p,dets,grid,basis,rounds,parent,training=True,key=key,config=architecture) + return binary_loss(z,y),jnp.mean((z>=0)!=y) + @jax.jit + def update(p,state,dets,grid,basis,rounds,parent,y,step): + (loss,error),gradient=jax.value_and_grad(objective,has_aux=True)(p,dets,grid,basis,rounds,parent,y,step) + updates,state=optim.update(gradient,state,p) + return optax.apply_updates(p,updates),state,loss,error,optax.global_norm(gradient) + return update diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/train.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/train.py new file mode 100644 index 0000000000000000000000000000000000000000..a0a04449c56aa9173a52cfb55906069f680c7634 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/axial_residual1/train.py @@ -0,0 +1,87 @@ +"""Fresh axial attention over a frozen session-trained parent; all d5 conditions.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +from pathlib import Path +import argparse,hashlib,json,shutil,time +import numpy as np +import jax +import jax.numpy as jnp +from data import load_all,reference,digest +from model import initialize,predict,DEFAULT +from state import save_state,load_state,optimizer,make_step + +def main(): + parser=argparse.ArgumentParser();parser.add_argument('--output',type=Path,required=True);parser.add_argument('--cache',type=Path) + parser.add_argument('--resume',type=Path);parser.add_argument('--steps',type=int,default=4096);parser.add_argument('--batch',type=int,default=256) + parser.add_argument('--learning-rate',type=float,default=.0002);parser.add_argument('--schedule-steps',type=int,default=4096) + parser.add_argument('--initial-seed',type=int,default=20261800);parser.add_argument('--seed',type=int,default=20261801);parser.add_argument('--dropout-seed',type=int,default=20261802) + parser.add_argument('--eval-every',type=int,default=512);parser.add_argument('--validation-shots',type=int,default=1024);parser.add_argument('--validation-batch',type=int,default=256) + args=parser.parse_args();args.output.mkdir(exist_ok=False);root=Path(__file__).resolve().parent + config={k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()};config['architecture']=DEFAULT + source=args.output/'training_source';source.mkdir();hashes={} + for name in ['model.py','data.py','state.py','train.py']: + shutil.copyfile(root/name,source/name);hashes[name]=digest(root/name) + parameters=initialize(args.initial_seed);optim=optimizer(args.learning_rate,args.schedule_steps);state=optim.init(parameters) + rng=np.random.default_rng(args.seed);start_step=0;dataset=load_all(cache=args.cache);seen=[np.zeros(len(x['train_labels']),bool) for x in dataset] + if args.resume: + previous=json.loads((args.resume.parent/'run_config.json').read_text()) + for name in ['batch','learning_rate','schedule_steps','initial_seed','seed','dropout_seed','architecture']:assert config[name]==previous[name],name + start_step=int(args.resume.stem.split('-')[-1]);assert start_step=0)&(q<=1)).all() + rows.append({'condition':row['name'],'failure':float(np.mean((q>=.5)!=y)), + 'negative_log_likelihood':float(np.mean(-y*np.log(qq)-(1-y)*np.log1p(-qq)))}) + report={'step':step,'validation_failure':float(np.mean([x['failure'] for x in rows])), + 'negative_log_likelihood':float(np.mean([x['negative_log_likelihood'] for x in rows])),'conditions':rows,'seconds':time.time()-beginning} + np.savez(args.output/f'weights-{step}.npz',**{k:np.asarray(v) for k,v in parameters.items()});save_state(args.output/f'optimizer-{step}.npz',state) + np.savez(args.output/f'seen-{step}.npz',**{item['row']['name']:mask for item,mask in zip(dataset,seen)}) + (args.output/f'rng-{step}.json').write_text(json.dumps(rng.bit_generator.state)+'\n') + checkpoint={'step':step,'training_draws':step*args.batch,'distinct_rows':sum(int(x.sum()) for x in seen), + 'distinct_by_condition':{item['row']['name']:int(mask.sum()) for item,mask in zip(dataset,seen)}, + 'adam_count':int(state[1][0].count),'weights_sha256':digest(args.output/f'weights-{step}.npz'),'complete':True} + assert checkpoint['adam_count']==step + (args.output/f'checkpoint-{step}.json').write_text(json.dumps(checkpoint,indent=2)+'\n') + (args.output/f'validation-{step}.json').write_text(json.dumps(report,indent=2)+'\n') + print(json.dumps({k:v for k,v in report.items() if k!='conditions'}),flush=True) + evaluate(start_step) + for step in range(start_step+1,args.steps+1): + ci=int(rng.integers(len(dataset)));item=dataset[ci];row=item['row'];ix=rng.integers(len(item['train_labels']),size=args.batch);seen[ci][ix]=True + parameters,state,loss,error,norm=update(parameters,state,item['train_dets'][ix],item['grid'],row['basis']=='X',row['rounds'],item['train_parent'][ix],item['train_labels'][ix],step) + if step==start_step+1 or step%32==0: + record={'step':step,'loss':float(loss),'error':float(error),'gradient_norm':float(norm),'elapsed':time.time()-started} + assert np.isfinite(record['loss']) and np.isfinite(record['gradient_norm']);records.append(record);print(json.dumps(record),flush=True) + if step%args.eval_every==0 or step==args.steps:evaluate(step) + provenance={'run_config':config,'frozen_parent':reference()[0],'training_rows':sum(len(x['train_labels']) for x in dataset), + 'data_references':{x['row']['name']:{'feature_plan_sha256':x['feature_plan_sha256'],'train':x['train_reference'],'validation':x['validation_reference']} for x in dataset}, + 'training':'Uniform condition/row sampling over all516880supplied d5training rows; binary logical BCE and dropout. Parent parameters frozen.', + 'loss':'Logical observable BCE on parent logit plus bounded4tanh learned correction', + 'validation':'Seed20261102 per-condition screens; full supplied validation required for checkpoint selection', + 'test_data_used':False,'records':records,'seconds':time.time()-started} + (args.output/'provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/FIT_SOURCE_LOCK.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/FIT_SOURCE_LOCK.json new file mode 100644 index 0000000000000000000000000000000000000000..5867a058ce67cd9372ea42ff1f5405e4b29cc59b --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/FIT_SOURCE_LOCK.json @@ -0,0 +1,15 @@ +{ + "created_utc": "2026-10-21T20:25:33.650374+00:00", + "source_sha256": { + "beam_calibration/core.py": "6cc89f9e5a3143564c57f8a79c7ea6e1d1cd5b2612dacfd5a52b58b6bcde41c7", + "beam_calibration/fit.py": "bb494e78078963e81f287dcd0925ce4c071276e791b1902202a17d7a028a2f41", + "beam_calibration/control_cpu.py": "8e7dcb714f5e5ca8a1d86188fc24d569bd0456d65a42a86f8e7a1bd56a6a0f8f", + "beam_calibration/plan.json": "6a16f161a24d54c05afe10004f0d6f119aa3879394195d63f9d1ce65830b52c1", + "beam_calibration/generate_beam_training.py": "b7d8aabc20b10577500518002df566b789b65fa5da398584e8d35185892ce41f", + "beam_calibration/generate_parent_training.py": "c8c11132e5eb98cae6e71936cdeba4a90211fe1cdc03dd63e5b7982f49d87073", + "full_frontier/beam_lookahead.py": "557ea3eef701c480b90896cda3e8f69e263bc852fcce805f054a90e43d7cffe5", + "beam_r3/reference.json": "9f0bbe90c63da1d9a13a27b9ecb61b07d689c64cc41055ba2c812e5b013c2701" + }, + "scope": "Frozen before any beam_calibration hardware-label fitting. Evidence generation is in progress; fits await and audit both complete caches.", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/PACKAGE_SOURCE_LOCK.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/PACKAGE_SOURCE_LOCK.json new file mode 100644 index 0000000000000000000000000000000000000000..8c2474e5151aa647d162887665121b79a7b6ebb5 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/PACKAGE_SOURCE_LOCK.json @@ -0,0 +1,19 @@ +{ + "created_utc": "2026-10-21T20:27:57.623048+00:00", + "queue_after": "full_frontier/resource_finished.json", + "source_sha256": { + "beam_calibration/runtime.py": "a3e6f859e6d4a350858c47ab362f5716155f3434e25661bb70e8e2896ab228c5", + "beam_calibration/export.py": "ad0c3d9cdf2897b350e4b9abb44a044bff5a84d81003775d5e7d682571fd2cd4", + "beam_calibration/runtime_control.py": "f93fe417f7b43a4f95314b84c6102f7a3eba6a4255600409f57555553bb1abc4", + "beam_calibration/core.py": "6cc89f9e5a3143564c57f8a79c7ea6e1d1cd5b2612dacfd5a52b58b6bcde41c7", + "beam_calibration/package.py": "7ce80033a384a5943a3e3d877dd7bde023322800871c7aab5aca551871e4c036", + "beam_calibration/after_resource.py": "cc2fdb0c0d24d46ad7e793913be53100f4969b67af72f3418802fcf1574b89fb", + "beam_calibration/plan.json": "6a16f161a24d54c05afe10004f0d6f119aa3879394195d63f9d1ce65830b52c1", + "beam_calibration/FIT_SOURCE_LOCK.json": "05595c096ef729d9981461316c883d106c2e95722b0f0ba1067cea86ff17fad9", + "full_frontier/beam_jax.py": "45ce979892fad6eaa88325477fb063e6513e635f89370d8a6a9c930f56f09cb1", + "beam_r3/plans/surface_code_bX_d5_r03_center_5_5.npz": "cc763f0638add95aa03f2b76b5acf8dcceb470de00182d6804366e3bfa0e3f0d", + "beam_r3/plans/surface_code_bZ_d5_r03_center_5_5.npz": "3a6746481fe2953c4b5b7615b365fccfcee538ff1b8fadc910420fb0f9ba681d" + }, + "scope": "Portable and packaging code frozen after actual CPU runtime controls, before fitted validation outcomes. GPU work only if a global candidate improves, serialized after the frontier resource study; no original queue reordered.", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/QUEUE_AMENDMENT.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/QUEUE_AMENDMENT.json new file mode 100644 index 0000000000000000000000000000000000000000..e01903a00b32bb18c2de97b6b263d5625298da47 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/QUEUE_AMENDMENT.json @@ -0,0 +1,10 @@ +{ + "created_utc": "2026-10-21T20:43:17.769949+00:00", + "previous_waiter_pid": 80427, + "previous_waiter_session": 95715, + "previous_queue_after": "full_frontier/resource_finished.json", + "new_queue": "GPUidle gap after completed block_physical resource study and failed/pretraining trajectorycontrol; finish calibrationverificationbeforetrajectoryrestart", + "reason": "Completed prespecifiedfitselectedparent-onlycalibration (no beam needed); verify strongestcandidate promptly whiletrajectorycontrolrecoveryprepared. Only operational scheduling changes, no training/selection/model changes.", + "original_source_lock_sha256": "a73ad807bf27cb8c65ed3b06fa287d2ce1fd1206b7a74f867221e35261b4edfa", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/after_resource.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/after_resource.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb41ebd7077bdd3a4053c51e86e548ddf45692b --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/after_resource.py @@ -0,0 +1,32 @@ +"""Publish only after serialized resource work, fitted assessment and actual replay.""" +from pathlib import Path +import hashlib,json,subprocess,time +root=Path(__file__).resolve().parent;r=root.parent;started=time.monotonic();report_path=root/'fit1/report.json';marker=r/'full_frontier/resource_finished.json' +while not report_path.exists(): + if time.monotonic()-started>86400:raise TimeoutError('Calibration did not finish') + time.sleep(2) +report=json.loads(report_path.read_text());assert report['status']=='complete';selected=report['selected'] +if selected is None: + (root/'beam_calibration1_finished.json').write_text(json.dumps({'complete':True,'published':False,'reason':'All six global validation choices failed to improve frozen reference','test_data_used':False})+'\n') +else: + while not marker.exists(): + if (r/'full_frontier/resource_failure.json').exists():raise RuntimeError('Prior GPU resource study failed; inspect and recover before advancing queue') + if time.monotonic()-started>86400:raise TimeoutError('Prior resource study did not complete') + time.sleep(2) + assert json.loads(marker.read_text())['complete'] + assert not subprocess.check_output(['nvidia-smi','--query-compute-apps=pid','--format=csv,noheader'],text=True).strip() + for path,digest in json.loads((root/'PACKAGE_SOURCE_LOCK.json').read_text())['source_sha256'].items():assert hashlib.sha256((r/path).read_bytes()).hexdigest()==digest,path + for filename in ['control_cpu.json','runtime_control_cpu.json']: + control=json.loads((root/filename).read_text());assert control['status']=='passed' + for path,digest in control['source_sha256'].items(): + source=r/path if '/' in path else root/path + assert hashlib.sha256(source.read_bytes()).hexdigest()==digest,path + current=json.loads((r/'CURRENT.json').read_text());score=json.loads(Path(current['score']).read_text());gain=sum((score['per_condition'][z['condition']]['errors']/score['per_condition'][z['condition']]['total']-z['failure'])/52 for z in selected['conditions']) + if gain<=1e-12: + (root/'beam_calibration1_finished.json').write_text(json.dumps({'complete':True,'published':False,'reason':'Selected global choice no longer improves then-current incumbent','test_data_used':False})+'\n') + else: + subprocess.run(['python',str(r/'run_limited.py'),str(root/'runtime_control.py'),'--device','gpu'],check=True) + subprocess.run(['python',str(root/'package.py')],check=True);out=r/'context_upgrades/beam_calibration1' + subprocess.run(['python',str(r/'verify_d5_candidate.py'),str(out)],check=True) + subprocess.run(['python',str(r/'audit_completed_candidate.py'),'--candidate',str(out),'--previous',current['predictions']],check=True) + done=json.loads((out/'finished.json').read_text());(root/'beam_calibration1_finished.json').write_text(json.dumps({'complete':True,'published':done['published'],'test_data_used':False})+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/beam_cpu.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/beam_cpu.py new file mode 100644 index 0000000000000000000000000000000000000000..17488892498084843e52fca611a36b28c83143e3 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/beam_cpu.py @@ -0,0 +1,45 @@ +"""Untrained NumPy sparse frontier prototype, capped separately by logical sector.""" +import numpy as np + +def decode(plan,dets,per_sector=128): + keys=np.array([0],np.uint32);mass=np.ones(1,np.float64) + pruned_steps=0;maximum_states=1;discarded_fractions=[] + for probability,mask,detector in zip(plan['probabilities'],plan['masks'],plan['measured']): + probability=float(probability);mask=np.uint32(mask) + if detector<0: + expanded=np.concatenate([keys,keys^mask]);values=np.concatenate([(1-probability)*mass,probability*mass]) + order=np.argsort(expanded,kind='stable');expanded=expanded[order];values=values[order] + starts=np.r_[0,np.flatnonzero(expanded[1:]!=expanded[:-1])+1] + keys=expanded[starts];mass=np.add.reduceat(values,starts) + positive=mass>0;keys,mass=keys[positive],mass[positive] + selected=[] + for sector in [0,1]: + indices=np.flatnonzero((keys&1)==sector) + if len(indices)>per_sector: + # Deterministic key tie break; never use sample IDs or other shots. + indices=indices[np.lexsort((keys[indices],-mass[indices]))[:per_sector]] + selected.extend(indices.tolist()) + if len(selected)=0:active&=~np.uint32(mask) + detectors=np.full((len(active_masks),width-1),-1,np.int32) + future=np.zeros((len(active_masks),width-1),np.float64) + active=np.asarray(active_masks,np.uint32)[:,None]&bits[None,:]!=0 + assigned=np.full(width-1,-1,np.int32);log_parity=np.zeros(width-1,np.float64) + for i in range(len(active_masks)-1,-1,-1): + p=float(plan['probabilities'][i]);mask=np.uint32(plan['masks'][i]);d=int(plan['measured'][i]);touched=(mask&bits)!=0 + if d>=0: + assert touched.sum()==1 + assigned[touched]=d;log_parity[touched]=0. + else: + assert 0<=p<.5 + detectors[i]=assigned;future[i]=-.5*np.expm1(log_parity) + assert (assigned[active[i]]>=0).all() + log_parity[touched]+=np.log1p(-2*p) + return {'detectors':detectors,'future':future,'active':active,'bits':bits} + +def decode(plan,dets,per_sector=128,lookahead=None): + lookahead=prepare_lookahead(plan) if lookahead is None else lookahead + keys=np.array([0],np.uint32);mass=np.ones(1,np.float64) + pruned_steps=0;maximum_states=1;discarded_fractions=[] + for operation,(probability,mask,detector) in enumerate(zip(plan['probabilities'],plan['masks'],plan['measured'])): + probability=float(probability);mask=np.uint32(mask) + if detector<0: + expanded=np.concatenate([keys,keys^mask]);values=np.concatenate([(1-probability)*mass,probability*mass]) + order=np.argsort(expanded,kind='stable');expanded=expanded[order];values=values[order] + starts=np.r_[0,np.flatnonzero(expanded[1:]!=expanded[:-1])+1] + keys=expanded[starts];mass=np.add.reduceat(values,starts) + positive=mass>0;keys,mass=keys[positive],mass[positive] + selected=[];scores=None + for sector in [0,1]: + indices=np.flatnonzero((keys&1)==sector) + if len(indices)>per_sector: + # Deterministic key tie break; never use sample IDs or other shots. + if scores is None: + active=lookahead['active'][operation];bits=lookahead['bits'][active] + ds=lookahead['detectors'][operation,active];q=lookahead['future'][operation,active] + mismatch=((keys[:,None]&bits[None,:])!=0)!=dets[ds][None,:] + with np.errstate(divide='ignore'): + future_log=np.where(mismatch,np.log(q)[None,:],np.log1p(-q)[None,:]).sum(axis=1) + scores=np.log(mass)+future_log + indices=indices[np.lexsort((keys[indices],-scores[indices]))[:per_sector]] + selected.extend(indices.tolist()) + if len(selected)=0)&(actual<=1)).all() + neutral={**state,'weights':np.zeros_like(state['weights'])};neutral_p=predict(parent,b,neutral);assert ((neutral_p>=.5)==(parent>=.5)).all();checks.append({'mode':mode,'gradient_max_abs_difference':delta,'independent_scipy_probability_exact':True,'order_subset_exact':True,'neutral_decisions_exact':True,'neutral_probability_max_abs':float(abs(neutral_p-parent).max())}) +y=np.array([1,0],np.uint8);base=np.log(np.array([.8,.2])/np.array([.2,.8]));value,_=objective_gradient(np.zeros(2),np.array([[1.,0.],[1.,0.]]),base,y,0.);assert abs(value+np.log(.8))<1e-14 +report={'status':'passed','checks':checks,'unsigned_label_reference_loss':value,'hardware_labels_accessed':False,'optimizer_updates':0,'test_data_used':False,'source_sha256':{p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in [root/'core.py',Path(__file__).resolve()]}};(root/'control_cpu.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report,indent=2),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/core.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/core.py new file mode 100644 index 0000000000000000000000000000000000000000..e61cfa6acdc064f80fa138239efca9330d32c8c2 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/core.py @@ -0,0 +1,36 @@ +"""Small logit residual with training-only feature normalization.""" +import numpy as np + + +def logit(probability): + p=np.clip(np.asarray(probability,np.float64),1e-7,1-1e-7) + assert p.ndim==1 + return np.log(p)-np.log1p(-p) + + +def raw_features(parent,beam=None): + base=logit(parent);columns=[base] + if beam is not None:columns.append(logit(beam)-base) + return base,np.ascontiguousarray(np.column_stack(columns),np.float64) + + +def normalize(raw,center,scale): + values=(raw-np.asarray(center)[None,:])/np.asarray(scale)[None,:] + return np.ascontiguousarray(np.column_stack([np.ones(len(raw)),values]),np.float64) + + +def sigmoid(z): + u=np.exp(-np.abs(z));return np.where(z>=0,1/(1+u),u/(1+u)) + + +def objective_gradient(weights,x,base,labels,regularization): + y=np.asarray(labels,np.float64);z=base+np.sum(x*weights[None,:],axis=1);penalty=np.ones(len(weights));penalty[0]=.01 + loss=float(np.mean(np.logaddexp(0,z)-y*z)+.5*regularization*np.sum(penalty*weights*weights)) + gradient=x.T@(sigmoid(z)-y)/len(y)+regularization*penalty*weights + return loss,gradient + + +def predict(parent,beam,state): + if len(state['center'])==1:beam=None + base,raw=raw_features(parent,beam);x=normalize(raw,state['center'],state['scale']);z=base+np.sum(x*np.asarray(state['weights'])[None,:],axis=1) + return sigmoid(z).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/export.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/export.py new file mode 100644 index 0000000000000000000000000000000000000000..705ae3aa1b07d5c829458962341ce5dd84426be2 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/export.py @@ -0,0 +1,11 @@ +"""Copy portable helpers under unique names; no data caches in the model.""" +from pathlib import Path +import hashlib,shutil +root=Path(__file__).resolve().parent;r=root.parent + + +def install(package): + package=Path(package);assert not (package/'d5_prebeam_calibration_contextual.py').exists() + files={'d5_prebeam_calibration_contextual.py':package/'d5_contextual.py','d5_contextual.py':root/'runtime.py','beam_calibration_core.py':root/'core.py','beam_calibration_frontier.py':r/'full_frontier/beam_jax.py'} + for name,source in files.items():shutil.copyfile(source,package/name) + return {name:hashlib.sha256((package/name).read_bytes()).hexdigest() for name in files} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/fit.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/fit.py new file mode 100644 index 0000000000000000000000000000000000000000..79a2792f20611ccf66f24475aac461e3ab31be66 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/fit.py @@ -0,0 +1,104 @@ +"""Train-only small corrections, then six prespecified global validation choices.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1' +os.environ['OMP_NUM_THREADS']='1' +os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:4]) +from pathlib import Path +import hashlib,json,time +import numpy as np +from scipy.optimize import minimize +from core import raw_features,normalize,objective_gradient,predict + +root=Path(__file__).resolve().parent;r=root.parent + +def digest(path):return hashlib.sha256(Path(path).read_bytes()).hexdigest() + +def write(path,value):Path(path).write_text(json.dumps(value,indent=2)+'\n') + +def aligned(path,ids,empty=False): + with np.load(path,allow_pickle=False) as f: + keys=f['sample_ids'];p=f['probabilities'] + assert keys.dtype==np.uint64 and keys.ndim==1 and len(np.unique(keys))==len(keys)==len(ids) + assert p.dtype==np.float32 and p.shape==keys.shape and np.isfinite(p).all() and ((p>=0)&(p<=1)).all() + order=np.argsort(keys);positions=np.searchsorted(keys[order],ids);assert (positions14400:raise TimeoutError('Training evidence incomplete') + time.sleep(2) + for path,sha in lock['source_sha256'].items():assert digest(r/path)==sha,path + assert json.loads((root/'control_cpu.json').read_text())['status']=='passed' + output=root/'fit1';output.mkdir(exist_ok=False);train=Path('/workspace/data/train');validation=Path('/workspace/data/validation') + train_rows={x['name']:x for x in json.loads((train/'manifest.json').read_text())['conditions']} + val_rows={x['name']:x for x in json.loads((validation/'manifest.json').read_text())['conditions']} + generation={k:json.loads((root/k/'provenance.json').read_text()) for k in ['beam_training','parent_training']} + reference=plan['reference'];package=Path(reference['current']['package']) + for filename,key in [('model_manifest.json','parent_manifest_sha256'),('model_config.json','parent_config_sha256'),('training_provenance.json','parent_provenance_sha256')]:assert digest(package/filename)==reference[key] + assert digest(r/'full_frontier/beam_lookahead.py')==plan['training_beam_source_sha256'] + data={};audit=[] + # Validate every ID and file before any hardware-label member is opened here. + for name in plan['conditions']: + row=train_rows[name];path=train/row['arrays'];assert digest(path)==row['arrays_sha256'] + with np.load(path,allow_pickle=False) as f:ids=f['sample_ids'] + assert len(ids)==19880 and len(np.unique(ids))==len(ids) + arrays={};files={} + for kind in generation: + record=next(z for z in generation[kind]['records'] if z['condition']==name);cache=root/kind/(name+'.npz') + assert record['arrays_sha256']==row['arrays_sha256'] and record['shots']==len(ids) + assert digest(cache)==record['output_sha256'];files[kind]=record['output_sha256'] + arrays[kind]=aligned(cache,ids,empty=kind=='beam_training') + parent=arrays['parent_training'][0];beam,empty=arrays['beam_training'];beam[empty]=parent[empty] + data[name]={'ids':ids,'parent':parent,'beam':beam} + audit.append({'condition':name,'shots':len(ids),'all_ids_exact_by_value':True,'arrays_sha256':row['arrays_sha256'],'cache_sha256':files,'empty_fallbacks':int(empty.sum())}) + write(output/'training_evidence_audit.json',{'status':'passed','conditions':audit,'training_rows':sum(len(z['ids']) for z in data.values()),'label_members_opened':False,'test_data_used':False}) + initial={basis:np.random.default_rng(seed).normal(0,.001,3) for basis,seed in plan['initial_seeds'].items()} + np.savez(output/'initial_weights.npz',**initial) + # All optimization and normalization are complete before validation labels. + fits=[] + for arm in plan['arms']: + for lam in plan['regularization']: + name=f'{arm}_lambda{lam:g}';folder=output/name;folder.mkdir();conditions=[] + for condition in plan['conditions']: + row=train_rows[condition];d=data[condition] + with np.load(train/row['arrays'],allow_pickle=False) as f: + assert np.array_equal(f['sample_ids'],d['ids']);y=f['labels'].astype(np.float64) + assert y.shape==d['ids'].shape and np.isin(y,[0,1]).all() + base,raw=raw_features(d['parent'],None if arm=='parent_only' else d['beam']);center=raw.mean(axis=0);scale=np.maximum(raw.std(axis=0),.05);x=normalize(raw,center,scale) + w0=initial[row['basis']][:x.shape[1]].copy();initial_loss=objective_gradient(w0,x,base,y,lam)[0] + result=minimize(objective_gradient,w0,args=(x,base,y,lam),method='L-BFGS-B',jac=True,options={'maxiter':500,'gtol':1e-8,'ftol':1e-12}) + assert result.success and np.isfinite(result.x).all(),str(result) + state={'condition':condition,'arm':arm,'regularization':lam,'center':center.tolist(),'scale':scale.tolist(),'weights':result.x.tolist()};write(folder/(condition+'.json'),state) + q=predict(d['parent'],d['beam'],state);qc=np.clip(q.astype(float),1e-7,1-1e-7) + conditions.append({'condition':condition,'shots':len(y),'initial_weights':w0.tolist(),'initial_objective':initial_loss,'final_objective':float(result.fun),'optimizer_iterations':int(result.nit),'function_evaluations':int(result.nfev),'optimizer_message':str(result.message),'gradient_max_abs':float(np.abs(result.jac).max()),'training_failure':float(((q>=.5)!=y).mean()),'training_nll':float((-y*np.log(qc)-(1-y)*np.log1p(-qc)).mean()),'state':state}) + fits.append({'name':name,'arm':arm,'regularization':lam,'conditions':conditions}) + write(output/'training.json',{'status':'complete','declared_plan':plan,'fits':fits,'initial_weights_sha256':digest(output/'initial_weights.npz'),'selection_data_opened':False,'source_lock':lock,'test_data_used':False}) + # Existing validation beams were generated before their labels; they have no empty cases. + validation_report=json.loads((r/'full_frontier/r3_assessment1/report.json').read_text());val={} + for condition in plan['conditions']: + row=val_rows[condition];path=validation/row['arrays'];assert digest(path)==row['arrays_sha256'] + with np.load(path,allow_pickle=False) as f:ids=f['sample_ids'];y=f['labels'].astype(np.float64) + assert len(ids)==5120 and len(np.unique(ids))==len(ids) and y.shape==ids.shape and np.isin(y,[0,1]).all() + beam_path=r/'full_frontier/r3_assessment1/beam_predictions'/(condition+'.npz');record=next(z for z in validation_report['generation'] if z['condition']==condition) + assert digest(beam_path)==record['prediction_sha256'] and record['empty_fallbacks']==0 + beam,_=aligned(beam_path,ids);parent,_=aligned(Path(reference['current']['predictions'])/(condition+'.npz'),ids) + val[condition]={'ids':ids,'y':y,'parent':parent,'beam':beam} + assessments=[] + for fit in fits: + conditions=[];folder=output/fit['name']/'validation_predictions';folder.mkdir();variance=0. + for item in fit['conditions']: + condition=item['condition'];d=val[condition];q=predict(d['parent'],d['beam'],item['state']);y=d['y'];old=(d['parent']>=.5)!=y;new=(q>=.5)!=y;diff=old.astype(int)-new.astype(int);qc=np.clip(q.astype(float),1e-7,1-1e-7) + np.savez(folder/(condition+'.npz'),sample_ids=d['ids'],probabilities=q) + gain=float(diff.mean()/52);variance+=float(diff.var(ddof=1)/len(y)/52**2) + conditions.append({'condition':condition,'failure':float(new.mean()),'parent_failure':float(old.mean()),'nll':float((-y*np.log(qc)-(1-y)*np.log1p(-qc)).mean()),'corrected':int((old&~new).sum()),'introduced':int((~old&new).sum()),'P_gain':gain}) + gain=sum(z['P_gain'] for z in conditions);assessments.append({'name':fit['name'],'arm':fit['arm'],'regularization':fit['regularization'],'conditions':conditions,'net_corrected':sum(z['corrected']-z['introduced'] for z in conditions),'P_gain':gain,'expected_full_P':reference['current']['P']+gain,'paired_naive_P_standard_error':variance**.5}) + # Fixed ordering resolves exact ties; no basis-specific selection. + best=max(assessments,key=lambda x:x['expected_full_P']) + report={'status':'complete','assessments':assessments,'selected':best if best['P_gain']>0 else None,'reference_P':reference['current']['P'],'seconds':time.time()-started,'scope':'Six prespecified global choices across both d5r3 conditions, all other128 retained. Expected score only, no actual exported replay or publication yet. Standard errors omit selection and shot dependence. Compare then-current incumbent before any publication.','test_data_used':False} + write(output/'report.json',report);print(json.dumps(report,indent=2),flush=True) + +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/generate_beam_training.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/generate_beam_training.py new file mode 100644 index 0000000000000000000000000000000000000000..d1cbfa570d7896c46d95684b1402fd032340eacd --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/generate_beam_training.py @@ -0,0 +1,34 @@ +"""CPU fullbeam training evidence, with no hardware logical-label access.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1' +available=sorted(os.sched_getaffinity(0));os.sched_setaffinity(0,available[4:20] if len(available)>=20 else available[:16]) +from pathlib import Path +import hashlib,json,multiprocessing,sys,time +import numpy as np +root=Path(__file__).resolve().parent;r=root.parent;sys.path.insert(0,str(r/'full_frontier')) +from beam_lookahead import prepare_lookahead,decode +PLAN=None;LOOK=None + +def initialize(path): + global PLAN,LOOK + with np.load(path,allow_pickle=False) as f:PLAN={k:f[k] for k in f.files} + LOOK=prepare_lookahead(PLAN) + +def work(ds): + q,report=decode(PLAN,ds,per_sector=512,lookahead=LOOK) + return float(q),report['status']=='empty_beam' + +def main(): + declared=json.loads((root/'plan.json').read_text());out=root/'beam_training';out.mkdir(exist_ok=False);records=[];rows=[z for z in json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'] if z['distance']==5 and z['rounds']==3];started=time.time() + for row in rows: + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:ids=f['sample_ids'];packed=f['detectors'] + ds=np.unpackbits(packed,axis=1,count=row['num_detectors'],bitorder='little');values=[];flags=[];begin=time.time();context=multiprocessing.get_context('spawn');path=r/'full_frontier/plans'/(row['name']+'.npz') + with context.Pool(16,initializer=initialize,initargs=(path,)) as pool: + for index,(q,empty) in enumerate(pool.imap(work,ds,chunksize=8),start=1): + values.append(q);flags.append(empty) + if index%1024==0:print(json.dumps({'condition':row['name'],'completed':index,'total':len(ids),'seconds':time.time()-begin}),flush=True) + q=np.asarray(values,np.float32);empty=np.asarray(flags,bool);assert np.isfinite(q).all() and ((q>=0)&(q<=1)).all();output=out/(row['name']+'.npz');np.savez(output,sample_ids=ids,probabilities=q,empty=empty) + records.append({'condition':row['name'],'shots':len(ids),'empty_count':int(empty.sum()),'seconds':time.time()-begin,'output_sha256':hashlib.sha256(output.read_bytes()).hexdigest(),'arrays_sha256':row['arrays_sha256'],'schedule_sha256':hashlib.sha256(path.read_bytes()).hexdigest()}) + report={'status':'complete','records':records,'seconds':time.time()-started,'scope':'All39760 supplied d5r3training syndromes, K512float64beam/float32savedprobabilities. No hardware logical-label member read. Empty flags saved for fallback to frozen parent in fitting/runtime. No model fitting or GPUwork.','test_data_used':False,'source_sha256':{str(p.relative_to(r)):hashlib.sha256(p.read_bytes()).hexdigest() for p in [Path(__file__).resolve(),r/'full_frontier/beam_lookahead.py']}};(out/'provenance.json').write_text(json.dumps(report,indent=2)+'\n') + +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/generate_parent_training.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/generate_parent_training.py new file mode 100644 index 0000000000000000000000000000000000000000..8d3e7a2e2be55cb2da9d4e07fe1eeb7cfdf43545 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/generate_parent_training.py @@ -0,0 +1,21 @@ +"""Frozen own-parent probabilities using cached physical priors; labels unopened.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='4' +os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:4]) +from pathlib import Path +import hashlib,json,sys,time +import numpy as np +root=Path(__file__).resolve().parent;r=root.parent;reference=json.loads((r/'beam_r3/reference.json').read_text());package=Path(reference['current']['package']);config=json.loads((package/'model_config.json').read_text());sys.path.insert(0,str(package)) +from d5_contextual import NeuralD5Decoder +out=root/'parent_training';out.mkdir(exist_ok=False);decoder=NeuralD5Decoder();records=[];started=time.time() +for row in [x for x in json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'] if x['distance']==5 and x['rounds']==3]: + settings=config['neural_d5'][row['group']];passes=settings['prior_pass'];cache=r/('learned_d5/full_training_priors3' if passes==3 else 'full_prior5/cache1')/(row['name']+'.npz') + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:ids=f['sample_ids'];packed=f['detectors'] + with np.load(cache,allow_pickle=False) as f:assert np.array_equal(f['sample_ids'],ids) and np.array_equal(f['indices'],np.arange(len(ids)));prior=f[f'priors{passes}'] + parts=[];begin=time.time() + for start in range(0,len(ids),128): + parts.append(decoder.predict(package,config['conditions'][row['name']],settings,packed[start:start+128],row['num_detectors'],batch=128,initial_priors=prior[start:start+128])) + if start//128%16==0:print(json.dumps({'condition':row['name'],'completed':min(start+128,len(ids)),'total':len(ids),'seconds':time.time()-begin}),flush=True) + q=np.concatenate(parts).astype(np.float32);assert np.isfinite(q).all() and ((q>=0)&(q<=1)).all();path=out/(row['name']+'.npz');np.savez(path,sample_ids=ids,probabilities=q) + records.append({'condition':row['name'],'shots':len(ids),'prior_pass':passes,'arrays_sha256':row['arrays_sha256'],'prior_cache_sha256':hashlib.sha256(cache.read_bytes()).hexdigest(),'output_sha256':hashlib.sha256(path.read_bytes()).hexdigest(),'seconds':time.time()-begin}) +report={'status':'complete','reference':reference,'records':records,'seconds':time.time()-started,'scope':'All39760 supplied d5r3training syndromes with ownfrozenr3publication; cachedphysicalpriors allIDschecked, no labelmembers opened. CPUbatch128, no optimizerupdates.','test_data_used':False,'source_sha256':hashlib.sha256(Path(__file__).read_bytes()).hexdigest()};(out/'provenance.json').write_text(json.dumps(report,indent=2)+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/package.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/package.py new file mode 100644 index 0000000000000000000000000000000000000000..33d0f2854d19bcceb7d6ede461e76c9a23d51709 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/package.py @@ -0,0 +1,55 @@ +"""Assemble the selected global r3 correction on the then-current incumbent.""" +from pathlib import Path +import hashlib,json,shutil +from export import install +root=Path(__file__).resolve().parent;r=root.parent + +def digest(path):return hashlib.sha256(Path(path).read_bytes()).hexdigest() + +def assemble(): + plan=json.loads((root/'plan.json').read_text());report=json.loads((root/'fit1/report.json').read_text());selected=report['selected'];assert selected is not None + reference=plan['reference'];reference_package=Path(reference['current']['package']);reference_config=json.loads((reference_package/'model_config.json').read_text()) + current=json.loads((r/'CURRENT.json').read_text());score=json.loads(Path(current['score']).read_text());out=r/'context_upgrades/beam_calibration1';out.mkdir(exist_ok=False);package=out/'package';predictions=out/'predictions' + shutil.copytree(current['package'],package);shutil.copytree(current['predictions'],predictions);config=json.loads((package/'model_config.json').read_text());folder=root/'fit1'/selected['name'];new_files=set();parent_copies={};history=[] + rows={z['name']:z for z in json.loads(Path('/workspace/data/validation/manifest.json').read_text())['conditions']};(package/'beam_calibration').mkdir() + def parent_copy(value,basis): + if isinstance(value,dict): + result={} + for key,entry in value.items(): + if key=='weights': + relative=f'weights/beam-calibration-parent-{basis}-{len(parent_copies)}.npz';shutil.copyfile(reference_package/entry,package/relative);new_files.add(relative);parent_copies[relative]={'original_relative_path':entry,'sha256':digest(package/relative)};result[key]=relative + else:result[key]=parent_copy(entry,basis) + return result + if isinstance(value,list):return [parent_copy(x,basis) for x in value] + return value + gain=0. + for assessment in selected['conditions']: + name=assessment['condition'];row=rows[name];group=row['group'];record=config['conditions'][name] + assert not config['neural_d5'][group].get('blend_turbo',False) and group not in config.get('frozen_stack',{}) + for key in ['neural_d5_plan','plan','turbo_plan','conditional_plan']: + if key in record:assert digest(package/record[key])==digest(reference_package/reference_config['conditions'][name][key]),(name,key) + state_relative='beam_calibration/'+name+'.json';shutil.copyfile(folder/(name+'.json'),package/state_relative);new_files.add(state_relative) + option={'state':state_relative,'parent':parent_copy(reference_config['neural_d5'][group],row['basis'])} + if selected['arm']=='parent_plus_beam': + relative='beam_calibration/'+name+'.npz';shutil.copyfile(r/'beam_r3/plans'/(name+'.npz'),package/relative);new_files.add(relative);option.update(plan=relative,per_sector_states=512) + config['neural_d5'][group]['beam_calibration']={record['neural_d5_plan']:option} + shutil.copyfile(folder/'validation_predictions'/(name+'.npz'),predictions/(name+'.npz')) + incumbent=score['per_condition'][name]['errors']/score['per_condition'][name]['total'];gain+=(incumbent-assessment['failure'])/52 + history.append({'condition':name,'then_current_failure':incumbent,'candidate_failure':assessment['failure'],'global_arm':selected['arm'],'global_regularization':selected['regularization']}) + expected=current['P']+gain + assert expected>current['P']+1e-12,'Candidate no longer improves the incumbent' + runtime=install(package);training_sources=package/'training_sources/beam_calibration1';training_sources.mkdir() + for filename in ['core.py','fit.py','generate_beam_training.py','generate_parent_training.py','control_cpu.py','runtime_control.py','runtime.py','export.py','package.py','after_resource.py','plan.json','FIT_SOURCE_LOCK.json','PACKAGE_SOURCE_LOCK.json']: + shutil.copyfile(root/filename,training_sources/filename) + for filename in ['beam_lookahead.py','beam_cpu.py']:shutil.copyfile(r/'full_frontier'/filename,training_sources/filename) + config['architecture']='Contextual exact d3 and d5 decoders with a selected train-fitted r3 logit correction' + (package/'model_config.json').write_text(json.dumps(config,indent=2)+'\n') + provenance=json.loads((package/'training_provenance.json').read_text());provenance['beam_calibration1']={'declared_plan':plan,'training':json.loads((root/'fit1/training.json').read_text()),'training_evidence_audit':json.loads((root/'fit1/training_evidence_audit.json').read_text()),'evidence_generation':{kind:json.loads((root/kind/'provenance.json').read_text()) for kind in ['beam_training','parent_training']},'six_global_selection_results':report,'then_current_selection':history,'frozen_parent_weight_copies':parent_copies,'runtime_export_sha256':runtime,'controls':{name:json.loads((root/name).read_text()) for name in ['control_cpu.json','runtime_control_cpu.json','runtime_control_gpu.json']},'test_data_used':False} + provenance['previous_package_validation']=provenance.pop('package_validation');(package/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + with (package/'README.md').open('a') as f:f.write('\nThe selected d5 three-round correction fits a small regularized logit model on all39,760 corresponding training shots. Parent-only and parent-plus-full-DEM-beam arms were compared with three fixed regularization values globally across both bases. The parent networks are frozen session-trained models. Feature normalization uses training only, and sample IDs never enter the model. Inference computes all evidence from input syndromes and packaged geometry; no cached predictions are packaged.\n') + manifest=json.loads((package/'model_manifest.json').read_text());paths={z['path'] for z in manifest['checkpoints']}|new_files + manifest['checkpoints']=[{'path':p,'sha256':digest(package/p),'format':'npz' if p.endswith('.npz') else 'json' if p.endswith('.json') else 'stim-dem'} for p in sorted(paths)];(package/'model_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + candidate={'package':str(package),'predictions':str(predictions),'score':str(out/'score.json'),'expected_P':expected,'selection':history,'validation_metadata':{'prediction_generation':'Two cached calibration predictions replace the corresponding incumbent conditions; all26d5 actual replay required before publication.','runtime_evidence':'Full actual d5 replay and workload projection remain required; optional beam adds inference cost.'}} + (out/'candidate.json').write_text(json.dumps(candidate,indent=2)+'\n');print(json.dumps(candidate,indent=2),flush=True) + +if __name__=='__main__':assemble() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/plan.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/plan.json new file mode 100644 index 0000000000000000000000000000000000000000..8276d246d48a39ce833d6802fd8bf365a51b5b95 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/plan.json @@ -0,0 +1,91 @@ +{ + "name": "beam_calibration1", + "status": "Declared before fitting; training evidence caches starting CPUonly", + "created_utc": "2026-10-21T20:21:49.646611+00:00", + "hypothesis": "The fullDEM beam supplies complementary probabilities but raw/equalr3globalchoices failed. Fit a small train-only correction; matchedparent-only arm identifies whether beam is needed. No accuracy improvement claimed.", + "reference": { + "current": { + "package": "/run-state/workspace/research/context_upgrades/r3_pattern1/package", + "predictions": "/run-state/workspace/research/context_upgrades/r3_pattern1/predictions", + "score": "/run-state/workspace/research/context_upgrades/r3_pattern1/score.json", + "P": 0.76744384765625 + }, + "records": [ + { + "condition": "surface_code_bX_d5_r03_center_5_5", + "group": "d5_bX_center_5_5", + "record": { + "dem": "dems/surface_code_bX_d5_r03_center_5_5.dem", + "circuit_sha256": "1cbfca6766d1e142e35233b2cbeeb039f41aebf5179c46acbd45824d928a78a6", + "conditional_plan": "conditional_plans/surface_code_bX_d5_r03_center_5_5.npz", + "turbo_plan": "turbo_plans/surface_code_bX_d5_r03_center_5_5.npz", + "neural_d5_plan": "neural_d5_plans/surface_code_bX_d5_r03_center_5_5.npz" + }, + "parent_settings": { + "confidence_joint": { + "weights": "weights/confidence-joint1-1024.npz" + }, + "prior_pass": 3, + "blend_turbo": false + }, + "plan_file": "plans/surface_code_bX_d5_r03_center_5_5.npz", + "plan_sha256": "cc763f0638add95aa03f2b76b5acf8dcceb470de00182d6804366e3bfa0e3f0d", + "source_schedule_sha256": "12e9b86c551a382dade82f1a1e3efe5aa26aa29edabd0e906607c2bde8b7dfc8", + "source_DEM_sha256": "b0f53a8924b9c471b5f574382d8162f5eef40e5003a85a15fcb62335a0af0e4c" + }, + { + "condition": "surface_code_bZ_d5_r03_center_5_5", + "group": "d5_bZ_center_5_5", + "record": { + "dem": "dems/surface_code_bZ_d5_r03_center_5_5.dem", + "circuit_sha256": "12b124311b84adfeeb4aa4e61b9b11707691692ee807a13cfd90ee73cf733f6c", + "conditional_plan": "conditional_plans/surface_code_bZ_d5_r03_center_5_5.npz", + "turbo_plan": "turbo_plans/surface_code_bZ_d5_r03_center_5_5.npz", + "neural_d5_plan": "neural_d5_plans/surface_code_bZ_d5_r03_center_5_5.npz" + }, + "parent_settings": { + "weights": "weights/prior-pass1-pass5-0.npz", + "prior_pass": 5, + "blend_turbo": false, + "training_checkpoint": "prior_pass1/pass5/0" + }, + "plan_file": "plans/surface_code_bZ_d5_r03_center_5_5.npz", + "plan_sha256": "3a6746481fe2953c4b5b7615b365fccfcee538ff1b8fadc910420fb0f9ba681d", + "source_schedule_sha256": "abebe153fc07da35e24b4f5e7aa7278896c2bb632a7d094ca95bcbaa58163ed7", + "source_DEM_sha256": "ed3409474d22d849cdc7b3deb9ded28d70aeff4a53cfb4a56a0d7411f5b57f88" + } + ], + "parent_manifest_sha256": "112a3c67e31cae326474ee93b1454382c8a14cc59900ff86344113d727fe4ce0", + "parent_config_sha256": "35badea7d1c73ab42edcb1193eff22613ad6f8aa0d8281596fe2c1fd78d73925", + "parent_provenance_sha256": "953d23dedc55520b654852a848cca45c0e555be23ff433f1368c5b5de1580df8", + "scope": "Nonlearned fullDEM schedules from authorized inputs, combined only with own session-trained parent. No syndrome/label/ID rows or predictions serialized as model state.", + "test_data_used": false + }, + "training_rows": 39760, + "conditions": [ + "surface_code_bX_d5_r03_center_5_5", + "surface_code_bZ_d5_r03_center_5_5" + ], + "beam_states_per_sector": 512, + "arms": [ + "parent_only", + "parent_plus_beam" + ], + "regularization": [ + 0.0001, + 0.001, + 0.01 + ], + "initial_seeds": { + "X": 20262100, + "Z": 20262101 + }, + "initialization": "Three coefficients perbasis drawnNormal(0,.001); parent_only uses firsttwo ofsameinitialvector, parent_plus_beam allthree. Eachlambda startsfromsamebasisinitialarray. No expert weights updated.", + "features": "Base offset logit(parent). Features parentlogit and beamlogit-minus-parentlogit, bothprobabilities clipped1e-7..1-1e-7 in float64. Center/scale fit onlyontraining, scalefloor.05; prependbias. Parent_only usesbiasandparentfeature.", + "loss": "Mean logicalBCE +.5lambda sum(weights^2*[.01,1,1]); independent perbasisfits, fullbatchL-BFGS-B max500,gtol1e-8,ftol1e-12.", + "selection": "Sixglobal(arm,lambda)choices across bothd5r3conditions againstfrozenownpublishedreference, full5120validationrows each; other128conditionsretained. No perbasisarm/lambdaselection. ActualGPU/portable/fullreplay/trustedscore/relocation/runtimebudgetrequired beforepublication; comparethen-currentstrongest.", + "fallback": "Emptybeam usesfrozenparent probability before featureconstruction. Parent_onlydoesnotexecute a beam ininference.", + "test_data_used": false, + "training_beam_source_sha256": "557ea3eef701c480b90896cda3e8f69e263bc852fcce805f054a90e43d7cffe5", + "reference_sha256": "9f0bbe90c63da1d9a13a27b9ecb61b07d689c64cc41055ba2c812e5b013c2701" +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/runtime.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..c9dfe4f8aea78c18d4e03b6a54532d985aa39df6 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/runtime.py @@ -0,0 +1,37 @@ +"""Portable train-fitted logit correction, with optional full-DEM beam evidence.""" +import json +import numpy as np +from d5_prebeam_calibration_contextual import NeuralD5Decoder as PreviousDecoder +from beam_calibration_core import predict as calibrate + + +class NeuralD5Decoder(PreviousDecoder): + def __init__(self): + super().__init__();self.beam_calibration_states={};self.beam_calibration_engines={} + + def predict(self,root,record,settings,packed,num_detectors,batch=512,initial_priors=None,**kwargs): + option=settings.get('beam_calibration',{}).get(record.get('neural_d5_plan','')) + if option is None:return super().predict(root,record,settings,packed,num_detectors,batch,initial_priors=initial_priors,**kwargs) + state_path=option['state'] + if state_path not in self.beam_calibration_states: + state=json.loads((root/state_path).read_text());width=len(state['center']) + assert width in [1,2] and len(state['scale'])==width and len(state['weights'])==width+1 + assert np.isfinite(np.array(state['weights']+state['center']+state['scale'])).all() and (np.array(state['scale'])>0).all() + self.beam_calibration_states[state_path]=state + state=self.beam_calibration_states[state_path];parent_settings=option['parent'] + parent_priors=initial_priors if settings.get('prior_pass')==parent_settings.get('prior_pass') else None + parent=super().predict(root,record,parent_settings,packed,num_detectors,batch,initial_priors=parent_priors,**kwargs) + if len(state['center'])==1:return calibrate(parent,None,state) + import jax + from beam_calibration_frontier import make_decoder + path=option['plan'];states=int(option['per_sector_states']);assert states==512 + if path not in self.beam_calibration_engines: + with np.load(root/path,allow_pickle=False) as f:plan={k:f[k] for k in f.files} + assert int(plan['num_detectors'])==num_detectors and int(plan['width'])<=30 + fn=make_decoder(int(plan['width']),states);arguments=[jax.device_put(plan[k]) for k in ['probabilities','masks','measured','detector_lookup','future','active']] + self.beam_calibration_engines[path]=(fn,arguments) + fn,arguments=self.beam_calibration_engines[path];dets=np.unpackbits(packed,axis=1,count=num_detectors,bitorder='little');parts=[];flags=[] + for start in range(0,len(dets),batch): + ds=dets[start:start+batch];n=len(ds);ds=np.pad(ds,((0,batch-n),(0,0)));q,empty=fn(ds,*arguments);parts.append(np.asarray(q)[:n]);flags.append(np.asarray(empty)[:n]) + beam=np.concatenate(parts).astype(np.float32);empty=np.concatenate(flags);beam[empty]=parent[empty] + return calibrate(parent,beam,state) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/runtime_control.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/runtime_control.py new file mode 100644 index 0000000000000000000000000000000000000000..73c76379449d93cb58be5553dc9a1d83d4bd6dcd --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/runtime_control.py @@ -0,0 +1,42 @@ +"""Portable numerical composition with nonzero synthetic correction coefficients.""" +import os,argparse +p=argparse.ArgumentParser();p.add_argument('--device',choices=['cpu','gpu'],default='cpu');args=p.parse_args() +os.environ['JAX_PLATFORMS']='cpu' if args.device=='cpu' else 'cuda';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:(4 if args.device=='cpu' else 22)]) +from pathlib import Path +import hashlib,importlib.util,json,shutil,sys,tempfile,time +import numpy as np +import jax +from export import install +from core import predict +root=Path(__file__).resolve().parent;r=root.parent;reference=json.loads((root/'plan.json').read_text())['reference'];package=Path(reference['current']['package']);config=json.loads((package/'model_config.json').read_text());probes=json.loads((r/'full_frontier/uncertain_r3_probes.json').read_text())['records'];numeric=json.loads((r/'full_frontier/beam_uncertain_r3_cpu.json').read_text())['records'];rows=json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'];checks=[];batch=4 if args.device=='cpu' else 512 +assert jax.default_backend()==args.device +with tempfile.TemporaryDirectory(prefix='beam-calibration-control-') as temporary: + target=Path(temporary)/'package';shutil.copytree(package,target);exported=install(target);(target/'beam_calibration').mkdir();sys.path.insert(0,str(target)) + from d5_prebeam_calibration_contextual import NeuralD5Decoder as Old + spec=importlib.util.spec_from_file_location('calibration_export_control',target/'d5_contextual.py');module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);old,new=Old(),module.NeuralD5Decoder() + for probe in probes: + row=next(x for x in rows if x['name']==probe['condition']);record=config['conditions'][row['name']];parent_settings=config['neural_d5'][row['group']];indices=np.array(probe['input_array_positions']);passes=parent_settings['prior_pass'] + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:packed=f['detectors'][indices];ids=f['sample_ids'][indices] + assert np.array_equal(ids,np.array(probe['sample_ids'],np.uint64));prior_path=r/('learned_d5/full_training_priors3' if passes==3 else 'full_prior5/cache1')/(row['name']+'.npz') + with np.load(prior_path,allow_pickle=False) as f:assert np.array_equal(f['sample_ids'][indices],ids);prior=f[f'priors{passes}'][indices] + relative='beam_calibration/'+row['name']+'.npz';shutil.copyfile(r/'beam_r3/plans'/(row['name']+'.npz'),target/relative);expected_beam=np.array(next(z['probabilities'] for z in numeric if z['condition']==row['name'] and z['method']=='independent' and z['per_sector_states']==512),np.float32) + parent=old.predict(target,record,parent_settings,packed,row['num_detectors'],batch=batch,initial_priors=prior) + for width in [1,2]: + state={'center':[.12,-.03][:width],'scale':[2.1,.8][:width],'weights':[.03,-.09,.12][:width+1]};state_path='beam_calibration/'+row['name']+f'_width{width}.json';(target/state_path).write_text(json.dumps(state)) + option={'state':state_path,'plan':relative,'per_sector_states':512,'parent':parent_settings};settings={**parent_settings,'beam_calibration':{record['neural_d5_plan']:option}} + old_engines=set(new.beam_calibration_engines);begin=time.time();actual=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch,initial_priors=prior);seconds=time.time()-begin;expected=predict(parent,expected_beam,state);delta=float(abs(expected-actual).max());assert delta<1e-4 and ((expected>=.5)==(actual>=.5)).all() + if width==1:assert set(new.beam_calibration_engines)==old_engines and np.array_equal(expected,actual) + reverse=new.predict(target,record,settings,packed[::-1].copy(),row['num_detectors'],batch=batch,initial_priors=prior[::-1].copy());single=new.predict(target,record,settings,packed[:1],row['num_detectors'],batch=batch,initial_priors=prior[:1]);assert np.array_equal(reverse,actual[::-1]) and np.array_equal(single,actual[:1]) + live=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch);live_delta=float(abs(live-actual).max());assert live_delta<1e-4 and ((live>=.5)==(actual>=.5)).all() + checks.append({'condition':row['name'],'feature_width':width,'batch':batch,'shots':len(packed),'independent_composition_max_abs':delta,'decisions_equal':True,'order_subset_exact':True,'no_new_beam_for_parent_only':width==1,'live_cached_max_abs':live_delta,'first_call_seconds':seconds}) + with np.load(target/relative,allow_pickle=False) as f:empty_plan={k:f[k] for k in f.files} + empty_plan['probabilities']=np.zeros_like(empty_plan['probabilities']);empty_relative='beam_calibration/empty_'+row['name']+'.npz';np.savez(target/empty_relative,**empty_plan) + settings['beam_calibration'][record['neural_d5_plan']]['plan']=empty_relative;fallback=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch,initial_priors=prior);assert np.array_equal(fallback,predict(parent,parent,state));checks.append({'condition':row['name'],'forced_empty_before_feature_fallback_exact':True}) + for basis,rounds in [('X',1),('Z',25)]: + row=next(z for z in rows if z['distance']==5 and z['basis']==basis and z['rounds']==rounds);record=config['conditions'][row['name']];settings={**config['neural_d5'][row['group']],'beam_calibration':{'unused_plan':{}}} + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:packed=f['detectors'][:2] + a=old.predict(target,record,config['neural_d5'][row['group']],packed,row['num_detectors'],batch=batch);b=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch);assert np.array_equal(a,b);checks.append({'condition':row['name'],'retained_current_path_exact':True}) + peak=int((jax.devices()[0].memory_stats() or {}).get('peak_bytes_in_use',0));assert peak<75*1024**3 +report={'status':'passed','device':str(jax.devices()[0]),'checks':checks,'peak_gpu_bytes':peak,'scope':'Exported wrapper with specified nonzero synthetic correction coefficients; no hardware labels or fitting. Independent NumPy beam/reference-parent composition, parent-only path, order/subsets, live conditioning, forced empty-before-feature fallback and retained paths. GPU mode uses production B512.','exported_source_sha256':exported,'test_data_used':False,'source_sha256':{str(p.relative_to(r)):hashlib.sha256(p.read_bytes()).hexdigest() for p in [root/'runtime.py',root/'export.py',root/'core.py',Path(__file__).resolve(),r/'full_frontier/beam_jax.py']}} +(root/f'runtime_control_{args.device}.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report,indent=2),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/verify_during_recovery.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/verify_during_recovery.py new file mode 100644 index 0000000000000000000000000000000000000000..9b442c20a141090cd5b12cf8a0c0b1998e1d00f1 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/beam_calibration1/verify_during_recovery.py @@ -0,0 +1,21 @@ +"""Use the current idle GPU window; resume trajectory only after this marker.""" +from pathlib import Path +import hashlib,json,shutil,subprocess +root=Path(__file__).resolve().parent;r=root.parent +assert not subprocess.check_output(['nvidia-smi','--query-compute-apps=pid','--format=csv,noheader'],text=True).strip() +assert json.loads((r/'block_physical/benchmark_finished.json').read_text())['complete'] +assert not (r/'feedback_trajectory/trajectory1').exists() +for lock in ['FIT_SOURCE_LOCK.json','PACKAGE_SOURCE_LOCK.json']: + for path,digest in json.loads((root/lock).read_text())['source_sha256'].items():assert hashlib.sha256((r/path).read_bytes()).hexdigest()==digest,path +for filename in ['control_cpu.json','runtime_control_cpu.json']: + report=json.loads((root/filename).read_text());assert report['status']=='passed' + for path,digest in report['source_sha256'].items(): + source=r/path if '/' in path else root/path;assert hashlib.sha256(source.read_bytes()).hexdigest()==digest,path +current=json.loads((r/'CURRENT.json').read_text());selected=json.loads((root/'fit1/report.json').read_text())['selected'];assert selected['arm']=='parent_only' +subprocess.run(['python',str(r/'run_limited.py'),str(root/'runtime_control.py'),'--device','gpu'],check=True) +subprocess.run(['python',str(root/'package.py')],check=True);out=r/'context_upgrades/beam_calibration1';package=out/'package' +provenance=json.loads((package/'training_provenance.json').read_text());provenance['beam_calibration1']['queue_amendment']=json.loads((root/'QUEUE_AMENDMENT.json').read_text());provenance['beam_calibration1']['verification_controller_sha256']=hashlib.sha256(Path(__file__).read_bytes()).hexdigest();(package/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') +for filename in ['QUEUE_AMENDMENT.json','verify_during_recovery.py']:shutil.copyfile(root/filename,package/'training_sources/beam_calibration1'/filename) +subprocess.run(['python',str(r/'verify_d5_candidate.py'),str(out)],check=True) +subprocess.run(['python',str(r/'audit_completed_candidate.py'),'--candidate',str(out),'--previous',current['predictions']],check=True) +done=json.loads((out/'finished.json').read_text());(root/'beam_calibration1_finished.json').write_text(json.dumps({'complete':True,'published':done['published'],'queue_amended':True,'test_data_used':False})+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/exact_d3__exact.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/exact_d3__exact.py new file mode 100644 index 0000000000000000000000000000000000000000..1ae5a8eb3e91ada61a5870063470a6d3453cf0e0 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/exact_d3__exact.py @@ -0,0 +1,213 @@ +"""Exact detector-error-model contraction by bounded-frontier elimination.""" +import collections +import numpy as np +import stim + + +def parse_errors(dem): + errors = [] + for ins in dem.flattened(): + if ins.type != 'error': + continue + ds, logical = set(), 0 + for t in ins.targets_copy(): + if t.is_relative_detector_id(): + ds.symmetric_difference_update([t.val]) + elif t.is_logical_observable_id(): + assert t.val == 0 + logical ^= 1 + errors.append((ds, logical, ins.args_copy()[0])) + return errors + + +def elimination_order(dem, errors): + blocks = collections.defaultdict(list) + for d, c in dem.get_detector_coordinates().items(): + blocks[c[-1]].append(d) + pending = [ds for ds, _, _ in errors if ds] + active, used, order = set(), set(), [] + for _, block in sorted(blocks.items()): + remain = set(block) + while remain: + def score(d): + new = set().union(*(e for e in pending if d in e)) - used + return len(active | new), -sum(d in e for e in pending), d + d = min(remain, key=score) + related = [e for e in pending if d in e] + active |= set().union(*related) - used + active.discard(d) + pending = [e for e in pending if d not in e] + used.add(d) + remain.remove(d) + order.append(d) + assert sorted(order) == list(range(dem.num_detectors)) + return order + + +def make_plan(dem): + errors = parse_errors(dem) + order = elimination_order(dem, errors) + rank = {d: i for i, d in enumerate(order)} + groups = collections.defaultdict(list) + for source_index, (ds, logical, p) in enumerate(errors): + first = min(ds, key=rank.get) if ds else order[0] + groups[first].append((ds, logical, p, source_index)) + slots, probabilities, masks, measured, source_indices = {}, [], [], [], [] + width = 1 + for d in order: + related = groups[d] + needed = set().union(*(ds for ds, _, _, _ in related)) | {d} + for node in sorted(needed): + if node not in slots: + free = next(i for i in range(1, 64) if i not in slots.values()) + slots[node] = free + width = max(width, free+1) + for ds, logical, p, source_index in related: + mask = logical + for node in ds: + mask ^= 1 << slots[node] + probabilities.append(p) + masks.append(mask) + measured.append(-1) + source_indices.append(source_index) + probabilities.append(0) + masks.append(1 << slots.pop(d)) + measured.append(d) + source_indices.append(-1) + assert not slots + return {'probabilities': np.array(probabilities, np.float32), + 'masks': np.array(masks, np.int32), 'measured': np.array(measured, np.int32), + 'width': np.array(width, np.int32), 'num_detectors': np.array(dem.num_detectors, np.int32), + 'source_indices': np.array(source_indices, np.int32), + 'order': np.array(order, np.int32)} + + +def make_decoder(width): + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + @jax.jit + def decode(dets, probabilities, masks, measured): + f = jnp.zeros((dets.shape[0], 1 << width), jnp.float32).at[:, 0].set(1) + def step(f, args): + p, mask, d = args + def error(f): + prob = p[:, None] if p.ndim else p + return (1-prob)*f + prob*f[:, indices ^ mask] + def project(f): + obs = dets[:, d].astype(jnp.int32) + dst = (indices[None, :] & ~mask) | (obs[:, None]*mask) + g = jnp.take_along_axis(f, dst, axis=1) * ((indices & mask)==0) + return g / jnp.maximum(jnp.sum(g, axis=1, keepdims=True), 1e-30) + return jax.lax.cond(d < 0, error, project, f), None + f, _ = jax.lax.scan(step, f, (probabilities, masks, measured)) + return jnp.sum(f[:, 1::2], axis=1) / jnp.maximum(jnp.sum(f, axis=1), 1e-30) + return decode + + +def make_marginals(width): + """Per-shot derivatives of log syndrome probability give all error marginals.""" + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + def log_partition(dets, probabilities, masks, measured): + f = jnp.zeros(1 << width, jnp.float32).at[0].set(1) + def step(carry, args): + f, logz = carry + p, mask, d = args + def error(carry): + f, logz = carry + return ((1-p)*f + p*f[indices ^ mask], logz) + def project(carry): + f, logz = carry + dst = (indices & ~mask) | (dets[d].astype(jnp.int32)*mask) + g = f[dst] * ((indices & mask)==0) + norm = jnp.maximum(jnp.sum(g), 1e-30) + return g/norm, logz+jnp.log(norm) + return jax.lax.cond(d<0, error, project, carry), None + (_, logz), _ = jax.lax.scan(jax.checkpoint(step), (f, jnp.float32(0)), (probabilities, masks, measured)) + return logz + return jax.jit(jax.vmap(jax.value_and_grad(log_partition, argnums=1), in_axes=(0,None,None,None))) + + +def make_marginals_fb(width, return_observable=False): + """Forward/backward contraction without generic gather-gradient scatter.""" + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + # The syndrome likelihood marginalizes the unobserved logical bit. Keep + # that bit in the forward state for its output, but omit it from histories + # and backward messages, halving the largest buffers. + backward_width=width-1 if return_observable else width + backward_indices=jnp.arange(1 << backward_width,dtype=jnp.int32) + @jax.jit + def marginals(dets, probabilities, masks, measured): + f=jnp.zeros((dets.shape[0],1<>1 if return_observable else masks + _,grad=jax.lax.scan(backward,jnp.ones((len(dets),1<=.5)!=a['labels'][:len(p)]), 'min': p.min(), 'max': p.max()}, flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__data.py new file mode 100644 index 0000000000000000000000000000000000000000..c4a53919501917da89ad95a4807e26e9972b8426 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__data.py @@ -0,0 +1,73 @@ +from pathlib import Path +import json,sys +import numpy as np +import stim +sys.path.insert(0,'/workspace/research/exact_d3') +from exact import make_plan,parse_errors + + +def _attach_arrays(item,root,splits): + row=item['row'] + for split in splits: + a=np.load(root/split/row['arrays']);ds=np.unpackbits(a['detectors'],axis=1,count=row['num_detectors'],bitorder='little') + item[split+'_dets']=np.pad(ds,((0,0),(0,201-ds.shape[1]))) + item[split+'_labels']=a['labels'] + + +def load_all(max_ops=3072,splits=('train','validation')): + assert set(splits)<=set(['train','validation']) + root=Path('/workspace/data') + rows=[r for r in json.loads((root/'train/manifest.json').read_text())['conditions'] if r['distance']==3] + groups=sorted({r['group'] for r in rows});result=[] + cache=Path('/workspace/research/learned_exact')/f'feature_cache-v2-{max_ops}' + cache.mkdir(exist_ok=True) + for row in rows: + cached=cache/f"{row['name']}-{row['noise_sha256'][:12]}-{row['circuit_sha256'][:12]}.npz" + if cached.exists(): + item=dict(np.load(cached,allow_pickle=False));item['row']=row + _attach_arrays(item,root,splits);result.append(item);continue + dem=stim.DetectorErrorModel.from_file(root/'train'/row['noise']);plan=make_plan(dem) + assert int(plan['width'])<=10 + count=len(plan['masks']);assert count<=max_ops,(row['name'],count) + coords=dem.get_detector_coordinates() + circuit=stim.Circuit.from_file(root/'train'/row['circuit']) + qubit_coords=circuit.get_final_qubit_coordinates() + first_measurement=next(ins for ins in circuit.flattened() if ins.name in ['M','MX','MR','MRX']) + spaces=sorted(tuple(qubit_coords[t.value][:2]) for t in first_measurement.targets_copy()) + assert len(spaces)==8 + idx={(int(c[2]),tuple(c[:2])):d for d,c in coords.items()} + errors=parse_errors(dem) + context=np.full((max_ops,40),200,np.int32) + static=np.zeros((max_ops,57),np.float32) + for op,source in enumerate(plan['source_indices']): + if source<0:continue + ds,logical,p=errors[source] + t=min(int(coords[d][2]) for d in ds) if ds else 0 + for dt in range(-2,3): + for s,spatial in enumerate(spaces):context[op,(dt+2)*8+s]=idx.get((t+dt,spatial),200) + for d in ds: + c=coords[d];static[op,(int(c[2])-t+2)*8+spaces.index(tuple(c[:2]))]=1 + static[op,40]=np.log(max(p,1e-7)/(1-max(p,1e-7)))/8 + static[op,41]=logical;static[op,42]=len(ds)/4 + static[op,43]=t/25;static[op,44]=row['rounds']/25 + static[op,45]=t==0;static[op,46]=t>=row['rounds']-1 + static[op,47+groups.index(row['group'])]=1 + static[op,55]=row['basis']=='X';static[op,56]=1 + masks=np.pad(plan['masks'],(0,max_ops-count)) + measured=np.pad(plan['measured'],(0,max_ops-count),constant_values=-1) + p=np.pad(plan['probabilities'],(0,max_ops-count)) + valid=((p>0)&(measured<0)).astype(np.float32) + logits=np.log(np.clip(p,1e-7,1-1e-7)/np.clip(1-p,1e-7,1-1e-7)).astype(np.float32) + item={'row':row,'context':context,'static':static,'masks':masks,'measured':measured,'base_logits':logits,'valid':valid} + grid=np.full((26,8),200,np.int32) + measurement_indices=np.full(201,208,np.int32) + for detector,c in coords.items(): + t=int(c[2]);s=spaces.index(tuple(c[:2]));grid[t,s]=detector + measurement_indices[detector]=t*8+s + item['grid']=grid;item['measurement_indices']=measurement_indices + item['counts']=np.maximum((grid!=200).sum(axis=0),1).astype(np.float32) + temporary=cached.with_suffix('.tmp.npz') + np.savez(temporary,**{k:v for k,v in item.items() if k!='row'});temporary.replace(cached) + _attach_arrays(item,root,splits) + result.append(item) + return result diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__gradient.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..a593f553a8ba67f3ff02717ba99d33c0eeb697e8 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__gradient.py @@ -0,0 +1,43 @@ +"""Differentiable conditional logical likelihood through exact DEM elimination.""" +import jax +import jax.numpy as jnp + + +def make_loss_and_grad(width): + indices=jnp.arange(1<=1 + run_config={k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()} + run_config['original_random_seed']=20261030 + run_config['feature_mode']='events+measurements+global_density+temporal_convolutions' + run_config['temporal_seed']=20261104 + run_config['rich_checkpoint_sha256']=hashlib.sha256(args.rich_checkpoint.read_bytes()).hexdigest() + run_config['hidden_width']=128 + run_config['logit_correction_bound']=4 + run_config['starting_checkpoint_sha256']=hashlib.sha256(args.init_weights.read_bytes()).hexdigest() if args.init_weights else None + run_config['train_manifest_sha256']=hashlib.sha256(Path('/workspace/data/train/manifest.json').read_bytes()).hexdigest() + run_config['validation_manifest_sha256']=hashlib.sha256(Path('/workspace/data/validation/manifest.json').read_bytes()).hexdigest() + source_root=Path(__file__).resolve().parent;research=source_root.parent + snapshot=args.output/'training_source';snapshot.mkdir(exist_ok=True) + source_hashes={} + for source in [Path(__file__).resolve(),source_root/'temporal_model.py',source_root/'data.py',source_root/'gradient.py',research/'exact_d3/exact.py']: + name=str(source.relative_to(research)).replace('/','__') + shutil.copyfile(source,snapshot/name);source_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + run_config['training_source_sha256']=source_hashes + run_config['runtime_versions']={'jax':jax.__version__,'numpy':np.__version__,'optax':optax.__version__} + (args.output/'run_config.json').write_text(json.dumps(run_config,indent=2)+'\n') + t=time.time();dataset=load_all();print(json.dumps({'loaded':len(dataset),'seconds':time.time()-t}),flush=True) + seen=[np.zeros(len(item['train_labels']),bool) for item in dataset] + assert len({len(item['train_labels']) for item in dataset})==1, 'Matched RNG protocol assumes equal condition sizes' + parameters=({k:jnp.asarray(v) for k,v in dict(np.load(args.init_weights,allow_pickle=False)).items()} if args.init_weights else initialize_from_rich(args.rich_checkpoint,seed=20261104)) + optimizer=optax.chain(optax.clip_by_global_norm(1.),optax.adamw(learning_rate=args.learning_rate,weight_decay=1e-4)) + state=optimizer.init(parameters);exact_gradient=make_loss_and_grad(10) + rng=np.random.default_rng(args.seed);optimizer_restored=False;rng_restored=False + if args.init_weights: + optfile=args.init_weights.with_name(args.init_weights.name.replace('weights-','optimizer-')) + if optfile.exists() and not args.reset_optimizer: + leaves,treedef=jax.tree_util.tree_flatten(state);saved=np.load(optfile,allow_pickle=False) + assert len(leaves)==len(saved.files) + state=jax.tree_util.tree_unflatten(treedef,[jnp.asarray(saved[f'arr_{i}']) for i in range(len(leaves))]);optimizer_restored=True + rngfile=args.init_weights.with_name(args.init_weights.name.replace('weights-','rng-')).with_suffix('.json') + if rngfile.exists() and not args.reset_rng:rng.bit_generator.state=json.loads(rngfile.read_text());rng_restored=True + @jax.jit + def batch_gradient(parameters,ds,ys,context,static,base_logits,valid,masks,measured,grid,measurement_indices,counts): + p,pullback=jax.vjp(lambda w:probabilities(w,ds,context,static,base_logits,valid,grid,measurement_indices,counts),parameters) + loss,pred,g=exact_gradient(ds,ys,p,masks,measured) + grads=pullback(g/len(ds))[0] + return grads,jnp.mean(loss),jnp.mean((pred>=.5)!=ys) + @jax.jit + def apply_gradient(parameters,state,grads): + changes,state=optimizer.update(grads,state,parameters) + return optax.apply_updates(parameters,changes),state,optax.global_norm(grads) + exact_decode=make_decoder(10) + @jax.jit + def infer(parameters,ds,context,static,base_logits,valid,masks,measured,grid,measurement_indices,counts): + p=probabilities(parameters,ds,context,static,base_logits,valid,grid,measurement_indices,counts) + return exact_decode(ds,p,masks,measured) + def evaluate(step): + results=[];start=time.time() + for item in dataset: + n=min(args.validation_shots,len(item['validation_labels']));pred=[] + selected=np.random.default_rng(20261028).choice(len(item['validation_labels']),n,replace=False) if args.uniform_validation else np.arange(n) + vd=item['validation_dets'][selected];vy=item['validation_labels'][selected] + for begin in range(0,n,args.validation_batch): + ds=vd[begin:begin+args.validation_batch];size=len(ds) + ds=np.pad(ds,((0,args.validation_batch-size),(0,0))) + p=np.asarray(infer(parameters,ds,*[item[k] for k in ['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts']]))[:size] + pred.append(p) + p=np.concatenate(pred) + pp=np.clip(p.astype(np.float64),1e-7,1-1e-7);yy=vy.astype(np.float64) + results.append({'condition':item['row']['name'],'failure':float(np.mean((p>=.5)!=vy)), + 'negative_log_likelihood':float(np.mean(-yy*np.log(pp)-(1-yy)*np.log1p(-pp))), + 'brier':float(np.mean((p-vy)**2))}) + result={'step':step,'validation_failure':float(np.mean([r['failure'] for r in results])), + 'negative_log_likelihood':float(np.mean([r['negative_log_likelihood'] for r in results])), + 'seconds':time.time()-start,'conditions':results} + (args.output/f'validation-{step}.json').write_text(json.dumps(result,indent=2)+'\n') + np.savez(args.output/f'weights-{step}.npz',**{k:np.asarray(v) for k,v in parameters.items()}) + leaves,_=jax.tree_util.tree_flatten(state) + np.savez(args.output/f'optimizer-{step}.npz',**{f'arr_{i}':np.asarray(v) for i,v in enumerate(leaves)}) + (args.output/f'rng-{step}.json').write_text(json.dumps(rng.bit_generator.state)+'\n') + (args.output/f'checkpoint-{step}.json').write_text(json.dumps({'step':step,'new_training_shots':step*args.batch*args.accumulate_conditions, + 'unique_training_rows_seen_in_this_phase':sum(int(x.sum()) for x in seen), + 'weights':f'weights-{step}.npz','weights_sha256':hashlib.sha256((args.output/f'weights-{step}.npz').read_bytes()).hexdigest(), + 'validation':f'validation-{step}.json','complete':True},indent=2)+'\n') + print(json.dumps({k:v for k,v in result.items() if k!='conditions'}),flush=True) + evaluate(0) + records=[] + for step in range(1,args.steps+1): + # Both arms consume the same condition and row RNG draws. The control + # reuses the first condition; the mixed arm uses every drawn condition. + sampled_conditions=rng.integers(len(dataset),size=args.accumulate_conditions) + total_grads=None;loss=0.;error=0. + for microbatch in range(args.accumulate_conditions): + condition_index=int(sampled_conditions[0 if args.condition_policy=='same' else microbatch]) + item=dataset[condition_index] + ix=rng.integers(len(item['train_labels']),size=args.batch);seen[condition_index][ix]=True + grads,part_loss,part_error=batch_gradient(parameters,item['train_dets'][ix],item['train_labels'][ix], + *[item[k] for k in ['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts']]) + total_grads=grads if total_grads is None else jax.tree_util.tree_map(jnp.add,total_grads,grads) + loss+=part_loss/args.accumulate_conditions;error+=part_error/args.accumulate_conditions + total_grads=jax.tree_util.tree_map(lambda g:g/args.accumulate_conditions,total_grads) + parameters,state,norm=apply_gradient(parameters,state,total_grads) + if step%16==0 or step==1: + record={'step':step,'loss':float(loss),'error':float(error),'gradient_norm':float(norm),'elapsed':time.time()-t} + records.append(record);print(json.dumps(record),flush=True) + if not np.isfinite(record['loss']):raise ValueError('Nonfinite training loss') + if step%args.eval_every==0 or step==args.steps:evaluate(step) + provenance={'initialization':('Continue from this session\'s trained checkpoint' if args.init_weights else 'Continue from rich weights trained from random initialization in this session; new temporal parameters randomly initialized now'), + 'original_initialization':'Rich network random seed20261030; new 32-channel temporal network random seed20261104; rich parameters retained, new input rows Normal(0,.001)', + 'rich_starting_checkpoint':str(args.rich_checkpoint), + 'rich_checkpoint_sha256':hashlib.sha256(args.rich_checkpoint.read_bytes()).hexdigest(), + 'architecture':'Rich 128-unit MLP plus 32-channel temporal encoder: four residual kernel3 convolutions with dilations1/2/4/8 and channel normalization. Receptive field31 rounds. Encoder sees events, measurements and presence masks. Per-edge 177 input features. Output4*tanh.', + 'training':'Uniform condition sampling from all 104 d3 conditions; batch drawn from the supplied training split only', + 'training_source_sha256':source_hashes,'runtime_versions':run_config['runtime_versions'], + 'eligible_training_shots':sum(len(item['train_labels']) for item in dataset), + 'unique_training_rows_seen_in_this_phase':sum(int(x.sum()) for x in seen), + 'unique_rows_by_condition':{item['row']['name']:int(x.sum()) for item,x in zip(dataset,seen)}, + 'steps':args.steps,'batch_size':args.batch,'accumulate_conditions':args.accumulate_conditions, + 'effective_batch_size':args.batch*args.accumulate_conditions,'condition_policy':args.condition_policy, + 'training_draws':args.steps*args.batch*args.accumulate_conditions, + 'condition_sampling':'Draw one condition index per microbatch in both arms; same-condition control reuses the first index while mixed arm uses all indices. Both arms consume identical RNG draws with equal-sized condition pools.','optimizer':'AdamW, weight decay 1e-4, global gradient norm clip 1', + 'learning_rate':args.learning_rate,'sampling_seed':args.seed,'optimizer_restored':optimizer_restored,'rng_restored':rng_restored,'explicit_optimizer_reset':args.reset_optimizer,'explicit_rng_reset':args.reset_rng, + 'sampling_state_source':str(rngfile) if rng_restored else f'Fresh NumPy default_rng seed {args.seed}', + 'session_trained_starting_checkpoint':str(args.init_weights) if args.init_weights else None, + 'starting_checkpoint_sha256':hashlib.sha256(args.init_weights.read_bytes()).hexdigest() if args.init_weights else None, + 'starting_training_provenance':json.loads((args.init_weights.parent/'provenance.json').read_text()) if args.init_weights else None, + 'loss':'Binary conditional logical likelihood computed by exact frontier elimination', + 'validation_shots_per_condition':args.validation_shots,'uniform_validation':args.uniform_validation, + 'uniform_validation_seed':20261028 if args.uniform_validation else None,'test_data_used':False,'log':records} + (args.output/'provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + + +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__temporal_model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__temporal_model.py new file mode 100644 index 0000000000000000000000000000000000000000..28c0a97b4469d5f6172b5c011b7e3788cd0e60a0 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/condition_same1/learned_exact__temporal_model.py @@ -0,0 +1,44 @@ +"""Long temporal context for contextual exact d3 decoding.""" +import numpy as np +import jax +import jax.numpy as jnp + + +def initialize_from_rich(path,seed=20261104,channels=32): + parameters={k:jnp.asarray(v) for k,v in dict(np.load(path,allow_pickle=False)).items()} + keys=jax.random.split(jax.random.key(seed),6) + parameters['w1']=jnp.concatenate([parameters['w1'],jax.random.normal(keys[0],(channels,parameters['w1'].shape[1]))*.001],axis=0) + parameters['temporal_in']=jax.random.normal(keys[1],(24,channels))*jnp.sqrt(2/24) + parameters['temporal_bias']=jnp.zeros(channels) + for i in range(4): + parameters[f'temporal_conv_{i}']=jax.random.normal(keys[i+2],(3,channels,channels))*jnp.sqrt(1/(3*channels)) + parameters[f'temporal_conv_bias_{i}']=jnp.zeros(channels) + return parameters + + +def probabilities(parameters,dets,context_indices,static,base_logits,valid,grid,measurement_indices,counts): + event_grid=dets[:,grid] + measurement_grid=(jnp.cumsum(event_grid.astype(jnp.int32),axis=1)&1).astype(jnp.float32) + observed=(grid!=200).astype(jnp.float32) + frames=jnp.concatenate([event_grid.astype(jnp.float32),measurement_grid*observed[None,:,:], + jnp.broadcast_to(observed[None,:,:],event_grid.shape)],axis=-1) + temporal=jax.nn.silu(frames@parameters['temporal_in']+parameters['temporal_bias']) + for i,dilation in enumerate([1,2,4,8]): + mean=jnp.mean(temporal,axis=-1,keepdims=True) + normalized=(temporal-mean)*jax.lax.rsqrt(jnp.mean((temporal-mean)**2,axis=-1,keepdims=True)+1e-5) + value=jax.lax.conv_general_dilated(normalized,parameters[f'temporal_conv_{i}'],(1,),padding='SAME', + rhs_dilation=(dilation,),dimension_numbers=('NWC','WIO','NWC')) + temporal=temporal+jax.nn.silu(value+parameters[f'temporal_conv_bias_{i}']) + times=jnp.clip(jnp.rint(static[:,43]*25).astype(jnp.int32),0,25) + temporal_context=temporal[:,times,:] + measured=jnp.pad(measurement_grid.reshape(len(dets),208),((0,0),(0,1))) + events=dets[:,context_indices].astype(jnp.float32) + measurements=measured[:,measurement_indices[context_indices]].astype(jnp.float32) + density=jnp.sum(event_grid,axis=1)/counts[None,:] + features=jnp.concatenate([events,measurements, + jnp.broadcast_to(density[:,None,:],(len(dets),len(context_indices),8)), + jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape)),temporal_context],axis=-1) + h=jax.nn.silu(features@parameters['w1']+parameters['b1']) + h=jax.nn.silu(h@parameters['w2']+parameters['b2']) + delta=4*jnp.tanh((h@parameters['w3']+parameters['b3'])[...,0]) + return (jax.nn.sigmoid(base_logits[None,:]+delta)*valid[None,:]).T diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/PROPOSAL.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/PROPOSAL.json new file mode 100644 index 0000000000000000000000000000000000000000..6b16c6eecbb531b7fec7c8080ed04c66b10ee2c5 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/PROPOSAL.json @@ -0,0 +1,17 @@ +{ + "name": "conditional_edge_projection_cpu_prototype", + "created_utc": "2026-10-22T00:19:02.702150+00:00", + "status": "CPU attachment prototype only; notGPUqueued, no main training protocol or validation selection yet.", + "hypothesis": "The original reconstruction loss supervises masked positions, but the first frozen edge-projection study uses unmasked representations. Instead give each fault endpoint its representation from a deterministic pass where its own bit is hidden. Original raw syndrome features remain in the frozen edge networks.", + "frozen_encoder": "masked_pretraining/auxiliary1/weights-8192.npz", + "frozen_encoder_sha256": "c2002e838abdc41fa166248396eaf63501905f000befab6f1b2bffefe00794f8", + "frozen_parent": "Own r3 parent: X confidence_joint1024 with two frozen experts and frozen confidence gate; Z fresh2full1536 with physical prior5. All originate from random initialization in this session.", + "new_parameters": "One zero-initialized64x64 first-hidden-layer input projection per frozen expert: Xexpert0,Xexpert1,Zsingle. No updates to original MLPs, gate or encoder.", + "partition_seed": 20263501, + "partition_count": 8, + "partition": "Same fixed26x24 colors as masked_conditional_features. Eightpasses, each token taken from its own maskedpass; masked events removed before cumulative parity. Colors depend only on ideal slot positions, not IDs/rows/batch data.", + "training_only_evidence": "masked_conditional_features/probe_report.json: conditional reconstruction NLL.344819 vspositionfreq.408062 on64originalreservedtrainingrows percondition,26d5conditions. No logical accuracy claim. CPU target-bit invariance/order/composition controls passed.", + "prototype": "Reuse unchanged exact frozen-parent edge equations; compare projected-head gradients to whole-contraction autodiff, neutral behavior, frozen-state and resume/order on real training X3/Z3N2. Only six discarded control updates includingreplay.", + "future_if_pursued": "Main should match original projection2048B512 data/draws/optimizer/checkpointchoices, with only token feature construction changed. ActualGPU resources and runtime feasibility must pass first. Decision to pursue main follows completed original fullvalidation. No current queue.", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/REPLAY_BACKEND_RECOVERY_PLAN.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/REPLAY_BACKEND_RECOVERY_PLAN.json new file mode 100644 index 0000000000000000000000000000000000000000..0de9b6c58d785f94ba9427737cccd99192841de3 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/REPLAY_BACKEND_RECOVERY_PLAN.json @@ -0,0 +1,16 @@ +{ + "created_utc": "2026-10-22T01:28:37.966406+00:00", + "failure": "GPU control and training workers explicitly set cuda, but their CPU preparation supervisor passed JAX_PLATFORMS=cpu to the shared full-package replay helper. GPU telemetry showed zero GPU compute and 4MiB device memory while replay consumed CPU and Xr1 took83s versus prior14s. The queue source demonstrates inheritedCPU; /proc environment read was denied and is not claimed as evidence.", + "stopped_processes": [ + 166742, + 167145, + 174418, + 174419 + ], + "preservation": "Original helper, partial predictions, complete cached predictions, original candidate metadata and logs preserved in cpu_replay_failure. Original cached validation predictions copied back; original helper will create a fresh cache before replay. No rows or probabilities modified.", + "repair": "Shared verify_d5_candidate.py now supplies JAX_PLATFORMS=cuda explicitly to full inference and relocated subset inference. Trusted scoring unchanged. This also repairs subsequent queued experiments using that helper.", + "scientific_effect": "None: all673 frozen study source/state pins remain unchanged. No new updates, checkpoints, choices or threshold changes. Timed replay restarts on the oneH100. No candidate publication occurred.", + "original_helper_sha256": "4f3725d3292df8a0e04a64d52ef365d19ba64be92ed1b92bb369848c36c48cf1", + "corrected_helper_sha256": "1d9d817f6df96abb654c141e4b2cc31c1bfa54b190e1a9d188d8a7f9f510fece", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/RUNTIME_PRECISION_RECOVERY_PLAN.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/RUNTIME_PRECISION_RECOVERY_PLAN.json new file mode 100644 index 0000000000000000000000000000000000000000..2c7b7d4a4f830086abb4b03d8b0f5a3315e2faf7 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/RUNTIME_PRECISION_RECOVERY_PLAN.json @@ -0,0 +1,96 @@ +{ + "created_utc": "2026-10-22T01:49:36.006244+00:00", + "status": "Fresh-process numerical controls passed; only runtime encoder precision repaired; full replay pending", + "cause_evidence": "Native fresh-process warmups Z1/X1 change conditional encoder tokens by0.00159/0.00149 and logical outputs by~5.53e-5; live priors are bitwise identical. Within-process permutation/subset checks are exact. This isolates the observed discrepancy to encoder compilation context, without claiming a particular backend kernel implementation.", + "repair": "Only conditional_edge_features.token_embeddings enters jax.default_matmul_precision(highest) during encoder tracing. Physical head, prior calculation, selected weights, geometry, masks, batch size, input features and equations unchanged. No weight fitting or new checkpoint choice.", + "fixed_selection": "Original Z1024 and incumbentX from three declared cached choices. No precision variant selected using validation score.", + "criteria": "Fresh-process encoder tokens/pooling<2e-5, priorlogits<1e-6, logicalprobabilities<2e-6 and zero decision changes for cold/Z1/X1 contexts. Full original readonly subset criterion remains<2e-6; all26 actual replay and trusted130score required.", + "controls": { + "status": "passed", + "scope": "Fixed trained weights and training detectors; Only encoder matmuls use highestfloat32; originalphysicalhead/priorprecision unchanged. Earlier condition warmup differs. Freshprocesses, no fitting, labels, IDs or score selection.", + "records": [ + { + "warm": "none", + "max_abs_versus_cold": { + "tokens": 0.0, + "pooled": 0.0, + "base": 0.0, + "logical": 0.0 + }, + "decision_differences": 0 + }, + { + "warm": "Z1", + "max_abs_versus_cold": { + "tokens": 0.0, + "pooled": 0.0, + "base": 0.0, + "logical": 0.0 + }, + "decision_differences": 0 + }, + { + "warm": "X1", + "max_abs_versus_cold": { + "tokens": 0.0, + "pooled": 0.0, + "base": 0.0, + "logical": 0.0 + }, + "decision_differences": 0 + } + ], + "source_sha256": { + "control_conditional_precise_stages.py": "4b3169ac1f4d342b734f9508d4a3e2a6643c934a35777bc78f32b0f71b8049ad", + "capture_conditional_precise_stages.py": "45a47113f8f0cc54d72cdca595902ffd0e50dba14b6876026f51fe28cd096bf4" + }, + "test_data_used": false + }, + "diagnosis": { + "status": "complete", + "scope": "Fixed trained weights and training detectors; only earlier condition warmup differs. Freshprocesses, no fitting, labels, IDs or score selection.", + "records": [ + { + "warm": "none", + "max_abs_versus_cold": { + "tokens": 0.0, + "pooled": 0.0, + "base": 0.0, + "logical": 0.0 + }, + "decision_differences": 0 + }, + { + "warm": "Z1", + "max_abs_versus_cold": { + "tokens": 0.0015914440155029297, + "pooled": 0.0015914440155029297, + "base": 0.0, + "logical": 5.53131103515625e-05 + }, + "decision_differences": 0 + }, + { + "warm": "X1", + "max_abs_versus_cold": { + "tokens": 0.0014914870262145996, + "pooled": 0.0014914870262145996, + "base": 0.0, + "logical": 5.525350570678711e-05 + }, + "decision_differences": 0 + } + ], + "source_sha256": { + "diagnose_conditional_fresh_stages.py": "6e4ed8fbc2f535a7c985647d3577b4806d5f30c35f21c9488937ba9e938771c2", + "capture_conditional_fresh_stages.py": "3d884292adffc9734564eaeb0a3e25a5049c64b87b99ecef86c06d72e1202f69" + }, + "test_data_used": false + }, + "source_file": "conditional_edge_features.py", + "source_before_sha256": "0dd05676027b5bbe71dc168f89a004f746b670e5533de27b6ec809843afabd25", + "source_after_sha256": "a54e0a0f50583372509f2fbbfd507099d2331fa721217e1e016e67c8852fc22c", + "frozen_scientific_pins_unchanged": 673, + "preserved_failure": "production_portability_failure contains native full/cached predictions, score, logs, failed contract and complete pre-repair package snapshot under contract/relocated. CPU backend failure remains separately preserved.", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/TRAINING_PLAN.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/TRAINING_PLAN.json new file mode 100644 index 0000000000000000000000000000000000000000..e9aaec2137f6d78fabc1287b19ee04a3e41c2194 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/TRAINING_PLAN.json @@ -0,0 +1,28 @@ +{ + "name": "conditional_edge_projection1", + "created_utc": "2026-10-22T00:35:23.164582+00:00", + "status": "Declared after completed unmasked projection results and before actual trainer controls or main fitting", + "prototype_control": "control_cpu.json", + "steps": 2048, + "batch": 512, + "learning_rate": 0.0005, + "sampling_seed": 20263201, + "optimizer": "Fresh separate X and Z AdamW states; beta defaults, constant learning rate. Clip norm1 across active basis projections only; decay1e-4 on projections only. All frozen parents, confidence gate and auxiliary encoder excluded from optimizer and weight decay.", + "initial_projection": "Allzero64x64 per Xexpert0/Xexpert1/Zsingle; no new random learned weights. All frozen weights descend own-session random initialization.", + "data": "All516880d5 supplied training labels, uniform condition then uniform row with replacement, conditions sorted by canonical name. Same frozen r3 parent and physical prior3X/prior5Z as declared prototype.", + "feature_computation": "Frozen auxiliary encoder8192. Eight deterministic ideal-slot masking partitions, seed20263501; each token embedding is taken only from the pass hiding its own detector event. Masked bits are zeroed before cumulative parity. Endpoint pooling unchanged; no dropout or logical probability inputs. Features evaluated live.", + "validation_screens": "Fixed1024 rows percondition fromseed20261102 at0 and every512updates, including1536; diagnostic only. Full selection remains512/1024/2048.", + "selection_steps": [ + 512, + 1024, + 2048 + ], + "selection": "Full26d5 validation, all13 durations perbasis; three raw checkpoints plus retain then-current. No blends or step0 selection.", + "mandatory_controls": "All26geometry support audits; actual CPU trainer full2 versus1+resume1, all-source/hash/ID/seen/RNG/active-basis Adam audit; CUDA CPU-reference gradients and native production B512 X25/Z25 discarded memory pilots; actual exported live/cached order/subset/globalclear controls; full0 frozen-parent cache agreement before main.", + "publication": "Full assembled d5 replay, trusted all130 scoring, readonly relocation/subset/ID checks and <3600second runtime projection before atomic publication. If numerical or resource controls fail, preserve evidence and resolve before any main fitting.", + "queue": "After d3_masked_head completes, subject to all CPU and actual GPU numerical/resource/runtime controls.", + "test_data_used": false, + "pre_main_clarification": "The implemented eval-every512 trainer also emits a1536 diagnostic screen, as its earlier sibling trainers do. This monitoring-only clarification is recorded before main execution/source freeze; no checkpoint selection or training math changes. Existing discarded CPU fixtures used the previous wording.", + "matched_control": "Completed masked_edge_projection/projection1: identical initial zero projections, frozen parents/encoder, all train/cache rows, sampler seed and every draw, optimizer schedule, separate basis clocks and three full checkpoint choices. Only token feature construction changes. Every draw/state descriptor and initial weights are compared explicitly; fitted weights and optimizer moments are expected to differ.", + "validation_cache": "Reuse original projection recovered exact-runtime validation priors with explicit ID equality joins; no historical prior cache. Full zero audit still required." +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/after_d3_head.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/after_d3_head.py new file mode 100644 index 0000000000000000000000000000000000000000..fa0a89b2067ef54ce26c519d7751b604b1871f82 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/after_d3_head.py @@ -0,0 +1,27 @@ +"""Serialize projection controls, initial cache audit, fitting and publication on one H100.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1';os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) +from pathlib import Path +import json,datetime,subprocess,time +from fixtures import digest,frozen_states +from audit_run import audit +from matched_draw_audit import audit as matched_audit +root=Path(__file__).resolve().parent;r=root.parent;marker=r/'d3_masked_head/d3_masked_head1_finished.json';started=time.monotonic() +while not marker.exists(): + if time.monotonic()-started>86400:raise TimeoutError('Prior d3 masked-head study incomplete') + time.sleep(2) +assert json.loads(marker.read_text())['complete'];assert not subprocess.check_output(['nvidia-smi','--query-compute-apps=pid','--format=csv,noheader'],text=True).strip();plan=json.loads((root/'plan.json').read_text()) +for path,sha in plan['frozen_source_sha256'].items():assert digest(r/path)==sha,path +for name in ['control_cpu.json','pipeline_control_cpu.json','dual_basis_control_cpu.json','geometry_control_cpu.json','backend_control_cpu.json','runtime_control_cpu.json']: + a=json.loads((root/name).read_text());assert a['status']=='passed' + for path,sha in a['source_sha256'].items():assert digest(root/path)==sha,(name,path) +for name in ['zero_parent_audit','projection1','validation-512','validation-1024','validation-2048']:assert not (root/name).exists(),name +def call(script,*args,gpu=False,log=None): + cmd=['python']+([str(r/'run_limited.py')] if gpu else [])+[str(root/script),*map(str,args)];env=os.environ.copy();env['JAX_PLATFORMS']='cuda' if gpu else 'cpu' + if log: + with (root/log).open('w') as stream:subprocess.run(cmd,stdout=stream,stderr=subprocess.STDOUT,check=True,env=env) + else:subprocess.run(cmd,check=True,env=env) +call('backend_control.py','--device','gpu',gpu=True);call('runtime_control.py','--device','gpu',gpu=True);call('evaluate.py','--zero','--output',root/'zero_parent_audit',gpu=True,log='zero_parent_audit.log');a=json.loads((root/'zero_parent_audit/report.json').read_text());assert a['status']=='passed' and a['zero_parent_audit'] and a['shots']==133120 and all(x['frozen_parent_decision_changes']==0 for x in a['conditions']);plan.update(status='All GPU production controls and full zero-projection parent/cache agreement passed; main fitting launched',launch_utc=datetime.datetime.now(datetime.timezone.utc).isoformat());(root/'plan.json').write_text(json.dumps(plan,indent=2)+'\n') +call('train.py','--output',root/'projection1',gpu=True,log='projection1.log');evidence=audit(root/'projection1');assert evidence['training_draws']==1048576 and evidence['eligible_rows']==516880;provenance=json.loads((root/'projection1/provenance.json').read_text());matched=matched_audit(root/'projection1');provenance.update(declared_plan=plan,sampling_audit=evidence,zero_parent_audit=a,matched_draw_audit=matched);(root/'projection1/provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') +for step in plan['selection_steps']:call('evaluate.py','--weights',root/f'projection1/weights-{step}.npz','--output',root/f'validation-{step}',gpu=True,log=f'validation-{step}.log') +previous=json.loads((r/'CURRENT.json').read_text())['predictions'];call('package.py');out=r/'context_upgrades/conditional_edge_projection1';subprocess.run(['python',str(r/'verify_d5_candidate.py'),str(out)],check=True);subprocess.run(['python',str(r/'audit_completed_candidate.py'),'--candidate',str(out),'--previous',previous],check=True);(root/'conditional_edge_projection1_finished.json').write_text(json.dumps({'complete':True,'test_data_used':False})+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/audit_run.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/audit_run.py new file mode 100644 index 0000000000000000000000000000000000000000..fbd742424d157cd224fd88bc6459278a7f28c8b2 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/audit_run.py @@ -0,0 +1,34 @@ +"""Replay every condition/row draw and the separate projection Adam clocks.""" +from pathlib import Path +import argparse,json,hashlib +import numpy as np + +def digest(p):return hashlib.sha256(Path(p).read_bytes()).hexdigest() +def audit(run): + run=Path(run);config=json.loads((run/'run_config.json').read_text());assert config['start_step']==0 and not config['resume'];plan=config['training_plan'];rows=sorted([x for x in json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'] if x['distance']==5],key=lambda x:x['name']);assert len(rows)==26 and len(config['data'])==26 + for name,sha in config['source_sha256'].items():assert digest(run/'training_source'/name)==sha + for record,row in zip(config['data'],rows): + assert record['condition']==row['name'] + for split,source in record['sources'].items(): + assert digest(source['cache'])==source['cache_sha256'];path=Path('/workspace/data')/split/row['arrays'];assert digest(path)==source['arrays_sha256'] + with np.load(path,allow_pickle=False) as f:ids=f['sample_ids'] + assert hashlib.sha256(ids.tobytes()).hexdigest()==source['ID_sha256'] + rng=np.random.default_rng(plan['sampling_seed']);seen=[np.zeros(x['shots'],bool) for x in rows];counts={'X':0,'Z':0};draw_hash=hashlib.sha256();steps=[] + for step in range(config['steps']+1): + if step: + ci=int(rng.integers(26));ix=rng.integers(rows[ci]['shots'],size=config['batch']);seen[ci][ix]=True;counts[rows[ci]['basis']]+=1;draw_hash.update(np.asarray([ci,*ix],dtype='bghij',q,k)/jnp.sqrt(jnp.float32(head_width)) + score=jnp.where(valid[None,:,None,None,:],score,-1e9) + weights=jax.nn.softmax(score,axis=-1) + value=jnp.einsum('bghij,bgjhd->bgihd',weights,v).reshape(batch,groups,length,width) + value=(value@parameters[prefix+'_out']+parameters[prefix+'_out_b'])*valid[None,:,:,None] + return jnp.swapaxes(value,1,2) if axis=='time' else value + +def logits(parameters,dets,grid,basis,rounds,parent,*,training=False,key=None,config=None): + c=DEFAULT if config is None else config + features,valid=token_features(dets,grid,basis,rounds,parent);mask=valid[None,:,:,None] + x=(features@parameters['input_w']+parameters['input_b']+parameters['space_embedding'][None,None]+parameters['time_embedding'][None,:,None])*mask + if key is None:key=jax.random.key(0) + keys=jax.random.split(key,3*c['depth']) + for i in range(c['depth']): + for rank,axis in enumerate(['time','space']): + prefix=f'b{i}_{axis}';normalized=layer_norm(x,parameters[prefix+'_scale'],parameters[prefix+'_bias']) + update=attention(parameters,prefix,normalized,valid,axis,c['heads']) + x=(x+dropout(update,keys[3*i+rank],c['dropout'],training))*mask + prefix=f'b{i}_ff';normalized=layer_norm(x,parameters[prefix+'_scale'],parameters[prefix+'_bias']) + update=jax.nn.gelu(normalized@parameters[prefix+'_w1']+parameters[prefix+'_b1'])@parameters[prefix+'_w2']+parameters[prefix+'_b2'] + x=(x+dropout(update,keys[3*i+2],c['dropout'],training))*mask + x=layer_norm(x,parameters['readout_scale'],parameters['readout_bias'])*mask + pooled=x.sum(axis=(1,2))/jnp.maximum(valid.sum(),1) + base=parent_logit(parent);z=jnp.clip(base,-8,8)/8 + hidden=jax.nn.silu(jnp.concatenate([pooled,z[:,None],jnp.abs(z)[:,None]],axis=1)@parameters['readout_w1']+parameters['readout_b1']) + correction=c['correction_bound']*jnp.tanh((hidden@parameters['readout_w2']+parameters['readout_b2'])[:,0]) + return base+correction + +def predict(parameters,dets,grid,basis,rounds,parent,config=None): + return jax.nn.sigmoid(logits(parameters,dets,grid,basis,rounds,parent,config=config)) + +def binary_loss(logit,labels): + return jnp.mean(jax.nn.softplus(logit)-labels*logit) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/backend_control.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/backend_control.py new file mode 100644 index 0000000000000000000000000000000000000000..213a8003c2c539f684673b7b54753deaec6de539 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/backend_control.py @@ -0,0 +1,32 @@ +"""CPU/CUDA gradient references and actual native B512 physical-gradient pilots.""" +import argparse,os +parser=argparse.ArgumentParser();parser.add_argument('--device',choices=['cpu','gpu'],required=True);args=parser.parse_args() +os.environ['JAX_PLATFORMS']='cpu' if args.device=='cpu' else 'cuda';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false';os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:(4 if args.device=='cpu' else 22)]) +from pathlib import Path +import json,time +import numpy as np +import jax,jax.numpy as jnp,optax +from fixtures import frozen_states,training_probe,digest +from features import token_embeddings,pool_endpoints,endpoint_plan +from core import make_kernels +from state import initialize,optimizer +from train import make_updates +root=Path(__file__).resolve().parent;encoder,parents,_,_,hashes=frozen_states();assert jax.default_backend()==args.device;rng=np.random.default_rng(20263210);projection=jax.tree_util.tree_map(lambda a:jnp.asarray(rng.normal(0,.01,a.shape),a.dtype),initialize());sg,_,mg,_=make_kernels();gradients={'X':mg,'Z':sg};encode=jax.jit(token_embeddings);pool=jax.jit(pool_endpoints);reference={};checks=[] +for basis in ['X','Z']: + row,dets,y,base,fields,source=training_probe(basis,3,2);indices,weights=endpoint_plan(fields[0],fields[1],fields[2],fields[6]) + with jax.default_matmul_precision('highest'): + embedding=pool(encode(encoder,dets,fields[5],basis=='X',3),indices,weights);g,loss,error=gradients[basis](projection[basis],parents[basis],dets,y,base,embedding,*fields) + arrays={'embedding':np.asarray(embedding),'loss':np.asarray(loss),**{f'gradient_{i}':np.asarray(v) for i,v in enumerate(jax.tree_util.tree_leaves(g))}};check={'condition':row['name'],'loss':float(loss),'gradient_norm':float(optax.global_norm(g))} + if args.device=='cpu':reference.update({basis+'__'+k:v for k,v in arrays.items()}) + else: + with np.load(root/'backend_reference.npz',allow_pickle=False) as f: + differences=[a-f[basis+'__'+k] for k,a in arrays.items() if k.startswith('gradient')];maximum=max(float(abs(x).max()) for x in differences);relative=float(np.sqrt(sum(np.sum(x.astype(float)**2) for x in differences)/sum(np.sum(f[basis+'__'+k].astype(float)**2) for k in arrays if k.startswith('gradient'))));emb_error=float(abs(arrays['embedding']-f[basis+'__embedding']).max());loss_error=float(abs(arrays['loss']-f[basis+'__loss'])) + assert emb_error<2e-5 and loss_error<2e-6 and maximum<1e-5 and relative<1e-4,(basis,emb_error,loss_error,maximum,relative);check.update(embedding_max_abs=emb_error,loss_max_abs=loss_error,gradient_max_abs=maximum,gradient_relative_l2=relative) + checks.append(check);print(json.dumps(check),flush=True) +if args.device=='cpu':np.savez(root/'backend_reference.npz',**reference) +pilots=[] +if args.device=='gpu': + original=[np.asarray(x).copy() for x in jax.tree_util.tree_leaves((encoder,parents))];zero=initialize();optim=optimizer(.0005);updates=make_updates(optim) + for basis in ['X','Z']: + row,dets,y,base,fields,source=training_probe(basis,25,512);indices,weights=endpoint_plan(fields[0],fields[1],fields[2],fields[6]);started=time.monotonic();embedding=pool(encode(encoder,dets,fields[5],basis=='X',25),indices,weights);result=updates[basis](zero[basis],optim.init(zero[basis]),parents[basis],dets,y,base,embedding,*fields);jax.block_until_ready(result);peak=int((jax.devices()[0].memory_stats() or {}).get('peak_bytes_in_use',0));assert 0self.max_executables:self.executables.popitem(last=False) + self.executables.move_to_end(key) + return self.executables[key](*args) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/conditional_tokens.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/conditional_tokens.py new file mode 100644 index 0000000000000000000000000000000000000000..c1e5d1454ff106599210e3d83abc2d2fc0be44e7 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/conditional_tokens.py @@ -0,0 +1,16 @@ +"""Deterministic conditional token representations from eight masking partitions.""" +import jax +import jax.numpy as jnp +from aux_encoder import features,encode + +def conditional_tokens(parameters,dets,grid,basis,rounds,colors): + valid=grid!=600 + def body(color,result): + mask=jnp.broadcast_to((colors==color)&valid,(len(dets),26,24));inputs,active,_,_=features(dets,grid,basis,rounds,mask);tokens=encode(parameters,inputs,active,training=False);return jnp.where(mask[:,:,:,None],tokens,result) + return jax.lax.fori_loop(0,8,body,jnp.zeros((len(dets),26,24,parameters['input_w'].shape[-1]),jnp.float32)) + +def conditional_logits(parameters,dets,grid,basis,rounds,colors): + h=conditional_tokens(parameters,dets,grid,basis,rounds,colors);return (h@parameters['mask_w'])[:,:,:,0]+parameters['mask_b'][0] + +# Fixed ideal-slot partition, generated once by seed20263501. +COLORS=[[6, 2, 0, 7, 3, 2, 3, 1, 2, 2, 4, 7, 4, 2, 4, 0, 2, 5, 0, 3, 6, 3, 6, 4], [1, 5, 4, 4, 5, 4, 5, 1, 0, 1, 3, 1, 5, 1, 7, 5, 1, 4, 6, 2, 3, 5, 6, 6], [7, 7, 6, 1, 2, 7, 7, 7, 1, 6, 0, 2, 6, 7, 2, 5, 1, 3, 4, 0, 4, 5, 1, 5], [4, 3, 0, 3, 3, 1, 5, 6, 0, 3, 2, 7, 4, 4, 4, 5, 7, 3, 2, 0, 0, 6, 1, 5], [4, 2, 7, 5, 1, 6, 3, 2, 1, 1, 0, 6, 7, 5, 2, 4, 4, 1, 5, 0, 2, 7, 1, 7], [3, 4, 2, 5, 2, 1, 6, 0, 0, 7, 2, 5, 4, 7, 6, 6, 4, 7, 6, 4, 7, 2, 3, 3], [0, 3, 4, 7, 4, 7, 2, 4, 7, 5, 7, 2, 1, 6, 4, 0, 6, 0, 4, 4, 4, 6, 1, 0], [5, 3, 7, 0, 7, 2, 3, 5, 5, 7, 0, 2, 0, 4, 0, 4, 1, 1, 1, 1, 4, 2, 7, 3], [0, 3, 2, 5, 7, 5, 1, 7, 6, 2, 0, 5, 7, 1, 6, 6, 6, 4, 6, 0, 6, 6, 2, 1], [6, 0, 4, 7, 1, 6, 2, 7, 0, 2, 4, 1, 3, 7, 7, 0, 3, 5, 4, 4, 5, 2, 2, 1], [2, 1, 5, 3, 2, 6, 4, 4, 4, 4, 0, 4, 0, 0, 2, 4, 3, 6, 3, 7, 4, 7, 2, 7], [2, 0, 4, 0, 4, 1, 6, 0, 1, 5, 2, 7, 4, 3, 6, 6, 0, 3, 0, 2, 1, 0, 6, 1], [1, 6, 1, 5, 4, 4, 1, 4, 6, 3, 0, 2, 6, 1, 1, 6, 0, 6, 0, 4, 6, 3, 3, 7], [5, 1, 7, 0, 2, 0, 0, 5, 3, 1, 2, 0, 3, 3, 3, 1, 3, 1, 3, 3, 7, 5, 3, 5], [2, 6, 4, 1, 7, 4, 7, 3, 2, 6, 4, 1, 2, 4, 6, 6, 3, 6, 5, 7, 3, 4, 6, 0], [7, 5, 4, 3, 0, 4, 6, 1, 4, 1, 2, 6, 5, 1, 3, 1, 3, 0, 7, 6, 5, 0, 0, 6], [1, 1, 3, 2, 6, 6, 7, 2, 5, 7, 7, 3, 6, 5, 4, 2, 6, 2, 5, 3, 3, 3, 1, 2], [4, 7, 4, 1, 4, 4, 1, 2, 0, 6, 6, 1, 1, 1, 6, 1, 0, 1, 4, 2, 4, 0, 3, 6], [7, 7, 6, 7, 0, 0, 7, 4, 5, 2, 4, 2, 2, 0, 6, 5, 6, 3, 5, 1, 6, 3, 2, 3], [6, 3, 5, 1, 7, 4, 0, 3, 4, 4, 5, 7, 5, 3, 1, 3, 2, 0, 7, 6, 5, 7, 3, 6], [6, 1, 7, 6, 4, 5, 3, 0, 6, 4, 4, 7, 5, 4, 1, 1, 3, 7, 5, 2, 0, 1, 7, 0], [1, 2, 3, 5, 7, 2, 1, 4, 4, 7, 7, 2, 1, 1, 5, 3, 7, 3, 4, 7, 3, 0, 0, 4], [7, 6, 0, 0, 7, 4, 2, 6, 0, 0, 2, 5, 5, 3, 4, 1, 0, 2, 2, 2, 3, 0, 2, 3], [1, 5, 2, 6, 3, 6, 0, 4, 5, 1, 6, 5, 4, 3, 5, 3, 6, 1, 3, 0, 0, 2, 7, 7], [5, 5, 6, 7, 3, 0, 7, 3, 6, 5, 4, 1, 4, 3, 6, 6, 5, 0, 4, 0, 7, 3, 3, 2], [6, 5, 3, 2, 6, 7, 6, 7, 7, 7, 5, 1, 1, 0, 1, 7, 6, 2, 4, 2, 0, 0, 1, 2]] diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/confidence_joint.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/confidence_joint.py new file mode 100644 index 0000000000000000000000000000000000000000..edd86a7ff804508c6566bb31150bd8e3058ddf3b --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/confidence_joint.py @@ -0,0 +1,38 @@ +"""Prototype confidence-aware gate; not yet a trained or queued candidate.""" +import jax.numpy as jnp +from joint_mixture import gate_features as syndrome_features +from joint_mixture import initialize_gate as initialize_base_gate +from joint_mixture import gate_logit, mixture_terms, mix_predictions + +GATE_FEATURES = 45 + + +def confidence_features(predictions): + """Four bounded shot-local features; no labels or batch statistics.""" + p = jnp.clip(predictions, 1e-6, 1-1e-6) + z = jnp.clip(jnp.log(p)-jnp.log1p(-p), -8., 8.)/8 + return jnp.concatenate([z,jnp.sqrt(z*z+1e-4)],axis=1) + + +def gate_features(dets, grid, static, predictions): + result = jnp.concatenate([syndrome_features(dets,grid,static),confidence_features(predictions)],axis=1) + assert result.shape==(len(dets),GATE_FEATURES) + return result + + +def initialize_gate(seed, hidden=32): + """Preserve the original seeded 41-input trunk; append zero new-input rows.""" + gate = initialize_base_gate(seed,hidden) + gate['w1'] = jnp.concatenate([gate['w1'],jnp.zeros((4,hidden),gate['w1'].dtype)],axis=0) + return gate + + +def expert_adjoint_factors(losses, labels, responsibility, gate_prediction_adjoint): + """Per-shot factors on expert BCE adjoints, including the confidence path. + + gate_prediction_adjoint is dL/dq through the gate only, for each per-shot + loss (not its batch mean). dq/dedge=(1-2*y)*exp(-BCE)*dBCE/dedge. + Labels enter this loss derivative only; all gate inputs use predictions. + """ + sign = 1-2*labels.astype(losses.dtype) + return responsibility+sign[:,None]*jnp.exp(-losses)*gate_prediction_adjoint diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/contextual.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/contextual.py new file mode 100644 index 0000000000000000000000000000000000000000..4cf2c34c34c65fed528cfd004dd0e82b4b70f69b --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/contextual.py @@ -0,0 +1,40 @@ +"""Portable structured decoder with frozen endpoint embeddings and learned projections.""" +import numpy as np +from d5_preconditional_edge_contextual import NeuralD5Decoder as PreviousDecoder +from conditional_edge_compiled import PersistentCompiled +from conditional_edge_features import token_embeddings,pool_endpoints,endpoint_plan +from conditional_edge_core import make_kernels + +class NeuralD5Decoder(PreviousDecoder): + def __init__(self): + super().__init__();_,single,_,mixture=make_kernels();self.edge_infer={'X':PersistentCompiled(mixture),'Z':PersistentCompiled(single)};self.edge_encode=PersistentCompiled(token_embeddings);self.edge_pool=PersistentCompiled(pool_endpoints);self.edge_weights={} + + def predict(self,root,record,settings,packed,num_detectors,batch=512,initial_priors=None,**kwargs): + extension=settings.get('conditional_edge_projection') + if extension is None:return super().predict(root,record,settings,packed,num_detectors,batch,initial_priors=initial_priors,**kwargs) + import jax.numpy as jnp + from conditional import load_state + from turbo import TurboDecoder + parent_settings=extension['parent_settings'];assert settings['prior_pass']==parent_settings['prior_pass'] and not parent_settings.get('blend_turbo',False) + plan=dict(np.load(root/record['neural_d5_plan'],allow_pickle=False));basis='X' if bool(np.max(plan['static'][:,127])) else 'Z';rounds=int(round(float(np.max(plan['static'][:,124]))*25));key=(extension['weights'],extension['encoder_weights'],basis) + if key not in self.edge_weights: + with np.load(root/extension['weights'],allow_pickle=False) as f:projection={k:jnp.asarray(f['X_'+k]) for k in ['expert0','expert1']} if basis=='X' else jnp.asarray(f['Z']) + with np.load(root/extension['encoder_weights'],allow_pickle=False) as f:encoder={k:jnp.asarray(f[k]) for k in f.files} + path=parent_settings['confidence_joint']['weights'] if basis=='X' else parent_settings['weights'] + with np.load(root/path,allow_pickle=False) as f: + if basis=='X': + parent={k:{} for k in ['expert0','expert1','gate']} + for name in f.files:part,field=name.split('__',1);parent[part][field]=jnp.asarray(f[name]) + else:parent={k:jnp.asarray(f[k]) for k in f.files} + self.edge_weights[key]=(projection,encoder,parent) + projection,encoder,parent=self.edge_weights[key];dets=np.unpackbits(packed,axis=1,count=num_detectors,bitorder='little');passes=settings['prior_pass'];assert passes==(3 if basis=='X' else 5) + if initial_priors is not None:priors=np.asarray(initial_priors) + elif 'turbo_plan' in record: + decoder=TurboDecoder(load_state(root/record['turbo_plan']),batch=batch);_,captured=decoder.predict(dets,damping=1.,iterations=passes,last_only=True,message_clip=8.,return_priors_at=(passes,),skip_final_logical=True);priors=captured[passes];del decoder + else: + fixed=dict(np.load(root/record['plan'],allow_pickle=False))['probabilities'];priors=np.broadcast_to(fixed[None],(len(dets),len(fixed))) + length=((int(plan['operation_count'])+511)//512)*512;assert priors.shape==(len(dets),int(plan['operation_count']));base=np.log(np.clip(priors,1e-7,1-1e-7)/np.clip(1-priors,1e-7,1-1e-7));base=np.pad(base,((0,0),(0,length-base.shape[1]))).astype(np.float32);dets=np.pad(dets,((0,0),(0,601-num_detectors))) + fields=[plan[k][:length] for k in ['context','static','valid','masks','measured']]+[plan[k] for k in ['grid','measurement_indices','counts']];indices,weights=endpoint_plan(fields[0],fields[1],fields[2],fields[6]);output=[] + for pos in range(0,len(dets),batch): + count=min(batch,len(dets)-pos);ds=np.pad(dets[pos:pos+batch],((0,batch-count),(0,0)));z=np.pad(base[pos:pos+batch],((0,batch-count),(0,0)));tokens=self.edge_encode(encoder,ds,plan['grid'],basis=='X',rounds);embedding=self.edge_pool(tokens,indices,weights);q=self.edge_infer[basis](projection,parent,ds,z,embedding,*fields);output.append(np.asarray(q)[:count]) + return np.concatenate(output).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/control_cpu.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/control_cpu.py new file mode 100644 index 0000000000000000000000000000000000000000..eacccadf4ee378926e5142722e4ea3c4cf9e1e7a --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/control_cpu.py @@ -0,0 +1,45 @@ +"""Endpoint geometry, neutral attachment, independent gradients and discarded state replay.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:4]) +from pathlib import Path +import json,tempfile +import numpy as np +import jax,jax.numpy as jnp,optax +from fixtures import frozen_states,training_probe,digest +from features import endpoint_plan,token_embeddings,pool_endpoints +from model import probabilities +from base_edge import probabilities as original_probabilities +from confidence_joint import gate_features,gate_logit,mix_predictions +from core import make_kernels +from exact import make_decoder +root=Path(__file__).resolve().parent;encoder,parents,_,_,hashes=frozen_states();original=[np.asarray(x).copy() for x in jax.tree_util.tree_leaves((encoder,parents))];sg,si,mg,mi=make_kernels();decode=make_decoder(14);checks=[];rng=np.random.default_rng(20263210);encode=jax.jit(token_embeddings) +for basis in ['X','Z']: + row,dets,y,base,fields,source=training_probe(basis,3);context,static,valid,masks,measured,grid,mapping,counts=fields;indices,weight=endpoint_plan(context,static,valid,mapping);tokens=encode(encoder,dets,grid,basis=='X',3);embedding=jax.jit(pool_endpoints)(tokens,indices,weight);reference=np.zeros(embedding.shape,np.float64);flat=np.asarray(tokens).reshape(len(dets),624,64) + for op in range(len(context)): + selected=indices[op][weight[op]>0] + if len(selected):reference[:,op]=flat[:,selected].astype(float).mean(axis=1) + pool_error=float(abs(np.asarray(embedding)-reference).max());assert pool_error<1e-6;assert not np.asarray(embedding)[:,valid==0].any() + frozen=parents[basis];is_mixture=basis=='X';projection={k:jnp.zeros((64,64),jnp.float32) for k in ['expert0','expert1']} if is_mixture else jnp.zeros((64,64),jnp.float32);infer=mi if is_mixture else si;gradient=mg if is_mixture else sg + zero=infer(projection,frozen,dets,base,embedding,*fields) + if is_mixture: + q=jnp.stack([decode(dets,original_probabilities(frozen[k],dets,base,context,static,valid,grid,mapping,counts),masks,measured) for k in ['expert0','expert1']],axis=1);expected=mix_predictions(q,gate_logit(frozen['gate'],gate_features(dets,grid,static,q))) + else:expected=decode(dets,original_probabilities(frozen,dets,base,context,static,valid,grid,mapping,counts),masks,measured) + neutral=float(abs(zero-expected).max());assert neutral<2e-7 + trial=jax.tree_util.tree_map(lambda v:jnp.asarray(rng.normal(0,.01,v.shape),v.dtype),projection) + def independent_loss(p): + q=infer(p,frozen,dets,base,embedding,*fields);return -jnp.mean(jnp.log(jnp.where(y.astype(bool),q,1-q))) + reference_loss,reference_gradient=jax.jit(jax.value_and_grad(independent_loss))(trial);actual,loss,error=gradient(trial,frozen,dets,y,base,embedding,*fields);leaves=jax.tree_util.tree_leaves(actual);ref=jax.tree_util.tree_leaves(reference_gradient);relative=float(np.sqrt(sum(np.sum((np.asarray(a)-np.asarray(b)).astype(float)**2) for a,b in zip(leaves,ref))/max(sum(np.sum(np.asarray(b,dtype=float)**2) for b in ref),1e-24)));assert relative<1e-4 and abs(float(loss-reference_loss))<1e-6 and float(optax.global_norm(actual))>0,(basis,relative,float(loss-reference_loss)) + optim=optax.chain(optax.clip_by_global_norm(1),optax.adamw(.001,weight_decay=1e-4));state=optim.init(projection) + @jax.jit + def update(p,s): + g,loss,_=gradient(p,frozen,dets,y,base,embedding,*fields);changes,s=optim.update(g,s,p);return optax.apply_updates(p,changes),s,loss + p1,s1,_=update(projection,state);p2,s2,_=update(p1,s1);leaves,tree=jax.tree_util.tree_flatten((p1,s1)) + with tempfile.TemporaryDirectory(prefix='masked-edge-control-') as temp: + path=Path(temp)/'state.npz';np.savez(path,**{f'arr_{i}':np.asarray(v) for i,v in enumerate(leaves)}) + with np.load(path,allow_pickle=False) as f:loaded=jax.tree_util.tree_unflatten(tree,[jnp.asarray(f[f'arr_{i}']) for i in range(len(leaves))]) + rp,rs,_=update(*loaded) + assert all(np.array_equal(a,b) for a,b in zip(jax.tree_util.tree_leaves((p2,s2)),jax.tree_util.tree_leaves((rp,rs)))) + assert int(s2[1][0].count)==2 and float(optax.global_norm(p2))>0 + q=infer(p2,frozen,dets,base,embedding,*fields);reverse=infer(p2,frozen,dets[::-1].copy(),base[::-1].copy(),embedding[::-1],*fields);assert np.array_equal(q,reverse[::-1]);assert all(np.array_equal(a,b) for a,b in zip(original,jax.tree_util.tree_leaves((encoder,parents)))) + checks.append({'condition':row['name'],'endpoint_pool_reference_max_abs':pool_error,'neutral_original_probability_max_abs':neutral,'independent_whole_contraction_gradient_relative_l2':relative,'loss_difference':float(abs(loss-reference_loss)),'trainable_projection_count':sum(v.size for v in jax.tree_util.tree_leaves(projection)),'actual_projection_gradient_norm':float(optax.global_norm(actual)),'frozen_encoder_parent_and_gate_exact':True,'two_updates_plus_serialized_resume_exact':True,'order_exact':True,'source':source});print(json.dumps(checks[-1]),flush=True) +report={'status':'passed','checks':checks,'discarded_optimizer_updates_including_resume':6,'scope':'CPU prototype only, four primary discarded updates plus two resumed replays. No main optimizer schedule, fitting or validation selection. No GPU queue.','source_sha256':{p.name:digest(p) for p in root.glob('*.py')},'frozen_weights_sha256':hashes,'test_data_used':False};(root/'control_cpu.json').write_text(json.dumps(report,indent=2)+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/core.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/core.py new file mode 100644 index 0000000000000000000000000000000000000000..fb4fd01bb1b68f654b4a488bbf921e11fc421c95 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/core.py @@ -0,0 +1,30 @@ +"""Projection-only exact gradients, including the frozen confidence gate's input path.""" +import jax +import jax.numpy as jnp +from model import probabilities +from checkpoint_gradient import make_loss_and_grad +from exact import make_decoder +from confidence_joint import gate_features,gate_logit,mixture_terms,mix_predictions,expert_adjoint_factors + +def make_kernels(width=14,block_size=32): + exact=make_loss_and_grad(width,block_size);decode=make_decoder(width) + @jax.jit + def single_gradient(projection,frozen,dets,labels,base,embedding,context,static,valid,masks,measured,grid,mapping,counts): + p,pull=jax.vjp(lambda v:probabilities(v,frozen,dets,base,embedding,context,static,valid,grid,mapping,counts),projection);loss,prediction,adjoint=exact(dets,labels,p,masks,measured) + return pull(adjoint/len(dets))[0],loss.mean(),jnp.mean((prediction>=.5)!=labels) + @jax.jit + def single_infer(projection,frozen,dets,base,embedding,context,static,valid,masks,measured,grid,mapping,counts): + return decode(dets,probabilities(projection,frozen,dets,base,embedding,context,static,valid,grid,mapping,counts),masks,measured) + @jax.jit + def mixture_gradient(projections,frozen,dets,labels,base,embedding,context,static,valid,masks,measured,grid,mapping,counts): + p0,pull0=jax.vjp(lambda v:probabilities(v,frozen['expert0'],dets,base,embedding,context,static,valid,grid,mapping,counts),projections['expert0']);loss0,pred0,g0=exact(dets,labels,p0,masks,measured) + p1,pull1=jax.vjp(lambda v:probabilities(v,frozen['expert1'],dets,base,embedding,context,static,valid,grid,mapping,counts),projections['expert1']);loss1,pred1,g1=exact(dets,labels,p1,masks,measured) + losses=jnp.stack([loss0,loss1],axis=1);prediction=jnp.stack([pred0,pred1],axis=1) + z,pull_gate=jax.vjp(lambda q:gate_logit(frozen['gate'],gate_features(dets,grid,static,q)),prediction);loss,responsibility,adjoint,coefficients=mixture_terms(losses,z);extra=pull_gate(adjoint)[0];factor=expert_adjoint_factors(losses,labels,responsibility,extra) + gradient={'expert0':pull0(g0*factor[:,0][None]/len(dets))[0],'expert1':pull1(g1*factor[:,1][None]/len(dets))[0]};q=mix_predictions(prediction,z) + return gradient,loss.mean(),jnp.mean((q>=.5)!=labels) + @jax.jit + def mixture_infer(projections,frozen,dets,base,embedding,context,static,valid,masks,measured,grid,mapping,counts): + q=jnp.stack([decode(dets,probabilities(projections[k],frozen[k],dets,base,embedding,context,static,valid,grid,mapping,counts),masks,measured) for k in ['expert0','expert1']],axis=1) + return mix_predictions(q,gate_logit(frozen['gate'],gate_features(dets,grid,static,q))) + return single_gradient,single_infer,mixture_gradient,mixture_infer diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/data.py new file mode 100644 index 0000000000000000000000000000000000000000..fe8a634ed17f258df46dcabfd138653a10595955 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/data.py @@ -0,0 +1,38 @@ +"""Full authorized hardware data with the frozen parent's physical-prior cache lineage.""" +from pathlib import Path +import json,hashlib +import numpy as np +from fixtures import frozen_states,digest,ROOT,RESEARCH +from features import endpoint_plan + +def align(ids,target): + assert len(np.unique(ids))==len(ids);order=np.argsort(ids);location=np.searchsorted(ids[order],target);assert np.all(location=0)&(probability<=1)).all();np.savez(a.output/(row['name']+'.npz'),sample_ids=ids,probabilities=probability);q=np.clip(probability.astype(np.float64),1e-7,1-1e-7);record={'condition':row['name'],'basis':basis,'rounds':row['rounds'],'shots':len(probability),'failure':float(np.mean((probability>=.5)!=y)),'negative_log_likelihood':float(np.mean(-y*np.log(q)-(1-y)*np.log1p(-q)))} + if a.zero: + with np.load(Path(reference['predictions'])/(row['name']+'.npz'),allow_pickle=False) as f:old=f['probabilities'][align(f['sample_ids'],ids)] + difference=float(abs(probability-old).max());decisions=int(np.sum((probability>=.5)!=(old>=.5)));assert difference<1e-5 and decisions==0,(row['name'],difference,decisions);record.update(frozen_parent_max_abs=difference,frozen_parent_decision_changes=decisions) + records.append(record);print(json.dumps(record),flush=True) + report={'status':'passed','zero_parent_audit':a.zero,'weights':None if a.zero else str(a.weights),'weights_sha256':None if a.zero else digest(a.weights),'shots':sum(x['shots'] for x in records),'d5_failure':float(np.mean([x['failure'] for x in records])),'negative_log_likelihood':float(np.mean([x['negative_log_likelihood'] for x in records])),'failure_by_basis':{b:float(np.mean([x['failure'] for x in records if x['basis']==b])) for b in ['X','Z']},'conditions':records,'data':data_record,'frozen_weights_sha256':hashes,'source_sha256':{n:digest(root/n) for n in ['evaluate.py','model.py','core.py','features.py','conditional_tokens.py','data.py','fixtures.py','state.py','aux_encoder.py','axial_base.py','confidence_joint.py','joint_mixture.py','checkpoint_gradient.py','exact.py']},'seconds':time.monotonic()-started,'selection_scope':'Step0 is a mandatory cache/neutral audit only and is excluded from candidate choices.' if a.zero else 'One originally declared full-validation checkpoint; retain all26conditions.','test_data_used':False};(a.output/'report.json').write_text(json.dumps(report,indent=2)+'\n') +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/exact.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/exact.py new file mode 100644 index 0000000000000000000000000000000000000000..1ae5a8eb3e91ada61a5870063470a6d3453cf0e0 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/exact.py @@ -0,0 +1,213 @@ +"""Exact detector-error-model contraction by bounded-frontier elimination.""" +import collections +import numpy as np +import stim + + +def parse_errors(dem): + errors = [] + for ins in dem.flattened(): + if ins.type != 'error': + continue + ds, logical = set(), 0 + for t in ins.targets_copy(): + if t.is_relative_detector_id(): + ds.symmetric_difference_update([t.val]) + elif t.is_logical_observable_id(): + assert t.val == 0 + logical ^= 1 + errors.append((ds, logical, ins.args_copy()[0])) + return errors + + +def elimination_order(dem, errors): + blocks = collections.defaultdict(list) + for d, c in dem.get_detector_coordinates().items(): + blocks[c[-1]].append(d) + pending = [ds for ds, _, _ in errors if ds] + active, used, order = set(), set(), [] + for _, block in sorted(blocks.items()): + remain = set(block) + while remain: + def score(d): + new = set().union(*(e for e in pending if d in e)) - used + return len(active | new), -sum(d in e for e in pending), d + d = min(remain, key=score) + related = [e for e in pending if d in e] + active |= set().union(*related) - used + active.discard(d) + pending = [e for e in pending if d not in e] + used.add(d) + remain.remove(d) + order.append(d) + assert sorted(order) == list(range(dem.num_detectors)) + return order + + +def make_plan(dem): + errors = parse_errors(dem) + order = elimination_order(dem, errors) + rank = {d: i for i, d in enumerate(order)} + groups = collections.defaultdict(list) + for source_index, (ds, logical, p) in enumerate(errors): + first = min(ds, key=rank.get) if ds else order[0] + groups[first].append((ds, logical, p, source_index)) + slots, probabilities, masks, measured, source_indices = {}, [], [], [], [] + width = 1 + for d in order: + related = groups[d] + needed = set().union(*(ds for ds, _, _, _ in related)) | {d} + for node in sorted(needed): + if node not in slots: + free = next(i for i in range(1, 64) if i not in slots.values()) + slots[node] = free + width = max(width, free+1) + for ds, logical, p, source_index in related: + mask = logical + for node in ds: + mask ^= 1 << slots[node] + probabilities.append(p) + masks.append(mask) + measured.append(-1) + source_indices.append(source_index) + probabilities.append(0) + masks.append(1 << slots.pop(d)) + measured.append(d) + source_indices.append(-1) + assert not slots + return {'probabilities': np.array(probabilities, np.float32), + 'masks': np.array(masks, np.int32), 'measured': np.array(measured, np.int32), + 'width': np.array(width, np.int32), 'num_detectors': np.array(dem.num_detectors, np.int32), + 'source_indices': np.array(source_indices, np.int32), + 'order': np.array(order, np.int32)} + + +def make_decoder(width): + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + @jax.jit + def decode(dets, probabilities, masks, measured): + f = jnp.zeros((dets.shape[0], 1 << width), jnp.float32).at[:, 0].set(1) + def step(f, args): + p, mask, d = args + def error(f): + prob = p[:, None] if p.ndim else p + return (1-prob)*f + prob*f[:, indices ^ mask] + def project(f): + obs = dets[:, d].astype(jnp.int32) + dst = (indices[None, :] & ~mask) | (obs[:, None]*mask) + g = jnp.take_along_axis(f, dst, axis=1) * ((indices & mask)==0) + return g / jnp.maximum(jnp.sum(g, axis=1, keepdims=True), 1e-30) + return jax.lax.cond(d < 0, error, project, f), None + f, _ = jax.lax.scan(step, f, (probabilities, masks, measured)) + return jnp.sum(f[:, 1::2], axis=1) / jnp.maximum(jnp.sum(f, axis=1), 1e-30) + return decode + + +def make_marginals(width): + """Per-shot derivatives of log syndrome probability give all error marginals.""" + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + def log_partition(dets, probabilities, masks, measured): + f = jnp.zeros(1 << width, jnp.float32).at[0].set(1) + def step(carry, args): + f, logz = carry + p, mask, d = args + def error(carry): + f, logz = carry + return ((1-p)*f + p*f[indices ^ mask], logz) + def project(carry): + f, logz = carry + dst = (indices & ~mask) | (dets[d].astype(jnp.int32)*mask) + g = f[dst] * ((indices & mask)==0) + norm = jnp.maximum(jnp.sum(g), 1e-30) + return g/norm, logz+jnp.log(norm) + return jax.lax.cond(d<0, error, project, carry), None + (_, logz), _ = jax.lax.scan(jax.checkpoint(step), (f, jnp.float32(0)), (probabilities, masks, measured)) + return logz + return jax.jit(jax.vmap(jax.value_and_grad(log_partition, argnums=1), in_axes=(0,None,None,None))) + + +def make_marginals_fb(width, return_observable=False): + """Forward/backward contraction without generic gather-gradient scatter.""" + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + # The syndrome likelihood marginalizes the unobserved logical bit. Keep + # that bit in the forward state for its output, but omit it from histories + # and backward messages, halving the largest buffers. + backward_width=width-1 if return_observable else width + backward_indices=jnp.arange(1 << backward_width,dtype=jnp.int32) + @jax.jit + def marginals(dets, probabilities, masks, measured): + f=jnp.zeros((dets.shape[0],1<>1 if return_observable else masks + _,grad=jax.lax.scan(backward,jnp.ones((len(dets),1<=.5)!=a['labels'][:len(p)]), 'min': p.min(), 'max': p.max()}, flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/export.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/export.py new file mode 100644 index 0000000000000000000000000000000000000000..03fdd1298e37fd6a82010122554eb3d68c327dac --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/export.py @@ -0,0 +1,14 @@ +"""Export unique runtime helper names without altering retained decoder modules.""" +from pathlib import Path +import hashlib,shutil + +def install(package): + root=Path(__file__).resolve().parent;package=Path(package);assert not (package/'d5_preconditional_edge_contextual.py').exists();shutil.copyfile(package/'d5_contextual.py',package/'d5_preconditional_edge_contextual.py');outputs=['d5_preconditional_edge_contextual.py'] + mapping={'conditional_tokens.py':'conditional_edge_tokens.py','contextual.py':'d5_contextual.py','features.py':'conditional_edge_features.py','core.py':'conditional_edge_core.py','model.py':'conditional_edge_model.py','aux_encoder.py':'conditional_edge_aux_encoder.py','axial_base.py':'conditional_edge_axial_base.py','confidence_joint.py':'conditional_edge_confidence_joint.py','joint_mixture.py':'conditional_edge_joint_mixture.py','checkpoint_gradient.py':'conditional_edge_checkpoint_gradient.py','compiled.py':'conditional_edge_compiled.py'} + replacements={'from conditional_tokens import':'from conditional_edge_tokens import','from aux_encoder import':'from conditional_edge_aux_encoder import','from axial_base import':'from conditional_edge_axial_base import','from model import':'from conditional_edge_model import','from checkpoint_gradient import':'from conditional_edge_checkpoint_gradient import','from confidence_joint import':'from conditional_edge_confidence_joint import','from joint_mixture import':'from conditional_edge_joint_mixture import'} + for source,target in mapping.items(): + text=(root/source).read_text() + for a,b in replacements.items():text=text.replace(a,b) + (package/target).write_text(text);outputs.append(target) + assert hashlib.sha256((root/'exact.py').read_bytes()).digest()==hashlib.sha256((package/'exact.py').read_bytes()).digest() + return {name:hashlib.sha256((package/name).read_bytes()).hexdigest() for name in outputs} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/features.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/features.py new file mode 100644 index 0000000000000000000000000000000000000000..1f8a80f56dbed93d6a6200899fb6cdb1f8d5183b --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/features.py @@ -0,0 +1,23 @@ +"""Frozen reconstruction features at fault endpoints, with no logical inputs.""" +import numpy as np +import jax +import jax.numpy as jnp +from aux_encoder import features as masked_features,encode + +def endpoint_plan(context,static,valid,measurement_indices): + context=np.asarray(context);static=np.asarray(static);valid=np.asarray(valid);mapping=np.asarray(measurement_indices);support=static[:,:120]>0;counts=support.sum(axis=1);assert counts.max()<=4 + indices=np.full((len(context),4),624,np.int32);weights=np.zeros((len(context),4),np.float32) + for op in range(len(context)): + selected=context[op,support[op]] + if not valid[op] or not len(selected):continue + assert np.all(selected<600);tokens=mapping[selected];assert np.all(tokens<624) and len(np.unique(tokens))==len(tokens) + indices[op,:len(tokens)]=tokens;weights[op,:len(tokens)]=1/len(tokens) + return indices,weights + +def token_embeddings(encoder,dets,grid,basis,rounds): + from conditional_tokens import conditional_tokens,COLORS + return conditional_tokens(encoder,dets,grid,basis,rounds,jnp.asarray(COLORS,jnp.int32)) + +def pool_endpoints(tokens,indices,weights): + flat=tokens.reshape(len(tokens),624,64);flat=jnp.pad(flat,((0,0),(0,1),(0,0))) + return (flat[:,indices,:]*weights[None,:,:,None]).sum(axis=2) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/fixtures.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/fixtures.py new file mode 100644 index 0000000000000000000000000000000000000000..f2436d4cd40ed3fc70831f62b007c53dd06648a6 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/fixtures.py @@ -0,0 +1,35 @@ +"""Authorized training probes and frozen own-session parents for numerical controls.""" +from pathlib import Path +import hashlib,json +import numpy as np +import jax.numpy as jnp +ROOT=Path(__file__).resolve().parent;RESEARCH=ROOT.parent + +def digest(path):return hashlib.sha256(Path(path).read_bytes()).hexdigest() +def frozen_states(): + proposal=json.loads((ROOT/'PROPOSAL.json').read_text());source=RESEARCH/proposal['frozen_encoder'];assert digest(source)==proposal['frozen_encoder_sha256'] + with np.load(source,allow_pickle=False) as f:encoder={k:jnp.asarray(f[k]) for k in f.files} + reference=json.loads((ROOT/'reference.json').read_text());package=Path(reference['package']) + for name,sha in reference['sha256'].items():assert digest(package/name)==sha + config=json.loads((package/'model_config.json').read_text());parents={};hashes={'encoder':digest(source)} + for basis in ['X','Z']: + settings=config['neural_d5'][f'd5_b{basis}_center_5_5'];path=package/(settings['confidence_joint']['weights'] if basis=='X' else settings['weights']);hashes[basis]=digest(path) + with np.load(path,allow_pickle=False) as f: + if basis=='X': + parent={k:{} for k in ['expert0','expert1','gate']} + for key in f.files:part,name=key.split('__',1);parent[part][name]=jnp.asarray(f[key]) + else:parent={k:jnp.asarray(f[k]) for k in f.files} + parents[basis]=parent + return encoder,parents,package,config,hashes + +def training_probe(basis,rounds,shots=2): + _,_,package,config,_=frozen_states();rows=json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'];row=next(x for x in rows if x['distance']==5 and x['basis']==basis and x['rounds']==rounds);record=config['conditions'][row['name']] + with np.load(package/record['neural_d5_plan'],allow_pickle=False) as f:plan={k:f[k] for k in f.files} + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:ids=f['sample_ids'][:shots];packed=f['detectors'][:shots];labels=f['labels'][:shots] + dets=np.pad(np.unpackbits(packed,axis=1,count=row['num_detectors'],bitorder='little'),((0,0),(0,601-row['num_detectors']))) + prior_pass=config['neural_d5'][row['group']]['prior_pass'];cache=RESEARCH/('learned_d5/full_training_priors3' if prior_pass==3 else 'full_prior5/cache1')/(row['name']+'.npz') + with np.load(cache,allow_pickle=False) as f: + source_ids=f['sample_ids'];order=np.argsort(source_ids);positions=np.searchsorted(source_ids[order],ids);assert np.all(positions0];support=plan['context'][op,plan['static'][op,:120]>0] + if plan['valid'][op] and len(support):assert np.array_equal(np.sort(flat[selected]),np.sort(support)) and abs(float(weights[op].sum())-1)<1e-6 + else:assert not weights[op].any() and np.all(indices[op]==624) + assert np.array_equal(np.sort(flat[flat!=600]),np.arange(row['num_detectors']));valid=np.flatnonzero(plan['grid'].reshape(-1)!=600);assert np.array_equal(plan['measurement_indices'][flat[valid]],valid) + records.append({'condition':row['name'],'feature_plan_sha256':digest(path),'endpoint_operations':int(np.count_nonzero(weights.sum(axis=1))),'valid_operations_without_endpoints':int(np.sum((plan['valid']>0)&(weights.sum(axis=1)==0))),'all_endpoint_detector_token_inverse_checks_passed':True}) +report={'status':'passed','conditions':records,'shot_arrays_or_logical_labels_read':False,'source_sha256':{n:digest(root/n) for n in ['geometry_control_cpu.py','features.py','fixtures.py']},'test_data_used':False};(root/'geometry_control_cpu.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({'status':'passed','conditions':len(records),'operations':sum(x['endpoint_operations'] for x in records)}),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/joint_mixture.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/joint_mixture.py new file mode 100644 index 0000000000000000000000000000000000000000..3d9d54003bcd8671bcd5332ee6ec050c80fdb99d --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/joint_mixture.py @@ -0,0 +1,75 @@ +"""Two trainable exact-decoder experts with a syndrome-only mixture gate.""" +import jax +import jax.numpy as jnp + +GATE_FEATURES = 41 + + +def gate_features(dets, grid, static): + """Shot-local features; no labels, IDs, expert probabilities, or batch statistics.""" + presence = grid != 600 + event = dets[:, grid].astype(jnp.float32) * presence[None] + count = jnp.maximum(presence.sum(axis=0), 1) + density = event.sum(axis=1) / count[None] + blocks = [density * 4] + for lag in [1, 2, 4, 8]: + valid = presence[:-lag] & presence[lag:] + denom = jnp.maximum(valid.sum(axis=0), 1) + site_valid = valid.any(axis=0) + a = event[:, :-lag] * valid[None] + b = event[:, lag:] * valid[None] + coincidence = (a * b).sum(axis=1) / denom[None] + covariance = coincidence - a.sum(axis=1) * b.sum(axis=1) / denom[None] ** 2 + divisor = jnp.maximum(site_valid.sum(), 1) + blocks.extend([(coincidence * site_valid).sum(axis=1, keepdims=True) * 8 / divisor, + (covariance * site_valid).sum(axis=1, keepdims=True) * 16 / divisor]) + valid_frame = presence.any(axis=1) + nframes = jnp.maximum(valid_frame.sum(), 1) + frame = event.sum(axis=2) / jnp.maximum(presence.sum(axis=1), 1)[None] + frame_mean = frame.sum(axis=1) / nframes + frame_var = (((frame - frame_mean[:, None]) ** 2) * valid_frame[None]).sum(axis=1) / nframes + rank = jnp.cumsum(valid_frame) - 1 + first_mask = valid_frame & (rank < jnp.maximum(nframes // 2, 1)) + last_mask = valid_frame & ~first_mask + first = (frame * first_mask[None]).sum(axis=1) / jnp.maximum(first_mask.sum(), 1) + last = (frame * last_mask[None]).sum(axis=1) / jnp.maximum(last_mask.sum(), 1) + last = jnp.where(last_mask.any(), last, first) + fraction = jnp.max(static[:, 124]) + basis = jnp.max(static[:, 127]) + blocks.append(4 * jnp.stack([density.mean(axis=1), density.std(axis=1), density.max(axis=1), + jnp.sqrt(jnp.maximum(frame_var, 0)), frame.max(axis=1), + jnp.abs(first - last)], axis=1)) + blocks.append(jnp.broadcast_to(jnp.stack([fraction, fraction ** 2, basis]), (len(dets), 3))) + result = jnp.concatenate(blocks, axis=1) + assert result.shape == (len(dets), GATE_FEATURES) + return result + + +def initialize_gate(seed, hidden=32): + key = jax.random.key(seed) + return {'w1': jax.random.normal(key, (GATE_FEATURES, hidden)) * jnp.sqrt(2 / GATE_FEATURES), + 'b1': jnp.zeros(hidden), 'w2': jnp.zeros((hidden, 1)), 'b2': jnp.zeros(1)} + + +def gate_logit(parameters, features): + hidden = jax.nn.silu(features @ parameters['w1'] + parameters['b1']) + return (hidden @ parameters['w2'] + parameters['b2'])[:, 0] + + +def mixture_terms(losses, logit): + """Stable BCE and exact expert/gate adjoints from per-expert BCE losses. + + losses has shape [batch,2]. Responsibilities multiply each expert BCE + gradient; gate_adjoint is d(BCE mixture)/d(single free gate logit). + """ + logits = jnp.stack([logit, jnp.zeros_like(logit)], axis=1) + log_a = jax.nn.log_softmax(logits, axis=1) + log_c = jax.scipy.special.logsumexp(log_a - losses, axis=1) + responsibility = jnp.exp(log_a - losses - log_c[:, None]) + a = jnp.exp(log_a) + return -log_c, responsibility, a[:, 0] - responsibility[:, 0], a + + +def mix_predictions(predictions, logit): + a = jax.nn.sigmoid(logit) + return a * predictions[:, 0] + (1 - a) * predictions[:, 1] diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/matched_draw_audit.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/matched_draw_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..78e3ffb15e361cfe7c0610f81f59e491655eb310 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/matched_draw_audit.py @@ -0,0 +1,27 @@ +"""Compare matched sampling, frozen states and optimizer schedule with completed unmasked run.""" +from pathlib import Path +import json,hashlib +import numpy as np +root=Path(__file__).resolve().parent + +def audit(run): + run=Path(run);old=root.parent/'masked_edge_projection/projection1';a=json.loads((run/'run_config.json').read_text());b=json.loads((old/'run_config.json').read_text()) + for key in ['steps','batch','eval_every','validation_shots','validation_batch','frozen_weights_sha256']: + assert a[key]==b[key],key + for key in ['steps','batch','learning_rate','sampling_seed','optimizer','initial_projection','data','selection_steps','selection']: + assert a['training_plan'][key]==b['training_plan'][key],key + for left,right in zip(a['data'],b['data']): + assert left['condition']==right['condition'] and left['feature_plan_sha256']==right['feature_plan_sha256'] and left['prior_pass']==right['prior_pass'] + for split in ['train','validation']: + for key in ['arrays_sha256','cache','cache_sha256','ID_sha256']:assert left['sources'][split][key]==right['sources'][split][key],(left['condition'],split,key) + for name in ['state.py','core.py','model.py','aux_encoder.py','axial_base.py','checkpoint_gradient.py','exact.py','joint_mixture.py','confidence_joint.py','fixtures.py','reference.json']: + assert (run/'training_source'/name).read_bytes()==(old/'training_source'/name).read_bytes(),name + with np.load(run/'weights-0.npz',allow_pickle=False) as x,np.load(old/'weights-0.npz',allow_pickle=False) as y:assert set(x.files)==set(y.files) and all(np.array_equal(x[k],y[k]) and not x[k].any() for k in x.files) + steps=[] + for step in [0,512,1024,1536,2048]: + x=json.loads((run/f'checkpoint-{step}.json').read_text());y=json.loads((old/f'checkpoint-{step}.json').read_text()) + for key in ['step','training_draws','basis_update_counts','draw_descriptor_sha256','unique_rows','frozen_encoder_parents_and_gate_exact']:assert x[key]==y[key],(step,key) + assert json.loads((run/f'rng-{step}.json').read_text())==json.loads((old/f'rng-{step}.json').read_text()) + with np.load(run/f'seen-{step}.npz',allow_pickle=False) as x,np.load(old/f'seen-{step}.npz',allow_pickle=False) as y:assert set(x.files)==set(y.files) and all(np.array_equal(x[k],y[k]) for k in x.files) + steps.append(step) + report={'status':'passed','reference':str(old),'steps':steps,'training_draws':1048576,'exact_initial_projections_frozen_weights_data_and_all_sampling':True,'scope':'Only endpoint token construction differs. Fitted weights and Adam moments are expected to differ; separate optimizer clocks are audited by audit_run.py.','source_sha256':hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),'test_data_used':False};(run/'matched_draw_audit.json').write_text(json.dumps(report,indent=2)+'\n');return report diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/model.py new file mode 100644 index 0000000000000000000000000000000000000000..46c84b3e2fb5c6c7c61d4a7986efb6c10d5c7ffb --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/model.py @@ -0,0 +1,11 @@ +"""A new endpoint-feature projection into an otherwise frozen edge network.""" +import jax +import jax.numpy as jnp + +def probabilities(projection,frozen,dets,base_logits,embedding,context,static,valid,grid,measurement_indices,counts): + assert projection.shape==(64,64) and frozen['w1'].shape==(394,64) + event_grid=dets[:,grid];measured=(jnp.cumsum(event_grid.astype(jnp.int32),axis=1)&1).reshape(len(dets),624);measured=jnp.pad(measured,((0,0),(0,1))) + events=dets[:,context].astype(jnp.float32);measurements=measured[:,measurement_indices[context]].astype(jnp.float32);density=jnp.sum(event_grid,axis=1)/counts[None,:] + x=jnp.concatenate([events,measurements,jnp.broadcast_to(density[:,None,:],(len(dets),len(context),24)),jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape)),base_logits[:,:,None]/8],axis=-1) + hidden=jax.nn.silu(x@frozen['w1']+frozen['b1']+embedding@projection);hidden=jax.nn.silu(hidden@frozen['w2']+frozen['b2']);delta=2*jnp.tanh((hidden@frozen['w3']+frozen['b3'])[...,0]) + return (jax.nn.sigmoid(base_logits+delta)*valid[None,:]).T diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/package.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/package.py new file mode 100644 index 0000000000000000000000000000000000000000..8d0fe0d3d62ed869c84fb173c6854cbf880bde2f --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/package.py @@ -0,0 +1,52 @@ +"""Select declared full-duration projection checkpoints and assemble a self-contained candidate.""" +from pathlib import Path +import json,shutil +import numpy as np +from fixtures import frozen_states,digest +from data import align +from export import install +root=Path(__file__).resolve().parent;r=root.parent;proposal=json.loads((root/'PROPOSAL.json').read_text());plan=json.loads((root/'plan.json').read_text());_,_,reference_package,reference_config,_=frozen_states();reference=json.loads((root/'reference.json').read_text());current=json.loads((r/'CURRENT.json').read_text());score=json.loads(Path(current['score']).read_text());run=root/'projection1';out=r/'context_upgrades/conditional_edge_projection1';out.mkdir(exist_ok=False);package,predictions=out/'package',out/'predictions';shutil.copytree(current['package'],package);shutil.copytree(current['predictions'],predictions);config=json.loads((package/'model_config.json').read_text());rows=[x for x in json.loads(Path('/workspace/data/validation/manifest.json').read_text())['conditions'] if x['distance']==5];new_files=set();history=[];copies={};selected_checkpoints={} +def copy_parent(value,basis): + if isinstance(value,dict): + result={} + for key,entry in value.items(): + if key=='weights': + relative=f'weights/conditional-edge-parent-{basis}-{len(copies)}.npz';shutil.copyfile(reference_package/entry,package/relative);new_files.add(relative);copies[relative]={'source':entry,'sha256':digest(package/relative)};result[key]=relative + else:result[key]=copy_parent(entry,basis) + return result + if isinstance(value,list):return [copy_parent(x,basis) for x in value] + return value +for basis in ['X','Z']: + members=[x for x in rows if x['basis']==basis];group=members[0]['group'];incumbent=float(np.mean([score['per_condition'][x['name']]['errors']/score['per_condition'][x['name']]['total'] for x in members]));choices={'incumbent':incumbent};options={} + for step in plan['selection_steps']: + values={};errors=[] + for row in members: + with np.load(Path('/workspace/data/validation')/row['arrays'],allow_pickle=False) as f:ids=f['sample_ids'];labels=f['labels'] + with np.load(root/f'validation-{step}'/(row['name']+'.npz'),allow_pickle=False) as f:q=f['probabilities'][align(f['sample_ids'],ids)] + assert q.dtype==np.float32 and np.isfinite(q).all() and ((q>=0)&(q<=1)).all();values[row['name']]=(ids,q);errors.append(float(np.mean((q>=.5)!=labels))) + key=f'projection-{step}';choices[key]=float(np.mean(errors));options[key]=(step,values) + selected=min(choices,key=choices.get) + if choices[selected]>=incumbent-1e-12:selected='incumbent' + history.append({'basis':basis,'group':group,'candidates':choices,'selected':selected}) + if selected=='incumbent':continue + step,values=options[selected];relative=f'weights/conditional-edge-projection1-{step}.npz';shutil.copyfile(run/f'weights-{step}.npz',package/relative);new_files.add(relative);selected_checkpoints[selected]=json.loads((run/f'checkpoint-{step}.json').read_text());encoder_relative='weights/conditional-edge-frozen-encoder8192.npz';shutil.copyfile(r/proposal['frozen_encoder'],package/encoder_relative);new_files.add(encoder_relative);parent=copy_parent(reference_config['neural_d5'][group],basis);config.get('frozen_stack',{}).pop(group,None) + for row in members: + for field in ['neural_d5_plan','plan','turbo_plan']: + if field in config['conditions'][row['name']]:assert digest(package/config['conditions'][row['name']][field])==digest(reference_package/reference_config['conditions'][row['name']][field]) + config['neural_d5'][group]={'prior_pass':parent['prior_pass'],'blend_turbo':False,'conditional_edge_projection':{'weights':relative,'encoder_weights':encoder_relative,'parent_settings':parent},'training_checkpoint':f'conditional_edge_projection/projection1/{step}'} + for name,(ids,q) in values.items():np.savez(predictions/(name+'.npz'),sample_ids=ids,probabilities=q) +if new_files: + exported=install(package);destination=package/'training_sources/conditional_edge_projection1';shutil.copytree(run/'training_source',destination) + for name in ['package.py','evaluate.py','audit_run.py','export.py','contextual.py','after_d3_head.py','matched_draw_audit.py','conditional_tokens.py','backend_control.py','geometry_control_cpu.py','control_cpu.py','pipeline_control_cpu.py','dual_basis_control_cpu.py','runtime_control.py','compiled.py']:shutil.copyfile(root/name,destination/name) + relative='weights/conditional-edge-initial-zero-projections.npz';shutil.copyfile(run/'weights-0.npz',package/relative);new_files.add(relative) + config['architecture']='Contextual exact decoders with selected frozen masked-syndrome endpoint embeddings and learned edge-input projections' + (package/'README.md').write_text('''# Learned structured logical decoder + +Run `bash infer.sh INPUT_ROOT OUTPUT_ROOT`. All130conditions support arbitrary positive shot counts, order and subsets. Outputs contain uint64 sample IDs and float32 official observable-flip probabilities; the decision threshold is0.5. IDs are copied and never enter model features. Dependencies are pinned in requirements.lock; inference needs no installation or network. + +Selected d5 routes use a frozen attention encoder trained in this session to reconstruct masked training detection events. At inference it makes eight passes with a fixed partition of ideal detector slots. Each token uses its embedding from the pass that hides its own event; hidden events are removed before cumulative parity. There is no dropout, ID dependence or row dependence. Mean embeddings at each fault's detector endpoints feed a learned input projection into frozen structured edge networks. The original edge-network and confidence-gate weights remain fixed; X's gate still depends on the resulting expert probabilities. Retained routes and all d3 decoders are specified in model_config.json. Every prediction is recomputed from the supplied events and packaged geometry. + +Only three new64x64 projection matrices were trained. They start at zero; their frozen encoder and decoder ancestors originated from random initialization in this session. Training uses all supplied d5 training labels with separate X/Z Adam clocks. Three declared checkpoints compete across all13durations perbasis, with the incumbent available. Full source, initialization, data/cache lineage, auxiliary-encoder selection, adaptive selection history and actual replay evidence are in training_provenance.json. Prediction caches are not packaged. Validation scores are distinguished from test scores, and local portability checks record their actual execution identity. +''') +else:exported=None +(package/'model_config.json').write_text(json.dumps(config,indent=2)+'\n');provenance=json.loads((package/'training_provenance.json').read_text());provenance['conditional_edge_projection1']={'declared_prototype':proposal,'declared_training_plan':plan,'frozen_parent':reference,'parent_weight_copies':copies,'encoder_training':json.loads((r/'masked_pretraining/auxiliary1/provenance.json').read_text()),'encoder_selection_record':json.loads((r/'masked_pretraining/transfer1/provenance.json').read_text()),'encoder_usage':'Use the original selected auxiliary checkpoint encoder before logical-head attachment, using eight fixed ideal-slot masking partitions and no dropout. Hardware-adapted encoder weights are not used.','training':json.loads((run/'provenance.json').read_text()),'selection_history':history,'selected_checkpoints':selected_checkpoints,'runtime_export_sha256':exported,'controls':{n:json.loads((root/n).read_text()) for n in ['control_cpu.json','pipeline_control_cpu.json','dual_basis_control_cpu.json','geometry_control_cpu.json','backend_control_cpu.json','backend_control_gpu.json','runtime_control_cpu.json','runtime_control_gpu.json']},'zero_parent_and_cache_audit':json.loads((root/'zero_parent_audit/report.json').read_text()),'matched_draw_audit':json.loads((run/'matched_draw_audit.json').read_text()),'partition_lineage':json.loads((root/'partition_lineage.json').read_text()),'test_data_used':False};provenance['previous_package_validation']=provenance.pop('package_validation');(package/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n');manifest=json.loads((package/'model_manifest.json').read_text());paths={x['path'] for x in manifest['checkpoints']}|new_files;manifest['checkpoints']=[{'path':p,'sha256':digest(package/p),'format':'npz' if p.endswith('.npz') else 'json' if p.endswith('.json') else 'stim-dem'} for p in sorted(paths)];(package/'model_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n');d3=score['failure_by_distance']['d3'];d5=sum(x['candidates'][x['selected']] for x in history)/2;candidate={'package':str(package),'predictions':str(predictions),'score':str(out/'score.json'),'expected_P':1-(d3+d5)/2,'failure_by_distance':{'d3':d3,'d5':d5},'selection':history,'validation_metadata':{'prediction_generation':'Cached full-validation checkpoints; complete assembled d5 replay required if improved.','runtime_evidence':'Actual full replay and projection below3600seconds required before publication.'}};(out/'candidate.json').write_text(json.dumps(candidate,indent=2)+'\n');print(json.dumps(candidate,indent=2)) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/pipeline_control_cpu.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/pipeline_control_cpu.py new file mode 100644 index 0000000000000000000000000000000000000000..e34755085f0fe61567d29fb22d2ba774de96a70e --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/pipeline_control_cpu.py @@ -0,0 +1,18 @@ +"""Actual full-data trainer and numeric checkpoint replay; all updates discarded.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:4]) +from pathlib import Path +import json,subprocess +import numpy as np +from fixtures import digest +from audit_run import audit +root=Path(__file__).resolve().parent +def run(name,steps,resume=None): + cmd=['python',str(root/'train.py'),'--output',str(root/name),'--steps',str(steps),'--batch','2','--eval-every','1','--validation-shots','1','--validation-batch','1'] + if resume:cmd+=['--resume',str(root/resume)] + with (root/(name+'.log')).open('w') as log:subprocess.run(cmd,stdout=log,stderr=subprocess.STDOUT,check=True) +run('smoke_full',2);run('smoke_split',1);run('smoke_resume',2,'smoke_split/weights-1.npz') +for kind in ['weights','optimizer','seen']: + with np.load(root/f'smoke_full/{kind}-2.npz',allow_pickle=False) as a,np.load(root/f'smoke_resume/{kind}-2.npz',allow_pickle=False) as b:assert set(a.files)==set(b.files) and all(np.array_equal(a[k],b[k]) for k in a.files) +assert json.loads((root/'smoke_full/rng-2.json').read_text())==json.loads((root/'smoke_resume/rng-2.json').read_text());evidence=audit(root/'smoke_full') +report={'status':'passed','actual_discarded_optimizer_updates':4,'training_draws_including_resume_replay':8,'actual_full_data_loader_resume_weights_Adam_seen_RNG_exact':True,'audit':evidence,'source_sha256':{f.name:digest(f) for f in root.glob('*.py')},'test_data_used':False};(root/'pipeline_control_cpu.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report,indent=2),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/reference.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/reference.json new file mode 100644 index 0000000000000000000000000000000000000000..c973158edbf68cb56dac6f3de80445f0f6b0ed43 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/reference.json @@ -0,0 +1,12 @@ +{ + "package": "/run-state/workspace/research/context_upgrades/r3_pattern1/package", + "predictions": "/run-state/workspace/research/context_upgrades/r3_pattern1/predictions", + "validation_P": 0.76744384765625, + "frozen_utc": "2026-10-21T18:48:29.847456+00:00", + "scope": "Frozen session-trained parent. X: confidence_joint1/1024, Z: fresh2_full/1536 with physical prior5; d3 irrelevant to this d5-only residual. All parent weights trained in this session; no released predictions or test data.", + "sha256": { + "model_config.json": "35badea7d1c73ab42edcb1193eff22613ad6f8aa0d8281596fe2c1fd78d73925", + "training_provenance.json": "953d23dedc55520b654852a848cca45c0e555be23ff433f1368c5b5de1580df8", + "model_manifest.json": "112a3c67e31cae326474ee93b1454382c8a14cc59900ff86344113d727fe4ce0" + } +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/runtime_control.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/runtime_control.py new file mode 100644 index 0000000000000000000000000000000000000000..e385a6f9087d825f66ff38efce2b7a516beac08e --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/runtime_control.py @@ -0,0 +1,33 @@ +"""Actual exported endpoint-projection runtime, including live priors and batch boundaries.""" +import argparse,os +p=argparse.ArgumentParser();p.add_argument('--device',choices=['cpu','gpu'],required=True);args=p.parse_args() +os.environ['JAX_PLATFORMS']='cpu' if args.device=='cpu' else 'cuda';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false';os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:(4 if args.device=='cpu' else 22)]) +from pathlib import Path +import json,shutil,sys,tempfile,importlib.util +import numpy as np +import jax,jax.numpy as jnp +from fixtures import frozen_states,training_probe,digest +from features import endpoint_plan,token_embeddings,pool_endpoints +from core import make_kernels +from state import initialize,save_weights +from export import install +root=Path(__file__).resolve().parent;encoder,parents,package,config,hashes=frozen_states();assert jax.default_backend()==args.device;batch=4 if args.device=='cpu' else 512;rng=np.random.default_rng(20263220);projections=jax.tree_util.tree_map(lambda x:jnp.asarray(rng.normal(0,.003,x.shape),x.dtype),initialize());_,single,_,mixture=make_kernels();infer={'X':mixture,'Z':single};encode=jax.jit(token_embeddings);pool=jax.jit(pool_endpoints);checks=[] +with tempfile.TemporaryDirectory(prefix='conditional-edge-runtime-') as temp: + target=Path(temp)/'package';shutil.copytree(package,target);exported=install(target);save_weights(target/'edge-control.npz',projections);np.savez(target/'encoder-control.npz',**{k:np.asarray(v) for k,v in encoder.items()});sys.path.insert(0,str(target));from d5_preconditional_edge_contextual import NeuralD5Decoder as Old + spec=importlib.util.spec_from_file_location('conditional_edge_exported_control',target/'d5_contextual.py');module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);new=module.NeuralD5Decoder();old=Old() + for basis,rounds,n in [('X',1,1),('Z',1,3),('X',3,batch+1),('Z',25,batch+1)]: + row,dets,labels,base,fields,source=training_probe(basis,rounds,n);record=config['conditions'][row['name']];parent_settings=config['neural_d5'][row['group']];settings={'prior_pass':parent_settings['prior_pass'],'blend_turbo':False,'conditional_edge_projection':{'weights':'edge-control.npz','encoder_weights':'encoder-control.npz','parent_settings':parent_settings}};plan=dict(np.load(target/record['neural_d5_plan'],allow_pickle=False));ops=int(plan['operation_count']) + cache=root.parent/('learned_d5/full_training_priors3' if basis=='X' else 'full_prior5/cache1')/(row['name']+'.npz') + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:ids=f['sample_ids'][:n];packed=f['detectors'][:n] + with np.load(cache,allow_pickle=False) as f: + order=np.argsort(f['sample_ids']);ix=order[np.searchsorted(f['sample_ids'][order],ids)];assert np.array_equal(f['sample_ids'][ix],ids);priors=f[f"priors{settings['prior_pass']}"][ix] + indices,weights=endpoint_plan(fields[0],fields[1],fields[2],fields[6]);values=[] + for pos in range(0,n,batch): + count=min(batch,n-pos);ds=np.pad(dets[pos:pos+batch],((0,batch-count),(0,0)));z=np.pad(base[pos:pos+batch],((0,batch-count),(0,0)));embedding=pool(encode(encoder,ds,fields[5],basis=='X',rounds),indices,weights);values.append(np.asarray(infer[basis](projections[basis],parents[basis],ds,z,embedding,*fields))[:count]) + expected=np.concatenate(values);cached=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch,initial_priors=priors);manual=float(abs(cached-expected).max());assert manual<2e-6 and np.array_equal(cached>=.5,expected>=.5),(row['name'],manual) + actual=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch);difference=float(abs(actual-cached).max());assert difference<1e-4 and np.array_equal(actual>=.5,cached>=.5),(row['name'],difference) + reverse=new.predict(target,record,settings,packed[::-1].copy(),row['num_detectors'],batch=batch);single_value=new.predict(target,record,settings,packed[:1],row['num_detectors'],batch=batch);assert np.array_equal(actual[::-1],reverse) and np.array_equal(actual[:1],single_value) + retained=new.predict(target,record,parent_settings,packed,row['num_detectors'],batch=batch);reference=old.predict(target,record,parent_settings,packed,row['num_detectors'],batch=batch);assert np.array_equal(retained,reference) + before=(new.edge_encode.compilations,new.edge_pool.compilations,new.edge_infer[basis].compilations);jax.clear_caches();again=new.predict(target,record,settings,packed,row['num_detectors'],batch=batch);after=(new.edge_encode.compilations,new.edge_pool.compilations,new.edge_infer[basis].compilations);assert np.array_equal(again,actual) and before==after + checks.append({'condition':row['name'],'shots':n,'batch':batch,'manual_max_abs':manual,'live_cached_max_abs':difference,'manual_and_live_cached_decisions_exact':True,'order_subset_retained_globalclear_exact':True});print(json.dumps(checks[-1]),flush=True) +report={'status':'passed','device':str(jax.devices()[0]),'cases':checks,'frozen_weights_sha256':hashes,'source_sha256':{n:digest(root/n) for n in ['runtime_control.py','contextual.py','export.py','features.py','conditional_tokens.py','core.py','model.py','fixtures.py','state.py','compiled.py','aux_encoder.py','axial_base.py','confidence_joint.py','joint_mixture.py','checkpoint_gradient.py','exact.py']},'exported_source_sha256':exported,'scope':'Discarded random projections; no fitting or selection. Real training probes only. Training labels are loaded by the shared control fixture but unused by runtime checks.','test_data_used':False};(root/f'runtime_control_{args.device}.json').write_text(json.dumps(report,indent=2)+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/runtime_precision_features.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/runtime_precision_features.py new file mode 100644 index 0000000000000000000000000000000000000000..5f527b744851c7471df870c29b2f51f764a1a135 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/runtime_precision_features.py @@ -0,0 +1,25 @@ +"""Frozen reconstruction features at fault endpoints, with no logical inputs.""" +import numpy as np +import jax +import jax.numpy as jnp +from conditional_edge_aux_encoder import features as masked_features,encode + +def endpoint_plan(context,static,valid,measurement_indices): + context=np.asarray(context);static=np.asarray(static);valid=np.asarray(valid);mapping=np.asarray(measurement_indices);support=static[:,:120]>0;counts=support.sum(axis=1);assert counts.max()<=4 + indices=np.full((len(context),4),624,np.int32);weights=np.zeros((len(context),4),np.float32) + for op in range(len(context)): + selected=context[op,support[op]] + if not valid[op] or not len(selected):continue + assert np.all(selected<600);tokens=mapping[selected];assert np.all(tokens<624) and len(np.unique(tokens))==len(tokens) + indices[op,:len(tokens)]=tokens;weights[op,:len(tokens)]=1/len(tokens) + return indices,weights + +def token_embeddings(encoder,dets,grid,basis,rounds): + from conditional_edge_tokens import conditional_tokens,COLORS + # Explicit float32 precision avoids compile-context-dependent encoder approximations. + with jax.default_matmul_precision('highest'): + return conditional_tokens(encoder,dets,grid,basis,rounds,jnp.asarray(COLORS,jnp.int32)) + +def pool_endpoints(tokens,indices,weights): + flat=tokens.reshape(len(tokens),624,64);flat=jnp.pad(flat,((0,0),(0,1),(0,0))) + return (flat[:,indices,:]*weights[None,:,:,None]).sum(axis=2) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/state.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/state.py new file mode 100644 index 0000000000000000000000000000000000000000..1cd93073d63ad3e707b59e7b8cfae63fec4a4453 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/state.py @@ -0,0 +1,19 @@ +"""Only the new projections own optimizer state; basis-specific clocks are explicit.""" +import numpy as np +import jax,jax.numpy as jnp,optax + +def initialize():return {'X':{k:jnp.zeros((64,64),jnp.float32) for k in ['expert0','expert1']},'Z':jnp.zeros((64,64),jnp.float32)} +def optimizer(rate):return optax.chain(optax.clip_by_global_norm(1.),optax.adamw(rate,weight_decay=1e-4)) +def save_weights(path,p):np.savez(path,X_expert0=np.asarray(p['X']['expert0']),X_expert1=np.asarray(p['X']['expert1']),Z=np.asarray(p['Z'])) +def load_weights(path): + with np.load(path,allow_pickle=False) as f: + assert set(f.files)=={'X_expert0','X_expert1','Z'} and all(f[k].shape==(64,64) and f[k].dtype==np.float32 for k in f.files) + return {'X':{'expert0':jnp.asarray(f['X_expert0']),'expert1':jnp.asarray(f['X_expert1'])},'Z':jnp.asarray(f['Z'])} +def save_state(path,state):np.savez(path,**{f'arr_{i}':np.asarray(v) for i,v in enumerate(jax.tree_util.tree_leaves(state))}) +def load_state(path,template): + leaves,tree=jax.tree_util.tree_flatten(template) + with np.load(path,allow_pickle=False) as f: + assert set(f.files)=={f'arr_{i}' for i in range(len(leaves))};values=[] + for i,v in enumerate(leaves): + a=f[f'arr_{i}'];assert a.shape==v.shape and a.dtype==v.dtype;values.append(jnp.asarray(a)) + return jax.tree_util.tree_unflatten(tree,values) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/train.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/train.py new file mode 100644 index 0000000000000000000000000000000000000000..5498fe1c2685d7272ee0f3978799431c31c44a16 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/conditional_edge_projection1/train.py @@ -0,0 +1,61 @@ +"""Fit only endpoint-feature projections over frozen session-trained encoders and decoders.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +from pathlib import Path +import argparse,json,hashlib,shutil,time +import numpy as np +import jax,jax.numpy as jnp,optax +from fixtures import frozen_states,digest +from data import load_all,fields +from features import token_embeddings,pool_endpoints +from core import make_kernels +from state import initialize,optimizer,save_weights,load_weights,save_state,load_state +ROOT=Path(__file__).resolve().parent + +def make_updates(optim): + sg,_,mg,_=make_kernels() + def wrap(gradient): + @jax.jit + def update(p,state,frozen,dets,y,base,embedding,*f): + g,loss,error=gradient(p,frozen,dets,y,base,embedding,*f);delta,state=optim.update(g,state,p) + return optax.apply_updates(p,delta),state,loss,error,optax.global_norm(g) + return update + return {'X':wrap(mg),'Z':wrap(sg)} + +def main(): + parser=argparse.ArgumentParser();parser.add_argument('--output',type=Path,required=True);parser.add_argument('--steps',type=int,default=2048);parser.add_argument('--batch',type=int,default=512);parser.add_argument('--eval-every',type=int,default=512);parser.add_argument('--validation-shots',type=int,default=1024);parser.add_argument('--validation-batch',type=int,default=512);parser.add_argument('--resume',type=Path);args=parser.parse_args();assert args.steps>0 and args.batch>0 and args.eval_every>0;args.output.mkdir(parents=True,exist_ok=False) + started=time.monotonic();plan=json.loads((ROOT/'TRAINING_PLAN.json').read_text());dataset,data_record=load_all();encoder,parents,_,_,weight_hashes=frozen_states();frozen_leaves=[np.asarray(x).copy() for x in jax.tree_util.tree_leaves((encoder,parents))];p=initialize();optim=optimizer(plan['learning_rate']);state={b:optim.init(p[b]) for b in ['X','Z']};rng=np.random.default_rng(plan['sampling_seed']);seen=[np.zeros(len(x['train_labels']),bool) for x in dataset];basis_counts={'X':0,'Z':0};start=0;draw_hash=hashlib.sha256() + if args.resume: + old=json.loads((args.resume.parent/'run_config.json').read_text()) + for key in ['batch','eval_every','validation_shots','validation_batch']:assert old[key]==getattr(args,key) + assert old['training_plan']==plan;start=int(args.resume.stem.split('-')[-1]);assert start=0)&(q<=1)).all();metrics.append({'condition':row['name'],'failure':float(np.mean((q>=.5)!=y)),'negative_log_likelihood':float(np.mean(-y*np.log(z)-(1-y)*np.log1p(-z)))}) + save_weights(args.output/f'weights-{step}.npz',p);save_state(args.output/f'optimizer-{step}.npz',state);np.savez(args.output/f'seen-{step}.npz',**{x['row']['name']:v for x,v in zip(dataset,seen)});(args.output/f'rng-{step}.json').write_text(json.dumps(rng.bit_generator.state,indent=2)+'\n') + assert sum(basis_counts.values())==step and all(int(state[b][1][0].count)==basis_counts[b] for b in ['X','Z']);assert all(np.array_equal(a,b) for a,b in zip(frozen_leaves,jax.tree_util.tree_leaves((encoder,parents)))) + record={'complete':True,'step':step,'training_draws':step*args.batch,'weights_sha256':digest(args.output/f'weights-{step}.npz'),'basis_update_counts':dict(basis_counts),'draw_descriptor_sha256':draw_hash.hexdigest(),'unique_rows':sum(int(x.sum()) for x in seen),'frozen_encoder_parents_and_gate_exact':True};(args.output/f'checkpoint-{step}.json').write_text(json.dumps(record,indent=2)+'\n') + report={'step':step,'validation_failure':float(np.mean([x['failure'] for x in metrics])),'negative_log_likelihood':float(np.mean([x['negative_log_likelihood'] for x in metrics])),'conditions':metrics,'seconds':time.monotonic()-begin};(args.output/f'validation-{step}.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({k:v for k,v in report.items() if k!='conditions'}),flush=True) + checkpoint(start);records=[] + for step in range(start+1,args.steps+1): + ci=int(rng.integers(26));item=dataset[ci];row=item['row'];basis=row['basis'];ix=rng.integers(len(item['train_labels']),size=args.batch);seen[ci][ix]=True;basis_counts[basis]+=1;draw_hash.update(np.asarray([ci,*ix],dtype='>1 if return_observable else masks + _,grad=jax.lax.scan(backward,jnp.ones((len(dets),1<=.5)!=a['labels'][:len(p)]), 'min': p.min(), 'max': p.max()}, flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint.py new file mode 100644 index 0000000000000000000000000000000000000000..edd86a7ff804508c6566bb31150bd8e3058ddf3b --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint.py @@ -0,0 +1,38 @@ +"""Prototype confidence-aware gate; not yet a trained or queued candidate.""" +import jax.numpy as jnp +from joint_mixture import gate_features as syndrome_features +from joint_mixture import initialize_gate as initialize_base_gate +from joint_mixture import gate_logit, mixture_terms, mix_predictions + +GATE_FEATURES = 45 + + +def confidence_features(predictions): + """Four bounded shot-local features; no labels or batch statistics.""" + p = jnp.clip(predictions, 1e-6, 1-1e-6) + z = jnp.clip(jnp.log(p)-jnp.log1p(-p), -8., 8.)/8 + return jnp.concatenate([z,jnp.sqrt(z*z+1e-4)],axis=1) + + +def gate_features(dets, grid, static, predictions): + result = jnp.concatenate([syndrome_features(dets,grid,static),confidence_features(predictions)],axis=1) + assert result.shape==(len(dets),GATE_FEATURES) + return result + + +def initialize_gate(seed, hidden=32): + """Preserve the original seeded 41-input trunk; append zero new-input rows.""" + gate = initialize_base_gate(seed,hidden) + gate['w1'] = jnp.concatenate([gate['w1'],jnp.zeros((4,hidden),gate['w1'].dtype)],axis=0) + return gate + + +def expert_adjoint_factors(losses, labels, responsibility, gate_prediction_adjoint): + """Per-shot factors on expert BCE adjoints, including the confidence path. + + gate_prediction_adjoint is dL/dq through the gate only, for each per-shot + loss (not its batch mean). dq/dedge=(1-2*y)*exp(-BCE)*dBCE/dedge. + Labels enter this loss derivative only; all gate inputs use predictions. + """ + sign = 1-2*labels.astype(losses.dtype) + return responsibility+sign[:,None]*jnp.exp(-losses)*gate_prediction_adjoint diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_gradient.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..f1249ff3a5dd9e17abd5362502702556083b536c --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_gradient.py @@ -0,0 +1,43 @@ +"""Prototype complete gate/expert chain rule through confidence features.""" +import jax +import jax.numpy as jnp +from confidence_joint import gate_features, gate_logit, mixture_terms, mix_predictions, expert_adjoint_factors + + +def make_gradient(probabilities, exact_gradient): + @jax.jit + def gradient(parameters,dets,labels,base,context,static,valid,masks,measured, + grid,measurement_indices,counts): + arguments = (dets,base,context,static,valid,grid,measurement_indices,counts) + p0,pull0 = jax.vjp(lambda w:probabilities(w,*arguments),parameters['expert0']) + loss0,pred0,g0 = exact_gradient(dets,labels,p0,masks,measured) + p1,pull1 = jax.vjp(lambda w:probabilities(w,*arguments),parameters['expert1']) + loss1,pred1,g1 = exact_gradient(dets,labels,p1,masks,measured) + losses = jnp.stack([loss0,loss1],axis=1) + predictions = jnp.stack([pred0,pred1],axis=1) + logit,pull_gate = jax.vjp(lambda w,q:gate_logit(w,gate_features(dets,grid,static,q)), + parameters['gate'],predictions) + loss,responsibility,adjoint,coefficients = mixture_terms(losses,logit) + # Each gate output depends only on its own shot, so this VJP yields + # per-shot prediction adjoints and the sum of parameter gradients. + gate_sum,extra_prediction_adjoint = pull_gate(adjoint) + factors = expert_adjoint_factors(losses,labels,responsibility,extra_prediction_adjoint) + batch = len(dets) + gradients = {'expert0':pull0(g0*factors[:,0][None]/batch)[0], + 'expert1':pull1(g1*factors[:,1][None]/batch)[0], + 'gate':jax.tree_util.tree_map(lambda x:x/batch,gate_sum)} + prediction = mix_predictions(predictions,logit) + return gradients,loss.mean(),jnp.mean((prediction>=.5)!=labels),coefficients.mean(axis=0) + return gradient + + +def make_infer(probabilities,exact_decode): + @jax.jit + def infer(parameters,dets,base,context,static,valid,masks,measured, + grid,measurement_indices,counts): + arguments = (dets,base,context,static,valid,grid,measurement_indices,counts) + q = jnp.stack([exact_decode(dets,probabilities(parameters[key],*arguments),masks,measured) + for key in ['expert0','expert1']],axis=1) + logit = gate_logit(parameters['gate'],gate_features(dets,grid,static,q)) + return mix_predictions(q,logit) + return infer diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_state.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_state.py new file mode 100644 index 0000000000000000000000000000000000000000..ddc34f50ab91aaff74b10066772f23f2617a1621 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_state.py @@ -0,0 +1,83 @@ +"""Serialization and distinct Adam clocks for two experts and a new gate.""" +from pathlib import Path +import hashlib +import numpy as np +import jax +import jax.numpy as jnp +import optax +from confidence_joint import initialize_gate + +PARTS = ('expert0', 'expert1', 'gate') + + +def save_weights(path, parameters): + np.savez(path, **{part + '__' + key: np.asarray(value) + for part, values in parameters.items() for key, value in values.items()}) + + +def load_weights(path): + result = {part: {} for part in PARTS} + with np.load(path, allow_pickle=False) as saved: + for key in saved.files: + part, name = key.split('__', 1) + result[part][name] = jnp.asarray(saved[key]) + assert all(result.values()) + return result + + +def save_state(path, state): + leaves, _ = jax.tree_util.tree_flatten(state) + np.savez(path, **{f'arr_{i}': np.asarray(leaf) for i, leaf in enumerate(leaves)}) + + +def load_state(path, template): + leaves, tree = jax.tree_util.tree_flatten(template) + loaded = [] + with np.load(path, allow_pickle=False) as saved: + assert set(saved.files) == {f'arr_{i}' for i in range(len(leaves))} + for i, leaf in enumerate(leaves): + value = saved[f'arr_{i}'] + assert value.shape == leaf.shape and value.dtype == leaf.dtype + loaded.append(jnp.asarray(value)) + return jax.tree_util.tree_unflatten(tree, loaded) + + +def initialize(parent_paths, seed, expert_lr, gate_lr): + assert len(parent_paths) == 2 + parameters = {'gate': initialize_gate(seed)} + state = {} + records = [] + for i, path in enumerate(map(Path, parent_paths)): + key = f'expert{i}' + with np.load(path, allow_pickle=False) as saved: + parameters[key] = {name: jnp.asarray(saved[name]) for name in saved.files} + assert set(parameters[key]) == {'w1', 'b1', 'w2', 'b2', 'w3', 'b3'} + assert parameters[key]['w1'].shape == (394, 64) + original = optax.chain(optax.clip_by_global_norm(1), optax.adamw(expert_lr, weight_decay=1e-4)) + optimizer_path = path.with_name(path.name.replace('weights-', 'optimizer-')) + original_state = load_state(optimizer_path, original.init(parameters[key])) + state[key] = original_state[1] + records.append({'part': key, 'weights': str(path), 'weights_sha256': hashlib.sha256(path.read_bytes()).hexdigest(), + 'optimizer': str(optimizer_path), 'optimizer_sha256': hashlib.sha256(optimizer_path.read_bytes()).hexdigest(), + 'initial_adam_count': int(state[key][0].count), 'moments_restored': True}) + state['gate'] = optax.adamw(gate_lr, weight_decay=1e-4).init(parameters['gate']) + return parameters, state, records + + +def make_apply(expert_lr, gate_lr): + optimizers = {part: optax.adamw(gate_lr if part == 'gate' else expert_lr, weight_decay=1e-4) for part in PARTS} + clip = optax.clip_by_global_norm(1) + @jax.jit + def apply(parameters, state, gradients): + norm = optax.global_norm(gradients) + gradients, _ = clip.update(gradients, clip.init(parameters), parameters) + new_parameters, new_state = {}, {} + for part in PARTS: + updates, new_state[part] = optimizers[part].update(gradients[part], state[part], parameters[part]) + new_parameters[part] = optax.apply_updates(parameters[part], updates) + return new_parameters, new_state, norm + return apply + + +def counts(state): + return {part: int(state[part][0].count) for part in PARTS} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_train.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_train.py new file mode 100644 index 0000000000000000000000000000000000000000..edc01d92926299868b536f0ad36ce13d205aa561 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__confidence_joint_train.py @@ -0,0 +1,145 @@ +"""Matched joint BCE training with syndrome and expert-confidence gate inputs.""" +import os +os.environ['OPENBLAS_NUM_THREADS'] = '1' +os.environ['OMP_NUM_THREADS'] = '1' +os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false' +from pathlib import Path +import argparse +import hashlib +import json +import shutil +import sys +import time +import numpy as np +import jax +from model import probabilities +from data import load_all +from confidence_joint_gradient import make_gradient, make_infer +from confidence_joint_state import initialize, make_apply, save_weights, load_weights, save_state, load_state, counts + +root = Path(__file__).resolve().parent +research = root.parent +sys.path.insert(0, str(research / 'learned_exact')) +from checkpoint_gradient import make_loss_and_grad +from exact import make_decoder + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--parents', type=Path, nargs=2, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--training-cache', type=Path, required=True) + parser.add_argument('--resume', type=Path) + parser.add_argument('--steps', type=int, default=2048) + parser.add_argument('--batch', type=int, default=1024) + parser.add_argument('--expert-lr', type=float, default=.0001) + parser.add_argument('--gate-lr', type=float, default=.001) + parser.add_argument('--gate-seed', type=int, default=20261310) + parser.add_argument('--seed', type=int, default=20261311) + parser.add_argument('--eval-every', type=int, default=512) + parser.add_argument('--validation-shots', type=int, default=1024) + parser.add_argument('--validation-batch', type=int, default=512) + args = parser.parse_args() + args.output.mkdir(exist_ok=False) + config = {key: ([str(p) for p in value] if key == 'parents' else str(value) if isinstance(value, Path) else value) + for key, value in vars(args).items()} + source_paths = [root / name for name in ['joint_mixture.py', 'confidence_joint.py', 'confidence_joint_gradient.py', 'confidence_joint_state.py', + 'confidence_joint_train.py', 'model.py', 'data.py']] + source_paths += [research / 'learned_exact/checkpoint_gradient.py', research / 'exact_d3/exact.py'] + snapshot = args.output / 'training_source'; snapshot.mkdir() + hashes = {} + for source in source_paths: + name = str(source.relative_to(research)).replace('/', '__') + shutil.copyfile(source, snapshot / name) + hashes[name] = hashlib.sha256(source.read_bytes()).hexdigest() + parameters, state, parent_records = initialize(args.parents, args.gate_seed, args.expert_lr, args.gate_lr) + rng = np.random.default_rng(args.seed) + if args.resume: + old = json.loads((args.resume.parent / 'run_config.json').read_text()) + for key in ['parents', 'expert_lr', 'gate_lr', 'gate_seed', 'seed', 'batch', 'training_cache']: + assert old[key] == config[key], key + parameters = load_weights(args.resume) + state = load_state(args.resume.with_name(args.resume.name.replace('weights-', 'optimizer-')), state) + rng.bit_generator.state = json.loads(args.resume.with_name(args.resume.name.replace('weights-', 'rng-')).with_suffix('.json').read_text()) + config.update(training_source_sha256=hashes, parent_records=parent_records, initial_adam_counts=counts(state), + initialization='Both experts copied with exact Adam state from declared session-trained parents; new gate retains the original seeded 41-input hidden weights, appends four zero confidence-input rows, and has zero output, giving the same initial equal mixture', + sampling='Same seed and draws as syndrome-only joint_mixture1; resumed phases restore only their own saved sampling state', + runtime_versions={'jax': jax.__version__, 'numpy': np.__version__}) + (args.output / 'run_config.json').write_text(json.dumps(config, indent=2) + '\n') + started = time.time() + dataset = load_all(prior_pass=3, training_cache=args.training_cache) + assert len(dataset) == 26 + print(json.dumps({'loaded_conditions': len(dataset), 'seconds': time.time() - started}), flush=True) + seen = [np.zeros(len(item['train_labels']), bool) for item in dataset] + os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'true' + os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.90' + gradient = make_gradient(probabilities, make_loss_and_grad(14, 32)) + infer = make_infer(probabilities, make_decoder(14)) + apply = make_apply(args.expert_lr, args.gate_lr) + feature_names = ['context', 'static', 'valid', 'masks', 'measured', 'grid', 'measurement_indices', 'counts'] + + def feature_values(item): + length = ((int(item['operation_count']) + 511) // 512) * 512 + return length, [item[k][:length] for k in feature_names[:5]] + [item[k] for k in feature_names[5:]] + + def evaluate(step): + results = []; beginning = time.time() + for item in dataset: + n = min(args.validation_shots, len(item['validation_labels'])) + selected = np.random.default_rng(20261102).choice(len(item['validation_labels']), n, replace=False) + length, features = feature_values(item) + parts = [] + for begin in range(0, n, args.validation_batch): + ix = selected[begin:begin + args.validation_batch]; size = len(ix) + ds = np.pad(item['validation_dets'][ix], ((0, args.validation_batch - size), (0, 0))) + base = np.pad(item['validation_base_logits'][ix, :length], ((0, args.validation_batch - size), (0, 0))) + parts.append(np.asarray(infer(parameters, ds, base, *features))[:size]) + p = np.concatenate(parts); y = item['validation_labels'][selected].astype(np.float64) + pp = np.clip(p.astype(np.float64), 1e-7, 1 - 1e-7) + results.append({'condition': item['row']['name'], 'failure': float(np.mean((p >= .5) != y)), + 'negative_log_likelihood': float(np.mean(-y * np.log(pp) - (1 - y) * np.log1p(-pp)))}) + report = {'step': step, 'validation_failure': float(np.mean([r['failure'] for r in results])), + 'negative_log_likelihood': float(np.mean([r['negative_log_likelihood'] for r in results])), + 'conditions': results, 'seconds': time.time() - beginning} + (args.output / f'validation-{step}.json').write_text(json.dumps(report, indent=2) + '\n') + save_weights(args.output / f'weights-{step}.npz', parameters) + save_state(args.output / f'optimizer-{step}.npz', state) + (args.output / f'rng-{step}.json').write_text(json.dumps(rng.bit_generator.state) + '\n') + (args.output / f'checkpoint-{step}.json').write_text(json.dumps({ + 'step': step, 'training_draws': step * args.batch, 'adam_counts': counts(state), + 'distinct_phase_rows': sum(int(mask.sum()) for mask in seen), + 'distinct_by_condition': {item['row']['name']: int(mask.sum()) for item, mask in zip(dataset, seen)}, + 'weights_sha256': hashlib.sha256((args.output / f'weights-{step}.npz').read_bytes()).hexdigest(), 'complete': True}, indent=2) + '\n') + print(json.dumps({k: v for k, v in report.items() if k != 'conditions'}), flush=True) + + evaluate(0) + records = [] + for step in range(1, args.steps + 1): + condition = int(rng.integers(len(dataset))); item = dataset[condition] + ix = rng.integers(len(item['train_labels']), size=args.batch); seen[condition][ix] = True + length, features = feature_values(item) + gradients, loss, error, coefficients = gradient(parameters, item['train_dets'][ix], item['train_labels'][ix], + item['train_base_logits'][ix, :length], *features) + parameters, state, norm = apply(parameters, state, gradients) + if step == 1 or step % 16 == 0: + record = {'step': step, 'loss': float(loss), 'error': float(error), 'gradient_norm': float(norm), + 'mean_coefficients': np.asarray(coefficients).tolist(), 'elapsed': time.time() - started} + assert np.isfinite(record['loss']) and np.isfinite(record['gradient_norm']) + records.append(record); print(json.dumps(record), flush=True) + if step % args.eval_every == 0 or step == args.steps: + evaluate(step) + provenance = {'run_config': config, 'parent_provenance': [json.loads((p.parent / 'provenance.json').read_text()) for p in args.parents], + 'training': 'All516880 supplied d5 training rows eligible; uniform condition and row sampling with replacement', + 'training_shots': sum(len(item['train_labels']) for item in dataset), + 'distinct_phase_rows': sum(int(mask.sum()) for mask in seen), + 'loss': 'Conditional logical BCE of two trainable exact-decoder probabilities; responsibilities and the complete expert-confidence gate chain rule scale expert BCE gradients; free gate-logit adjoint is coefficient minus responsibility', + 'optimizer': 'Separate restored expert AdamW states and fresh gate AdamW; one global norm1 clipping over the full joint gradient, weight_decay1e-4; rates in run_config', + 'gate': '41 fixed-scale syndrome/basis/duration features plus two expert clipped logits/8 and their smooth absolute values;45 inputs,32-unit SiLU hidden layer,one free logit,other logit zero;no label/ID/position features;confidence features are fully differentiated', + 'validation': 'Uniform1024-shot per-condition screens by default, seed20261102; full validation required for checkpoint selection', + 'independence': 'This joint model uses all training rows; it does not retain the reserved-parent training-label holdout claim', + 'test_data_used': False, 'records': records, 'seconds': time.time() - started} + (args.output / 'provenance.json').write_text(json.dumps(provenance, indent=2) + '\n') + + +if __name__ == '__main__': + main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__data.py new file mode 100644 index 0000000000000000000000000000000000000000..7cbe1229eafbaaa3a06eaaeca9a0c38cb411f34f --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__data.py @@ -0,0 +1,95 @@ +"""Static physical features and authorized cached training/validation priors.""" +from pathlib import Path +import json,sys +import numpy as np +import stim +sys.path.insert(0,'/workspace/research/exact_d3') +from exact import parse_errors +from conditional import load_state + +ROOT=Path(__file__).resolve().parent + + +def build_features(row,package,max_ops=2048): + dem=stim.DetectorErrorModel.from_file(Path('/workspace/data/train')/row['noise']) + errors=parse_errors(dem);coords=dem.get_detector_coordinates() + if row['rounds']==1: + plan=dict(np.load(package/f"plans/{row['name']}.npz"));supports=[(ds,l) for ds,l,p in errors] + measured=plan['measured'] + else: + state=load_state(package/f"conditional_plans/{row['name']}.npz");plan=state['on'] + on=set(state['on_indices'].tolist());groups={} + for ds,l,p in errors: + key=tuple(sorted(ds&on)),l + if key[0] or key[1]:groups.setdefault(key,len(groups)) + supports=list(groups) + measured=np.where(plan['measured']>=0,state['on_indices'][np.maximum(plan['measured'],0)],-1) + assert int(plan['width'])<=14 + circuit=stim.Circuit.from_file(Path('/workspace/data/train')/row['circuit']) + qubits=circuit.get_final_qubit_coordinates() + first=next(ins for ins in circuit.flattened() if ins.name in ['M','MX','MR','MRX']) + spaces=sorted(tuple(qubits[t.value][:2]) for t in first.targets_copy());assert len(spaces)==24 + idx={(int(c[2]),tuple(c[:2])):d for d,c in coords.items()} + context=np.full((max_ops,120),600,np.int32);static=np.zeros((max_ops,129),np.float32) + n=len(plan['masks']);assert n<=max_ops + for op,source in enumerate(plan['source_indices']): + if source<0:continue + ds,logical=supports[source];t=min(int(coords[d][2]) for d in ds) if ds else 0 + for dt in range(-2,3): + for s,space in enumerate(spaces):context[op,(dt+2)*24+s]=idx.get((t+dt,space),600) + for d in ds: + c=coords[d];static[op,(int(c[2])-t+2)*24+spaces.index(tuple(c[:2]))]=1 + p=float(plan['probabilities'][op]);static[op,120]=np.log(max(p,1e-7)/max(1-p,1e-7))/8 + static[op,121:]=[logical,len(ds)/4,t/25,row['rounds']/25,t==0,t>=row['rounds']-1,row['basis']=='X',1] + grid=np.full((26,24),600,np.int32);measurement_indices=np.full(601,624,np.int32) + for d,c in coords.items(): + t=int(c[2]);s=spaces.index(tuple(c[:2]));grid[t,s]=d;measurement_indices[d]=t*24+s + return {'context':context,'static':static,'masks':np.pad(plan['masks'],(0,max_ops-n)), + 'measured':np.pad(measured,(0,max_ops-n),constant_values=-1), + 'valid':np.pad((plan['source_indices']>=0).astype(np.float32),(0,max_ops-n)), + 'grid':grid,'measurement_indices':measurement_indices, + 'counts':np.maximum((grid!=600).sum(axis=0),1).astype(np.float32),'operation_count':np.array(n,np.int32)} + + +def load_all(splits=('train','validation'),max_ops=2048,prior_pass=1,training_cache=None): + assert prior_pass in [1,3,5] + assert training_cache is None or prior_pass in [1,3] + rows=[r for r in json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'] if r['distance']==5] + package=Path(json.loads(Path('/workspace/research/CURRENT.json').read_text())['package']) + plans=ROOT/'feature_plans';plans.mkdir(exist_ok=True);items=[] + for row in rows: + path=plans/f"{row['name']}.npz" + if not path.exists():np.savez(path,**build_features(row,package,max_ops)) + item=dict(np.load(path,allow_pickle=False));item['row']=row + if splits: + cache=np.load(Path('/workspace/research/exact_d3/turbo_cache')/f"{row['name']}.npz") + feedback=np.load(Path('/workspace/research/exact_d3/feedback5_cache')/f"{row['name']}.npz") if prior_pass>1 else None + for split in splits: + assert split in ['train','validation'] + arrays=np.load(Path('/workspace/data')/split/row['arrays']) + if split=='train' and training_cache is not None: + full=np.load(Path(training_cache)/f"{row['name']}.npz",allow_pickle=False) + indices=full['indices'];p=full[f'priors{prior_pass}'] + assert np.array_equal(arrays['sample_ids'][indices],full['sample_ids']) + physical_prediction=full['probabilities3'] + else: + indices=cache['train4096_indices'] if split=='train' else np.arange(len(arrays['labels'])) + key='train4096' if split=='train' else 'validation' + p=cache[key+'_on_prior'] if prior_pass==1 else feedback[f'{key}_priors{prior_pass}'] + physical_prediction=cache[key][:,0] if prior_pass==1 else feedback[key][:,prior_pass-1] + assert p.shape[1]==int(item['operation_count']) + if feedback is not None:assert np.array_equal(feedback['train4096_indices'],cache['train4096_indices']) + logits=np.log(np.clip(p,1e-7,1-1e-7)/np.clip(1-p,1e-7,1-1e-7)) + item[split+'_base_logits']=np.pad(logits,((0,0),(0,max_ops-p.shape[1]))).astype(np.float32) + ds=np.unpackbits(arrays['detectors'][indices],axis=1,count=row['num_detectors'],bitorder='little') + item[split+'_dets']=np.pad(ds,((0,0),(0,601-ds.shape[1]))) + item[split+'_labels']=arrays['labels'][indices] + item[split+'_sample_ids']=arrays['sample_ids'][indices] + item[split+'_physical_probabilities']=physical_prediction.astype(np.float32) + items.append(item) + return items + + +if __name__=='__main__': + items=load_all(splits=()) + print(json.dumps({'conditions':len(items),'maximum_operations':max(int(x['operation_count']) for x in items)})) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__joint_mixture.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__joint_mixture.py new file mode 100644 index 0000000000000000000000000000000000000000..3d9d54003bcd8671bcd5332ee6ec050c80fdb99d --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__joint_mixture.py @@ -0,0 +1,75 @@ +"""Two trainable exact-decoder experts with a syndrome-only mixture gate.""" +import jax +import jax.numpy as jnp + +GATE_FEATURES = 41 + + +def gate_features(dets, grid, static): + """Shot-local features; no labels, IDs, expert probabilities, or batch statistics.""" + presence = grid != 600 + event = dets[:, grid].astype(jnp.float32) * presence[None] + count = jnp.maximum(presence.sum(axis=0), 1) + density = event.sum(axis=1) / count[None] + blocks = [density * 4] + for lag in [1, 2, 4, 8]: + valid = presence[:-lag] & presence[lag:] + denom = jnp.maximum(valid.sum(axis=0), 1) + site_valid = valid.any(axis=0) + a = event[:, :-lag] * valid[None] + b = event[:, lag:] * valid[None] + coincidence = (a * b).sum(axis=1) / denom[None] + covariance = coincidence - a.sum(axis=1) * b.sum(axis=1) / denom[None] ** 2 + divisor = jnp.maximum(site_valid.sum(), 1) + blocks.extend([(coincidence * site_valid).sum(axis=1, keepdims=True) * 8 / divisor, + (covariance * site_valid).sum(axis=1, keepdims=True) * 16 / divisor]) + valid_frame = presence.any(axis=1) + nframes = jnp.maximum(valid_frame.sum(), 1) + frame = event.sum(axis=2) / jnp.maximum(presence.sum(axis=1), 1)[None] + frame_mean = frame.sum(axis=1) / nframes + frame_var = (((frame - frame_mean[:, None]) ** 2) * valid_frame[None]).sum(axis=1) / nframes + rank = jnp.cumsum(valid_frame) - 1 + first_mask = valid_frame & (rank < jnp.maximum(nframes // 2, 1)) + last_mask = valid_frame & ~first_mask + first = (frame * first_mask[None]).sum(axis=1) / jnp.maximum(first_mask.sum(), 1) + last = (frame * last_mask[None]).sum(axis=1) / jnp.maximum(last_mask.sum(), 1) + last = jnp.where(last_mask.any(), last, first) + fraction = jnp.max(static[:, 124]) + basis = jnp.max(static[:, 127]) + blocks.append(4 * jnp.stack([density.mean(axis=1), density.std(axis=1), density.max(axis=1), + jnp.sqrt(jnp.maximum(frame_var, 0)), frame.max(axis=1), + jnp.abs(first - last)], axis=1)) + blocks.append(jnp.broadcast_to(jnp.stack([fraction, fraction ** 2, basis]), (len(dets), 3))) + result = jnp.concatenate(blocks, axis=1) + assert result.shape == (len(dets), GATE_FEATURES) + return result + + +def initialize_gate(seed, hidden=32): + key = jax.random.key(seed) + return {'w1': jax.random.normal(key, (GATE_FEATURES, hidden)) * jnp.sqrt(2 / GATE_FEATURES), + 'b1': jnp.zeros(hidden), 'w2': jnp.zeros((hidden, 1)), 'b2': jnp.zeros(1)} + + +def gate_logit(parameters, features): + hidden = jax.nn.silu(features @ parameters['w1'] + parameters['b1']) + return (hidden @ parameters['w2'] + parameters['b2'])[:, 0] + + +def mixture_terms(losses, logit): + """Stable BCE and exact expert/gate adjoints from per-expert BCE losses. + + losses has shape [batch,2]. Responsibilities multiply each expert BCE + gradient; gate_adjoint is d(BCE mixture)/d(single free gate logit). + """ + logits = jnp.stack([logit, jnp.zeros_like(logit)], axis=1) + log_a = jax.nn.log_softmax(logits, axis=1) + log_c = jax.scipy.special.logsumexp(log_a - losses, axis=1) + responsibility = jnp.exp(log_a - losses - log_c[:, None]) + a = jnp.exp(log_a) + return -log_c, responsibility, a[:, 0] - responsibility[:, 0], a + + +def mix_predictions(predictions, logit): + a = jax.nn.sigmoid(logit) + return a * predictions[:, 0] + (1 - a) * predictions[:, 1] diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__model.py new file mode 100644 index 0000000000000000000000000000000000000000..bf5272d4652dd29dab2b118849d17648019d93a3 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_d5__model.py @@ -0,0 +1,55 @@ +"""Small shared network corrects soft-conditioned relevant-CSS edge logits.""" +import jax +import jax.numpy as jnp + + +def initialize(seed,hidden=64,input_features=394): + assert input_features in (394,414) + keys=jax.random.split(jax.random.key(seed),3) + return {'w1':jax.random.normal(keys[0],(input_features,hidden))*jnp.sqrt(2/input_features),'b1':jnp.zeros(hidden), + 'w2':jax.random.normal(keys[1],(hidden,hidden))*jnp.sqrt(2/hidden),'b2':jnp.zeros(hidden), + 'w3':jax.random.normal(keys[2],(hidden,1))*.001,'b3':jnp.zeros(1)} + + +def endpoint_features(events,measurements,density,static): + """Twenty endpoint/history summaries computed solely from the input syndrome.""" + support=static[:,:120].reshape(-1,5,24) + selected=jnp.max(support,axis=1) + endpoint_counts=jnp.maximum(support.sum(axis=-1),1) + spatial_count=jnp.maximum(selected.sum(axis=-1),1) + event_frames=events.reshape(len(events),-1,5,24) + measurement_frames=measurements.reshape(len(events),-1,5,24) + endpoint_events=(event_frames*support[None,:,:,:]).sum(axis=-1) + endpoint_measurements=(measurement_frames*support[None,:,:,:]).sum(axis=-1) + spatial_events=(event_frames*selected[None,:,None,:]).sum(axis=-1)/spatial_count[None,:,None] + endpoint_total=endpoint_events.sum(axis=-1) + endpoint_fraction=endpoint_total/jnp.maximum(support.sum(axis=(1,2)),1)[None,:] + endpoint_parity=jnp.mod(endpoint_total,2) + selected_density=(density[:,None,:]*selected[None,:,:]).sum(axis=-1)/spatial_count[None,:] + adjacent=(event_frames[:,:,:-1,:]*event_frames[:,:,1:,:]*selected[None,:,None,:]).sum(axis=(2,3))/(4*spatial_count[None,:]) + measurement_mean=(measurement_frames*selected[None,:,None,:]).sum(axis=(2,3))/(5*spatial_count[None,:]) + return jnp.concatenate([ + endpoint_events/endpoint_counts[None,:,:], + endpoint_measurements/endpoint_counts[None,:,:],spatial_events, + selected_density[:,:,None],endpoint_fraction[:,:,None],endpoint_parity[:,:,None], + adjacent[:,:,None],measurement_mean[:,:,None]],axis=-1) + + +def probabilities(parameters,dets,base_logits,context,static,valid,grid,measurement_indices,counts): + event_grid=dets[:,grid] + measured=(jnp.cumsum(event_grid.astype(jnp.int32),axis=1)&1).reshape(len(dets),624) + measured=jnp.pad(measured,((0,0),(0,1))) + events=dets[:,context].astype(jnp.float32) + measurements=measured[:,measurement_indices[context]].astype(jnp.float32) + density=jnp.sum(event_grid,axis=1)/counts[None,:] + features=jnp.concatenate([events,measurements, + jnp.broadcast_to(density[:,None,:],(len(dets),len(context),24)), + jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape)),base_logits[:,:,None]/8],axis=-1) + if parameters['w1'].shape[0]==414: + features=jnp.concatenate([features,endpoint_features(events,measurements,density,static)],axis=-1) + else: + assert parameters['w1'].shape[0]==394 + h=jax.nn.silu(features@parameters['w1']+parameters['b1']) + h=jax.nn.silu(h@parameters['w2']+parameters['b2']) + delta=2*jnp.tanh((h@parameters['w3']+parameters['b3'])[...,0]) + return (jax.nn.sigmoid(base_logits+delta)*valid[None,:]).T diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_exact__checkpoint_gradient.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_exact__checkpoint_gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..b567a7a009cbed51d34288e7492dc5ee45710488 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/confidence_joint1/learned_exact__checkpoint_gradient.py @@ -0,0 +1,73 @@ +"""Exact logical BCE gradient with block recomputation of frontier histories.""" +import jax +import jax.numpy as jnp + + +def make_loss_and_grad(width, block_size=32): + indices = jnp.arange(1 << width, dtype=jnp.int32) + + @jax.jit + def evaluate(dets, labels, probabilities, masks, measured): + nops, batch = probabilities.shape + padding = (-nops) % block_size + p = jnp.pad(probabilities, ((0, padding), (0, 0))) + m = jnp.pad(masks, (0, padding)) + d = jnp.pad(measured, (0, padding), constant_values=-1) + blocks = (p.reshape(-1, block_size, batch), m.reshape(-1, block_size), + d.reshape(-1, block_size)) + + def forward(f, args): + probability, mask, detector = args + + def error(f): + out = (1 - probability[:, None]) * f + probability[:, None] * f[:, indices ^ mask] + return out, jnp.ones(batch) + + def project(f): + dst = (indices[None, :] & ~mask) | (dets[:, detector, None].astype(jnp.int32) * mask) + g = jnp.take_along_axis(f, dst, axis=1) * ((indices & mask) == 0) + norm = jnp.maximum(g.sum(axis=1), 1e-30) + return g / norm[:, None], norm + + out, norm = jax.lax.cond(detector < 0, error, project, f) + return out, (f, norm) + + def forward_block(f, args): + def step(carry, item): + result, _ = forward(carry, item) + return result, None + out, _ = jax.lax.scan(step, f, args) + return out, f + + initial = jnp.zeros((batch, 1 << width), jnp.float32).at[:, 0].set(1) + f, checkpoints = jax.lax.scan(forward_block, initial, blocks) + indicator = ((indices[None, :] & 1) == labels[:, None]) + correct = jnp.maximum(jnp.sum(f * indicator, axis=1), 1e-10) + prediction = jnp.sum(f[:, 1::2], axis=1) + terminal = 1 - indicator / correct[:, None] + + def backward(b, args): + probability, mask, detector, old, norm = args + + def error(b): + flipped = b[:, indices ^ mask] + grad = jnp.sum(old * (flipped - b), axis=1) + return (1 - probability[:, None]) * b + probability[:, None] * flipped, grad + + def project(b): + obs = ((indices[None, :] & mask) == dets[:, detector, None].astype(jnp.int32) * mask) + values = b[:, indices & ~mask] + mean = jnp.sum(old * obs * values, axis=1) / norm + return (values - mean[:, None]) * obs / norm[:, None], jnp.zeros(batch) + + return jax.lax.cond(detector < 0, error, project, b) + + def backward_block(b, args): + probabilities, masks, measured, checkpoint = args + _, (history, norms) = jax.lax.scan(forward, checkpoint, (probabilities, masks, measured)) + return jax.lax.scan(backward, b, (probabilities, masks, measured, history, norms), reverse=True) + + _, gradients = jax.lax.scan(backward_block, terminal, (*blocks, checkpoints), reverse=True) + return -jnp.log(correct), prediction, gradients.reshape(-1, batch)[:nops] + + return evaluate diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/PROPOSAL.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/PROPOSAL.json new file mode 100644 index 0000000000000000000000000000000000000000..533b80ac82bf43d93f129b7e91f4eec08ccf77ed --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/PROPOSAL.json @@ -0,0 +1,77 @@ +{ + "name": "d3_latent_readout1", + "created_utc": "2026-10-22T06:18:52.852012+00:00", + "status": "Prospective protocol; main requires completed Brier plus queued engineering and new pipeline controls.", + "hypothesis": "A persistent latent quality state shared by interior readout signatures touching the geometrically central data qubit can represent fault correlations absent from an independent corrected DEM, improving logical decisions with only three fitted parameters per patch/basis.", + "geometry": { + "family": "Unique local data qubit nearest mean of ideal patch data coordinates, fixed without performance selection.", + "channels": "Audited grouped DEM signatures for interior time-adjacent ancilla measurement flips, L0 zero, adjacent to central data family. Grouped signatures do not uniquely identify microscopic cause.", + "boundaries": "Unchanged. Interior times monotonic in elimination order; retain one quality bit." + }, + "model": { + "latent_bits": 1, + "physical_frontier_width": 10, + "stationary_pi": ".001+.499*sigmoid(theta0)", + "rho": ".999*sigmoid(theta1)", + "alpha": ".99*sigmoid(theta2)", + "transition": "Pr(Q_new=1|Q_old)=pi+(Q_old-pi)*rho**time_gap", + "rates": "p_Q=p+alpha*min(p/pi,(1-p)/(1-pi))*(Q-pi)", + "normalization": "Joint normalization over latent and physical frontier after each measured detector.", + "trainable_scalars": 24, + "shared_within_group": "Three latent logits shared by every original neural expert and all durations in each of eight groups.", + "frozen": "Original session-trained physical neural experts, their ensemble coefficients, existing r1 replacement and r3 affine refinement. No conditional endpoint encoder/projection; replace d3 route, do not stack learned residuals." + }, + "initialization": { + "seed": 20268310, + "distribution": "Independent normal(0,.01) jitter around logits giving pi=.1,rho=.8,alpha=.25, eight groups in sorted order, NumPy default_rng; fresh latent parameters.", + "nonzero_amplitude_reason": "A mean-preserving stationary latent model has zero first derivative in amplitude at zero; exact-zero initialization cannot learn the intended correlations.", + "frozen_parent_reference": { + "package": "/run-state/workspace/research/context_upgrades/masked_pretraining1/package", + "predictions": "/run-state/workspace/research/context_upgrades/masked_pretraining1/predictions", + "score": "/run-state/workspace/research/context_upgrades/masked_pretraining1/score.json", + "P": 0.7675189678485577, + "frozen_utc": "2026-10-21T23:49:09.177417+00:00", + "scope": "Frozen own-session d3 parent, cache lineage inherited from d3_masked_head; validation outputs must equal current published d3 before training.", + "sha256": { + "model_config.json": "aa8a5875819da706b8e00dce90c3908e56ae29bc072bf8406d9ac68100db4cf5", + "model_manifest.json": "9427be1bbd3f51ae07778e075f09b0a16ecae3d9e0db7ef1d01ffddd2d1cffc7", + "training_provenance.json": "dba6a9a4a18707ae0e95c231fe2b0db1097ed062de0a110722de73ce2d3c85ed" + } + }, + "precision": "HIGHEST float32 MLP/temporal convolution, inherited from training-only diagnosed precise parent; different numerical graph from native published cache. Full neutral audit records that difference and no neutral checkpoint is eligible." + }, + "implementation": { + "frontier": "Pallas persistent forward and three forward-mode directional derivatives. CPU interpret and independent JAX/float64 enumeration controls required.", + "row_batch": 256, + "optimizer": "Independent per-group Adam, clipping gradient norm1, no weight decay.", + "learning_rate": 0.02, + "dropout": false, + "EMA": false, + "calibration": false + }, + "training": { + "steps": 8192, + "batch": 256, + "sampling_seed": 20268320, + "stream": "At absolute step s, NumPy default_rng(SeedSequence([seed,s])); uniform one of sorted104conditions then256 independent uniform row draws. All2067520 training rows eligible.", + "learning_rate": 0.02, + "checkpoints": [ + 2048, + 4096, + 8192 + ], + "r1": "Draw/log all eight r1 conditions but skip optimizer; there are no selected interior channels. Existing r1 predictions retained.", + "selection": "Only one GLOBAL d3 choice among fixed2048/4096/8192checkpoints and then-current incumbent. No per-group/per-duration blends or extra checkpoints. All104d3 conditions evaluated, d5 unchanged; actual assembled replay and all130trusted score decide publication." + }, + "controls_required": [ + "Prototype geometry, float64 enumeration, Jacobian, Pallas CPU/static/GPU controls", + "Frozen neural attachment CPU and GPU, including B256 complete loss gradients", + "Actual pipeline CPU64 versus32+resume32, all8groups and allr1 bypass behavior, full counters/seen/Adam clocks", + "Actual GPU pipeline pilot versus CPU reference and exported runtime arbitraryN/order/subsets", + "Full104zero-amplitude numerical audit against independent HIGHEST parent and native incumbent (diagnostic only); finite q and maxdiff against HIGHEST<5e-6, decision differences reported", + "Projected inference runtime below3600seconds before main; actual packaged replay required for promotion" + ], + "publication": "Only after main sources/inputpins frozen, full selection and actual assembled inference/scorer/readonlyrelocation. Use qecbench.package atomic replacement. Preserve every failed control and amendment.", + "data_restrictions": "Train and validation only; no labels or IDs decoded by geometry/model features. IDs copied/equality-aligned only. No external checkpoints/test data/released per-shot answers.", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/R1_ROUTING_CLARIFICATION.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/R1_ROUTING_CLARIFICATION.json new file mode 100644 index 0000000000000000000000000000000000000000..80021e87a809636a8faf58f61b631157bafa7ed9 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/R1_ROUTING_CLARIFICATION.json @@ -0,0 +1,9 @@ +{ + "created_utc": "2026-10-22T06:20:49.027717+00:00", + "scope": "Clarification before any validation or main fitting, consistent with all-r1 bypass in proposal.", + "r1_inference": "All eight r1 conditions retain then-current incumbent routes and outputs, including any completed Brier route. New latent geometry has no interior channel there.", + "r1_training_accounting": "r1 draws do not update parameters or Adam. Diagnostic training loss uses frozen original HIGHEST physical parent with current latent parameters; this does not select or alter r1 predictions.", + "r1_validation": "Copy own incumbent predictions for unchanged r1, verify exact inference via exported-route controls/actual candidate replay. This is analogous to retaining d5; every condition remains in full score.", + "r_ge_3": "Replace existing learned d3 residual route with original frozen physical parent plus one fitted quality chain and existing r3 affine refinement.", + "test_data_used": false +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/audit_run.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/audit_run.py new file mode 100644 index 0000000000000000000000000000000000000000..8bd378c2a6e9062c85d85995cc11a9710ea08d4d --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/audit_run.py @@ -0,0 +1,35 @@ +"""Independent counter-stream, bypass and active-group optimizer audit.""" +from pathlib import Path +import json,hashlib +import numpy as np +from common import ROOT,RESEARCH,digest +from state import optimizer,load_state +from data import frozen_states,initial + +def audit(run): + run=Path(run);config=json.loads((run/'run_config.json').read_text());assert config['start_step']==0;rows=sorted(config['data']);groups=config['groups'];sizes=[config['data'][n]['reference']['shots'] for n in rows];counts=np.zeros(104,np.int64);seen=[np.zeros(n,bool) for n in sizes];updates=np.zeros(8,np.int64);bypasses=np.zeros(104,np.int64);dh=hashlib.sha256();steps=[] + for name,sha in config['training_source_sha256'].items():assert digest(run/'training_source'/name)==sha and digest(ROOT/name)==sha + frozen,_=frozen_states();p=initial(frozen);opt=optimizer(config['settings']['learning_rate']);template={g:opt.init(p[g]) for g in groups} + for step in range(config['steps']+1): + if step: + rng=np.random.default_rng(np.random.SeedSequence([config['settings']['sampling_seed']+config['seed_offset'],step]));ci=int(rng.integers(104));ix=rng.integers(sizes[ci],size=config['batch']);counts[ci]+=config['batch'];seen[ci][ix]=True + if config['data'][rows[ci]]['bypass']:bypasses[ci]+=1 + else: + group=next(g for g in groups if rows[ci].startswith('surface_code_b'+g.split('_b')[1][0]) and rows[ci].endswith(g.split('_center_')[1])) + updates[groups.index(group)]+=1 + desc={'step':step,'condition_index':ci,'condition':rows[ci],'shots':config['batch'],'row_draws_sha256':hashlib.sha256(ix.astype('=.5,rq>=.5) + checks.append({'reference':label,'gradient_max_abs':mx,'gradient_relative_l2':rel,'reference_norm':norm,'q_max_abs':pmax,'loss_max_abs':lmax}) + record={'condition':row['condition'],'batch':batch,'checks':checks,'compile_included_seconds':compile_seconds} + if batch==256: + raw,adam,loss,pred,norm=update(args[0],opt.init(args[0]),*args[1:]);jax.block_until_ready((raw,adam,loss));assert int(adam[1][0].count)==1 and np.isfinite(np.asarray(raw)).all() + times=[] + for _ in range(5): + tick=time.monotonic();jax.block_until_ready(gradient(*args));times.append(time.monotonic()-tick) + record.update(gradient_seconds=times,median_gradient_seconds=float(np.median(times)),discarded_optimizer_updates=1,discarded_optimizer_row_draws=256) + records.append(record);print(json.dumps(record),flush=True) +peak=int(jax.devices()[0].memory_stats()['peak_bytes_in_use']);assert 0=.5,qq>=.5) + record={'condition':item['row']['name'],'group':group,'rounds':rounds,'batch':batch,'members':len(state['coefficients']),'gradient_max_abs':mx,'gradient_relative_l2':rel,'reference_norm':norm,'relative_bound_applied':norm>=1e-4,'probability_max_abs':pmax,'loss_max_abs':lmax};records.append(record) + values.update({str(ci)+'_'+k:v for k,v in [('gradient',g),('loss',l),('q',q)]});print(json.dumps(record),flush=True) +np.savez(out/'reference.npz',**values) +report={'status':'passed','utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'records':records,'seconds':time.monotonic()-start,'training_row_occurrences':528,'optimizer_updates':0,'test_data_used':False,'source_sha256':protocol['source_sha256'],'fixtures_sha256':digest(out/'reference.npz'), + 'access':'Training loader reads train labels and IDs, IDs are unused; only fixed rows256:256+B contribute to controls. No validation/test access.'} +(out/'report.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({k:v for k,v in report.items() if k not in ['records','source_sha256']}),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/certify_controls.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/certify_controls.py new file mode 100644 index 0000000000000000000000000000000000000000..4336cbf248d1f0c8e7cd93b7230fbe3f4a07ffbb --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/certify_controls.py @@ -0,0 +1,29 @@ +"""Verify controls and measured neutral inference feasibility before main fitting.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1' +from pathlib import Path +import json,datetime,sys +from common import ROOT,RESEARCH,digest +from audit_run import audit +audit(ROOT/'control1/full');audit(ROOT/'gpu_control1') +required=['control1/report.json','control1/full/sampling_audit.json','backend_reference1/report.json','runtime_control_cpu.json','backend_gpu.json','gpu_pipeline_audit.json','runtime_control_gpu.json','neutral_validation/report.json'] +for name in required: + report=json.loads((ROOT/name).read_text());assert report['status']=='passed',name + for path,sha in report['source_sha256'].items(): + p=Path(path) if Path(path).is_absolute() else ROOT/path;assert digest(p)==sha,(name,path) +prototype=RESEARCH/'d3_latent_readout_prototype' +prototype_reports=['geometry_control.json','control1/report.json','gradient_control1/report.json','pallas_control1/report.json','static_lowering.json','gpu1/report.json','neural_control1/report.json'] +for name in prototype_reports:assert json.loads((prototype/name).read_text())['status']=='passed',name +assert (ROOT/'kernel.py').read_bytes()==(prototype/'pallas_model.py').read_bytes() +sys.path.insert(0,str(RESEARCH));from runtime_estimate import find_component_seconds +current=json.loads((ROOT/'incumbent_at_launch.json').read_text());prov=json.loads((Path(current['package'])/'training_provenance.json').read_text()) +old_d3=find_component_seconds(prov,3);d5=find_component_seconds(prov,5);neutral=json.loads((ROOT/'neutral_validation/report.json').read_text());new96=neutral['new_route_inference_seconds'] +# Deliberately include the entire old d3 runtime as an upper allowance for retained r1. +projected=(new96+old_d3+d5)*25000/5120;assert projected<3600,projected +resource={'status':'passed','new96_measured_seconds':new96,'old_full_d3_seconds_allowance_for_retained_r1':old_d3,'retained_d5_seconds':d5,'projected_total_seconds':projected, + 'scope':'Conservative component projection, not a timed test benchmark. Actual assembled replay required for promotion.', + 'source_sha256':{str(ROOT/'certify_controls.py'):digest(ROOT/'certify_controls.py'),str(ROOT/'neutral_validation/report.json'):digest(ROOT/'neutral_validation/report.json')},'test_data_used':False} +(ROOT/'premain_runtime_projection.json').write_text(json.dumps(resource,indent=2)+'\n');required.append('premain_runtime_projection.json') +report={'status':'passed','utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'controls':{name:digest(ROOT/name) for name in required}, + 'prototype_controls':{str(prototype/name):digest(prototype/name) for name in prototype_reports},'main_training_started':False,'test_data_used':False} +(ROOT/'prepared_controls.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/common.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/common.py new file mode 100644 index 0000000000000000000000000000000000000000..95a5a4ba70ca1f18f62eea9d7c047de3e4fb643d --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/common.py @@ -0,0 +1,12 @@ +from pathlib import Path +import hashlib,json,numpy as np +ROOT=Path(__file__).resolve().parent;RESEARCH=ROOT.parent +def digest(path):return hashlib.sha256(Path(path).read_bytes()).hexdigest() +def proposal():return json.loads((ROOT/'PROPOSAL.json').read_text()) +def reference(): + info=json.loads((ROOT/'reference.json').read_text());package=Path(info['package']) + for path,sha in info['sha256'].items():assert digest(package/path)==sha + return info,package,json.loads((package/'model_config.json').read_text()) +def align(ids,target): + assert ids.dtype==target.dtype==np.uint64 and len(np.unique(ids))==len(ids) and len(np.unique(target))==len(target) + order=np.argsort(ids);ix=np.searchsorted(ids[order],target);assert np.all(ixself.max_executables:self.executables.popitem(last=False) + self.executables.move_to_end(key) + return self.executables[key](*args) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/control_pipeline.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/control_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..75f458f236726139eb36b46efa09d8329a9133db --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/control_pipeline.py @@ -0,0 +1,22 @@ +"""Actual latent-quality trainer exact resume; every control state discarded.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:4]) +from pathlib import Path +import subprocess,json,time +import numpy as np +from common import ROOT,digest +out=ROOT/'control1';out.mkdir(exist_ok=False);start=time.monotonic() +def run(name,steps,resume=None): + cmd=['python',str(ROOT/'train.py'),'--control','--steps',str(steps),'--batch','2','--output',str(out/name)] + if resume:cmd+=['--resume',str(out/resume)] + with (out/(name+'.log')).open('w') as log:subprocess.run(cmd,stdout=log,stderr=subprocess.STDOUT,check=True) +run('full',64);run('split',32);run('resume',64,'split/weights-32.npz') +for kind in ['weights','optimizer','counts','seen']: + with np.load(out/'full'/f'{kind}-64.npz',allow_pickle=False) as a,np.load(out/'resume'/f'{kind}-64.npz',allow_pickle=False) as b: + assert set(a.files)==set(b.files) + for k in a.files:assert np.array_equal(a[k],b[k]),(kind,k) +assert json.loads((out/'full/checkpoint-64.json').read_text())==json.loads((out/'resume/checkpoint-64.json').read_text()) +from audit_run import audit +audit(out/'full') +with np.load(out/'full/counts-64.npz',allow_pickle=False) as f:assert np.all(f['updates_by_group']>0) and f['bypasses_by_condition'].sum()>0 +report={'status':'passed','actual_trainer_full64_vs32plusresume_raw_Adam_counts_seen_descriptors_bitwise':True,'discarded_training_attempts':128,'discarded_hardware_row_draws':256,'control_states_eligible_for_main':False,'seconds':time.monotonic()-start,'source_sha256':json.loads((out/'full/run_config.json').read_text())['training_source_sha256'],'test_data_used':False};(out/'report.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/core.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/core.py new file mode 100644 index 0000000000000000000000000000000000000000..713f684ba82f051612e97e951cdb94b43dcb3625 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/core.py @@ -0,0 +1,25 @@ +"""Frozen neural rates and exact three-parameter directional latent derivatives.""" +import jax,jax.numpy as jnp +from parent_model import probabilities +from kernel import make_decoder + +def make_kernels(interpret=False): + forward=make_decoder(interpret=interpret);derivative=make_decoder(interpret=interpret,derivatives=True) + def values(theta,frozen,dets,coefficients,a,b,context,static,base,valid,masks,measured,grid,mapping,counts,quality_times,transition_gaps,*,with_derivatives): + qs=[];js=[] + for k in range(len(coefficients)): + state=jax.tree_util.tree_map(lambda x:x[k],frozen) + p=probabilities(state,dets,context,static,base,valid,grid,mapping,counts) + q,j=(derivative if with_derivatives else forward)(theta,dets,p,masks,measured,quality_times,transition_gaps) + qs.append(q[0]);js.append(j.T if with_derivatives else jnp.zeros((len(dets),3),jnp.float32)) + q=a*jnp.sum(jnp.stack(qs,axis=1)*coefficients[None],axis=1)+b + jac=a[:,None]*jnp.sum(jnp.stack(js,axis=1)*coefficients[None,:,None],axis=1) + return q,jac + @jax.jit + def infer(*args):return values(*args,with_derivatives=False)[0] + @jax.jit + def gradient(theta,frozen,dets,y,*args): + q,jac=values(theta,frozen,dets,*args,with_derivatives=True);correct=jnp.where(y,q,1-q);safe=jnp.maximum(correct,1e-10) + factor=jnp.where(correct>1e-10,jnp.where(y,-1.,1.)/safe,0.)/len(dets) + return jnp.sum(jac*factor[:,None],axis=0),-jnp.log(safe).mean(),q + return infer,gradient diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/data.py new file mode 100644 index 0000000000000000000000000000000000000000..f40622c79bfcc7c31a641646f2c470b415f77233 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/data.py @@ -0,0 +1,61 @@ +"""Explicit split access and frozen original physical parameters.""" +import json,numpy as np,jax.numpy as jnp +from pathlib import Path +from scipy.special import expit,logit +from common import ROOT,RESEARCH,digest,proposal,reference +from r3_model import codes +from short_model import predict as short_predict + +def frozen_states(): + _,package,config=reference();hashes={x['path']:x['sha256'] for x in json.loads((package/'model_manifest.json').read_text())['checkpoints']};groups={};pins={} + for group,record in sorted(config['temporal_contextual'].items()): + assert not record.get('blend_contextual',False);states=[];coefficients=[] + for member in record.get('members',[record]): + path=package/member['weights'];assert digest(path)==hashes[member['weights']];pins[str(path)]=digest(path) + with np.load(path,allow_pickle=False) as f:states.append({k:jnp.asarray(f[k]) for k in f.files}) + coefficients.append(member.get('coefficient',1.)) + coefficients=np.asarray(coefficients,np.float32);coefficients/=coefficients.sum() + groups[group]={'weights':{k:jnp.stack([s[k] for s in states]) for k in states[0]},'coefficients':coefficients} + assert len(groups)==8 and sum(len(x['coefficients']) for x in groups.values())==11 + return groups,pins + +def initial(groups): + rng=np.random.default_rng(proposal()['initialization']['seed']);centre=np.array([logit((.1-.001)/.499),logit(.8/.999),logit(.25/.99)]) + return {g:jnp.asarray((centre+rng.normal(0,.01,3)).astype(np.float32)) for g in sorted(groups)} + +def affine(item,packed): + a=np.ones(len(packed),np.float32);b=np.zeros(len(packed),np.float32) + if item['short']:a[:]=0;b=short_predict(packed,item['short_state']) + elif item['r3_state'] is not None: + state=item['r3_state'];key=codes(packed);ix=np.searchsorted(state['codes'],key);safe=np.minimum(ix,len(state['codes'])-1);seen=(ix=0)) + item={'row':row,'record':record,'plan':plan,'geometry':geometry,'bypass':row['rounds']==1,'short':'short_syndrome_weights' in record,'short_state':None,'r3_state':None,'geometry_sha256':digest(gp),'plan_sha256':digest(path)} + for key,field in [('short_syndrome_weights','short_state'),('r3_pattern','r3_state')]: + if key in record: + path=package/(record[key] if key=='short_syndrome_weights' else record[key]['weights']) + with np.load(path,allow_pickle=False) as f:item[field]={k:f[k] for k in f.files} + for split in splits: + sr=manifests[split][row['name']];path=source/split/sr['arrays'];assert digest(path)==sr['arrays_sha256'] + with np.load(path,allow_pickle=False) as f:packed=f['detectors'];y=f['labels'];ids=f['sample_ids'] + ds=np.pad(np.unpackbits(packed,axis=1,count=row['num_detectors'],bitorder='little'),((0,0),(0,201-row['num_detectors'])));a,b=affine(item,packed) + item.update({split+'_dets':ds,split+'_labels':y,split+'_ids':ids,split+'_a':a,split+'_b':b,split+'_reference':{'arrays_sha256':sr['arrays_sha256'],'shots':len(y)}}) + items.append(item) + assert len(items)==104 and sum(x['bypass'] for x in items)==8 and sum(x['short'] for x in items)==3 + return items + +def args(item):return [item['plan'][k] for k in ['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts']]+[item['geometry'][k] for k in ['quality_times','transition_gaps']] diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/evaluate.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..29619407dc965a3b31fe18bb56d3b5cafc13c4a1 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/evaluate.py @@ -0,0 +1,48 @@ +"""All-duration d3 validation; zero amplitude is an ineligible numerical audit.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +from pathlib import Path +import argparse,json,time,datetime,numpy as np,jax,jax.numpy as jnp +from common import ROOT,digest,reference,align +from data import load_all,frozen_states,initial,args as plan_args +from core import make_kernels +from compiled import PersistentCompiled +from neutral_core import infer as independent + +def main(): + parser=argparse.ArgumentParser();parser.add_argument('--weights',type=Path);parser.add_argument('--output',type=Path,required=True);parser.add_argument('--neutral',action='store_true');args=parser.parse_args() + assert args.neutral==(args.weights is None);assert jax.default_backend()=='gpu';args.output.mkdir(exist_ok=False);start=time.monotonic() + items=load_all(('validation',));frozen,_=frozen_states();parameters=initial(frozen);incumbent=json.loads((ROOT/'incumbent_at_launch.json').read_text());parent_reference=reference()[0] + if args.weights: + with np.load(args.weights,allow_pickle=False) as f:parameters={k:jnp.asarray(f[k]) for k in f.files} + else:parameters={g:p.at[2].set(-jnp.inf) for g,p in parameters.items()} + infer=PersistentCompiled(make_kernels()[0],max_executables=32);parent=PersistentCompiled(independent,max_executables=32);records=[];inference_seconds=0.;parent_seconds=0. + for item in items: + row=item['row'];g=row['group'];ds=item['validation_dets'];y=item['validation_labels'];ids=item['validation_ids'];state=frozen[g];bypass=item['bypass'];values=[];parents=[] + with np.load(Path(incumbent['predictions'])/(row['name']+'.npz'),allow_pickle=False) as f:old=f['probabilities'][align(f['sample_ids'],ids)] + if bypass:q=old.copy() + else: + for pos in range(0,len(ds),256): + n=min(256,len(ds)-pos);dets=np.pad(ds[pos:pos+256],((0,256-n),(0,0)));a=np.pad(item['validation_a'][pos:pos+256],(0,256-n));b=np.pad(item['validation_b'][pos:pos+256],(0,256-n)) + tick=time.monotonic();q0=np.asarray(infer(parameters[g],state['weights'],dets,state['coefficients'],a,b,*plan_args(item)));inference_seconds+=time.monotonic()-tick;values.append(q0[:n]) + if args.neutral: + tick=time.monotonic();pr=np.asarray(parent(state['weights'],dets,state['coefficients'],a,b,*plan_args(item)[:-2]));parent_seconds+=time.monotonic()-tick;parents.append(pr[:n]) + q=np.concatenate(values).astype(np.float32) + assert q.dtype==np.float32 and q.shape==y.shape and np.isfinite(q).all() and np.all((q>=0)&(q<=1)) + np.savez(args.output/(row['name']+'.npz'),sample_ids=ids,probabilities=q) + record={'condition':row['name'],'group':g,'basis':row['basis'],'rounds':row['rounds'],'shots':len(q),'retained_incumbent_r1':bypass,'failure':float(np.mean((q>=.5)!=y)), + 'negative_log_likelihood':float(-np.log(np.maximum(np.where(y,q,1-q),1e-10)).mean()),'incumbent_decision_differences':int(np.sum((q>=.5)!=(old>=.5)))} + if args.neutral: + if not bypass: + pr=np.concatenate(parents);mx=float(abs(q-pr).max());assert mx<5e-6,(row['name'],mx) + with np.load(Path(parent_reference['predictions'])/(row['name']+'.npz'),allow_pickle=False) as f:native=f['probabilities'][align(f['sample_ids'],ids)] + record.update(independent_highest_max_abs=mx,independent_highest_decision_differences=int(np.sum((q>=.5)!=(pr>=.5))),native_original_parent_max_abs=float(abs(q-native).max()),native_original_parent_decision_differences=int(np.sum((q>=.5)!=(native>=.5)))) + else:record.update(independent_highest_max_abs=None,independent_highest_decision_differences=None) + records.append(record) + if len(records)%13==0:print(json.dumps({'conditions':len(records),'seconds':time.monotonic()-start,'inference_seconds':inference_seconds}),flush=True) + report={'status':'passed','utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'records':records,'neutral_diagnostic_only':args.neutral,'eligible_for_selection':not args.neutral, + 'd3_failure':float(np.mean([x['failure'] for x in records])),'negative_log_likelihood':float(np.mean([x['negative_log_likelihood'] for x in records])), + 'seconds':time.monotonic()-start,'new_route_inference_seconds':inference_seconds,'independent_parent_inference_seconds':parent_seconds,'r1_retained':8,'test_data_used':False, + 'weights_sha256':digest(args.weights) if args.weights else None,'source_sha256':{str(ROOT/name):digest(ROOT/name) for name in ['evaluate.py','neutral_core.py','ordinary.py','core.py','kernel.py','parent_model.py','compiled.py','data.py','common.py']}} + (args.output/'report.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({k:v for k,v in report.items() if k not in ['records','source_sha256']}),flush=True) +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/export.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/export.py new file mode 100644 index 0000000000000000000000000000000000000000..8a1010e12827f1dc4f516812c75480e30779f189 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/export.py @@ -0,0 +1,14 @@ +"""Install only self-contained runtime modules with explicit import names.""" +from pathlib import Path +import shutil +from common import ROOT,digest + +def install(package): + package=Path(package);old=package/'temporal_contextual.py';backup=package/'d3_pre_latent_temporal.py' + assert not backup.exists();shutil.copyfile(old,backup) + sources={'core.py':'d3_lr_core.py','kernel.py':'d3_lr_kernel.py','parent_model.py':'d3_lr_parent.py','compiled.py':'d3_lr_compiled.py','runtime.py':'temporal_contextual.py'} + for src,dst in sources.items(): + text=(ROOT/src).read_text() + if src=='core.py':text=text.replace('from parent_model import','from d3_lr_parent import').replace('from kernel import','from d3_lr_kernel import') + (package/dst).write_text(text) + return {p:digest(package/p) for p in [backup.name,*sources.values()]} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/gpu_pipeline_audit.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/gpu_pipeline_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..ed3bbd34990c89d149f79eaa2c911bc04cf098ab --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/gpu_pipeline_audit.py @@ -0,0 +1,28 @@ +"""Audit the discarded actual GPU pilot against its fixed CPU counterpart.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1' +from pathlib import Path +import json,datetime,numpy as np +from common import ROOT,digest +from audit_run import audit +cpu=ROOT/'control1/full';gpu=ROOT/'gpu_control1';audit(gpu);records=[] +for kind in ['weights','optimizer','counts','seen']: + with np.load(cpu/f'{kind}-64.npz',allow_pickle=False) as a,np.load(gpu/f'{kind}-64.npz',allow_pickle=False) as b: + assert set(a.files)==set(b.files) + for name in a.files: + x,y=a[name],b[name];assert x.dtype==y.dtype and x.shape==y.shape + if kind in ['counts','seen'] or np.issubdtype(x.dtype,np.integer):assert np.array_equal(x,y) + else: + diff=x.astype(float)-y.astype(float);mx=float(abs(diff).max());norm=float(np.linalg.norm(x.astype(float)));rel=float(np.linalg.norm(diff)/max(norm,1e-30)) + bound=3e-4 if kind=='weights' else 3e-6;relative_bound=3e-4 if kind=='weights' else 3e-3 + assert mx=1e-5}) +a=json.loads((cpu/'checkpoint-64.json').read_text());b=json.loads((gpu/'checkpoint-64.json').read_text()) +for k in a: + if k!='weights_sha256':assert a[k]==b[k],k +with np.load(cpu/'weights-0.npz',allow_pickle=False) as a,np.load(gpu/'weights-0.npz',allow_pickle=False) as b: + for k in a.files:assert np.array_equal(a[k],b[k]) +report={'status':'passed','utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'records':records,'sampling_counts_seen_clocks_and_initialization_bitwise':True, + 'discarded_training_attempts':64,'discarded_training_draws':128,'eligible_for_selection':False,'test_data_used':False, + 'source_sha256':{str(ROOT/name):digest(ROOT/name) for name in ['gpu_pipeline_audit.py','audit_run.py','train.py','core.py','state.py','data.py','common.py','kernel.py','parent_model.py']}} +(ROOT/'gpu_pipeline_audit.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({k:v for k,v in report.items() if k not in ['records','source_sha256']}),flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/kernel.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/kernel.py new file mode 100644 index 0000000000000000000000000000000000000000..915bf151945e7b41d06d1875e52c0b00d65f59d8 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/kernel.py @@ -0,0 +1,72 @@ +"""Persistent one-quality-bit contraction with directional forward derivatives.""" +import jax +import jax.numpy as jnp +from jax import lax +from jax.experimental import pallas as pl +from jax.experimental.pallas import triton as plt +from jax._src import core,dispatch +from jax.interpreters import mlir,ad +from jax._src.pallas.triton import lowering +from jax._src.lib.triton import dialect as tt + +gather_p=core.Primitive('latent_frontier_vector_gather') +gather_p.def_impl(lambda x,i:dispatch.apply_primitive(gather_p,x,i)) +def abstract(x,i): + assert x.ndim==i.ndim==1 and x.shape==i.shape and i.dtype==jnp.int32 + return core.ShapedArray(x.shape,x.dtype) +gather_p.def_abstract_eval(abstract) +mlir.register_lowering(gather_p,mlir.lower_fun(lambda x,i:x[i],multiple_results=False)) +@lowering.register_lowering(gather_p) +def lower(ctx,x,i):return tt.gather(x,i,axis=0) +def gather(x,i):return gather_p.bind(x,i) +def gather_jvp(primals,tangents): + x,i=primals;dx,_=tangents + return gather(x,i),jnp.zeros_like(x) if isinstance(dx,ad.Zero) else gather(dx,i) +ad.primitive_jvps[gather_p]=gather_jvp + +def physical(t0,t1,t2): + return .001+.499*jax.nn.sigmoid(t0),.999*jax.nn.sigmoid(t1),.99*jax.nn.sigmoid(t2) + +def make_decoder(width=10,interpret=False,derivatives=False,num_warps=8): + size=1<<(width+1);quality_bit=1<0,transition,lambda f:f,f) + def error(f): + u=jnp.where(qt>=0,alpha*jnp.minimum(p/pi,(1-p)/(1-pi)),0.) + rate=p+u*(quality.astype(jnp.float32)-pi) + return (1-rate)*f+rate*gather(f,indices^mask) + def project(f): + g=gather(f,(indices&~mask)|(obs*mask))*((indices&mask)==0) + return g/jnp.maximum(jnp.sum(g),1e-30) + return lax.cond(detector<0,error,project,f) + if derivatives:return jax.jvp(step,(t0,t1,t2,f),(*unit,df)) + return step(t0,t1,t2,f),df + f,df=lax.fori_loop(0,nops,iteration,(f,df)) + def observable(f):return jnp.sum(jnp.where((indices&1)!=0,f,0))/jnp.maximum(jnp.sum(f),1e-30) + if derivatives:q,dq=jax.jvp(observable,(f,),(df,)) + else:q=observable(f);dq=jnp.float32(0) + pl.store(out,(direction,shot),q);pl.store(jac,(direction,shot),dq) + values=pl.pallas_call(kernel,out_shape=(jax.ShapeDtypeStruct((directions,batch),jnp.float32),jax.ShapeDtypeStruct((directions,batch),jnp.float32)),grid=(batch,directions),interpret=interpret,backend='triton',compiler_params=plt.CompilerParams(num_warps=num_warps,num_stages=1))( + theta,dets,probabilities,masks,measured,quality_times,transition_gaps) + return values + return jax.jit(decode) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/neutral_core.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/neutral_core.py new file mode 100644 index 0000000000000000000000000000000000000000..bf64d81947bf398c621e33bddcef22bae083c449 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/neutral_core.py @@ -0,0 +1,13 @@ +"""Independent HIGHEST physical parent for the zero-amplitude numerical audit.""" +import jax,jax.numpy as jnp +from ordinary import make_independent +from parent_model import probabilities +decode=make_independent() +@jax.jit +def infer(frozen,dets,coefficients,a,b,context,static,base,valid,masks,measured,grid,mapping,counts): + values=[] + for k in range(len(coefficients)): + state=jax.tree_util.tree_map(lambda x:x[k],frozen) + p=probabilities(state,dets,context,static,base,valid,grid,mapping,counts) + values.append(decode(dets,p,masks,measured)[1]) + return a*jnp.sum(jnp.stack(values,axis=1)*coefficients[None],axis=1)+b diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/ordinary.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/ordinary.py new file mode 100644 index 0000000000000000000000000000000000000000..df83d5751bc42b3d942b40d318d8d3da7d169d71 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/ordinary.py @@ -0,0 +1,51 @@ +"""Exact joint contraction of one quality Markov chain and physical frontier.""" +import jax +import jax.numpy as jnp + +def make_decoder(width=10): + indices=jnp.arange(1<0,transition,lambda x:x,f) + def error(carry): + f,logz=carry + u=jnp.where(qt>=0,alpha*jnp.minimum(p/pi,(1-p)/(1-pi)),0.) + rates=p[:,None]+u[:,None]*(jnp.arange(2,dtype=jnp.float32)[None]-pi) + return (1-rates[:,:,None])*f+rates[:,:,None]*f[:,:,indices^mask],logz + def project(carry): + f,logz=carry;obs=dets[:,d].astype(jnp.int32) + dst=(indices[None,:]&~mask)|(obs[:,None]*mask) + g=jnp.take_along_axis(f,dst[:,None,:],axis=2)*((indices&mask)==0)[None,None,:] + norm=jnp.maximum(g.sum(axis=(1,2)),1e-30) + return g/norm[:,None,None],logz+jnp.log(norm) + return jax.lax.cond(d<0,error,project,(f,logz)),None + (f,logz),_=jax.lax.scan(step,(initial,jnp.zeros(batch)),(probabilities,masks,measured,quality_times,transition_gaps)) + q=f[:,:,1::2].sum(axis=(1,2))/jnp.maximum(f.sum(axis=(1,2)),1e-30) + return logz,q + return decode + +def make_independent(width=10): + indices=jnp.arange(1<=0)&(q<=1)) + values[row['name']]=(ids,q);errors.append(float(np.mean((q>=.5)!=y))) + choices[str(step)]=float(np.mean(errors));options[str(step)]=values + assert abs(choices[str(step)]-json.loads((folder/'report.json').read_text())['d3_failure'])<1e-12 + selected=min(choices,key=choices.get) + if choices[selected]>=choices['incumbent']-1e-12:selected='incumbent' + selection={'scope':plan['training']['selection'],'choices':choices,'selected':selected,'zero_amplitude_eligible':False} + config=json.loads((package/'model_config.json').read_text());new_files=set();exported=None + if selected!='incumbent': + weight='weights/d3-latent-quality-'+selected+'.npz';shutil.copyfile(ROOT/'fit1'/f'weights-{selected}.npz',package/weight);new_files.add(weight) + initial='weights/d3-latent-quality-initial.npz';shutil.copyfile(ROOT/'fit1/weights-0.npz',package/initial);new_files.add(initial) + (package/'d3_latent_geometry').mkdir();parent_config=reference()[2] + for item in load_all(splits=()): + row=item['row'];record=config['conditions'][row['name']] + if not item['bypass']: + for key in ['d3_masked_aux','d3_batch_ema','d3_coupled_pretraining','d3_conditional_edge']:record.pop(key,None) + assert record==parent_config['conditions'][row['name']],row['name'] + assert config['temporal_contextual'][row['group']]==parent_config['temporal_contextual'][row['group']] + path='d3_latent_geometry/'+row['name']+'.npz';shutil.copyfile(ROOT/'geometry1'/(row['name']+'.npz'),package/path);new_files.add(path) + record['d3_latent_readout']={'weights':weight,'geometry':path,'group':row['group']} + ids,q=options[selected][row['name']];np.savez(predictions/(row['name']+'.npz'),sample_ids=ids,probabilities=q) + exported=install(package);config['architecture']='Frozen session-trained structured d3 neural decoder with one fitted persistent readout-quality chain; incumbent r1 and d5 routes retained' + source=package/'training_sources/d3_latent_readout1';source.mkdir() + for path in [*ROOT.glob('*.py'),ROOT/'PROPOSAL.json',ROOT/'R1_ROUTING_CLARIFICATION.json',ROOT/'reference.json',ROOT/'plan.json']:shutil.copyfile(path,source/path.name) + (package/'README.md').write_text((ROOT/'PACKAGE_README.md').read_text()) + (package/'model_config.json').write_text(json.dumps(config,indent=2)+'\n') + controls={name:json.loads((ROOT/name).read_text()) for name in json.loads((ROOT/'prepared_controls.json').read_text())['controls']} + provenance=json.loads((package/'training_provenance.json').read_text());provenance['d3_latent_readout1']={'proposal':plan,'routing_clarification':json.loads((ROOT/'R1_ROUTING_CLARIFICATION.json').read_text()),'frozen_plan':json.loads((ROOT/'plan.json').read_text()),'training':json.loads((ROOT/'fit1/provenance.json').read_text()),'sampling_audit':json.loads((ROOT/'fit1/sampling_audit.json').read_text()),'controls':controls,'selection':selection,'exported_source_sha256':exported,'test_data_used':False} + provenance['previous_package_validation']=provenance.pop('package_validation');(package/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + manifest=json.loads((package/'model_manifest.json').read_text());paths={x['path'] for x in manifest['checkpoints']}|new_files + manifest['checkpoints']=[{'path':p,'sha256':digest(package/p),'format':'npz' if p.endswith('.npz') else 'json' if p.endswith('.json') else 'stim-dem'} for p in sorted(paths)];(package/'model_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + d3=choices[selected];d5=score['failure_by_distance']['d5'];candidate={'package':str(package),'predictions':str(predictions),'score':str(out/'score.json'),'expected_P':1-(d3+d5)/2,'failure_by_distance':{'d3':d3,'d5':d5},'selection':[selection], + 'validation_metadata':{'prediction_generation':'Three fixed global d3 latent-quality checkpoints; incumbent r1/d5 retained. Assembled replay required.','runtime_evidence':'Actual full d3 replay, readonly relocation and projected runtime below3600seconds required before publication.'}} + (out/'candidate.json').write_text(json.dumps(candidate,indent=2)+'\n');print(json.dumps(candidate,indent=2),flush=True) +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/parent_model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/parent_model.py new file mode 100644 index 0000000000000000000000000000000000000000..cc9185d9b299909862aec3b8f4d2a299d0b8a641 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/parent_model.py @@ -0,0 +1,46 @@ +"""Long temporal context for contextual exact d3 decoding.""" +import numpy as np +import jax +import jax.numpy as jnp +def matmul(x,w): + return jnp.matmul(x,w,precision=jax.lax.Precision.HIGHEST) + + +def initialize_from_rich(path,seed=20261104,channels=32): + parameters={k:jnp.asarray(v) for k,v in dict(np.load(path,allow_pickle=False)).items()} + keys=jax.random.split(jax.random.key(seed),6) + parameters['w1']=jnp.concatenate([parameters['w1'],jax.random.normal(keys[0],(channels,parameters['w1'].shape[1]))*.001],axis=0) + parameters['temporal_in']=jax.random.normal(keys[1],(24,channels))*jnp.sqrt(2/24) + parameters['temporal_bias']=jnp.zeros(channels) + for i in range(4): + parameters[f'temporal_conv_{i}']=jax.random.normal(keys[i+2],(3,channels,channels))*jnp.sqrt(1/(3*channels)) + parameters[f'temporal_conv_bias_{i}']=jnp.zeros(channels) + return parameters + + +def probabilities(parameters,dets,context_indices,static,base_logits,valid,grid,measurement_indices,counts): + event_grid=dets[:,grid] + measurement_grid=(jnp.cumsum(event_grid.astype(jnp.int32),axis=1)&1).astype(jnp.float32) + observed=(grid!=200).astype(jnp.float32) + frames=jnp.concatenate([event_grid.astype(jnp.float32),measurement_grid*observed[None,:,:], + jnp.broadcast_to(observed[None,:,:],event_grid.shape)],axis=-1) + temporal=jax.nn.silu(matmul(frames,parameters['temporal_in'])+parameters['temporal_bias']) + for i,dilation in enumerate([1,2,4,8]): + mean=jnp.mean(temporal,axis=-1,keepdims=True) + normalized=(temporal-mean)*jax.lax.rsqrt(jnp.mean((temporal-mean)**2,axis=-1,keepdims=True)+1e-5) + value=jax.lax.conv_general_dilated(normalized,parameters[f'temporal_conv_{i}'],(1,),padding='SAME', + rhs_dilation=(dilation,),dimension_numbers=('NWC','WIO','NWC'),precision=jax.lax.Precision.HIGHEST) + temporal=temporal+jax.nn.silu(value+parameters[f'temporal_conv_bias_{i}']) + times=jnp.clip(jnp.rint(static[:,43]*25).astype(jnp.int32),0,25) + temporal_context=temporal[:,times,:] + measured=jnp.pad(measurement_grid.reshape(len(dets),208),((0,0),(0,1))) + events=dets[:,context_indices].astype(jnp.float32) + measurements=measured[:,measurement_indices[context_indices]].astype(jnp.float32) + density=jnp.sum(event_grid,axis=1)/counts[None,:] + features=jnp.concatenate([events,measurements, + jnp.broadcast_to(density[:,None,:],(len(dets),len(context_indices),8)), + jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape)),temporal_context],axis=-1) + h=jax.nn.silu(matmul(features,parameters['w1'])+parameters['b1']) + h=jax.nn.silu(matmul(h,parameters['w2'])+parameters['b2']) + delta=4*jnp.tanh((matmul(h,parameters['w3'])+parameters['b3'])[...,0]) + return (jax.nn.sigmoid(base_logits[None,:]+delta)*valid[None,:]).T diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/plan.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/plan.json new file mode 100644 index 0000000000000000000000000000000000000000..e1d60f411e1c188f52073455faba1736264a89be --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/plan.json @@ -0,0 +1,2527 @@ +{ + "name": "d3_latent_readout1", + "created_utc": "2026-10-22T06:18:52.852012+00:00", + "status": "All latent-quality controls passed; main launched", + "hypothesis": "A persistent latent quality state shared by interior readout signatures touching the geometrically central data qubit can represent fault correlations absent from an independent corrected DEM, improving logical decisions with only three fitted parameters per patch/basis.", + "geometry": { + "family": "Unique local data qubit nearest mean of ideal patch data coordinates, fixed without performance selection.", + "channels": "Audited grouped DEM signatures for interior time-adjacent ancilla measurement flips, L0 zero, adjacent to central data family. Grouped signatures do not uniquely identify microscopic cause.", + "boundaries": "Unchanged. Interior times monotonic in elimination order; retain one quality bit." + }, + "model": { + "latent_bits": 1, + "physical_frontier_width": 10, + "stationary_pi": ".001+.499*sigmoid(theta0)", + "rho": ".999*sigmoid(theta1)", + "alpha": ".99*sigmoid(theta2)", + "transition": "Pr(Q_new=1|Q_old)=pi+(Q_old-pi)*rho**time_gap", + "rates": "p_Q=p+alpha*min(p/pi,(1-p)/(1-pi))*(Q-pi)", + "normalization": "Joint normalization over latent and physical frontier after each measured detector.", + "trainable_scalars": 24, + "shared_within_group": "Three latent logits shared by every original neural expert and all durations in each of eight groups.", + "frozen": "Original session-trained physical neural experts, their ensemble coefficients, existing r1 replacement and r3 affine refinement. No conditional endpoint encoder/projection; replace d3 route, do not stack learned residuals." + }, + "initialization": { + "seed": 20268310, + "distribution": "Independent normal(0,.01) jitter around logits giving pi=.1,rho=.8,alpha=.25, eight groups in sorted order, NumPy default_rng; fresh latent parameters.", + "nonzero_amplitude_reason": "A mean-preserving stationary latent model has zero first derivative in amplitude at zero; exact-zero initialization cannot learn the intended correlations.", + "frozen_parent_reference": { + "package": "/run-state/workspace/research/context_upgrades/masked_pretraining1/package", + "predictions": "/run-state/workspace/research/context_upgrades/masked_pretraining1/predictions", + "score": "/run-state/workspace/research/context_upgrades/masked_pretraining1/score.json", + "P": 0.7675189678485577, + "frozen_utc": "2026-10-21T23:49:09.177417+00:00", + "scope": "Frozen own-session d3 parent, cache lineage inherited from d3_masked_head; validation outputs must equal current published d3 before training.", + "sha256": { + "model_config.json": "aa8a5875819da706b8e00dce90c3908e56ae29bc072bf8406d9ac68100db4cf5", + "model_manifest.json": "9427be1bbd3f51ae07778e075f09b0a16ecae3d9e0db7ef1d01ffddd2d1cffc7", + "training_provenance.json": "dba6a9a4a18707ae0e95c231fe2b0db1097ed062de0a110722de73ce2d3c85ed" + } + }, + "precision": "HIGHEST float32 MLP/temporal convolution, inherited from training-only diagnosed precise parent; different numerical graph from native published cache. Full neutral audit records that difference and no neutral checkpoint is eligible." + }, + "implementation": { + "frontier": "Pallas persistent forward and three forward-mode directional derivatives. CPU interpret and independent JAX/float64 enumeration controls required.", + "row_batch": 256, + "optimizer": "Independent per-group Adam, clipping gradient norm1, no weight decay.", + "learning_rate": 0.02, + "dropout": false, + "EMA": false, + "calibration": false + }, + "training": { + "steps": 8192, + "batch": 256, + "sampling_seed": 20268320, + "stream": "At absolute step s, NumPy default_rng(SeedSequence([seed,s])); uniform one of sorted104conditions then256 independent uniform row draws. All2067520 training rows eligible.", + "learning_rate": 0.02, + "checkpoints": [ + 2048, + 4096, + 8192 + ], + "r1": "Draw/log all eight r1 conditions but skip optimizer; there are no selected interior channels. Existing r1 predictions retained.", + "selection": "Only one GLOBAL d3 choice among fixed2048/4096/8192checkpoints and then-current incumbent. No per-group/per-duration blends or extra checkpoints. All104d3 conditions evaluated, d5 unchanged; actual assembled replay and all130trusted score decide publication." + }, + "controls_required": [ + "Prototype geometry, float64 enumeration, Jacobian, Pallas CPU/static/GPU controls", + "Frozen neural attachment CPU and GPU, including B256 complete loss gradients", + "Actual pipeline CPU64 versus32+resume32, all8groups and allr1 bypass behavior, full counters/seen/Adam clocks", + "Actual GPU pipeline pilot versus CPU reference and exported runtime arbitraryN/order/subsets", + "Full104zero-amplitude numerical audit against independent HIGHEST parent and native incumbent (diagnostic only); finite q and maxdiff against HIGHEST<5e-6, decision differences reported", + "Projected inference runtime below3600seconds before main; actual packaged replay required for promotion" + ], + "publication": "Only after main sources/inputpins frozen, full selection and actual assembled inference/scorer/readonlyrelocation. Use qecbench.package atomic replacement. Preserve every failed control and amendment.", + "data_restrictions": "Train and validation only; no labels or IDs decoded by geometry/model features. IDs copied/equality-aligned only. 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0000000000000000000000000000000000000000..400f095040ae12274d54382322dc0ac68e186148 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/queue.py @@ -0,0 +1,53 @@ +"""Serialized GPU controls, neutral audit, final source freeze, and main study.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1' +from pathlib import Path +import json,datetime,time,subprocess +from common import ROOT,RESEARCH,digest,proposal +start=time.monotonic();prototype=RESEARCH/'d3_latent_readout_prototype';marker=prototype/'gpu_queue.json' +record={'status':'waiting for Brier and latent prototype GPU controls','created_utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'test_data_used':False} +def save():(ROOT/'queue_status.json').write_text(json.dumps(record,indent=2)+'\n') +save() +while not marker.exists() or json.loads(marker.read_text())['status'] not in ['passed','failed']: + if time.monotonic()-start>6*3600:raise TimeoutError('Prior GPU work incomplete') + time.sleep(10) +if json.loads(marker.read_text())['status']!='passed': + record['status']='deferred: prototype GPU control failed';save();raise SystemExit(0) +prepared=json.loads((ROOT/'PREPARED_SOURCES.json').read_text()) +for path,sha in prepared['source_sha256'].items():assert digest(ROOT/path)==sha,path +for name in ['control1/report.json','backend_reference1/report.json','runtime_control_cpu.json']:assert json.loads((ROOT/name).read_text())['status']=='passed',name +assert json.loads((RESEARCH/'d3_conditional_edge_brier/d3_conditional_edge_brier1_finished.json').read_text())['complete'] +(ROOT/'incumbent_at_launch.json').write_text((RESEARCH/'CURRENT.json').read_text()) +def run(name,args=(),gpu=False): + if gpu:assert not subprocess.check_output(['nvidia-smi','--query-compute-apps=pid','--format=csv,noheader'],text=True).strip() + record.update(status='running '+name,updated_utc=datetime.datetime.now(datetime.timezone.utc).isoformat());save() + with (ROOT/(name.replace('.py','')+'_queue.log')).open('w') as log: + subprocess.run(['python']+([str(RESEARCH/'run_limited.py')] if gpu else [])+[str(ROOT/name),*map(str,args)],stdout=log,stderr=subprocess.STDOUT,check=True,env=dict(os.environ,JAX_PLATFORMS='cuda' if gpu else 'cpu')) +run('train.py',['--control','--steps','64','--batch','2','--output',ROOT/'gpu_control1'],gpu=True) +run('gpu_pipeline_audit.py');run('backend_gpu.py',gpu=True);run('runtime_control.py',['--device','gpu'],gpu=True) +run('evaluate.py',['--neutral','--output',ROOT/'neutral_validation'],gpu=True);run('certify_controls.py') +files={p.resolve() for p in ROOT.glob('*.py')};files|={p.resolve() for p in ROOT.glob('*.json') if p.name not in ['queue_status.json','plan.json']} +files.add((ROOT/'PACKAGE_README.md').resolve()) +for path,sha in json.loads((RESEARCH/'d3_conditional_edge_projection/plan.json').read_text())['frozen_source_sha256'].items(): + p=(RESEARCH/path).resolve();assert digest(p)==sha,path;files.add(p) +for folder in ['geometry1','control1','gpu_control1','backend_reference1','neutral_validation']: + files|={p.resolve() for p in (ROOT/folder).rglob('*') if p.is_file() and p.suffix in ['.py','.json','.npz']} +controls=json.loads((ROOT/'prepared_controls.json').read_text()) +for path in controls['prototype_controls']: + p=Path(path);files.add(p.resolve());report=json.loads(p.read_text()) + for key in ['source_sha256','sources']: + pins=report.get(key,{}) + if isinstance(pins,str): + names={'geometry_control.json':'geometry.py','static_lowering.json':'pallas_model.py'} + assert p.name in names,p.name + pins={str(prototype/names[p.name]):pins} + for source,sha in pins.items(): + q=Path(source) if Path(source).is_absolute() else prototype/source + assert digest(q)==sha,(path,source);files.add(q.resolve()) +def key(p): + try:return str(p.relative_to(RESEARCH)) + except ValueError:return str(p) +for path,sha in prepared['source_sha256'].items():assert digest(ROOT/path)==sha,path +plan=dict(proposal());plan.update(frozen_utc=datetime.datetime.now(datetime.timezone.utc).isoformat(),status='All controls passed; latent-quality scientific sources frozen',frozen_source_sha256={key(p):digest(p) for p in sorted(files)}) +assert not (ROOT/'plan.json').exists();(ROOT/'plan.json').write_text(json.dumps(plan,indent=2)+'\n') +run('run_study.py');record.update(status='complete',completed_utc=datetime.datetime.now(datetime.timezone.utc).isoformat());save() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/r3_model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/r3_model.py new file mode 100644 index 0000000000000000000000000000000000000000..505d8fb3aa5a3adc8eaafab99f276bb111da04b4 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/r3_model.py @@ -0,0 +1,23 @@ +"""Categorical logical probabilities keyed only by 24 hard detection events.""" +import numpy as np +from scipy.special import expit + + +def codes(packed): + assert packed.dtype == np.uint8 and packed.ndim == 2 and packed.shape[1] == 3 + return np.sum(packed.astype(np.uint32) * np.array([1, 256, 65536], np.uint32), + axis=1, dtype=np.uint32) + + +def predict(packed, state, fallback, mode='raw'): + assert mode in ['raw', 'equal'] + key = codes(packed) + known = state['codes'] + at = np.searchsorted(known, key) + safe = np.minimum(at, len(known)-1) + seen = (at < len(known)) & (known[safe] == key) + p = np.asarray(fallback, dtype=np.float32).copy() + assert p.shape == (len(packed),) + learned = expit(state['prior_logits'][safe[seen]] + state['residual_logits'][safe[seen]]).astype(np.float32) + p[seen] = learned if mode == 'raw' else ((learned + p[seen])/2).astype(np.float32) + return p diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/reference.json b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/reference.json new file mode 100644 index 0000000000000000000000000000000000000000..14cce42e0f301a8332721001b8ed8e703dfe43d6 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/reference.json @@ -0,0 +1,13 @@ +{ + "package": "/run-state/workspace/research/context_upgrades/masked_pretraining1/package", + "predictions": "/run-state/workspace/research/context_upgrades/masked_pretraining1/predictions", + "score": "/run-state/workspace/research/context_upgrades/masked_pretraining1/score.json", + "P": 0.7675189678485577, + "frozen_utc": "2026-10-21T23:49:09.177417+00:00", + "scope": "Frozen own-session d3 parent, cache lineage inherited from d3_masked_head; validation outputs must equal current published d3 before training.", + "sha256": { + "model_config.json": "aa8a5875819da706b8e00dce90c3908e56ae29bc072bf8406d9ac68100db4cf5", + "model_manifest.json": "9427be1bbd3f51ae07778e075f09b0a16ecae3d9e0db7ef1d01ffddd2d1cffc7", + "training_provenance.json": "dba6a9a4a18707ae0e95c231fe2b0db1097ed062de0a110722de73ce2d3c85ed" + } +} diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/run_study.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/run_study.py new file mode 100644 index 0000000000000000000000000000000000000000..93748f37f3886cd0834d63a53554dad28908dd60 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/run_study.py @@ -0,0 +1,22 @@ +"""Run the frozen latent-quality study and promote only a verified improvement.""" +import os +os.environ['JAX_PLATFORMS']='cpu';os.environ['OPENBLAS_NUM_THREADS']='1' +os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) +from pathlib import Path +import json,datetime,subprocess +from common import ROOT,RESEARCH,proposal,digest +from audit_run import audit +plan=json.loads((ROOT/'plan.json').read_text());assert not (ROOT/'fit1').exists();assert json.loads((ROOT/'prepared_controls.json').read_text())['status']=='passed' +assert not subprocess.check_output(['nvidia-smi','--query-compute-apps=pid','--format=csv,noheader'],text=True).strip() +for path,sha in plan['frozen_source_sha256'].items():assert digest(RESEARCH/path)==sha,path +assert json.loads((ROOT/'incumbent_at_launch.json').read_text())==json.loads((RESEARCH/'CURRENT.json').read_text()) +plan.update(status='All latent-quality controls passed; main launched',launch_utc=datetime.datetime.now(datetime.timezone.utc).isoformat());(ROOT/'plan.json').write_text(json.dumps(plan,indent=2)+'\n') +def call(script,*args,gpu=False,log): + with (ROOT/log).open('w') as f:subprocess.run(['python']+([str(RESEARCH/'run_limited.py')] if gpu else [])+[str(ROOT/script),*map(str,args)],stdout=f,stderr=subprocess.STDOUT,check=True,env=dict(os.environ,JAX_PLATFORMS='cuda' if gpu else 'cpu')) +call('train.py','--output',ROOT/'fit1',gpu=True,log='fit1.log');record=audit(ROOT/'fit1');assert record['training_draws']==2097152 and record['eligible_rows']==2067520 +for step in proposal()['training']['checkpoints']:call('evaluate.py','--weights',ROOT/'fit1'/f'weights-{step}.npz','--output',ROOT/f'validation-{step}',gpu=True,log=f'validation-{step}.log') +for path,sha in plan['frozen_source_sha256'].items():assert digest(RESEARCH/path)==sha,path +previous=json.loads((RESEARCH/'CURRENT.json').read_text())['predictions'];call('package.py',log='package.log');out=RESEARCH/'context_upgrades/d3_latent_readout1' +subprocess.run(['python',str(RESEARCH/'verify_d3_candidate.py'),str(out)],check=True,env=dict(os.environ,JAX_PLATFORMS='cuda')) +subprocess.run(['python',str(RESEARCH/'audit_completed_candidate.py'),'--candidate',str(out),'--previous',previous],check=True) +(ROOT/'d3_latent_readout1_finished.json').write_text(json.dumps({'complete':True,'test_data_used':False})+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/runtime.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..83709b9863c43c8ddcabb241d44a5fcf7acd8849 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/runtime.py @@ -0,0 +1,42 @@ +"""Portable one-quality-chain route; original surrounding routes remain callable.""" +import numpy as np +from d3_pre_latent_temporal import TemporalContextualDecoder as ParentDecoder +from d3_lr_compiled import PersistentCompiled +from d3_lr_core import make_kernels + +class TemporalContextualDecoder(ParentDecoder): + def __init__(self): + import jax + super().__init__();self.latent_weights={};self.latent_groups={};self.latent_plans={} + self.latent_infer=PersistentCompiled(make_kernels(interpret=jax.default_backend()=='cpu')[0],max_executables=32) + def predict(self,root,record,group_record,packed,num_detectors,batch=None): + settings=record.get('d3_latent_readout') + if settings is None: + if batch is None:return super().predict(root,record,group_record,packed,num_detectors) + return super().predict(root,record,group_record,packed,num_detectors,batch) + if batch is None:batch=256 + import jax.numpy as jnp + key=(settings['weights'],settings['group']) + if key not in self.latent_weights: + with np.load(root/settings['weights'],allow_pickle=False) as f:self.latent_weights[key]=jnp.asarray(f[settings['group']]) + group=settings['group'] + if group not in self.latent_groups: + weights=[];coefficients=[] + for member in group_record.get('members',[group_record]): + with np.load(root/member['weights'],allow_pickle=False) as f:weights.append({k:jnp.asarray(f[k]) for k in f.files}) + coefficients.append(member.get('coefficient',1.)) + coefficients=np.asarray(coefficients,np.float32);coefficients/=coefficients.sum() + self.latent_groups[group]=({k:jnp.stack([w[k] for w in weights]) for k in weights[0]},coefficients) + gp=settings['geometry'] + if gp not in self.latent_plans: + with np.load(root/record['rich_plan'],allow_pickle=False) as f:plan={k:f[k] for k in f.files} + with np.load(root/gp,allow_pickle=False) as f:geometry={k:f[k] for k in f.files} + self.latent_plans[gp]=(plan,geometry) + plan,geometry=self.latent_plans[gp];frozen,coefficients=self.latent_groups[group] + ds=np.pad(np.unpackbits(packed,axis=1,count=num_detectors,bitorder='little'),((0,0),(0,201-num_detectors)));values=[] + for pos in range(0,len(ds),batch): + n=min(batch,len(ds)-pos);dets=np.pad(ds[pos:pos+batch],((0,batch-n),(0,0))) + q=self.latent_infer(self.latent_weights[key],frozen,dets,coefficients,np.ones(batch,np.float32),np.zeros(batch,np.float32), + *[plan[k] for k in ['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts']],geometry['quality_times'],geometry['transition_gaps']) + values.append(np.asarray(q)[:n]) + return np.concatenate(values).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/runtime_control.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/runtime_control.py new file mode 100644 index 0000000000000000000000000000000000000000..1a9c022ae9cdb05cb1a70a22b5e706c4155de5ea --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/runtime_control.py @@ -0,0 +1,40 @@ +"""Exported route, affine refinement, arbitrary N/order, and unchanged r1 fallback.""" +import argparse,os +p=argparse.ArgumentParser();p.add_argument('--device',choices=['cpu','gpu'],required=True);a=p.parse_args() +os.environ['JAX_PLATFORMS']='cpu' if a.device=='cpu' else 'cuda';os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:(3 if a.device=='cpu' else 22)]) +from pathlib import Path +import json,tempfile,shutil,sys,importlib.util,datetime,time +import numpy as np,jax,jax.numpy as jnp +from common import ROOT,digest,reference +from data import load_all,frozen_states,args as plan_args +from core import make_kernels +from export import install +assert jax.default_backend()==a.device;start=time.monotonic() +items=load_all(('train',));states,_=frozen_states();infer,_=make_kernels(interpret=a.device=='cpu');_,parent,config=reference();weights=ROOT/'control1/full/weights-64.npz' +with np.load(weights,allow_pickle=False) as f:parameters={k:jnp.asarray(f[k]) for k in f.files} +batch=4 if a.device=='cpu' else 256;cases=[('d3_bX_center_3_5',1),('d3_bZ_center_5_3',3),('d3_bZ_center_7_5',25)];records=[] +with tempfile.TemporaryDirectory(prefix='d3-latent-runtime-') as temp: + target=Path(temp)/'package';shutil.copytree(parent,target);exported=install(target);shutil.copyfile(weights,target/'control-weights.npz');sys.path.insert(0,str(target)) + spec=importlib.util.spec_from_file_location('d3_latent_runtime_control',target/'temporal_contextual.py');module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);decoder=module.TemporalContextualDecoder();parent_decoder=module.ParentDecoder();from r3_pattern_runtime import refine + for group,rounds in cases: + item=next(x for x in items if x['row']['group']==group and x['row']['rounds']==rounds);row=item['row'];n=1 if rounds==1 else batch+1 + with np.load(Path('/workspace/data/train')/row['arrays'],allow_pickle=False) as f:packed=f['detectors'][:n] + record=dict(item['record']) + if rounds==1: + actual=decoder.predict(target,record,config['temporal_contextual'][group],packed,row['num_detectors']) + expected=parent_decoder.predict(target,record,config['temporal_contextual'][group],packed,row['num_detectors']);assert np.array_equal(actual,expected) + records.append({'condition':row['name'],'shots':n,'unchanged_parent_default_batch_bitwise':True});continue + path=row['name']+'-geometry.npz';np.savez(target/path,**item['geometry']);record['d3_latent_readout']={'weights':'control-weights.npz','geometry':path,'group':group};state=states[group];manual=[] + for pos in range(0,n,batch): + size=min(batch,n-pos);ds=np.pad(item['train_dets'][pos:pos+batch],((0,batch-size),(0,0)));aa=np.pad(item['train_a'][pos:pos+batch],(0,batch-size));bb=np.pad(item['train_b'][pos:pos+batch],(0,batch-size)) + manual.append(np.asarray(infer(parameters[group],state['weights'],ds,state['coefficients'],aa,bb,*plan_args(item)))[:size]) + manual=np.concatenate(manual) + def call(packed):return refine(target,record,packed,decoder.predict(target,record,config['temporal_contextual'][group],packed,row['num_detectors'],batch=batch)) + actual=call(packed);maximum=float(abs(actual-manual).max());assert maximum<2e-6 and np.array_equal(actual>=.5,manual>=.5) + reverse=call(packed[::-1].copy());single=call(packed[:1]);assert np.array_equal(reverse,actual[::-1]) and np.array_equal(single,actual[:1]) + compilations=decoder.latent_infer.compilations;jax.clear_caches();again=call(packed);assert np.array_equal(again,actual) and compilations==decoder.latent_infer.compilations + records.append({'condition':row['name'],'shots':n,'batch':batch,'manual_exported_max_abs':maximum,'order_singleton_and_globalclear_bitwise':True});print(json.dumps(records[-1]),flush=True) +report={'status':'passed','utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'device':str(jax.devices()[0]),'records':records,'seconds':time.monotonic()-start,'control_weights_sha256':digest(weights),'exported_source_sha256':exported, + 'source_sha256':{n:digest(ROOT/n) for n in ['runtime_control.py','runtime.py','export.py','compiled.py','core.py','kernel.py','parent_model.py','data.py','common.py']},'test_data_used':False} +(ROOT/f'runtime_control_{a.device}.json').write_text(json.dumps(report,indent=2)+'\n') diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/short_model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/short_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3bd23ad7ad4caa7b03269e10741553b87d9b5a87 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/short_model.py @@ -0,0 +1,8 @@ +"""Categorical logical decoder indexed only by an8-bit detection syndrome.""" +import numpy as np +from scipy.special import expit + +def predict(packed,state): + assert packed.dtype==np.uint8 and packed.ndim==2 and packed.shape[1]==1 + key=packed[:,0] + return expit(state['physical_logits'][key]+state['residual_logits'][key]).astype(np.float32) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/state.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/state.py new file mode 100644 index 0000000000000000000000000000000000000000..bd028658994c0c2e207f7c50c26a9c502aed2805 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/state.py @@ -0,0 +1,18 @@ +import numpy as np,jax,jax.numpy as jnp,optax +from core import make_kernels +def optimizer(lr=.02):return optax.chain(optax.clip_by_global_norm(1.),optax.adam(lr)) +def save_state(path,state):np.savez(path,**{f'arr_{i}':np.asarray(x) for i,x in enumerate(jax.tree_util.tree_leaves(state))}) +def load_state(path,template): + leaves,tree=jax.tree_util.tree_flatten(template);values=[] + with np.load(path,allow_pickle=False) as f: + assert set(f.files)=={f'arr_{i}' for i in range(len(leaves))} + for i,x in enumerate(leaves): + v=f[f'arr_{i}'];assert v.shape==x.shape and v.dtype==x.dtype;values.append(jnp.asarray(v)) + return jax.tree_util.tree_unflatten(tree,values) +def make_step(opt,interpret=False): + _,gradient=make_kernels(interpret) + @jax.jit + def update(p,state,*inputs): + g,loss,q=gradient(p,*inputs);updates,state=opt.update(g,state,p) + return optax.apply_updates(p,updates),state,loss,q,optax.global_norm(g) + return update diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/train.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/train.py new file mode 100644 index 0000000000000000000000000000000000000000..ba7965fcfbe66e6f9f6452b48f4233079a1eb465 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/d3_latent_readout1/train.py @@ -0,0 +1,60 @@ +"""Replayable latent-quality training with independent active-group Adam states.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1';os.environ['OMP_NUM_THREADS']='1';os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +from pathlib import Path +import argparse,json,hashlib,shutil,time +import numpy as np +import jax,jax.numpy as jnp +from common import ROOT,RESEARCH,proposal,digest +from data import load_all,frozen_states,initial,args as plan_args +from core import make_kernels +from state import optimizer,make_step,save_state,load_state + +def descriptor(step,batch,seed,items): + rng=np.random.default_rng(np.random.SeedSequence([seed,step]));ci=int(rng.integers(len(items)));ix=rng.integers(len(items[ci]['train_labels']),size=batch) + return ci,ix,{'step':step,'condition_index':ci,'condition':items[ci]['row']['name'],'shots':batch,'row_draws_sha256':hashlib.sha256(ix.astype('0 and batch>0 + if not args.control:assert steps==8192 and batch==256 + args.output.mkdir(parents=True,exist_ok=False);begin=time.monotonic();items=load_all(('train',));frozen,frozen_metadata=frozen_states();parameters=initial(frozen);groups=sorted(parameters);assert sum(x.size for x in parameters.values())==24 + opt=optimizer(settings['learning_rate']);states={g:opt.init(parameters[g]) for g in groups};infer,_=make_kernels(interpret=jax.default_backend()=='cpu');update=make_step(opt,interpret=jax.default_backend()=='cpu') + counts=np.zeros(104,np.int64);seen=[np.zeros(len(x['train_labels']),bool) for x in items];updates=np.zeros(8,np.int64);bypasses=np.zeros(104,np.int64);start=0 + config={'steps':steps,'batch':batch,'control':args.control,'seed_offset':offset,'settings':settings,'proposal':plan,'frozen_states':frozen_metadata,'data':{x['row']['name']:{'short':x['short'],'bypass':x['bypass'],'reference':x['train_reference'],'geometry_sha256':x['geometry_sha256'],'plan_sha256':x['plan_sha256']} for x in items},'groups':groups,'runtime_versions':{'jax':jax.__version__,'numpy':np.__version__}} + if args.resume: + old=json.loads((args.resume.parent/'run_config.json').read_text()) + for k in ['batch','control','seed_offset','settings','proposal','frozen_states','data','groups']:assert config[k]==old[k],k + start=int(args.resume.stem.split('-')[-1]);assert start=.5)!=y)),'gradient_norm':float(norm),'elapsed':time.monotonic()-begin};assert np.isfinite([record['loss'],record['gradient_norm']]).all();records.append(record);print(json.dumps(record),flush=True) + if step in settings['checkpoints'] or step==steps:checkpoint(step) + provenance={'status':'complete','run_config':config,'training_draws':int(counts.sum()),'training_draws_this_invocation':(steps-start)*batch,'draw_descriptor_sha256':dh.hexdigest(),'updates_by_group':dict(zip(groups,map(int,updates))),'bypass_steps':int(bypasses.sum()),'unique_training_rows':sum(int(x.sum()) for x in seen),'eligible_rows':sum(len(x) for x in seen),'hardware_validation_read':False,'test_data_used':False,'records':records,'seconds':time.monotonic()-begin} + (args.output/'provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/exact_d3__exact.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/exact_d3__exact.py new file mode 100644 index 0000000000000000000000000000000000000000..1ae5a8eb3e91ada61a5870063470a6d3453cf0e0 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/exact_d3__exact.py @@ -0,0 +1,213 @@ +"""Exact detector-error-model contraction by bounded-frontier elimination.""" +import collections +import numpy as np +import stim + + +def parse_errors(dem): + errors = [] + for ins in dem.flattened(): + if ins.type != 'error': + continue + ds, logical = set(), 0 + for t in ins.targets_copy(): + if t.is_relative_detector_id(): + ds.symmetric_difference_update([t.val]) + elif t.is_logical_observable_id(): + assert t.val == 0 + logical ^= 1 + errors.append((ds, logical, ins.args_copy()[0])) + return errors + + +def elimination_order(dem, errors): + blocks = collections.defaultdict(list) + for d, c in dem.get_detector_coordinates().items(): + blocks[c[-1]].append(d) + pending = [ds for ds, _, _ in errors if ds] + active, used, order = set(), set(), [] + for _, block in sorted(blocks.items()): + remain = set(block) + while remain: + def score(d): + new = set().union(*(e for e in pending if d in e)) - used + return len(active | new), -sum(d in e for e in pending), d + d = min(remain, key=score) + related = [e for e in pending if d in e] + active |= set().union(*related) - used + active.discard(d) + pending = [e for e in pending if d not in e] + used.add(d) + remain.remove(d) + order.append(d) + assert sorted(order) == list(range(dem.num_detectors)) + return order + + +def make_plan(dem): + errors = parse_errors(dem) + order = elimination_order(dem, errors) + rank = {d: i for i, d in enumerate(order)} + groups = collections.defaultdict(list) + for source_index, (ds, logical, p) in enumerate(errors): + first = min(ds, key=rank.get) if ds else order[0] + groups[first].append((ds, logical, p, source_index)) + slots, probabilities, masks, measured, source_indices = {}, [], [], [], [] + width = 1 + for d in order: + related = groups[d] + needed = set().union(*(ds for ds, _, _, _ in related)) | {d} + for node in sorted(needed): + if node not in slots: + free = next(i for i in range(1, 64) if i not in slots.values()) + slots[node] = free + width = max(width, free+1) + for ds, logical, p, source_index in related: + mask = logical + for node in ds: + mask ^= 1 << slots[node] + probabilities.append(p) + masks.append(mask) + measured.append(-1) + source_indices.append(source_index) + probabilities.append(0) + masks.append(1 << slots.pop(d)) + measured.append(d) + source_indices.append(-1) + assert not slots + return {'probabilities': np.array(probabilities, np.float32), + 'masks': np.array(masks, np.int32), 'measured': np.array(measured, np.int32), + 'width': np.array(width, np.int32), 'num_detectors': np.array(dem.num_detectors, np.int32), + 'source_indices': np.array(source_indices, np.int32), + 'order': np.array(order, np.int32)} + + +def make_decoder(width): + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + @jax.jit + def decode(dets, probabilities, masks, measured): + f = jnp.zeros((dets.shape[0], 1 << width), jnp.float32).at[:, 0].set(1) + def step(f, args): + p, mask, d = args + def error(f): + prob = p[:, None] if p.ndim else p + return (1-prob)*f + prob*f[:, indices ^ mask] + def project(f): + obs = dets[:, d].astype(jnp.int32) + dst = (indices[None, :] & ~mask) | (obs[:, None]*mask) + g = jnp.take_along_axis(f, dst, axis=1) * ((indices & mask)==0) + return g / jnp.maximum(jnp.sum(g, axis=1, keepdims=True), 1e-30) + return jax.lax.cond(d < 0, error, project, f), None + f, _ = jax.lax.scan(step, f, (probabilities, masks, measured)) + return jnp.sum(f[:, 1::2], axis=1) / jnp.maximum(jnp.sum(f, axis=1), 1e-30) + return decode + + +def make_marginals(width): + """Per-shot derivatives of log syndrome probability give all error marginals.""" + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + def log_partition(dets, probabilities, masks, measured): + f = jnp.zeros(1 << width, jnp.float32).at[0].set(1) + def step(carry, args): + f, logz = carry + p, mask, d = args + def error(carry): + f, logz = carry + return ((1-p)*f + p*f[indices ^ mask], logz) + def project(carry): + f, logz = carry + dst = (indices & ~mask) | (dets[d].astype(jnp.int32)*mask) + g = f[dst] * ((indices & mask)==0) + norm = jnp.maximum(jnp.sum(g), 1e-30) + return g/norm, logz+jnp.log(norm) + return jax.lax.cond(d<0, error, project, carry), None + (_, logz), _ = jax.lax.scan(jax.checkpoint(step), (f, jnp.float32(0)), (probabilities, masks, measured)) + return logz + return jax.jit(jax.vmap(jax.value_and_grad(log_partition, argnums=1), in_axes=(0,None,None,None))) + + +def make_marginals_fb(width, return_observable=False): + """Forward/backward contraction without generic gather-gradient scatter.""" + import jax + import jax.numpy as jnp + indices = jnp.arange(1 << width, dtype=jnp.int32) + # The syndrome likelihood marginalizes the unobserved logical bit. Keep + # that bit in the forward state for its output, but omit it from histories + # and backward messages, halving the largest buffers. + backward_width=width-1 if return_observable else width + backward_indices=jnp.arange(1 << backward_width,dtype=jnp.int32) + @jax.jit + def marginals(dets, probabilities, masks, measured): + f=jnp.zeros((dets.shape[0],1<>1 if return_observable else masks + _,grad=jax.lax.scan(backward,jnp.ones((len(dets),1<=.5)!=a['labels'][:len(p)]), 'min': p.min(), 'max': p.max()}, flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__data.py new file mode 100644 index 0000000000000000000000000000000000000000..7cbe1229eafbaaa3a06eaaeca9a0c38cb411f34f --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__data.py @@ -0,0 +1,95 @@ +"""Static physical features and authorized cached training/validation priors.""" +from pathlib import Path +import json,sys +import numpy as np +import stim +sys.path.insert(0,'/workspace/research/exact_d3') +from exact import parse_errors +from conditional import load_state + +ROOT=Path(__file__).resolve().parent + + +def build_features(row,package,max_ops=2048): + dem=stim.DetectorErrorModel.from_file(Path('/workspace/data/train')/row['noise']) + errors=parse_errors(dem);coords=dem.get_detector_coordinates() + if row['rounds']==1: + plan=dict(np.load(package/f"plans/{row['name']}.npz"));supports=[(ds,l) for ds,l,p in errors] + measured=plan['measured'] + else: + state=load_state(package/f"conditional_plans/{row['name']}.npz");plan=state['on'] + on=set(state['on_indices'].tolist());groups={} + for ds,l,p in errors: + key=tuple(sorted(ds&on)),l + if key[0] or key[1]:groups.setdefault(key,len(groups)) + supports=list(groups) + measured=np.where(plan['measured']>=0,state['on_indices'][np.maximum(plan['measured'],0)],-1) + assert int(plan['width'])<=14 + circuit=stim.Circuit.from_file(Path('/workspace/data/train')/row['circuit']) + qubits=circuit.get_final_qubit_coordinates() + first=next(ins for ins in circuit.flattened() if ins.name in ['M','MX','MR','MRX']) + spaces=sorted(tuple(qubits[t.value][:2]) for t in first.targets_copy());assert len(spaces)==24 + idx={(int(c[2]),tuple(c[:2])):d for d,c in coords.items()} + context=np.full((max_ops,120),600,np.int32);static=np.zeros((max_ops,129),np.float32) + n=len(plan['masks']);assert n<=max_ops + for op,source in enumerate(plan['source_indices']): + if source<0:continue + ds,logical=supports[source];t=min(int(coords[d][2]) for d in ds) if ds else 0 + for dt in range(-2,3): + for s,space in enumerate(spaces):context[op,(dt+2)*24+s]=idx.get((t+dt,space),600) + for d in ds: + c=coords[d];static[op,(int(c[2])-t+2)*24+spaces.index(tuple(c[:2]))]=1 + p=float(plan['probabilities'][op]);static[op,120]=np.log(max(p,1e-7)/max(1-p,1e-7))/8 + static[op,121:]=[logical,len(ds)/4,t/25,row['rounds']/25,t==0,t>=row['rounds']-1,row['basis']=='X',1] + grid=np.full((26,24),600,np.int32);measurement_indices=np.full(601,624,np.int32) + for d,c in coords.items(): + t=int(c[2]);s=spaces.index(tuple(c[:2]));grid[t,s]=d;measurement_indices[d]=t*24+s + return {'context':context,'static':static,'masks':np.pad(plan['masks'],(0,max_ops-n)), + 'measured':np.pad(measured,(0,max_ops-n),constant_values=-1), + 'valid':np.pad((plan['source_indices']>=0).astype(np.float32),(0,max_ops-n)), + 'grid':grid,'measurement_indices':measurement_indices, + 'counts':np.maximum((grid!=600).sum(axis=0),1).astype(np.float32),'operation_count':np.array(n,np.int32)} + + +def load_all(splits=('train','validation'),max_ops=2048,prior_pass=1,training_cache=None): + assert prior_pass in [1,3,5] + assert training_cache is None or prior_pass in [1,3] + rows=[r for r in json.loads(Path('/workspace/data/train/manifest.json').read_text())['conditions'] if r['distance']==5] + package=Path(json.loads(Path('/workspace/research/CURRENT.json').read_text())['package']) + plans=ROOT/'feature_plans';plans.mkdir(exist_ok=True);items=[] + for row in rows: + path=plans/f"{row['name']}.npz" + if not path.exists():np.savez(path,**build_features(row,package,max_ops)) + item=dict(np.load(path,allow_pickle=False));item['row']=row + if splits: + cache=np.load(Path('/workspace/research/exact_d3/turbo_cache')/f"{row['name']}.npz") + feedback=np.load(Path('/workspace/research/exact_d3/feedback5_cache')/f"{row['name']}.npz") if prior_pass>1 else None + for split in splits: + assert split in ['train','validation'] + arrays=np.load(Path('/workspace/data')/split/row['arrays']) + if split=='train' and training_cache is not None: + full=np.load(Path(training_cache)/f"{row['name']}.npz",allow_pickle=False) + indices=full['indices'];p=full[f'priors{prior_pass}'] + assert np.array_equal(arrays['sample_ids'][indices],full['sample_ids']) + physical_prediction=full['probabilities3'] + else: + indices=cache['train4096_indices'] if split=='train' else np.arange(len(arrays['labels'])) + key='train4096' if split=='train' else 'validation' + p=cache[key+'_on_prior'] if prior_pass==1 else feedback[f'{key}_priors{prior_pass}'] + physical_prediction=cache[key][:,0] if prior_pass==1 else feedback[key][:,prior_pass-1] + assert p.shape[1]==int(item['operation_count']) + if feedback is not None:assert np.array_equal(feedback['train4096_indices'],cache['train4096_indices']) + logits=np.log(np.clip(p,1e-7,1-1e-7)/np.clip(1-p,1e-7,1-1e-7)) + item[split+'_base_logits']=np.pad(logits,((0,0),(0,max_ops-p.shape[1]))).astype(np.float32) + ds=np.unpackbits(arrays['detectors'][indices],axis=1,count=row['num_detectors'],bitorder='little') + item[split+'_dets']=np.pad(ds,((0,0),(0,601-ds.shape[1]))) + item[split+'_labels']=arrays['labels'][indices] + item[split+'_sample_ids']=arrays['sample_ids'][indices] + item[split+'_physical_probabilities']=physical_prediction.astype(np.float32) + items.append(item) + return items + + +if __name__=='__main__': + items=load_all(splits=()) + print(json.dumps({'conditions':len(items),'maximum_operations':max(int(x['operation_count']) for x in items)})) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__model.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__model.py new file mode 100644 index 0000000000000000000000000000000000000000..bf5272d4652dd29dab2b118849d17648019d93a3 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__model.py @@ -0,0 +1,55 @@ +"""Small shared network corrects soft-conditioned relevant-CSS edge logits.""" +import jax +import jax.numpy as jnp + + +def initialize(seed,hidden=64,input_features=394): + assert input_features in (394,414) + keys=jax.random.split(jax.random.key(seed),3) + return {'w1':jax.random.normal(keys[0],(input_features,hidden))*jnp.sqrt(2/input_features),'b1':jnp.zeros(hidden), + 'w2':jax.random.normal(keys[1],(hidden,hidden))*jnp.sqrt(2/hidden),'b2':jnp.zeros(hidden), + 'w3':jax.random.normal(keys[2],(hidden,1))*.001,'b3':jnp.zeros(1)} + + +def endpoint_features(events,measurements,density,static): + """Twenty endpoint/history summaries computed solely from the input syndrome.""" + support=static[:,:120].reshape(-1,5,24) + selected=jnp.max(support,axis=1) + endpoint_counts=jnp.maximum(support.sum(axis=-1),1) + spatial_count=jnp.maximum(selected.sum(axis=-1),1) + event_frames=events.reshape(len(events),-1,5,24) + measurement_frames=measurements.reshape(len(events),-1,5,24) + endpoint_events=(event_frames*support[None,:,:,:]).sum(axis=-1) + endpoint_measurements=(measurement_frames*support[None,:,:,:]).sum(axis=-1) + spatial_events=(event_frames*selected[None,:,None,:]).sum(axis=-1)/spatial_count[None,:,None] + endpoint_total=endpoint_events.sum(axis=-1) + endpoint_fraction=endpoint_total/jnp.maximum(support.sum(axis=(1,2)),1)[None,:] + endpoint_parity=jnp.mod(endpoint_total,2) + selected_density=(density[:,None,:]*selected[None,:,:]).sum(axis=-1)/spatial_count[None,:] + adjacent=(event_frames[:,:,:-1,:]*event_frames[:,:,1:,:]*selected[None,:,None,:]).sum(axis=(2,3))/(4*spatial_count[None,:]) + measurement_mean=(measurement_frames*selected[None,:,None,:]).sum(axis=(2,3))/(5*spatial_count[None,:]) + return jnp.concatenate([ + endpoint_events/endpoint_counts[None,:,:], + endpoint_measurements/endpoint_counts[None,:,:],spatial_events, + selected_density[:,:,None],endpoint_fraction[:,:,None],endpoint_parity[:,:,None], + adjacent[:,:,None],measurement_mean[:,:,None]],axis=-1) + + +def probabilities(parameters,dets,base_logits,context,static,valid,grid,measurement_indices,counts): + event_grid=dets[:,grid] + measured=(jnp.cumsum(event_grid.astype(jnp.int32),axis=1)&1).reshape(len(dets),624) + measured=jnp.pad(measured,((0,0),(0,1))) + events=dets[:,context].astype(jnp.float32) + measurements=measured[:,measurement_indices[context]].astype(jnp.float32) + density=jnp.sum(event_grid,axis=1)/counts[None,:] + features=jnp.concatenate([events,measurements, + jnp.broadcast_to(density[:,None,:],(len(dets),len(context),24)), + jnp.broadcast_to(static[None,:,:],(len(dets),*static.shape)),base_logits[:,:,None]/8],axis=-1) + if parameters['w1'].shape[0]==414: + features=jnp.concatenate([features,endpoint_features(events,measurements,density,static)],axis=-1) + else: + assert parameters['w1'].shape[0]==394 + h=jax.nn.silu(features@parameters['w1']+parameters['b1']) + h=jax.nn.silu(h@parameters['w2']+parameters['b2']) + delta=2*jnp.tanh((h@parameters['w3']+parameters['b3'])[...,0]) + return (jax.nn.sigmoid(base_logits+delta)*valid[None,:]).T diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__train.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__train.py new file mode 100644 index 0000000000000000000000000000000000000000..978be4e4764e5c606d611a47d1dd44340a252d61 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_d5__train.py @@ -0,0 +1,176 @@ +"""Train effective d5 CSS weights from random initialization on hardware labels.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1' +os.environ['OMP_NUM_THREADS']='1' +os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +from pathlib import Path +import argparse,json,time,sys,hashlib,shutil +import numpy as np +import jax,jax.numpy as jnp,optax +from model import initialize,probabilities +from data import load_all +sys.path.insert(0,'/workspace/research/learned_exact') +from gradient import make_loss_and_grad +from exact import make_decoder + + +def main(): + parser=argparse.ArgumentParser();parser.add_argument('--steps',type=int,default=1024) + parser.add_argument('--batch',type=int,default=256);parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--validation-shots',type=int,default=512);parser.add_argument('--validation-batch',type=int,default=512) + parser.add_argument('--init-weights',type=Path);parser.add_argument('--learning-rate',type=float,default=.001) + parser.add_argument('--eval-every',type=int,default=256);parser.add_argument('--seed',type=int,default=20261101) + parser.add_argument('--initial-seed',type=int,default=20261101) + parser.add_argument('--prior-pass',type=int,choices=[1,3,5],default=1) + parser.add_argument('--training-cache',type=Path) + parser.add_argument('--gradient-block-size',type=int,default=0) + parser.add_argument('--bucket-operations',action='store_true') + parser.add_argument('--accumulate-conditions',type=int,default=1) + parser.add_argument('--uncertainty-weight-floor',type=float,default=1.) + parser.add_argument('--hidden-width',type=int,default=64) + parser.add_argument('--endpoint-features',action='store_true') + parser.add_argument('--reset-optimizer',action='store_true') + parser.add_argument('--reset-rng',action='store_true') + args=parser.parse_args();args.output.mkdir(parents=True,exist_ok=True) + assert args.accumulate_conditions>=1 and 0=.5)!=ys),jnp.sum(loss*example_weights)/jnp.sum(example_weights) + @jax.jit + def apply_gradient(parameters,state,grads): + changes,state=optimizer.update(grads,state,parameters) + return optax.apply_updates(parameters,changes),state,optax.global_norm(grads) + @jax.jit + def infer(parameters,ds,base,context,static,valid,masks,measured,grid,measurement_indices,counts): + p=probabilities(parameters,ds,base,context,static,valid,grid,measurement_indices,counts) + return decode(ds,p,masks,measured) + feature_names=['context','static','valid','masks','measured','grid','measurement_indices','counts'] + def evaluate(step): + results=[];start=time.time() + for item in dataset: + n=min(args.validation_shots,len(item['validation_labels'])) + ix=np.random.default_rng(20261102).choice(len(item['validation_labels']),n,replace=False) + length=((int(item['operation_count'])+511)//512)*512 + features=[item[k][:length] for k in feature_names[:5]]+[item[k] for k in feature_names[5:]] + parts=[] + for begin in range(0,n,args.validation_batch): + selected=ix[begin:begin+args.validation_batch];size=len(selected) + ds=np.pad(item['validation_dets'][selected],((0,args.validation_batch-size),(0,0))) + base=np.pad(item['validation_base_logits'][selected,:length],((0,args.validation_batch-size),(0,0))) + parts.append(np.asarray(infer(parameters,ds,base,*features))[:size]) + p=np.concatenate(parts);y=item['validation_labels'][ix].astype(np.float64) + pp=np.clip(p.astype(np.float64),1e-7,1-1e-7) + results.append({'condition':item['row']['name'],'failure':float(np.mean((p>=.5)!=y)), + 'negative_log_likelihood':float(np.mean(-y*np.log(pp)-(1-y)*np.log1p(-pp)))}) + result={'step':step,'validation_failure':float(np.mean([r['failure'] for r in results])), + 'negative_log_likelihood':float(np.mean([r['negative_log_likelihood'] for r in results])), + 'seconds':time.time()-start,'conditions':results} + (args.output/f'validation-{step}.json').write_text(json.dumps(result,indent=2)+'\n') + np.savez(args.output/f'weights-{step}.npz',**{k:np.asarray(v) for k,v in parameters.items()}) + leaves,_=jax.tree_util.tree_flatten(state);np.savez(args.output/f'optimizer-{step}.npz',**{f'arr_{i}':np.asarray(v) for i,v in enumerate(leaves)}) + (args.output/f'rng-{step}.json').write_text(json.dumps(rng.bit_generator.state)+'\n') + (args.output/f'checkpoint-{step}.json').write_text(json.dumps({'step':step,'training_samples':step*args.batch*args.accumulate_conditions, + 'unique_training_rows_seen_in_this_phase':sum(int(mask.sum()) for mask in seen), + 'weights_sha256':hashlib.sha256((args.output/f'weights-{step}.npz').read_bytes()).hexdigest(),'complete':True})+'\n') + print(json.dumps({k:v for k,v in result.items() if k!='conditions'}),flush=True) + evaluate(0);records=[] + for step in range(1,args.steps+1): + total_grads=None;loss=0.;error=0.;objective=0. + for _ in range(args.accumulate_conditions): + condition_index=int(rng.integers(len(dataset)));item=dataset[condition_index] + ix=rng.integers(len(item['train_labels']),size=args.batch);seen[condition_index][ix]=True + length=((int(item['operation_count'])+511)//512)*512 if args.bucket_operations else len(item['valid']) + features=[item[k][:length] for k in feature_names[:5]]+[item[k] for k in feature_names[5:]] + physical=item['train_physical_probabilities'][ix] + example_weights=(args.uncertainty_weight_floor+(1-args.uncertainty_weight_floor)*4*physical*(1-physical)).astype(np.float32) + grads,batch_loss,batch_error,batch_objective=batch_gradient(parameters,item['train_dets'][ix],item['train_labels'][ix],item['train_base_logits'][ix,:length],*features,example_weights) + total_grads=grads if total_grads is None else jax.tree_util.tree_map(jnp.add,total_grads,grads) + loss+=batch_loss/args.accumulate_conditions;error+=batch_error/args.accumulate_conditions + objective+=batch_objective/args.accumulate_conditions + total_grads=jax.tree_util.tree_map(lambda g:g/args.accumulate_conditions,total_grads) + parameters,state,norm=apply_gradient(parameters,state,total_grads) + if step==1 or step%16==0: + r={'step':step,'loss':float(loss),'objective':float(objective),'error':float(error),'gradient_norm':float(norm),'elapsed':time.time()-started} + assert np.isfinite(r['loss']);records.append(r);print(json.dumps(r),flush=True) + if step%args.eval_every==0 or step==args.steps:evaluate(step) + provenance={'initialization':(f'Continue from the recorded session-trained checkpoint; original random initialization seed {args.initial_seed}' if args.init_weights else + f'All parameters initialized in this session: variance-scaled normal hidden layers, Normal(0,.001) output weights, zero biases, seed {args.initial_seed}'), + 'prior_pass':args.prior_pass,'initial_seed':args.initial_seed, + 'training_source_sha256':source_hashes,'runtime_versions':config['runtime_versions'], + 'hidden_width':actual_hidden, + 'input_features':input_features,'feature_mode':feature_mode, + 'architecture':f'Two {actual_hidden}-unit SiLU layers; {input_features} features; bounded 2*tanh correction to relevant-CSS edge logits from the recorded physical feedback pass; exact width-14 contraction predicts logical probability. D5 joint conditioning remains approximate.', + 'training':('All supplied training shots per each of all26 d5 conditions; uniform condition and within-condition sampling for optimizer batches.' if args.training_cache else + '4096 training shots per each of all 26 d5 conditions, sampled without replacement with seed 20261023. Uniform condition and within-condition sampling for optimizer batches.'), + 'training_cache':str(args.training_cache) if args.training_cache else None, + 'training_cache_provenance':json.loads((args.training_cache/'provenance.json').read_text()) if args.training_cache else None, + 'training_shots':sum(len(item['train_labels']) for item in dataset), + 'training_pool_interpretation':'Eligible training rows; optimizer samples conditions and rows with replacement. Distinct rows used in this phase are recorded separately.', + 'unique_training_rows_seen_in_this_phase':sum(int(mask.sum()) for mask in seen), + 'unique_rows_by_condition':{item['row']['name']:int(mask.sum()) for item,mask in zip(dataset,seen)}, + 'gradient_block_size':args.gradient_block_size, + 'bucket_operations':args.bucket_operations, + 'accumulate_conditions':args.accumulate_conditions, + 'effective_batch_size':args.batch*args.accumulate_conditions, + 'uncertainty_weight_floor':args.uncertainty_weight_floor, + 'example_weighting':('Uniform BCE' if args.uncertainty_weight_floor==1 else + 'Frozen physical pass3 confidence gives w=floor+(1-floor)*4*p*(1-p), normalized within each condition batch. All weights positive; full validation remains unweighted.'), + 'loss':'Conditional logical binary cross entropy, differentiated through exact relevant-CSS contraction; physical CSS feedback prior held fixed', + 'steps':args.steps,'batch':args.batch,'learning_rate':args.learning_rate,'sampling_seed':args.seed, + 'optimizer':'AdamW, weight decay 1e-4, global gradient norm clip 1','optimizer_restored':restored, + 'rng_restored':rng_restored, + 'explicit_optimizer_reset':args.reset_optimizer,'explicit_rng_reset':args.reset_rng, + 'sampling_state_source':str(rngfile) if rng_restored else f'Fresh NumPy default_rng seed {args.seed}', + 'starting_checkpoint':str(args.init_weights) if args.init_weights else None, + 'starting_checkpoint_sha256':hashlib.sha256(args.init_weights.read_bytes()).hexdigest() if args.init_weights else None, + 'starting_training_provenance':json.loads((args.init_weights.parent/'provenance.json').read_text()) if args.init_weights else None, + 'validation':'Uniform subset per all 26 conditions, sample seed 20261102; full validation required before publication', + 'validation_shots_per_condition':args.validation_shots,'test_data_used':False,'records':records} + (args.output/'provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + + +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_exact__checkpoint_gradient.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_exact__checkpoint_gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..b567a7a009cbed51d34288e7492dc5ee45710488 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_exact__checkpoint_gradient.py @@ -0,0 +1,73 @@ +"""Exact logical BCE gradient with block recomputation of frontier histories.""" +import jax +import jax.numpy as jnp + + +def make_loss_and_grad(width, block_size=32): + indices = jnp.arange(1 << width, dtype=jnp.int32) + + @jax.jit + def evaluate(dets, labels, probabilities, masks, measured): + nops, batch = probabilities.shape + padding = (-nops) % block_size + p = jnp.pad(probabilities, ((0, padding), (0, 0))) + m = jnp.pad(masks, (0, padding)) + d = jnp.pad(measured, (0, padding), constant_values=-1) + blocks = (p.reshape(-1, block_size, batch), m.reshape(-1, block_size), + d.reshape(-1, block_size)) + + def forward(f, args): + probability, mask, detector = args + + def error(f): + out = (1 - probability[:, None]) * f + probability[:, None] * f[:, indices ^ mask] + return out, jnp.ones(batch) + + def project(f): + dst = (indices[None, :] & ~mask) | (dets[:, detector, None].astype(jnp.int32) * mask) + g = jnp.take_along_axis(f, dst, axis=1) * ((indices & mask) == 0) + norm = jnp.maximum(g.sum(axis=1), 1e-30) + return g / norm[:, None], norm + + out, norm = jax.lax.cond(detector < 0, error, project, f) + return out, (f, norm) + + def forward_block(f, args): + def step(carry, item): + result, _ = forward(carry, item) + return result, None + out, _ = jax.lax.scan(step, f, args) + return out, f + + initial = jnp.zeros((batch, 1 << width), jnp.float32).at[:, 0].set(1) + f, checkpoints = jax.lax.scan(forward_block, initial, blocks) + indicator = ((indices[None, :] & 1) == labels[:, None]) + correct = jnp.maximum(jnp.sum(f * indicator, axis=1), 1e-10) + prediction = jnp.sum(f[:, 1::2], axis=1) + terminal = 1 - indicator / correct[:, None] + + def backward(b, args): + probability, mask, detector, old, norm = args + + def error(b): + flipped = b[:, indices ^ mask] + grad = jnp.sum(old * (flipped - b), axis=1) + return (1 - probability[:, None]) * b + probability[:, None] * flipped, grad + + def project(b): + obs = ((indices[None, :] & mask) == dets[:, detector, None].astype(jnp.int32) * mask) + values = b[:, indices & ~mask] + mean = jnp.sum(old * obs * values, axis=1) / norm + return (values - mean[:, None]) * obs / norm[:, None], jnp.zeros(batch) + + return jax.lax.cond(detector < 0, error, project, b) + + def backward_block(b, args): + probabilities, masks, measured, checkpoint = args + _, (history, norms) = jax.lax.scan(forward, checkpoint, (probabilities, masks, measured)) + return jax.lax.scan(backward, b, (probabilities, masks, measured, history, norms), reverse=True) + + _, gradients = jax.lax.scan(backward_block, terminal, (*blocks, checkpoints), reverse=True) + return -jnp.log(correct), prediction, gradients.reshape(-1, batch)[:nops] + + return evaluate diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_exact__gradient.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_exact__gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..a593f553a8ba67f3ff02717ba99d33c0eeb697e8 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh2_full/learned_exact__gradient.py @@ -0,0 +1,43 @@ +"""Differentiable conditional logical likelihood through exact DEM elimination.""" +import jax +import jax.numpy as jnp + + +def make_loss_and_grad(width): + indices=jnp.arange(1<>1 if return_observable else masks + _,grad=jax.lax.scan(backward,jnp.ones((len(dets),1<=.5)!=a['labels'][:len(p)]), 'min': p.min(), 'max': p.max()}, flush=True) diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__data.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__data.py new file mode 100644 index 0000000000000000000000000000000000000000..c4a53919501917da89ad95a4807e26e9972b8426 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__data.py @@ -0,0 +1,73 @@ +from pathlib import Path +import json,sys +import numpy as np +import stim +sys.path.insert(0,'/workspace/research/exact_d3') +from exact import make_plan,parse_errors + + +def _attach_arrays(item,root,splits): + row=item['row'] + for split in splits: + a=np.load(root/split/row['arrays']);ds=np.unpackbits(a['detectors'],axis=1,count=row['num_detectors'],bitorder='little') + item[split+'_dets']=np.pad(ds,((0,0),(0,201-ds.shape[1]))) + item[split+'_labels']=a['labels'] + + +def load_all(max_ops=3072,splits=('train','validation')): + assert set(splits)<=set(['train','validation']) + root=Path('/workspace/data') + rows=[r for r in json.loads((root/'train/manifest.json').read_text())['conditions'] if r['distance']==3] + groups=sorted({r['group'] for r in rows});result=[] + cache=Path('/workspace/research/learned_exact')/f'feature_cache-v2-{max_ops}' + cache.mkdir(exist_ok=True) + for row in rows: + cached=cache/f"{row['name']}-{row['noise_sha256'][:12]}-{row['circuit_sha256'][:12]}.npz" + if cached.exists(): + item=dict(np.load(cached,allow_pickle=False));item['row']=row + _attach_arrays(item,root,splits);result.append(item);continue + dem=stim.DetectorErrorModel.from_file(root/'train'/row['noise']);plan=make_plan(dem) + assert int(plan['width'])<=10 + count=len(plan['masks']);assert count<=max_ops,(row['name'],count) + coords=dem.get_detector_coordinates() + circuit=stim.Circuit.from_file(root/'train'/row['circuit']) + qubit_coords=circuit.get_final_qubit_coordinates() + first_measurement=next(ins for ins in circuit.flattened() if ins.name in ['M','MX','MR','MRX']) + spaces=sorted(tuple(qubit_coords[t.value][:2]) for t in first_measurement.targets_copy()) + assert len(spaces)==8 + idx={(int(c[2]),tuple(c[:2])):d for d,c in coords.items()} + errors=parse_errors(dem) + context=np.full((max_ops,40),200,np.int32) + static=np.zeros((max_ops,57),np.float32) + for op,source in enumerate(plan['source_indices']): + if source<0:continue + ds,logical,p=errors[source] + t=min(int(coords[d][2]) for d in ds) if ds else 0 + for dt in range(-2,3): + for s,spatial in enumerate(spaces):context[op,(dt+2)*8+s]=idx.get((t+dt,spatial),200) + for d in ds: + c=coords[d];static[op,(int(c[2])-t+2)*8+spaces.index(tuple(c[:2]))]=1 + static[op,40]=np.log(max(p,1e-7)/(1-max(p,1e-7)))/8 + static[op,41]=logical;static[op,42]=len(ds)/4 + static[op,43]=t/25;static[op,44]=row['rounds']/25 + static[op,45]=t==0;static[op,46]=t>=row['rounds']-1 + static[op,47+groups.index(row['group'])]=1 + static[op,55]=row['basis']=='X';static[op,56]=1 + masks=np.pad(plan['masks'],(0,max_ops-count)) + measured=np.pad(plan['measured'],(0,max_ops-count),constant_values=-1) + p=np.pad(plan['probabilities'],(0,max_ops-count)) + valid=((p>0)&(measured<0)).astype(np.float32) + logits=np.log(np.clip(p,1e-7,1-1e-7)/np.clip(1-p,1e-7,1-1e-7)).astype(np.float32) + item={'row':row,'context':context,'static':static,'masks':masks,'measured':measured,'base_logits':logits,'valid':valid} + grid=np.full((26,8),200,np.int32) + measurement_indices=np.full(201,208,np.int32) + for detector,c in coords.items(): + t=int(c[2]);s=spaces.index(tuple(c[:2]));grid[t,s]=detector + measurement_indices[detector]=t*8+s + item['grid']=grid;item['measurement_indices']=measurement_indices + item['counts']=np.maximum((grid!=200).sum(axis=0),1).astype(np.float32) + temporary=cached.with_suffix('.tmp.npz') + np.savez(temporary,**{k:v for k,v in item.items() if k!='row'});temporary.replace(cached) + _attach_arrays(item,root,splits) + result.append(item) + return result diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__fresh_specialize_train.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__fresh_specialize_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fb454c4b65ff7fdfa34bd8c65693a6bd3ed285b0 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__fresh_specialize_train.py @@ -0,0 +1,161 @@ +# Derived from specialize_train.py SHA256 5a2e7ba25969f4599cb65d60cbd53544fd77d7a6926ec0d03662b72b151acf95; fresh-seed parent provenance only, unchanged update equations. +"""Fine-tune eight existing session-trained d3 models on their respective groups.""" +import os +os.environ['OPENBLAS_NUM_THREADS']='1' +os.environ['OMP_NUM_THREADS']='1' +os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false' +from pathlib import Path +import argparse, hashlib, json, shutil, sys, time +import numpy as np +import jax, jax.numpy as jnp, optax +from data import load_all +from temporal_model import probabilities +from gradient import make_loss_and_grad +sys.path.insert(0,'/workspace/research/exact_d3') +from exact import make_decoder + +FIELDS=['context','static','base_logits','valid','masks','measured','grid','measurement_indices','counts'] + +def sha(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + +def main(): + parser=argparse.ArgumentParser() + parser.add_argument('--package',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--steps',type=int,default=256) + parser.add_argument('--eval-every',type=int,default=128) + parser.add_argument('--batch',type=int,default=1024) + parser.add_argument('--validation-shots',type=int,default=1024) + parser.add_argument('--validation-batch',type=int,default=1024) + parser.add_argument('--learning-rate',type=float,default=.0001) + parser.add_argument('--seed',type=int,default=20261200) + parser.add_argument('--groups',nargs='+') + args=parser.parse_args() + assert args.steps>0 and args.eval_every>0 and args.batch>0 + args.output.mkdir(parents=True,exist_ok=False) + config=json.loads((args.package/'model_config.json').read_text()) + groups=sorted(config['temporal_contextual']) + if args.groups: + assert set(args.groups)<=set(groups) + groups=[g for g in groups if g in args.groups] + assert groups + source=Path(__file__).resolve();research=source.parent.parent + source_hashes={} + snapshot=args.output/'training_source';snapshot.mkdir() + for path in [source,source.parent/'temporal_model.py',source.parent/'data.py', + source.parent/'gradient.py',research/'exact_d3/exact.py']: + name=str(path.relative_to(research)).replace('/','__') + shutil.copyfile(path,snapshot/name);source_hashes[name]=sha(path) + parents=args.output/'parents';parents.mkdir() + shutil.copyfile(args.package/'training_provenance.json',parents/'training_provenance.json') + shutil.copyfile(args.package/'model_config.json',parents/'model_config.json') + run_config={k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()} + run_config.update(training_source_sha256=source_hashes, + runtime_versions={'jax':jax.__version__,'numpy':np.__version__,'optax':optax.__version__}, + parent_config_sha256=sha(parents/'model_config.json'), + parent_provenance_sha256=sha(parents/'training_provenance.json'), + train_manifest_sha256=sha('/workspace/data/train/manifest.json'), + validation_manifest_sha256=sha('/workspace/data/validation/manifest.json'), + groups=groups) + (args.output/'run_config.json').write_text(json.dumps(run_config,indent=2)+'\n') + started=time.time();dataset=load_all() + dataset=[item for item in dataset if item['row']['group'] in groups] + assert len(dataset)==13*len(groups) + optimizer=optax.chain(optax.clip_by_global_norm(1.),optax.adamw(args.learning_rate,weight_decay=1e-4)) + exact_gradient=make_loss_and_grad(10);exact_decode=make_decoder(10) + + @jax.jit + def update(parameters,state,ds,ys,context,static,base_logits,valid,masks,measured,grid,measurement_indices,counts): + p,pullback=jax.vjp(lambda w:probabilities(w,ds,context,static,base_logits,valid,grid,measurement_indices,counts),parameters) + loss,pred,g=exact_gradient(ds,ys,p,masks,measured) + grads=pullback(g/len(ds))[0] + changes,state=optimizer.update(grads,state,parameters) + return optax.apply_updates(parameters,changes),state,jnp.mean(loss),jnp.mean((pred>=.5)!=ys),optax.global_norm(grads) + + @jax.jit + def infer(parameters,ds,context,static,base_logits,valid,masks,measured,grid,measurement_indices,counts): + p=probabilities(parameters,ds,context,static,base_logits,valid,grid,measurement_indices,counts) + return exact_decode(ds,p,masks,measured) + + reports={} + for group_index,group in enumerate(groups): + items=[item for item in dataset if item['row']['group']==group] + assert sorted(item['row']['rounds'] for item in items)==list(range(1,26,2)) + out=args.output/group;out.mkdir() + settings=config['temporal_contextual'][group] + parent=args.package/settings['weights'];parent_copy=parents/f'{group}.npz' + shutil.copyfile(parent,parent_copy) + parameters={k:jnp.asarray(v) for k,v in dict(np.load(parent_copy,allow_pickle=False)).items()} + state=optimizer.init(parameters) + seed=args.seed+sorted(config['temporal_contextual']).index(group) + rng=np.random.default_rng(seed);seen=[np.zeros(len(i['train_labels']),bool) for i in items] + records=[] + + def evaluate(step): + results=[];begin=time.time() + for item in items: + n=min(args.validation_shots,len(item['validation_labels'])) + ix=np.random.default_rng(20261028).choice(len(item['validation_labels']),n,replace=False) + vd=item['validation_dets'][ix];vy=item['validation_labels'][ix];parts=[] + for start in range(0,n,args.validation_batch): + ds=vd[start:start+args.validation_batch];size=len(ds) + ds=np.pad(ds,((0,args.validation_batch-size),(0,0))) + parts.append(np.asarray(infer(parameters,ds,*[item[k] for k in FIELDS]))[:size]) + p=np.concatenate(parts);pp=np.clip(p.astype(np.float64),1e-7,1-1e-7);yy=vy.astype(np.float64) + results.append({'condition':item['row']['name'],'failure':float(np.mean((p>=.5)!=vy)), + 'negative_log_likelihood':float(np.mean(-yy*np.log(pp)-(1-yy)*np.log1p(-pp)))}) + result={'group':group,'step':step,'validation_failure':float(np.mean([r['failure'] for r in results])), + 'negative_log_likelihood':float(np.mean([r['negative_log_likelihood'] for r in results])), + 'seconds':time.time()-begin,'conditions':results} + (out/f'validation-{step}.json').write_text(json.dumps(result,indent=2)+'\n') + np.savez(out/f'weights-{step}.npz',**{k:np.asarray(v) for k,v in parameters.items()}) + leaves,_=jax.tree_util.tree_flatten(state) + np.savez(out/f'optimizer-{step}.npz',**{f'arr_{i}':np.asarray(v) for i,v in enumerate(leaves)}) + (out/f'rng-{step}.json').write_text(json.dumps(rng.bit_generator.state)+'\n') + checkpoint={'group':group,'step':step,'training_draws':step*args.batch, + 'unique_training_rows_seen_in_this_phase':sum(int(x.sum()) for x in seen), + 'unique_rows_by_condition':{i['row']['name']:int(x.sum()) for i,x in zip(items,seen)}, + 'weights':f'weights-{step}.npz','weights_sha256':sha(out/f'weights-{step}.npz'),'complete':True} + (out/f'checkpoint-{step}.json').write_text(json.dumps(checkpoint,indent=2)+'\n') + print(json.dumps({k:v for k,v in result.items() if k!='conditions'}),flush=True) + + evaluate(0) + for step in range(1,args.steps+1): + ci=int(rng.integers(len(items)));item=items[ci] + ix=rng.integers(len(item['train_labels']),size=args.batch);seen[ci][ix]=True + parameters,state,loss,error,norm=update(parameters,state,item['train_dets'][ix],item['train_labels'][ix],*[item[k] for k in FIELDS]) + if step==1 or step%16==0: + record={'group':group,'step':step,'loss':float(loss),'error':float(error), + 'gradient_norm':float(norm),'elapsed':time.time()-started} + assert np.isfinite(record['loss']);records.append(record);print(json.dumps(record),flush=True) + if step%args.eval_every==0 or step==args.steps:evaluate(step) + provenance={'initialization':'Continue the temporal component of the selected session-trained group decoder; no new random parameters', + 'original_initialization':'Fresh rich trunk seed20261300 and temporal encoder seed20261301; parent may be a recorded within-trajectory parameter average; all fitting occurred in this session', + 'group':group,'parent_model':settings,'parent_weights_sha256':sha(parent_copy), + 'parent_provenance_sha256':run_config['parent_provenance_sha256'], + 'parent_record':'../parents/training_provenance.json', + 'training':'Uniform sampling over all13 odd round counts for this geometry/basis group, supplied training labels only', + 'eligible_training_shots':sum(len(i['train_labels']) for i in items), + 'training_draws':args.steps*args.batch, + 'unique_training_rows_seen_in_this_phase':sum(int(x.sum()) for x in seen), + 'unique_rows_by_condition':{i['row']['name']:int(x.sum()) for i,x in zip(items,seen)}, + 'steps':args.steps,'batch_size':args.batch,'learning_rate':args.learning_rate, + 'optimizer':'Fresh AdamW moments for every group, weight decay1e-4, global norm clip1', + 'optimizer_restored':False,'rng_restored':False,'sampling_seed':seed, + 'validation':'Fixed random screen per condition using seed20261028; full validation is used by the separate selector', + 'validation_shots_requested_per_condition':args.validation_shots, + 'loss':'Uniform binary conditional logical likelihood through exact frontier contraction', + 'training_source_sha256':source_hashes,'test_data_used':False,'log':records} + (out/'provenance.json').write_text(json.dumps(provenance,indent=2)+'\n');reports[group]=provenance + (args.output/'provenance.json').write_text(json.dumps({'experiment':'group-specific d3 continuation','groups':reports, + 'initialization':'Continue session-trained selected temporal components; no new random parameters', + 'training':'Each group samples uniformly over its own13 durations; supplied training labels only', + 'eligible_training_shots':sum(len(i['train_labels']) for i in dataset), + 'steps':args.steps*len(groups),'steps_per_group':args.steps,'batch_size':args.batch, + 'learning_rate':args.learning_rate,'optimizer_restored':False,'rng_restored':False, + 'training_source_sha256':source_hashes, + 'run_config':run_config,'parent_record':'parents/training_provenance.json','new_random_parameters':0, + 'training_draws':args.steps*args.batch*len(groups),'test_data_used':False},indent=2)+'\n') + +if __name__=='__main__':main() diff --git a/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__gradient.py b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..a593f553a8ba67f3ff02717ba99d33c0eeb697e8 --- /dev/null +++ b/tasks/surface-code-decoding/surface-code-decoding-astra-xhigh-1h100-48h-20261020b/submissions/S044/training_sources/fresh_specialize1/learned_exact__gradient.py @@ -0,0 +1,43 @@ +"""Differentiable conditional logical likelihood through exact DEM elimination.""" +import jax +import jax.numpy as jnp + + +def make_loss_and_grad(width): + indices=jnp.arange(1<