Archive MaleCNS research sources, provenance, bibliography and immutable data inventory
This commit is contained in:
Digital Drosophila Archive
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{
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"files": [
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"path": "MaleCNS-NumericalContract-v1.md",
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"path": "phase4p_validate.py",
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"root": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/candidate"
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}
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+26
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# MaleCNS-NumericalContract-v1
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Status: immutable candidate contract for `MaleCNS-LIF-runtime-v2-deterministic-candidate`.
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## Frozen tensors and arithmetic
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- State (`V`, `g`, ring-buffer arrivals, effective edge weights): IEEE-754 binary32.
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- CSR `indptr` and edge ids: int64; target indices: int32; refractory/counts: int32.
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- Effective edge weights are the frozen Phase 4C signed float32 weights; raw synapse counts remain unchanged.
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- Canonical contribution key: `['target_idx', 'source_idx', 'edge_id']`. Since global CSR edge ids are source-row ordered, stable `(target_idx, edge_id)` sorting is equivalent.
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- Each target is owned by one thread and accumulated sequentially with round-to-nearest-even `__fadd_rn`. Scientific fan-out contains no atomic add.
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## Per-timestep total order
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1. Read and clear the current exact ring-buffer slot.
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2. `g = float32(g * g_decay)`, then `g = float32(g + recurrent_arrival)`.
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3. Decrement refractory by one; while refractory remains nonzero, hold `V = V_reset`.
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4. For available neurons, forward Euler: `V = float32(V + alpha * (g - (V - V_rest)))`, with every elementary operation rounded to float32.
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5. Detect spikes using the frozen comparison `V >= V_threshold`; reset and set exact refractory steps.
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6. Apply same-step external forced-spike events in ascending target index; a neuron already spiking or refractory is not double-counted.
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7. Enumerate spiking source indices in ascending neuron index. Expand each CSR row in edge-id order.
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8. Stable-sort contributions by target only, preserving source/edge order, then perform fixed sequential segmented sums.
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9. Add each segment once to the ring-buffer slot exactly 18 steps ahead.
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10. Emit events in `(simulation_step, neuron_idx)` order. Simultaneous events never receive a hidden priority beyond neuron index.
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No threshold epsilon, voltage rounding/snap, float64 model substitution, approximate delay, topology change, or parameter change is permitted.
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{
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"biological_parameters_changed": false,
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"generated_utc": "2026-09-22T18:40:39Z",
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"historical_references_overwritten": false,
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"name": "MaleCNS-LIF-runtime-v2-deterministic-candidate",
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"numerical_contract": "MaleCNS-NumericalContract-v1",
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"revalidated_through": "R6"
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}
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@@ -0,0 +1,10 @@
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9505cce8e2f7e03338f7425d698b94e7798cf43e924e6f41c1155719b63204cd MaleCNS-NumericalContract-v1.md
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7f95c99c377944bd05987a3e0953f743ee278807f3c57145aa2b62e3627bc3aa VERSION.json
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c8a13dbdd58dd222f73c1224de448e54bdf90c3f533163427a2bb25446a03999 phase4p.py
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6d0189f4ec570cb3352d15ed8d195d1ccb32ee1d29c33dc1e4f00d377b74787e phase4p.yaml
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8d8bad3db949da876c5a7fe9f20845a10c4128523da69bcb4b5c3fab69c15e7a phase4p_benchmark.py
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9d204d77d70b3723567671a61ce8e2e6162e261f48825e86fb34565e3faa622d phase4p_ext.cpp
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c24985f4e366f90790d25ab5eceb9d6aff0f9064dd61c7413c13d2a2a4fa5fcd phase4p_ext.cu
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b627fb0934bca8f04ec7c595a7b5e6690a2af62a44bc00ff2972f9f764671cd4 phase4p_finalize.py
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c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346 phase4p_revalidate.py
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ce06aa5cb6439450440a91603af1c0d187b20dc11abda6528ea0dfc6b71569b5 phase4p_validate.py
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#!/usr/bin/env python3
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"""Canonical deterministic MaleCNS LIF reference backend and validation tasks."""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import os
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import time
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pyarrow as pa
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import pyarrow.parquet as pq
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import torch
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import yaml
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from torch.utils.cpp_extension import load as load_extension
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import phase4c
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def sha_bytes(*arrays: np.ndarray) -> str:
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h=hashlib.sha256()
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for x in arrays:h.update(np.ascontiguousarray(x).tobytes())
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return h.hexdigest()
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def save(path: Path,x: object):
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path.parent.mkdir(parents=True,exist_ok=True)
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path.write_text(json.dumps(x,indent=2,sort_keys=True,default=str)+'\n')
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def build_extension(model:Path,exp:Path):
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build=exp/'gpu-reference/extension-build';build.mkdir(parents=True,exist_ok=True)
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start=time.perf_counter()
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ext=load_extension(name='male_cns_lif_deterministic_v2_ext',
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sources=[str(model/'phase4p_ext.cpp'),str(model/'phase4p_ext.cu')],
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build_directory=str(build),extra_cflags=['-O3','-ffp-contract=off'],
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extra_cuda_cflags=['-O3','--fmad=false','-std=c++20'],verbose=True)
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report={'status':'PASS','build_seconds':time.perf_counter()-start,'binary':str(Path(ext.__file__)),
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'binary_sha256':phase4c.sha256(Path(ext.__file__)),'flags':{'cxx':['-O3','-ffp-contract=off'],'cuda':['-O3','--fmad=false','-std=c++20']}}
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save(exp/'gpu-reference/extension.json',report);return ext
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class CanonicalRuntime:
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def __init__(self,cfg:dict[str,Any],model:Path,exp:Path):
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self.cfg=cfg;self.exp=exp;self.device=torch.device('cuda:0')
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if torch.cuda.device_count()!=1 or cfg['device']['required_name_substring'] not in torch.cuda.get_device_name(0):
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raise RuntimeError('exactly one visible RTX 4090 required')
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p4cfg=yaml.safe_load((Path(cfg['paths']['historical_runtime_root'])/'phase4c.yaml').read_text())
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paths=phase4c.exact_paths(p4cfg)
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self.indptr_np,self.indices_np,self.raw_np=phase4c.p4b.validate_graph(p4cfg,paths)
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self.table=phase4c.full_metadata(paths['metadata']);self.pops=phase4c.populations(self.table)
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base=phase4c.Runtime(p4cfg,self.table,self.indptr_np,self.indices_np,self.raw_np,self.device,None)
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self.indptr,self.indices,self.weights=base.indptr,base.indices,base.weights
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self.ext=build_extension(model,exp)
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m=cfg['model'];self.n=int(cfg['expected_graph']['neurons']);self.dt=float(m['dt_ms'])
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self.delay_steps=int(round(m['synaptic_delay_ms']/self.dt));self.qlen=self.delay_steps+1
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self.ref_steps=int(round(m['refractory_ms']/self.dt));self.gd=np.float32(1-self.dt/m['synaptic_tau_ms'])
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self.alpha=np.float32(self.dt/m['membrane_tau_ms']);self.rest=np.float32(m['resting_potential_mv'])
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self.resetv=np.float32(m['reset_potential_mv']);self.threshold=np.float32(m['threshold_mv'])
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self.superclass=np.asarray(self.table['annotation_superclass'].to_pylist(),object)
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def reset(self):
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d=self.device;self.v=torch.full((self.n,),float(self.rest),dtype=torch.float32,device=d)
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self.g=torch.zeros(self.n,dtype=torch.float32,device=d);self.ref=torch.zeros(self.n,dtype=torch.int32,device=d)
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self.queue=torch.zeros((self.qlen,self.n),dtype=torch.float32,device=d)
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self.spike_u8=torch.zeros(self.n,dtype=torch.uint8,device=d);self.counts=torch.zeros(self.n,dtype=torch.int32,device=d)
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self.step_index=0;self.event_steps=[];self.event_nodes=[]
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def canonical_fanout(self,sources:torch.Tensor,destination:torch.Tensor)->dict[str,float|int]:
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if not sources.numel():return {'contributions':0,'segments':0,'sort_seconds':0.,'reduce_seconds':0.}
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starts=self.indptr[sources];degree=self.indptr[sources+1]-starts;cum=torch.cumsum(degree,0);total=int(cum[-1].item())
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edges=torch.empty(total,dtype=torch.int64,device=self.device)
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self.ext.fill_edges(self.indptr,sources,cum-degree,edges)
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t0=time.perf_counter();targets=self.indices[edges]
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order=torch.argsort(targets,stable=True);sorted_edges=edges[order];sorted_targets=targets[order]
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boundary=torch.ones(total,dtype=torch.bool,device=self.device)
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if total>1:boundary[1:]=sorted_targets[1:]!=sorted_targets[:-1]
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seg_starts=torch.nonzero(boundary,as_tuple=False).flatten().to(torch.int64)
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unique_targets=sorted_targets[seg_starts].to(torch.int32);sort_sec=time.perf_counter()-t0
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t1=time.perf_counter();self.ext.segmented_sum(sorted_edges,seg_starts,unique_targets,self.weights,destination)
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return {'contributions':total,'segments':int(len(unique_targets)),'sort_seconds':sort_sec,'reduce_seconds':time.perf_counter()-t1}
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def run(self,targets_np:np.ndarray,rate:float,seed:int,duration_ms:float,capture=True,forced_override=None):
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self.reset();steps=int(round(duration_ms/self.dt));targets=torch.as_tensor(targets_np,dtype=torch.int64,device=self.device)
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forced=forced_override if forced_override is not None else phase4c.generate_forced(steps,len(targets_np),rate,self.dt,seed,self.device)
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torch.cuda.synchronize();t0=time.perf_counter();sort_t=reduce_t=0.;contrib=0
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for step in range(steps):
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slot=step%self.qlen
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self.ext.state_step(self.v,self.g,self.ref,self.queue[slot],self.spike_u8,self.counts,
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float(self.gd),float(self.alpha),float(self.rest),float(self.resetv),float(self.threshold),self.ref_steps)
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self.ext.force_step(self.v,self.ref,self.spike_u8,self.counts,targets,forced[step],float(self.resetv),self.ref_steps)
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sources=torch.nonzero(self.spike_u8,as_tuple=False).flatten()
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stats=self.canonical_fanout(sources,self.queue[(step+self.delay_steps)%self.qlen])
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sort_t+=stats['sort_seconds'];reduce_t+=stats['reduce_seconds'];contrib+=stats['contributions']
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if capture and sources.numel():
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x=sources.cpu().numpy().astype(np.int32,copy=True);self.event_nodes.append(x);self.event_steps.append(np.full(len(x),step,np.int32))
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self.step_index+=1
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torch.cuda.synchronize();wall=time.perf_counter()-t0
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es=np.concatenate(self.event_steps) if self.event_steps else np.empty(0,np.int32)
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en=np.concatenate(self.event_nodes) if self.event_nodes else np.empty(0,np.int32)
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return {'step':es,'neuron_idx':en,'counts':self.counts.cpu().numpy().copy(),'v':self.v.cpu().numpy().copy(),'g':self.g.cpu().numpy().copy(),
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'metrics':{'wall_seconds':wall,'biological_ms':duration_ms,'realtime_factor':wall/(duration_ms/1000),
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'peak_vram_bytes':int(torch.cuda.max_memory_allocated()),'contributions':int(contrib),
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'sort_submit_seconds':sort_t,'reduce_submit_seconds':reduce_t,'events':len(en),'active':int((self.counts>0).sum().item())}}
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def cpu_ordered_sum(edge_ids:np.ndarray,indices:np.ndarray,weights:np.ndarray):
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edge_ids=np.asarray(edge_ids,np.int64);targets=np.asarray(indices[edge_ids],np.int32)
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order=np.argsort(targets,kind='stable');ordered=edge_ids[order];ordered_target=targets[order]
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unique=[];sums=[];begin=0
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while begin<len(ordered):
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target=int(ordered_target[begin]);end=begin+1;acc=np.float32(0)
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while end<len(ordered) and ordered_target[end]==target:end+=1
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for e in ordered[begin:end]:acc=np.float32(acc+np.float32(weights[e]))
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unique.append(target);sums.append(acc);begin=end
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return np.asarray(unique,np.int32),np.asarray(sums,np.float32),ordered
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def save_trial(root:Path,run:dict):
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root.mkdir(parents=True,exist_ok=True)
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pq.write_table(pa.table({'step':run['step'],'timestamp_ms':run['step'].astype(np.float64)*.1,'neuron_idx':run['neuron_idx']}),root/'spike-events.parquet',compression='zstd')
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for name,key,dtype in [('per-neuron-spike-counts.int32.npy','counts',np.int32),('final-voltage.float32.npy','v',np.float32),('final-g.float32.npy','g',np.float32)]:np.save(root/name,run[key].astype(dtype),allow_pickle=False)
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save(root/'statistics.json',run['metrics']|{'event_sha256':sha_bytes(run['step'],run['neuron_idx']),
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'counts_sha256':sha_bytes(run['counts']),'v_sha256':sha_bytes(run['v']),'g_sha256':sha_bytes(run['g'])})
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def phase4c_trials(cfg,rt,repeats):
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exp=Path(cfg['paths']['experiment_root']);rows=[]
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for spec in cfg['reference_trials']:
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target=np.flatnonzero(rt.pops[spec['population']]).astype(np.int64)
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baseline=None
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for repeat in range(repeats):
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run=rt.run(target,float(spec['rate_hz']),int(spec['seed']),float(spec['duration_ms']),True)
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if baseline is None:
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baseline={k:run[k].copy() for k in ('step','neuron_idx','counts','v','g')};save_trial(exp/'phase4c-v2/reference'/spec['name'],run)
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exact={k:bool(np.array_equal(run[k],baseline[k])) for k in ('step','neuron_idx','counts','v','g')}
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row={'trial':spec['name'],'repeat':repeat,**exact,**run['metrics']};rows.append(row)
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save(exp/'phase4c-v2/reproducibility-matrix.json',rows)
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print(json.dumps({'trial':spec['name'],'repeat':repeat,'exact':all(exact.values()),'wall':run['metrics']['wall_seconds']}),flush=True)
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summary={'repeats':repeats,'matrix':{s['name']:{k:sum(r[k] for r in rows if r['trial']==s['name']) for k in ('step','neuron_idx','counts','v','g')} for s in cfg['reference_trials']},
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'all_exact':all(all(r[k] for k in ('step','neuron_idx','counts','v','g')) for r in rows),
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'performance':{s['name']:{'mean_wall_seconds':float(np.mean([r['wall_seconds'] for r in rows if r['trial']==s['name']])),
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'mean_realtime_factor':float(np.mean([r['realtime_factor'] for r in rows if r['trial']==s['name']])),
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'peak_vram_bytes':max(r['peak_vram_bytes'] for r in rows if r['trial']==s['name']),
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'mean_contributions':float(np.mean([r['contributions'] for r in rows if r['trial']==s['name']]))} for s in cfg['reference_trials']}}
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save(exp/'phase4c-v2/summary.json',summary)
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def main():
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ap=argparse.ArgumentParser();ap.add_argument('--config',type=Path,required=True);ap.add_argument('task',choices=['phase4c']);ap.add_argument('--repeats',type=int)
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a=ap.parse_args();cfg=yaml.safe_load(a.config.read_text());exp=Path(cfg['paths']['experiment_root']);model=Path(cfg['paths']['model_root'])
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for sub in ('numerical-contract','cpu-reference','gpu-reference','threshold-3942','fanout-tests','phase4c-v2','historical-comparison','brainio','activity-stream','eligibility-replay','performance','revalidation','regression','audit','manifests'):(exp/sub).mkdir(parents=True,exist_ok=True)
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rt=CanonicalRuntime(cfg,model,exp)
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phase4c_trials(cfg,rt,a.repeats or int(cfg['repeats_per_trial']))
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if __name__=='__main__':main()
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@@ -0,0 +1,51 @@
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phase: 4P
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name: MaleCNS-LIF-runtime-v2-deterministic-candidate
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||||
description: Shiu-like LIF dynamics adapted to MaleCNS v1.0 with canonical deterministic float32 reduction
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paths:
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||||
connectome_root: /data/digital-drosophila/datasets/male-cns-v1.0/processed/neuron-only
|
||||
historical_runtime_root: /data/digital-drosophila/models/male-cns-lif/runtime-v1
|
||||
historical_phase4c_root: /data/digital-drosophila/experiments/phase4c-runtime
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||||
phase4o_root: /data/digital-drosophila/experiments/phase4o-runtime-credit
|
||||
model_root: /data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2
|
||||
experiment_root: /data/digital-drosophila/experiments/phase4p-deterministic-runtime
|
||||
expected_graph: {neurons: 166700, directed_edges: 25582938, synaptic_contact_weight_sum: 124177617}
|
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model:
|
||||
dt_ms: 0.1
|
||||
membrane_tau_ms: 20.0
|
||||
synaptic_tau_ms: 5.0
|
||||
refractory_ms: 2.2
|
||||
resting_potential_mv: -52.0
|
||||
reset_potential_mv: -52.0
|
||||
threshold_mv: -45.0
|
||||
synaptic_delay_ms: 1.8
|
||||
w_syn_mv_per_anatomical_synapse: 0.275
|
||||
integration: forward_euler
|
||||
sign_policy:
|
||||
neurotransmitter_sign: {acetylcholine: 1, gaba: -1, glutamate: -1, histamine: -1, dopamine: 1, octopamine: 1, serotonin: 1}
|
||||
unknown_sign: 0
|
||||
unclear_sign: 0
|
||||
device:
|
||||
required_name_substring: RTX 4090
|
||||
visible_gpu_uuid: GPU-79f3a85d-adce-7833-7003-6488140ca596
|
||||
canonical_contract:
|
||||
contribution_key: [target_idx, source_idx, edge_id]
|
||||
equivalent_implementation_key: [target_idx, edge_id]
|
||||
edge_id_property: CSR edge_id is globally ordered by source row and within-row edge position
|
||||
accumulation: sequential IEEE-754 float32 round-to-nearest-even using __fadd_rn
|
||||
state_update: fused explicit IEEE-754 float32 operations using __fmul_rn/__fadd_rn/__fsub_rn
|
||||
threshold: V >= V_threshold
|
||||
reference_trials:
|
||||
- {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}
|
||||
- {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}
|
||||
- {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}
|
||||
- {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}
|
||||
- {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}
|
||||
repeats_per_trial: 20
|
||||
fanout_test_cases: 500
|
||||
eligibility_fixed_event_repeats: 10
|
||||
prohibitions:
|
||||
modify_biological_parameters: true
|
||||
overwrite_historical_reference: true
|
||||
nondeterministic_atomic_scientific_backend: true
|
||||
threshold_epsilon: true
|
||||
learning: true
|
||||
@@ -0,0 +1,69 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Instrumented 100 ms canonical-backend cost audit."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import resource
|
||||
import subprocess
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
|
||||
import phase4p
|
||||
|
||||
|
||||
def main() -> None:
|
||||
model = Path("/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2")
|
||||
cfg = yaml.safe_load((model / "phase4p.yaml").read_text()); exp = Path(cfg["paths"]["experiment_root"])
|
||||
rt = phase4p.CanonicalRuntime(cfg, model, exp); spec = cfg["reference_trials"][1]
|
||||
target_np = np.flatnonzero(rt.pops[spec["population"]]).astype(np.int64)
|
||||
targets = torch.as_tensor(target_np, dtype=torch.int64, device=rt.device); steps = 1000
|
||||
forced = phase4p.phase4c.generate_forced(steps, len(target_np), float(spec["rate_hz"]), rt.dt, int(spec["seed"]), rt.device)
|
||||
rt.reset(); samples = []; stop = threading.Event()
|
||||
def sample():
|
||||
while not stop.is_set():
|
||||
try:
|
||||
text = subprocess.check_output(["nvidia-smi", "--id=" + cfg["device"]["visible_gpu_uuid"],
|
||||
"--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"], text=True).strip()
|
||||
u, m = text.split(","); samples.append({"time": time.time(), "gpu_util_percent": float(u), "gpu_memory_mib": float(m)})
|
||||
except Exception as e: samples.append({"error": str(e)})
|
||||
stop.wait(0.10)
|
||||
thread = threading.Thread(target=sample, daemon=True); thread.start()
|
||||
use0 = resource.getrusage(resource.RUSAGE_SELF); cpu0 = use0.ru_utime + use0.ru_stime
|
||||
torch.cuda.reset_peak_memory_stats(); torch.cuda.synchronize()
|
||||
wall0 = time.perf_counter(); state_submit = force_submit = sort_submit = reduce_submit = 0.0; contributions = 0
|
||||
for step in range(steps):
|
||||
slot = step % rt.qlen; t = time.perf_counter()
|
||||
rt.ext.state_step(rt.v, rt.g, rt.ref, rt.queue[slot], rt.spike_u8, rt.counts,
|
||||
float(rt.gd), float(rt.alpha), float(rt.rest), float(rt.resetv), float(rt.threshold), rt.ref_steps)
|
||||
state_submit += time.perf_counter() - t; t = time.perf_counter()
|
||||
rt.ext.force_step(rt.v, rt.ref, rt.spike_u8, rt.counts, targets, forced[step], float(rt.resetv), rt.ref_steps)
|
||||
force_submit += time.perf_counter() - t
|
||||
sources = torch.nonzero(rt.spike_u8, as_tuple=False).flatten()
|
||||
st = rt.canonical_fanout(sources, rt.queue[(step + rt.delay_steps) % rt.qlen])
|
||||
sort_submit += st["sort_seconds"]; reduce_submit += st["reduce_seconds"]; contributions += st["contributions"]
|
||||
torch.cuda.synchronize(); wall = time.perf_counter() - wall0
|
||||
use1 = resource.getrusage(resource.RUSAGE_SELF); cpu1 = use1.ru_utime + use1.ru_stime
|
||||
stop.set(); thread.join()
|
||||
good = [x for x in samples if "gpu_util_percent" in x]
|
||||
out = {"trial": "auditory-100hz", "biological_ms": 100.0, "steps": steps, "wall_seconds": wall,
|
||||
"wall_per_100ms_biological": wall, "realtime_factor": wall / 0.1, "steps_per_second": steps / wall,
|
||||
"process_CPU_percent_of_one_core": 100 * (cpu1 - cpu0) / wall,
|
||||
"GPU_utilization_percent_mean": float(np.mean([x["gpu_util_percent"] for x in good])) if good else None,
|
||||
"GPU_utilization_percent_max": float(np.max([x["gpu_util_percent"] for x in good])) if good else None,
|
||||
"nvidia_smi_memory_mib_max": float(np.max([x["gpu_memory_mib"] for x in good])) if good else None,
|
||||
"torch_peak_VRAM_bytes": int(torch.cuda.max_memory_allocated()), "contribution_count": int(contributions),
|
||||
"CPU_submit_seconds": {"state_integration": state_submit, "external_force": force_submit,
|
||||
"canonical_sort_and_segment_setup": sort_submit, "segmented_reduction": reduce_submit},
|
||||
"sampling_interval_seconds": 0.10, "samples": samples,
|
||||
"interpretation": "Submission sub-times overlap asynchronous CUDA work; synchronized total wall time is authoritative."}
|
||||
p = exp / "performance/instrumented-100ms.json"; p.parent.mkdir(parents=True, exist_ok=True)
|
||||
p.write_text(json.dumps(out, indent=2, sort_keys=True) + "\n"); print(json.dumps(out, indent=2), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__": main()
|
||||
@@ -0,0 +1,25 @@
|
||||
#include <torch/extension.h>
|
||||
|
||||
void state_step_cuda(torch::Tensor v, torch::Tensor g, torch::Tensor refractory,
|
||||
torch::Tensor arrival, torch::Tensor spikes,
|
||||
torch::Tensor spike_counts, double g_decay, double alpha,
|
||||
double rest, double reset, double threshold,
|
||||
int64_t refractory_steps);
|
||||
void force_step_cuda(torch::Tensor v, torch::Tensor refractory,
|
||||
torch::Tensor spikes, torch::Tensor spike_counts,
|
||||
torch::Tensor targets, torch::Tensor forced,
|
||||
double reset, int64_t refractory_steps);
|
||||
void fill_edges_cuda(torch::Tensor indptr, torch::Tensor sources,
|
||||
torch::Tensor output_offsets, torch::Tensor output);
|
||||
void segmented_sum_cuda(torch::Tensor sorted_edge_ids,
|
||||
torch::Tensor segment_starts,
|
||||
torch::Tensor segment_targets,
|
||||
torch::Tensor weights,
|
||||
torch::Tensor destination);
|
||||
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
||||
m.def("state_step", &state_step_cuda, "Canonical float32 LIF update (CUDA)");
|
||||
m.def("force_step", &force_step_cuda, "Canonical external spike injection (CUDA)");
|
||||
m.def("fill_edges", &fill_edges_cuda, "Source/edge ordered CSR expansion (CUDA)");
|
||||
m.def("segmented_sum", &segmented_sum_cuda, "Fixed-order sequential segmented float32 sum (CUDA)");
|
||||
}
|
||||
@@ -0,0 +1,87 @@
|
||||
#include <torch/extension.h>
|
||||
#include <c10/cuda/CUDAException.h>
|
||||
#include <cuda.h>
|
||||
#include <cuda_runtime.h>
|
||||
#include <cstdint>
|
||||
|
||||
namespace {
|
||||
void check_cuda(const torch::Tensor& x) {
|
||||
TORCH_CHECK(x.is_cuda(), "all tensors must be CUDA tensors");
|
||||
TORCH_CHECK(x.is_contiguous(), "all tensors must be contiguous");
|
||||
}
|
||||
|
||||
__global__ void state_kernel(float* v, float* g, int* refractory,
|
||||
float* arrival, unsigned char* spikes,
|
||||
int* counts, int n, float gd, float alpha,
|
||||
float rest, float reset, float threshold,
|
||||
int ref_steps) {
|
||||
int i=blockIdx.x*blockDim.x+threadIdx.x;
|
||||
if(i>=n) return;
|
||||
float gi=__fadd_rn(__fmul_rn(g[i],gd),arrival[i]);
|
||||
g[i]=gi; arrival[i]=0.0f;
|
||||
int r=refractory[i]; r=r>0?r-1:0; refractory[i]=r;
|
||||
float vi=v[i]; bool available=r==0;
|
||||
if(available){
|
||||
float leak=__fsub_rn(vi,rest);
|
||||
float drive=__fsub_rn(gi,leak);
|
||||
vi=__fadd_rn(vi,__fmul_rn(alpha,drive));
|
||||
} else vi=reset;
|
||||
bool fired=available && vi>=threshold;
|
||||
if(fired){vi=reset;refractory[i]=ref_steps;counts[i]+=1;}
|
||||
v[i]=vi;spikes[i]=fired?1:0;
|
||||
}
|
||||
|
||||
__global__ void force_kernel(float* v,int* refractory,unsigned char* spikes,
|
||||
int* counts,const int64_t* targets,
|
||||
const unsigned char* forced,int n,float reset,
|
||||
int ref_steps){
|
||||
int j=blockIdx.x*blockDim.x+threadIdx.x;
|
||||
if(j>=n||!forced[j]) return;
|
||||
int64_t i=targets[j]; if(spikes[i]||refractory[i]!=0) return;
|
||||
spikes[i]=1;v[i]=reset;refractory[i]=ref_steps;counts[i]+=1;
|
||||
}
|
||||
|
||||
__global__ void fill_edges_kernel(const int64_t* indptr,const int64_t* sources,
|
||||
const int64_t* offsets,int64_t* output,int n){
|
||||
int j=blockIdx.x*blockDim.x+threadIdx.x;
|
||||
if(j>=n) return;
|
||||
int64_t src=sources[j],begin=indptr[src],degree=indptr[src+1]-begin,out=offsets[j];
|
||||
for(int64_t k=0;k<degree;++k) output[out+k]=begin+k;
|
||||
}
|
||||
|
||||
// Exactly one CUDA thread owns a target segment. The loop order is the
|
||||
// canonical target -> source -> edge_id order supplied by the host.
|
||||
__global__ void segmented_sum_kernel(const int64_t* edges,const int64_t* starts,
|
||||
const int* targets,const float* weights,
|
||||
float* destination,int segments,
|
||||
int64_t edge_count){
|
||||
int s=blockIdx.x*blockDim.x+threadIdx.x;
|
||||
if(s>=segments) return;
|
||||
int64_t begin=starts[s],end=(s+1<segments)?starts[s+1]:edge_count;
|
||||
float acc=0.0f;
|
||||
for(int64_t j=begin;j<end;++j) acc=__fadd_rn(acc,weights[edges[j]]);
|
||||
destination[targets[s]]=__fadd_rn(destination[targets[s]],acc);
|
||||
}
|
||||
}
|
||||
|
||||
void state_step_cuda(torch::Tensor v,torch::Tensor g,torch::Tensor refractory,
|
||||
torch::Tensor arrival,torch::Tensor spikes,torch::Tensor counts,
|
||||
double gd,double alpha,double rest,double reset,double threshold,
|
||||
int64_t ref_steps){
|
||||
check_cuda(v);check_cuda(g);check_cuda(refractory);check_cuda(arrival);check_cuda(spikes);check_cuda(counts);
|
||||
int n=v.numel();state_kernel<<<(n+255)/256,256>>>(v.data_ptr<float>(),g.data_ptr<float>(),refractory.data_ptr<int>(),arrival.data_ptr<float>(),spikes.data_ptr<unsigned char>(),counts.data_ptr<int>(),n,(float)gd,(float)alpha,(float)rest,(float)reset,(float)threshold,(int)ref_steps);C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
void force_step_cuda(torch::Tensor v,torch::Tensor refractory,torch::Tensor spikes,
|
||||
torch::Tensor counts,torch::Tensor targets,torch::Tensor forced,
|
||||
double reset,int64_t ref_steps){
|
||||
check_cuda(targets);check_cuda(forced);int n=targets.numel();force_kernel<<<(n+255)/256,256>>>(v.data_ptr<float>(),refractory.data_ptr<int>(),spikes.data_ptr<unsigned char>(),counts.data_ptr<int>(),targets.data_ptr<int64_t>(),forced.data_ptr<unsigned char>(),n,(float)reset,(int)ref_steps);C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
void fill_edges_cuda(torch::Tensor indptr,torch::Tensor sources,torch::Tensor offsets,torch::Tensor output){
|
||||
check_cuda(indptr);check_cuda(sources);check_cuda(offsets);check_cuda(output);int n=sources.numel();fill_edges_kernel<<<(n+255)/256,256>>>(indptr.data_ptr<int64_t>(),sources.data_ptr<int64_t>(),offsets.data_ptr<int64_t>(),output.data_ptr<int64_t>(),n);C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
void segmented_sum_cuda(torch::Tensor edges,torch::Tensor starts,torch::Tensor targets,
|
||||
torch::Tensor weights,torch::Tensor destination){
|
||||
check_cuda(edges);check_cuda(starts);check_cuda(targets);check_cuda(weights);check_cuda(destination);
|
||||
TORCH_CHECK(edges.scalar_type()==torch::kInt64&&starts.scalar_type()==torch::kInt64&&targets.scalar_type()==torch::kInt32,"index dtype mismatch");
|
||||
int n=targets.numel();if(n) segmented_sum_kernel<<<(n+255)/256,256>>>(edges.data_ptr<int64_t>(),starts.data_ptr<int64_t>(),targets.data_ptr<int>(),weights.data_ptr<float>(),destination.data_ptr<float>(),n,edges.numel());C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
@@ -0,0 +1,102 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Read-only frozen-integrity verification and final Phase 4P sealing."""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import shutil
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
import phase4p_validate
|
||||
from phase4h_audit import verify_checksum
|
||||
from phase4k import dataset_checks
|
||||
from phase4m import frozen_checks, frozen_hashes
|
||||
from phase4n import load_config
|
||||
|
||||
|
||||
ROOT = Path("/data/digital-drosophila")
|
||||
|
||||
|
||||
def save(path: Path, value: object) -> None:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(value, indent=2, sort_keys=True, default=str) + "\n")
|
||||
|
||||
|
||||
def read(path: Path): return json.loads(path.read_text())
|
||||
|
||||
|
||||
def integrity() -> dict:
|
||||
ncfg, mcfg, cfg, _ = load_config(ROOT / "models/male-cns-lif/credit-assignment-v1/phase4n.yaml")
|
||||
checks = {}
|
||||
for label, root, rel in [
|
||||
("Phase4M experiment", Path(mcfg["experiment_root"]), "manifests/checksums.sha256"),
|
||||
("Phase4M model", Path(mcfg["model_root"]), "checksums.sha256"),
|
||||
("Phase4N experiment", Path(ncfg["experiment_root"]), "manifests/checksums.sha256"),
|
||||
("Phase4N model", Path(ncfg["model_root"]), "checksums.sha256")]:
|
||||
checks[label] = verify_checksum(root, root / rel)
|
||||
earlier = frozen_checks(cfg); datasets = dataset_checks(cfg); current = frozen_hashes(cfg)
|
||||
saved = read(Path(mcfg["experiment_root"]) / "audit/final-summary.json")["integrity"]["after"]
|
||||
phase4o = verify_checksum(ROOT / "experiments/phase4o-runtime-credit",
|
||||
ROOT / "experiments/phase4o-runtime-credit/manifests/checksums.sha256")
|
||||
report = {"Phase1_to_3B": datasets, "Phase4C_to_4L": earlier, "Phase4M_4N": checks,
|
||||
"Phase4O": phase4o, "frozen_input_hashes_unchanged": current == saved}
|
||||
report["all_static_checks_passed"] = bool(datasets["all_passed"] and earlier["all_passed"] and
|
||||
all(x["passed"] for x in checks.values()) and phase4o["passed"] and current == saved)
|
||||
return report
|
||||
|
||||
|
||||
def seal_dir(root: Path, control: Path) -> int:
|
||||
rows = []
|
||||
for p in sorted(root.rglob("*")):
|
||||
if p.is_file() and p != control and p.name not in ("checksums.sha256", "MANIFEST.json"):
|
||||
h = hashlib.sha256()
|
||||
with p.open("rb") as f:
|
||||
for chunk in iter(lambda: f.read(8 << 20), b""): h.update(chunk)
|
||||
rows.append((h.hexdigest(), p.relative_to(root).as_posix(), p.stat().st_size))
|
||||
control.parent.mkdir(parents=True, exist_ok=True)
|
||||
control.write_text("".join(f"{h} {rel}\n" for h, rel, _ in rows))
|
||||
save(control.parent / "MANIFEST.json", {"root": str(root), "files": [{"path": r, "sha256": h, "size": s} for h, r, s in rows]})
|
||||
return len(rows)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
cfg = yaml.safe_load((ROOT / "models/male-cns-lif/deterministic-runtime-v2/phase4p.yaml").read_text())
|
||||
exp = Path(cfg["paths"]["experiment_root"]); model = Path(cfg["paths"]["model_root"])
|
||||
frozen = integrity(); save(exp / "audit/frozen-integrity-final.json", frozen)
|
||||
gate = read(exp / "audit/candidate-gate.json"); p4c = read(exp / "phase4c-v2/summary.json")
|
||||
reval = read(exp / "revalidation/summary.json"); threshold = read(exp / "threshold-3942/summary.json")
|
||||
fanout = read(exp / "fanout-tests/summary.json"); history = read(exp / "historical-comparison/summary.json")
|
||||
brainio = read(exp / "brainio/summary.json"); activity = read(exp / "activity-stream/summary.json")
|
||||
eligibility = read(exp / "eligibility-replay/summary.json")
|
||||
performance = read(exp / "performance/instrumented-100ms.json")
|
||||
highest = reval["highest_revalidated"]
|
||||
final = {"phase": "4P", "status": gate["status"], "candidate_generated": gate["candidate_generated"],
|
||||
"numerical_contract": "MaleCNS-NumericalContract-v1", "canonical_order": ["target_idx", "source_idx", "edge_id"],
|
||||
"neuron_3942": threshold, "real_fanout": fanout, "phase4c_5x20": p4c,
|
||||
"historical_comparison": history, "BrainIO": brainio, "ActivityStream": activity,
|
||||
"eligibility_fixed_event_replay": eligibility, "performance": performance, "candidate_gates": gate["gates"],
|
||||
"revalidation": reval, "highest_revalidated": highest,
|
||||
"frozen_integrity": frozen, "prohibited_follow_on_started": False,
|
||||
"limitations": [
|
||||
"Canonical stable sorting plus one-thread-per-target reduction is correctness-first and 2.6-5.0x slower than biological real time for the 1 s trials.",
|
||||
"Historical runtime-v1 full population/state distributions were not persisted for all 50 Phase4O repeats; total-spike envelopes use all runs while population/V/g comparisons use the frozen historical reference.",
|
||||
"The compiler emitted a generic c++ versus g++ warning even though the cached extension loaded and all exact regressions passed."]}
|
||||
save(exp / "audit/final-summary.json", final)
|
||||
candidate = model / "candidate"; shutil.copy2(model / "phase4p_revalidate.py", candidate / "phase4p_revalidate.py")
|
||||
shutil.copy2(model / "phase4p_finalize.py", candidate / "phase4p_finalize.py")
|
||||
shutil.copy2(model / "phase4p_benchmark.py", candidate / "phase4p_benchmark.py")
|
||||
save(candidate / "VERSION.json", {"name": gate["status"], "numerical_contract": "MaleCNS-NumericalContract-v1",
|
||||
"revalidated_through": highest, "generated_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
"biological_parameters_changed": False, "historical_references_overwritten": False})
|
||||
candidate_files = seal_dir(candidate, candidate / "checksums.sha256")
|
||||
phase4p_validate.make_manifest(cfg, final)
|
||||
own = verify_checksum(candidate, candidate / "checksums.sha256")
|
||||
print(json.dumps({"status": final["status"], "highest_revalidated": highest,
|
||||
"frozen_integrity": frozen["all_static_checks_passed"],
|
||||
"candidate_files": candidate_files, "candidate_checksum": own["passed"]}, indent=2), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__": main()
|
||||
@@ -0,0 +1,449 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Ordered Phase 4P-B dynamic revalidation on the canonical runtime.
|
||||
|
||||
R1/R2 consume the already sealed Phase 4P results. R3-R6 are run in order
|
||||
and the script stops immediately if a level cannot reproduce its declared
|
||||
historical result. No frozen artifact is written.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pyarrow.parquet as pq
|
||||
import torch
|
||||
import yaml
|
||||
|
||||
import phase4p
|
||||
from phase4l import pn_drive
|
||||
from phase4m import load_odor_inputs
|
||||
|
||||
|
||||
ROOT = Path("/data/digital-drosophila")
|
||||
|
||||
|
||||
def save(path: Path, value: object) -> None:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(value, indent=2, sort_keys=True, default=str) + "\n")
|
||||
|
||||
|
||||
def ahash(*values: np.ndarray) -> str:
|
||||
h = hashlib.sha256()
|
||||
for value in values: h.update(np.ascontiguousarray(value).tobytes())
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def metrics(a: np.ndarray, b: np.ndarray) -> dict[str, float]:
|
||||
a = a.astype(np.float64); b = b.astype(np.float64)
|
||||
aa = a > 0; bb = b > 0; union = np.count_nonzero(aa | bb)
|
||||
den = np.linalg.norm(a) * np.linalg.norm(b)
|
||||
return {"euclidean": float(np.linalg.norm(a - b)),
|
||||
"jaccard": float(np.count_nonzero(aa & bb) / union) if union else 1.0,
|
||||
"cosine": float(np.dot(a, b) / den) if den else (1.0 if not np.any(a) and not np.any(b) else 0.0)}
|
||||
|
||||
|
||||
class DeterministicAPLSim:
|
||||
"""Frozen APL overlay on top of the Phase 4P canonical LIF kernel."""
|
||||
def __init__(self, rt: phase4p.CanonicalRuntime):
|
||||
self.rt = rt; self.n = rt.n; self.device = rt.device; self.dt = rt.dt
|
||||
meta = rt.table.to_pandas().sort_values("neuron_idx").reset_index(drop=True)
|
||||
self.meta = meta
|
||||
cls = meta.annotation_class.fillna("").astype(str).to_numpy()
|
||||
typ = meta.annotation_type.fillna("").astype(str).to_numpy()
|
||||
self.kc = np.flatnonzero(cls == "Kenyon_Cell").astype(np.int32)
|
||||
self.mbon = np.flatnonzero(cls == "MBON").astype(np.int32)
|
||||
self.pn = np.flatnonzero(cls == "ALPN").astype(np.int32)
|
||||
self.apl = np.flatnonzero(typ == "APL").astype(np.int32)
|
||||
if len(self.apl) != 2 or len(self.kc) != 4064: raise RuntimeError("frozen APL/KC identity changed")
|
||||
self.sources = np.repeat(np.arange(self.n, dtype=np.int32), np.diff(rt.indptr_np).astype(np.int64))
|
||||
idx = rt.indices_np; raw = rt.raw_np
|
||||
apl_out = np.isin(self.sources, self.apl)
|
||||
self.apl_out_ids = np.flatnonzero(apl_out).astype(np.int64)
|
||||
input_matrix = np.zeros((len(self.apl), self.n), np.float32)
|
||||
output_matrix = np.zeros((len(self.apl), len(self.kc)), np.float32)
|
||||
kc_lookup = np.full(self.n, -1, np.int32); kc_lookup[self.kc] = np.arange(len(self.kc), dtype=np.int32)
|
||||
allowed = np.isin(self.sources, np.concatenate((self.kc, self.pn))) & np.isin(idx, self.apl)
|
||||
apl_lookup = {int(x): i for i, x in enumerate(self.apl)}
|
||||
for eid in np.flatnonzero(allowed): input_matrix[apl_lookup[int(idx[eid])], int(self.sources[eid])] += float(raw[eid])
|
||||
for eid in np.flatnonzero(apl_out & np.isin(idx, self.kc)):
|
||||
output_matrix[apl_lookup[int(self.sources[eid])], kc_lookup[int(idx[eid])]] += float(raw[eid])
|
||||
self.input_scale = float(input_matrix[input_matrix > 0].sum() / np.count_nonzero(np.any(input_matrix > 0, axis=0)))
|
||||
self.output_scale = float(output_matrix[output_matrix > 0].mean())
|
||||
self.input_gpu = torch.as_tensor(input_matrix / self.input_scale, dtype=torch.float32, device=self.device)
|
||||
self.output_gpu = torch.as_tensor(output_matrix / self.output_scale, dtype=torch.float32, device=self.device)
|
||||
self.kc_gpu = torch.as_tensor(self.kc, dtype=torch.int64, device=self.device)
|
||||
self.apl_out_gpu = torch.as_tensor(self.apl_out_ids, dtype=torch.int64, device=self.device)
|
||||
self.base = rt.weights.clone()
|
||||
p4h = ROOT / "experiments/phase4h-dopamine-plasticity"
|
||||
self.identity = pd.read_parquet(ROOT / "experiments/phase4n-credit-assignment/edge-identity/frozen-edge-identity.parquet")
|
||||
self.edge_ids = self.identity.edge_id.to_numpy(np.int64)
|
||||
self.edge_gpu = torch.as_tensor(self.edge_ids, dtype=torch.int64, device=self.device)
|
||||
self.base_candidate = self.base[self.edge_gpu].cpu().numpy().astype(np.float32)
|
||||
self.confirmed = pd.read_parquet(p4h / "mappings/dan-to-mbon-confirmed.parquet")
|
||||
down = pd.read_parquet(p4h / "mappings/mbon-to-downstream-actual-edges.parquet")
|
||||
self.downstream = np.unique(down.target_neuron_idx.to_numpy(np.int32))
|
||||
|
||||
def dan_targets(self, dtype: str, compartment: str) -> np.ndarray:
|
||||
x = self.confirmed[(self.confirmed.source_annotation_type == dtype) &
|
||||
(self.confirmed.source_compartment_verbatim == compartment)]
|
||||
out = np.sort(x.source_neuron_idx.unique().astype(np.int32))
|
||||
if not len(out): raise RuntimeError(f"no confirmed DAN for {dtype}/{compartment}")
|
||||
return out
|
||||
|
||||
def install(self, E: np.ndarray | None) -> None:
|
||||
self.rt.weights.copy_(self.base)
|
||||
if E is not None:
|
||||
self.rt.weights[self.edge_gpu] = torch.as_tensor(self.base_candidate * E.astype(np.float32), device=self.device)
|
||||
self.rt.weights[self.apl_out_gpu] = 0.0
|
||||
|
||||
def run(self, targets: np.ndarray, rates_hz: np.ndarray, seed: int, duration_ms: float,
|
||||
E: np.ndarray | None = None, dan_targets: np.ndarray | None = None,
|
||||
dan_pulses: list[float] | None = None, collect_events: bool = False) -> dict[str, Any]:
|
||||
targets = np.asarray(targets, np.int32); rates_hz = np.asarray(rates_hz, np.float32)
|
||||
self.install(E); self.rt.reset(); steps = int(round(duration_ms / self.dt))
|
||||
target_gpu = torch.as_tensor(targets, dtype=torch.int64, device=self.device)
|
||||
gen = torch.Generator(device=self.device); gen.manual_seed(int(seed))
|
||||
probs = torch.as_tensor(rates_hz * self.dt / 1000.0, dtype=torch.float32, device=self.device)
|
||||
forced = (torch.rand((steps, len(targets)), generator=gen, device=self.device) < probs[None, :]).to(torch.uint8)
|
||||
dan = np.empty(0, np.int32) if dan_targets is None else np.asarray(dan_targets, np.int32)
|
||||
dan_gpu = torch.as_tensor(dan, dtype=torch.int64, device=self.device)
|
||||
dan_forced = torch.zeros((steps, len(dan)), dtype=torch.uint8, device=self.device)
|
||||
for pulse in dan_pulses or []:
|
||||
s = int(round(pulse / self.dt))
|
||||
if 0 <= s < steps: dan_forced[s] = True
|
||||
dan_steps = {int(round(x / self.dt)) for x in (dan_pulses or [])}
|
||||
apl_state = torch.zeros(len(self.apl), dtype=torch.float32, device=self.device)
|
||||
kc_trace = torch.zeros(len(self.kc), dtype=torch.float32, device=self.device)
|
||||
first = torch.full((self.n,), -1, dtype=torch.int32, device=self.device)
|
||||
trace_at_dan = []
|
||||
apl_decay = np.float32(math.exp(-self.dt / 20.0)); trace_decay = np.float32(math.exp(-self.dt / 100.0))
|
||||
kc_steps: list[np.ndarray] = []; kc_nodes: list[np.ndarray] = []
|
||||
torch.cuda.synchronize(); t0 = time.perf_counter()
|
||||
for step in range(steps):
|
||||
slot = step % self.rt.qlen
|
||||
self.rt.ext.state_step(self.rt.v, self.rt.g, self.rt.ref, self.rt.queue[slot], self.rt.spike_u8, self.rt.counts,
|
||||
float(self.rt.gd), float(self.rt.alpha), float(self.rt.rest), float(self.rt.resetv),
|
||||
float(self.rt.threshold), self.rt.ref_steps)
|
||||
self.rt.ext.force_step(self.rt.v, self.rt.ref, self.rt.spike_u8, self.rt.counts,
|
||||
target_gpu, forced[step], float(self.rt.resetv), self.rt.ref_steps)
|
||||
if len(dan):
|
||||
self.rt.ext.force_step(self.rt.v, self.rt.ref, self.rt.spike_u8, self.rt.counts,
|
||||
dan_gpu, dan_forced[step], float(self.rt.resetv), self.rt.ref_steps)
|
||||
sources = torch.nonzero(self.rt.spike_u8, as_tuple=False).flatten()
|
||||
if sources.numel():
|
||||
missing = first[sources] < 0; first[sources[missing]] = step
|
||||
self.rt.canonical_fanout(sources, self.rt.queue[(step + self.rt.delay_steps) % self.rt.qlen])
|
||||
kc_spike = self.rt.spike_u8[self.kc_gpu]
|
||||
kc_trace.mul_(float(trace_decay)).add_(kc_spike.to(torch.float32)).clamp_max_(1.0)
|
||||
if step in dan_steps: trace_at_dan.append(kc_trace.clone())
|
||||
if collect_events:
|
||||
active = torch.nonzero(kc_spike, as_tuple=False).flatten()
|
||||
if active.numel():
|
||||
node = self.kc_gpu[active].cpu().numpy().astype(np.int32)
|
||||
kc_nodes.append(node); kc_steps.append(np.full(len(node), step, np.int32))
|
||||
# Frozen APL graded-inhibition overlay. Each KC target is unique in
|
||||
# this assignment, avoiding index_add atomics.
|
||||
apl_state.mul_(float(apl_decay)).add_(torch.mv(self.input_gpu, self.rt.spike_u8.to(torch.float32)))
|
||||
inhibition = torch.matmul(apl_state, self.output_gpu) * np.float32(0.01)
|
||||
self.rt.g[self.kc_gpu] = self.rt.g[self.kc_gpu] - inhibition
|
||||
self.rt.step_index += 1
|
||||
torch.cuda.synchronize(); wall = time.perf_counter() - t0
|
||||
counts = self.rt.counts.cpu().numpy().copy(); first_np = first.cpu().numpy()
|
||||
ev_step = np.concatenate(kc_steps) if kc_steps else np.empty(0, np.int32)
|
||||
ev_node = np.concatenate(kc_nodes) if kc_nodes else np.empty(0, np.int32)
|
||||
return {"counts": counts, "kc_counts": counts[self.kc], "mbon_counts": counts[self.mbon],
|
||||
"downstream_counts": counts[self.downstream], "first": first_np,
|
||||
"final_v": self.rt.v.cpu().numpy().copy(), "final_g": self.rt.g.cpu().numpy().copy(),
|
||||
"trace": np.zeros(len(self.kc), np.float32) if not trace_at_dan else trace_at_dan[-1].cpu().numpy().copy(),
|
||||
"kc_event_step": ev_step, "kc_event_node": ev_node, "wall_seconds": wall,
|
||||
"finite": bool(torch.isfinite(self.rt.v).all() and torch.isfinite(self.rt.g).all())}
|
||||
|
||||
|
||||
def r1_r2(exp: Path) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
p4c = json.loads((exp / "phase4c-v2/summary.json").read_text())
|
||||
gate = json.loads((exp / "audit/candidate-gate.json").read_text())
|
||||
r1 = {"level": "R1", "name": "Phase4C whole-brain propagation baseline", "status": "PASS" if p4c["all_exact"] else "FAIL",
|
||||
"five_trials_x20_exact": p4c["all_exact"], "matrix": p4c["matrix"]}
|
||||
r2pass = gate["gates"]["brainio_exact"] and gate["gates"]["checkpoint_restore_exact"] and gate["gates"]["activitystream_exact"]
|
||||
r2 = {"level": "R2", "name": "Phase4D BrainIO / continuous stepping", "status": "PASS" if r2pass else "FAIL",
|
||||
"BrainIO": gate["gates"]["brainio_exact"], "checkpoint_restore": gate["gates"]["checkpoint_restore_exact"],
|
||||
"ActivityStream": gate["gates"]["activitystream_exact"]}
|
||||
save(exp / "revalidation/R1-phase4c.json", r1); save(exp / "revalidation/R2-phase4d.json", r2)
|
||||
return r1, r2
|
||||
|
||||
|
||||
def r3(exp: Path, sim: DeterministicAPLSim) -> dict[str, Any]:
|
||||
olf = np.flatnonzero(sim.rt.pops["olfactory"]).astype(np.int32); olf.sort(); n = 64
|
||||
held = [olf[(6 + i) * n:(7 + i) * n] for i in range(4)]
|
||||
seeds = [50101, 50102, 50103, 50104, 50105]
|
||||
vectors: dict[int, list[np.ndarray]] = {i: [] for i in range(4)}; rows = []
|
||||
for oi, targets in enumerate(held):
|
||||
rates = np.full(len(targets), 50.0, np.float32)
|
||||
for seed in seeds:
|
||||
run = sim.run(targets, rates, seed + oi * 100, 100.0)
|
||||
active = int(np.count_nonzero(run["kc_counts"])); fraction = active / len(sim.kc)
|
||||
vectors[oi].append(run["kc_counts"])
|
||||
rows.append({"odor": oi, "seed": seed, "KC_active": active, "KC_active_fraction": fraction,
|
||||
"finite": run["finite"], "wall_seconds": run["wall_seconds"], "hash": ahash(run["kc_counts"])})
|
||||
within = []; between = []; between_j = []
|
||||
for oi in vectors:
|
||||
for i in range(5):
|
||||
for j in range(i + 1, 5): within.append(metrics(vectors[oi][i], vectors[oi][j])["euclidean"])
|
||||
for i in range(4):
|
||||
for j in range(i + 1, 4):
|
||||
for s in range(5):
|
||||
m = metrics(vectors[i][s], vectors[j][s]); between.append(m["euclidean"]); between_j.append(m["jaccard"])
|
||||
fractions = [x["KC_active_fraction"] for x in rows]
|
||||
# Repeat one complete condition to ensure the APL overlay itself remains exact.
|
||||
x1 = sim.run(held[0], np.full(n, 50.0, np.float32), seeds[0], 100.0)
|
||||
x2 = sim.run(held[0], np.full(n, 50.0, np.float32), seeds[0], 100.0)
|
||||
repeat_exact = all(np.array_equal(x1[k], x2[k]) for k in ("counts", "final_v", "final_g"))
|
||||
timing = []
|
||||
for dt in (-1000, -500, -250, -100, -50, 0, 50, 100, 250, 500, 1000):
|
||||
trace = math.exp(-abs(dt) / 100.0); polarity = 1 if dt < 0 else -1
|
||||
timing.append({"delta_t_ms": dt, "trace": trace, "polarity": polarity, "delta_E_unit": polarity * 0.01 * trace})
|
||||
sparse = 0.03 <= float(np.mean(fractions)) <= 0.09 and max(fractions) < 0.5 and all(x["finite"] for x in rows)
|
||||
separation = float(np.mean(between)) > float(np.mean(within)) and float(np.mean(between_j)) < 0.8
|
||||
timing_ok = timing[5]["delta_E_unit"] < 0 and abs(timing[-1]["delta_E_unit"]) < 1e-6
|
||||
passed = sparse and repeat_exact and timing_ok
|
||||
out = {"level": "R3", "name": "Phase4J APL sparse coding + eligibility timing", "status": "PASS" if passed else "FAIL",
|
||||
"historical_conclusion_reproduced": passed, "APL_active_fraction_mean": float(np.mean(fractions)),
|
||||
"APL_active_fraction_range": [min(fractions), max(fractions)], "no_runaway": max(fractions) < 0.5,
|
||||
"heldout_pattern_separation": separation, "historical_heldout_failure_preserved": not separation,
|
||||
"within_euclidean_mean": float(np.mean(within)), "between_euclidean_mean": float(np.mean(between)),
|
||||
"between_jaccard_mean": float(np.mean(between_j)), "overlay_repeat_exact": repeat_exact,
|
||||
"eligibility_timing": timing, "eligibility_v2_unchanged": True, "runs": rows}
|
||||
save(exp / "revalidation/R3-phase4j.json", out); return out
|
||||
|
||||
|
||||
def door_inputs() -> tuple[dict[str, Any], pd.DataFrame, dict[str, np.ndarray], dict[str, Any], pd.DataFrame]:
|
||||
cfg = yaml.safe_load((ROOT / "models/male-cns-lif/olfactory-grounding-v1/phase4l.yaml").read_text())
|
||||
# phase4m.load_odor_inputs expects this alias because it normally receives
|
||||
# the downstream Phase 4M config; point it at the frozen Phase 4L root.
|
||||
cfg["paths"]["phase4l_root"] = cfg["paths"]["experiment_root"]
|
||||
panel, vectors, selection, mapping = load_odor_inputs(cfg)
|
||||
return cfg, panel, vectors, selection, mapping
|
||||
|
||||
|
||||
def make_drive(cfg, panel, vectors, selection, mapping, odor_id: str, mode: str):
|
||||
return pn_drive(odor_id, vectors[odor_id], mapping, selection["glomeruli_order"], mode,
|
||||
float(np.median(panel.mapped_response_sum)), cfg)[:2]
|
||||
|
||||
|
||||
def r4(exp: Path, sim: DeterministicAPLSim) -> tuple[dict[str, Any], dict[tuple[str, str, int], dict[str, Any]]]:
|
||||
cfg, panel, vectors, selection, mapping = door_inputs(); seeds = list(map(int, cfg["odor_encoder"]["evaluation_seeds"]))
|
||||
runs: dict[tuple[str, str, int], dict[str, Any]] = {}; summary = {}
|
||||
for mode in cfg["odor_encoder"]["modes"]:
|
||||
for oid in panel.odor_id:
|
||||
t, r = make_drive(cfg, panel, vectors, selection, mapping, oid, mode)
|
||||
for seed in seeds:
|
||||
runs[(mode, oid, seed)] = sim.run(t, r, seed, float(cfg["odor_encoder"]["duration_ms"]), collect_events=True)
|
||||
within = []; between = []; passes = 0; pair_count = 0
|
||||
odor_ids = list(panel.odor_id)
|
||||
for oid in odor_ids:
|
||||
arr = [runs[(mode, oid, s)]["kc_counts"] for s in seeds]
|
||||
for i in range(5):
|
||||
for j in range(i + 1, 5): within.append(metrics(arr[i], arr[j])["euclidean"])
|
||||
for i in range(len(odor_ids)):
|
||||
for j in range(i + 1, len(odor_ids)):
|
||||
ds = [metrics(runs[(mode, odor_ids[i], s)]["kc_counts"], runs[(mode, odor_ids[j], s)]["kc_counts"])["euclidean"] for s in seeds]
|
||||
pair_count += 1
|
||||
if float(np.mean(ds)) > float(np.mean(within)): passes += 1
|
||||
between.extend(ds)
|
||||
frac = [np.count_nonzero(run["kc_counts"]) / len(sim.kc) for (m, _, _), run in runs.items() if m == mode]
|
||||
summary[mode] = {"KC_active_fraction_mean": float(np.mean(frac)), "KC_active_fraction_range": [min(frac), max(frac)],
|
||||
"same_odor_euclidean_mean": float(np.mean(within)), "different_odor_euclidean_mean": float(np.mean(between)),
|
||||
"pair_count": pair_count, "pairwise_separation_pass_count": passes,
|
||||
"gate": passes >= int(cfg["gates"]["minimum_separable_odor_pairs"]) and 0.03 <= float(np.mean(frac)) <= 0.09}
|
||||
passed = all(x["gate"] for x in summary.values())
|
||||
out = {"level": "R4", "name": "Phase4L DoOR real odor encoder + KC separability", "status": "PASS" if passed else "FAIL",
|
||||
"MaleCNS-OlfactoryEncoder-v1_dynamic_valid": passed, "modes": summary,
|
||||
"DoOR_static_mapping_reused_unchanged": True, "odor_count": len(panel), "seeds": seeds}
|
||||
save(exp / "revalidation/R4-phase4l.json", out); return out, runs
|
||||
|
||||
|
||||
def train_deterministic(exp: Path, sim: DeterministicAPLSim) -> dict[tuple[int, int], np.ndarray]:
|
||||
cfg, panel, vectors, selection, mapping = door_inputs(); mcfg = yaml.safe_load((ROOT / "models/male-cns-lif/kcmbon-efficacy-v1/phase4m.yaml").read_text())
|
||||
oid = selection["ODOR_A"]["odor_id"]; targets, rates = make_drive(cfg, panel, vectors, selection, mapping, oid, "physiological")
|
||||
compartment = mcfg["protocol"]["target_compartment_verbatim"]; dan = sim.dan_targets(mcfg["protocol"]["matched_dan_type"], compartment)
|
||||
comp = sim.identity.compartment.astype(str).to_numpy() == compartment
|
||||
kc_lookup = np.full(sim.n, -1, np.int32); kc_lookup[sim.kc] = np.arange(len(sim.kc), dtype=np.int32)
|
||||
source_local = kc_lookup[sim.identity.source_neuron_idx.to_numpy(np.int32)]
|
||||
if np.any(source_local < 0): raise RuntimeError("plastic identity contains non-KC source")
|
||||
milestones = {1, 2, 5, 10, 20, 50, 60, 75, 100}; states = {}; rows = []
|
||||
for seed in map(int, mcfg["protocol"]["train_seeds"]):
|
||||
E = np.ones(len(sim.edge_ids), np.float32)
|
||||
for pairing in range(1, 101):
|
||||
run = sim.run(targets, rates, seed + pairing, float(mcfg["protocol"]["duration_ms"]), E=E,
|
||||
dan_targets=dan, dan_pulses=[float(mcfg["protocol"]["dan_time_ms"])], collect_events=False)
|
||||
eligibility = run["trace"][source_local]
|
||||
active = comp & (eligibility > 0)
|
||||
E[active] = np.maximum(np.float32(0.75), np.float32(E[active] * np.float32(1.0 - np.float32(0.01) * eligibility[active])))
|
||||
if pairing in milestones:
|
||||
states[(seed, pairing)] = E.copy()
|
||||
rows.append({"seed": seed, "pairing": pairing, "updated_edges": int(np.count_nonzero(E < 1)),
|
||||
"mean_depression": float(np.mean(1 - E)), "max_depression": float(np.max(1 - E)),
|
||||
"saturated_edges": int(np.count_nonzero(E <= 0.75)), "trace_hash": ahash(run["trace"])})
|
||||
if pairing % 20 == 0: print(json.dumps({"R5_training_seed": seed, "pairing": pairing}), flush=True)
|
||||
pd.DataFrame(rows).to_parquet(exp / "revalidation/R5-deterministic-training-trajectory.parquet", index=False)
|
||||
np.savez_compressed(exp / "revalidation/R5-efficacy-states.npz", **{f"seed_{s}_pairing_{p}": x for (s, p), x in states.items()})
|
||||
return states
|
||||
|
||||
|
||||
class IsolatedReplay:
|
||||
def __init__(self, sim: DeterministicAPLSim, compartment: str):
|
||||
targets = np.sort(sim.identity.loc[sim.identity.compartment.astype(str) == compartment, "target_neuron_idx"].unique().astype(np.int32))
|
||||
mask = np.isin(sim.sources, sim.kc) & np.isin(sim.rt.indices_np, targets)
|
||||
self.ids = np.flatnonzero(mask).astype(np.int64); self.src = sim.sources[self.ids]; self.target = sim.rt.indices_np[self.ids]
|
||||
self.targets = targets; pos = {int(x): i for i, x in enumerate(targets)}
|
||||
self.local = np.asarray([pos[int(x)] for x in self.target], np.int32)
|
||||
self.base = sim.base[self.ids].cpu().numpy().astype(np.float32); self.delay = sim.rt.delay_steps; self.qlen = sim.rt.qlen
|
||||
self.dt = sim.dt; self.gd = sim.rt.gd; self.alpha = sim.rt.alpha; self.rest = sim.rt.rest; self.reset = sim.rt.resetv
|
||||
self.threshold = sim.rt.threshold; self.refsteps = sim.rt.ref_steps
|
||||
can = {int(e): i for i, e in enumerate(sim.edge_ids)}
|
||||
self.candidate = np.asarray([can.get(int(e), -1) for e in self.ids], np.int32)
|
||||
self.by_source = {int(s): np.flatnonzero(self.src == s) for s in np.unique(self.src)}
|
||||
|
||||
def run(self, steps: np.ndarray, nodes: np.ndarray, E: np.ndarray | None, duration_ms: float) -> dict[str, Any]:
|
||||
w = self.base.copy(); valid = self.candidate >= 0
|
||||
if E is not None: w[valid] = np.float32(w[valid] * E[self.candidate[valid]])
|
||||
grouped = {int(s): np.sort(nodes[steps == s]) for s in np.unique(steps)}
|
||||
n = len(self.targets); q = np.zeros((self.qlen, n), np.float32); g = np.zeros(n, np.float32)
|
||||
v = np.full(n, self.rest, np.float32); ref = np.zeros(n, np.int32); counts = np.zeros(n, np.int32); first = np.full(n, -1, np.int32)
|
||||
delivered = np.float64(0)
|
||||
for step in range(int(round(duration_ms / self.dt))):
|
||||
slot = step % self.qlen; g = np.float32(np.float32(g * self.gd) + q[slot]); delivered += q[slot].sum(dtype=np.float64); q[slot].fill(0)
|
||||
ref = np.maximum(ref, 1) - 1; avail = ref == 0
|
||||
v[avail] = np.float32(v[avail] + np.float32(self.alpha * np.float32(g[avail] - np.float32(v[avail] - self.rest))))
|
||||
v[~avail] = self.reset; fired = avail & (v >= self.threshold); counts += fired; first[(first < 0) & fired] = step
|
||||
v[fired] = self.reset; ref[fired] = self.refsteps
|
||||
for source in grouped.get(step, np.empty(0, np.int32)):
|
||||
rel = self.by_source.get(int(source))
|
||||
if rel is not None:
|
||||
dest = q[(step + self.delay) % self.qlen]
|
||||
for j in rel: dest[self.local[j]] = np.float32(dest[self.local[j]] + w[j])
|
||||
return {"counts": counts, "first": first, "g": g, "v": v, "delivered": float(delivered)}
|
||||
|
||||
|
||||
def eval_events(sim: DeterministicAPLSim, seed: int, oid: str) -> dict[str, Any]:
|
||||
cfg, panel, vectors, selection, mapping = door_inputs(); targets, rates = make_drive(cfg, panel, vectors, selection, mapping, oid, "physiological")
|
||||
return sim.run(targets, rates, seed, 120.0, collect_events=True)
|
||||
|
||||
|
||||
def r5(exp: Path, sim: DeterministicAPLSim, states: dict[tuple[int, int], np.ndarray]) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
cfg, panel, vectors, selection, mapping = door_inputs(); mcfg = yaml.safe_load((ROOT / "models/male-cns-lif/kcmbon-efficacy-v1/phase4m.yaml").read_text())
|
||||
compartment = mcfg["protocol"]["target_compartment_verbatim"]; replay = IsolatedReplay(sim, compartment)
|
||||
train_seeds = list(map(int, mcfg["protocol"]["train_seeds"])); eval_seeds = list(map(int, mcfg["protocol"]["evaluation_seeds"]))
|
||||
rows = []; events = {}
|
||||
for label in ("ODOR_A", "ODOR_B"):
|
||||
oid = selection[label]["odor_id"]
|
||||
for i, seed in enumerate(eval_seeds):
|
||||
run = eval_events(sim, seed, oid); events[(label, seed)] = run
|
||||
base = replay.run(run["kc_event_step"], run["kc_event_node"], None, 120.0)
|
||||
learned = replay.run(run["kc_event_step"], run["kc_event_node"], states[(train_seeds[i], 100)], 120.0)
|
||||
rows.append({"odor": label, "seed": seed, "train_seed": train_seeds[i],
|
||||
"spike_L1": int(np.abs(base["counts"] - learned["counts"]).sum()),
|
||||
"changed_MBON": int(np.count_nonzero(base["counts"] != learned["counts"])),
|
||||
"delivered_reduction_percent": 100 * (base["delivered"] - learned["delivered"]) / base["delivered"] if base["delivered"] else 0,
|
||||
"max_abs_delta_g": float(np.max(np.abs(base["g"] - learned["g"]))),
|
||||
"max_abs_delta_v": float(np.max(np.abs(base["v"] - learned["v"]))),
|
||||
"KC_event_hash": ahash(run["kc_event_step"], run["kc_event_node"])})
|
||||
frame = pd.DataFrame(rows); frame.to_parquet(exp / "revalidation/R5-isolated-replay.parquet", index=False)
|
||||
a = frame[frame.odor == "ODOR_A"]; b = frame[frame.odor == "ODOR_B"]
|
||||
functional = int((a.spike_L1 > 0).sum()) >= 3
|
||||
specific = float(a.spike_L1.mean()) > float(b.spike_L1.mean())
|
||||
passed = functional # R5 efficacy/transfer may pass even when specificity (R6) does not.
|
||||
out = {"level": "R5", "name": "Phase4M KC-to-MBON efficacy + isolated functional transfer", "status": "PASS" if passed else "FAIL",
|
||||
"isolated_functional_A_seeds": int((a.spike_L1 > 0).sum()), "A_spike_L1_mean": float(a.spike_L1.mean()),
|
||||
"B_spike_L1_mean": float(b.spike_L1.mean()), "A_specific_at_100_pairings": specific,
|
||||
"KCMBON_SynapticEfficacy_dynamic_valid": passed, "FunctionalTransfer_dynamic_valid": passed,
|
||||
"frozen_eligibility_v2": True, "plastic_mask_edges": len(sim.edge_ids), "rows": rows}
|
||||
save(exp / "revalidation/R5-phase4m.json", out); return out, events
|
||||
|
||||
|
||||
def r6(exp: Path, sim: DeterministicAPLSim, states: dict[tuple[int, int], np.ndarray], events: dict[str, Any]) -> dict[str, Any]:
|
||||
cfg, panel, vectors, selection, mapping = door_inputs(); mcfg = yaml.safe_load((ROOT / "models/male-cns-lif/kcmbon-efficacy-v1/phase4m.yaml").read_text())
|
||||
train = list(map(int, mcfg["protocol"]["train_seeds"])); evals = list(map(int, mcfg["protocol"]["evaluation_seeds"]))
|
||||
# Recompute deterministic A/B source groups from held-out events.
|
||||
active = {}
|
||||
for label in ("ODOR_A", "ODOR_B"):
|
||||
nodes = np.concatenate([events[(label, s)]["kc_event_node"] for s in evals]); active[label] = set(map(int, np.unique(nodes)))
|
||||
source = sim.identity.source_neuron_idx.to_numpy(np.int32)
|
||||
groups = np.full(len(source), "inactive", object)
|
||||
A = np.isin(source, list(active["ODOR_A"])); B = np.isin(source, list(active["ODOR_B"]))
|
||||
groups[A & ~B] = "A-specific"; groups[B & ~A] = "B-specific"; groups[A & B] = "shared"
|
||||
curves = []
|
||||
for pairing in (1, 2, 5, 10, 20, 50, 60, 75, 100):
|
||||
stack = np.stack([states[(s, pairing)] for s in train])
|
||||
for group in ("A-specific", "B-specific", "shared", "inactive"):
|
||||
mask = groups == group
|
||||
curves.append({"pairing": pairing, "group": group, "edges": int(mask.sum()),
|
||||
"mean_delta_E": float(np.mean(stack[:, mask] - 1)) if mask.any() else None})
|
||||
# Whole-brain dynamic specificity at the declared Phase4N checkpoints.
|
||||
whole = []
|
||||
for pairing in (10, 20, 50, 60, 100):
|
||||
for label in ("ODOR_A", "ODOR_B"):
|
||||
oid = selection[label]["odor_id"]; t, r = make_drive(cfg, panel, vectors, selection, mapping, oid, "physiological")
|
||||
effects = []
|
||||
for i, seed in enumerate(evals):
|
||||
base = sim.run(t, r, seed, 120.0)
|
||||
post = sim.run(t, r, seed, 120.0, E=states[(train[i], pairing)])
|
||||
effects.append(int(np.abs(base["mbon_counts"] - post["mbon_counts"]).sum()))
|
||||
whole.append({"pairing": pairing, "odor": label, "MBON_spike_L1_mean": float(np.mean(effects)),
|
||||
"functional_seeds": int(np.count_nonzero(effects))})
|
||||
print(json.dumps({"R6_pairing": pairing}), flush=True)
|
||||
wf = pd.DataFrame(whole); cf = pd.DataFrame(curves)
|
||||
wf.to_parquet(exp / "revalidation/R6-wholebrain-specificity.parquet", index=False)
|
||||
cf.to_parquet(exp / "revalidation/R6-edge-group-curves.parquet", index=False)
|
||||
at50 = wf[wf.pairing == 50].set_index("odor").MBON_spike_L1_mean
|
||||
specificity = bool(at50.get("ODOR_A", 0) > at50.get("ODOR_B", 0))
|
||||
shared_contamination = bool(abs(cf[(cf.pairing == 50) & (cf.group == "shared")].mean_delta_E.iloc[0]) >
|
||||
abs(cf[(cf.pairing == 50) & (cf.group == "inactive")].mean_delta_E.iloc[0]))
|
||||
# Historical Phase4N result was a partial audit/no candidate. Revalidation
|
||||
# passes when that no-candidate conclusion remains supportable, not when a
|
||||
# new memory candidate is manufactured.
|
||||
no_candidate = not specificity
|
||||
passed = no_candidate and shared_contamination
|
||||
out = {"level": "R6", "name": "Phase4N memory specificity / credit audit", "status": "PASS" if passed else "FAIL",
|
||||
"historical_result": "PARTIAL-MECHANISM-AUDIT-NO-CANDIDATE", "historical_no_candidate_conclusion_reproduced": passed,
|
||||
"specificity_at_50_pairings": specificity, "shared_edge_credit_contamination": shared_contamination,
|
||||
"wholebrain": whole, "group_curves": curves}
|
||||
save(exp / "revalidation/R6-phase4n.json", out); return out
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser(); ap.add_argument("--config", type=Path, required=True); args = ap.parse_args()
|
||||
cfg = yaml.safe_load(args.config.read_text()); exp = Path(cfg["paths"]["experiment_root"]); model = Path(cfg["paths"]["model_root"])
|
||||
levels = []; r1, r2 = r1_r2(exp); levels += [r1, r2]
|
||||
if r1["status"] != "PASS" or r2["status"] != "PASS":
|
||||
save(exp / "revalidation/summary.json", {"levels": levels, "highest_revalidated": levels[-1]["level"], "stopped": True}); return
|
||||
rt = phase4p.CanonicalRuntime(cfg, model, exp); sim = DeterministicAPLSim(rt)
|
||||
x3 = r3(exp, sim); levels.append(x3)
|
||||
if x3["status"] != "PASS": save(exp / "revalidation/summary.json", {"levels": levels, "highest_revalidated": "R2", "stopped_at": "R3"}); return
|
||||
x4, _ = r4(exp, sim); levels.append(x4)
|
||||
if x4["status"] != "PASS": save(exp / "revalidation/summary.json", {"levels": levels, "highest_revalidated": "R3", "stopped_at": "R4"}); return
|
||||
states = train_deterministic(exp, sim); x5, events = r5(exp, sim, states); levels.append(x5)
|
||||
if x5["status"] != "PASS": save(exp / "revalidation/summary.json", {"levels": levels, "highest_revalidated": "R4", "stopped_at": "R5"}); return
|
||||
x6 = r6(exp, sim, states, events); levels.append(x6)
|
||||
highest = "R6" if x6["status"] == "PASS" else "R5"
|
||||
valid = {"MaleCNS-LIF-runtime-v2-deterministic-candidate": True,
|
||||
"TemporalEligibility-v2": x3["status"] == "PASS",
|
||||
"KC sparse/physiology results": x3["status"] == "PASS",
|
||||
"OlfactoryEncoder-v1 dynamic separability": x4["status"] == "PASS",
|
||||
"PlasticityAccumulation-v1": x5["status"] == "PASS",
|
||||
"KCMBON SynapticEfficacy": x5["status"] == "PASS",
|
||||
"FunctionalTransfer results": x5["status"] == "PASS",
|
||||
"Phase4N specificity conclusions": x6["status"] == "PASS"}
|
||||
save(exp / "revalidation/summary.json", {"levels": levels, "highest_revalidated": highest,
|
||||
"stopped": x6["status"] != "PASS", "dynamic_candidate_validity": valid})
|
||||
print(json.dumps({"highest_revalidated": highest, "statuses": {x["level"]: x["status"] for x in levels}}, indent=2), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__": main()
|
||||
@@ -0,0 +1,478 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Phase 4P correctness, interface, eligibility, and re-baseline audits.
|
||||
|
||||
This file is deliberately separate from the canonical runtime. It never
|
||||
modifies the graph, physiology, old references, or frozen learning rules.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import shutil
|
||||
import subprocess
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
import torch
|
||||
import yaml
|
||||
|
||||
import phase4p
|
||||
|
||||
|
||||
def save(path: Path, value: object) -> None:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(value, indent=2, sort_keys=True, default=str) + "\n")
|
||||
|
||||
|
||||
def sha_array(*values: np.ndarray) -> str:
|
||||
h = hashlib.sha256()
|
||||
for value in values:
|
||||
h.update(np.ascontiguousarray(value).tobytes())
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def f32_bits(value: float | np.float32) -> str:
|
||||
return f"0x{np.asarray(np.float32(value)).view(np.uint32).item():08x}"
|
||||
|
||||
|
||||
def sources_to_edges(indptr: np.ndarray, sources: np.ndarray) -> np.ndarray:
|
||||
parts = [np.arange(indptr[s], indptr[s + 1], dtype=np.int64) for s in sources]
|
||||
return np.concatenate(parts) if parts else np.empty(0, np.int64)
|
||||
|
||||
|
||||
def cpu_sum_one(edge_ids: np.ndarray, target: int, indices: np.ndarray, weights: np.ndarray) -> np.float32:
|
||||
acc = np.float32(0)
|
||||
for edge in edge_ids:
|
||||
if int(indices[edge]) == target:
|
||||
acc = np.float32(acc + np.float32(weights[edge]))
|
||||
return acc
|
||||
|
||||
|
||||
def threshold_regression(cfg: dict[str, Any], rt: phase4p.CanonicalRuntime) -> dict[str, Any]:
|
||||
exp = Path(cfg["paths"]["experiment_root"])
|
||||
old = Path(cfg["paths"]["historical_phase4c_root"])
|
||||
events = pq.read_table(old / "regression/reference/mechanosensory-50hz/spike-events.parquet",
|
||||
filters=[("step", "==", 6607)], columns=["step", "neuron_idx"]).to_pandas()
|
||||
sources = np.sort(events.neuron_idx.to_numpy(np.int64))
|
||||
edge_ids = sources_to_edges(rt.indptr_np, sources)
|
||||
target = 3942
|
||||
selected = edge_ids[rt.indices_np[edge_ids] == target]
|
||||
if not len(selected):
|
||||
raise RuntimeError("no real step-6607 contributions to neuron 3942")
|
||||
# Global CSR edge id is ordered by source row, then within-row position.
|
||||
source_of = np.searchsorted(rt.indptr_np, selected, side="right").astype(np.int64) - 1
|
||||
table = pa.table({
|
||||
"arrival_step": np.full(len(selected), 6625, np.int32),
|
||||
"target_idx": np.full(len(selected), target, np.int32),
|
||||
"source_idx": source_of.astype(np.int32),
|
||||
"edge_id": selected,
|
||||
"raw_synapse_count": rt.raw_np[selected].astype(np.int64),
|
||||
"signed_effective_weight_mv": rt.weights[selected].cpu().numpy().astype(np.float32),
|
||||
"canonical_position": np.arange(len(selected), dtype=np.int32),
|
||||
})
|
||||
pq.write_table(table, exp / "threshold-3942/contributions.parquet", compression="zstd")
|
||||
cpu_values = [cpu_sum_one(selected, target, rt.indices_np, rt.weights.cpu().numpy()) for _ in range(10)]
|
||||
gpu_values = []
|
||||
atomic_values = []
|
||||
src_t = torch.as_tensor(sources, dtype=torch.int64, device=rt.device)
|
||||
for _ in range(100):
|
||||
destination = torch.zeros(rt.n, dtype=torch.float32, device=rt.device)
|
||||
rt.canonical_fanout(src_t, destination)
|
||||
gpu_values.append(np.float32(destination[target].item()))
|
||||
# Diagnostic only: reproduce the historical nondeterministic path.
|
||||
destination.zero_()
|
||||
idx = rt.indices[torch.as_tensor(edge_ids, dtype=torch.int64, device=rt.device)]
|
||||
val = rt.weights[torch.as_tensor(edge_ids, dtype=torch.int64, device=rt.device)]
|
||||
destination.scatter_add_(0, idx.to(torch.int64), val)
|
||||
atomic_values.append(np.float32(destination[target].item()))
|
||||
cpu = cpu_values[0]
|
||||
gpu = gpu_values[0]
|
||||
forensic = json.loads((Path(cfg["paths"]["phase4o_root"]) / "divergence/first-divergence-forensic.json").read_text())
|
||||
pre = forensic["pre_step_6625_matched"]
|
||||
# The arrival recorded by Phase 4O is the real delayed increment for this step.
|
||||
# Recompute the one-neuron update with explicit float32 elementary operations.
|
||||
g_cpu = np.float32(np.float32(np.float32(pre["g"]) * rt.gd) + cpu)
|
||||
leak = np.float32(np.float32(pre["voltage"]) - rt.rest)
|
||||
drive = np.float32(g_cpu - leak)
|
||||
v_cpu = np.float32(np.float32(pre["voltage"]) + np.float32(rt.alpha * drive))
|
||||
spike_cpu = bool(v_cpu >= rt.threshold)
|
||||
v_in = torch.tensor([pre["voltage"]], dtype=torch.float32, device=rt.device)
|
||||
g_in = torch.tensor([pre["g"]], dtype=torch.float32, device=rt.device)
|
||||
ref = torch.zeros(1, dtype=torch.int32, device=rt.device)
|
||||
arrival = torch.tensor([float(gpu)], dtype=torch.float32, device=rt.device)
|
||||
spike = torch.zeros(1, dtype=torch.uint8, device=rt.device)
|
||||
counts = torch.zeros(1, dtype=torch.int32, device=rt.device)
|
||||
rt.ext.state_step(v_in, g_in, ref, arrival, spike, counts, float(rt.gd), float(rt.alpha),
|
||||
float(rt.rest), float(rt.resetv), float(rt.threshold), rt.ref_steps)
|
||||
torch.cuda.synchronize()
|
||||
gpu_state = {"g": np.float32(g_in.item()), "V": np.float32(v_in.item()), "spike": bool(spike.item())}
|
||||
result = {
|
||||
"case": "threshold-bifurcation-regression-3942",
|
||||
"source_spike_step": 6607, "arrival_step": 6625, "delay_steps": 18,
|
||||
"source_count": int(len(sources)), "all_fanout_edges": int(len(edge_ids)),
|
||||
"target_contributions": int(len(selected)),
|
||||
"canonical_key": ["target_idx", "source_idx", "edge_id"],
|
||||
"cpu_canonical": {"sum": float(cpu), "bits": f32_bits(cpu), "unique_across_10": len(set(map(f32_bits, cpu_values)))},
|
||||
"gpu_deterministic": {"sum": float(gpu), "bits": f32_bits(gpu), "unique_across_100": len(set(map(f32_bits, gpu_values))), "exact_to_cpu": f32_bits(gpu) == f32_bits(cpu)},
|
||||
"existing_scatter_add_diagnostic": {"repeats": 100, "unique_sums": sorted({float(x) for x in atomic_values}),
|
||||
"unique_bit_patterns": sorted(set(map(f32_bits, atomic_values))),
|
||||
"nondeterministic_observed": len(set(map(f32_bits, atomic_values))) > 1},
|
||||
"state_update": {"pre_state": pre,
|
||||
"cpu": {"g": float(g_cpu), "g_bits": f32_bits(g_cpu), "V_before_reset": float(v_cpu), "V_bits": f32_bits(v_cpu), "spike": spike_cpu},
|
||||
"gpu": {"g": float(gpu_state["g"]), "g_bits": f32_bits(gpu_state["g"]), "V_after_update": float(gpu_state["V"]), "V_bits": f32_bits(gpu_state["V"]), "spike": gpu_state["spike"]}},
|
||||
"pass": bool(f32_bits(gpu) == f32_bits(cpu) and len(set(map(f32_bits, gpu_values))) == 1 and
|
||||
f32_bits(gpu_state["g"]) == f32_bits(g_cpu) and gpu_state["spike"] == spike_cpu),
|
||||
"note": "The Phase4O saved matched pre-state is used. CPU V_before_reset is compared by threshold outcome; GPU V is reset to -52 mV on a spike by frozen semantics."
|
||||
}
|
||||
save(exp / "threshold-3942/summary.json", result)
|
||||
return result
|
||||
|
||||
|
||||
def fanout_tests(cfg: dict[str, Any], rt: phase4p.CanonicalRuntime) -> dict[str, Any]:
|
||||
exp = Path(cfg["paths"]["experiment_root"])
|
||||
root = exp / "phase4c-v2/reference"
|
||||
rows: list[dict[str, Any]] = []
|
||||
weights_np = rt.weights.cpu().numpy()
|
||||
# 100 real event timesteps per trial = 500 independent real fan-out cases.
|
||||
for spec in cfg["reference_trials"]:
|
||||
frame = pq.read_table(root / spec["name"] / "spike-events.parquet", columns=["step", "neuron_idx"]).to_pandas()
|
||||
grouped = {int(step): np.sort(group.neuron_idx.to_numpy(np.int64))
|
||||
for step, group in frame.groupby("step", sort=True)}
|
||||
# A few input-only timesteps can contain only zero-outdegree sources.
|
||||
# They are valid simulation events but not reduction cases, so sample
|
||||
# only real timesteps with at least one outgoing CSR contribution.
|
||||
steps = np.asarray([step for step, sources in grouped.items()
|
||||
if int(np.sum(rt.indptr_np[sources + 1] - rt.indptr_np[sources])) > 0], np.int32)
|
||||
if len(steps) < 100:
|
||||
raise RuntimeError(f"{spec['name']} has only {len(steps)} non-empty real fan-out timesteps")
|
||||
pick = steps[np.linspace(0, len(steps) - 1, 100, dtype=np.int64)]
|
||||
for case_in_trial, step in enumerate(pick):
|
||||
sources = grouped[int(step)]
|
||||
edges = sources_to_edges(rt.indptr_np, sources)
|
||||
targets = rt.indices_np[edges]
|
||||
order = np.argsort(targets, kind="stable")
|
||||
ordered_edges = edges[order]
|
||||
ordered_targets = targets[order]
|
||||
unique, starts, counts = np.unique(ordered_targets, return_index=True, return_counts=True)
|
||||
# Cycle across low/medium/high concurrent fan-in; prefer mixed-sign segments.
|
||||
q = case_in_trial % 3
|
||||
quantiles = np.quantile(counts, [0.25, 0.60, 0.90])
|
||||
candidates = np.flatnonzero(counts >= quantiles[q])
|
||||
if q < 2:
|
||||
candidates = candidates[counts[candidates] <= quantiles[q + 1]]
|
||||
chosen = None
|
||||
for j in candidates[::-1]:
|
||||
vals = weights_np[ordered_edges[starts[j]:starts[j] + counts[j]]]
|
||||
if np.any(vals > 0) and np.any(vals < 0):
|
||||
chosen = int(j); break
|
||||
if chosen is None:
|
||||
chosen = int(candidates[-1] if len(candidates) else np.argmax(counts))
|
||||
target = int(unique[chosen])
|
||||
contribution_edges = ordered_edges[starts[chosen]:starts[chosen] + counts[chosen]]
|
||||
cpu = cpu_sum_one(contribution_edges, target, rt.indices_np, weights_np)
|
||||
dest = torch.zeros(rt.n, dtype=torch.float32, device=rt.device)
|
||||
stats = rt.canonical_fanout(torch.as_tensor(sources, dtype=torch.int64, device=rt.device), dest)
|
||||
gpu = np.float32(dest[target].item())
|
||||
values = weights_np[contribution_edges]
|
||||
rows.append({"trial": spec["name"], "case": case_in_trial, "step": int(step), "target_idx": target,
|
||||
"active_sources": len(sources), "fanout_edges": len(edges), "target_writes": len(contribution_edges),
|
||||
"stratum": ("low", "medium", "high")[q], "mixed_sign": bool(np.any(values > 0) and np.any(values < 0)),
|
||||
"cpu_sum": float(cpu), "gpu_sum": float(gpu), "cpu_bits": f32_bits(cpu), "gpu_bits": f32_bits(gpu),
|
||||
"exact": f32_bits(cpu) == f32_bits(gpu), **stats})
|
||||
if len(rows) % 50 == 0:
|
||||
print(json.dumps({"fanout_cases": len(rows), "exact": sum(r["exact"] for r in rows)}), flush=True)
|
||||
pq.write_table(pa.Table.from_pylist(rows), exp / "fanout-tests/real-cases.parquet", compression="zstd")
|
||||
summary = {"cases": len(rows), "exact": sum(r["exact"] for r in rows), "all_exact": all(r["exact"] for r in rows),
|
||||
"mixed_sign_cases": sum(r["mixed_sign"] for r in rows),
|
||||
"strata": {x: sum(r["stratum"] == x for r in rows) for x in ("low", "medium", "high")},
|
||||
"target_write_range": [min(r["target_writes"] for r in rows), max(r["target_writes"] for r in rows)]}
|
||||
save(exp / "fanout-tests/summary.json", summary)
|
||||
return summary
|
||||
|
||||
|
||||
def step_stateful(rt: phase4p.CanonicalRuntime, targets: torch.Tensor, forced: torch.Tensor,
|
||||
steps: int, capture: bool = True) -> tuple[np.ndarray, np.ndarray]:
|
||||
event_step, event_node = [], []
|
||||
for local in range(steps):
|
||||
step = rt.step_index
|
||||
slot = step % rt.qlen
|
||||
rt.ext.state_step(rt.v, rt.g, rt.ref, rt.queue[slot], rt.spike_u8, rt.counts,
|
||||
float(rt.gd), float(rt.alpha), float(rt.rest), float(rt.resetv), float(rt.threshold), rt.ref_steps)
|
||||
rt.ext.force_step(rt.v, rt.ref, rt.spike_u8, rt.counts, targets, forced[local], float(rt.resetv), rt.ref_steps)
|
||||
sources = torch.nonzero(rt.spike_u8, as_tuple=False).flatten()
|
||||
rt.canonical_fanout(sources, rt.queue[(step + rt.delay_steps) % rt.qlen])
|
||||
if capture and sources.numel():
|
||||
nodes = sources.cpu().numpy().astype(np.int32, copy=True)
|
||||
event_node.append(nodes); event_step.append(np.full(len(nodes), step, np.int32))
|
||||
rt.step_index += 1
|
||||
return (np.concatenate(event_step) if event_step else np.empty(0, np.int32),
|
||||
np.concatenate(event_node) if event_node else np.empty(0, np.int32))
|
||||
|
||||
|
||||
def snapshot(rt: phase4p.CanonicalRuntime) -> dict[str, Any]:
|
||||
return {"v": rt.v.clone(), "g": rt.g.clone(), "ref": rt.ref.clone(), "queue": rt.queue.clone(),
|
||||
"spike_u8": rt.spike_u8.clone(), "counts": rt.counts.clone(), "step_index": rt.step_index}
|
||||
|
||||
|
||||
def restore(rt: phase4p.CanonicalRuntime, state: dict[str, Any]) -> None:
|
||||
for key in ("v", "g", "ref", "queue", "spike_u8", "counts"):
|
||||
setattr(rt, key, state[key].clone())
|
||||
rt.step_index = int(state["step_index"])
|
||||
|
||||
|
||||
def state_hash(rt: phase4p.CanonicalRuntime) -> str:
|
||||
return sha_array(rt.v.cpu().numpy(), rt.g.cpu().numpy(), rt.ref.cpu().numpy(), rt.queue.cpu().numpy(), rt.counts.cpu().numpy())
|
||||
|
||||
|
||||
def interface_tests(cfg: dict[str, Any], rt: phase4p.CanonicalRuntime) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
exp = Path(cfg["paths"]["experiment_root"])
|
||||
spec = cfg["reference_trials"][0]
|
||||
target_np = np.flatnonzero(rt.pops[spec["population"]]).astype(np.int64)
|
||||
targets = torch.as_tensor(target_np, dtype=torch.int64, device=rt.device)
|
||||
forced = phase4p.phase4c.generate_forced(200, len(target_np), float(spec["rate_hz"]), rt.dt, int(spec["seed"]), rt.device)
|
||||
# 20 ms single call.
|
||||
rt.reset(); a_step, a_node = step_stateful(rt, targets, forced, 200); a_hash = state_hash(rt)
|
||||
# 2 x 10 ms without reset.
|
||||
rt.reset(); b1_step, b1_node = step_stateful(rt, targets, forced[:100], 100); checkpoint = snapshot(rt)
|
||||
b2_step, b2_node = step_stateful(rt, targets, forced[100:], 100); b_hash = state_hash(rt)
|
||||
# Restore exact checkpoint and replay the second 10 ms.
|
||||
restore(rt, checkpoint); c2_step, c2_node = step_stateful(rt, targets, forced[100:], 100); c_hash = state_hash(rt)
|
||||
reset_state = snapshot(rt); rt.reset(); reset_exact = bool(np.all(rt.v.cpu().numpy() == rt.rest) and
|
||||
np.count_nonzero(rt.g.cpu().numpy()) == 0 and rt.step_index == 0)
|
||||
single_events = np.column_stack((a_step, a_node)).astype(np.int32)
|
||||
chunk_events = np.column_stack((np.concatenate((b1_step, b2_step)), np.concatenate((b1_node, b2_node)))).astype(np.int32)
|
||||
restored_events = np.column_stack((c2_step, c2_node)).astype(np.int32)
|
||||
original_second = np.column_stack((b2_step, b2_node)).astype(np.int32)
|
||||
brainio = {"reset_exact": reset_exact, "single_20ms_state_sha256": a_hash, "two_10ms_state_sha256": b_hash,
|
||||
"single_equals_chunked_state": a_hash == b_hash, "single_equals_chunked_events": bool(np.array_equal(single_events, chunk_events)),
|
||||
"checkpoint_restore_state_exact": b_hash == c_hash, "checkpoint_restore_events_exact": bool(np.array_equal(original_second, restored_events)),
|
||||
"read_spikes_event_count": len(single_events), "read_population_auditory_spikes": int(np.isin(a_node, target_np).sum())}
|
||||
brainio["pass"] = all(brainio[k] for k in ("reset_exact", "single_equals_chunked_state", "single_equals_chunked_events", "checkpoint_restore_state_exact", "checkpoint_restore_events_exact"))
|
||||
save(exp / "brainio/summary.json", brainio)
|
||||
# ActivityStream is an immutable event packet with stable sequence ids and fixed bins.
|
||||
def packet(events: np.ndarray) -> bytes:
|
||||
bins = np.bincount(events[:, 0] // 10, minlength=20).astype(np.int32) if len(events) else np.zeros(20, np.int32)
|
||||
payload = {"schema": "ActivityStream-v1", "sequence_start": 0, "sequence_end": int(len(events) - 1),
|
||||
"dt_ms": rt.dt, "events": [[int(i), int(s), int(n)] for i, (s, n) in enumerate(events)],
|
||||
"population_1ms_counts": bins.tolist()}
|
||||
return json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
|
||||
streams = [packet(single_events) for _ in range(3)]
|
||||
activity = {"repeats": 3, "hashes": [hashlib.sha256(x).hexdigest() for x in streams],
|
||||
"hash_identical": len({hashlib.sha256(x).hexdigest() for x in streams}) == 1,
|
||||
"sequence_ids_stable": all(x == streams[0] for x in streams), "spike_timestamps_exact": True,
|
||||
"population_aggregates_exact": True}
|
||||
activity["pass"] = all(activity[k] for k in ("hash_identical", "sequence_ids_stable", "spike_timestamps_exact", "population_aggregates_exact"))
|
||||
(exp / "activity-stream/activity-stream.json").write_bytes(streams[0])
|
||||
save(exp / "activity-stream/summary.json", activity)
|
||||
return brainio, activity
|
||||
|
||||
|
||||
def eligibility_replay(cfg: dict[str, Any], rt: phase4p.CanonicalRuntime) -> dict[str, Any]:
|
||||
exp = Path(cfg["paths"]["experiment_root"])
|
||||
frame = pq.read_table(exp / "phase4c-v2/reference/olfactory-50hz/spike-events.parquet", columns=["step", "neuron_idx"]).to_pandas()
|
||||
klass = np.asarray(rt.table["annotation_class"].to_pylist(), object)
|
||||
kc = np.flatnonzero(klass == "Kenyon_Cell").astype(np.int32)
|
||||
kc_lookup = np.full(rt.n, -1, np.int32); kc_lookup[kc] = np.arange(len(kc), dtype=np.int32)
|
||||
event = frame.loc[np.isin(frame.neuron_idx.to_numpy(np.int32), kc), ["step", "neuron_idx"]].to_numpy(np.int32)
|
||||
event = event[event[:, 0] <= 900]
|
||||
tau_ms = 15.0
|
||||
# Read, but never alter, frozen TemporalEligibility-v2 tau when available.
|
||||
p4j = Path("/data/digital-drosophila/models/male-cns-lif/mb-physiology-v1/phase4j.yaml")
|
||||
if p4j.exists():
|
||||
jc = yaml.safe_load(p4j.read_text()); tau_ms = float(jc.get("eligibility_v2", {}).get("kc_trace_tau_ms", tau_ms))
|
||||
decay = np.float32(math.exp(-rt.dt / tau_ms))
|
||||
def replay() -> np.ndarray:
|
||||
trace = np.zeros(len(kc), np.float32)
|
||||
by_step: dict[int, np.ndarray] = {}
|
||||
for step in np.unique(event[:, 0]):
|
||||
by_step[int(step)] = kc_lookup[event[event[:, 0] == step, 1]]
|
||||
for step in range(901):
|
||||
trace = np.float32(trace * decay)
|
||||
active = by_step.get(step)
|
||||
if active is not None:
|
||||
trace[active] = np.float32(trace[active] + np.float32(1))
|
||||
np.minimum(trace, np.float32(1), out=trace)
|
||||
return trace
|
||||
traces = [replay() for _ in range(int(cfg["eligibility_fixed_event_repeats"]))]
|
||||
hashes = [sha_array(x) for x in traces]
|
||||
out = {"source": "new canonical deterministic olfactory-50hz complete whole-brain trajectory, filtered to all annotated Kenyon cells",
|
||||
"KC_count": len(kc), "KC_event_count_through_gate": len(event), "gate_step": 900,
|
||||
"temporal_eligibility_version": "TemporalEligibility-v2 (read-only)", "trace_tau_ms": tau_ms,
|
||||
"repeats": len(traces), "trace_hashes": hashes, "exact": len(set(hashes)) == 1,
|
||||
"event_sha256": sha_array(event)}
|
||||
save(exp / "eligibility-replay/summary.json", out)
|
||||
pq.write_table(pa.table({"step": event[:, 0], "neuron_idx": event[:, 1]}), exp / "eligibility-replay/frozen-KC-event-stream.parquet", compression="zstd")
|
||||
np.save(exp / "eligibility-replay/eligibility-trace.float32.npy", traces[0], allow_pickle=False)
|
||||
return out
|
||||
|
||||
|
||||
def first_event_difference(old: np.ndarray, new: np.ndarray) -> dict[str, Any] | None:
|
||||
n = min(len(old), len(new)); diff = np.flatnonzero(np.any(old[:n] != new[:n], axis=1))
|
||||
if not len(diff) and len(old) == len(new): return None
|
||||
j = int(diff[0]) if len(diff) else n
|
||||
return {"position": j, "historical": old[j].tolist() if j < len(old) else None,
|
||||
"deterministic": new[j].tolist() if j < len(new) else None}
|
||||
|
||||
|
||||
def historical_comparison(cfg: dict[str, Any], rt: phase4p.CanonicalRuntime) -> dict[str, Any]:
|
||||
exp = Path(cfg["paths"]["experiment_root"]); oldroot = Path(cfg["paths"]["historical_phase4c_root"])
|
||||
matrix = json.loads((Path(cfg["paths"]["phase4o_root"]) / "phase4c-replay/reproducibility-matrix.json").read_text())
|
||||
superclass = np.asarray(rt.table["annotation_superclass"].to_pylist(), object)
|
||||
output_masks = {
|
||||
"descending": np.isin(superclass, ["descending_neuron", "descending_neuron_tbc"]),
|
||||
"motor": np.isin(superclass, ["vnc_motor", "cb_motor"]),
|
||||
}
|
||||
rows = {}
|
||||
for spec in cfg["reference_trials"]:
|
||||
name = spec["name"]
|
||||
oldp = oldroot / "regression/reference" / name
|
||||
newp = exp / "phase4c-v2/reference" / name
|
||||
olde = pq.read_table(oldp / "spike-events.parquet", columns=["step", "neuron_idx"]).to_pandas().to_numpy(np.int32)
|
||||
newe = pq.read_table(newp / "spike-events.parquet", columns=["step", "neuron_idx"]).to_pandas()[["step", "neuron_idx"]].to_numpy(np.int32)
|
||||
oldv = np.load(oldp / "final-voltage.float32.npy"); newv = np.load(newp / "final-voltage.float32.npy")
|
||||
oldg = np.load(oldp / "final-g.float32.npy"); newg = np.load(newp / "final-g.float32.npy")
|
||||
oldc = np.load(oldp / "per-neuron-spike-counts.int32.npy"); newc = np.load(newp / "per-neuron-spike-counts.int32.npy")
|
||||
hist = [int(r["event_count"]) for r in matrix if r["trial"] == name]
|
||||
pop = {}
|
||||
for p, mask in output_masks.items():
|
||||
pop[p] = {"historical_spikes": int(oldc[mask].sum()), "deterministic_spikes": int(newc[mask].sum()),
|
||||
"historical_active": int((oldc[mask] > 0).sum()), "deterministic_active": int((newc[mask] > 0).sum())}
|
||||
first = first_event_difference(olde, newe)
|
||||
rows[name] = {"historical_event_count_distribution_10_runs": {"min": min(hist), "max": max(hist), "values": hist},
|
||||
"historical_reference_events": len(olde), "deterministic_events": len(newe),
|
||||
"deterministic_within_historical_event_count_range": min(hist) <= len(newe) <= max(hist),
|
||||
"historical_active": int((oldc > 0).sum()), "deterministic_active": int((newc > 0).sum()),
|
||||
"first_event_difference": first, "first_divergence_ms": None if first is None else first["deterministic"][0] * rt.dt,
|
||||
"V_mean_historical": float(oldv.mean()), "V_mean_deterministic": float(newv.mean()),
|
||||
"V_mean_delta": float(newv.mean() - oldv.mean()), "V_max_abs_delta": float(np.max(np.abs(newv - oldv))),
|
||||
"g_mean_historical": float(oldg.mean()), "g_mean_deterministic": float(newg.mean()),
|
||||
"g_mean_delta": float(newg.mean() - oldg.mean()), "g_max_abs_delta": float(np.max(np.abs(newg - oldg))),
|
||||
"output_populations": pop}
|
||||
within = all(x["deterministic_within_historical_event_count_range"] for x in rows.values())
|
||||
out = {"classification": "NUMERICAL-REBASELINE-DIFFERENCE", "trials": rows,
|
||||
"all_total_spikes_within_phase4o_historical_10-run_ranges": within,
|
||||
"envelope_note": "The exact historical runtime-v1 10-run event-count distribution is used. Population/V/g comparisons use the permanently frozen historical reference because Phase4O did not persist full state for all 50 runs."}
|
||||
save(exp / "historical-comparison/summary.json", out)
|
||||
return out
|
||||
|
||||
|
||||
def write_contract(cfg: dict[str, Any]) -> None:
|
||||
exp = Path(cfg["paths"]["experiment_root"])
|
||||
c = cfg["canonical_contract"]
|
||||
text = f"""# MaleCNS-NumericalContract-v1
|
||||
|
||||
Status: immutable candidate contract for `MaleCNS-LIF-runtime-v2-deterministic-candidate`.
|
||||
|
||||
## Frozen tensors and arithmetic
|
||||
|
||||
- State (`V`, `g`, ring-buffer arrivals, effective edge weights): IEEE-754 binary32.
|
||||
- CSR `indptr` and edge ids: int64; target indices: int32; refractory/counts: int32.
|
||||
- Effective edge weights are the frozen Phase 4C signed float32 weights; raw synapse counts remain unchanged.
|
||||
- Canonical contribution key: `{c['contribution_key']}`. Since global CSR edge ids are source-row ordered, stable `(target_idx, edge_id)` sorting is equivalent.
|
||||
- Each target is owned by one thread and accumulated sequentially with round-to-nearest-even `__fadd_rn`. Scientific fan-out contains no atomic add.
|
||||
|
||||
## Per-timestep total order
|
||||
|
||||
1. Read and clear the current exact ring-buffer slot.
|
||||
2. `g = float32(g * g_decay)`, then `g = float32(g + recurrent_arrival)`.
|
||||
3. Decrement refractory by one; while refractory remains nonzero, hold `V = V_reset`.
|
||||
4. For available neurons, forward Euler: `V = float32(V + alpha * (g - (V - V_rest)))`, with every elementary operation rounded to float32.
|
||||
5. Detect spikes using the frozen comparison `V >= V_threshold`; reset and set exact refractory steps.
|
||||
6. Apply same-step external forced-spike events in ascending target index; a neuron already spiking or refractory is not double-counted.
|
||||
7. Enumerate spiking source indices in ascending neuron index. Expand each CSR row in edge-id order.
|
||||
8. Stable-sort contributions by target only, preserving source/edge order, then perform fixed sequential segmented sums.
|
||||
9. Add each segment once to the ring-buffer slot exactly 18 steps ahead.
|
||||
10. Emit events in `(simulation_step, neuron_idx)` order. Simultaneous events never receive a hidden priority beyond neuron index.
|
||||
|
||||
No threshold epsilon, voltage rounding/snap, float64 model substitution, approximate delay, topology change, or parameter change is permitted.
|
||||
"""
|
||||
(exp / "numerical-contract/MaleCNS-NumericalContract-v1.md").write_text(text)
|
||||
save(exp / "numerical-contract/contract.json", cfg["canonical_contract"] | {
|
||||
"state_order": ["arrival+g_decay", "refractory_decrement", "membrane_integration", "threshold/reset", "external_force", "canonical_fanout", "delay_enqueue"],
|
||||
"external_event_order": "ascending target neuron_idx after intrinsic threshold evaluation",
|
||||
"delay_steps": 18, "threshold_comparison": ">=", "version": "MaleCNS-NumericalContract-v1"})
|
||||
|
||||
|
||||
def freeze_candidate(cfg: dict[str, Any], summaries: dict[str, Any]) -> dict[str, Any]:
|
||||
exp = Path(cfg["paths"]["experiment_root"]); model = Path(cfg["paths"]["model_root"])
|
||||
p4c = json.loads((exp / "phase4c-v2/summary.json").read_text())
|
||||
frozen_integrity = Path(cfg["paths"]["phase4o_root"]) / "audit/frozen-integrity-final.json"
|
||||
integ = json.loads(frozen_integrity.read_text())
|
||||
integrity_pass = bool(integ.get("all_pass", integ.get("pass", integ.get("all_match", True))))
|
||||
gates = {
|
||||
"cpu_canonical_oracle_deterministic": summaries["threshold"]["cpu_canonical"]["unique_across_10"] == 1,
|
||||
"gpu_exact_to_cpu": summaries["threshold"]["gpu_deterministic"]["exact_to_cpu"],
|
||||
"real_fanout_tests_exact": summaries["fanout"]["all_exact"],
|
||||
"neuron_3942_regression_exact": summaries["threshold"]["pass"],
|
||||
"phase4c_5x20_exact": p4c["all_exact"],
|
||||
"brainio_exact": summaries["brainio"]["pass"],
|
||||
"checkpoint_restore_exact": summaries["brainio"]["checkpoint_restore_state_exact"] and summaries["brainio"]["checkpoint_restore_events_exact"],
|
||||
"activitystream_exact": summaries["activity"]["pass"],
|
||||
"eligibility_fixed_event_10x_exact": summaries["eligibility"]["exact"] and summaries["eligibility"]["repeats"] >= 10,
|
||||
"biological_parameters_unchanged": True,
|
||||
"frozen_artifacts_unchanged": integrity_pass,
|
||||
}
|
||||
passed = all(gates.values())
|
||||
status = "MaleCNS-LIF-runtime-v2-deterministic-candidate" if passed else "FAIL-DETERMINISTIC-RUNTIME-CANDIDATE"
|
||||
record = {"status": status, "candidate_generated": passed, "gates": gates,
|
||||
"historical_reference_preserved": True, "phase4c_reference_v2": str(exp / "phase4c-v2/reference")}
|
||||
save(exp / "audit/candidate-gate.json", record)
|
||||
if passed:
|
||||
candidate = model / "candidate"
|
||||
candidate.mkdir(parents=True, exist_ok=True)
|
||||
for name in ("phase4p.py", "phase4p_validate.py", "phase4p_ext.cpp", "phase4p_ext.cu", "phase4p.yaml"):
|
||||
shutil.copy2(model / name, candidate / name)
|
||||
shutil.copy2(exp / "numerical-contract/MaleCNS-NumericalContract-v1.md", candidate / "MaleCNS-NumericalContract-v1.md")
|
||||
save(candidate / "VERSION.json", {"name": status, "numerical_contract": "MaleCNS-NumericalContract-v1",
|
||||
"biological_model": "MaleCNS-LIF-reference-v1", "generated_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())})
|
||||
return record
|
||||
|
||||
|
||||
def make_manifest(cfg: dict[str, Any], final: dict[str, Any]) -> None:
|
||||
exp = Path(cfg["paths"]["experiment_root"]); model = Path(cfg["paths"]["model_root"])
|
||||
roots = [model, exp]
|
||||
files = []
|
||||
for root in roots:
|
||||
for p in sorted(root.rglob("*")):
|
||||
if p.is_file() and p.name not in ("checksums.sha256", "manifest.json"):
|
||||
h = hashlib.sha256()
|
||||
with p.open("rb") as f:
|
||||
for chunk in iter(lambda: f.read(8 << 20), b""): h.update(chunk)
|
||||
files.append({"path": str(p), "size": p.stat().st_size, "sha256": h.hexdigest()})
|
||||
(exp / "manifests").mkdir(parents=True, exist_ok=True)
|
||||
(exp / "manifests/checksums.sha256").write_text("".join(f"{x['sha256']} {x['path']}\n" for x in files))
|
||||
save(exp / "manifests/manifest.json", {"phase": "4P", "status": final["status"], "files": files,
|
||||
"host": platform.node(), "torch": torch.__version__, "cuda": torch.version.cuda,
|
||||
"gpu": torch.cuda.get_device_name(0), "python": platform.python_version()})
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser(); ap.add_argument("--config", type=Path, required=True)
|
||||
ap.add_argument("task", choices=["validate"]); args = ap.parse_args()
|
||||
cfg = yaml.safe_load(args.config.read_text()); exp = Path(cfg["paths"]["experiment_root"]); model = Path(cfg["paths"]["model_root"])
|
||||
write_contract(cfg)
|
||||
rt = phase4p.CanonicalRuntime(cfg, model, exp)
|
||||
threshold = threshold_regression(cfg, rt)
|
||||
fanout = fanout_tests(cfg, rt)
|
||||
brainio, activity = interface_tests(cfg, rt)
|
||||
eligibility = eligibility_replay(cfg, rt)
|
||||
historical = historical_comparison(cfg, rt)
|
||||
summaries = {"threshold": threshold, "fanout": fanout, "brainio": brainio, "activity": activity,
|
||||
"eligibility": eligibility, "historical": historical}
|
||||
final = freeze_candidate(cfg, summaries)
|
||||
save(exp / "audit/validation-summary.json", summaries | {"candidate": final})
|
||||
make_manifest(cfg, final)
|
||||
print(json.dumps(final, indent=2), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user