diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 6ba8f4f..4ca4be7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -52,6 +52,22 @@ jobs: working-directory: packages/js - run: pytest tests/test_experiment.py - run: git diff --exit-code -- site/js/runtime.js site/js/dodge-runtime.js site/toy-bundle.json site/license.txt + synaptic-reference: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + - run: python -m pip install -e . 'Brian2==2.9.0' 'numpy==1.26.4' + - name: Independent synaptic dynamics oracle + run: python scripts/validate_synaptic_reference.py --output synaptic-reference.json + - uses: actions/upload-artifact@v4 + if: always() + with: + name: synaptic-reference + path: synaptic-reference.json godot: runs-on: ubuntu-latest timeout-minutes: 10 diff --git a/README.md b/README.md index 7fbbbe5..f0fe01a 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,15 @@ # flybrain-sdk +> Development branch: [experimental CUDA backend](docs/cuda.md). [Full FlyWire benchmark](docs/validation/flywire-full-a16.md): 139,255 neurons, all 16.85M source rows, CPU/CUDA parity and 10 seconds of continuous simulation on A16-8Q. CUDA median 11.55 seconds per simulated second (not real time). [Train an external reflex readout](examples/REFLEX_TRAINING.md). Released v0.4.0a4 is CPU-only. + + **No CUDA required.** A Python SDK for connecting small connectome simulations to games and experiments, starting with a working NumPy CPU backend. -**Full-brain CUDA benchmark (experimental branch):** [139,255 FlyWire neurons on a real NVIDIA GPU](https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html). Complete published proofread graph, CPU/CUDA checks, reproducible timing and evidence. Released v0.4.0a4 remains CPU-only. +**Full-brain sensory result:** [The GPU odor probe activates the input cells but exposes a propagation limit in the benchmark weight preset](docs/validation/flywire-odor-a16.md). Numerical parity does not establish biological behavior. [Published report](https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html#olfaction). + +**Full-brain voxel recording:** [Watch the complete GPU brain drive a ground body](https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html). The fixed readout curves away from food; the input-silenced control stays still. [Evidence, assumptions and reproduction](docs/validation/flywire-voxel-a16.md). +**Synaptic dynamics verified on GPU:** [The separate mV engine matches Brian2 and passes actual CUDA tests](docs/synaptic-dynamics.md). [A complete FlyWire sensory probe now reaches downstream populations](docs/validation/flywire-synaptic-odor-a16.md); the candidate DNa02 readout does not simply encode stimulus side. Navigation remains unproven. **Live game:** [Open the browser sandbox](https://freeman-1984-coder.github.io/flybrain-sdk/live.html) — add obstacles, tune mappings, inspect neural control, save and verify recordings. [Adapter guide](docs/live-sandbox.md). @@ -23,7 +29,7 @@ brain.step(100) print(brain.action().to_dict()) ``` -**0.4 alpha:** the bundled offline demo is a hand-authored 12-neuron circuit. A separate 3.8 MB MaleCNS model now runs 313 real source neurons and 20,607 anatomical edges with explicitly assumed LIF parameters. [Model card and reproducible recipe](models/male-cns-escape-v1/README.md). CUDA and WASM are reserved interfaces, not implemented runtimes. No GPU, credentials, or network access are needed to run the toy demo after installation. +**0.4 alpha:** the bundled offline demo is a hand-authored 12-neuron circuit. A separate 3.8 MB MaleCNS model now runs 313 real source neurons and 20,607 anatomical edges with explicitly assumed LIF parameters. [Model card and reproducible recipe](models/male-cns-escape-v1/README.md). This development branch adds an experimental CUDA runtime; WASM remains unimplemented. No GPU, credentials, or network access are needed to run the toy demo after installation. ## Make your own demo @@ -105,7 +111,7 @@ Game movement and rendering stay in your application; see [game_loop.py](example | --- | --- | | `cpu` | Working reference implementation, NumPy edge lists, float64 | | `wasm` | Reserved name; raises `BackendUnavailableError` | -| `cuda` | Reserved name; raises `BackendUnavailableError`; no CUDA dependencies | +| `cuda` | Experimental optional CuPy runtime; 12/12 A16 hardware cases passed; unavailable without NVIDIA/CuPy | See [dynamics and backend contract](docs/architecture.md) for equations, spike timing and limitations. diff --git a/docs/api.md b/docs/api.md index f9a8864..d235091 100644 --- a/docs/api.md +++ b/docs/api.md @@ -138,7 +138,9 @@ Use `flybrain.experiment.replay_experiment(recording, download=False)` to verify accepts an observation with `offer(seq, observation)`, then commits the actual engine control with `acknowledge(seq, applied)`. Only one action may be pending. Identical pending offers reuse their result without reintegrating the brain. -`snapshot()` is allowed at acknowledged boundaries; `from_snapshot()` restores -the built-in linear encoder and rate readout. The engine must checkpoint its own +`snapshot()` is allowed at acknowledged boundaries; `from_snapshot(data, backend=None)` restores +the built-in linear encoder and rate readout. In this CUDA development branch, +pass `backend="cpu"` or `backend="cuda"` to select the restore device explicitly. +The engine must checkpoint its own world at the matching sequence. See the [Godot example](../examples/godot/README.md) for transport, numeric validation, timing limits and recovery rules. diff --git a/docs/cuda.md b/docs/cuda.md new file mode 100644 index 0000000..3cb6cd0 --- /dev/null +++ b/docs/cuda.md @@ -0,0 +1,95 @@ +# Experimental CUDA backend — A16 hardware validation passed + +This development branch implements a CuPy/CUDA reference backend. **All 12 required hardware cases passed on a Vultr NVIDIA A16-2Q on 2026-09-12 UTC.** The published v0.4.0a4 remains CPU-only. This is experimental compatibility support, not a speedup claim: the 313-cell model took 2.64 seconds per simulated second on this GPU, versus 0.108 seconds on the same host CPU. See [the measured report](validation/a16-20260912.md). + +## Optional installation + +The basic `pip install -e .` path still installs only NumPy. Importing `flybrain` or creating a CPU brain does not import CuPy or initialize CUDA. On an NVIDIA host with a suitable driver, use a separate Python 3.12 environment: + +```sh +pip install -e '.[dev]' 'cupy-cuda12x[ctk]==14.2.0' +python scripts/validate_cuda.py --output cuda-validation.json +``` + +CuPy 14 requires Python 3.10+; the CPU SDK still supports Python 3.9. Match the CuPy wheel to the host driver/toolkit and install only one CuPy distribution. The `ctk` extra installs compatible CUDA components; it cannot supply the host NVIDIA driver. See [CuPy installation](https://docs.cupy.dev/en/stable/install.html). + +After hardware validation: + +```python +from flybrain import FlyBrain, available_backends + +print(available_backends()) # dependency/device probe, not a conformance certificate +brain = FlyBrain.load("male-cns-escape-v1", backend="cuda", download=True) +brain.stimulate("looming_left", duration_ms=100) +brain.advance(duration_ms=100) +print(brain.action().to_dict()) +brain.save("gpu.checkpoint.json") +cpu = FlyBrain.restore("gpu.checkpoint.json", backend="cpu") +``` + +Unavailable dependencies, devices or kernel compilation raise `BackendUnavailableError`. There is no silent CPU fallback. Python-only `backend="cuda"` does not add CUDA to the browser runtime; WASM is still unimplemented. + +## What the implementation does + +- One CUDA thread owns one postsynaptic row. Stable ordering keeps original parallel-edge accumulation order. It uses float64 and disables fused multiply-add to preserve separate reference operations; the reported hardware suite measures conformance. +- The graph, voltage, spikes, refractory counters, filtered rates and silencing mask reside on one GPU. Each instance owns a stream so external CuPy stream contexts do not reorder its operations. +- One kernel advances one tick using previous-tick spikes. It writes separate output buffers. A scalar error flag is checked before committing the tick, preserving state on nonfinite voltage errors. +- Selected observations transfer only requested cells/fields. Full observations and checkpoints explicitly transfer full state. Restore uses the existing CPU validation rules, with temporary CPU graph/state allocation. +- Current vectors are currently prepared on the host and uploaded every tick. Each tick also synchronizes an error flag. This first implementation prioritizes compatibility; it is **not** the future device-resident batched schedule and may be slower than CPU on small models. + +The [CuPy RawKernel API](https://docs.cupy.dev/en/stable/reference/generated/cupy.RawKernel.html) provides runtime compilation. Larger graphs, batching, mixed precision and trainable dynamics need separate work and evidence. + +## The hardware gate + +`validate_cuda.py` requires an actual CuPy-visible NVIDIA device, runs the hardware suite with `FLYBRAIN_REQUIRE_CUDA=1`, rejects skipped tests, then times toy and real circuits on CPU and GPU. Without a device it writes a failed report and exits nonzero. Ordinary CPU CI skips those hardware tests explicitly. + +The suite has 12 required cases. It compares every cell over 180 ticks for toy, real and signed/parallel-edge synthetic graphs at two timesteps (1,080 comparison ticks), plus cross-device checkpoints with pending stimuli, custom readout, interventions, selected observations, invalid-state handling and stream isolation. Spikes/clock must match exactly; voltage/rates use absolute tolerance `1e-10`. A report includes environment versions, device, source hashes, passed test counts, configuration, warmup and per-run timings. This covers the reported hardware and cases only. + +Acceptance before merging/advertising CUDA: + +1. No skipped or failed hardware tests on at least one NVIDIA GPU. +2. Checkpoint continuation and direct/named current semantics match CPU. +3. Honest CPU/GPU wall times with compilation/loading separated and synchronization included. +4. Store the report with the exact tested source identity and inspect any failures. +5. Re-run normal CPU packaging/CI; missing CUDA never breaks CPU installation. + +## Optional rented T4 run + +A prepared runner is available at `scripts/validate_cuda_modal.py`. Its configuration is one T4, one CPU core, 4 GiB RAM, at most one container, a 600-second function timeout, no retries, and scale-to-zero. There is no persistent volume, web endpoint or schedule. Only source code, the hardware test, validator and the small model are uploaded; no credentials or local home directory are included. + +After the account and spending authorization are resolved, a maintainer can run: + +```sh +# Separate Python 3.10+ environment on the controlling computer. +pip install 'modal==1.5.5' +modal setup +modal run scripts/validate_cuda_modal.py --output cuda-validation.json +``` + +This command starts remote compute and can incur charges. As checked on 2026-09-10, [Modal's published pricing](https://modal.com/pricing) lists T4 at $0.000164/second, CPU at $0.0000131/core/second, and RAM at $0.00000222/GiB/second. Ten minutes at the configured allocations is about $0.112 in execution charges, before build/startup/other charges. This estimate is not an enforced account-wide dollar cap. Review current pricing and available account credits before running; no credits are assumed. Use a $1 trial budget and verify the ephemeral app stops in the provider console when the run finishes or is canceled. + +The runner definition was checked against the local Modal SDK without invoking any remote function. It remains untested remotely. [Modal GPU documentation](https://modal.com/docs/guide/gpu) describes device selection. Recheck container termination and actual billed usage before considering the rental step finished. + +## Game integration (development branch only) + +The draft now includes the v0.4.0a4 Godot and project-generation changes. +`make_demo(..., backend="cuda")` selects CUDA for Python sessions. +`ExternalController.from_snapshot(data, backend="cpu")` explicitly restores a +GPU checkpoint onto CPU, or vice versa with `backend="cuda"`. The new hardware +cases compare both session and acknowledged external feedback against CPU for toy +and real circuits, including CUDA → CPU → CUDA continuation. Both cases passed on the reported A16 device. + +On a configured NVIDIA host, the experimental Godot bridge accepts +`python examples/godot/bridge.py --backend cuda`. Its chosen backend applies to +fresh sessions and imported checkpoints. The default remains CPU; a saved backend +label cannot change the bridge's explicitly selected device. Actual Godot with +CUDA has not been verified. + +## Complete FlyWire brain benchmark + +The [full FlyWire v783 report](validation/flywire-full-a16.md) validates all 139,255 +proofread neurons and 16,847,997 source rows on A16-8Q. CPU/CUDA parity, backend +checkpoint replay, 10 seconds of continuous simulated time, and 171 regression +tests passed. CUDA median 11.55 seconds per simulated second versus CPU 110.96 +seconds on that host. This is faster than the SDK CPU reference, but not real time. +[Reproduce the full-graph test](fullbrain-validation.md). diff --git a/docs/fullbrain-validation.md b/docs/fullbrain-validation.md new file mode 100644 index 0000000..634b275 --- /dev/null +++ b/docs/fullbrain-validation.md @@ -0,0 +1,71 @@ +# Full FlyWire CUDA validation + +**Functional limitation:** the [full-brain odor probe](validation/flywire-odor-a16.md) found that this benchmark’s normalized weights cannot drive an initially resting, externally unstimulated neuron to threshold. Use these results for numerical conformance, not sensory-to-motor behavior. + +This opt-in runner loads all **139,255 proofread FlyWire v783 neurons** and every +row in the official proofread connections table. It uses the SDK's actual +`Connectome`, `CPUBackend`, and `CUDABackend`, without replacing their execution +paths. No neuron selection, minimum-count pruning, or synthetic graph is used. + +The pinned source contains **16,847,997 neuron-pair/neuropil rows**, representing +**54,492,922 synaptic contacts**. Multiple rows may connect the same pair in +different neuropils. All endpoints match the official 139,255-neuron list. + +## Reproduce on an NVIDIA host + +Use the experimental CUDA branch. A 32 GB system-memory host gives headroom for +this initial Python object-based importer. GPU memory requirements are measured +by the runner; the raw file size is not the required GPU memory. + +```sh +python -m venv .venv +. .venv/bin/activate +pip install -e '.[datasets]' 'cupy-cuda12x[ctk]==14.2.0' pytest +mkdir -p data results +curl -fL 'https://zenodo.org/records/10676866/files/proofread_root_ids_783.npy?download=1' -o data/proofread_root_ids_783.npy +curl -fL 'https://zenodo.org/records/10676866/files/proofread_connections_783.feather?download=1' -o data/proofread_connections_783.feather +python scripts/validate_fullbrain.py --data-dir data --output results/fullbrain.json +``` + +Download source: [FlyWire Consortium, v783.0, Zenodo](https://zenodo.org/records/10676866). +The runner checks both published MD5 values, then records SHA-256 values and byte +sizes. A missing or different file fails the test. The raw data are not bundled +in the package or redistributed by this repository. + +The neuron list stores IDs as `uint64`, while the connection table uses `int64`. +Normalize both to the same integer type before lookup: mixed signed/unsigned +NumPy searches can promote large IDs to `float64` and lose their identity. The +runner rejects floating-point IDs and includes regression cases above 2^53. + +## What is checked + +- All official neuron IDs are unique; all source edge endpoints resolve. +- Every published proofread connection row is retained, including weak connections, + self-connections, and parallel rows for different neuropils. +- The complete network rests without input, then generates spikes under seeded input. +- CPU and CUDA states across every neuron are compared every 25 ticks over 200 + active ticks: spikes and refractory counters exactly, voltage/rates within 1e-10. +- A JSON checkpoint reproduces 30 subsequent CUDA ticks exactly. +- Three 1,000 ms runs per backend follow a 100-tick warmup. Compilation/loading are + reported separately; timed steps include host input upload and error synchronization. +- 10,000 additional CUDA ticks check finite, bounded states and record population + activity, device allocations, and peak host memory. These observation-heavy + timings are separate from the throughput benchmark. + +## What this means biologically + +The whole published proofread **brain** graph is used; this is not the whole +nervous system or the raw EM volume, and not every unproofread segment is a neuron +in this graph. Each source row aggregates anatomical contacts between a neuron +pair in a neuropil. A connection row is not one anatomical synapse. + +Dynamics are dimensionless, simplified LIF assumptions. Contact counts are scaled +to incoming absolute weight sum 2.5. Each neuron's sign uses its outgoing +contact-weighted transmitter scores: GABA/glutamate negative, other labels positive. +Unknown transmitter evidence uses positive sign and is counted. This deliberately +simple policy does not model receptor-specific effects or neuromodulation. + +The benchmark uses artificial currents, not reconstructed vision, behavior, +physiological calibration, or biological learning. Passing establishes full-graph +software execution and numerical checks for the reported configuration. It does +not establish that a living fly's complete brain function has been reproduced. diff --git a/docs/olfactory-experiment.md b/docs/olfactory-experiment.md new file mode 100644 index 0000000..a5b3f98 --- /dev/null +++ b/docs/olfactory-experiment.md @@ -0,0 +1,107 @@ +# Full-brain food-odor experiment + +**GPU probe completed; navigation not demonstrated.** +See the [negative result and parameter diagnosis](validation/flywire-odor-a16.md). The full FlyWire graph has +passed the [CUDA numerical benchmark](fullbrain-validation.md). This experiment +adds anatomically identified sensory input. It does not establish that the fly +recognizes a banana, seeks food, or flies. No CUDA is needed to build the mapping, +use the local odor adapter or run unit tests. The full-brain probe requires CUDA +and fails if unavailable; it cannot silently run a smaller circuit or use CPU. + +## What “banana odor” means here + +A banana is a mixture of volatile chemicals, varying with ripeness. There is no +single banana neuron. [Banana odor experiments](https://pubmed.ncbi.nlm.nih.gov/37357260/) +report ripeness and concentration dependent attraction. Our first stimulus is +**one ethyl-acetate-responsive receptor channel, Or42b → ORN_DM1**. The receptor +response has [experimental support](https://www.nature.com/articles/s41598-017-13015-w). +This is neither a reconstructed banana blend nor the complete response to ethyl +acetate, which can recruit other receptors too. + +The pinned [FlyWire annotations](https://github.com/flyconnectome/flywire_annotations/tree/8587524c1748ce5ef2080822a2fc890fc03bf597) +resolve 35 left and 33 right ORN_DM1 cells. All 68 exact root IDs occur in the +official 139,255-cell v783 graph. The input population is small; **the simulated +network remains the complete graph**, including all edges used by the full-brain +benchmark. IDs are preserved as decimal strings, avoiding rounding above 2^53. +Unannotated sides, unknown selected IDs and source checksum changes fail closed. + +The mapping also identifies 685 ALPNs (antennal-lobe projection neurons), 96 +MBONs, 1,303 descending neurons and one DNa02 per side for observation. A group +label describes anatomical identity; it does not establish that every group +member participates in this particular odor response. + +## Reproduce the mapping and GPU probe + +Start with the CUDA development branch and the full-brain data/environment +instructions in [fullbrain-validation.md](fullbrain-validation.md). Download the +annotation file into your experiment folder, outside the repository: + +```sh +curl --fail --location \ + https://raw.githubusercontent.com/flyconnectome/flywire_annotations/8587524c1748ce5ef2080822a2fc890fc03bf597/supplemental_files/Supplemental_file1_neuron_annotations.tsv \ + --output annotations.tsv +python scripts/build_olfactory_map.py \ + --annotations annotations.tsv \ + --roots data/proofread_root_ids_783.npy \ + --output results/olfactory-map.json +python scripts/probe_fullbrain_odor.py \ + --annotations annotations.tsv --data-dir data \ + --output results/fullbrain-odor.json +``` + +Annotation SHA-256: +`9a4f8b2f843196074431ebd7cd883536afa1be86c8a4ce90970441e8be81d1be`. +The output includes the pinned source URL, commit, checksum, selections and all +assumptions. The annotation repository does not specify a license in its pinned +root; **do not assume our MIT license covers upstream annotations**. We download +them at the user's request and do not bundle the annotation table in the SDK. +Cite Schlegel et al. (2024), Matsliah et al. (2024), Berg et al. (2025/2026), and +Dorkenwald et al. (2024), as requested by the source repository. The graph has +separate source/license metadata in the full-brain data instructions. + +## Sensory and world assumptions + +`flybrain.olfaction.OlfactoryDrive` takes two local antenna concentration samples. +It injects only the selected sensory neurons. It receives no banana coordinates, +target bearing, desired turn or reward. Its stateless response is +`current = 5 * concentration / (0.2 + concentration)` with dimensionless units; +these engineering defaults are **not fitted dose-response parameters**, pA, ppm, +or measured spike rates. A concentration of one produces current 4.1667. Other +channels can be added explicitly by creating additional adapters and summing +their vectors. There is intentionally no scientifically unqualified `banana` +preset. + +`sample_odor(antenna_xyz, source_xyz)` is a game-side isotropic Gaussian field. +Sample it separately at each antenna. It models neither turbulent plumes nor +obstacle-induced airflow. The body/world owns spatial coordinates; the neural +controller receives concentrations only. The initial world will be a kinematic +body in a voxel scene, not a reconstruction of flight musculature or a full +ventral nerve cord. FlyWire v783 is a whole-brain dataset, not the whole animal's +nervous system. + +## Falsifiable first gate + +Five conditions reset to the identical resting checkpoint: no odor, left odor, +right odor, bilateral odor, and bilateral odor with the 68 ORNs silenced. Each +has 100 ms pre-stimulus, 500 ms stimulus, and 200 ms recovery. The graph, initial +state and dynamics stay fixed, with no background current, noise or tuning. +Spikes are counted every tick and rates/voltages sampled every 10 ms. Reports +retain every condition, the hardware, graph and script hashes, including failures. + +Input/silencing checks are a software validation gate. The `completed` report +status means the experiment executed, **not** that navigation was successful. +If sensory neurons fire but ALPNs or descending neurons do not, the current LIF +preset has not demonstrated transmission to motor output. This is a result to +investigate, not a reason to inject current directly into motor neurons or to +hide a seek-food rule in the renderer. + +The existing benchmark uses homogeneous dimensionless LIF neurons and incoming +absolute-weight normalization. Its numerical accuracy does not validate its +physiology. Before a biological behavior claim, compare odor propagation under +published synaptic/membrane dynamics and calibrated sensory drive. A later +external learned readout must be labeled separately from innate circuit behavior. + +For context, the [Minecraft project's own architecture report](https://github.com/blendi-remade/fly-brain-minecraft/blob/main/docs/ARCHITECTURE.md) +states that its odor stimulus causes activity without a walking command, so a +handwritten body reflex implements odor taxis. Its visual appeal is useful +inspiration; the video alone is not evidence of neural food seeking. diff --git a/docs/synaptic-dynamics.md b/docs/synaptic-dynamics.md new file mode 100644 index 0000000..d8cf5db --- /dev/null +++ b/docs/synaptic-dynamics.md @@ -0,0 +1,148 @@ +# Synaptic LIF research engine: independent CPU reference + +**CPU reference and actual CUDA numerics verified; full-graph sensory propagation +observed. Odor-directed navigation is not established.** This is the next implementation step after the +[full-brain odor probe](validation/flywire-odor-a16.md) showed that the numerical +benchmark's incoming normalization cannot support sensory propagation. + +The existing dimensionless `FlyBrain` API, models and checkpoints keep their +original meaning. The new interface lives in `flybrain.experimental.synaptic`; +it is not yet exposed by `FlyBrain.load()` or a model catalog entry. It requires +explicit millivolt weights and rejects dimensionless weight units. The original +benchmark is retained as a reproducible historical numerical test. + +## Model and source + +Nominal equations and parameters follow [Shiu et al., Nature (2024)](https://www.nature.com/articles/s41586-024-07763-9) +and the author's [pinned Brian2 implementation](https://github.com/philshiu/Drosophila_brain_model/blob/91bdd1e7dcf193f3e7ca5a8933497fcef63b7960/model.py): + +``` +dv/dt = (v_rest - v + g) / tau_mem +dg/dt = -g / tau_syn +``` + +`v` and `g` have mV units. Here `g` is a voltage-valued synaptic drive, not a +conductance in siemens. A source spike schedules a signed increment to `g` after +the transmission delay. The new engine uses the exact solution of the two +coupled linear equations between events, including the equal-time-constant limit. + +| Parameter | Nominal value | +|---|---:| +| Step | 0.1 ms | +| Rest / reset | −52 mV | +| Strict spike threshold | greater than −45 mV | +| Membrane time constant | 20 ms | +| Synaptic decay time constant | 5 ms | +| Refractory interval | 2.2 ms | +| Transmission delay | 1.8 ms | + +The paper uses **0.275 mV per anatomical contact**, with transmitter-dependent +signs. This is a fitted model parameter, not a measurement of every synapse. +Callers must supply already-converted edge weights. The engine never normalizes +them by incoming degree. Receptor/neuromodulator mechanisms, membrane compartments, +gap junctions, body mechanics, learning and model calibration are not implemented. +Applying these nominal parameters to another graph/version is a new experiment, +not automatic replication of the paper's biological results. + +## Event schedule and checkpoints + +One step represents the schedule at `t = previous_tick * dt`: + +1. Update refractory eligibility and integrate `v` and `g` exactly. +2. Detect threshold crossings. +3. Deliver delayed synaptic events and declared external voltage jumps. +4. Reset spiking cells' `v` and `g`, then update recorded rates. + +Both states are frozen and protected from synaptic writes during refractoriness, +matching Brian2's `(unless refractory)` semantics. Eligibility uses integer tick +differences `>= refractory_ticks`, verified against Brian2 2.9.0. Source neurons +listed in `input_ids` have zero refractory duration, as in the paper's Poisson +targets. An externally injected jump can trigger a spike on the next step; an +input coinciding with a reset can be cleared. The oracle exercises this ordering. +Delay and refractory durations must be exact multiples of the timestep; there is +no hidden rounding to a different biological duration. + +Snapshots contain `g`, membrane voltage, rates, silencing, last-spike times and +the complete circular buffer of delayed spikes. A fingerprint binds the graph, +weights, dynamics and declared input neurons. Cross-model restores are rejected. +Restore validates state before replacing it. Silencing suppresses future spikes; +events already queued before silencing retain their scheduled delivery. + +`step(voltage_jumps_mv)` accepts a finite vector, with nonzero entries only at +declared inputs. This engine has no internal RNG. A later Poisson encoder must +save its own RNG state with the experiment. The original paper uses stochastic +inputs with jump amplitude `0.275 * 250 = 68.75 mV`; validation here uses known +event times, separating solver correctness from random-number generator choice. + +## Reproduce independent verification + +In an isolated Python 3.12 environment: + +```sh +pip install -e . 'Brian2==2.9.0' 'numpy==1.26.4' +python scripts/validate_synaptic_reference.py --output synaptic-reference.json +``` + +Brian2 2.9.0 accesses an API removed in NumPy 2, so this **oracle environment** +pins NumPy 1.26.4. The SDK's ordinary CPU installation still supports NumPy 2; +Brian2 is not added to runtime dependencies. + +The oracle independently constructs the same equations in Brian2 and compares +every cell at every step, with explicit scheduled input events. Four cases cover +0.1/0.2 ms steps, positive/negative and parallel edges, recurrent edges, an +autapse, zero/positive delays, simultaneous input/reset, isolated cells and equal +time constants. Each case includes real downstream spikes in the synthetic test +network. These small fixtures validate the solver; they are not fly models. + +Local result: **all four cases passed**, identical spike ticks, membrane errors +at most approximately `5.05e-13 mV`, synaptic-state errors at most approximately +`3.56e-14 mV`. JSON checkpoints taken while an event is in flight replay exactly. +[Machine-readable oracle report](validation/synaptic-brian2-reference.json). +Eleven dependency-free unit cases additionally cover delayed delivery, downstream +activation, invalid units/input selections, incompatible or malformed checkpoints, +silencing, and the near-equal time-constant limit. CI runs the Brian2 oracle in its own environment. + +For equal time constants, the oracle uses the same symbolic time constant in both +equations. Brian2's generic expression for distinct symbolic constants divides +by their difference when they happen to have equal numeric values. Using the +equivalent single-symbol equations permits its independent exact solver to +derive the degenerate case, rather than evaluating that removable singularity. + +## Required next gates + +The [actual A16 results](validation/flywire-synaptic-odor-a16.md) cover the CUDA +schedule, full graph, active CPU/GPU comparison and matched sensory controls. +Signals reach downstream populations, but DNa02 retains a left bias for both +left and right stimuli. Establishing a useful motor mapping remains a separate +experiment. Preserve negative results; target coordinates must not reach a +hidden seek-food controller. +## Experimental CUDA implementation and full-graph odor protocol + +`flybrain.experimental.synaptic_cuda.SynapticCUDA` is an optional port of this +reference. **All five hardware cases passed on an A16-8Q**, separately from the previous +dimensionless CUDA benchmark. See the linked report for exact scope and source. Importing its module is safe +without CuPy; constructing the engine requires a working NVIDIA device. + +The actual-hardware gate must execute all five GPU cases without skips: + +```bash +python scripts/validate_synaptic_cuda.py --output synaptic-cuda.json +``` + +After that gate passes, run the complete FlyWire odor protocol using the same +verified source files and annotations as [the earlier experiment](olfactory-experiment.md): + +```bash +python scripts/probe_synaptic_odor.py --data-dir data \ + --annotations data/annotations.tsv --output synaptic-odor.json +``` + +It retains every official neuron and aggregate edge, uses explicitly signed +0.275 mV/contact weights without incoming normalization, and stimulates only +the 68 annotated DM1 inputs with nominal 150 Hz discrete Poisson events and +68.75 mV input jumps. These are research assumptions, not a calibrated banana +dose. It first checks 200 active full-graph CPU/GPU ticks and 20 ticks of JSON +checkpoint replay, including the input RNG. Then it records all five matched +800 ms conditions: no odor, left, right, bilateral, bilateral with ORNs silenced. +Enabled input cells share identical random draws across conditions. Negative +results are retained; completing the protocol does not establish navigation. diff --git a/docs/validation/a16-20260912.json b/docs/validation/a16-20260912.json new file mode 100644 index 0000000..265dfad --- /dev/null +++ b/docs/validation/a16-20260912.json @@ -0,0 +1,141 @@ +{ + "format": "flybrain-cuda-validation", + "schema_version": 1, + "status": "passed", + "started_at": "2026-09-12T15:00:22.452207+00:00", + "source_sha256": { + "scripts/validate_cuda.py": "113060288dc4865065542870ba102d085785327edcb6a6eb7a781e47a0528dea", + "src/flybrain/__init__.py": "de9596a06a4de0a04303795a10ae0a6d8cbbc7705a5e60ad566b4c1bfad65971", + "src/flybrain/__main__.py": "c125d73bf421cac3c6a8f4185ae56516db20cc91831e006c42c544c7b873dfb9", + "src/flybrain/backends/__init__.py": "6c94a5cc4be4916e569562041036a6e169b13b228d2bc2dffc0494cff90bd64a", + "src/flybrain/backends/base.py": "bb28e7aa30f3cb3b676243e6e3767242d5daa27458b5e5a96b218232aaa1de81", + "src/flybrain/backends/cpu.py": 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"16ec1436ca16842edcc095a249e061ed797c94d644dcf428ee20399fca6bc102" + }, + "environment": { + "python": "3.12.3", + "platform": "Linux-6.8.0-138-generic-x86_64-with-glibc2.39", + "numpy": "2.2.6", + "cupy": "14.2.0", + "device": "NVIDIA A16-2Q", + "compute_capability": [ + 8, + 6 + ], + "device_memory_bytes": 1964965888, + "cuda_driver": 12040, + "cuda_runtime": 12090 + }, + "conformance": { + "tests": 12, + "failures": 0, + "errors": 0, + "skipped": 0 + }, + "benchmarks": [ + { + "model": "toy-v1", + "fingerprint": "ba1701cf092e8b6acb8d36aa70f86f46b4521e4bb5f6f463efb3d8824b4bac6c", + "neurons": 12, + "edges": 12, + "config": { + "dt_ms": 1.0, + "tau_ms": 10.0, + "rest": 0.0, + "reset": 0.0, + "threshold": 1.0, + "refractory_ms": 2.0, + "input_gain": 2.0, + "rate_tau_ms": 50.0, + "action_rate_hz": 100.0 + }, + "simulated_ms_per_repeat": 1000, + "warmup_ms": 100, + "timings": { + "cpu_load_seconds": 0.0002442290000317371, + "cpu": { + "seconds": [ + 0.02785182699994948, + 0.02760493999994651, + 0.027172675999963758 + ], + "median_seconds": 0.02760493999994651 + }, + "cuda_load_seconds": 0.12077638699997806, + "cuda": { + "seconds": [ + 2.557725829999981, + 2.629756971000006, + 2.527393785999948 + ], + "median_seconds": 2.557725829999981 + } + } + }, + { + "model": "male-cns-escape-v1", + "fingerprint": "47a0c91b3eba9c606c29faef58fe15c8846fc049c4314a5e3b6912ab22c5e68a", + "neurons": 313, + "edges": 20607, + "config": { + "dt_ms": 1.0, + "tau_ms": 10.0, + "rest": 0.0, + "reset": 0.0, + "threshold": 1.0, + "refractory_ms": 2.0, + "input_gain": 2.0, + "rate_tau_ms": 50.0, + "action_rate_hz": 100.0 + }, + "simulated_ms_per_repeat": 1000, + "warmup_ms": 100, + "timings": { + "cpu_load_seconds": 0.1308009670000274, + "cpu": { + "seconds": [ + 0.11087149200000113, + 0.10837631200001852, + 0.10773501699998178 + ], + "median_seconds": 0.10837631200001852 + }, + "cuda_load_seconds": 0.11967941799991877, + "cuda": { + "seconds": [ + 2.63953883399995, + 2.582605983999997, + 2.640352118999999 + ], + "median_seconds": 2.63953883399995 + } + } + } + ], + "limitations": [ + "Correctness-first CUDA: host current upload and scalar synchronization each tick.", + "These timings cover only toy and 313-cell circuit, not whole-brain scaling.", + "Loading/compilation reported separately; execution includes inputs and action readout.", + "Conformance applies to tested cases and reported device/software, not all CUDA hardware." + ], + "finished_at": "2026-09-12T15:01:02.554374+00:00" +} diff --git a/docs/validation/a16-20260912.md b/docs/validation/a16-20260912.md new file mode 100644 index 0000000..af9814b --- /dev/null +++ b/docs/validation/a16-20260912.md @@ -0,0 +1,50 @@ +# Actual NVIDIA validation — 12 September 2026 UTC + +Source commit: `35628f379d79b5ceefcec8fb0acc9bf04baa610b`. +[Machine-readable conformance report](a16-20260912.json) includes SHA-256 for +every Python runtime source, hardware test, and validator. Those hashes were +checked against the local source before accepting the report. + +- Vultr Frankfurt, NVIDIA A16-2Q vGPU, advertised 2 GB framebuffer. +- Python 3.12.3, NumPy 2.2.6, CuPy 14.2.0. +- NVIDIA driver 550.90.07, reported driver API 12.4, runtime API 12.9. +- Hardware gate: **12 passed, zero skipped**, 22.43 seconds. +- Full Python suite on this host: **161 passed, 1 optional import skipped**, + 48.83 seconds. PyArrow was not installed; the separate GitHub quality job tests + the Arrow importer with that dependency installed. +- GitHub CI on this commit passed all seven jobs, including Python across + Linux/macOS/Windows, packaging, JavaScript parity, and actual Godot checks. + +## Measured small-circuit latency + +Three repeats, median wall seconds per 1,000 ms simulated time, after 100 ms +warmup. Execution includes input handling, readout and GPU synchronization. +Loading/compilation is separate in the JSON. + +| Model | CPU | CUDA | +|---|---:|---:| +| Artificial toy, 12 cells | 0.02760 s | 2.55773 s | +| MaleCNS escape, 313 cells | 0.10838 s | 2.63954 s | + +**CUDA is slower on these small circuits.** This reference path uploads currents +and synchronizes an error flag on each tick. A fractional virtual GPU also has +scheduling overhead. These data do not establish whole-brain scaling, speedup, +or full-card A16 performance. Keep CPU as the default. Device-resident multi-tick +schedules and batching are the next optimization target, with the same +conformance gate retained. + +The first device attempt failed at NVRTC compilation because `math.h` was not +provided by the pip CUDA environment. Removing that unnecessary host header +allowed NVRTC's built-in device math to compile. The complete gate then passed; +CPU-only tests could not detect this deployment issue. + +## External readout learning + +`examples/train_reflex.py --backend cuda` ran a reward-trained two-choice action +readout using the unchanged 313-neuron circuit. On 80 balanced held-out trials, +accuracy rose from **50% to 100%** after 24 batches over 64 training trials. +Runtime was 33.87 seconds. CPU independently reproduced the same accuracy. + +Only three external readout weights were trained. This is a simple, resetting +left/right obstacle task with varied stimulus strength and distractors, not +biological plasticity or broad game skill. See the [experiment guide](../../examples/REFLEX_TRAINING.md). diff --git a/docs/validation/benchmark-voltage-bound.json b/docs/validation/benchmark-voltage-bound.json new file mode 100644 index 0000000..4a7dc63 --- /dev/null +++ b/docs/validation/benchmark-voltage-bound.json @@ -0,0 +1,21 @@ +{ + "config": { + "dt_ms": 1.0, + "tau_ms": 10.0, + "rest": 0.0, + "reset": 0.0, + "threshold": 1.0, + "refractory_ms": 2.0, + "input_gain": 2.0, + "rate_tau_ms": 50.0, + "action_rate_hz": 100.0 + }, + "incoming_weight": 2.5, + "presynaptic_current": 100.0, + "postsynaptic_current": 0.0, + "ticks": 2000, + "max_voltage": 0.9179135027773137, + "postsynaptic_spikes": 0, + "analytic_bound": 0.9179135027773139, + "scope": "CPU two-cell diagnostic for the benchmark weight policy, not a full-brain run" +} diff --git a/docs/validation/flywire-full-a16.json b/docs/validation/flywire-full-a16.json new file mode 100644 index 0000000..b289898 --- /dev/null +++ b/docs/validation/flywire-full-a16.json @@ -0,0 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0.9999983134575562, + "mean_rate_hz": 67.0790936263084, + "spiking_neurons": 8562 + } + ] + }, + "gpu_pool_used_bytes": 210532864, + "gpu_pool_reserved_bytes": 214013952, + "host_peak_rss_kib_linux": 7122432 +} diff --git a/docs/validation/flywire-full-a16.md b/docs/validation/flywire-full-a16.md new file mode 100644 index 0000000..4256543 --- /dev/null +++ b/docs/validation/flywire-full-a16.md @@ -0,0 +1,63 @@ +# Full FlyWire v783 on NVIDIA A16-8Q + +Actual-device run, 2026-09-12 UTC. Tested source commit: +`5cb825507a05a50c0a0c7200e3c130d547f77663`. +The later Windows import fix only makes Linux RSS collection optional outside Linux; +no simulation, importer mapping, or benchmark algorithm was changed by that fix. + +- All 139,255 proofread neurons, including 616 isolated neurons. +- All 16,847,997 published neuron-pair/neuropil rows retained (0 discarded). +- 54,492,922 anatomical synaptic contacts represented by those rows. +- Both official MD5 checks passed; SHA-256 values are in the report. +- 12/12 actual CUDA hardware checks passed; full suite **171 passed, 0 skipped**. +- 200 active CPU/CUDA ticks, comparing every neuron every 25 ticks: exact spikes + and refractory state; voltage and rate maximum difference was 0 at all checkpoints. +- JSON backend-state roundtrip reproduced 30 subsequent CUDA ticks exactly. +- 10,000 additional CUDA ticks (10 seconds simulated), finite/bounded full states + checked every 100 ticks: 93,609,984 spikes, 139,254 neurons fired at least once. + +| SDK backend | Three wall times for 1,000 ms simulation | Median | +| --- | --- | --- | +| NumPy CPU | 110.955, 110.815, 113.702 s | 110.955 s | +| CuPy CUDA | 11.552, 11.558, 11.549 s | 11.552 s | + +CUDA was **9.61x faster than this SDK CPU reference on this host**, still about +11.55 wall seconds per simulated second (not real time). Timings include per-tick +host input uploads and scalar error synchronization. This is not a comparison +against an optimized third-party CPU simulator. Both use float64 and dt=1 ms. + +Model import: 63.95 s. CPU backend initialization: 3.27 s. CUDA initialization and +compilation: 6.77 s. Stability pass including activity collection: 122.03 s. +Final CuPy pool in-use allocation: 210,532,864 bytes (~200.8 MiB); reserved: +214,013,952 bytes. These are not peak total GPU memory. Host process peak RSS: +7,122,432 KiB (~6.79 GiB). The Python object graph is costly in host memory. + +Environment: A16-8Q, 8 GiB VRAM; 3 vCPU / 32 GiB system RAM; Ubuntu 24.04; +Python 3.12.3, NumPy 2.5.3, CuPy 14.2.0, NVIDIA driver 550.90.07, +CUDA runtime API 12090. See frozen dependencies for the complete installation. + +## Scope and assumptions + +This is the complete published **proofread brain graph**, not the entire CNS, +not raw unproofread EM fragments, and not a physiological or behavioral model. +The original 313-cell Reflex School remains a separate experiment. +Artificial currents drive this benchmark. No learning or body/game integration +is claimed here. GABA/glutamate signs, other/unknown positive signs, homogeneous +LIF parameters, and incoming normalization to 2.5 are explicit assumptions. +1,250 neurons have no outgoing transmitter-score evidence; the policy counts them +and assumes positive sign. No edges are dropped to hide missing annotations. + +The first attempt failed closed because NumPy mixed int64/uint64 lookups lost +precision on long neuron IDs. Normalizing both source columns to uint64 fixed +this; four regression tests protect neighboring large IDs and unknown IDs. + +The report's checkpoint is the backend state with the same model, not a benchmark +of serializing the entire graph through FlyBrain.save(). The whole-brain entry +point is currently a validation script rather than a catalog-ready game model. + +- [Full JSON report](flywire-full-a16.json) +- [Full pytest XML](flywire-full-pytest.xml) +- [44 source SHA-256 values](flywire-full-source-sha256.json) +- [Frozen requirements](flywire-full-requirements.txt) +- [Reproduction guide](../fullbrain-validation.md) +- [Official dataset](https://zenodo.org/records/10676866) diff --git a/docs/validation/flywire-full-pytest.xml b/docs/validation/flywire-full-pytest.xml new file mode 100644 index 0000000..5b1529f --- /dev/null +++ b/docs/validation/flywire-full-pytest.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/validation/flywire-full-requirements.txt b/docs/validation/flywire-full-requirements.txt new file mode 100644 index 0000000..26707dd --- /dev/null +++ b/docs/validation/flywire-full-requirements.txt @@ -0,0 +1,20 @@ +cuda-pathfinder==1.8.1 +cuda-toolkit==12.9.2.0 +cupy-cuda12x==14.2.0 +# Editable install with no version control (flybrain-sdk==0.5.0.dev0) +-e /root/fullbrain/source +iniconfig==2.3.0 +numpy==2.5.3 +nvidia-cublas-cu12==12.9.2.10 +nvidia-cuda-nvrtc-cu12==12.9.86 +nvidia-cuda-runtime-cu12==12.9.79 +nvidia-cufft-cu12==11.4.1.4 +nvidia-curand-cu12==10.3.10.19 +nvidia-cusolver-cu12==11.7.5.82 +nvidia-cusparse-cu12==12.5.10.65 +nvidia-nvjitlink-cu12==12.9.86 +packaging==26.3 +pluggy==1.6.0 +pyarrow==25.0.1 +Pygments==2.21.0 +pytest==9.1.1 diff --git a/docs/validation/flywire-full-source-sha256.json b/docs/validation/flywire-full-source-sha256.json new file mode 100644 index 0000000..5602a60 --- /dev/null +++ b/docs/validation/flywire-full-source-sha256.json @@ -0,0 +1,46 @@ +{ + "src/flybrain/session.py": "0e04b67cc0f32f4d5b109b2e8bfdb9082d0f27e295df2718b5a597f8a2e3192a", + "src/flybrain/demos.py": "ad45e3a91920affae56b74f2b3d2d1d0b8eab41d705acf3ccd4fbba8b8a5cee6", + "src/flybrain/brain.py": "e9ea19aea644bcbdcc686b6e79ee5767f4c620cdeb063cf69fd7a014a87e9450", + "src/flybrain/viewer.py": 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"DNa02_right": 1, + "ALPN": 685, + "MBON": 96, + "descending": 1303 + } + }, + "device": { + "name": "NVIDIA A16-8Q", + "memory_bytes": 8394047488, + "cupy": "14.2.0", + "driver": 12040, + "runtime": 12090 + }, + "load_seconds": 67.89835428700002, + "cuda_kernel_sha256": "da6f944b0075733d668615ec73f2822cdf740d404d06d2e8309afec1a63b9e9b", + "findings": { + "input_stimulation_and_silencing_checks": "passed", + "bilateral_descending_spikes": 0, + "interpretation": "Presence of descending spikes does not prove odor-directed navigation; absence means this preset has not demonstrated the required motor response. No behavior, banana recognition, flight or learning was tested here." + }, + "finished_utc": "2026-09-13T01:56:35Z" +} diff --git a/docs/validation/flywire-odor-a16.md b/docs/validation/flywire-odor-a16.md new file mode 100644 index 0000000..f1b03d3 --- /dev/null +++ b/docs/validation/flywire-odor-a16.md @@ -0,0 +1,78 @@ +# Full FlyWire odor probe — input works, downstream spiking absent + +**Negative functional result, 2026-09-13 UTC.** All 139,255 neurons and 16,847,997 +source rows ran on NVIDIA A16-8Q. This is not a subgraph. CUDA's 12 hardware cases +passed; the full suite passed **181 tests, zero skips**. The five-condition +experiment completed, but this preset did **not** demonstrate odor transmission +through the olfactory projection neurons or motor output. + +| Condition | Input ORN spikes | ALPN spikes | MBON spikes | Descending spikes | +|---|---:|---:|---:|---:| +| no_odor | 0 | 0 | 0 | 0 | +| left_odor | 3,500 | 0 | 0 | 0 | +| right_odor | 3,300 | 0 | 0 | 0 | +| bilateral_odor | 6,800 | 0 | 0 | 0 | +| bilateral_odor_ORNs_silenced | 0 | 0 | 0 | 0 | + +Each condition: 100 ms rest + 500 ms stimulus + 200 ms recovery, from the same +resting checkpoint. No global background current. Only annotated ORN_DM1 cells +receive external input. Left/right cell counts are 35/33. A single responsive +Or42b channel is approximated; no complete banana mixture is claimed. + +The maximum observed ALPN membrane potential was 0.2516453 (threshold 1). +Maximum descending membrane potential was 0.00129747. Zero spikes therefore +means no threshold crossing; it does not mean absolutely no subthreshold signal. +The GPU executed 4,000 total ticks. Per-condition wall times were 9.62–9.69 s, +including selected-neuron observations; this was not a throughput benchmark. + +## Why this preset cannot support the requested behavior + +The historical numerical benchmark normalized every neuron's incoming absolute +weight sum to 2.5. With its 1 ms step, 10 ms membrane time constant and two-tick +refractory hold, an input cell can spike at most once every three ticks. From +rest and with zero external postsynaptic current, the largest possible voltage +is bounded by the all-excitatory, perfectly synchronized case: + +``` +leak = exp(-1/10) +V_max <= 2.5 * (1 - leak) / (1 - leak**3) = 0.9179135 < threshold 1 +``` + +Inhibition only lowers this upper bound. Thus increasing odor intensity alone +cannot fix transmission in this preset. A separate CPU two-cell diagnostic with +maximal 3-tick firing spacing reached 0.9179135027773137 and zero downstream +spikes over 2,000 ticks, matching the independent bound. That small diagnostic +explains the parameter issue; it is not a substitute for the full-brain GPU run. +The original full-brain benchmark remains valid for numerical conformance, +checkpointing and timing under its artificial global input, **not physiology**. + +## Next implementation gate + +Use an explicitly separate dynamics configuration based on the published +[Shiu et al. model](https://www.nature.com/articles/s41586-024-07763-9), rather than +silently changing the historical benchmark. Its membrane and synaptic states, +synaptic decay, transmission delay, refractory behavior and Poisson sensory +stimulation must be checked against an independent reference before making +behavior claims. Preserve the full graph, source/weight provenance and the same +negative controls. Only after propagation is demonstrated should a voxel-world +body consume neural outputs. Any learned or handcrafted readout must be labeled +as such; target coordinates must not bypass the neural path. + +## Evidence and reproduction + +- [Protocol, annotation download and commands](../olfactory-experiment.md). +- [Raw report with all 400 sampled frames](flywire-odor-a16.json). +- [Actual GPU test suite XML](flywire-odor-pytest.xml). +- [Tested Python source SHA-256 values](flywire-odor-source-sha256.txt). +- [Analytical-bound CPU diagnostic](benchmark-voltage-bound.json). +- Tested source: `357e6546cad18b45b7033c9725c140d0b8901dec`. The subsequent + `930b5f9` commit only formats an annotation metadata string. +- Downloaded evidence archive SHA-256: + `e41c10b70bf46a51569afb3e404213f5ee01924adcc8e2d324380d7dd82881aa`. + All source hashes were matched to the published tested commit. +- CUDA backend/kernel unchanged from the earlier numerical benchmark. CuPy + 14.2.0, CUDA runtime API 12090, driver API 12040, 8,394,047,488 device bytes. + +The temporary GPU instance was terminated after results were retrieved and +verified. No persistent GPU endpoint or live browser inference is provided by +this report. diff --git a/docs/validation/flywire-odor-pytest.xml b/docs/validation/flywire-odor-pytest.xml new file mode 100644 index 0000000..b28b61b --- /dev/null +++ b/docs/validation/flywire-odor-pytest.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/validation/flywire-odor-source-sha256.txt b/docs/validation/flywire-odor-source-sha256.txt new file mode 100644 index 0000000..16db212 --- /dev/null +++ b/docs/validation/flywire-odor-source-sha256.txt @@ -0,0 +1,37 @@ +0e04b67cc0f32f4d5b109b2e8bfdb9082d0f27e295df2718b5a597f8a2e3192a source/src/flybrain/session.py +ad45e3a91920affae56b74f2b3d2d1d0b8eab41d705acf3ccd4fbba8b8a5cee6 source/src/flybrain/demos.py +e9ea19aea644bcbdcc686b6e79ee5767f4c620cdeb063cf69fd7a014a87e9450 source/src/flybrain/brain.py +26d71040003ac8deff35bd5c0fb42be9d47a0af49a41754795b20fb608910cad source/src/flybrain/viewer.py 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{ + "ticks": 200, + "observations": [ + { + "tick": 40, + "errors": { + "voltage_mv": 0.0, + "synaptic_mv": 0.0, + "rates_hz": 0.0 + } + }, + { + "tick": 80, + "errors": { + "voltage_mv": 0.0, + "synaptic_mv": 0.0, + "rates_hz": 0.0 + } + }, + { + "tick": 120, + "errors": { + "voltage_mv": 0.0, + "synaptic_mv": 0.0, + "rates_hz": 0.0 + } + }, + { + "tick": 160, + "errors": { + "voltage_mv": 0.0, + "synaptic_mv": 0.0, + "rates_hz": 0.0 + } + }, + { + "tick": 200, + "errors": { + "voltage_mv": 0.0, + "synaptic_mv": 0.0, + "rates_hz": 0.0 + } + } + ], + "input_events": 201, + "status": "passed", + "json_checkpoint_replay_ticks": 20, + "wall_seconds": 27.289984679999975 + }, + "findings": { + "input_and_silencing_controls": "passed", + "bilateral_descending_spikes": 2883, + "interpretation": "Report includes absent responses. Descending spikes alone do not prove odor-directed navigation. No banana recognition, flight or learning tested." + }, + "finished_utc": "2026-09-13T03:01:55Z" +} diff --git a/docs/validation/flywire-synaptic-odor-a16.md b/docs/validation/flywire-synaptic-odor-a16.md new file mode 100644 index 0000000..43fe1e5 --- /dev/null +++ b/docs/validation/flywire-synaptic-odor-a16.md @@ -0,0 +1,90 @@ +# Complete FlyWire synaptic CUDA odor experiment + +**Sensory propagation observed, 2026-09-13 UTC. Navigation not established.** +The separate mV synaptic engine ran all **139,255 neurons**, retaining all +**16,847,997 aggregate edges / 54,492,922 anatomical contacts**, without pruning. +The historical normalized benchmark and its negative result remain unchanged. + +| Condition | ORN left / right spikes | ALPN | MBON | Descending | DNa02 left / right | +|---|---:|---:|---:|---:|---:| +| No odor | 0 / 0 | 0 | 0 | 0 | 0 / 0 | +| Left odor | 2,545 / 0 | 23,298 | 3,286 | 2,778 | 29 / 6 | +| Right odor | 0 / 2,466 | 23,305 | 3,175 | 2,799 | 32 / 8 | +| Bilateral odor | 2,545 / 2,466 | 23,503 | 3,276 | 2,883 | 32 / 9 | +| Bilateral, ORNs silenced | 0 / 0 | 0 | 0 | 0 | 0 / 0 | + +Only annotated DM1 sensory cells received external input. Each condition started +from the same resting checkpoint: 100 ms rest, 500 ms stimulus, 200 ms recovery; +dt=0.1 ms, 8,000 steps per condition. NumPy PCG64 seed 20260913 draws are matched +across conditions. Input uses the discrete PoissonGroup approximation p=rate*dt, +150 Hz nominal rate and 68.75 mV jumps. Input events can be cleared by same-tick +reset, so injected events and observed ORN spikes are intentionally distinct. + +## What the results establish + +- Five actual-GPU conformance cases executed: **zero skipped, failed or errored**. + These cover CPU parity, delays, inhibition, equal decay constants, stream + ownership, cross-backend checkpoints, silencing and overflow rollback. +- The complete graph passed **200 active CPU/GPU ticks** with 201 sensory input + events. At five checkpoints, every neuron's voltage, synaptic state and rate + had maximum absolute difference **0**. Spike/history/refractory timestamps + were exactly equal. This covers the measured trajectory, not all possible inputs. +- Full-graph JSON state plus input RNG replayed **20 future ticks exactly**. +- The same hardware environment passed **204 software tests, zero skips**. +- Sensory-only stimulation now drives ALPN, MBON and descending spikes. The + no-odor and input-silenced controls remain silent in these recorded populations. + +## What remains unresolved + +DNa02 activity is left-biased for both left and right stimulation. The observed +pair therefore does not simply encode stimulus side. Counts in aggregate output +populations are not proof of correct body commands, attraction or reliable +navigation. This protocol has one seed, nominal parameters and an 800 ms horizon; +it does not establish robust physiology across doses, seeds or behavioral states. + +Time matters: at 150 ms (50 ms after stimulus onset), the right-odor DNa02 +rates are left 18.15 Hz / right 34.07 Hz, whereas left odor gives 32.84 / 0 Hz. +Thus the cumulative left bias does **not** establish absence of all transient +side information. The current raw rate-difference motor mapping still needs +closed-loop evaluation rather than an inference from these early samples. + +Activity also persists after stimulus offset. In the 600–800 ms window, the +bilateral condition adds 6,842 ALPN, 924 MBON and 859 descending spikes. These +are new spike counts, not just the tail of the rate estimator. The finite +recording does not establish indefinite persistence, biological memory or its +mechanism. Longer recovery and recurrent-gain/transmitter assumptions need +checking before calling this a calibrated sensorimotor model. See the +[derived post-stimulus counts](synaptic-post-stimulus.json), linked by raw-report hash. + +The graph uses assumed transmitter signs, 0.275 mV/contact weights and no incoming +normalization. These fitted research parameters do not measure every synapse. +The [independent Brian2 validation](synaptic-brian2-reference.json) establishes +the implemented equations/schedule, not biological validity of this particular +graph/parameter combination. The stimulus is one Or42b/DM1 channel, not a complete +ethyl-acetate response, banana mixture or visual-recognition model. + +Each 800 ms condition took 102.1–104.1 seconds, including observations. These +are experiment wall times, not an isolated kernel benchmark or real-time claim. +The raw report's `mapping.stimulus.current_units` describes the historical +dimensionless adapter bundled with the annotation map; it is not used here. +The actual mV input units, dynamics and weights are defined in `protocol`, +`config` and `model.weight_units`. + +## Evidence and reproduction + +- [Raw five-condition report](flywire-synaptic-odor-a16.json) +- [Five-case actual CUDA gate](synaptic-cuda-a16.json) +- [Software test XML](synaptic-gpu-pytest.xml) +- [64 tested Python file SHA-256 values](synaptic-gpu-source-sha256.txt) +- [Reproduction commands and physical assumptions](../synaptic-dynamics.md) +- Tested source: `592738a3a963961e81a6cc948d0b5d3dfca3471a`. +- Graph arrays SHA-256: + `f6dc0d6348cc7db3be7514323a57142549327e679f9aee709b2343351685f5b5`. +- Retrieved evidence archive SHA-256: + `cfeaabc6fb28b90f76b2e4cd0ac013c7cf3a87d44d1e813141d5474e181d9a47`. +- NVIDIA A16-8Q, 8,394,047,488 bytes device memory; CuPy 14.2.0; + CUDA driver API 12040 / runtime API 12090; Python 3.12.3, NumPy 2.5.3. +- Started `2026-09-13T02:51:35Z`; finished `2026-09-13T03:01:55Z`. + +The archive checksum and all 64 recorded source file hashes were verified after +download. The source graph files also passed the pinned official checksums. diff --git a/docs/validation/flywire-voxel-a16.md b/docs/validation/flywire-voxel-a16.md new file mode 100644 index 0000000..faa0e1b --- /dev/null +++ b/docs/validation/flywire-voxel-a16.md @@ -0,0 +1,69 @@ +# Full-brain CUDA voxel experiment: movement, no successful foraging + +**Recorded on NVIDIA A16-8Q, 2026-09-13.** All 139,255 FlyWire v783 neurons and +16,847,997 aggregate edges were simulated on the GPU in both runs. A fixed, +untrained readout converted DNa02 rates to a kinematic ground body's speed and +turn. There was no target-bearing controller or direct motor stimulation. + +| Recorded condition | Simulated time | Path length | Initial / final food distance | Food contact | +|---|---:|---:|---:|---| +| Neural control | 2 s | 3.840 | 2.778 / 4.380 | No | +| ORN inputs silenced | 2 s | 0 | 2.778 / 2.778 | No | + +Distances use arbitrary game units. The active run initially approached to +2.266 units, then curved left and moved away. It did not reach the 0.6-unit +contact radius. Neither run encountered a block; collision mechanics are tested +separately in the CPU environment tests, not established by these trajectories. +This is evidence of an implemented neural-to-body closed loop, **not successful +odor-directed navigation, training or biological locomotion**. + +## Exact control boundary + +- World observations contain only left and right local antenna concentrations. +- Input rates are `180*c/(0.2+c)` Hz; per-tick Bernoulli events generate 68.75 mV + jumps on the 35 left / 33 right annotated DM1 input cells. +- All graph cells update at 0.1 ms. After 200 neural steps, the 20 ms body frame + consumes DNa02 left/right exponentially smoothed rates, with a 50 ms time constant. +- Speed = `clip(0.03*(left+right), 0, 3)` game units/s. +- Turn = `2*tanh((right-left)/40)` radians/s. Positive turns toward world +z. +- Food coordinates reach the odor field, rendering and scoring only, never the + motor readout. Input silencing operates on the brain, not on renderer animation. + +The speed mapping is an engineering hypothesis with no biological validation. +The ground body, Gaussian field, gains, initial scene and seed are explicit in +the recording. The model covers one food-odor channel and no vision. DNa02's +side bias and post-stimulus persistence in the [sensory controls](flywire-synaptic-odor-a16.md) +remain reasons to investigate calibration and a more informative readout. + +## Validation and reproduction + +- **207 software tests passed on the GPU host, zero skips**: + [test XML](voxel-gpu-pytest.xml). +- At frame 20 in each run, brain state (including all delayed events), world and + input RNG were serialized through JSON. The next complete 20 ms frame was + replayed and compared: exact full-brain state, world record and RNG equality. +- All 100 frames per run are preserved. Wall times: active 243.654 s; silenced + 248.907 s. These include observations and the extra replay checks; not real time. +- Tested source: `e89b8feaf7f2381eeaad625de0cc0897bb6ac773`. +- [Tested Python source hashes](voxel-gpu-source-sha256.txt) and the hashes in the + raw recording were verified after download. +- Evidence archive SHA-256: + `4e3dc17aa64c878c3447320ad9098f94a93a79c05655fb710f21f8b64af64455`. +- Started `2026-09-13T03:04:58Z`; finished `2026-09-13T03:14:25Z`. +- NVIDIA A16-8Q / CuPy 14.2.0 / CUDA driver API 12040 / runtime API 12090. + +```bash +python scripts/run_fullbrain_voxel.py --data-dir data \ + --annotations data/annotations.tsv --output fullbrain-voxel.json +``` + +Use the source files and dependencies from the [full-brain odor protocol](../olfactory-experiment.md). +[The browser page](https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html) +replays the [complete JSON recording](../../site/experiments/fullbrain-voxel.json). +It supports selecting the control, scrubbing time and rotating the view; it does +not perform live GPU inference. It refuses to substitute generated trajectories +when the recording is absent or incomplete. + +The temporary GPU instance was destroyed after evidence retrieval and checksum +verification. The provider instance list was confirmed empty; the task SSH key +was deleted. 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-0,0 +1,19 @@ +cuda-pathfinder==1.8.1 +cuda-toolkit==12.9.2.0 +cupy-cuda12x==14.2.0 +# Editable install with no version control (flybrain-sdk==0.5.0.dev0) +-e /root/flybrain-test +iniconfig==2.3.0 +numpy==2.2.6 +nvidia-cublas-cu12==12.9.2.10 +nvidia-cuda-nvrtc-cu12==12.9.86 +nvidia-cuda-runtime-cu12==12.9.79 +nvidia-cufft-cu12==11.4.1.4 +nvidia-curand-cu12==10.3.10.19 +nvidia-cusolver-cu12==11.7.5.82 +nvidia-cusparse-cu12==12.5.10.65 +nvidia-nvjitlink-cu12==12.9.86 +packaging==26.3 +pluggy==1.6.0 +Pygments==2.21.0 +pytest==8.4.2 diff --git a/docs/validation/synaptic-brian2-reference.json b/docs/validation/synaptic-brian2-reference.json new file mode 100644 index 0000000..fd091e7 --- /dev/null +++ b/docs/validation/synaptic-brian2-reference.json @@ -0,0 +1,114 @@ +{ + "status": "passed", + "oracle": "Brian2", + "version": "2.9.0", + "numpy": "1.26.4", + "cases": [ + { + "config": { + "dt_ms": 0.1, + "rest_mv": -52.0, + "reset_mv": -52.0, + "threshold_mv": -45.0, + "membrane_tau_ms": 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b/docs/validation/synaptic-cuda-a16.json new file mode 100644 index 0000000..1c34432 --- /dev/null +++ b/docs/validation/synaptic-cuda-a16.json @@ -0,0 +1,24 @@ +{ + "status": "passed", + "python": "3.12.3", + "source_sha256": { + "src/flybrain/experimental/__init__.py": "570d34f551fd63b33bff564c022ad01fc463b0e1649d9f7035e361b421533bd5", + "src/flybrain/experimental/synaptic.py": "4c3b7b48e659314a1dd839819ddce4bdd1622d21dd8753400035c1df8deaa726", + "src/flybrain/experimental/synaptic_cuda.py": "8b0bc40f8bf0075e6d81caadf3393673110aa48b66415303a08020cb9055dfa8", + "tests/test_synaptic_cuda_hardware.py": "70761bdbb7cbd4fdaa82ea85b40494a84fb532d9d6af350449aa33e46219bb1e", + "scripts/validate_synaptic_cuda.py": "7f51e7147d8c51d879a663e85841077197196851648e780d05fb86eceaf13c80" + }, + "device": { + "name": "NVIDIA A16-8Q", + "cupy": "14.2.0", + "memory_bytes": 8394047488, + "driver": 12040, + "runtime": 12090 + }, + "cases": { + "tests": 5, + "skipped": 0, + "errors": 0, + "failures": 0 + } 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mode 100644 index 0000000..ab6dc10 --- /dev/null +++ b/docs/voxel-foraging.md @@ -0,0 +1,51 @@ +# Full-brain voxel odor experiment + +The requested experiment couples the **entire FlyWire graph on an NVIDIA GPU** +to a small voxel world. A fruit mesh will represent a simplified food-odor +source. It does not imply banana vision, a complete odor blend, flight mechanics +or naturally validated food-seeking behavior. + +## Implemented environment + +`flybrain.experimental.voxel_arena.VoxelArena` provides floor motion, solid cubic +obstacles, collision detection, two antenna concentration samples, food-contact +scoring and JSON world-state replay. It is a kinematic ground body in arbitrary +game units. Odor uses a Gaussian field without turbulence or obstacle occlusion. + +The control boundary is explicit: + +1. The arena exposes only `odor_left` and `odor_right` to the sensory adapter. +2. The adapter schedules events on annotated ORNs in the full network. +3. A separately declared readout converts neural activity to speed and turn. +4. `arena.apply({"speed": ..., "turn": ...}, duration_ms=20)` moves the body. +5. `arena.scene()` supplies positions/food/obstacles to rendering and evaluation; + it must not be passed to the neural controller or readout. + +Tests verify that relocating food changes the sensed odor, but cannot change +the trajectory under identical commands. Zero movement commands keep the body +stationary. Obstacles stop motion, and JSON restore reproduces future pose, +concentration and collision results. + +## Implemented closed loop and remaining behavior work + +The [full-graph odor controls](validation/flywire-synaptic-odor-a16.md) establish +that sensory-only input reaches downstream populations. DNa02 is a candidate steering +readout based on [descending control experiments](https://www.nature.com/articles/s41586-024-07523-9), +but this does not establish an appropriate forward-speed mapping, odor response +or behavior in our graph/dynamics. Any gain, baseline gait, trained readout or +external reflex must be disclosed in the recording and reproduction recipe. + +The [actual GPU recording](validation/flywire-voxel-a16.md) identifies graph, +configuration, source hashes and GPU. It contains an active and input-silenced +control with the same initial state and RNG seed, recording body pose, input +events, neural output and applied commands. Replaying +it in a browser is **recorded GPU output**, not live browser GPU inference. +World, brain, delayed events and sensory RNG restore together; the next complete +frame was replayed exactly in each run. Report +failures to move or reach food; do not choose successful seeds and hide others. + +Run `scripts/run_fullbrain_voxel.py` with the pinned dataset files to produce +the full-brain recording. `site/foraging.html` renders its saved frames. The active +run moved, curved left and ended farther from food; the silenced run remained +stationary. Neither reached food. Calibration and a useful motor readout remain +unfinished. No successful foraging or training result is claimed. diff --git a/examples/REFLEX_TRAINING.md b/examples/REFLEX_TRAINING.md new file mode 100644 index 0000000..77dde9a --- /dev/null +++ b/examples/REFLEX_TRAINING.md @@ -0,0 +1,35 @@ +# Reflex School: train an external action readout + +Run from the repository checkout: + +```sh +pip install -e '.[dev]' +python examples/train_reflex.py --backend cpu --output reflex-training.json +# On a validated CUDA host, choose --backend cuda instead. +``` + +Open the generated `reflex-training.html` to replay the same held-out obstacles +before and after training. It also writes portable readout weights to +`reflex-training.checkpoint.json` and full results to JSON. The HTML uses only +embedded results, needs no server/GPU, and does not perform live inference. + +The pinned 313-neuron MaleCNS escape circuit receives left/right looming pulses +with randomized amplitude and weak opposite-side interference. After 80 ms, +two GF firing rates and a constant bias become the only controller features. +The controller chooses left or right. Escaping opposite the obstacle gets reward +1; moving toward it gets 0. Batched REINFORCE updates three external weights for +24 rounds using 64 training trials. A separate seed generates 80 balanced test +trials. Their results never select epochs or update weights. Zero initial weights +give a 50% deterministic tie-break baseline on this balanced task. + +With CUDA selected, both LIF simulation and readout optimization execute through +CuPy on the GPU. Stimulus generation, reporting and held-out metrics run on CPU. +This tiny experiment does not imply GPU speedup. All seeds, weights, rates and +actions are saved so the result can be inspected and reproduced. + +**Scientific scope:** the anatomical connectivity stays fixed. The learning is +an engineered external readout, not biological synaptic plasticity. LIF parameters, +sensory projections, reward and game rules are designed by us. Trials reset the +brain and test only a two-choice reflex; this is not a trained whole fruit fly or +evidence of complex game skill. The animation stretches each saved 80 ms response +for visibility; it is an illustration of recorded decisions, not physical replay. diff --git a/examples/godot/bridge.py b/examples/godot/bridge.py index 65827ea..e32c1b1 100644 --- a/examples/godot/bridge.py +++ b/examples/godot/bridge.py @@ -10,8 +10,9 @@ class Bridge: - def __init__(self, model="toy", download=False): - demo = make_demo("dodge", model=model, download=download) + def __init__(self, model="toy", download=False, *, backend="cpu"): + self.backend = backend + demo = make_demo("dodge", model=model, download=download, backend=backend) self.initial = ExternalController(demo.brain, demo.encoder, demo.readout).snapshot() self.controller = None self.session_id = None @@ -19,9 +20,11 @@ def __init__(self, model="toy", download=False): def dispatch(self, path, data): if path == "/start": if "checkpoint_json" in data: - controller = ExternalController.from_snapshot(json.loads(data["checkpoint_json"])) + controller = ExternalController.from_snapshot( + json.loads(data["checkpoint_json"]), backend=self.backend + ) else: - controller = ExternalController.from_snapshot(self.initial) + controller = ExternalController.from_snapshot(self.initial, backend=self.backend) if controller.period_ms != 20: raise ValueError("Godot reference arena requires 20 ms") self.controller, self.session_id = controller, uuid.uuid4().hex @@ -44,23 +47,27 @@ def dispatch(self, path, data): raise ValueError("unknown endpoint") -def make_server(port=8766, *, model="toy", download=False): - bridge = Bridge(model, download) +def make_server(port=8766, *, model="toy", download=False, backend="cpu"): + bridge = Bridge(model, download, backend=backend) class Handler(BaseHTTPRequestHandler): def do_POST(self): try: + length = int(self.headers.get("Content-Length", "0")) + if not 0 < length <= 16_000_000: + raise ValueError("request limit: 16 MB") + self.connection.settimeout(5) + # Consume the bounded body before replying, including rejected + # origins. Closing with unread bytes can reset TCP on Windows + # and discard the HTTP error response. Never dispatch an origin. + body = self.rfile.read(length) # Browsers receive no cross-origin access to this local dev service. if ( self.headers.get("Origin") or self.headers.get_content_type() != "application/json" ): raise ValueError("native JSON client required") - length = int(self.headers.get("Content-Length", "0")) - if not 0 < length <= 16_000_000: - raise ValueError("request limit: 16 MB") - self.connection.settimeout(5) - data = json.loads(self.rfile.read(length)) + data = json.loads(body) if not isinstance(data, dict): raise ValueError("JSON object required") result = bridge.dispatch(self.path, data) @@ -87,9 +94,12 @@ def log_message(self, *args): parser.add_argument("--port", type=int, default=8766) parser.add_argument("--model", default="toy") parser.add_argument("--download", action="store_true") + parser.add_argument("--backend", choices=("cpu", "cuda"), default="cpu") args = parser.parse_args() print("Preparing model before accepting game connections…", flush=True) - with make_server(args.port, model=args.model, download=args.download) as server: + with make_server( + args.port, model=args.model, download=args.download, backend=args.backend + ) as server: print(f"Godot bridge: http://127.0.0.1:{server.server_port} · {args.model}", flush=True) try: server.serve_forever() diff --git a/examples/reflex_replay.html b/examples/reflex_replay.html new file mode 100644 index 0000000..11c314b --- /dev/null +++ b/examples/reflex_replay.html @@ -0,0 +1,24 @@ + + +Reflex School · flybrain-sdk + +
FLYBRAIN / REFLEX SCHOOL实验记录
+

同一回路，学会不同的动作。

来袭在左，就向右闪。来袭在右，就向左闪。固定的 313 神经元 MaleCNS 子回路接收刺激；一个很小的外部动作读出层，通过奖励学习如何解释神经活动。

+
训练前 · 留出关卡—
训练后 · 相同关卡—
没有参与训练—
+

01 / 还没学会

零权重读出；平局默认向右。

02 / 奖励训练之后

连接回路不变，仅更新 3 个读出权重。

+
1 / 80
+

+

奖励如何改变动作

绿线：训练集动作准确率 · 蓝线：实际采样奖励。先固定训练轮数，再做一次留出评估。

这个实验说明什么

这是一个简单的两选一反射任务。学到的是外部读出，不是果蝇突触可塑性。关卡之间重置神经状态；留出集改变刺激强度与干扰，尚未证明复杂游戏中的泛化能力。

画面是保存下来的真实实验动作的示意回放。80ms 的神经反应被放慢展示，页面不会连接或租用 GPU，也不在浏览器里重新训练。

+ + diff --git a/examples/train_reflex.py b/examples/train_reflex.py new file mode 100644 index 0000000..6ac2be0 --- /dev/null +++ b/examples/train_reflex.py @@ -0,0 +1,179 @@ +"""Train an external action readout from a fixed real connectome's firing rates. + +One-step contextual bandit: dodge opposite the approaching obstacle (+1), or +collide (0). This is engineered readout learning, not biological plasticity. +No labels, cue direction, or reward enter the readout's inference features. +""" + +import argparse +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + +from flybrain import FlyBrain + +ROOT = Path(__file__).resolve().parents[1] +IDS = ("10010", "10001") + + +def collect(model, backend, seed, count): + rng = np.random.default_rng(seed) + sides = np.tile([0, 1], count // 2) + rng.shuffle(sides) + records = [] + for index, side in enumerate(sides): + brain = FlyBrain.load(model, backend=backend) + strength = float(rng.uniform(0.65, 1.0)) + distractor = float(rng.uniform(0, 0.25)) + channels = ("looming_left", "looming_right") + brain.stimulate(channels[side], strength=strength, duration_ms=80) + brain.stimulate(channels[1 - side], strength=distractor, duration_ms=80) + brain.advance(duration_ms=80) + rates = list(brain.observe(IDS, fields=("rates_hz",)).values["rates_hz"]) + records.append( + { + "trial": index, + "obstacle_side": int(side), + "strength": strength, + "distractor": distractor, + "rates_hz": rates, + "target": int(1 - side), + } + ) + return records + + +def features(records): + # Rates only; bias permits a learned action preference. + rates = np.asarray([r["rates_hz"] for r in records], dtype=np.float64) / 100 + return np.column_stack([rates, np.ones(len(records))]) + + +def probabilities(xp, x, weights): + return 1 / (1 + xp.exp(-xp.clip(x @ weights, -30, 30))) + + +def train(x, targets, *, xp=np, epochs=24, seed=23): + """REINFORCE with sampled actions and binary reward; fixed 0.5 baseline.""" + rng = np.random.default_rng(seed) + x = xp.asarray(x) + targets = xp.asarray(targets) + weights = xp.zeros(x.shape[1], dtype=xp.float64) + curve = [] + for epoch in range(epochs): + p = probabilities(xp, x, weights) + action = (xp.asarray(rng.random(len(x))) < p).astype(xp.int32) + reward = (action == targets).astype(xp.float64) + weights += 0.8 * x.T @ ((reward - 0.5) * (action - p)) / len(x) + greedy = probabilities(xp, x, weights) >= 0.5 + curve.append( + { + "epoch": epoch + 1, + "sampled_reward": float(reward.mean()), + "train_accuracy": float((greedy == targets).mean()), + } + ) + return weights, curve + + +def evaluate(records, weights): + p = probabilities(np, features(records), np.asarray(weights)) + trials = [ + { + **r, + "probability_right": float(prob), + "action": int(prob >= 0.5), + "success": int(prob >= 0.5) == r["target"], + } + for r, prob in zip(records, p) + ] + return {"accuracy": float(np.mean([t["success"] for t in trials])), "trials": trials} + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--backend", choices=("cpu", "cuda"), default="cpu") + parser.add_argument("--output", type=Path, default=Path("reflex-training.json")) + args = parser.parse_args() + start = time.perf_counter() + brain = FlyBrain.load(ROOT / "models/male-cns-escape-v1/model.json") + model = brain.model + xp = np + if args.backend == "cuda": + from flybrain.backends.cuda import _load_cupy + + xp = _load_cupy() + training = collect(model, args.backend, 101, 64) + # Separate seeds and amplitudes; held-out outcomes never update weights. + heldout = collect(model, args.backend, 202, 80) + before = evaluate(heldout, [0, 0, 0]) + weights, curve = train(features(training), [r["target"] for r in training], xp=xp) + weights = weights.get().tolist() if args.backend == "cuda" else weights.tolist() + after = evaluate(heldout, weights) + checkpoint = { + "format": "flybrain-external-reflex-readout-v1", + "neuron_ids": list(IDS), + "scale_hz": 100, + "bias_feature": 1, + "weights": weights, + "model_fingerprint": model.fingerprint, + "actions": ["left", "right"], + } + # Evaluate again after an actual JSON round trip of the portable weights. + restored = json.loads(json.dumps(checkpoint)) + assert evaluate(heldout, restored["weights"]) == after + report = { + "format": "flybrain-reflex-training-v1", + "backend": args.backend, + "model": model.name, + "neurons": len(model.neuron_ids), + "model_fingerprint": model.fingerprint, + "checkpoint": checkpoint, + "source_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), + "train_seed": 101, + "evaluation_seed": 202, + "optimizer_seed": 23, + "training_trials": len(training), + "evaluation_trials": len(heldout), + "epochs": len(curve), + "curve": curve, + "before": before, + "after": after, + "seconds": time.perf_counter() - start, + "limitations": [ + "Fixed real anatomical subgraph; LIF parameters and input mapping assumed.", + "Only the external three-weight action readout is learned.", + "Two-choice 80ms obstacle task; trials reset neural state.", + "Held-out trials vary stimulus amplitude, not task or environment.", + "Untrained zero weights choose right on ties; chance baseline is 50%.", + "No claim of biological learning, whole-fly intelligence or broad generalization.", + ], + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n") + args.output.with_suffix(".checkpoint.json").write_text(json.dumps(checkpoint, indent=2) + "\n") + template = Path(__file__).with_name("reflex_replay.html") + if template.exists(): + args.output.with_suffix(".html").write_text( + template.read_text().replace( + "__EXPERIMENT_JSON__", json.dumps(report).replace("<", "\\u003c") + ), + encoding="utf-8", + ) + print( + json.dumps( + { + "before": before["accuracy"], + "after": after["accuracy"], + "backend": args.backend, + "seconds": report["seconds"], + } + ) + ) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index c2c2cc8..340cd71 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "flybrain-sdk" -version = "0.4.0a4" +version = "0.5.0.dev0" description = "CPU-first connectome simulation for games and experiments. No CUDA required." readme = "README.md" requires-python = ">=3.9" diff --git a/scripts/build_olfactory_map.py b/scripts/build_olfactory_map.py new file mode 100644 index 0000000..44a9188 --- /dev/null +++ b/scripts/build_olfactory_map.py @@ -0,0 +1,122 @@ +"""Resolve pinned FlyWire annotations against the complete official v783 ID set. + +No download or inferred hemisphere assignment. The output records exact IDs, +source checksums and mapping assumptions for downstream CUDA experiments. +""" + +import argparse +import csv +import hashlib +import json +from pathlib import Path + +import numpy as np + +ANNOTATION_COMMIT = "8587524c1748ce5ef2080822a2fc890fc03bf597" +ANNOTATION_URL = ( + "https://raw.githubusercontent.com/flyconnectome/flywire_annotations/" + + ANNOTATION_COMMIT + + "/supplemental_files/Supplemental_file1_neuron_annotations.tsv" +) +ANNOTATION_SHA256 = "9a4f8b2f843196074431ebd7cd883536afa1be86c8a4ce90970441e8be81d1be" +ROOTS_SHA256 = "7c7b7e818e9232e5ab64793d52ba20574dbe9da3a6509fbc74193b0f259a01be" + + +def sha256(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def resolve_populations(rows, neuron_ids): + """Keep IDs as strings throughout; reject uncertain/missing bilateral inputs.""" + known = set(neuron_ids) + groups = {f"ORN_DM1_{side}": [] for side in ("left", "right")} + groups.update({f"DNa02_{side}": [] for side in ("left", "right")}) + groups.update({"ALPN": [], "MBON": [], "descending": []}) + seen = set() + for row in rows: + neuron_id = row["root_id"] + if not neuron_id.isascii() or not neuron_id.isdecimal(): + raise ValueError("root_id must be an exact decimal string") + if neuron_id in seen: + raise ValueError("duplicate annotation root_id") + seen.add(neuron_id) + cell_type, side = row["cell_type"], row["side"] + targets = [] + if cell_type == "ORN_DM1": + if row["super_class"] != "sensory" or row["cell_class"] != "olfactory": + raise ValueError("ORN_DM1 has conflicting sensory annotation") + if side not in ("left", "right"): + raise ValueError("ORN_DM1 side is missing/ambiguous; never split arbitrarily") + targets.append(f"ORN_DM1_{side}") + if cell_type == "DNa02": + if row["super_class"] != "descending" or side not in ("left", "right"): + raise ValueError("DNa02 has conflicting descending/side annotation") + targets.append(f"DNa02_{side}") + for column, value in ( + ("cell_class", "ALPN"), + ("cell_class", "MBON"), + ("super_class", "descending"), + ): + if row[column] == value: + targets.append(value) + if targets and neuron_id not in known: + raise ValueError(f"selected annotation references unknown neuron {neuron_id}") + for target in targets: + groups[target].append(neuron_id) + if any(not values for values in groups.values()): + raise ValueError("one or more required sensory/readout populations are empty") + return {name: sorted(values, key=int) for name, values in groups.items()} + + +def build(annotations, roots): + if sha256(annotations) != ANNOTATION_SHA256 or sha256(roots) != ROOTS_SHA256: + raise ValueError("pinned source checksum mismatch") + ids = np.load(roots, allow_pickle=False) + if ids.dtype.kind not in "iu" or len(ids) != 139255 or len(np.unique(ids)) != 139255: + raise ValueError("expected all 139255 official integer neuron IDs") + with annotations.open(newline="", encoding="utf-8") as f: + groups = resolve_populations(csv.DictReader(f, delimiter="\t"), map(str, ids)) + if len(groups["ORN_DM1_left"]) != 35 or len(groups["ORN_DM1_right"]) != 33: + raise ValueError("pinned DM1 population counts changed") + return { + "schema": "flybrain-olfactory-map-v1", + "model": "flywire-full-v783", + "neuron_count": 139255, + "annotation_source": { + "url": ANNOTATION_URL, + "commit": ANNOTATION_COMMIT, + "sha256": ANNOTATION_SHA256, + "license": "not specified in the pinned repository; do not infer SDK MIT coverage", + "citation": "Schlegel et al. 2024; Matsliah et al. 2024; Berg et al. 2025/2026", + }, + "root_ids_sha256": ROOTS_SHA256, + "stimulus": { + "label": "ethyl-acetate / Or42b channel approximation", + "receptor": "Or42b", + "glomerulus": "DM1", + "evidence": "https://www.nature.com/articles/s41598-017-13015-w", + "scope": ( + "One responsive receptor channel; not the complete ethyl-acetate or banana response" + ), + "current_units": "dimensionless; not measured current or calibrated odor concentration", + "hemispheres": "source annotation side; no invented left/right assignments", + }, + "groups": groups, + "counts": {name: len(values) for name, values in groups.items()}, + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--annotations", type=Path, required=True) + parser.add_argument("--roots", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + report = build(args.annotations, args.roots) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + print(json.dumps(report["counts"])) + + +if __name__ == "__main__": + main() diff --git a/scripts/probe_fullbrain_odor.py b/scripts/probe_fullbrain_odor.py new file mode 100644 index 0000000..e0dd6f3 --- /dev/null +++ b/scripts/probe_fullbrain_odor.py @@ -0,0 +1,166 @@ +"""CUDA-only full FlyWire sensory propagation experiment, not a behavior claim. + +Five matched conditions start from exactly the same saved resting state. No +global background current, random input, decoder training or graph pruning. +Reports every condition, including absent downstream responses. Source data and +annotations must already exist locally; see docs/olfactory-experiment.md. +""" + +import argparse +import json +import time +from pathlib import Path + +import numpy as np +from build_olfactory_map import build +from validate_fullbrain import digest, load_fullbrain + +from flybrain.backends.cuda import CUDABackend +from flybrain.config import LIFConfig +from flybrain.neurons import NeuronIndex +from flybrain.olfaction import OlfactoryDrive + +CONDITIONS = ( + ("no_odor", 0.0, 0.0, False), + ("left_odor", 1.0, 0.0, False), + ("right_odor", 0.0, 1.0, False), + ("bilateral_odor", 1.0, 1.0, False), + ("bilateral_odor_ORNs_silenced", 1.0, 1.0, True), +) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--data-dir", type=Path, required=True) + parser.add_argument("--annotations", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + report = { + "schema": "flybrain-full-odor-probe-v1", + "status": "running", + "started_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "script_sha256": digest(__file__), + "config": LIFConfig().to_dict(), + "protocol": { + "pre_ms": 100, + "odor_ms": 500, + "post_ms": 200, + "trace_interval_ms": 10, + "stimulus": "Or42b/DM1 single-channel food-odor approximation", + "max_current": 5.0, + "half_concentration": 0.2, + "background_current": 0.0, + "weight_policy": "unchanged full-brain benchmark", + "concentration_units": "dimensionless, not a measured chemical dose", + }, + "conditions": [], + } + args.output.parent.mkdir(parents=True, exist_ok=True) + + def save(): + args.output.write_text(json.dumps(report, indent=2, allow_nan=False) + "\n") + + save() + try: + started = time.perf_counter() + mapping = build(args.annotations, args.data_dir / "proofread_root_ids_783.npy") + model, report["model"] = load_fullbrain(args.data_dir) + report["mapping"] = mapping + index = NeuronIndex(model) + groups = {name: index.resolve(ids) for name, ids in mapping["groups"].items()} + selected = tuple(sorted({i for values in groups.values() for i in values})) + selected_index = {i: j for j, i in enumerate(selected)} + positions = {k: [selected_index[i] for i in v] for k, v in groups.items()} + brain = CUDABackend(model, LIFConfig()) # No CPU fallback permitted. + brain._stream.synchronize() + cp = brain._cp + props = cp.cuda.runtime.getDeviceProperties(cp.cuda.Device().id) + report["device"] = { + "name": props["name"].decode(), + "memory_bytes": props["totalGlobalMem"], + "cupy": cp.__version__, + "driver": cp.cuda.runtime.driverGetVersion(), + "runtime": cp.cuda.runtime.runtimeGetVersion(), + } + report["load_seconds"] = time.perf_counter() - started + report["cuda_kernel_sha256"] = digest( + Path(__file__).parents[1] / "src/flybrain/backends/cuda.py" + ) + odor = OlfactoryDrive( + model, + left_ids=mapping["groups"]["ORN_DM1_left"], + right_ids=mapping["groups"]["ORN_DM1_right"], + ) + zero = np.zeros(len(model.neuron_ids)) + # Same graph + backend state, through JSON, as in the full benchmark. + rest = json.loads(json.dumps(brain.snapshot(), allow_nan=False)) + for name, left, right, silence in CONDITIONS: + brain.restore(rest) + if silence: + brain.set_silenced(groups["ORN_DM1_left"] + groups["ORN_DM1_right"], True) + current = odor.current(left=left, right=right) + item = { + "name": name, + "antenna_concentrations": [left, right], + "silence_input_neurons": silence, + "spikes": {group: 0 for group in groups}, + "trace": [], + } + started = time.perf_counter() + spike_totals = np.zeros(len(selected), dtype=np.int64) + for tick in range(800): + brain.step(current if 100 <= tick < 600 else zero) + observed = brain.observe_selected(selected, ("spikes",)) + spike_totals += np.asarray(observed["spikes"], dtype=np.int64) + if (tick + 1) % 10 == 0: + observed = brain.observe_selected(selected, ("voltage", "rates_hz")) + voltage = np.asarray(observed["voltage"]) + rates = np.asarray(observed["rates_hz"]) + if not np.isfinite(voltage).all() or not np.isfinite(rates).all(): + raise ValueError("nonfinite neural response") + item["trace"].append( + { + "time_ms": tick + 1, + "groups": { + group: { + "mean_rate_hz": float(rates[pos].mean()), + "max_voltage": float(voltage[pos].max()), + } + for group, pos in positions.items() + }, + } + ) + item["spikes"] = {k: int(spike_totals[p].sum()) for k, p in positions.items()} + item["wall_seconds"] = time.perf_counter() - started + report["conditions"].append(item) + save() + print(json.dumps({k: v for k, v in item.items() if k != "trace"}), flush=True) + conditions = {item["name"]: item for item in report["conditions"]} + for name in ("no_odor", "bilateral_odor_ORNs_silenced"): + if any(conditions[name]["spikes"].values()): + raise AssertionError("unexpected spikes in matched no-input control") + for side in ("left", "right"): + counts = conditions[f"{side}_odor"]["spikes"] + if counts[f"ORN_DM1_{side}"] == 0: + raise AssertionError("input stimulation failed to produce ORN spikes") + report["findings"] = { + "input_stimulation_and_silencing_checks": "passed", + "bilateral_descending_spikes": conditions["bilateral_odor"]["spikes"]["descending"], + "interpretation": ( + "Presence of descending spikes does not prove odor-directed navigation; " + "absence means this preset has not demonstrated the required motor response. " + "No behavior, banana recognition, flight or learning was tested here." + ), + } + report["status"] = "completed" + except Exception as error: + report["status"] = "failed" + report["error"] = f"{type(error).__name__}: {error}" + raise + finally: + report["finished_utc"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + save() + + +if __name__ == "__main__": + main() diff --git a/scripts/probe_synaptic_odor.py b/scripts/probe_synaptic_odor.py new file mode 100644 index 0000000..1d24748 --- /dev/null +++ b/scripts/probe_synaptic_odor.py @@ -0,0 +1,254 @@ +"""Full FlyWire CUDA odor experiment with separately versioned mV dynamics. + +Keeps every official neuron/edge. Seeded discrete Poisson inputs target only +annotated DM1 ORNs. No CPU fallback, background current, navigation controller, +parameter search, graph pruning or success-based selection of conditions. +""" + +import argparse +import json +import time +from pathlib import Path + +import numpy as np +from build_olfactory_map import build +from validate_fullbrain import digest, load_fullbrain + +from flybrain.experimental.synaptic import SynapticCPU, SynapticLIFConfig +from flybrain.experimental.synaptic_cuda import SynapticCUDA +from flybrain.neurons import NeuronIndex + +CONDITIONS = ( + ("no_odor", False, False, False), + ("left_odor", True, False, False), + ("right_odor", False, True, False), + ("bilateral_odor", True, True, False), + ("bilateral_odor_ORNs_silenced", True, True, True), +) + + +def input_events(rng, left_count, right_count, probability, left, right): + """Matched random draws even when a side is disabled; one event/cell/tick. + + Bernoulli p=rate*dt is the discrete PoissonGroup approximation. It is not + an exact continuous-time Poisson process or a calibrated chemical dose. + """ + events = rng.random(left_count + right_count) < probability + if not left: + events[:left_count] = False + if not right: + events[left_count:] = False + return events + + +def compare(cpu, gpu): + expected, actual = cpu.snapshot(), gpu.snapshot() + for field in ( + "engine", + "fingerprint", + "tick", + "spikes", + "history", + "silenced", + "last_spike_tick", + ): + if expected[field] != actual[field]: + raise AssertionError(f"CPU/GPU mismatch: {field}") + errors = {} + for field in ("voltage_mv", "synaptic_mv", "rates_hz"): + a, b = np.asarray(expected[field]), np.asarray(actual[field]) + np.testing.assert_allclose(a, b, atol=1e-9, rtol=0, err_msg=field) + errors[field] = float(np.max(np.abs(a - b))) + return errors + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--data-dir", type=Path, required=True) + parser.add_argument("--annotations", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + config = SynapticLIFConfig() + seed, rate_hz, jump_mv, contact_mv = 20260913, 150.0, 68.75, 0.275 + probability = rate_hz * config.dt_ms / 1000 + report = { + "schema": "flybrain-full-synaptic-odor-v1", + "status": "running", + "started_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "config": config.to_dict(), + "numpy": np.__version__, + "source_sha256": { + str(p.relative_to(Path(__file__).resolve().parents[1])): digest(p) + for p in ( + Path(__file__).resolve(), + Path(__file__).with_name("validate_fullbrain.py").resolve(), + Path(__file__).with_name("build_olfactory_map.py").resolve(), + *sorted( + (Path(__file__).resolve().parents[1] / "src/flybrain/experimental").glob("*.py") + ), + ) + }, + "protocol": { + "pre_ms": 100, + "odor_ms": 500, + "post_ms": 200, + "trace_interval_ms": 10, + "seed": seed, + "rng": "NumPy PCG64", + "input_rate_hz": rate_hz, + "input_jump_mv": jump_mv, + "contact_mv": contact_mv, + "background_input": 0, + "sensory_model": "Bernoulli p=rate*dt, at most one input event/cell/tick", + "stimulus": "Or42b/DM1 single-channel food-odor approximation", + "calibration": "nominal research parameters; not a measured banana dose", + "input_refractory_ms": 0, + "parameter_source": ( + "https://github.com/philshiu/Drosophila_brain_model/" + "blob/91bdd1e7dcf193f3e7ca5a8933497fcef63b7960/model.py" + ), + }, + "conditions": [], + } + args.output.parent.mkdir(parents=True, exist_ok=True) + + def save(): + temporary = args.output.with_suffix(".partial.json") + temporary.write_text(json.dumps(report, indent=2, allow_nan=False) + "\n") + temporary.replace(args.output) + + save() + try: + mapping = build(args.annotations, args.data_dir / "proofread_root_ids_783.npy") + model, report["model"] = load_fullbrain(args.data_dir, contact_mv=contact_mv) + report["mapping"] = mapping + index = NeuronIndex(model) + groups = {name: index.resolve(ids) for name, ids in mapping["groups"].items()} + left_ids, right_ids = (mapping["groups"][f"ORN_DM1_{side}"] for side in ("left", "right")) + input_ids = left_ids + right_ids + inputs = np.asarray(index.resolve(input_ids), dtype=np.int64) + selected = tuple(sorted({i for values in groups.values() for i in values})) + offsets = {i: j for j, i in enumerate(selected)} + positions = {name: [offsets[i] for i in values] for name, values in groups.items()} + brain = SynapticCUDA(model, config, input_ids=input_ids, weight_units="mV") + props = brain._cp.cuda.runtime.getDeviceProperties(brain._device.id) + report["device"] = { + "name": props["name"].decode(), + "memory_bytes": props["totalGlobalMem"], + "cupy": brain._cp.__version__, + "driver": brain._cp.cuda.runtime.driverGetVersion(), + "runtime": brain._cp.cuda.runtime.runtimeGetVersion(), + } + rest = json.loads(json.dumps(brain.snapshot(), allow_nan=False)) + jumps = np.zeros(brain.n) + cpu = SynapticCPU(model, config, input_ids=input_ids, weight_units="mV") + rng = np.random.default_rng(seed) + parity = {"ticks": 200, "observations": [], "input_events": 0} + started = time.perf_counter() + for tick in range(200): + events = input_events(rng, len(left_ids), len(right_ids), probability, True, True) + jumps[inputs] = events * jump_mv + parity["input_events"] += int(events.sum()) + cpu.step(jumps) + brain.step(jumps) + if (tick + 1) % 40 == 0: + parity["observations"].append({"tick": tick + 1, "errors": compare(cpu, brain)}) + if parity["input_events"] == 0: + raise AssertionError("full-graph parity must exercise active sensory input") + # Round-trip device state through JSON and replay the same input RNG. + checkpoint = json.loads(json.dumps(brain.snapshot(), allow_nan=False)) + rng_state = json.loads(json.dumps(rng.bit_generator.state)) + + def replay(): + for _ in range(20): + events = input_events(rng, len(left_ids), len(right_ids), probability, True, True) + jumps[inputs] = events * jump_mv + brain.step(jumps) + return brain.snapshot() + + expected = replay() + brain.restore(checkpoint) + rng.bit_generator.state = rng_state + if replay() != expected: + raise AssertionError("full-graph device + input RNG checkpoint replay differed") + parity.update( + status="passed", + json_checkpoint_replay_ticks=20, + wall_seconds=time.perf_counter() - started, + ) + report["full_graph_cpu_cuda_parity"] = parity + save() + print("Full-graph active parity and delayed checkpoint replay passed", flush=True) + del cpu, checkpoint, expected + for name, left, right, silence in CONDITIONS: + brain.restore(rest) + if silence: + brain.set_silenced(inputs, True) + rng = np.random.default_rng(seed) + totals = np.zeros(len(selected), dtype=np.int64) + item = {"name": name, "silenced": silence, "input_events": [0, 0], "trace": []} + started = time.perf_counter() + for tick in range(8000): + active = 1000 <= tick < 6000 + events = input_events( + rng, + len(left_ids), + len(right_ids), + probability, + left and active, + right and active, + ) + jumps[inputs] = events * jump_mv + item["input_events"][0] += int(events[: len(left_ids)].sum()) + item["input_events"][1] += int(events[len(left_ids) :].sum()) + brain.step(jumps) + totals += np.asarray( + brain.observe_selected(selected, ("spikes",))["spikes"], dtype=np.int64 + ) + if (tick + 1) % 100 == 0: + observed = brain.observe_selected(selected, ("voltage_mv", "rates_hz")) + voltage, rates = (np.asarray(observed[k]) for k in ("voltage_mv", "rates_hz")) + item["trace"].append( + { + "time_ms": (tick + 1) * config.dt_ms, + "groups": { + group: { + "mean_rate_hz": float(rates[pos].mean()), + "max_voltage_mv": float(voltage[pos].max()), + "cumulative_spikes": int(totals[pos].sum()), + } + for group, pos in positions.items() + }, + } + ) + item["spikes"] = {group: int(totals[pos].sum()) for group, pos in positions.items()} + item["wall_seconds"] = time.perf_counter() - started + report["conditions"].append(item) + save() + print(json.dumps({k: v for k, v in item.items() if k != "trace"}), flush=True) + conditions = {item["name"]: item for item in report["conditions"]} + for name in ("no_odor", "bilateral_odor_ORNs_silenced"): + if any(conditions[name]["spikes"].values()): + raise AssertionError("spikes in matched no-input control") + for side in ("left", "right"): + if conditions[f"{side}_odor"]["spikes"][f"ORN_DM1_{side}"] == 0: + raise AssertionError("stimulated ORNs did not spike") + report["findings"] = { + "input_and_silencing_controls": "passed", + "bilateral_descending_spikes": conditions["bilateral_odor"]["spikes"]["descending"], + "interpretation": ( + "Report includes absent responses. Descending spikes alone do not prove " + "odor-directed navigation. No banana recognition, flight or learning tested." + ), + } + report["status"] = "completed" + except Exception as error: + report.update(status="failed", error=f"{type(error).__name__}: {error}") + raise + finally: + report["finished_utc"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + save() + + +if __name__ == "__main__": + main() diff --git a/scripts/run_fullbrain_voxel.py b/scripts/run_fullbrain_voxel.py new file mode 100644 index 0000000..b0545ab --- /dev/null +++ b/scripts/run_fullbrain_voxel.py @@ -0,0 +1,214 @@ +"""Record a complete FlyWire CUDA brain controlling a ground voxel body. + +Experimental engineering readout: DNa02 rate difference controls turn; summed +rates control speed. The speed mapping is not biologically established. No +target coordinate, odor concentration or rescue reflex enters the motor readout. +Both active and input-silenced runs are retained, regardless of food contact. +""" + +import argparse +import json +import math +import time +from pathlib import Path + +import numpy as np +from build_olfactory_map import build +from validate_fullbrain import digest, load_fullbrain + +from flybrain.experimental.synaptic import SynapticLIFConfig +from flybrain.experimental.synaptic_cuda import SynapticCUDA +from flybrain.experimental.voxel_arena import VoxelArena +from flybrain.neurons import NeuronIndex + + +def motor_readout(left_hz, right_hz): + """Fixed, untrained engineering gains. Inputs are neural firing rates only.""" + return { + "speed": min(3.0, max(0.0, (left_hz + right_hz) * 0.03)), + "turn": 2.0 * math.tanh((right_hz - left_hz) / 40.0), + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--data-dir", type=Path, required=True) + parser.add_argument("--annotations", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + root = Path(__file__).resolve().parents[1] + config, seed = SynapticLIFConfig(), 20260913 + report = { + "schema": "flybrain-voxel-recording-v1", + "status": "running", + "started_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "config": config.to_dict(), + "seed": seed, + "source_sha256": { + str(p.relative_to(root)): digest(p) + for p in ( + Path(__file__).resolve(), + root / "scripts/validate_fullbrain.py", + root / "scripts/build_olfactory_map.py", + root / "src/flybrain/olfaction.py", + *sorted((root / "src/flybrain/experimental").glob("*.py")), + ) + }, + "protocol": { + "duration_ms": 2000, + "body_step_ms": 20, + "neural_steps_per_frame": 200, + "input_jump_mv": 68.75, + "contact_mv": 0.275, + "odor_rate_hz": "180*c/(0.2+c); c is a dimensionless antenna sample", + "stimulus": "DM1 single-channel food odor, not a full banana mixture", + "readout": "speed=clip(0.03*(DNa02_L+DNa02_R),0,3); turn=2*tanh((DNa02_R-DNa02_L)/40)", + "units": "rates Hz; speed game units/s; turn radians/s", + "limitations": [ + "Untrained engineering readout; forward-speed mapping not established biologically", + "Kinematic walking body, not flight or musculoskeletal simulation", + "Toy Gaussian odor field without wind, turbulence or obstacle occlusion", + "Recorded GPU frames, not live browser inference", + "No direct motor input, background drive, odor-bearing controller or rescue reflex", + "Success or failure of both runs reported without seed selection", + ], + }, + "runs": [], + } + args.output.parent.mkdir(parents=True, exist_ok=True) + + def save(): + temp = args.output.with_suffix(".partial.json") + temp.write_text(json.dumps(report, indent=2, allow_nan=False) + "\n") + temp.replace(args.output) + + save() + try: + mapping = build(args.annotations, args.data_dir / "proofread_root_ids_783.npy") + model, report["model"] = load_fullbrain(args.data_dir, contact_mv=0.275) + report["mapping"] = mapping + # The map's historical dimensionless-current label does not configure this engine. + report["mapping_input_units_note"] = "Actual input units and strengths are in protocol." + index = NeuronIndex(model) + left_ids, right_ids = (mapping["groups"][f"ORN_DM1_{s}"] for s in ("left", "right")) + input_ids = left_ids + right_ids + inputs = np.asarray(index.resolve(input_ids), dtype=np.int64) + outputs = index.resolve(mapping["groups"]["DNa02_left"] + mapping["groups"]["DNa02_right"]) + brain = SynapticCUDA(model, config, input_ids=input_ids, weight_units="mV") + props = brain._cp.cuda.runtime.getDeviceProperties(brain._device.id) + report["device"] = { + "name": props["name"].decode(), + "memory_bytes": props["totalGlobalMem"], + "cupy": brain._cp.__version__, + "driver": brain._cp.cuda.runtime.driverGetVersion(), + "runtime": brain._cp.cuda.runtime.runtimeGetVersion(), + } + rest = json.loads(json.dumps(brain.snapshot(), allow_nan=False)) + jumps = np.zeros(brain.n) + for silence in (False, True): + world = VoxelArena(food=(-1.6, 0.5, -0.6)) + brain.restore(rest) + brain.set_silenced(inputs, silence) + rng = np.random.default_rng(seed) + run = { + "name": "ORNs_silenced" if silence else "neural_control", + "initial": world.scene(), + "frames": [], + } + started = time.perf_counter() + + def advance_frame(): + odor = world.observe() + rates = np.asarray( + [ + 180 * odor[f"odor_{side}"] / (0.2 + odor[f"odor_{side}"]) + for side in ("left", "right") + ] + ) + probability = np.repeat(rates, [len(left_ids), len(right_ids)]) * 0.0001 + input_count = np.zeros(2, dtype=np.int64) + output_count = np.zeros(2, dtype=np.int64) + for _ in range(200): + events = rng.random(len(inputs)) < probability + jumps[inputs] = events * 68.75 + input_count += [ + int(events[: len(left_ids)].sum()), + int(events[len(left_ids) :].sum()), + ] + brain.step(jumps) + output_count += np.asarray( + brain.observe_selected(outputs, ("spikes",))["spikes"], dtype=np.int64 + ) + neural = brain.observe_selected(outputs, ("rates_hz",))["rates_hz"] + action = motor_readout(*neural) + world.apply(action, 20) + return { + **world.scene(), + "odor": odor, + "input_rate_hz": rates.tolist(), + "input_events": input_count.tolist(), + "output_spikes": output_count.tolist(), + "output_rates_hz": list(neural), + "action": action, + } + + for frame in range(100): + record = advance_frame() + run["frames"].append(record) + if frame == 19: + # A saved body state plus brain delay buffers and sensory RNG, + # restored before the next frame. No hidden renderer state. + saved_brain = json.loads(json.dumps(brain.snapshot(), allow_nan=False)) + saved_world = json.loads(json.dumps(world.snapshot(), allow_nan=False)) + saved_rng = json.loads(json.dumps(rng.bit_generator.state)) + expected_frame = advance_frame() + expected_brain = brain.snapshot() + expected_rng = rng.bit_generator.state + brain.restore(saved_brain) + world = VoxelArena.from_snapshot(saved_world) + rng.bit_generator.state = saved_rng + if ( + advance_frame() != expected_frame + or brain.snapshot() != expected_brain + or rng.bit_generator.state != expected_rng + ): + raise AssertionError("joint brain/world/RNG replay differed") + brain.restore(saved_brain) + world = VoxelArena.from_snapshot(saved_world) + rng.bit_generator.state = saved_rng + run["checkpoint_replay"] = { + "at_frame": 20, + "future_frames": 1, + "full_brain_world_and_rng_exact": True, + } + if (frame + 1) % 20 == 0: + print( + json.dumps( + { + "run": run["name"], + "frame": frame + 1, + "position": world.scene()["position"], + "rates_hz": record["output_rates_hz"], + } + ), + flush=True, + ) + run["wall_seconds"] = time.perf_counter() - started + run["final_distance"] = math.dist(world.scene()["position"], world.food) + run["initial_distance"] = math.dist(run["initial"]["position"], world.food) + run["reached_food"] = world.reached_food + if silence and world.scene()["position"] != run["initial"]["position"]: + raise AssertionError("silenced control moved unexpectedly") + report["runs"].append(run) + save() + report["status"] = "completed" + except Exception as error: + report.update(status="failed", error=f"{type(error).__name__}: {error}") + raise + finally: + report["finished_utc"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + save() + + +if __name__ == "__main__": + main() diff --git a/scripts/validate_cuda.py b/scripts/validate_cuda.py new file mode 100644 index 0000000..6a3973e --- /dev/null +++ b/scripts/validate_cuda.py @@ -0,0 +1,142 @@ +"""Actual-device conformance gate and small-model latency report; never an emulator.""" + +import argparse +import hashlib +import json +import os +import platform +import statistics +import subprocess +import sys +import tempfile +import time +import xml.etree.ElementTree as ET +from datetime import datetime, timezone +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def validate(report): + import numpy as np + + from flybrain import FlyBrain + from flybrain.backends.cuda import _load_cupy + + cp = _load_cupy() + device = cp.cuda.runtime.getDeviceProperties(cp.cuda.Device().id) + name = device["name"] + report["environment"] = { + "python": platform.python_version(), + "platform": platform.platform(), + "numpy": np.__version__, + "cupy": cp.__version__, + "device": name.decode() if isinstance(name, bytes) else name, + "compute_capability": [device["major"], device["minor"]], + "device_memory_bytes": device["totalGlobalMem"], + "cuda_driver": cp.cuda.runtime.driverGetVersion(), + "cuda_runtime": cp.cuda.runtime.runtimeGetVersion(), + } + # A positive device count is not a conformance pass. Require every hardware + # test to execute successfully; a skipped test fails this gate. + with tempfile.TemporaryDirectory() as folder: + results = Path(folder) / "pytest.xml" + subprocess.run( + [ + sys.executable, + "-m", + "pytest", + "tests/test_cuda_hardware.py", + "-q", + f"--junitxml={results}", + ], + cwd=ROOT, + env={**os.environ, "FLYBRAIN_REQUIRE_CUDA": "1"}, + check=True, + timeout=300, + ) + suites = list(ET.parse(results).getroot().iter("testsuite")) + counts = { + k: sum(int(s.get(k, 0)) for s in suites) + for k in ("tests", "failures", "errors", "skipped") + } + if counts["tests"] < 12 or any(counts[k] for k in ("failures", "errors", "skipped")): + raise RuntimeError(f"Incomplete CUDA validation: {counts}") + report["conformance"] = counts + report["benchmarks"] = [] + for model in ("toy", ROOT / "models/male-cns-escape-v1/model.json"): + loaded = FlyBrain.load(model) + measurements = {} + for backend in ("cpu", "cuda"): + cold_start = time.perf_counter() + brain = FlyBrain.load(model, backend=backend) + measurements[backend + "_load_seconds"] = time.perf_counter() - cold_start + brain.stimulate(brain.sensory_channels[0], duration_ms=4000) + brain.advance(duration_ms=100) + times = [] + for _ in range(3): + start = time.perf_counter() + brain.advance(duration_ms=1000) + brain.action() + if backend == "cuda": + cp.cuda.get_current_stream().synchronize() + times.append(time.perf_counter() - start) + measurements[backend] = {"seconds": times, "median_seconds": statistics.median(times)} + report["benchmarks"].append( + { + "model": loaded.model.name, + "fingerprint": loaded.model.fingerprint, + "neurons": len(loaded.model.neuron_ids), + "edges": len(loaded.model.synapses), + "config": loaded.config.to_dict(), + "simulated_ms_per_repeat": 1000, + "warmup_ms": 100, + "timings": measurements, + } + ) + report["limitations"] = [ + "Correctness-first CUDA: host current upload and scalar synchronization each tick.", + "These timings cover only toy and 313-cell circuit, not whole-brain scaling.", + "Loading/compilation reported separately; execution includes inputs and action readout.", + "Conformance applies to tested cases and reported device/software, not all CUDA hardware.", + ] + report["status"] = "passed" + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + report = { + "format": "flybrain-cuda-validation", + "schema_version": 1, + "status": "failed", + "started_at": datetime.now(timezone.utc).isoformat(), + "source_sha256": { + str(p.relative_to(ROOT)): hashlib.sha256(p.read_bytes()).hexdigest() + for p in sorted( + list((ROOT / "src/flybrain").rglob("*.py")) + + [ROOT / "tests/test_cuda_hardware.py", Path(__file__).resolve()] + ) + }, + } + code = 0 + try: + validate(report) + except Exception as error: + report["error"] = f"{type(error).__name__}: {error}" + code = 1 + report["finished_at"] = datetime.now(timezone.utc).isoformat() + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + print( + json.dumps( + {"status": report["status"], "output": str(args.output), "error": report.get("error")}, + indent=2, + ) + ) + return code + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/validate_cuda_modal.py b/scripts/validate_cuda_modal.py new file mode 100644 index 0000000..dbb6977 --- /dev/null +++ b/scripts/validate_cuda_modal.py @@ -0,0 +1,59 @@ +"""Optional, manually invoked Modal T4 validation. Running this can incur charges. + +Only the source package, one test file, validator and small checked-in model are +uploaded. No home directory, credential files, raw datasets or .git are included. +""" + +import json +from pathlib import Path + +import modal + +ROOT = Path(__file__).resolve().parents[1] +image = ( + modal.Image.debian_slim(python_version="3.12") + .pip_install("numpy==2.2.6", "pytest==8.4.2", "cupy-cuda12x[ctk]==14.2.0") + .add_local_dir(ROOT / "src/flybrain", "/project/src/flybrain", ignore=["**/__pycache__/**"]) + .add_local_file(ROOT / "tests/test_cuda_hardware.py", "/project/tests/test_cuda_hardware.py") + .add_local_file(ROOT / "scripts/validate_cuda.py", "/project/scripts/validate_cuda.py") + .add_local_file( + ROOT / "models/male-cns-escape-v1/model.json", + "/project/models/male-cns-escape-v1/model.json", + ) + .env({"PYTHONPATH": "/project/src"}) +) +app = modal.App("flybrain-cuda-validation", image=image) + + +@app.function( + gpu="T4", + cpu=1, + memory=4096, + timeout=600, + retries=0, + max_containers=1, + min_containers=0, + scaledown_window=2, +) +def validate(): + import subprocess + import sys + + path = Path("/tmp/cuda-validation.json") + result = subprocess.run( + [sys.executable, "/project/scripts/validate_cuda.py", "--output", str(path)], + cwd="/project", + timeout=480, + ) + report = json.loads(path.read_text()) if path.exists() else {"status": "failed"} + report["runner_exit_code"] = result.returncode + return report + + +@app.local_entrypoint() +def main(output: str = "cuda-validation.json"): + report = validate.remote() + Path(output).write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + print(f"CUDA report: {output}; status: {report['status']}") + if report["status"] != "passed" or report["runner_exit_code"] != 0: + raise RuntimeError("CUDA validation failed; inspect the report and remote logs") diff --git a/scripts/validate_fullbrain.py b/scripts/validate_fullbrain.py new file mode 100644 index 0000000..b72cb0b --- /dev/null +++ b/scripts/validate_fullbrain.py @@ -0,0 +1,336 @@ +"""Opt-in full FlyWire v783 hardware benchmark; never substitutes a subgraph. + +Uses the SDK's unmodified CPUBackend / CUDABackend and Connectome classes. +Download the two official Zenodo files before running. No network access here. +""" + +import argparse +import gc +import hashlib +import json +import platform +import time +from pathlib import Path + +import numpy as np + +from flybrain.backends.cpu import CPUBackend +from flybrain.backends.cuda import CUDABackend +from flybrain.config import LIFConfig, finite_number +from flybrain.model import Connectome, Synapse + +SOURCES = { + "proofread_root_ids_783.npy": "e0e6c19732fd8c7a4e39a2d170105421", + "proofread_connections_783.feather": "f48f972d262323a102aed49af1396b8a", +} + + +def digest(path, algorithm="sha256"): + h = hashlib.new(algorithm) + with Path(path).open("rb") as f: + for block in iter(lambda: f.read(8 * 1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def map_source_ids(ids, source_ids): + """Match large IDs exactly; mixed uint64/int64 search may promote to float64.""" + ids, source_ids = np.asarray(ids), np.asarray(source_ids) + if ids.dtype.kind not in "iu" or source_ids.dtype.kind not in "iu": + raise ValueError("Source neuron IDs must have integer storage") + if (ids < 0).any() or (source_ids < 0).any(): + raise ValueError("Source neuron IDs must be nonnegative") + ids = ids.astype(np.uint64, copy=False) + source_ids = source_ids.astype(np.uint64, copy=False) + indices = np.searchsorted(ids, source_ids) + if (indices >= len(ids)).any() or not np.array_equal(ids[indices], source_ids): + raise ValueError("Source edge references an unknown neuron") + return indices + + +def load_fullbrain(data_dir, *, contact_mv=None): + """Load every source row; optional mV weights require a synaptic mV engine. + + Default retains the historical numerical benchmark's normalized weights. + Neither weighting policy establishes biological validation. + """ + if contact_mv is not None: + contact_mv = finite_number(contact_mv, "contact_mv") + if contact_mv <= 0: + raise ValueError("contact_mv must be positive") + import pyarrow.feather as feather + + sources = {} + for filename, expected in SOURCES.items(): + path = data_dir / filename + if digest(path, "md5") != expected: + raise ValueError(f"Official source checksum mismatch: {filename}") + sources[filename] = {"md5": expected, "sha256": digest(path), "bytes": path.stat().st_size} + ids = np.sort(np.load(data_dir / "proofread_root_ids_783.npy", allow_pickle=False)) + if len(ids) != 139255 or len(np.unique(ids)) != len(ids): + raise ValueError("Expected all 139255 unique official proofread IDs") + nts = ["ach_avg", "gaba_avg", "glut_avg", "oct_avg", "ser_avg", "da_avg"] + columns = ["pre_pt_root_id", "post_pt_root_id", "syn_count", *nts] + table = feather.read_table(data_dir / "proofread_connections_783.feather", columns=columns) + raw_pre = table[columns[0]].to_numpy() + raw_post = table[columns[1]].to_numpy() + pre, post = map_source_ids(ids, raw_pre), map_source_ids(ids, raw_post) + counts = table["syn_count"].to_numpy().astype(np.int64) + if (counts <= 0).any(): + raise ValueError("Source synapse counts must be positive") + # Explicit assumed Dale-like sign per source neuron: weighted mean NT scores + # over all outgoing contacts. GABA/glutamate negative; other labels positive. + # This approximation does not implement receptors or neuromodulation. + scores = np.zeros((len(ids), len(nts))) + nonfinite = 0 + for i, name in enumerate(nts): + values = table[name].to_numpy() + nonfinite += int((~np.isfinite(values)).sum()) + scores[:, i] = np.bincount(pre, weights=np.nan_to_num(values) * counts, minlength=len(ids)) + labels = scores.argmax(axis=1) + signs = np.where(np.isin(labels, [1, 2]), -1.0, 1.0) + incoming = np.bincount(post, weights=counts, minlength=len(ids)) + weights = ( + signs[pre] * 2.5 * counts / incoming[post] + if contact_mv is None + else signs[pre] * contact_mv * counts + ) + report = { + "dataset": "FlyWire v783 / Zenodo 10676866", + "source_url": "https://zenodo.org/records/10676866", + "sources": sources, + "neurons": len(ids), + "source_rows": len(table), + "retained_rows": len(table), + "discarded_rows": 0, + "synaptic_contacts": int(counts.sum()), + "rows_are": "neuron-pair/neuropil aggregates, retained as parallel weighted edges", + "isolated_neurons": int( + ( + np.bincount(pre, minlength=len(ids)) + np.bincount(post, minlength=len(ids)) == 0 + ).sum() + ), + "nt_score_nonfinite_entries": nonfinite, + "neurons_without_nt_evidence": int((scores.sum(axis=1) == 0).sum()), + "assumed_sign_counts": { + "positive": int((signs > 0).sum()), + "negative": int((signs < 0).sum()), + }, + "weight_policy": ( + "incoming abs-sum 2.5; GABA/GLUT negative, others/unknown positive" + if contact_mv is None + else ( + f"{contact_mv} mV/contact; GABA/GLUT negative, " + "others/unknown positive; no normalization" + ) + ), + "weight_units": "normalized" if contact_mv is None else "mV", + "minimum_contacts": 1, + "additional_pruning": False, + "graph_arrays_sha256": hashlib.sha256( + ids.tobytes() + + pre.astype(" 0, "Parity must exercise active spikes, not only rest" + report["parity"] = { + "ticks": 200, + "all_neurons_compared_every": 25, + "exact_spikes_and_refractory": True, + "atol": 1e-10, + "total_spikes": spikes, + "checks": parity, + } + print("Full-network CPU/CUDA parity passed", flush=True) + save() + checkpoint = json.loads(json.dumps(gpu.snapshot(), allow_nan=False)) + for tick in range(30): + gpu.step(currents[tick % 8]) + end = gpu.snapshot() + gpu.restore(checkpoint) + for tick in range(30): + gpu.step(currents[tick % 8]) + assert gpu.snapshot() == end + report["checkpoint_replay_exact"] = True + timings = {} + for name, backend in (("cpu", cpu), ("cuda", gpu)): + for _ in range(100): + backend.step(currents[0]) + durations = [] + for repeat in range(3): + start = time.perf_counter() + for tick in range(1000): + backend.step(currents[(tick // 125) % 8]) + if name == "cuda": + gpu._stream.synchronize() + durations.append(time.perf_counter() - start) + print( + f"{name} repeat {repeat}: {durations[-1]:.3f}s per simulated second", flush=True + ) + timings[name] = { + "wall_seconds": durations, + "median_seconds": float(np.median(durations)), + "simulated_ms_per_repeat": 1000, + "warmup_ticks": 100, + "includes": "SDK step, host input upload and per-tick error sync", + } + report["timings"] = timings + save() + trace, population_spikes = [], 0 + ever_active = np.zeros(n, dtype=bool) + start = time.perf_counter() + # Inspect actual device state under its owning stream every tick, accumulating + # spikes on device; periodic complete snapshots check finite bounded states. + with gpu._device, gpu._stream: + active = cp.zeros(n, dtype=cp.bool_) + total = cp.zeros((), dtype=cp.int64) + for tick in range(args.stability_ticks): + gpu.step(currents[(tick // 125) % 8]) + with gpu._device, gpu._stream: + active |= gpu._spikes + total += gpu._spikes.sum() + if tick % 100 == 99: + state = gpu.snapshot() + voltage, rates = np.asarray(state["voltage"]), np.asarray(state["rates_hz"]) + assert np.isfinite(voltage).all() and np.isfinite(rates).all() + assert (voltage < 1.0).all() and (rates >= 0).all() and (rates <= 1000).all() + trace.append( + { + "tick": gpu.tick, + "voltage_min": float(voltage.min()), + "voltage_max": float(voltage.max()), + "mean_rate_hz": float(rates.mean()), + "spiking_neurons": int(sum(state["spikes"])), + } + ) + if tick % 1000 == 999: + print(f"Stability {tick + 1}/{args.stability_ticks} ticks", flush=True) + with gpu._device, gpu._stream: + population_spikes = int(total.get()) + ever_active = active.get() + report["stability"] = { + "ticks": args.stability_ticks, + "simulated_ms": args.stability_ticks, + "wall_seconds_including_observation": time.perf_counter() - start, + "total_spikes": population_spikes, + "ever_spiking_neurons": int(ever_active.sum()), + "trace": trace, + } + report["gpu_pool_used_bytes"] = cp.get_default_memory_pool().used_bytes() + report["gpu_pool_reserved_bytes"] = cp.get_default_memory_pool().total_bytes() + report["host_peak_rss_kib_linux"] = None + if platform.system() == "Linux": + import resource + + report["host_peak_rss_kib_linux"] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + report["status"] = "passed" + except Exception as exc: + report["status"] = "failed" + report["error"] = f"{type(exc).__name__}: {exc}" + raise + finally: + save() + print("FULL BRAIN CUDA VALIDATION PASSED", flush=True) + + +if __name__ == "__main__": + main() diff --git a/scripts/validate_synaptic_cuda.py b/scripts/validate_synaptic_cuda.py new file mode 100644 index 0000000..2d51733 --- /dev/null +++ b/scripts/validate_synaptic_cuda.py @@ -0,0 +1,80 @@ +"""Fail-closed actual-GPU gate for the experimental synaptic mV engine.""" + +import argparse +import hashlib +import json +import os +import platform +import subprocess +import sys +import tempfile +import xml.etree.ElementTree as ET +from pathlib import Path + + +def main(): + from flybrain.backends.cuda import _load_cupy + + root = Path(__file__).resolve().parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", required=True, type=Path) + args = parser.parse_args() + report = { + "status": "failed", + "python": platform.python_version(), + "source_sha256": { + str(p.relative_to(root)): hashlib.sha256(p.read_bytes()).hexdigest() + for p in [ + *sorted((root / "src/flybrain/experimental").glob("*.py")), + root / "tests/test_synaptic_cuda_hardware.py", + Path(__file__), + ] + }, + } + code = 0 + try: + cp = _load_cupy() + props = cp.cuda.runtime.getDeviceProperties(cp.cuda.Device().id) + report["device"] = { + "name": props["name"].decode(), + "cupy": cp.__version__, + "memory_bytes": props["totalGlobalMem"], + "driver": cp.cuda.runtime.driverGetVersion(), + "runtime": cp.cuda.runtime.runtimeGetVersion(), + } + with tempfile.TemporaryDirectory() as directory: + xml = Path(directory) / "pytest.xml" + subprocess.run( + [ + sys.executable, + "-m", + "pytest", + "tests/test_synaptic_cuda_hardware.py", + "-q", + f"--junitxml={xml}", + ], + cwd=root, + env={**os.environ, "FLYBRAIN_REQUIRE_CUDA": "1"}, + check=True, + timeout=300, + ) + suites = ET.parse(xml).getroot().iter("testsuite") + counts = {k: 0 for k in ("tests", "skipped", "errors", "failures")} + for suite in suites: + for k in counts: + counts[k] += int(suite.get(k, 0)) + report["cases"] = counts + if counts["tests"] != 5 or any(counts[k] for k in ("skipped", "errors", "failures")): + raise RuntimeError("all five actual GPU cases must execute and pass") + report["status"] = "passed" + except Exception as error: + report["error"] = f"{type(error).__name__}: {error}" + code = 1 + finally: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n") + raise SystemExit(code) + + +if __name__ == "__main__": + main() diff --git a/scripts/validate_synaptic_reference.py b/scripts/validate_synaptic_reference.py new file mode 100644 index 0000000..95d1877 --- /dev/null +++ b/scripts/validate_synaptic_reference.py @@ -0,0 +1,168 @@ +"""Independent Brian2 oracle for the experimental mV synaptic CPU engine. + +Install Brian2==2.9.0 and numpy==1.26.4 separately; no Brian2 runtime dependency. +Tests explicit scheduled input events (not matching two different RNGs). +""" + +import argparse +import hashlib +import json +from pathlib import Path + +import numpy as np + +from flybrain import Connectome, Synapse +from flybrain.experimental.synaptic import SynapticCPU, SynapticLIFConfig + + +def compare_case(config): + import brian2 as b + + b.start_scope() + b.prefs.codegen.target = "numpy" + b.defaultclock.dt = config.dt_ms * b.ms + # Parallel excitatory edges, inhibitory input, recurrent excitation, autapse, + # and a disconnected neuron. Counts below are fixture choices, not fly data. + edges = [ + (0, 2, 40.0), + (0, 2, 15.0), + (1, 2, -33.0), + (2, 3, 55.0), + (3, 2, 10.0), + (2, 2, 2.0), + (3, 4, -20.0), + ] + model = Connectome( + "oracle-fixture", + tuple(str(i) for i in range(6)), + tuple(Synapse(*edge) for edge in edges), + {}, + {}, + {}, + ) + cpu = SynapticCPU(model, config, input_ids=("0", "1"), weight_units="mV") + n_steps = round(100 / config.dt_ms) + # Rounding here constructs a known clock-aligned experimental schedule. + events = sorted( + [(round(t / config.dt_ms), 0) for t in [1.0, 5.0, 10.0, 17.0, 25.0, 40.0]] + + [(round(t / config.dt_ms), 1) for t in [6.0, 17.0, 23.0, 40.0]] + + [(round(1 / config.dt_ms) + offset, 0) for offset in (1, 2)] + ) + injected = np.zeros((n_steps, 6)) + for tick, neuron in events: + injected[tick, neuron] = 68.75 # 0.275 mV * 250 in the published stimulus. + namespace = { + "v_rest": config.rest_mv * b.mV, + "v_reset": config.reset_mv * b.mV, + "v_threshold": config.threshold_mv * b.mV, + "tau_mem": config.membrane_tau_ms * b.ms, + "tau_syn": config.synapse_tau_ms * b.ms, + } + # Use the SAME symbol when the time constants are equal so Brian2 can + # simplify the degenerate linear system analytically, rather than evaluate + # a generic expression containing division by (tau_mem - tau_syn) at zero. + syn_tau_symbol = "tau_mem" if config.synapse_tau_ms == config.membrane_tau_ms else "tau_syn" + group = b.NeuronGroup( + 6, + f"""dv/dt = (v_rest - v + g)/tau_mem : volt (unless refractory) + dg/dt = -g/{syn_tau_symbol} : volt (unless refractory) + rfc : second""", + threshold="v > v_threshold", + reset="v = v_reset; g = 0*mV", + refractory="rfc", + method="linear", + namespace=namespace, + ) + group.v = config.rest_mv * b.mV + group.g = 0 * b.mV + group.rfc = config.refractory_ms * b.ms + group.rfc[:2] = 0 * b.ms + synapses = b.Synapses( + group, group, "weight : volt", on_pre="g_post += weight", delay=config.delay_ms * b.ms + ) + synapses.connect(i=[e[0] for e in edges], j=[e[1] for e in edges]) + synapses.weight = np.array([e[2] for e in edges]) * b.mV + source = b.SpikeGeneratorGroup( + 2, [e[1] for e in events], np.array([e[0] for e in events]) * config.dt_ms * b.ms + ) + external = b.Synapses(source, group, on_pre="v_post += 68.75*mV") + external.connect(i=[0, 1], j=[0, 1]) + monitor = b.StateMonitor(group, ("v", "g"), record=True, when="end") + spikes = b.SpikeMonitor(group) + b.Network(group, synapses, source, external, monitor, spikes).run(n_steps * config.dt_ms * b.ms) + expected_v = np.array(monitor.v / b.mV).T + expected_g = np.array(monitor.g / b.mV).T + expected_spikes = np.zeros((n_steps, 6), dtype=bool) + expected_ticks = np.rint(np.array(spikes.t / b.ms) / config.dt_ms).astype(int) + expected_spikes[expected_ticks, np.array(spikes.i)] = True + actual_v, actual_g, actual_spikes = [], [], [] + checkpoint = None + for tick in range(n_steps): + cpu.step(injected[tick]) + observed = cpu.observe_selected(tuple(range(6))) + actual_v.append(observed["voltage_mv"]) + actual_g.append(observed["synaptic_mv"]) + actual_spikes.append(observed["spikes"]) + # Save with the first spike still queued for transmission in default case. + if tick == round(1 / config.dt_ms) + 2: + checkpoint = json.loads(json.dumps(cpu.snapshot(), allow_nan=False)) + actual_v, actual_g = np.asarray(actual_v), np.asarray(actual_g) + np.testing.assert_allclose(actual_v, expected_v, rtol=0, atol=1e-9) + np.testing.assert_allclose(actual_g, expected_g, rtol=0, atol=1e-9) + np.testing.assert_array_equal(actual_spikes, expected_spikes) + assert expected_spikes[:, 2:4].any(), "oracle must exercise downstream propagation" + final = cpu.snapshot() + cpu.restore(checkpoint) + for tick in range(cpu.tick, n_steps): + cpu.step(injected[tick]) + assert cpu.snapshot() == final, "delay-buffer checkpoint replay differs" + return { + "config": config.to_dict(), + "ticks": n_steps, + "voltage_max_error_mv": float(np.max(np.abs(actual_v - expected_v))), + "synaptic_max_error_mv": float(np.max(np.abs(actual_g - expected_g))), + "spikes_per_cell": expected_spikes.sum(axis=0).tolist(), + "spike_times_equal": True, + "checkpoint_replay_exact": True, + } + + +def main(): + import brian2 + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + report = { + "status": "running", + "oracle": "Brian2", + "version": brian2.__version__, + "numpy": np.__version__, + "cases": [], + } + args.output.parent.mkdir(parents=True, exist_ok=True) + report["implementation_sha256"] = hashlib.sha256( + (Path(__file__).parents[1] / "src/flybrain/experimental/synaptic.py").read_bytes() + ).hexdigest() + report["validation_script_sha256"] = hashlib.sha256(Path(__file__).read_bytes()).hexdigest() + try: + for config in ( + SynapticLIFConfig(), + SynapticLIFConfig(dt_ms=0.2), + SynapticLIFConfig(delay_ms=0), + SynapticLIFConfig(synapse_tau_ms=20), + ): + result = compare_case(config) + report["cases"].append(result) + print(json.dumps(result), flush=True) + report["status"] = "passed" + except Exception as error: + report["status"] = "failed" + report["error"] = f"{type(error).__name__}: {error}" + raise + finally: + args.output.write_text(json.dumps(report, indent=2, allow_nan=False) + "\n") + + +if __name__ == "__main__": + main() diff --git a/site/demos.html b/site/demos.html index ebf1af5..0c0c076 100644 --- a/site/demos.html +++ b/site/demos.html @@ -9,7 +9,7 @@

Observe. Connect.
Make it your own.

One Python session loop connects neural activity to a small game environment or synthesized sound. Run a preset, inspect the inputs and controls, then replace an adapter.

Choose the experience: the game sandbox and circuit lab run live simulations. The demos below are verified recordings from Python; playback controls inspect a run without recalculating its brain.
-

Full FlyWire brain · CUDA benchmark

All 139,255 proofread neurons and 16.85 million source rows on a real NVIDIA GPU. CPU/CUDA numerical checks, throughput, checkpoint replay and continuous simulation; this is a full-graph software benchmark, not a trained game policy.

Read the measured results →

Reflex School · reward-trained readout

A fixed real 313-neuron circuit, three learned external action weights, and an actual A16 GPU run. On 80 balanced held-out two-choice trials, accuracy changed from 50% to 100%. This is readout learning, not biological plasticity.

Compare before and after →

Godot connectome game adapter

+

Full-brain voxel world · recorded GPU experiment

All 139,255 FlyWire neurons run on an A16 GPU and control a ground body through an explicit DNa02 readout. Compare the actual trajectory with a silenced-input control. It moves, but curves away from food; no successful navigation or training is claimed.

Watch the voxel experiment →

Full FlyWire brain · CUDA benchmark

All 139,255 proofread neurons and 16.85 million source rows on a real NVIDIA GPU. CPU/CUDA numerical checks, throughput, checkpoint replay and continuous simulation; this is a full-graph software benchmark, not a trained game policy.

Read the measured results →

Reflex School · reward-trained readout

A fixed real 313-neuron circuit, three learned external action weights, and an actual A16 GPU run. On 80 balanced held-out two-choice trials, accuracy changed from 50% to 100%. This is readout learning, not biological plasticity.

Compare before and after →

Godot connectome game adapter

No CUDA required. Run an actual Godot 4 scene with the Python CPU SDK. Godot owns obstacles and movement; a replaceable encoder and neural readout supply steering. Includes pause, paired world/brain saves, restore and stop-on-disconnect behavior.

Python 3.9+ and Godot 4.7.2 · two terminals
git clone --branch v0.4.0a4 https://github.com/freeman-1984-coder/flybrain-sdk.git
 cd flybrain-sdk
diff --git a/site/experiments/fullbrain-synaptic-odor.json b/site/experiments/fullbrain-synaptic-odor.json
new file mode 100644
index 0000000..e13360f
--- /dev/null
+++ b/site/experiments/fullbrain-synaptic-odor.json
@@ -0,0 +1,18441 @@
+{
+  "schema": "flybrain-full-synaptic-odor-v1",
+  "status": "completed",
+  "started_utc": "2026-09-13T02:51:35Z",
+  "config": {
+    "dt_ms": 0.1,
+    "rest_mv": -52.0,
+    "reset_mv": -52.0,
+    "threshold_mv": -45.0,
+    "membrane_tau_ms": 20.0,
+    "synapse_tau_ms": 5.0,
+    "refractory_ms": 2.2,
+    "delay_ms": 1.8,
+    "rate_tau_ms": 50.0
+  },
+  "numpy": "2.5.3",
+  "source_sha256": {
+    "scripts/probe_synaptic_odor.py": "393ae10e825cff56b58cd80f2ab8e559b55cec600886ae85131427545d1cffdf",
+    "scripts/validate_fullbrain.py": "4d543ed43e38cbed609d0cd54003f76049347844d7fa8af58d1ac25753157370",
+    "scripts/build_olfactory_map.py": "00c87876ce4dd1475f225333ea5776b33f1473da6d98157f21f3fd3f98efa8ae",
+    "src/flybrain/experimental/__init__.py": "570d34f551fd63b33bff564c022ad01fc463b0e1649d9f7035e361b421533bd5",
+    "src/flybrain/experimental/synaptic.py": "4c3b7b48e659314a1dd839819ddce4bdd1622d21dd8753400035c1df8deaa726",
+    "src/flybrain/experimental/synaptic_cuda.py": "8b0bc40f8bf0075e6d81caadf3393673110aa48b66415303a08020cb9055dfa8"
+  },
+  "protocol": {
+    "pre_ms": 100,
+    "odor_ms": 500,
+    "post_ms": 200,
+    "trace_interval_ms": 10,
+    "seed": 20260913,
+    "rng": "NumPy PCG64",
+    "input_rate_hz": 150.0,
+    "input_jump_mv": 68.75,
+    "contact_mv": 0.275,
+    "background_input": 0,
+    "sensory_model": "Bernoulli p=rate*dt, at most one input event/cell/tick",
+    "stimulus": "Or42b/DM1 single-channel food-odor approximation",
+    "calibration": "nominal research parameters; not a measured banana dose",
+    "input_refractory_ms": 0,
+    "parameter_source": "https://github.com/philshiu/Drosophila_brain_model/blob/91bdd1e7dcf193f3e7ca5a8933497fcef63b7960/model.py"
+  },
+  "conditions": [
+    {
+      "name": "no_odor",
+      "silenced": false,
+      "input_events": [
+        0,
+        0
+      ],
+      "trace": [
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+      "ORN_DM1_right": 33,
+      "DNa02_left": 1,
+      "DNa02_right": 1,
+      "ALPN": 685,
+      "MBON": 96,
+      "descending": 1303
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+  },
+  "device": {
+    "name": "NVIDIA A16-8Q",
+    "memory_bytes": 8394047488,
+    "cupy": "14.2.0",
+    "driver": 12040,
+    "runtime": 12090
+  },
+  "full_graph_cpu_cuda_parity": {
+    "ticks": 200,
+    "observations": [
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+        "tick": 40,
+        "errors": {
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+          "synaptic_mv": 0.0,
+          "rates_hz": 0.0
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+      },
+      {
+        "tick": 80,
+        "errors": {
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+          "rates_hz": 0.0
+        }
+      },
+      {
+        "tick": 120,
+        "errors": {
+          "voltage_mv": 0.0,
+          "synaptic_mv": 0.0,
+          "rates_hz": 0.0
+        }
+      },
+      {
+        "tick": 160,
+        "errors": {
+          "voltage_mv": 0.0,
+          "synaptic_mv": 0.0,
+          "rates_hz": 0.0
+        }
+      },
+      {
+        "tick": 200,
+        "errors": {
+          "voltage_mv": 0.0,
+          "synaptic_mv": 0.0,
+          "rates_hz": 0.0
+        }
+      }
+    ],
+    "input_events": 201,
+    "status": "passed",
+    "json_checkpoint_replay_ticks": 20,
+    "wall_seconds": 27.289984679999975
+  },
+  "findings": {
+    "input_and_silencing_controls": "passed",
+    "bilateral_descending_spikes": 2883,
+    "interpretation": "Report includes absent responses. Descending spikes alone do not prove odor-directed navigation. No banana recognition, flight or learning tested."
+  },
+  "finished_utc": "2026-09-13T03:01:55Z"
+}
diff --git a/site/experiments/fullbrain-voxel.json b/site/experiments/fullbrain-voxel.json
new file mode 100644
index 0000000..fdf1ebd
--- /dev/null
+++ b/site/experiments/fullbrain-voxel.json
@@ -0,0 +1,12574 @@
+{
+  "schema": "flybrain-voxel-recording-v1",
+  "status": "completed",
+  "started_utc": "2026-09-13T03:04:58Z",
+  "config": {
+    "dt_ms": 0.1,
+    "rest_mv": -52.0,
+    "reset_mv": -52.0,
+    "threshold_mv": -45.0,
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+    "synapse_tau_ms": 5.0,
+    "refractory_ms": 2.2,
+    "delay_ms": 1.8,
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+  "seed": 20260913,
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+    "scripts/validate_fullbrain.py": "4d543ed43e38cbed609d0cd54003f76049347844d7fa8af58d1ac25753157370",
+    "scripts/build_olfactory_map.py": "00c87876ce4dd1475f225333ea5776b33f1473da6d98157f21f3fd3f98efa8ae",
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+    "src/flybrain/experimental/synaptic.py": "4c3b7b48e659314a1dd839819ddce4bdd1622d21dd8753400035c1df8deaa726",
+    "src/flybrain/experimental/synaptic_cuda.py": "8b0bc40f8bf0075e6d81caadf3393673110aa48b66415303a08020cb9055dfa8",
+    "src/flybrain/experimental/voxel_arena.py": "d358df33830ca4d96fe8c5c8549cef383760213d00f8e153f64e2b9c5e7484b3"
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+  "protocol": {
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+    "body_step_ms": 20,
+    "neural_steps_per_frame": 200,
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+    "odor_rate_hz": "180*c/(0.2+c); c is a dimensionless antenna sample",
+    "stimulus": "DM1 single-channel food odor, not a full banana mixture",
+    "readout": "speed=clip(0.03*(DNa02_L+DNa02_R),0,3); turn=2*tanh((DNa02_R-DNa02_L)/40)",
+    "units": "rates Hz; speed game units/s; turn radians/s",
+    "limitations": [
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+      "Kinematic walking body, not flight or musculoskeletal simulation",
+      "Toy Gaussian odor field without wind, turbulence or obstacle occlusion",
+      "Recorded GPU frames, not live browser inference",
+      "No direct motor input, background drive, odor-bearing controller or rescue reflex",
+      "Success or failure of both runs reported without seed selection"
+    ]
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+  },
+  "mapping_input_units_note": "Actual input units and strengths are in protocol.",
+  "device": {
+    "name": "NVIDIA A16-8Q",
+    "memory_bytes": 8394047488,
+    "cupy": "14.2.0",
+    "driver": 12040,
+    "runtime": 12090
+  },
+  "finished_utc": "2026-09-13T03:14:25Z"
+}
diff --git a/site/foraging.html b/site/foraging.html
new file mode 100644
index 0000000..dff590c
--- /dev/null
+++ b/site/foraging.html
@@ -0,0 +1,27 @@
+
+
+
+
+Full-brain voxel odor experiment · flybrain-sdk
+
+
+
+
+
+

+
FLYBRAIN / VOXEL EXPERIMENT神经信号对照实验 ↗

+
把完整脑，放进一个小世界。

+
地上的香蕉代表一个简化食物气味源。两侧触角把浓度变成神经输入；完整 FlyWire 网络在 NVIDIA GPU 上演算，DNa02 神经活动经过明确的动作映射，控制方块世界里的步行身体。

+
GPU 实验录制 · 浏览器回放 · No CUDA required to view

+
All 139,255 FlyWire neurons and 16,847,997 connection rows run on an NVIDIA A16 GPU. The fixed neural readout moves the body but fails to reach food; silencing sensory inputs leaves it stationary. This is recorded simulation, not live inference or learned navigation.

+
完整 FlyWire 网络—
本组是否接触食物—
最终距食物 / 游戏单位—

+

+
使用下方实验摘要和 JSON 记录查看结果。
正在读取实际 GPU 记录……

+
0 / 2000 ms

+
绿色：实际轨迹黄色：食物气味源拖动场景：旋转观察角度

+
这一帧，脑子输出了什么？
等待实际记录。
如何读这个实验
比较正常神经输入和关闭嗅觉入口的同初始状态对照。轨迹来自实际记录；网页不会让角色自动朝向香蕉。
结果以记录为准，包括没有接近或接触食物的情况。

+
哪些是连接组，哪些是我们的假设？
全部脑神经元和连接来自 FlyWire v783。输入只覆盖一个 DM1 食物气味通道，不代表完整香蕉气味或视觉识别。脑模型采用简化的突触 LIF 动力学。
动作层是未经训练的工程映射：DNa02 两侧放电频率之差控制转向、频率之和控制速度。尤其是速度映射没有生物学验证。身体是地面运动模型，没有飞行、肌肉或六足步态仿真。记录中的移动不等于可靠觅食。
下载全部帧与参数 · 复现方法与限制 · 开源仓库

+
页面只播放实验数据，不在浏览器里运行全脑 CUDA。

+

diff --git a/site/fullbrain.html b/site/fullbrain.html
index 9fcb1bd..d78885c 100644
--- a/site/fullbrain.html
+++ b/site/fullbrain.html
@@ -9,19 +9,35 @@
 
这次加载全部已校对的 FlyWire v783 脑神经元，运行 SDK 自己的 CUDA 后端。没有抽取小回路，也没有额外删除弱连接。原始报告、校验值和复现脚本均公开。

 
全部已校对神经元139,255包含无连接的神经元
保留的连接表行16,847,997按神经元对与脑区汇总
这些行代表的突触接触54,492,922官方表内的接触计数总和

 
通过了什么：完整图的 LIF 仿真、全体神经元的 CPU/CUDA 数值对照、状态恢复，以及连续运行检查。范围：使用真实解剖连接，动力学、输入和递质作用是简化假设。这不是完整果蝇行为或意识的复现，也不是训练成果。

-
新增功能验证：气味入口已激活，但基准测试权重阻止了下游放电。当前配置不能用于证明果蝇会追逐食物。

+
新的全脑实验：采用经验证的突触动力学后，气味刺激已传到下降神经元。动作方向与可靠觅食仍需独立验证。

 
模拟 1 秒，需要多久？
同一台 NVIDIA A16-8Q 服务器、同一个网络和输入。预热后各测三次，以下取中位数；加载与编译单独计时。

 
后端模拟 1 秒的实际耗时相对实时
NumPy CPU110.955 秒0.01×
CuPy CUDA11.552 秒0.09×

 
本次 CUDA 吞吐为 CPU 的 9.61 倍。计时包含逐步输入上传和错误同步，不包括数据下载、模型加载、编译和网页渲染。这个数字仅适用于此配置和测试输入。

 
逐项核对
  • 两份原始文件通过官方 MD5 校验；报告另存 SHA-256。
  • 16,847,997 行原始连接全部保留，额外丢弃 0 行；最低接触数为 1。
  • 先验证 20 步静息，再比较 200 步有活动输入；每 25 步检查全部神经元。脉冲和不应期计数完全一致，电压最大差异 0。
  • 完整后端状态通过 JSON 往返，恢复后 30 步结果完全复现。
  • 另外连续运行 10,000 步，即 10 秒模拟时间；每 100 步检查全部状态，未出现非有限或越界状态。
  • 连续运行期间记录 93,609,984 次脉冲，139,254 个神经元至少放电一次。输入是人工电流，不是视觉重建。

 
实际资源占用
测试卡显存 7.82 GiB；结束时 CuPy 内存池在用约 200.8 MiB。这不包含驱动和全部 CUDA 上下文开销，也不代表显存峰值。进程主机内存峰值约 6.79 GiB；当前导入器使用 Python 对象，因此主机内存开销明显大于纯图数组。

-
气味输入：能激活入口，还不能驱动觅食

+
打开完整脑方块环境回放 → 正常神经输入驱动了移动，但当前动作映射让角色偏离食物；关闭输入的对照保持静止。

+
新模型：气味信号已经传到下游

+
2026 年 9 月 13 日，完整 FlyWire 网络在真实 A16-8Q 上换用独立验证的突触 LIF 模型：包含突触衰减、1.8 毫秒传递延迟和明确的毫伏单位。保留所有连接，不再使用阻止信号传播的输入权重归一化。

+

+
+
+
+
+
+
条件嗅觉投射脉冲蘑菇体输出脉冲下降神经元脉冲DNa02 左 / 右
无气味0000 / 0
左侧气味23,2983,2862,77829 / 6
右侧气味23,3053,1752,79932 / 8
双侧气味23,5033,2762,88332 / 9
双侧气味，关闭嗅觉入口0000 / 0

+
信号传播已观察到，但不能直接推断会追食物。左右气味都产生了 DNa02 左侧较强的响应，没有出现简单的左右反转。这是当前动作映射需要解决的问题，不能在网页里悄悄补上自动寻路。

+
这里比较的是累计脉冲。刺激刚开始时存在短暂的左右差异，因此不能断言完全没有方向信息。另外，双侧刺激停止后的 200 毫秒内，下降神经元仍新增了 859 次脉冲；这种持续活动需要进一步校准，不能直接称作生物记忆。

+
新引擎通过 5 组真实 GPU 检查；完整图在 200 个有嗅觉输入的时间步中，5 个检查点的全体神经元电压、突触状态和频率最大差异为 0。全脑状态连同输入随机数状态，恢复后 20 步完全重现。同机 204 项软件测试通过，零跳过。

+
每组包含 100 毫秒静息、500 毫秒刺激、200 毫秒恢复，以 0.1 毫秒步长运行。每组实际耗时约 102–104 秒，包含记录开销。这不是实时模拟，也不与上方不同参数的旧基准直接比较。输入是 Or42b/DM1 单通道食物气味近似，尚未校准为香蕉的真实化学剂量。

+
下载新模型五组原始记录 来源、参数与复现证据 ↗

+
Full FlyWire synaptic CUDA experiment: sensory-only input reaches ALPN, MBON and descending populations; matched no-odor and silenced-input controls remain silent. DNa02 laterality does not reverse with stimulus side. Navigation is not established.

+
旧基准配置：为什么信号停在入口

 
2026 年 9 月 13 日，在 NVIDIA A16-8Q 上运行相同的完整 139,255 神经元网络，只刺激经标注确认的 68 个 ORN_DM1 感觉神经元。每组从同一静息状态开始，包含 100 毫秒静息、500 毫秒刺激和 200 毫秒恢复。

 
输入近似乙酸乙酯响应中的 Or42b 单个通道，不是完整的香蕉气味，也不是视觉识别。

 
条件感觉入口脉冲嗅觉投射神经元脉冲下降神经元脉冲
无气味000
仅左侧气味3,50000
仅右侧气味3,30000
双侧气味6,80000
双侧气味，关闭输入神经元000

 
结果：入口刺激和关闭操作有效，但这组参数没有产生下游放电。嗅觉投射神经元在采样时刻观察到的最高电压约 0.252，阈值为 1；有微弱膜电位响应，不等于完全没有信号。

 
原因已经定位到基准测试的权重配置：每个神经元的输入连接绝对权重之和被归一化到 2.5。在当前不应期下，即使上游以允许的最高频率同步放电，无外部电流的下游电压上限也只有 0.918，低于阈值 1。因此不能靠提高气味强度解决，也不能把之前的数值测试当作已验证的感觉—动作模型。

-
这次 GPU 环境中 181 项软件测试通过。数值验证通过与行为验证通过是不同的结论。下一步是引入并验证有依据的突触衰减、传递延迟与感觉刺激，再连接三维环境中的运动输出。

+
这次 GPU 环境中 181 项软件测试通过。数值验证通过与行为验证通过是不同的结论。上方的新模型实验已经补充并验证突触衰减、传递延迟与感觉刺激；本节保留旧配置的负面结果，便于复现和比较。

 
下载五组完整实验记录 参数诊断与复现说明 ↗

 
Full-brain olfactory CUDA probe: sensory inputs fire, downstream spiking absent with the benchmark normalization. This is a recorded negative functional result, not live inference, banana recognition, or successful navigation.

 
开发者如何复现
使用实验分支 feat/cuda-reference，按复现指南下载官方数据并安装可选 CUDA 依赖。

diff --git a/site/index.html b/site/index.html
index b2ead23..3a04e9e 100644
--- a/site/index.html
+++ b/site/index.html
@@ -17,4 +17,4 @@
 brain.save("brain.checkpoint.json")
 restored = FlyBrain.restore(
     "brain.checkpoint.json"
-)
What runs today: a synthetic offline circuit and an opt-in, 313-neuron real MaleCNS subgraph on your CPU. Select cells, inject currents, inspect activity and bind your own output channels. Try it in the browser, export a recording for Python replay, or download an editable HTML demo. The real model uses assumed dynamics. The experimental CUDA branch now has a complete FlyWire brain benchmark: 139,255 neurons. The released package remains CPU-only; WASM is planned.
A small API, from input to action

Keep the game loop yours.

01 / STIMULATE

Send a sensory signal

Food, looming on the left or right, and touch are mapped to explicit inputs in the demo circuit.

02 / STEP

Advance neural time

Run fixed simulation ticks on NumPy. Inspect voltages, spikes and firing rates. No GPU setup.

03 / ACT

Read motor intensity

Bind your own named output channels, or use the toy walk, turn and jump values. Save an experiment and continue its trajectory.

A model catalog, not a giant install

Download only what you want to use.

Source URLs, versions, sizes and licenses live in a lightweight catalog. Assets stream to a local cache when you explicitly request them.

Model sourceTodayDownload
Toy LIF circuitRunnable, syntheticIncluded
MaleCNS escape circuitRunnable, experimental real subgraph3.8 MB, SHA256 verified
MaleCNS v1.0Raw connectome dataChoose connections, annotations or transmitters
FlyWire v783Raw connectome dataChoose connections or neuron IDs

Explore model downloads and limitations →

Build in the open

The next circuit could be yours.

Contribute a model source, a real-data importer, an engine adapter or a WASM implementation. You can start with a laptop and a focused pull request.

Start contributing
\ No newline at end of file +)
What runs today: a synthetic offline circuit and an opt-in, 313-neuron real MaleCNS subgraph on your CPU. Select cells, inject currents, inspect activity and bind your own output channels. Try it in the browser, export a recording for Python replay, or download an editable HTML demo. The real model uses assumed dynamics. The experimental CUDA branch now has a complete FlyWire brain benchmark: 139,255 neurons. Watch the full-brain GPU voxel recording: neural output drives movement, but the current readout does not reach food. The released v0.4 alpha remains CPU-only; WASM is planned.
A small API, from input to action

Keep the game loop yours.

01 / STIMULATE

Send a sensory signal

Food, looming on the left or right, and touch are mapped to explicit inputs in the demo circuit.

02 / STEP

Advance neural time

Run fixed simulation ticks on NumPy. Inspect voltages, spikes and firing rates. No GPU setup.

03 / ACT

Read motor intensity

Bind your own named output channels, or use the toy walk, turn and jump values. Save an experiment and continue its trajectory.

A model catalog, not a giant install

Download only what you want to use.

Source URLs, versions, sizes and licenses live in a lightweight catalog. Assets stream to a local cache when you explicitly request them.

Model sourceTodayDownload
Toy LIF circuitRunnable, syntheticIncluded
MaleCNS escape circuitRunnable, experimental real subgraph3.8 MB, SHA256 verified
MaleCNS v1.0Raw connectome dataChoose connections, annotations or transmitters
FlyWire v783Raw connectome dataChoose connections or neuron IDs

Explore model downloads and limitations →

Build in the open

The next circuit could be yours.

Contribute a model source, a real-data importer, an engine adapter or a WASM implementation. You can start with a laptop and a focused pull request.

Start contributing
\ No newline at end of file diff --git a/site/js/foraging.js b/site/js/foraging.js new file mode 100644 index 0000000..0f223a1 --- /dev/null +++ b/site/js/foraging.js @@ -0,0 +1,42 @@ +'use strict'; +const $=id=>document.getElementById(id), canvas=$('world'), ctx=canvas.getContext('2d'); +let data=null, run=null, playing=false, elapsed=0, last=performance.now(), angle=.65, drag=null; +const colors={top:'#344b49',left:'#253c3e',right:'#1d3037'}; +function project(x,y,z){const dx=x+1,dz=z+1,c=Math.cos(angle),s=Math.sin(angle);return [500+(dx*c-dz*s)*38,230+(dx*s+dz*c)*18-y*39]} +function polygon(points,color){ctx.fillStyle=color;ctx.beginPath();points.forEach((p,i)=>i?ctx.lineTo(...p):ctx.moveTo(...p));ctx.closePath();ctx.fill()} +function cube(x,z){const p=(dx,y,dz)=>project(x+dx,y,z+dz),sx=Math.sin(angle)>=0?.5:-.5,sz=Math.cos(angle)>=0?.5:-.5;polygon([p(-.5,0,sz),p(.5,0,sz),p(.5,1,sz),p(-.5,1,sz)],colors.left);polygon([p(sx,0,-.5),p(sx,0,.5),p(sx,1,.5),p(sx,1,-.5)],colors.right);polygon([p(-.5,1,-.5),p(.5,1,-.5),p(.5,1,.5),p(-.5,1,.5)],colors.top)} +function draw(){ + ctx.clearRect(0,0,1000,520);ctx.fillStyle='#101d25';ctx.fillRect(0,0,1000,520); + for(let x=-7;x<7;x++)for(let z=-7;z<7;z++)polygon([project(x,0,z),project(x+1,0,z),project(x+1,0,z+1),project(x,0,z+1)],(x+z)%2?'#1c302f':'#213633'); + if(!run)return; + const index=Math.max(0,Math.min(run.frames.length-1,Math.floor(elapsed/20)-1)); + const frame=elapsed<20?run.initial:run.frames[index]; + const blocks=[...frame.blocks].sort((a,b)=>(a[0]*Math.sin(angle)+a[1]*Math.cos(angle))-(b[0]*Math.sin(angle)+b[1]*Math.cos(angle))); + blocks.forEach(b=>cube(...b)); + ctx.strokeStyle='#86efc0';ctx.lineWidth=2;ctx.beginPath(); + [run.initial,...run.frames.slice(0,elapsed<20?0:index+1)].forEach((v,i)=>{const p=project(v.position[0],.03,v.position[2]);i?ctx.lineTo(...p):ctx.moveTo(...p)});ctx.stroke(); + const food=project(...frame.food);ctx.font='32px system-ui';ctx.textAlign='center';ctx.fillText('🍌',food[0],food[1]);ctx.font='12px system-ui';ctx.fillStyle='#f7d183';ctx.fillText('食物气味源',food[0],food[1]-33); + const [fx,fy,fz]=frame.position, at=project(fx,fy,fz), head=project(fx+.23*Math.cos(frame.yaw),fy,fz+.23*Math.sin(frame.yaw)); + ctx.fillStyle='#7baac777';ctx.beginPath();ctx.ellipse(at[0]-7,at[1]-7,13,5,-.4,0,Math.PI*2);ctx.ellipse(at[0]+7,at[1]-7,13,5,.4,0,Math.PI*2);ctx.fill(); + ctx.strokeStyle='#e1e8d2';ctx.lineWidth=8;ctx.lineCap='round';ctx.beginPath();ctx.moveTo(...at);ctx.lineTo(...head);ctx.stroke();ctx.lineCap='butt'; + ctx.fillStyle='#ffffff';ctx.beginPath();ctx.arc(head[0],head[1],4,0,Math.PI*2);ctx.fill(); + ctx.textAlign='left';ctx.font='14px system-ui';ctx.fillStyle='#b3c9c7';ctx.fillText(run.name==='neural_control'?'完整脑神经输出 → 身体':'关闭嗅觉输入 · 完整脑仍在 GPU 演算',25,32); + $('time').textContent=`${Math.round(elapsed/20)*20} / ${data.protocol.duration_ms} ms`;$('seek').value=elapsed; + const rates=frame.output_rates_hz||[0,0],action=frame.action||{speed:0,turn:0}; + $('neural').textContent=`DNa02 左 / 右：${rates.map(v=>v.toFixed(1)+' Hz').join(' / ')}`; + $('motor').textContent=`速度 ${action.speed.toFixed(2)} 单位/秒 · 转向 ${action.turn.toFixed(2)} 弧度/秒`; + $('odor').textContent=frame.odor?`触角浓度 左 ${frame.odor.odor_left.toFixed(3)} / 右 ${frame.odor.odor_right.toFixed(3)}（无量纲）`:'尚未施加第一帧刺激。'; +} +function select(index){run=data.runs[index];elapsed=0;playing=false;$('play').textContent='播放';$('result').textContent=run.reached_food?'已接触':'未接触';$('distance').textContent=run.final_distance.toFixed(2);$('summary').textContent=`初始距离 ${run.initial_distance.toFixed(2)} → 最终 ${run.final_distance.toFixed(2)}。${run.reached_food?'本次记录接触了食物；单次结果不能证明可靠导航。':'本次记录未接触食物。'}`;$('status').textContent=`${data.device.name} 实际录制 · 此组模拟 2 秒，耗时 ${run.wall_seconds.toFixed(1)} 秒。`;draw()} +$('play').onclick=()=>{if(elapsed>=data.protocol.duration_ms)elapsed=0;playing=!playing;$('play').textContent=playing?'暂停':'播放'}; +$('restart').onclick=()=>{elapsed=0;draw()};$('seek').oninput=e=>{elapsed=Number(e.target.value);playing=false;$('play').textContent='播放';draw()};$('condition').onchange=e=>select(Number(e.target.value)); +canvas.onpointerdown=e=>{drag=e.clientX;canvas.setPointerCapture(e.pointerId)};canvas.onpointermove=e=>{if(drag!==null){angle+=(e.clientX-drag)*.008;drag=e.clientX;draw()}};canvas.onpointerup=canvas.onpointercancel=()=>drag=null; +function animate(now){if(playing&&data){elapsed=Math.min(data.protocol.duration_ms,elapsed+Math.min(now-last,100)*Number($('speed').value));if(elapsed>=data.protocol.duration_ms){playing=false;$('play').textContent='重播'}draw()}last=now;requestAnimationFrame(animate)} +draw();requestAnimationFrame(animate); +fetch('experiments/fullbrain-voxel.json').then(r=>{if(!r.ok)throw new Error('尚无完整实验记录');return r.json()}).then(value=>{ + if(value.schema!=='flybrain-voxel-recording-v1'||value.status!=='completed'||value.model.neurons!==139255||value.runs.length!==2||value.runs.some(r=>r.frames.length!==100))throw new Error('记录不完整，暂停回放'); + data=value;$('neurons').textContent=data.model.neurons.toLocaleString();$('condition').replaceChildren(...data.runs.map((r,i)=>{const option=document.createElement('option');option.value=i;option.textContent=r.name==='neural_control'?'正常嗅觉 → 神经控制':'对照：关闭嗅觉入口';return option})); + for(const id of ['condition','play','restart','seek'])$(id).disabled=false; + $('provenance').textContent=`${data.model.neurons.toLocaleString()} neurons · ${data.model.retained_rows.toLocaleString()} edges · seed ${data.seed} · ${data.started_utc} · ${data.model.graph_arrays_sha256.slice(0,16)}…`; + select(0); +}).catch(error=>{$('status').textContent=`无法播放：${error.message}。页面不会用生成轨迹代替 GPU 记录。`;$('status').classList.add('error')}); diff --git a/site/llms.txt b/site/llms.txt index 3b78a2c..9a8c2ab 100644 --- a/site/llms.txt +++ b/site/llms.txt @@ -60,3 +60,7 @@ Package name: flybrain-sdk. Python import: from flybrain import FlyBrain. Do not describe raw source datasets as already runnable biological brains. Full-brain sensory update (2026-09-13): The A16-8Q five-condition ORN_DM1 probe ran all 139,255 neurons, but produced no ALPN or descending spikes. The benchmark incoming abs-weight sum 2.5 imposes a subthreshold bound of 0.9179135 for neurons without external current under its refractory rule. Do not describe that benchmark preset as supporting sensory-to-motor behavior or banana seeking. [Evidence and interpretation](https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html#olfaction). + +New synaptic-engine result (2026-09-13): The independently checked mV LIF engine passed five actual A16 GPU cases, 200 active full-graph CPU/GPU ticks and exact 20-tick checkpoint replay. Full FlyWire sensory-only input produced ALPN, MBON and descending spikes; no-odor and input-silenced controls stayed silent. DNa02 remained left-biased for both stimulus sides, so odor-directed navigation is not established. This result uses different dynamics/weights from the historical normalized benchmark. [Raw evidence and limitations](https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html#synaptic). + +[Full-brain GPU voxel recording](https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html): all 139,255 neurons run on actual A16 GPU; two 2-second closed-loop runs, 100 frames each. A fixed, untrained DNa02-to-speed/turn readout produces movement but curves away from food (distance 2.778 to 4.380 game units); silencing input ORNs leaves the body stationary. Neither run reaches food. 207 hardware-host software tests passed, plus exact joint brain/world/RNG next-frame replay. Recorded browser playback, not live inference, flight, biological locomotion or learned navigation. diff --git a/site/sitemap.xml b/site/sitemap.xml index 98565cf..bd5f242 100644 --- a/site/sitemap.xml +++ b/site/sitemap.xml @@ -5,4 +5,5 @@ https://freeman-1984-coder.github.io/flybrain-sdk/live.html https://freeman-1984-coder.github.io/flybrain-sdk/reflex.html https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html + https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html diff --git a/src/flybrain/__init__.py b/src/flybrain/__init__.py index c452c58..9e33480 100644 --- a/src/flybrain/__init__.py +++ b/src/flybrain/__init__.py @@ -10,7 +10,7 @@ from .registry import fetch_model, list_models, model_info from .sensory import Stimulus -__version__ = "0.4.0a4" +__version__ = "0.5.0.dev0" __all__ = [ "FlyBrain", "LIFConfig", diff --git a/src/flybrain/backends/__init__.py b/src/flybrain/backends/__init__.py index 116c5bb..60430dc 100644 --- a/src/flybrain/backends/__init__.py +++ b/src/flybrain/backends/__init__.py @@ -10,13 +10,19 @@ def available_backends() -> dict: - return {"cpu": True, "wasm": False, "cuda": False} + from .cuda import cuda_available + + return {"cpu": True, "wasm": False, "cuda": cuda_available()} def create_backend(name: str, model: Connectome, config: LIFConfig) -> Backend: if name == "cpu": return CPUBackend(model, config) - if name in ("wasm", "cuda"): + if name == "cuda": + from .cuda import CUDABackend + + return CUDABackend(model, config) + if name == "wasm": raise BackendUnavailableError( f"Backend {name!r} is a reserved interface, not implemented in this release. " "Use backend='cpu'. No CUDA required." diff --git a/src/flybrain/backends/cuda.py b/src/flybrain/backends/cuda.py new file mode 100644 index 0000000..27ced9e --- /dev/null +++ b/src/flybrain/backends/cuda.py @@ -0,0 +1,202 @@ +"""Experimental CuPy/CUDA LIF backend; import is safe without CuPy or a GPU. + +Graph and state stay on the selected CUDA device. Each tick currently uploads +one host current vector and reads one error flag. This correctness-first path +is not the planned batched GPU scheduler and makes no speedup promise. +""" + +from importlib import import_module +from math import ceil, exp + +import numpy as np + +from ..config import LIFConfig +from ..errors import BackendUnavailableError +from ..model import Connectome +from .base import Backend, SimulationState +from .cpu import CPUBackend + +# One thread owns a postsynaptic row. Stable input order retains parallel edges +# and avoids nondeterministic floating-point atomic accumulation. Disable FMA to +# preserve the reference's separate multiplication and addition operations. +_KERNEL = r""" +extern "C" __global__ void lif_tick( + int n, const long long* row, const int* pre, const double* weights, + const double* current, const double* old_v, const unsigned char* old_spikes, + const long long* old_refr, const double* old_rates, const unsigned char* silent, + double* v, unsigned char* spikes, long long* refr, double* rates, int* error, + double rest, double reset, double threshold, double leak, double rate_decay, + double rate_hz, long long hold) { + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n) return; + double synaptic = 0.0; + for (long long e = row[i]; e < row[i+1]; ++e) + synaptic += weights[e] * (double)old_spikes[pre[e]]; + bool available = old_refr[i] == 0 && !silent[i]; + double value = available + ? rest + (old_v[i] - rest) * leak + (current[i] + synaptic) * (1.0 - leak) + : reset; + if (!isfinite(value)) atomicExch(error, 1); + unsigned char spike = available && value >= threshold; + v[i] = spike ? reset : value; + spikes[i] = spike; + refr[i] = spike ? hold : (old_refr[i] > 0 ? old_refr[i] - 1 : 0); + rates[i] = old_rates[i] * rate_decay + ((double)spike * rate_hz) * (1.0-rate_decay); +} +""" + + +def _load_cupy(): + try: + cp = import_module("cupy") + if cp.cuda.runtime.getDeviceCount() < 1: + raise RuntimeError("no visible NVIDIA device") + if cp.cuda.runtime.is_hip: + raise RuntimeError("ROCm is not supported by this CUDA backend") + return cp + except Exception as error: + raise BackendUnavailableError( + "CUDA requires a working NVIDIA driver, visible GPU and compatible CuPy. " + "Install the appropriate cupy-cuda12x or cupy-cuda13x wheel. " + f"CPU remains available without them. Cause: {error}" + ) from error + + +def cuda_available() -> bool: + """Probe optional dependency and device; does not certify kernel conformance.""" + try: + _load_cupy() + return True + except BackendUnavailableError: + return False + + +class CUDABackend(Backend): + name = "cuda" + + def __init__(self, model: Connectome, config: LIFConfig) -> None: + cp = _load_cupy() + self._cp, self._model, self.config = cp, model, config + self.n = len(model.neuron_ids) + if self.n > np.iinfo(np.int32).max: + raise ValueError("CUDA reference backend supports at most int32 neuron indices") + self._tick = 0 + self._leak = exp(-config.dt_ms / config.tau_ms) + self._decay = exp(-config.dt_ms / config.rate_tau_ms) + self._hold = ceil(config.refractory_ms / config.dt_ms) + post = np.array([e.post for e in model.synapses], dtype=np.int32) + order = np.argsort(post, kind="stable") + rows = np.empty(self.n + 1, dtype=np.int64) + rows[0] = 0 + rows[1:] = np.cumsum(np.bincount(post, minlength=self.n)) + try: + self._device = cp.cuda.Device() + self._stream = cp.cuda.Stream(non_blocking=True) + with self._device, self._stream: + self._row = cp.asarray(rows) + self._pre = cp.asarray(np.array([e.pre for e in model.synapses], np.int32)[order]) + self._weights = cp.asarray( + np.array([e.weight for e in model.synapses], np.float64)[order] + ) + self._voltage = cp.full(self.n, config.rest, dtype=cp.float64) + self._spikes = cp.zeros(self.n, dtype=cp.bool_) + self._refractory = cp.zeros(self.n, dtype=cp.int64) + self._rates = cp.zeros(self.n, dtype=cp.float64) + self._silenced = cp.zeros(self.n, dtype=cp.bool_) + self._next = [cp.empty_like(x) for x in self._state_arrays()] + self._error = cp.zeros(1, dtype=cp.int32) + self._kernel = cp.RawKernel(_KERNEL, "lif_tick", options=("--fmad=false",)) + self._kernel.compile() + except Exception as error: + raise BackendUnavailableError( + f"CUDA initialization or compilation failed: {error}" + ) from error + + def _state_arrays(self): + return self._voltage, self._spikes, self._refractory, self._rates + + @property + def tick(self) -> int: + return self._tick + + def step(self, current: np.ndarray) -> None: + current = np.ascontiguousarray(current, dtype=np.float64) + if current.shape != (self.n,) or not np.isfinite(current).all(): + raise ValueError("current must be a finite vector of length neuron_count") + with self._device, self._stream: + self._error.fill(0) + self._kernel( + ((self.n + 127) // 128,), + (128,), + ( + np.int32(self.n), + self._row, + self._pre, + self._weights, + self._cp.asarray(current), + *self._state_arrays(), + self._silenced, + *self._next, + self._error, + np.float64(self.config.rest), + np.float64(self.config.reset), + np.float64(self.config.threshold), + np.float64(self._leak), + np.float64(self._decay), + np.float64(1000.0 / self.config.dt_ms), + np.int64(self._hold), + ), + ) + # Synchronize only a scalar error flag before committing this tick. + if self._error.get()[0]: + raise ValueError("nonfinite voltage; reduce synapse weights or stimulus current") + old = self._state_arrays() + self._voltage, self._spikes, self._refractory, self._rates = self._next + self._next = old + self._tick += 1 + + def observe_selected(self, indices: tuple, fields: tuple) -> dict: + arrays = {"voltage": self._voltage, "spikes": self._spikes, "rates_hz": self._rates} + with self._device, self._stream: + selected = self._cp.asarray(indices, dtype=self._cp.int64) + return {k: tuple(arrays[k][selected].get().tolist()) for k in fields} + + def set_silenced(self, indices: tuple, enabled: bool) -> None: + with self._device, self._stream: + self._silenced[self._cp.asarray(indices, dtype=self._cp.int64)] = enabled + + def observe(self) -> SimulationState: + values = self.observe_selected(tuple(range(self.n)), ("voltage", "spikes", "rates_hz")) + return SimulationState(self.tick, self.tick * self.config.dt_ms, **values) + + def snapshot(self) -> dict: + with self._device, self._stream: + return { + "tick": self.tick, + "voltage": self._voltage.get().tolist(), + "spikes": self._spikes.get().tolist(), + "refractory": self._refractory.get().tolist(), + "rates_hz": self._rates.get().tolist(), + "silenced": self._silenced.get().tolist(), + } + + def restore(self, state: dict) -> None: + # Share the authoritative CPU validator, including schema-1 compatibility. + # This temporarily allocates a CPU graph/state only during restoration. + checked = CPUBackend(self._model, self.config) + checked.restore(state) + cp = self._cp + with self._device, self._stream: + values = [ + cp.asarray(x) + for x in ( + checked._voltage, + checked._spikes, + checked._refractory, + checked._rates, + checked._silenced, + ) + ] + self._stream.synchronize() + self._voltage, self._spikes, self._refractory, self._rates, self._silenced = values + self._tick = checked.tick diff --git a/src/flybrain/errors.py b/src/flybrain/errors.py index 4126866..8a5680c 100644 --- a/src/flybrain/errors.py +++ b/src/flybrain/errors.py @@ -2,7 +2,7 @@ class BackendUnavailableError(RuntimeError): - """A recognized backend has no runtime in this release.""" + """A backend cannot run because its runtime, device or compiler is unavailable.""" class CheckpointError(ValueError): diff --git a/src/flybrain/experimental/__init__.py b/src/flybrain/experimental/__init__.py new file mode 100644 index 0000000..42da4be --- /dev/null +++ b/src/flybrain/experimental/__init__.py @@ -0,0 +1 @@ +"""Research interfaces kept separate from the released dimensionless LIF API.""" diff --git a/src/flybrain/experimental/synaptic.py b/src/flybrain/experimental/synaptic.py new file mode 100644 index 0000000..680e809 --- /dev/null +++ b/src/flybrain/experimental/synaptic.py @@ -0,0 +1,234 @@ +"""CPU reference for a delayed, exponentially decaying synaptic LIF model. + +Equations/nominal defaults follow Shiu et al. (Nature 2024), with an explicit +Brian2-style schedule. Voltages, synaptic state and edge weights use millivolts. +No incoming-weight normalization. This is not wired into FlyBrain.load yet: +dimensionless checkpoints/weights must never be silently reinterpreted as mV. +""" + +import hashlib +import json +from dataclasses import asdict, dataclass +from math import exp, expm1, isclose + +import numpy as np + +from ..config import finite_number, positive_int +from ..neurons import NeuronIndex + + +@dataclass(frozen=True) +class SynapticLIFConfig: + dt_ms: float = 0.1 + rest_mv: float = -52.0 + reset_mv: float = -52.0 + threshold_mv: float = -45.0 + membrane_tau_ms: float = 20.0 + synapse_tau_ms: float = 5.0 + refractory_ms: float = 2.2 + delay_ms: float = 1.8 + rate_tau_ms: float = 50.0 + + def __post_init__(self): + for name, value in asdict(self).items(): + object.__setattr__(self, name, finite_number(value, name)) + for name in ("dt_ms", "membrane_tau_ms", "synapse_tau_ms", "rate_tau_ms"): + if getattr(self, name) <= 0: + raise ValueError(f"{name} must be positive") + if self.rest_mv >= self.threshold_mv or self.reset_mv >= self.threshold_mv: + raise ValueError("rest and reset must be below threshold") + for name in ("delay_ms", "refractory_ms"): + value = getattr(self, name) + if value < 0 or not isclose( + value / self.dt_ms, round(value / self.dt_ms), rel_tol=0, abs_tol=1e-9 + ): + raise ValueError(f"{name} must be a nonnegative integer multiple of dt_ms") + + def to_dict(self): + return asdict(self) + + +class SynapticCPU: + """Sparse CPU research engine; edge weights must explicitly declare mV units. + + A step receives externally scheduled voltage jumps (mV) for input_ids only. + These cells have zero refractory duration, matching the paper's Poisson + targets. Input jumps happen after threshold detection, before resets. They + can therefore cause a spike on the next step, or be cleared by a same-step + reset. No RNG is hidden in this object; callers checkpoint their input RNG. + """ + + name = "cpu" + engine = "lif-synaptic-mv-v1" + + def __init__(self, model, config=None, *, input_ids=(), weight_units): + if weight_units != "mV": + raise ValueError("synaptic weights must explicitly use mV") + self.config = config or SynapticLIFConfig() + if not isinstance(self.config, SynapticLIFConfig): + raise ValueError("SynapticLIFConfig required; dimensionless config is incompatible") + c = self.config + self.n = len(model.neuron_ids) + self._pre = np.array([e.pre for e in model.synapses], dtype=" 0: + self._coupling = ( + self._em + * -expm1(-c.dt_ms * decay_difference) + / (c.membrane_tau_ms * decay_difference) + ) + else: + self._coupling = ( + self._es + * expm1(c.dt_ms * decay_difference) + / (c.membrane_tau_ms * decay_difference) + ) + self._rate_decay = exp(-c.dt_ms / c.rate_tau_ms) + self.tick = 0 + self._voltage = np.full(self.n, c.rest_mv) + self._g = np.zeros(self.n) + self._spikes = np.zeros(self.n, dtype=bool) + self._last_spike = np.full(self.n, -round(c.refractory_ms / c.dt_ms) - 1, dtype=np.int64) + self._rates = np.zeros(self.n) + self._silenced = np.zeros(self.n, dtype=bool) + self._history = np.zeros((self._delay + 1, self.n), dtype=bool) + h = hashlib.sha256() + h.update(json.dumps([model.neuron_ids, c.to_dict()], sort_keys=True).encode()) + for array in (self._pre, self._post, self._weights, self._inputs): + h.update(array.tobytes()) + self._fingerprint = h.hexdigest() + + def step(self, voltage_jumps_mv): + jumps = np.asarray(voltage_jumps_mv, dtype=np.float64) + if jumps.shape != (self.n,) or not np.isfinite(jumps).all(): + raise ValueError("input jumps must be a finite vector matching the graph") + if np.any(jumps[~self._inputs] != 0): + raise ValueError("voltage jumps are allowed only at declared input neurons") + c = self.config + available = (self.tick - self._last_spike >= self._ref_steps) & ~self._silenced + # Exact coupled integration uses the OLD synaptic state in the voltage update. + v = self._voltage.copy() + g = self._g.copy() + v[available] = ( + c.rest_mv + (v[available] - c.rest_mv) * self._em + g[available] * self._coupling + ) + g[available] *= self._es + spikes = available & (v > c.threshold_mv) + # Thresholds -> delayed synaptic events -> external input -> reset. + # Both v and g are read-only during refractory time, as in Brian2's + # `(unless refractory)`, including writes from arriving synapses. + history = self._history.copy() + history[self.tick % len(history)] = spikes + arriving = history[(self.tick - self._delay) % len(history)] + synaptic = np.bincount( + self._post, weights=self._weights * arriving[self._pre], minlength=self.n + ) + g[available] += synaptic[available] + v[available] += jumps[available] + v[spikes | self._silenced] = c.reset_mv + g[spikes | self._silenced] = 0 + if not np.isfinite(v).all() or not np.isfinite(g).all(): + raise ValueError("nonfinite synaptic state; check weights and inputs") + self._last_spike[spikes] = self.tick + self._voltage, self._g, self._spikes, self._history = v, g, spikes, history + self._rates = self._rates * self._rate_decay + spikes * (1000 / c.dt_ms) * ( + 1 - self._rate_decay + ) + self.tick += 1 + + def observe_selected(self, indices, fields=("voltage_mv", "synaptic_mv", "spikes", "rates_hz")): + indices = self._checked_indices(indices) + arrays = { + "voltage_mv": self._voltage, + "synaptic_mv": self._g, + "spikes": self._spikes, + "rates_hz": self._rates, + } + return {name: tuple(arrays[name][list(indices)].tolist()) for name in fields} + + def set_silenced(self, indices, enabled): + if type(enabled) is not bool: + raise ValueError("enabled must be boolean") + self._silenced[list(self._checked_indices(indices))] = enabled + + def _checked_indices(self, indices): + indices = tuple(indices) + if any( + isinstance(i, (bool, np.bool_)) + or not isinstance(i, (int, np.integer)) + or i < 0 + or i >= self.n + for i in indices + ): + raise ValueError("selection requires valid integer neuron indices") + return indices + + def snapshot(self): + return { + "engine": self.engine, + "fingerprint": self._fingerprint, + "tick": self.tick, + "voltage_mv": self._voltage.tolist(), + "synaptic_mv": self._g.tolist(), + "spikes": self._spikes.tolist(), + "rates_hz": self._rates.tolist(), + "last_spike_tick": self._last_spike.tolist(), + "history": self._history.tolist(), + "silenced": self._silenced.tolist(), + } + + def restore(self, state): + if state.get("engine") != self.engine or state.get("fingerprint") != self._fingerprint: + raise ValueError("checkpoint graph, input population or dynamics mismatch") + tick = positive_int(state["tick"], "tick", allow_zero=True) + arrays = {} + for key, dtype in ( + ("voltage_mv", float), + ("synaptic_mv", float), + ("rates_hz", float), + ("spikes", bool), + ("silenced", bool), + ("last_spike_tick", int), + ): + value = state[key] + if not isinstance(value, list) or len(value) != self.n: + raise ValueError(f"{key} must have exactly {self.n} entries") + if dtype in (bool, int) and any(type(v) is not dtype for v in value): + raise ValueError(f"{key} has invalid entry types") + arrays[key] = np.array( + [finite_number(v, key) for v in value] if dtype is float else value, dtype=dtype + ) + if not np.isfinite(arrays[key]).all(): + raise ValueError(f"{key} must be finite") + history = state["history"] + if not isinstance(history, list) or len(history) != self._delay + 1: + raise ValueError("checkpoint has wrong delay buffer length") + if any( + not isinstance(row, list) or len(row) != self.n or any(type(v) is not bool for v in row) + for row in history + ): + raise ValueError("delay buffer must contain one boolean per neuron per slot") + last = arrays["last_spike_tick"] + minimum = -round(self.config.refractory_ms / self.config.dt_ms) - 1 + if (last < minimum).any() or (last >= tick).any(): + raise ValueError("last spike timestamps are out of range") + rates = arrays["rates_hz"] + if (rates < 0).any() or (rates > 1000 / self.config.dt_ms).any(): + raise ValueError("rates are out of range") + self.tick = tick + self._voltage, self._g = arrays["voltage_mv"], arrays["synaptic_mv"] + self._rates, self._spikes = rates, arrays["spikes"] + self._silenced, self._last_spike = arrays["silenced"], last + self._history = np.array(history, dtype=bool) diff --git a/src/flybrain/experimental/synaptic_cuda.py b/src/flybrain/experimental/synaptic_cuda.py new file mode 100644 index 0000000..541d6be --- /dev/null +++ b/src/flybrain/experimental/synaptic_cuda.py @@ -0,0 +1,206 @@ +"""Optional CUDA port of the separately versioned mV synaptic research engine. + +Two ordered kernels implement integrate/threshold, then synaptic/input/reset. +Only after a successful step are state buffers and delayed-spike history committed. +Importing this module does not import CuPy or initialize a device. +""" + +import numpy as np + +from ..backends.cuda import _load_cupy +from ..errors import BackendUnavailableError +from .synaptic import SynapticCPU + +_KERNELS = r""" +extern "C" __global__ void integrate( + int n, long long tick, const double* old_v, const double* old_g, + const long long* last_spike, const long long* ref_steps, + const unsigned char* silent, double* v, double* g, + unsigned char* spikes, unsigned char* available, + double rest, double threshold, double em, double es, double coupling) { + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n) return; + bool active = tick - last_spike[i] >= ref_steps[i] && !silent[i]; + available[i] = active; + v[i] = active ? rest + (old_v[i] - rest) * em + old_g[i] * coupling : old_v[i]; + g[i] = active ? old_g[i] * es : old_g[i]; + spikes[i] = active && v[i] > threshold; +} +extern "C" __global__ void events_and_reset( + int n, long long tick, const long long* row, const int* pre, + const double* weights, const unsigned char* arriving, const double* jumps, + const unsigned char* silent, const unsigned char* available, + const unsigned char* spikes, const double* old_rates, + const long long* old_last_spike, double* v, double* g, + double* rates, long long* last_spike, int* error, + double reset, double rate_decay, double rate_hz) { + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n) return; + if (available[i]) { + double synaptic = 0.0; + for (long long e = row[i]; e < row[i+1]; ++e) + synaptic += weights[e] * (double)arriving[pre[e]]; + g[i] += synaptic; + v[i] += jumps[i]; + } + if (spikes[i] || silent[i]) { v[i] = reset; g[i] = 0.0; } + rates[i] = old_rates[i] * rate_decay + ((double)spikes[i] * rate_hz) * (1.0-rate_decay); + last_spike[i] = spikes[i] ? tick : old_last_spike[i]; + if (!isfinite(v[i]) || !isfinite(g[i]) || !isfinite(rates[i])) atomicExch(error, 1); +} +""" + + +class SynapticCUDA: + """Research CUDA engine with the same explicit units/API as SynapticCPU.""" + + name = "cuda" + engine = SynapticCPU.engine + + def __init__(self, model, config=None, *, input_ids=(), weight_units): + self._reference = SynapticCPU(model, config, input_ids=input_ids, weight_units=weight_units) + ref = self._reference + self.config, self.n, self.tick = ref.config, ref.n, 0 + if self.n > np.iinfo(np.int32).max: + raise ValueError("CUDA indices require neuron count within int32 range") + cp = self._cp = _load_cupy() + try: + self._device = cp.cuda.Device() + self._stream = cp.cuda.Stream(non_blocking=True) + with self._device, self._stream: + order = np.argsort(ref._post, kind="stable") + rows = np.empty(self.n + 1, dtype=np.int64) + rows[0] = 0 + np.cumsum(np.bincount(ref._post, minlength=self.n), out=rows[1:]) + self._row = cp.asarray(rows) + self._pre = cp.asarray(ref._pre[order].astype(np.int32)) + self._weights = cp.asarray(ref._weights[order]) + self._refs = cp.asarray(ref._ref_steps) + self._integrate = cp.RawKernel(_KERNELS, "integrate", options=("--fmad=false",)) + self._events = cp.RawKernel(_KERNELS, "events_and_reset", options=("--fmad=false",)) + self._integrate.compile() + self._events.compile() + self._state = self._upload(ref.snapshot()) + self._next = { + k: cp.empty_like(self._state[k]) + for k in ("voltage_mv", "synaptic_mv", "spikes", "rates_hz", "last_spike_tick") + } + self._available = cp.empty(self.n, dtype=cp.bool_) + self._error = cp.zeros(1, dtype=cp.int32) + self._stream.synchronize() + except Exception as error: + raise BackendUnavailableError( + f"Synaptic CUDA initialization failed: {error}" + ) from error + + def _upload(self, snapshot): + dtypes = { + "voltage_mv": np.float64, + "synaptic_mv": np.float64, + "rates_hz": np.float64, + "last_spike_tick": np.int64, + "spikes": np.bool_, + "silenced": np.bool_, + "history": np.bool_, + } + return {key: self._cp.asarray(snapshot[key], dtype=dtype) for key, dtype in dtypes.items()} + + def step(self, voltage_jumps_mv): + ref, cp, c = self._reference, self._cp, self.config + jumps = np.asarray(voltage_jumps_mv, dtype=np.float64) + if jumps.shape != (self.n,) or not np.isfinite(jumps).all(): + raise ValueError("input jumps must be a finite vector matching the graph") + if np.any(jumps[~ref._inputs] != 0): + raise ValueError("voltage jumps are allowed only at declared input neurons") + with self._device, self._stream: + old, new = self._state, self._next + device_jumps = cp.asarray(jumps) + self._error.fill(0) + shape = ((self.n + 255) // 256,), (256,) + self._integrate( + *shape, + ( + np.int32(self.n), + np.int64(self.tick), + old["voltage_mv"], + old["synaptic_mv"], + old["last_spike_tick"], + self._refs, + old["silenced"], + new["voltage_mv"], + new["synaptic_mv"], + new["spikes"], + self._available, + np.float64(c.rest_mv), + np.float64(c.threshold_mv), + np.float64(ref._em), + np.float64(ref._es), + np.float64(ref._coupling), + ), + ) + slot = (self.tick - ref._delay) % (ref._delay + 1) + arriving = new["spikes"] if ref._delay == 0 else old["history"][slot] + self._events( + *shape, + ( + np.int32(self.n), + np.int64(self.tick), + self._row, + self._pre, + self._weights, + arriving, + device_jumps, + old["silenced"], + self._available, + new["spikes"], + old["rates_hz"], + old["last_spike_tick"], + new["voltage_mv"], + new["synaptic_mv"], + new["rates_hz"], + new["last_spike_tick"], + self._error, + np.float64(c.reset_mv), + np.float64(ref._rate_decay), + np.float64(1000 / c.dt_ms), + ), + ) + if int(self._error.get()[0]): + raise ValueError("nonfinite synaptic state; check weights and inputs") + cp.copyto(old["history"][self.tick % (ref._delay + 1)], new["spikes"]) + for key in new: + old[key], new[key] = new[key], old[key] + self.tick += 1 + + def observe_selected(self, indices, fields=("voltage_mv", "synaptic_mv", "spikes", "rates_hz")): + indices = self._reference._checked_indices(indices) + with self._device, self._stream: + selected = self._cp.asarray(indices, dtype=self._cp.int64) + return {name: tuple(self._state[name][selected].get().tolist()) for name in fields} + + def set_silenced(self, indices, enabled): + if type(enabled) is not bool: + raise ValueError("enabled must be boolean") + indices = self._reference._checked_indices(indices) + with self._device, self._stream: + self._state["silenced"][self._cp.asarray(indices, dtype=self._cp.int64)] = enabled + + def snapshot(self): + with self._device, self._stream: + state = {key: array.get().tolist() for key, array in self._state.items()} + return { + "engine": self.engine, + "fingerprint": self._reference._fingerprint, + "tick": self.tick, + **state, + } + + def restore(self, state): + # Validation mutates only the CPU validator. Published device state is + # replaced after all transfers complete, leaving it intact on failure. + self._reference.restore(state) + with self._device, self._stream: + uploaded = self._upload(self._reference.snapshot()) + self._stream.synchronize() + self._state = uploaded + self.tick = self._reference.tick diff --git a/src/flybrain/experimental/voxel_arena.py b/src/flybrain/experimental/voxel_arena.py new file mode 100644 index 0000000..e287116 --- /dev/null +++ b/src/flybrain/experimental/voxel_arena.py @@ -0,0 +1,121 @@ +"""Deterministic ground arena for neural-controller experiments. + +The body is an engineered kinematic approximation, not a fly biomechanics +model. Food affects antenna observations and contact scoring only. Movement +accepts explicit speed/turn commands and never follows a target automatically. +""" + +import math + +from ..config import finite_number, positive_int +from ..olfaction import sample_odor + + +class VoxelArena: + """A floor, solid cubes and one odor source, in arbitrary game units. + + x/z span the floor; y is up. Positive yaw turns toward +z. The fly's body + remains at y=0.5. Obstacles affect collisions but do not occlude the toy + Gaussian odor field. A neural adapter receives observe(), not scene(). + """ + + def __init__(self, *, food=(4.0, 0.5, 2.0), blocks=((0.0, 0.0), (0.0, 1.0))): + food = tuple(food) + if len(food) != 3: + raise ValueError("food requires x/y/z") + self.food = tuple(finite_number(v, "food") for v in food) + self.blocks = tuple(tuple(finite_number(v, "block") for v in b) for b in blocks) + if any(len(b) != 2 for b in self.blocks): + raise ValueError("block requires floor x/z") + self.x, self.z, self.yaw = -4.0, -2.0, 0.0 + self.frame, self.contacts = 0, 0 + self.time_ms = 0.0 + self.reached_food = False + self.radius = 0.18 + + def observe(self): + # Antennae extend forward and symmetrically to each side of the body. + forward = (math.cos(self.yaw), math.sin(self.yaw)) + left = (math.sin(self.yaw), -math.cos(self.yaw)) + return { + f"odor_{side}": sample_odor( + ( + self.x + 0.25 * forward[0] + sign * 0.16 * left[0], + 0.5, + self.z + 0.25 * forward[1] + sign * 0.16 * left[1], + ), + self.food, + spread=4.0, + ) + for side, sign in (("left", 1), ("right", -1)) + } + + def _blocked(self, x, z): + if abs(x) > 7 - self.radius or abs(z) > 7 - self.radius: + return True + for bx, bz in self.blocks: + dx, dz = max(abs(x - bx) - 0.5, 0), max(abs(z - bz) - 0.5, 0) + if dx * dx + dz * dz < self.radius**2: + return True + return False + + def apply(self, action, duration_ms=20.0): + dt = finite_number(duration_ms, "duration_ms") / 1000 + if not 0 < dt <= 0.1: + raise ValueError("body step must be positive and at most 100 ms") + speed = finite_number(action["speed"], "speed") + turn = finite_number(action["turn"], "turn") + if not 0 <= speed <= 3 or abs(turn) > 4: + raise ValueError("speed must be 0..3 units/s; turn must be -4..4 radians/s") + self.yaw = (self.yaw + turn * dt + math.pi) % (2 * math.pi) - math.pi + # Substeps prevent crossing a cube at the maximum supported speed/dt. + steps = max(1, math.ceil(speed * dt / (self.radius / 2))) + collision = False + for _ in range(steps): + nx = self.x + math.cos(self.yaw) * speed * dt / steps + nz = self.z + math.sin(self.yaw) * speed * dt / steps + if self._blocked(nx, nz): + collision = True + break + self.x, self.z = nx, nz + self.contacts += int(collision) + self.frame += 1 + self.time_ms += dt * 1000 + self.reached_food |= math.dist((self.x, 0.5, self.z), self.food) <= 0.6 + return {"collision": collision, "reached_food": self.reached_food} + + def scene(self): + """Renderer/evaluator state, never a neural sensory input.""" + return { + "position": [self.x, 0.5, self.z], + "yaw": self.yaw, + "food": list(self.food), + "blocks": [list(b) for b in self.blocks], + "time_ms": self.time_ms, + "frame": self.frame, + "contacts": self.contacts, + "reached_food": self.reached_food, + } + + def snapshot(self): + return {"type": "voxel-odor-arena-v1", **self.scene()} + + @classmethod + def from_snapshot(cls, state): + if state["type"] != "voxel-odor-arena-v1": + raise ValueError("unsupported world state") + arena = cls(food=state["food"], blocks=state["blocks"]) + position = tuple(state["position"]) + if len(position) != 3 or position[1] != 0.5: + raise ValueError("ground body position requires y=0.5") + arena.x, arena.z = (finite_number(position[i], "position") for i in (0, 2)) + arena.yaw = finite_number(state["yaw"], "yaw") + arena.time_ms = finite_number(state["time_ms"], "time_ms") + if arena._blocked(arena.x, arena.z) or arena.time_ms < 0: + raise ValueError("invalid body placement or time") + arena.frame = positive_int(state["frame"], "frame", allow_zero=True) + arena.contacts = positive_int(state["contacts"], "contacts", allow_zero=True) + if type(state["reached_food"]) is not bool: + raise ValueError("reached_food must be boolean") + arena.reached_food = state["reached_food"] + return arena diff --git a/src/flybrain/external.py b/src/flybrain/external.py index 2436062..70f92fb 100644 --- a/src/flybrain/external.py +++ b/src/flybrain/external.py @@ -116,7 +116,7 @@ def snapshot(self): ) @classmethod - def from_snapshot(cls, data): + def from_snapshot(cls, data, *, backend=None): if ( data["format"] != "flybrain-external" or type(data["schema_version"]) is not int @@ -124,7 +124,7 @@ def from_snapshot(cls, data): ): raise ValueError("unsupported external checkpoint") result = cls( - FlyBrain.from_snapshot(data["brain"]), + FlyBrain.from_snapshot(data["brain"], backend=backend), LinearEncoder.from_snapshot(data["encoder"]), RateReadout.from_snapshot(data["readout"]), period_ms=data["period_ms"], diff --git a/src/flybrain/olfaction.py b/src/flybrain/olfaction.py new file mode 100644 index 0000000..2fd93c1 --- /dev/null +++ b/src/flybrain/olfaction.py @@ -0,0 +1,71 @@ +"""Explicit bilateral odor-to-current adapter; no banana recognition or navigation. + +Concentrations and currents are dimensionless engineering quantities. Receptor +identity can be evidence-based while the transfer function remains an assumption. +The adapter accepts only local antenna samples, never a target position/bearing. +""" + +from math import exp + +import numpy as np + +from .config import finite_number +from .neurons import NeuronIndex + + +class OlfactoryDrive: + """Inject a saturating stimulus into two explicitly selected ORN populations. + + ``left_ids``/``right_ids`` must come from annotations, not an arbitrary split. + This stateless adapter has no hidden adaptation or random generator to save. + Use one adapter per receptor channel and add their current vectors if needed. + """ + + def __init__(self, model, *, left_ids, right_ids, max_current=5.0, half_concentration=0.2): + index = NeuronIndex(model) + self.left = index.resolve(left_ids) + self.right = index.resolve(right_ids) + if set(self.left) & set(self.right): + raise ValueError("left and right ORN populations must not overlap") + self.n = len(model.neuron_ids) + self.max_current = finite_number(max_current, "max_current") + self.half_concentration = finite_number(half_concentration, "half_concentration") + if self.max_current <= 0 or self.half_concentration <= 0: + raise ValueError("max_current and half_concentration must be positive") + + def amplitude(self, concentration): + concentration = finite_number(concentration, "concentration") + if concentration < 0: + raise ValueError("concentration must be nonnegative") + # This form avoids overflow when concentration is close to float max. + fraction = 0.0 if concentration == 0 else 1 / (1 + self.half_concentration / concentration) + return self.max_current * fraction + + def current(self, *, left, right): + """A fresh host vector; only the selected ORNs receive external current.""" + amplitudes = self.amplitude(left), self.amplitude(right) + result = np.zeros(self.n, dtype=np.float64) + result[list(self.left)] = amplitudes[0] + result[list(self.right)] = amplitudes[1] + return result + + +def sample_odor(position, source, *, spread=4.0, strength=1.0): + """Isotropic Gaussian concentration field in arbitrary world units. + + A game-side approximation, not a fluid/chemistry model: no wind, turbulence, + obstacle occlusion or measured banana emissions. Evaluate at each antenna. + """ + position, source = tuple(position), tuple(source) + if len(position) != 3 or len(source) != 3: + raise ValueError("position and source must be three-dimensional") + position = [finite_number(v, "position") for v in position] + source = [finite_number(v, "source") for v in source] + spread = finite_number(spread, "spread") + strength = finite_number(strength, "strength") + if spread <= 0 or strength < 0: + raise ValueError("spread must be positive and strength nonnegative") + delta = (np.asarray(position) - np.asarray(source)) / spread + with np.errstate(over="ignore"): + distance_squared = float(delta @ delta) + return strength * exp(-0.5 * distance_squared) diff --git a/tests/test_api.py b/tests/test_api.py index 6df0353..e35007d 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -59,10 +59,9 @@ def test_nondefault_dt_and_clear_stimuli(): assert brain.state.time_ms == 5 -@pytest.mark.parametrize("backend", ["cuda", "wasm"]) -def test_backend_slots_fail_explicitly(backend): +def test_wasm_slot_fails_explicitly(): with pytest.raises(BackendUnavailableError, match="not implemented"): - FlyBrain.load(backend=backend) + FlyBrain.load(backend="wasm") def test_unknown_backend_and_raw_model(): diff --git a/tests/test_cuda_hardware.py b/tests/test_cuda_hardware.py new file mode 100644 index 0000000..1be8063 --- /dev/null +++ b/tests/test_cuda_hardware.py @@ -0,0 +1,171 @@ +"""Run on actual CUDA only. FLYBRAIN_REQUIRE_CUDA=1 makes absence a failure.""" + +import os +from pathlib import Path + +import numpy as np +import pytest + +from flybrain import BackendUnavailableError, Connectome, FlyBrain, LIFConfig, Synapse +from flybrain.backends import CPUBackend +from flybrain.backends.cuda import CUDABackend, _load_cupy + +ROOT = Path(__file__).resolve().parents[1] + + +@pytest.fixture(scope="module") +def gpu(): + try: + return _load_cupy() + except BackendUnavailableError as error: + if os.environ.get("FLYBRAIN_REQUIRE_CUDA") == "1": + pytest.fail(f"Required CUDA hardware is unavailable: {error}") + pytest.skip(f"Actual CUDA device required: {error}") + + +def assert_state(a, b): + assert a.tick == b.tick + assert a.time_ms == b.time_ms + assert a.spikes == b.spikes + np.testing.assert_allclose(a.voltage, b.voltage, atol=1e-10, rtol=0) + np.testing.assert_allclose(a.rates_hz, b.rates_hz, atol=1e-10, rtol=0) + + +@pytest.mark.parametrize("model", ["toy", "real", "parallel-inhibitory"]) +@pytest.mark.parametrize("dt", [0.5, 1.0]) +def test_every_cell_tick_matches_cpu(gpu, model, dt): + if model == "parallel-inhibitory": + rng = np.random.default_rng(41) + edges = tuple( + Synapse(int(a), int(b), float(w)) + for a, b, w in zip( + rng.integers(0, 257, 4096), rng.integers(0, 257, 4096), rng.uniform(-8, 12, 4096) + ) + ) + graph = Connectome( + "random signed graph", + tuple(map(str, range(257))), + edges + (Synapse(0, 1, 8), Synapse(0, 1, 8)), + {}, + {}, + {}, + ) + else: + graph = FlyBrain.load( + "toy" if model == "toy" else ROOT / "models/male-cns-escape-v1/model.json" + ).model + config = LIFConfig(dt_ms=dt) + cpu, cuda = CPUBackend(graph, config), CUDABackend(graph, config) + rng = np.random.default_rng(17) + currents = rng.uniform(-1, 4, (180, len(graph.neuron_ids))) + for tick, current in enumerate(currents): + if tick in (31, 79): + cpu.set_silenced((0, 1), tick == 31) + cuda.set_silenced((0, 1), tick == 31) + cpu.step(current) + cuda.step(current) + assert_state(cpu.observe(), cuda.observe()) + if tick == 50: + # GPU->CPU->GPU with active silencing and refractory state. + cpu.restore(cuda.snapshot()) + cuda.restore(cpu.snapshot()) + + +def test_pending_inputs_and_custom_readout_cross_device(gpu, tmp_path): + brain = FlyBrain.load() + brain.bind_readout({"flash": ["motor.walk"]}) + brain.stimulate("food", duration_ms=120) + brain.drive.current(["relay.food"], amplitude=-1, duration_ms=50) + brain.intervene.silence(["motor.jump"]) + brain.step(17) + path = brain.save(tmp_path / "cpu.json") + cuda = FlyBrain.restore(path, backend="cuda") + for _ in range(150): + assert_state(brain.step(), cuda.step()) + assert cuda.action().to_dict() == pytest.approx(brain.action().to_dict(), abs=1e-10) + restored = FlyBrain.restore(cuda.save(tmp_path / "gpu.json"), backend="cpu") + assert_state(restored.step(20), cuda.step(20)) + + +def test_invalid_tick_and_restore_leave_state_unchanged(gpu): + graph = Connectome( + "overflow", ("a", "b"), (Synapse(0, 1, 1e308), Synapse(0, 1, 1e308)), {}, {}, {} + ) + cuda = CUDABackend(graph, LIFConfig()) + cuda.step(np.array([20.0, 0.0])) + before = cuda.snapshot() + for current in (np.array([np.nan, 0]), np.zeros(3), np.zeros(2)): + with pytest.raises(ValueError): + cuda.step(current) + assert cuda.snapshot() == before + bad = dict(before, refractory=[999, 0]) + with pytest.raises(ValueError): + cuda.restore(bad) + assert cuda.snapshot() == before + + +def test_selected_observation_is_detached(gpu): + cuda = CUDABackend(FlyBrain.load().model, LIFConfig()) + current = np.arange(cuda.n * 2, dtype=np.float64)[::-2] + cuda.step(current) + reference = CPUBackend(FlyBrain.load().model, LIFConfig()) + reference.step(current) + assert_state(reference.observe(), cuda.observe()) + values = cuda.observe_selected((1, 4), ("voltage", "rates_hz")) + before = values["voltage"] + cuda.step(np.full(cuda.n, 20.0)) + assert values["voltage"] == before + assert len(values["voltage"]) == 2 + + +def test_backend_owns_stream_ordering(gpu): + cpu = CPUBackend(FlyBrain.load().model, LIFConfig()) + cuda = CUDABackend(FlyBrain.load().model, LIFConfig()) + for _ in range(10): + with gpu.cuda.Stream(non_blocking=True): + cuda.step(np.full(cuda.n, 2.0)) + cuda.set_silenced((0,), True) + cpu.step(np.full(cpu.n, 2.0)) + cpu.set_silenced((0,), True) + assert_state(cpu.observe(), cuda.observe()) + + +@pytest.mark.parametrize("model", ["toy", ROOT / "models/male-cns-escape-v1/model.json"]) +def test_feedback_sessions_and_external_cross_device_restore(gpu, model): + from flybrain.demos import DodgeArena, make_demo + from flybrain.external import ExternalController + from flybrain.session import Session, compare + + expected = make_demo("dodge", model=model) + accelerated = make_demo("dodge", model=model, backend="cuda") + demo = make_demo("dodge", model=model, backend="cuda") + external = ExternalController(demo.brain, demo.encoder, demo.readout) + world = demo.environment + for seq in range(60): + obs = world.observe() + action = external.offer(seq, obs) + tick = external.brain.progress.tick + assert external.offer(seq, obs) == action + assert external.brain.progress.tick == tick + applied = world.apply(action["requested"], 20) + external.acknowledge(seq, applied) + reference = expected.step() + frame = accelerated.step() + for controls in (action["requested"], frame.requested): + compare(controls, reference.requested) + compare(applied, reference.applied) + compare(frame.applied, reference.applied) + compare(world.snapshot(), reference.environment) + compare(frame.environment, reference.environment) + assert_state(expected.brain.state, external.brain.state) + assert_state(expected.brain.state, accelerated.brain.state) + if seq in (29, 44): + backend = "cpu" if seq == 29 else "cuda" + external = ExternalController.from_snapshot(external.snapshot(), backend=backend) + accelerated = Session.from_snapshot( + accelerated.snapshot(), + environment_factory=DodgeArena.from_snapshot, + backend=backend, + ) + assert external.brain.snapshot()["backend"] == backend + assert accelerated.brain.snapshot()["backend"] == backend diff --git a/tests/test_cuda_optional.py b/tests/test_cuda_optional.py new file mode 100644 index 0000000..0cb3c3c --- /dev/null +++ b/tests/test_cuda_optional.py @@ -0,0 +1,32 @@ +"""Dependency and diagnostic behavior, not simulated GPU conformance.""" + +import subprocess +import sys + +import pytest + +from flybrain import BackendUnavailableError, FlyBrain, available_backends +from flybrain.backends import cuda + + +def test_cpu_import_does_not_import_cupy(): + subprocess.run( + [ + sys.executable, + "-c", + "import sys; from flybrain import FlyBrain; " + "FlyBrain.load().step(); assert 'cupy' not in sys.modules", + ], + check=True, + ) + + +def test_missing_cupy_has_actionable_error(monkeypatch): + def missing(name): + raise ModuleNotFoundError("test: no CuPy") + + monkeypatch.setattr(cuda, "import_module", missing) + assert available_backends() == {"cpu": True, "cuda": False, "wasm": False} + with pytest.raises(BackendUnavailableError, match="NVIDIA driver"): + FlyBrain.load(backend="cuda") + assert FlyBrain.load(backend="cpu").step().tick == 1 diff --git a/tests/test_fullbrain_ids.py b/tests/test_fullbrain_ids.py new file mode 100644 index 0000000..9e80286 --- /dev/null +++ b/tests/test_fullbrain_ids.py @@ -0,0 +1,38 @@ +"""Regression for official uint64 neuron-list / int64 Feather endpoint IDs.""" + +import importlib.util +from pathlib import Path + +import numpy as np +import pytest + +spec = importlib.util.spec_from_file_location( + "fullbrain_runner", Path(__file__).parents[1] / "scripts/validate_fullbrain.py" +) +runner = importlib.util.module_from_spec(spec) +spec.loader.exec_module(runner) + + +def test_mixed_integer_dtypes_preserve_neighboring_large_ids(): + base = 720575940600000000 + ids = np.array([base, base + 1, base + 2, base + 100], dtype=np.uint64) + endpoints = np.array([base + 2, base, base + 100, base + 1], dtype=np.int64) + assert runner.map_source_ids(ids, endpoints).tolist() == [2, 0, 3, 1] + + +@pytest.mark.parametrize("unknown", [720575940600000003, 720575940600000101]) +def test_unknown_large_id_is_not_rounded_to_an_existing_id(unknown): + ids = np.array([720575940600000002, 720575940600000100], dtype=np.uint64) + with pytest.raises(ValueError, match="unknown neuron"): + runner.map_source_ids(ids, np.array([unknown], dtype=np.int64)) + + +def test_float_ids_are_rejected(): + with pytest.raises(ValueError, match="integer storage"): + runner.map_source_ids(np.array([1], dtype=np.uint64), np.array([1.0])) + + +@pytest.mark.parametrize("scale", [0, -1, True, float("inf"), float("nan")]) +def test_invalid_physical_contact_scale_rejected_before_data_io(tmp_path, scale): + with pytest.raises(ValueError): + runner.load_fullbrain(tmp_path, contact_mv=scale) diff --git a/tests/test_godot_bridge.py b/tests/test_godot_bridge.py index f34f7a4..240e6aa 100644 --- a/tests/test_godot_bridge.py +++ b/tests/test_godot_bridge.py @@ -53,3 +53,14 @@ def post(path, data, origin=None): finally: server.shutdown() thread.join(timeout=5) + + +def test_bridge_pins_selected_backend_when_restoring(): + service = bridge.Bridge(backend="cpu") + saved = dict(service.initial) + saved["brain"] = dict(saved["brain"], backend="cuda") + # Selection is caller-owned, even when saved metadata names another device. + # This checks routing only; it does not pretend this fixture ran on CUDA. + service.dispatch("/start", {"checkpoint_json": json.dumps(saved)}) + assert service.controller.brain.snapshot()["backend"] == "cpu" + assert service.controller.offer(0, {"danger_left": 1, "danger_right": 0})["brain_tick"] == 20 diff --git a/tests/test_olfaction.py b/tests/test_olfaction.py new file mode 100644 index 0000000..a46a6a6 --- /dev/null +++ b/tests/test_olfaction.py @@ -0,0 +1,114 @@ +import importlib.util +from pathlib import Path + +import numpy as np +import pytest + +from flybrain import Connectome, LIFConfig, Synapse +from flybrain.backends.cpu import CPUBackend +from flybrain.olfaction import OlfactoryDrive, sample_odor + +spec = importlib.util.spec_from_file_location( + "olfactory_map", Path(__file__).parents[1] / "scripts/build_olfactory_map.py" +) +mapping = importlib.util.module_from_spec(spec) +spec.loader.exec_module(mapping) + + +@pytest.fixture +def model(): + return Connectome( + "fixture", + ("101", "102", "103", "104"), + (Synapse(0, 2, 30.0), Synapse(1, 3, 30.0)), + {}, + {}, + {}, + ) + + +def test_local_antenna_stimulus_only_drives_annotated_side(model): + odor = OlfactoryDrive(model, left_ids=["101"], right_ids=["102"]) + left = odor.current(left=1, right=0) + np.testing.assert_array_equal(np.flatnonzero(left), [0]) + brain = CPUBackend(model, LIFConfig()) + for _ in range(100): + brain.step(left) + # A fixture tests transport/isolation, not a biological odor pathway. + state = brain.observe_selected((0, 1, 2, 3), ("rates_hz",))["rates_hz"] + assert state[0] > 0 and state[2] > 0 + assert state[1] == 0 and state[3] == 0 + brain.set_silenced((0,), True) + for _ in range(1000): + brain.step(left) + assert brain.observe_selected((2,), ("rates_hz",))["rates_hz"][0] < 1e-5 + + +@pytest.mark.parametrize("bad", [-1, float("nan"), float("inf"), True]) +def test_invalid_concentration_rejected(model, bad): + odor = OlfactoryDrive(model, left_ids=["101"], right_ids=["102"]) + with pytest.raises(ValueError): + odor.current(left=bad, right=0) + + +def test_response_bounded_and_monotone(model): + odor = OlfactoryDrive(model, left_ids=["101"], right_ids=["102"]) + values = [odor.amplitude(v) for v in [0, 1e-6, 0.1, 1, 10, 1e308]] + assert values == sorted(values) + assert values[0] == 0 and values[-1] <= odor.max_current + with pytest.raises(ValueError, match="overlap"): + OlfactoryDrive(model, left_ids=["101"], right_ids=["101"]) + with pytest.raises(ValueError, match="known"): + OlfactoryDrive(model, left_ids=["unknown"], right_ids=["102"]) + + +def test_world_field_uses_local_sample_and_is_mirror_symmetric(): + source = (0, 2, 0) + assert sample_odor(source, source) == 1 + assert sample_odor((-1, 0, 0), source) == sample_odor((1, 0, 0), source) + assert sample_odor((0, 1, 0), source) > sample_odor((0, -1, 0), source) + assert sample_odor((0, 0, 0), source, strength=0) == 0 + with pytest.raises(ValueError): + sample_odor((float("nan"), 0, 0), source) + + +def rows(): + selections = [ + ("ORN_DM1", "left", "olfactory", "sensory"), + ("ORN_DM1", "right", "olfactory", "sensory"), + ("DNa02", "left", "", "descending"), + ("DNa02", "right", "", "descending"), + ("PN", "left", "ALPN", "central"), + ("MBON", "right", "MBON", "central"), + ] + return [ + dict(root_id=str(720575940600000000 + i), cell_type=t, side=s, cell_class=c, super_class=sc) + for i, (t, s, c, sc) in enumerate(selections) + ] + + +def test_annotation_ids_preserved_and_unknown_side_never_guessed(): + data = rows() + ids = [r["root_id"] for r in data] + groups = mapping.resolve_populations(data, ids) + assert groups["ORN_DM1_right"] == ["720575940600000001"] + data[1]["side"] = "" + with pytest.raises(ValueError, match="ambiguous"): + mapping.resolve_populations(data, ids) + + +def test_incompatible_annotation_graph_fails_closed(): + data = rows() + with pytest.raises(ValueError, match="unknown neuron"): + mapping.resolve_populations(data, [r["root_id"] for r in data[1:]]) + with pytest.raises(ValueError, match="duplicate"): + mapping.resolve_populations(data + [data[0]], [r["root_id"] for r in data]) + + +def test_source_drift_rejected_before_loading_ids(tmp_path): + changed = tmp_path / "annotations.tsv" + roots = tmp_path / "roots.npy" + changed.write_text("changed") + roots.write_text("not a numpy file") + with pytest.raises(ValueError, match="checksum"): + mapping.build(changed, roots) diff --git a/tests/test_reflex_training.py b/tests/test_reflex_training.py new file mode 100644 index 0000000..7377eda --- /dev/null +++ b/tests/test_reflex_training.py @@ -0,0 +1,28 @@ +"""The example's controller sees neural features, never the answer label.""" + +import importlib.util +from pathlib import Path + +import numpy as np + +SPEC = importlib.util.spec_from_file_location( + "train_reflex", Path(__file__).resolve().parents[1] / "examples/train_reflex.py" +) +EXAMPLE = importlib.util.module_from_spec(SPEC) +SPEC.loader.exec_module(EXAMPLE) + + +def test_labels_do_not_enter_features(): + a = [{"rates_hz": [95, 12], "target": 0, "obstacle_side": 1}] + b = [{"rates_hz": [95, 12], "target": 1, "obstacle_side": 0}] + np.testing.assert_array_equal(EXAMPLE.features(a), EXAMPLE.features(b)) + + +def test_reward_changes_learned_action_without_changing_features(): + x = np.tile([[1, 0, 1], [0, 1, 1]], (32, 1)) + target = np.tile([1, 0], 32) + for labels in (target, 1 - target): + weights, curve = EXAMPLE.train(x, labels) + action = EXAMPLE.probabilities(np, x, weights) >= 0.5 + np.testing.assert_array_equal(action, labels) + assert len(curve) == 24 diff --git a/tests/test_synaptic.py b/tests/test_synaptic.py new file mode 100644 index 0000000..1f42dc4 --- /dev/null +++ b/tests/test_synaptic.py @@ -0,0 +1,120 @@ +import json + +import numpy as np +import pytest + +from flybrain import Connectome, Synapse +from flybrain.experimental.synaptic import SynapticCPU, SynapticLIFConfig + + +def model(weight=55.0): + return Connectome( + "fixture", ("input", "output", "isolated"), (Synapse(0, 1, weight),), {}, {}, {} + ) + + +def brain(**kwargs): + return SynapticCPU(model(), input_ids=("input",), weight_units="mV", **kwargs) + + +def test_exact_transmission_delay_and_sensory_input_schedule(): + b = brain() + b.step([68.75, 0, 0]) + assert b.snapshot()["spikes"] == [False, False, False] + b.step([0, 0, 0]) + assert b.snapshot()["spikes"] == [True, False, False] + # Source spike at t=0.1 ms arrives at 1.9 ms, not before. + for _ in range(17): + b.step([0, 0, 0]) + assert b.snapshot()["synaptic_mv"][1] == 0 + b.step([0, 0, 0]) + assert b.snapshot()["synaptic_mv"][1] == 55 + assert b.snapshot()["voltage_mv"][1] == -52 + b.step([0, 0, 0]) + assert b.snapshot()["voltage_mv"][1] > -52 + + +def test_synaptic_propagation_without_external_postsynaptic_current(): + b = brain() + counts = np.zeros(3, dtype=int) + for tick in range(1000): + b.step([68.75 if tick < 500 and tick % 30 == 0 else 0.0, 0.0, 0.0]) + counts += b.snapshot()["spikes"] + assert counts[0] > 0 and counts[1] > 0 and counts[2] == 0 + + +def test_inflight_delay_buffer_survives_json_checkpoint(): + b = brain() + b.step([68.75, 0, 0]) + b.step([0, 0, 0]) + saved = json.loads(json.dumps(b.snapshot())) + restored = brain() + restored.restore(saved) + for _ in range(150): + b.step([0, 0, 0]) + restored.step([0, 0, 0]) + assert b.snapshot() == restored.snapshot() + + +def test_checkpoint_rejects_other_graph_and_invalid_state_atomically(): + b = brain() + original = b.snapshot() + different = SynapticCPU(model(56.0), input_ids=("input",), weight_units="mV") + with pytest.raises(ValueError, match="mismatch"): + different.restore(original) + for key, value in [ + ("voltage_mv", [float("nan"), 0, 0]), + ("voltage_mv", [True, 0, 0]), + ("history", [[True]]), + ("last_spike_tick", [1, 1, 1]), + ]: + corrupted = {**original, key: value} + with pytest.raises(ValueError): + b.restore(corrupted) + assert b.snapshot() == original + + +def test_silencing_stops_new_spikes_but_preserves_already_queued_events(): + b = brain() + b.step([68.75, 0, 0]) + b.step([0, 0, 0]) + b.set_silenced((0,), True) + spikes = [] + for _ in range(150): + b.step([68.75, 0, 0]) + spikes.append(b.snapshot()["spikes"]) + assert not np.asarray(spikes)[:, 0].any() + assert np.asarray(spikes)[:, 1].any() + + +def test_units_and_declared_inputs_cannot_be_silently_bypassed(): + with pytest.raises(ValueError, match="mV"): + SynapticCPU(model(), weight_units="normalized") + b = brain() + saved = b.snapshot() + with pytest.raises(ValueError, match="declared input"): + b.step([0, 68.75, 0]) + assert b.snapshot() == saved + for indices in [(-1,), (3,), (True,), (0.5,)]: + with pytest.raises(ValueError): + b.set_silenced(indices, True) + + +def test_nearly_equal_time_constants_approach_exact_equal_limit(): + exact = brain(config=SynapticLIFConfig(synapse_tau_ms=20)) + nearby = brain(config=SynapticLIFConfig(synapse_tau_ms=20 + 1e-10)) + for tick in range(200): + jumps = [68.75 if tick == 0 else 0, 0, 0] + exact.step(jumps) + nearby.step(jumps) + np.testing.assert_allclose( + exact.snapshot()["voltage_mv"], nearby.snapshot()["voltage_mv"], atol=1e-9, rtol=0 + ) + + +@pytest.mark.parametrize( + "params", [dict(dt_ms=1), dict(delay_ms=-1), dict(synapse_tau_ms=0), dict(dt_ms=float("nan"))] +) +def test_invalid_dynamics_rejected(params): + with pytest.raises(ValueError): + SynapticLIFConfig(**params) diff --git a/tests/test_synaptic_cuda_hardware.py b/tests/test_synaptic_cuda_hardware.py new file mode 100644 index 0000000..b16ac0d --- /dev/null +++ b/tests/test_synaptic_cuda_hardware.py @@ -0,0 +1,142 @@ +"""Actual GPU conformance; fixtures validate math, not full-brain behavior.""" + +import json +import os + +import numpy as np +import pytest + +from flybrain import Connectome, Synapse +from flybrain.backends.cuda import _load_cupy +from flybrain.errors import BackendUnavailableError +from flybrain.experimental.synaptic import SynapticCPU, SynapticLIFConfig +from flybrain.experimental.synaptic_cuda import SynapticCUDA + + +@pytest.fixture +def device(): + try: + return _load_cupy() + except BackendUnavailableError as error: + if os.environ.get("FLYBRAIN_REQUIRE_CUDA") == "1": + pytest.fail(str(error)) + pytest.skip(f"Actual CUDA device required: {error}") + + +def graph(): + return Connectome( + "synaptic-gpu-fixture", + tuple(str(i) for i in range(6)), + tuple( + Synapse(*e) + for e in [ + (0, 2, 40.0), + (0, 2, 15.0), + (1, 2, -33.0), + (2, 3, 55.0), + (3, 2, 10.0), + (2, 2, 2.0), + (3, 4, -20.0), + ] + ), + {}, + {}, + {}, + ) + + +def equal(cpu, gpu): + a, b = cpu.snapshot(), gpu.snapshot() + for field in ( + "engine", + "fingerprint", + "tick", + "spikes", + "history", + "last_spike_tick", + "silenced", + ): + assert a[field] == b[field], field + for field in ("voltage_mv", "synaptic_mv", "rates_hz"): + np.testing.assert_allclose(a[field], b[field], atol=1e-9, rtol=0, err_msg=field) + + +@pytest.mark.parametrize( + "config", + [ + SynapticLIFConfig(), + SynapticLIFConfig(dt_ms=0.2), + SynapticLIFConfig(delay_ms=0), + SynapticLIFConfig(synapse_tau_ms=20), + ], +) +def test_cpu_cuda_and_cross_backend_delayed_checkpoint(device, config): + cpu = SynapticCPU(graph(), config, input_ids=("0", "1"), weight_units="mV") + gpu = SynapticCUDA(graph(), config, input_ids=("0", "1"), weight_units="mV") + stream = device.cuda.Stream(non_blocking=True) + downstream = 0 + for tick in range(600): + jumps = np.zeros(6) + if tick in (0, 1, 2, 30, 75, 100, 180, 200, 230): + jumps[0] = 68.75 + if tick in (25, 75, 160, 190): + jumps[1] = 68.75 + cpu.step(jumps) + # The engine must continue to own its stream in other CuPy contexts. + with stream: + gpu.step(jumps) + equal(cpu, gpu) + downstream += int(cpu.snapshot()["spikes"][3]) + if tick == 4: + # A spike is in the delay buffer for positive-delay configurations. + saved_cpu = json.loads(json.dumps(cpu.snapshot())) + saved_gpu = json.loads(json.dumps(gpu.snapshot())) + gpu.restore(saved_cpu) + cpu.restore(saved_gpu) + assert downstream > 0 + checkpoint = gpu.snapshot() + corrupted = {**checkpoint, "voltage_mv": [float("nan")] * 6} + with pytest.raises(ValueError): + gpu.restore(corrupted) + assert gpu.snapshot() == checkpoint + with pytest.raises(ValueError, match="declared input"): + gpu.step([0, 0, 1, 0, 0, 0]) + assert gpu.snapshot() == checkpoint + + +def test_silencing_queued_events_and_nonfinite_step_rollback(device): + cpu = SynapticCPU(graph(), input_ids=("0", "1"), weight_units="mV") + gpu = SynapticCUDA(graph(), input_ids=("0", "1"), weight_units="mV") + for tick in range(200): + jumps = np.zeros(6) + jumps[0] = 68.75 if tick < 10 else 0 + if tick == 5: + cpu.set_silenced((0,), True) + gpu.set_silenced((0,), True) + if tick == 50: + cpu.set_silenced((0,), False) + gpu.set_silenced((0,), False) + cpu.step(jumps) + gpu.step(jumps) + equal(cpu, gpu) + before = gpu.snapshot() + with pytest.raises(ValueError, match="finite"): + gpu.step([float("inf"), 0, 0, 0, 0, 0]) + assert gpu.snapshot() == before + # Exercise a kernel-detected overflow, not just host input validation. + overflow_graph = Connectome( + "overflow-fixture", + ("a", "b", "c"), + (Synapse(0, 2, 1.7e308), Synapse(1, 2, 1.7e308)), + {}, + {}, + {}, + ) + overflow = SynapticCUDA( + overflow_graph, SynapticLIFConfig(delay_ms=0), input_ids=("a", "b"), weight_units="mV" + ) + overflow.step([68.75, 68.75, 0]) + before = overflow.snapshot() + with pytest.raises(ValueError, match="nonfinite"): + overflow.step([0, 0, 0]) + assert overflow.snapshot() == before diff --git a/tests/test_synaptic_odor_protocol.py b/tests/test_synaptic_odor_protocol.py new file mode 100644 index 0000000..82ba332 --- /dev/null +++ b/tests/test_synaptic_odor_protocol.py @@ -0,0 +1,51 @@ +"""Controls must share noise and preserve exact stimulus replay.""" + +import importlib.util +import json +import sys +from pathlib import Path + +import numpy as np + + +def load_protocol(): + scripts = Path(__file__).parents[1] / "scripts" + spec = importlib.util.spec_from_file_location( + "synaptic_odor_protocol", scripts / "probe_synaptic_odor.py" + ) + module = importlib.util.module_from_spec(spec) + sys.path.insert(0, str(scripts)) + try: + spec.loader.exec_module(module) + finally: + sys.path.pop(0) + return module + + +def test_controls_share_each_enabled_neurons_random_inputs(): + protocol = load_protocol() + generators = [np.random.default_rng(20260913) for _ in range(4)] + counts = np.zeros(68, dtype=int) + for _ in range(1000): + both, left, right, off = [ + protocol.input_events(rng, 35, 33, 0.015, enable_left, enable_right) + for rng, (enable_left, enable_right) in zip( + generators, [(True, True), (True, False), (False, True), (False, False)] + ) + ] + np.testing.assert_array_equal(both, left | right) + assert not left[35:].any() and not right[:35].any() and not off.any() + counts += both + assert (counts > 0).all() + assert all(rng.bit_generator.state == generators[0].bit_generator.state for rng in generators) + + +def test_input_rng_json_restore_replays_future_stimuli(): + protocol = load_protocol() + rng = np.random.default_rng(20260913) + protocol.input_events(rng, 35, 33, 0.015, False, False) + state = json.loads(json.dumps(rng.bit_generator.state)) + expected = [protocol.input_events(rng, 35, 33, 0.015, True, True) for _ in range(100)] + rng.bit_generator.state = state + actual = [protocol.input_events(rng, 35, 33, 0.015, True, True) for _ in range(100)] + np.testing.assert_array_equal(actual, expected) diff --git a/tests/test_voxel_arena.py b/tests/test_voxel_arena.py new file mode 100644 index 0000000..dc89ed9 --- /dev/null +++ b/tests/test_voxel_arena.py @@ -0,0 +1,46 @@ +import json + +import pytest + +from flybrain.experimental.voxel_arena import VoxelArena + + +def test_food_cannot_steer_the_body_without_neural_commands(): + a = VoxelArena(food=(4, 0.5, 2)) + b = VoxelArena(food=(-6, 0.5, -6)) + assert a.observe() != b.observe() + assert set(a.observe()) == {"odor_left", "odor_right"} + initial = a.scene()["position"] + for _ in range(10): + a.apply({"speed": 0, "turn": 0}) + assert a.scene()["position"] == initial + for _ in range(100): + action = {"speed": 2, "turn": 0.7} + a.apply(action) + b.apply(action) + assert a.scene()["position"] == b.scene()["position"] + assert a.yaw == b.yaw + + +def test_solid_blocks_stop_fast_body_without_tunneling(): + arena = VoxelArena(blocks=((-2, -2),)) + for _ in range(20): + arena.apply({"speed": 3, "turn": 0}, 100) + assert -2.8 < arena.x <= -2.68 + assert arena.contacts > 0 + + +def test_world_json_checkpoint_replays_odor_contacts_and_pose(): + arena = VoxelArena() + for _ in range(40): + arena.apply({"speed": 1, "turn": 0.1}) + restored = VoxelArena.from_snapshot(json.loads(json.dumps(arena.snapshot()))) + for _ in range(100): + action = {"speed": 3, "turn": 0} + assert arena.apply(action) == restored.apply(action) + assert arena.observe() == restored.observe() + assert arena.snapshot() == restored.snapshot() + before = arena.snapshot() + with pytest.raises(ValueError): + arena.apply({"speed": 2, "turn": float("nan")}) + assert arena.snapshot() == before