From be9586885032477304df2aa3256b321dffd0c213 Mon Sep 17 00:00:00 2001 From: Bru Date: Mon, 24 Aug 2026 21:50:19 +0200 Subject: [PATCH 1/6] Speed up raw IO hot paths for many-small-read workloads - Raw.get_data: skip full times-axis allocation when tmin/tmax unset; resolve picks=None to arange directly instead of string machinery - _picks_to_idx: fast return for integer arrays already in range - _mult_cal_one: fuse gather+cast+calibration into one pass - FIF reader: read simple numeric tags through a PID-keyed memory map; select buffer entries via searchsorted on bounds - EDF/BDF reader: vectorized uniform-sfreq fast path writing straight into the output buffer (incl. uniform stim channels via legacy bitmask), optional numba kernels for window decode and int24 samples with numpy fallbacks (all outputs bit-identical to the previous implementation) - BaseRaw._get_windows: internal batched window reader reusing one buffer Benchmarks (128ch x 1800s @512Hz, random 2s windows): EDF/BDF/BrainVision/ FIF per-call latency reduced ~4.3-7x vs main; preloaded access now at the numpy floor. --- .gitignore | 5 +- benchmarks/io_dl/RESULTS.md | 461 +++++++++++++ benchmarks/io_dl/ab_test.py | 101 +++ .../backends-data_big-20260824-161621.json | 74 ++ .../backends-data_big-20260824-161927.json | 92 +++ .../backends-data_big-20260824-174932.json | 104 +++ benchmarks/io_dl/bench_backends.py | 313 +++++++++ benchmarks/io_dl/bench_io.py | 284 ++++++++ benchmarks/io_dl/check_equivalence.py | 76 +++ benchmarks/io_dl/check_equivalence_big.py | 79 +++ benchmarks/io_dl/generate_data.py | 296 ++++++++ benchmarks/io_dl/profile_io.py | 253 +++++++ benchmarks/io_dl/results-20260824-154321.json | 248 +++++++ benchmarks/io_dl/results-20260824-160007.json | 248 +++++++ benchmarks/io_dl/workspaces/W5/bench_cache.py | 229 +++++++ .../W5/cache_results_20260824-204900.json | 193 ++++++ .../W5/cache_results_20260824-205105.json | 295 ++++++++ .../W5/cache_results_20260824-205253.json | 295 ++++++++ benchmarks/io_dl/workspaces/W5/findings.md | 141 ++++ .../workspaces/W7/bench_w7_consolidated.py | 156 +++++ .../io_dl/workspaces/W7/bench_w7_write.py | 644 ++++++++++++++++++ .../io_dl/workspaces/W7/consolidated.json | 11 + benchmarks/io_dl/workspaces/W7/findings.md | 174 +++++ benchmarks/io_dl/workspaces/W7/results.json | 9 + doc/changes/dev/14202.bugfix.rst | 6 + mne/_fiff/_mmap_cache.py | 38 ++ mne/_fiff/pick.py | 12 + mne/_fiff/utils.py | 57 +- mne/io/base.py | 65 +- mne/io/edf/_bdf_numba.py | 33 + mne/io/edf/_edf_numba.py | 41 ++ mne/io/edf/edf.py | 138 +++- mne/io/fiff/raw.py | 86 ++- mne/utils/mixin.py | 9 +- 34 files changed, 5250 insertions(+), 16 deletions(-) create mode 100644 benchmarks/io_dl/RESULTS.md create mode 100644 benchmarks/io_dl/ab_test.py create mode 100644 benchmarks/io_dl/backends-data_big-20260824-161621.json create mode 100644 benchmarks/io_dl/backends-data_big-20260824-161927.json create mode 100644 benchmarks/io_dl/backends-data_big-20260824-174932.json create mode 100644 benchmarks/io_dl/bench_backends.py create mode 100644 benchmarks/io_dl/bench_io.py create mode 100644 benchmarks/io_dl/check_equivalence.py create mode 100644 benchmarks/io_dl/check_equivalence_big.py create mode 100644 benchmarks/io_dl/generate_data.py create mode 100644 benchmarks/io_dl/profile_io.py create mode 100644 benchmarks/io_dl/results-20260824-154321.json create mode 100644 benchmarks/io_dl/results-20260824-160007.json create mode 100644 benchmarks/io_dl/workspaces/W5/bench_cache.py create mode 100644 benchmarks/io_dl/workspaces/W5/cache_results_20260824-204900.json create mode 100644 benchmarks/io_dl/workspaces/W5/cache_results_20260824-205105.json create mode 100644 benchmarks/io_dl/workspaces/W5/cache_results_20260824-205253.json create mode 100644 benchmarks/io_dl/workspaces/W5/findings.md create mode 100644 benchmarks/io_dl/workspaces/W7/bench_w7_consolidated.py create mode 100644 benchmarks/io_dl/workspaces/W7/bench_w7_write.py create mode 100644 benchmarks/io_dl/workspaces/W7/consolidated.json create mode 100644 benchmarks/io_dl/workspaces/W7/findings.md create mode 100644 benchmarks/io_dl/workspaces/W7/results.json create mode 100644 doc/changes/dev/14202.bugfix.rst create mode 100644 mne/_fiff/_mmap_cache.py create mode 100644 mne/io/edf/_bdf_numba.py create mode 100644 mne/io/edf/_edf_numba.py diff --git a/.gitignore b/.gitignore index 21275b21c0b..0ebf0394659 100644 --- a/.gitignore +++ b/.gitignore @@ -103,4 +103,7 @@ venv/ .hypothesis/ .ruff_cache/ .ipynb_checkpoints/ -/.claude/ \ No newline at end of file +/.claude/ +# IO benchmark synthetic data (regenerate via benchmarks/io_dl/generate_data.py) +benchmarks/io_dl/data/ +benchmarks/io_dl/data_big/ diff --git a/benchmarks/io_dl/RESULTS.md b/benchmarks/io_dl/RESULTS.md new file mode 100644 index 00000000000..70e627e4cc0 --- /dev/null +++ b/benchmarks/io_dl/RESULTS.md @@ -0,0 +1,461 @@ +# MNE-Python IO for deep learning: benchmark + first speedups + +Date: 2026-08-24 · Machine: Apple Silicon (arm64), macOS, py 3.12.12, numpy 2.4.6, +MNE 1.12.1 (this working tree). All numbers below are medians; see +"Methodology notes" for why interleaved A/B is authoritative here. + +## 1. Which three formats? + +| Rank | Format | Why it dominates DL workloads | +|---|---|---| +| 1 | **EDF/EDF+** | The big DL corpora are EDF: TUH EEG Corpus (~25k studies, braindecode's flagship dataset), PhysioNet (CHB-MIT, Sleep-EDF). BIDS-recommended. | +| 2 | **BrainVision (.vhdr/.eeg/.vmrk)** | The other BIDS-recommended format; standard on OpenNeuro and MOABB; mne-bids converts to it by default. | +| 3 | **FIF** | MNE-native; what DL pipelines persist preprocessed data/caches to (braindecode preprocessing saves `.fif`). | + +(Honorable mention: EEGLAB `.set`, common but usually converted to one of the above.) + +## 2. Benchmark design + +Files in this directory: + +- `generate_data.py` – writes the *same* synthetic signal (64 ch × 300 s @ 256 Hz, + ~±100 µV band-limited noise) to `bench.edf`, `bench.vhdr(+.eeg,.vmrk)`, + `bench_raw.fif`, plus a raw float32 `bench.bin` baseline. EDF is int16 by design + (half the on-disk bytes of float32). +- `bench_io.py` – scenario suite (see below), GC disabled during timing. +- `profile_io.py` – cProfile attribution + deepcopy tracer (rebinds every + `from copy import deepcopy` reference in loaded `mne.*` modules). +- `ab_test.py` – interleaved A/B between the installed (unpatched) release copy + and this working tree; cancels machine drift. +- `check_equivalence.py` – md5-compares outputs of every access pattern between + unpatched and patched code (must be bit-identical). + +Scenarios (the DL-relevant access patterns): + +| Scenario | Meaning | +|---|---| +| `open_meta` | `read_raw_X(preload=False)` — header parse only | +| `full_load` | `read_raw_X(preload=True)` — end-to-end load | +| `seq_1s` | 300 sequential 1 s `get_data()` calls (streaming/eval) | +| `rand_windows` | 300 shuffled 2 s windows via `get_data()`, `preload=False` — **the training-loop pattern** (dataset opens raw once, sampler draws windows) | +| `preloaded_windows` | same windows on a preloaded raw — pure per-call Python overhead | +| floors | numpy `memmap` / `fromfile` equivalents reading identical bytes | + +### Methodology notes (things that silently corrupt results) + +1. **Import resolution**: running scripts from inside `benchmarks/io_dl/` or with + `python -c` resolves `mne` differently (cwd lands on `sys.path` for `-c`; + script dir replaces cwd for files). Both bench scripts now pin the working + tree explicitly. Verify with the printed `mne loaded from:` line. +2. **Sustained-load drift**: whole-suite runs back-to-back showed ±20–100 % + swings under background load (even the numpy floor drifted 4→11 ms). + Conclusions here rely on interleaved A/B (`ab_test.py`), not cross-run diffs. +3. cProfile inflates tiny-function costs ~3×; use it for attribution only. + +## 3. Results + +### 3.1 Baseline (unpatched tree) — where does time go? + +Steady-state cost per `get_data()` call, preloaded raw, all three formats: +**~100 µs**, identical across formats ⇒ pure Python overhead in the shared path, +zero relation to disk. The numpy floor for the same window is ~15 µs ⇒ MNE was +~6× off the floor before any I/O happens. + +Attribution (cProfile, 200 calls): + +- `_handle_tmin_tmax` touches `self.times` even when `tmin=tmax=None` + (`mne/utils/mixin.py`) and Raw's `times` property allocates + `np.arange(n_times)/sfreq` each call — **600 KB allocated+discarded per window** + at 300 s; scales linearly with recording length (≈59 MB per call for an 8 h TUH + recording!). +- Channel picks resolved **twice** per call (once in `get_data`, again in + `_parse_get_set_params`); the first goes None→`"all"`→string machinery with + ~65 `list.index` lookups per call. +- EDF reader extras (non-preloaded only): `from scipy.interpolate import + interp1d` executed inside `_read_segment_file` (first call in a fresh DataLoader + worker pays a ~0.4 s import/JIT spike), a wasted `.copy()` of every channel's + block, and an extra full-size temp buffer. +- Per-call fixed cost curve (unpatched, ms/call vs window length): + EDF flat ≈0.37 ms up to 2 s windows; BV/FIF ≈0.12–0.15 ms — i.e. small reads + were almost entirely fixed overhead. + +### 3.2 Patches applied (this tree) + +1. `mne/utils/mixin.py::_handle_tmin_tmax`: use integer `n_times` when available + (Raw); Epochs/Evoked keep their cheap stored-times path. +2. `mne/io/base.py::get_data`: `picks=None` short-circuits to + `np.arange(nchan)` — exactly equivalent to `_picks_to_idx(..., "all", + exclude=())` semantics, skips name resolution entirely. +3. `mne/io/edf/edf.py::_read_segment_file`: `interp1d` import moved into the + mixed-sfreq stim-interpolation branch; removed the per-channel `.copy()` + (arithmetic is out-of-place; TAL views stay valid until consumed). + +### 3.3 Speedups (interleaved A/B, best-of-medians) + +Preloaded random-window `get_data()` (pure access cost): + +| arm | µs/call | +|---|---| +| installed = release (unpatched) | 101 | +| tree = patched | 31 | + +⇒ **3.2× faster**; now within ~2× of the raw-numpy floor instead of ~6×. + +Non-preloaded random windows (includes reader): + +| format | unpatched µs/call | patched µs/call | speedup | +|---|---|---|---| +| EDF | 426 | 335 | **1.27×** | +| BrainVision | ~172 | ~111 | **1.55×** | +| FIF | ~192 | ~130 | **1.47×** | + +Whole-suite runs agree (preloaded_windows ~100→~34 µs/call; seq_1s BV/FIF +~150→~82 µs/call) once drift is controlled. + +Correctness: `check_equivalence.py` shows bit-identical output vs the release +copy for every pattern (full read, boundary-crossing windows, tmin/tmax, int / +str / slice / negative picks); upstream suites pass +(`test_edf.py` + `test_brainvision.py` 116 passed, `test_raw.py` 63 passed, +`test_epochs.py` 239 passed, targeted evoked tests passed). + +## 4. The deepcopy question ("I hear there's a problem") + +Traced with a tracer that rebinds *every* `deepcopy` reference held by loaded +`mne.*` modules (a plain `copy.deepcopy` monkeypatch misses `from copy import +deepcopy` bindings — first tracer attempt caught almost nothing): + +| Operation | deepcopy calls | total time | +|---|---|---| +| `read_raw_X(preload=True)` ×3 formats | 141 (mostly Info internals) | 0.64 ms | +| load+crop+reref+notch+resample | 2 | 0.07 ms | +| Epochs creation + iterate 20 epochs | 238 | 1.65 ms | +| `deepcopy(info)` alone, 64 ch | 1 | 0.05 ms | + +Verdict: on current main, **deepcopy is not the bottleneck for DL-style data +access**. It becomes measurable only with large Infos — copies scale ~66 µs +(32 ch) → ~400 µs (512 ch), roughly ×1.5–2 with a montage/dig set — or when an +operation copies Info thousands of times. If complaints trace to older MNE +versions, note `Info.__deepcopy__` already contains fast paths (chs shallow-copy +trick) that recent releases added. + +The real fixed-cost culprits were the ones fixed above (times array materialized +per call; double pick resolution), plus the EDF reader's per-channel Python loop. + +## 5. Remaining bottlenecks, ranked (candidates for next PRs) + +1. **EDF non-preloaded small reads still pay ~300 µs fixed cost** (per-channel + Python loop over blocks + temp buffer + block bookkeeping). Vectorizing the + uniform-sfreq case (the overwhelmingly common one) could cut most of it. +2. **File opened per `get_data()` call** in all three readers (~5–10 µs + + syscalls). Keeping an open handle per Raw would help streaming patterns. +3. **Batched window reads**: a `raw.get_windows(starts, width)` style API could + amortize validation/picks across hundreds of windows (the remaining ~30 µs + fixed cost per call would drop to ~µs amortized). +4. **`save()` to FIF is slow**: ~105 ms for a 19.7 MB payload (~190 MB/s) — this + is the caching path DL pipelines hit constantly; tag serialization looks + unoptimized (separate investigation). +5. Output dtype is always float64; DL users immediately cast to float32. + An opt-in `dtype="float32"` read path halves memory traffic. +6. `open_meta` ≈ 2 ms/format — matters when instantiating datasets over ~10k + files (20 s just for headers); mostly Python-side parsing/checks. + +## 6. Provider-library dissection & BIG multi-format study (session 2) + +Setup: second preset `--preset big` = **128 ch × 1800 s @ 512 Hz** (118M samples; +float32 472 MB / int16 236 MB), same signal written to every format: +`data_big/` holds EDF(int16), **BDF(int24)**, BrainVision(f32), FIF(f32), +NPZ(f32), HDF5(f32; three chunk layouts), Zarr(f32; two layouts), raw .bin. +Backends compared by `bench_backends.py`; per-call latencies via interleaved A/B. + +### 6.1 Full-file loads (BIG) + +| backend | time | effective MB/s | +|---|---|---| +| floor_memmap (f32 bin) | 152–156 ms | ~3050 | +| h5 contiguous | 153 ms | ~3077 | +| npz | 206 ms | ~2289 | +| **edfio eager EDF** | **244 ms** | ~967 | +| mne_edf | 454–500 ms | ~950–1040 (vs 236 MB payload) | +| mne_fif | 535–552 ms | ~855 | +| mne_bv | 814–842 ms | ~560 | +| mne_bdf | 995–1003 ms | ~470 | +| zarr t10s chunks | 4688–5099 ms | ~100 (!!) | + +Corroborates the MNE-forum report (Dec 2023): edfio ≈ 2× faster than MNE on +uniform-sfreq EDF; MNE's catastrophic mixed-sfreq cases (minutes) come from its +upsample-to-max-sfreq policy. Upstream floated making edfio an optional reader +backend — worth pursuing for `read_raw_edf`. + +### 6.2 Random-window reads (the DL pattern; 300 × 2 s windows) + +| backend | µs/window | +|---|---| +| h5_win (chunks = 128×512 f32) | **53–94** — beats memmap floor! | +| floor_memmap | 142 | +| h5 contiguous | 28–94 | +| h5_t10s (per-channel slabs) | 725–930 | +| zarr_win | 1678–2535 | +| mne_fif / mne_bv non-preloaded | 1298–1696 | +| edfio lazy per-signal slices | ~1158 | +| zarr_t10s | ~31700 | + +Two big lessons: +1. **Chunk geometry must mirror access geometry.** Window-shaped HDF5 chunks + turn each sample into ONE contiguous ~512 KB read and even beat the raw + memmap "floor" (which touches 128 scattered pages per window). Time-slab + chunking is 13× worse; misaligned Zarr chunks are pathological. +2. **Zarr v3's per-chunk Python dispatch makes it a poor local training + backend** despite identical chunk shapes to HDF5 (~20× slower here). + HDF5 or plain memmap/npy for single-node; Zarr for remote/parallel. + +### 6.3 Where MNE's remaining window cost lives (128 ch) + +After this round of patches, steady-state per-window (get_data incl. .sum()): + +| format | non-preloaded | preloaded | +|---|---|---| +| EDF | ~400–430 µs | ~79 µs | +| BDF | ~1000 µs | ~79 µs | +| BrainVision | ~515 µs | ~80 µs | +| FIF | ~416 µs | ~78 µs | + +Attribution experiments (`_getitem` bypass): at 128 ch, public-API validation +was consuming **55–58 %** of BV/FIF window latency (double pick resolution, +astype copies, reductions). The `_picks_to_idx` integer-fast-exit patch below +reclaims much of it; a future batched `raw.get_windows(starts, width)` API +could amortize nearly all of it (validation once, then tight read loop). + +### 6.4 Patches added in session 2 (this tree) + +4. `mne/io/edf/edf.py::_read_segment_file`: vectorized fast path for + uniform-sfreq EDF/BDF with no TAL/stim channels among requested ones + (gated to decoded outputs ≤ 32 MB so huge sequential loads keep the legacy + cache-friendly loop). Replaces the per-channel Python loop with one + reshape + strided gather + 3 vector ops; writes straight into the output + buffer when no projector/compensation is active and cals == 1 (always true + for EDF/BDF). Bit-identical output (md5-verified on small set; max |diff| + = 0 on BIG cross-checks). +5. `mne/_fiff/pick.py::_picks_to_idx`: fast return for integer arrays already + in range (skips astype copy, two boolean-reduction passes, modulo pass). + Benefits every hot call site resolving array picks. + +Measured against **pristine main** (interleaved subprocesses, best-of): + +| metric (BIG file) | pristine | patched | speedup | +|---|---|---|---| +| EDF random windows | 1317–1383 µs | 405–414 µs | **3.3×** | +| EDF 4-channel windows | 592–599 µs | 90–97 µs | **6.3×** | +| BDF random windows | ~2000 µs | ~1000 µs | **~2.0×** | +| full sequential load | ~equal within machine noise (see caveat) | | | + +Caveat learned the hard way: whole-file loads (≈944 MB float64 output alloc + +page faults) swing ±100 % with background system state on this laptop; only +interleaved same-window comparisons are trustworthy. Two earlier "regressions" +were artifacts: (a) comparing against the *installed release* instead of +pristine main — main itself got faster than 1.12.1 on full loads; (b) harness +rows that re-opened/reallocated the Raw inside the timed region. + +Correctness: 179 io tests + 368-test epochs/raw/bv batches pass; outputs +bit-identical to both the installed release and stashed-pristine main on every +pattern tested (windows, boundary spans, subsets, negative picks, tmin/tmax). + +## 7. How others speed this up (survey notes) + +- **Pre-conversion + memory-mapping**: PyRain (RainBench) reports 27–60× + dataloading speedups over NetCDF/Dask using mmap'd samples for randomized + sliding-window access; explicitly recommends mmap over chunked stores for + fragmented random access on local disks. LaBraM pre-packages EEG into HDF5 + windows. Braindecode caches preprocessed data as FIF. +- **Chunk-layout alignment**: h5py/Zarr guidance and benchmarks + (e.g., rabernat/zarr_hdf_benchmarks) — our §6.2 confirms: shape chunks like + the reads. For (n_ch, n_time) EEG with 2 s window sampling: + chunks=(n_ch, window) is optimal; (1, time_slab) is terrible for multi-channel + windows; fully contiguous is best for full loads but mediocre for random + windows on spinning rust/NVMe (still fine on macOS page cache). +- **Reader-backend competition**: pyedflib (Cython/C), edfio (vectorized numpy, + lazy loading, partial digital slices) — edfio's design (one-shot frombuffer + + record reshape + vectorized calibration + lazy per-signal loading) is the + model for MNE's EDF path; adopting it as an optional backend was proposed + upstream by MNE maintainers. +- **Amortization APIs**: DALI/WebDataset-style pre-decoded batches; in MNE + terms, reading K consecutive windows through one resolved-picks call. + +## 8. Recommended next steps (updated ranking) + +1. Land sessions-1+2 patches (bit-exact, tested): shared-path fixes, EDF + vectorized fast path, `_picks_to_idx` fast exit. ~2–6× on DL window loops. +2. Add `raw.get_windows(starts, stop)`-style batched reader (amortizes the + remaining ~30–90 µs/call Python overhead across windows; prototype shows + ≥50 % headroom for BV/FIF). +3. Optional edfio-backed `read_raw_edf` engine (fast path for the corpus-scale + mixed-sfreq pathology; upstream discussion exists). +4. Publish chunk-layout guidance + a converter recipe (BIDS→HDF5 win-chunks) + for training pipelines; keep Zarr for cloud/parallel contexts. +5. BDF int24 decode vectorization (mne_bdf is now the slowest full-loader). +6. Investigate FIF save throughput separately (fixed overhead dominates small + saves; large saves run at ~300 MB/s). + +## 9. Reproduce + +```bash +python benchmarks/io_dl/generate_data.py # small set +python benchmarks/io_dl/generate_data.py --preset big # BIG set (~2.5 GB) +python benchmarks/io_dl/bench_io.py # small-suite timings +python benchmarks/io_dl/bench_backends.py --dir data_big # multi-backend table +AB_FMT=edf AB_PRELOAD=0 python benchmarks/io_dl/ab_test.py 5 # interleaved A/B +python benchmarks/io_dl/profile_io.py # profiles + deepcopy traces +python benchmarks/io_dl/check_equivalence.py # bit-equality vs release +python benchmarks/io_dl/check_equivalence_big.py # numeric equality, BIG +``` + +Result JSONs: `results-*.json`, `backends-*.json` in this directory. + + +## 10. Session 3: profiling to the metal + batched reads + +Method: accumulator-wrapping of hot functions (no profiler inflation) plus +interleaved subprocess A/B against both pristine main and the installed +release. py-spy available as cross-check. + +### 10.1 What the traces showed (BV, BIG file, per window) + +| component | before | after fix | +|---|---|---| +| `_mult_cal_one` (cast+index+scale = **3 passes** + alloc) | ~169 µs (61 %) | **one fused pass** | +| `get_data` validation layers | ~30 µs | ~21 µs | +| open+seek+fromfile glue | ~88 µs | unchanged | + +### 10.2 Patches added (session 3) + +6. `mne/_fiff/utils.py::_mult_cal_one`: fuse gather + float-cast + calibration + into a single strided multiply into the output view (`np.multiply(one[idx], + cals, out=data_view)`), eliminating a full-size intermediate allocation and + two extra passes. Numerically identical (elementwise ops on same values); + all suites pass, outputs bit-identical. +7. `mne/io/edf/_bdf_numba.py` (new) + hook in `_read_ch`: numba-accelerated + int24→int32 BDF decoder following MNE's existing optional-numba pattern + (`mne/_numba.py`), with graceful fallback to the vectorized-numpy path. + +### 10.3 Cumulative effect (BIG file, 2 s windows, non-preloaded) + +Interleaved A/B vs installed release: + +| format | session start | now | total speedup | +|---|---|---|---| +| BrainVision | ~1300 µs | ~225–232 µs | **~5.7×** | +| FIF | ~1700 µs | ~291 µs | **~5.8×** | +| EDF | ~2080 µs | ~475 µs | **~4.4×** | +| BDF | ~2150 µs | ~561 µs | **~3.8×** | + +Preloaded access is unchanged at ~78–80 µs/window (= numpy slice floor). + +### 10.4 The batched-read ceiling (adopt today without touching MNE) + +Reusing one output buffer and calling internal read machinery directly +(`raw._read_segment(..., data_buffer=buf, sel=arange)`) vs public `get_data`, +measured in a single run (within-run ratios are meaningful): + +| format | public | internal | buffered | +|---|---|---|---| +| EDF | 420 µs | 417 µs | **338 µs** | +| BDF | 553 µs | 541 µs | **501 µs** | +| BV | 538 µs | 208 µs | **199 µs** | +| FIF | 537 µs | 296 µs | **266 µs** | + +This is the case for an upstream batched API (`raw.get_windows(starts, +width)`): resolve picks/cals once, fill a preallocated buffer per window — +PyTorch's own data-loading tutorial measures the same pattern (`__getitems__`) +at ~2.9× marginal throughput. + +### 10.5 Where the remaining time goes (honest accounting) + +Per 2 s window at 128 ch (~512 KB payload): +- preloaded path is already AT the numpy floor; +- non-preloaded paths now spend their budget on genuine I/O + format decode: + EDF overreads whole records (format-inherent ~1.5×), BDF decodes 3 bytes per + sample by design, BV/FIF do one contiguous read + one cast/scale pass. +- Next levers, in order: persistent mmap/handle per Raw (saves ~30–60 µs of + syscall glue per call), upstreaming the batched API, FIF tag-layer dispatch + caching (`_compare_version` etc. seen in early profiles). + + +## 11. Session 4: BIDS format completeness + batched reader + memmap path + +### 11.1 BIDS-accepted formats — full coverage check + +Per the current BIDS spec (EEG section): EEG MUST be EDF, BrainVision, +EEGLAB (.set/.fdt), or Biosemi (.bdf); iEEG additionally allows MEF and NWB. +Benchmark status of every accepted format: + +| BIDS format | status in this benchmark | +|---|---| +| EDF / EDF+ | done (sessions 1–3) | +| BrainVision | done (sessions 1–3) | +| Biosemi BDF | done (session 2, incl. numba int24 decoder) | +| **EEGLAB .set/.fdt** | **added (this session)** — writer via scipy.savemat + f32 .fdt | +| **NWB** | **added (this session)** — written with pynwb; read directly (MNE has no NWB reader) | +| MEF (iEEG) | deferred: pymef is installed but its write API requires manual segment-metadata assembly; reading benchmarks need a real corpus | + +### 11.2 New-format results (BIG file: 128 ch, 1800 s @ 512 Hz) + +| backend | full load | windows (µs/win) | +|---|---|---| +| mne_set (EEGLAB) | 511 ms (~924 MB/s) | 986 | +| **nwb (pynwb/h5py direct)** | **136 ms (~3.5 GB/s)** | **100** | + +NWB's time-major storage makes a window one contiguous row-block — near-optimal +for DL sampling without any chunk tuning. EEGLAB performs like BrainVision +(multiplexed binary + header parse). Practical guidance for BIDS corpora: +EDF/BrainVision are fine after our reader fixes; if you control the export +format for training-only copies, time-major stores (NWB-style or +window-chunked HDF5) give the best random-window behavior. + +### 11.3 Speed patches added (session 4) + +8. `BaseRaw._get_windows(starts, width, *, out=None, sel=None)` (internal API): + resolves channel selection once and fills an optional reusable + ``(n_win, n_ch, width)`` buffer — zero per-window allocations. Verified + bit-identical to per-window `get_data`; fork-safe. +9. `_read_segments_file` (generic binary reader used by BrainVision et al.): + persistent `np.memmap` cached on the Raw's extras, keyed by PID so forked + DataLoader workers create their own mapping. Removes per-call + open/seek/read syscalls. BV windows: ~230 → **~189 µs** public-API. + +Batched vs per-call (300 × 2 s windows, identical per-window reductions, +buffer reused across repetitions): EDF 1.27×, BDF 1.15×, BV 1.14×, +FIF 1.05× — plus the elimination of ~315 MB/epoch of allocation churn. + +All suites green (246 io tests), outputs bit-identical to release on small set +and to pristine main on the BIG set (worst diff 0.0). + +## 12. Session 5: multi-agent round — FIF mmap, searchsorted, stim fast path + +Deployed per workstream (subagent infra was flaky; W1/W2 landed via direct execution): + +| Workstream | Outcome | +|---|---| +| W1 FIF mmap | **LANDED**: `mne/_fiff/_mmap_cache.py` (PID-keyed, mtime+size validated) + partial-tag byte-offset reads in `Raw._read_segment_file`; gzip/file-like/odd-tag fallback to legacy loop. Gates: test_raw_fiff 37✔, equivalence ✔, A/B below | +| W2 EDF/BDF numba | **LANDED** (by agent): fused decode kernel `mne/io/edf/_edf_numba.py`, `fastmath=False` required for bit-exactness; verified numpy fallback under `MNE_USE_NUMBA=false`; also fixed NumPy-2 uint32 overflow in `_bdf_numba.decode_int24` no-numba path + missing `has_numba` guard | +| opencode idea #8 | **LANDED**: `searchsorted` on sorted bounds replaces O(n_ent) mask per call (`fiff/raw.py`); property-tested vs boolean mask on 2000 random span cases | +| opencode idea #9 | **LANDED**: EDF/BDF fast path no longer abandons when uniform stim channels are among picks — replicates legacy post-calibration truncating bitmask per row; synthetic EDF+STATUS channel verified md5-identical vs release for all/stim-only/mixed picks | +| W5 stores | Chunk-cache tuning ruled out (±5%); zarr v3 ≈10× slower locally with identical chunks → remote/parallel-only guidance; h5py driver="core" −26% on slabs at ~1 GiB RAM cost | +| W7 writes | "FIF save fixed overhead" root-caused to lazy `from mne_bids import BIDSPath` inside `_check_fname` (~105 ms first save/process; warm saves ~1220 MB/s). pybv local install has a 10× write regression + in-place caller mutation bug (upstream is fine) | +| W3 glue / W6 MEF / W4 edfio | pending (infra flakiness / next round) | + +### Session-5 cumulative A/B (BIG file, 2 s windows, public get_data, best-of interleaved) + +| format | pristine main | patched tree | speedup | +|---|---|---|---| +| EDF | 1472 µs | **290 µs** | **5.1×** | +| BDF | 2166 µs | **405 µs** | **5.4×** | +| BrainVision | 927 µs | **189 µs** | **4.9×** | +| FIF | 1036 µs | **239 µs** | **4.3×** | + +All outputs bit-exact (small-set md5 identical vs release; BIG-set worst diff 0.0); +246 io/fiff/pick tests green. + +### External-auditor ideas adopted vs queued +Adopted: searchsorted bounds (#8), stim-in-fast-path (#9), codex's FIF mmap prototype. +Queued: EDF mixed-sfreq partial fast path (decode uniform EEG blocks, interp stim +separately — needs careful interp semantics), float32 output read path, +open_meta header-parse cost, batched-API upstream proposal, edfio engine backend. diff --git a/benchmarks/io_dl/ab_test.py b/benchmarks/io_dl/ab_test.py new file mode 100644 index 00000000000..eb6f0a4763f --- /dev/null +++ b/benchmarks/io_dl/ab_test.py @@ -0,0 +1,101 @@ +"""Clean interleaved A/B with explicit environment control. + +Arm INSTALLED: site-packages mne (unpatched release copy) +Arm TREE: working tree mne (patched) + +Also reports component breakdown inside each process. +""" + +import json +import os +import subprocess +import sys +from pathlib import Path + +TREE = "/Users/bruaristimunha/Projects/libraries/mne_python/mne_python_more_io_speed" +HERE = Path(__file__).parent + +CHILD = r''' +import gc, json, os, sys, time +import numpy as np +which = sys.argv[1] +# `python -c` puts cwd ('') first on sys.path; remove anything that could +# resolve to the working tree unless this is the TREE arm +TREE = "{tree}" +if which == "tree": + if TREE not in sys.path: + sys.path.insert(0, TREE) +else: + sys.path[:] = [p for p in sys.path if p not in ("", ".", TREE)] + os.environ.pop("PYTHONPATH", None) +import mne +mne.set_log_level("ERROR") +fmt, pl = sys.argv[2], sys.argv[3] == "1" +READERS = { + "edf": lambda p_: mne.io.read_raw_edf("{here}/data/bench.edf", preload=p_), + "brainvision": lambda p_: mne.io.read_raw_brainvision("{here}/data/bench.vhdr", preload=p_), + "fif": lambda p_: mne.io.read_raw_fif("{here}/data/bench_raw.fif", preload=p_), +} +raw = READERS[fmt](pl) +rng = np.random.default_rng(0) +starts = rng.integers(0, raw.n_times - 513, size=4000).astype(int) +stops = starts + 512 + +def bench(fn, warmup=300): + for s in starts[:warmup]: + fn(int(s)) + ts = [] + gc.disable() + for s, e in zip(starts[warmup:], stops[warmup:]): + t0 = time.perf_counter_ns() + fn(s) + ts.append(time.perf_counter_ns() - t0) + gc.enable() + arr = np.asarray(ts) / 1e3 + return dict(med=float(np.median(arr)), p10=float(np.percentile(arr, 10))) + +out = dict(which=which, file=mne.__file__) +out["get_data_only"] = bench(lambda s: raw.get_data(start=s, stop=s + 512)) +out["get_data_sum"] = bench(lambda s: raw.get_data(start=s, stop=s + 512).sum()) +mm = np.memmap("{here}/data/bench.bin", dtype=" 1 else 5 + acc = {"INSTALLED": [], "TREE": []} + for i in range(rounds): + row = {} + for key, which in (("INSTALLED", "installed"), ("TREE", "tree")): + r = run(which) + acc[key].append(r) + row[key] = r + src = "TREE" if "more_io_speed" in r["file"] else "site-packages" + print(f"round {i+1} {key:<10} src={src:<13} " + f"data={r['get_data_only']['med']:6.1f}us " + f"data+sum={r['get_data_sum']['med']:6.1f}us " + f"floor={r['memmap_copy']['med']:5.1f}us") + print("\n=== best-of medians ===") + for key in ("INSTALLED", "TREE"): + best = min(acc[key], key=lambda r: r["get_data_only"]["med"]) + g, gs, f = best["get_data_only"]["med"], best["get_data_sum"]["med"], best["memmap_copy"]["med"] + print(f"{key:<10} get_data={g:6.1f}us get_data+sum={gs:6.1f}us memmap-floor={f:5.1f}us") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/backends-data_big-20260824-161621.json b/benchmarks/io_dl/backends-data_big-20260824-161621.json new file mode 100644 index 00000000000..690d762ef5a --- /dev/null +++ b/benchmarks/io_dl/backends-data_big-20260824-161621.json @@ -0,0 +1,74 @@ +{ + "floor_memmap": { + "full_ms": 151.852959, + "win_ms": 42.519625, + "mbps": 3107.3428078540105, + "us_per_win": 141.73208333333332 + }, + "mne_edf": { + "full_ms": 453.770375, + "win_ms": 575.13625, + "mbps": 1039.8633890544309, + "us_per_win": 1917.1208333333334 + }, + "mne_bdf": { + "full_ms": 994.840167, + "win_ms": 954.506333, + "mbps": 474.3065425503673, + "us_per_win": 3181.6877766666667 + }, + "mne_bv": { + "full_ms": 842.084667, + "win_ms": 479.360625, + "mbps": 560.3465049197957, + "us_per_win": 1597.86875 + }, + "mne_fif": { + "full_ms": 534.52, + "win_ms": 418.168083, + "mbps": 882.7718326723041, + "us_per_win": 1393.89361 + }, + "edfio_edf_eager": { + "full_ms": 255.659167, + "win_ms": null, + "mbps": 922.9578065550061, + "us_per_win": null + }, + "npz": { + "full_ms": 206.126375, + "win_ms": null, + "mbps": 2289.17429901923, + "us_per_win": null + }, + "h5_full": { + "full_ms": 153.334458, + "win_ms": 16.073, + "mbps": 3077.320037222162, + "us_per_win": 53.57666666666667 + }, + "h5_t10s": { + "full_ms": 247.235, + "win_ms": 217.567209, + "mbps": 1908.5453111412219, + "us_per_win": 725.22403 + }, + "h5_win": { + "full_ms": 213.12475, + "win_ms": 38.790833, + "mbps": 2214.004708509922, + "us_per_win": 129.30277666666666 + }, + "zarr_t10s": { + "full_ms": 4688.113042, + "win_ms": 9798.711709, + "mbps": 100.65013274481532, + "us_per_win": 32662.37236333333 + }, + "zarr_win": { + "full_ms": 765.368083, + "win_ms": 760.479458, + "mbps": 616.5127740243122, + "us_per_win": 2534.9315266666667 + } +} \ No newline at end of file diff --git a/benchmarks/io_dl/backends-data_big-20260824-161927.json b/benchmarks/io_dl/backends-data_big-20260824-161927.json new file mode 100644 index 00000000000..4e90c4489de --- /dev/null +++ b/benchmarks/io_dl/backends-data_big-20260824-161927.json @@ -0,0 +1,92 @@ +{ + "floor_memmap": { + "full_ms": 155.608959, + "win_ms": 42.666833, + "mbps": 3032.339545437098, + "us_per_win": 142.22277666666668 + }, + "mne_edf": { + "full_ms": 497.573125, + "win_ms": 608.397875, + "mbps": 948.3213145806458, + "us_per_win": 2027.9929166666666 + }, + "mne_edf_preloaded": { + "full_ms": null, + "win_ms": 683.8465, + "mbps": null, + "us_per_win": 2279.4883333333332 + }, + "mne_bdf": { + "full_ms": 1003.320542, + "win_ms": 852.924292, + "mbps": 470.2975572087908, + "us_per_win": 2843.0809733333335 + }, + "mne_bv": { + "full_ms": 814.662583, + "win_ms": 389.382458, + "mbps": 579.2081407033272, + "us_per_win": 1297.9415266666665 + }, + "mne_fif": { + "full_ms": 551.795, + "win_ms": 508.93025, + "mbps": 855.1349686024702, + "us_per_win": 1696.4341666666667 + }, + "mne_fif_preloaded": { + "full_ms": null, + "win_ms": 704.338583, + "mbps": null, + "us_per_win": 2347.7952766666667 + }, + "edfio_edf_eager": { + "full_ms": 244.071083, + "win_ms": null, + "mbps": 966.7782889298688, + "us_per_win": null + }, + "edfio_edf_lazy_wins": { + "full_ms": null, + "win_ms": 347.37475, + "mbps": null, + "us_per_win": 1157.9158333333332 + }, + "npz": { + "full_ms": 228.083208, + "win_ms": null, + "mbps": 2068.802890566148, + "us_per_win": null + }, + "h5_full": { + "full_ms": 158.631, + "win_ms": 28.184583, + "mbps": 2974.571174612781, + "us_per_win": 93.94861 + }, + "h5_t10s": { + "full_ms": 263.300583, + "win_ms": 279.080709, + "mbps": 1792.093259436497, + "us_per_win": 930.2690300000002 + }, + "h5_win": { + "full_ms": 217.69475, + "win_ms": 53.925958, + "mbps": 2167.5267777472814, + "us_per_win": 179.75319333333334 + }, + "zarr_t10s": { + "full_ms": 5098.628375, + "win_ms": 9509.274958, + "mbps": 92.54630172962938, + "us_per_win": 31697.583193333336 + }, + "zarr_win": { + "full_ms": 632.534083, + "win_ms": 503.473208, + "mbps": 745.9822524693898, + "us_per_win": 1678.2440266666665 + } +} \ No newline at end of file diff --git a/benchmarks/io_dl/backends-data_big-20260824-174932.json b/benchmarks/io_dl/backends-data_big-20260824-174932.json new file mode 100644 index 00000000000..bdb7eea0a4d --- /dev/null +++ b/benchmarks/io_dl/backends-data_big-20260824-174932.json @@ -0,0 +1,104 @@ +{ + "floor_memmap": { + "full_ms": 123.97925, + "win_ms": 34.394292, + "mbps": 3805.953012298429, + "us_per_win": 114.64764000000001 + }, + "mne_edf": { + "full_ms": 464.572083, + "win_ms": 463.496, + "mbps": 1015.6856541033267, + "us_per_win": 1544.9866666666667 + }, + "mne_edf_preloaded": { + "full_ms": null, + "win_ms": 561.771959, + "mbps": null, + "us_per_win": 1872.5731966666667 + }, + "mne_bdf": { + "full_ms": 844.267375, + "win_ms": 643.647791, + "mbps": 558.8978254667248, + "us_per_win": 2145.4926366666664 + }, + "mne_bv": { + "full_ms": 528.579208, + "win_ms": 298.229583, + "mbps": 892.6934560770691, + "us_per_win": 994.0986099999999 + }, + "mne_fif": { + "full_ms": 474.158625, + "win_ms": 327.07125, + "mbps": 995.150515294328, + "us_per_win": 1090.2375 + }, + "mne_fif_preloaded": { + "full_ms": null, + "win_ms": 584.748375, + "mbps": null, + "us_per_win": 1949.16125 + }, + "mne_set": { + "full_ms": 510.646625, + "win_ms": 295.776875, + "mbps": 924.0425313689285, + "us_per_win": 985.9229166666667 + }, + "nwb": { + "full_ms": 135.857542, + "win_ms": 29.9985, + "mbps": 3473.191057733107, + "us_per_win": 99.995 + }, + "edfio_edf_eager": { + "full_ms": 171.696875, + "win_ms": null, + "mbps": null, + "us_per_win": null + }, + "edfio_edf_lazy_wins": { + "full_ms": null, + "win_ms": 314.790875, + "mbps": null, + "us_per_win": 1049.3029166666668 + }, + "npz": { + "full_ms": 152.86025, + "win_ms": null, + "mbps": 3086.8665987396985, + "us_per_win": null + }, + "h5_full": { + "full_ms": 104.1435, + "win_ms": 15.96875, + "mbps": 4530.855982370479, + "us_per_win": 53.229166666666664 + }, + "h5_t10s": { + "full_ms": 177.1665, + "win_ms": 160.295667, + "mbps": 2663.3658169010505, + "us_per_win": 534.31889 + }, + "h5_win": { + "full_ms": 148.633666, + "win_ms": 27.039625, + "mbps": 3174.6455072971153, + "us_per_win": 90.13208333333333 + }, + "zarr_t10s": { + "full_ms": 4115.615792, + "win_ms": 8396.288958, + "mbps": 114.65093532715262, + "us_per_win": 27987.629859999997 + }, + "zarr_win": { + "full_ms": 540.063791, + "win_ms": 373.51525, + "mbps": 873.710120662394, + "us_per_win": 1245.0508333333332 + } +} \ No newline at end of file diff --git a/benchmarks/io_dl/bench_backends.py b/benchmarks/io_dl/bench_backends.py new file mode 100644 index 00000000000..b593e28861c --- /dev/null +++ b/benchmarks/io_dl/bench_backends.py @@ -0,0 +1,313 @@ +"""Compare storage backends / provider libraries on identical signal data. + +Works on any preset dir (data/ or data_big/). Sections: + +FULL : end-to-end load of the entire recording through each backend +WINDOWS: 300 shuffled windows (win = 2 s) -- the DL training pattern + +Backends: + mne_ : MNE readers (preload True/False) + edfio : edfio.read_edf eager + .data; lazy variant for windows + h5_ : h5py datasets (t10s / win / full chunk layouts) + zarr_ : zarr arrays (t10s / win) + npz : numpy .npz (whole-array) + floor_memmap : raw float32 .bin via np.memmap + +Usage: + python benchmarks/io_dl/bench_backends.py [--dir data] [--repeats 3] +""" + +import argparse +import gc +import json +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).parent + +TREE_ROOT = HERE.resolve().parents[1].parent +import sys # noqa: E402 + +sys.path.insert(0, str(TREE_ROOT)) + +import mne # noqa: E402 + +mne.set_log_level("ERROR") + + +def timed(fn, repeats=3, warmup=1): + for _ in range(warmup): + fn() + out = [] + gc.collect() + gc.disable() + try: + for _ in range(repeats): + t0 = time.perf_counter_ns() + fn() + out.append((time.perf_counter_ns() - t0) / 1e6) + finally: + gc.enable() + return float(np.median(out)) + + +def rand_starts(rng, n_times, win, n): + return rng.integers(0, n_times - win - 1, size=n).astype(int) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--dir", default="data_big") + ap.add_argument("--repeats", type=int, default=3) + ap.add_argument("--n-win", type=int, default=300) + ap.add_argument("--seed", type=int, default=99) + args = ap.parse_args() + + d = HERE / args.dir + meta = json.loads((d / "meta.json").read_text()) + n_ch, sfreq, n_times = meta["n_ch"], meta["sfreq"], meta["n_times"] + base = d.name.replace("data", "bench") + win = int(2 * sfreq) + rng = np.random.default_rng(args.seed) + starts = rand_starts(rng, n_times, win, args.n_win) + + results = {} + print(f"\n=== {d.name}: {n_ch} ch, {sfreq:.0f} Hz, {meta['dur']:.0f} s | " + f"win={win} ({win/sfreq:.1f} s) x {args.n_win} ===") + + # ---------------- FULL loads ------------------------------------------- + print(f"\n{'backend':<24}{'full load':>12} {'MB/s':>8} | " + f"{'300 windows':>12} {'us/win':>8}") + rows = [] + + def record(name, full_fn=None, win_fn=None, full_bytes=None): + f_ms = timed(full_fn, args.repeats) if full_fn else None + w_ms = timed(win_fn, max(args.repeats, 3)) if win_fn else None + mbps = full_bytes / 1e6 / (f_ms / 1000) if (full_bytes and f_ms) else None + usw = w_ms * 1000 / args.n_win if w_ms else None + results[name] = dict( + full_ms=f_ms, win_ms=w_ms, mbps=mbps, us_per_win=usw, + ) + print(f"{name:<24}" + f"{f_ms if f_ms else float('nan'):>9.1f}ms " + f"{mbps if mbps else float('nan'):>7.0f} | " + f"{w_ms if w_ms else float('nan'):>11.1f}ms " + f"{usw if usw else float('nan'):>7.1f}") + + # floors ----------------------------------------------------------------- + binp = d / f"{base}.bin" + shape = (n_ch, n_times) + nbytes_f32 = n_ch * n_times * 4 + + def mm_full(): + a = np.memmap(binp, "12} {'--':>8} | {w_ms:>11.1f}ms {usw:>7.1f}") + + # EEGLAB (.set/.fdt) ------------------------------------------------------ + set_path = d / f"{base}.set" + if set_path.exists(): + from mne.io import read_raw_eeglab + + def set_full(): + r = read_raw_eeglab(set_path, preload=True) + s = float(r.get_data().sum()) + del r + return s + + def set_wins(): + r = read_raw_eeglab(set_path, preload=False) + acc = 0.0 + for s in starts: + acc += float(r.get_data(start=int(s), stop=int(s) + win).sum()) + del r + return acc + + record("mne_set", set_full, set_wins, nbytes_f32) + + # NWB (time-major HDF5; window = contiguous row block) -------------------- + nwb_path = d / f"{base}.nwb" + if nwb_path.exists(): + + def nwb_full(): + from pynwb import NWBHDF5IO + + with NWBHDF5IO(nwb_path, "r") as io: + a = np.asarray(io.read().acquisition["ElectricalSeries"].data[:]) + s = float(a.astype(np.float64).sum()) + return s + + def nwb_wins(): + from pynwb import NWBHDF5IO + + io = NWBHDF5IO(nwb_path, "r") + arr = io.read().acquisition["ElectricalSeries"].data + acc = 0.0 + for s in starts: + w = np.asarray(arr[s : s + win]).T.astype(np.float64) + acc += float(w.sum()) + io.close() + return acc + + record("nwb", nwb_full, nwb_wins, nbytes_f32) + + # edfio ------------------------------------------------------------------ + try: + from edfio import read_bdf, read_edf + + def edfio_full_eager(): + e = read_edf(d / f"{base}.edf", lazy_load_data=False) + s = sum(float(sig.data.sum()) for sig in e.signals) + return s + + def edfio_lazy_wins(): + from edfio.edf_signal import _calculate_gain_and_offset + + e = read_edf(d / f"{base}.edf", lazy_load_data=True) + acc = 0.0 + for s in starts: + t0s, t1s = s / sfreq, (s + win) / sfreq + tot = 0.0 + for sg in e.signals: + dg = sg.get_digital_slice(t0s, t1s) + gain, offset = _calculate_gain_and_offset( + sg.digital_min, sg.digital_max, + sg.physical_min, sg.physical_max, + ) + tot += float(((dg + offset) * gain).sum()) + acc += tot + return acc + + record("edfio_edf_eager", edfio_full_eager, None, + meta["sizes"].get("edf", 0)) + record("edfio_edf_lazy_wins", None, edfio_lazy_wins, None) + except Exception as exc: # noqa: BLE001 + print(f"edfio section skipped: {exc!r}") + + # npz ---------------------------------------------------------------------- + npzp = d / f"{base}.npz" + if npzp.exists(): + + def npz_full(): + a = np.load(npzp)["data"] + s = float(a.astype(np.float64).sum()) + del a + return s + + record("npz", npz_full, None, nbytes_f32) + + # hdf5 layouts -------------------------------------------------------------- + h5p = d / f"{base}.h5" + if h5p.exists(): + import h5py + + with h5py.File(h5p, "r") as f: + layouts = list(f.keys()) + for lay in layouts: + + def h5_full(lay=lay): + with h5py.File(h5p, "r") as f: + a = f[lay][:] + s = float(a.astype(np.float64).sum()) + return s + + def h5_wins(lay=lay): + f = h5py.File(h5p, "r")[lay] + acc = 0.0 + for s in starts: + acc += float(f[:, s : s + win].astype(np.float64).sum()) + f.file.close() + return acc + + record(f"h5_{lay}", h5_full, h5_wins, nbytes_f32) + + # zarr layouts ---------------------------------------------------------------- + for lay in ("t10s", "win"): + zp = d / f"{base}_{lay}.zarr" + if not zp.exists(): + continue + import zarr + + arr = zarr.open_array(store=str(zp), mode="r") + + def z_full(arr=arr): + a = arr[:] + s = float(a.astype(np.float64).sum()) + del a + return s + + def z_wins(arr=arr): + acc = 0.0 + for s in starts: + acc += float(arr[:, s : s + win].astype(np.float64).sum()) + return acc + + record(f"zarr_{lay}", z_full, z_wins, nbytes_f32) + + stamp = time.strftime("%Y%m%d-%H%M%S") + out = HERE / f"backends-{args.dir}-{stamp}.json" + out.write_text(json.dumps(results, indent=2)) + print(f"\nsaved -> {out}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/bench_io.py b/benchmarks/io_dl/bench_io.py new file mode 100644 index 00000000000..c6564c58d05 --- /dev/null +++ b/benchmarks/io_dl/bench_io.py @@ -0,0 +1,284 @@ +"""Benchmark MNE-Python raw IO under deep-learning access patterns. + +Run generate_data.py first. Then: + + python benchmarks/io_dl/bench_io.py # full suite + python benchmarks/io_dl/bench_io.py --quick # smoke test + python benchmarks/io_dl/bench_io.py --curve # fixed-overhead curve only + +Scenarios +--------- +open_meta : read_raw_X(preload=False) -- header/metadata parse only +full_load : read_raw_X(preload=True) -- end-to-end load +seq_1s : sequential 1 s get_data() over whole file (streaming/eval) +rand_windows : 300 shuffled 2 s get_data() calls, preload=False + (the canonical DL training-loop pattern) +preloaded_windows: same windows on an already-preloaded raw (access-only cost) +floors : numpy memmap / np.fromfile equivalents on identical bytes + +Every number is median of N repetitions (min shown too), GC disabled. +""" + +import argparse +import gc +import json +import platform +import sys +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).parent +sys.path.insert(0, str(HERE)) + +# Always benchmark THIS working tree, regardless of interpreter resolution +# (script dir / cwd / PYTHONPATH pitfalls). +TREE_ROOT = HERE.resolve().parents[1] +sys.path.insert(0, str(TREE_ROOT)) + +import mne + +mne.set_log_level("ERROR") +from mne.io import read_raw_edf, read_raw_fif, read_raw_brainvision # noqa: E402 + + +# ---------------------------------------------------------------- utilities +def timed(fn, repeats=5, warmup=1): + """Return list of runtimes in ms.""" + for _ in range(warmup): + fn() + out = [] + was_enabled = gc.isenabled() + gc.collect() + gc.disable() + try: + for _ in range(repeats): + t0 = time.perf_counter_ns() + fn() + out.append((time.perf_counter_ns() - t0) / 1e6) + finally: + if was_enabled: + gc.enable() + return np.asarray(out) + + +def fmt(ms): + med = np.median(ms) + mn = ms.min() + return f"{med:9.2f} / {mn:8.2f}" + + +def row(label, unit, val_ms, extra=""): + print(f" {label:<34}{fmt(val_ms)} {unit:<12}{extra}") + + +# ---------------------------------------------------------------- readers +def make_readers(data_dir): + return { + "edf": lambda preload: read_raw_edf( + data_dir / "bench.edf", preload=preload, verbose="ERROR" + ), + "brainvision": lambda preload: read_raw_brainvision( + data_dir / "bench.vhdr", preload=preload, verbose="ERROR" + ), + "fif": lambda preload: read_raw_fif( + data_dir / "bench_raw.fif", preload=preload, verbose="ERROR" + ), + } + + +def rand_starts(rng, n_times, win, n_win): + return rng.integers(0, n_times - win - 1, size=n_win) + + +# ---------------------------------------------------------------- scenarios +def bench_format(name, reader, meta, cfg): + n_times, sfreq = meta["n_times"], meta["sfreq"] + res = {} + + # S0: metadata-only open + res["open_meta"] = timed(lambda: reader(preload=False), cfg["repeats"]) + # S1: full load + res["full_load"] = timed(lambda: reader(preload=True), cfg["repeats"]) + + # Steady-state access patterns: Raw objects are opened ONCE (as DL + # dataset classes do); we time only the data-access loop. + + # S3/S4: random 2 s windows <-- canonical DL training pattern + rng = np.random.default_rng(cfg["seed"]) + starts = [int(s) for s in rand_starts(rng, n_times, cfg["win"], cfg["n_win"])] + + def rwin(raw): + acc = 0.0 + for s in starts: + acc += float(raw.get_data(start=s, stop=s + cfg["win"]).sum()) + return acc + + def seq(raw_nop): + acc = 0.0 + for i in range(int(meta["dur"])): + acc += float( + raw_nop.get_data(start=i * int(sfreq), stop=(i + 1) * int(sfreq)).sum() + ) + return acc + + # S2: sequential streaming over 1 s chunks, no preload + raw_seq = reader(preload=False) + res["seq_1s"] = timed(lambda: seq(raw_seq), max(3, cfg["repeats"] - 2)) + + raw_rand = reader(preload=False) + res["rand_windows"] = timed(lambda: rwin(raw_rand), cfg["repeats"]) + + raw_pre = reader(preload=True) + res["preloaded_windows"] = timed(lambda: rwin(raw_pre), cfg["repeats"]) + return res + + +def bench_floors(meta, cfg): + """Numpy-only baselines on the identical float32 payload.""" + n_ch, n_times = meta["n_ch"], meta["n_times"] + bin_path = HERE / "data" / "bench.bin" + rng = np.random.default_rng(cfg["seed"]) + starts = rand_starts(rng, n_times, cfg["win"], cfg["n_win"]) + shape = (n_ch, n_times) + res = {} + + def mm_windows(): + mm = np.memmap(bin_path, dtype="8} | " + " | ".join(f"{k:>12}" for k in readers)) + rows = [] + for wlen in sizes_s: + win = int(wlen * meta["sfreq"]) + vals = [] + for name, reader in readers.items(): + starts = rand_starts(rng, meta["n_times"], win, cfg["n_win"]) + raw = reader(preload=False) + + def run(): + for s in starts: + raw.get_data(start=int(s), stop=int(s) + win) + + ms_per_call = np.median(timed(run, 3, 1)) / cfg["n_win"] + vals.append(ms_per_call) + rows.append((wlen, vals)) + print(f"{wlen:>7.3f}s | " + " | ".join(f"{v:12.3f}" for v in vals)) + return rows + + +# ---------------------------------------------------------------- main +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--quick", action="store_true") + ap.add_argument("--curve", action="store_true") + ap.add_argument("--repeats", type=int, default=None) + ap.add_argument("--n-win", type=int, default=None) + ap.add_argument("--seed", type=int, default=1234) + args = ap.parse_args() + + cfg = dict( + seed=args.seed, + repeats=args.repeats or (2 if args.quick else 5), + n_win=args.n_win or (50 if args.quick else 300), + win=512, # 2 s @ 256 Hz, typical DL window + ) + + meta = json.loads((HERE / "data" / "meta.json").read_text()) + dur = meta["dur"] + readers = make_readers(HERE / "data") + + print(f"\nmne {mne.__version__} | numpy {np.__version__} | " + f"{platform.machine()} | py {platform.python_version()}") + print(f"mne loaded from: {Path(mne.__file__).parent}") + print(f"data: {meta['n_ch']} ch, {meta['sfreq']:.0f} Hz, {dur:.0f} s | " + f"win={cfg['win']} samples ({cfg['win']/meta['sfreq']:.1f} s), " + f"n_win={cfg['n_win']}") + + hdr = f"{'scenario':<36}{'median/min (ms)':<24}{'unit':<12}" + all_results = {"config": cfg, "meta": {k: v for k, v in meta.items() if k != "ch_names"}} + + if not args.curve: + print(f"\n=== floors (numpy on identical float32 bytes) ===\n{hdr}") + floors = bench_floors(meta, cfg) + for k, v in floors.items(): + row(k, "ms", v) + all_results.update({k: v.tolist() for k, v in floors.items()}) + + for name, reader in readers.items(): + print(f"\n=== format: {name} ===\n{hdr}") + r = bench_format(name, reader, meta, cfg) + for k, v in r.items(): + n_calls = cfg["n_win"] if "windows" in k else (int(dur) if k == "seq_1s" else 1) + extra = f"({v.mean()/max(n_calls,1)*1000:8.1f} us/call)" if n_calls > 1 else "" + row(k, "ms", v, extra) + all_results[f"fmt_{name}"] = {k: v.tolist() for k, v in r.items()} + + print(f"\n=== micro (copies) ===\n{hdr}") + mic = bench_micro(cfg) + for k, v in mic.items(): + row(k, "ms", v) + all_results["micro"] = {k: v.tolist() for k, v in mic.items()} + + bench_curve(readers, meta, cfg) + + stamp = time.strftime("%Y%m%d-%H%M%S") + out = HERE / f"results-{stamp}.json" + out.write_text(json.dumps(all_results, indent=2)) + print(f"\nsaved -> {out}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/check_equivalence.py b/benchmarks/io_dl/check_equivalence.py new file mode 100644 index 00000000000..5a3f78ecb0b --- /dev/null +++ b/benchmarks/io_dl/check_equivalence.py @@ -0,0 +1,76 @@ +"""Compare get_data outputs: working-tree mne vs site-packages mne (oracle). + +Run pair-wise via subprocess so both versions load in isolation. +""" + +import hashlib +import json +import sys + + +def which_mne(): + import os + + if os.environ.get("USE_TREE_MNE") == "1": + sys.path.insert(0, "/Users/bruaristimunha/Projects/libraries/" + "mne_python/mne_python_more_io_speed") + import mne + + mne.set_log_level("ERROR") + return mne, mne.__file__ + + +def digests(): + mne, src = which_mne() + here = __file__.rsplit("/", 1)[0] + readers = { + "edf": lambda: mne.io.read_raw_edf(f"{here}/data/bench.edf", preload=False), + "brainvision": lambda: mne.io.read_raw_brainvision( + f"{here}/data/bench.vhdr", preload=False + ), + "fif": lambda: mne.io.read_raw_fif(f"{here}/data/bench_raw.fif", preload=False), + } + out = {"mne_file": src} + import numpy as np + + for name, reader in readers.items(): + raw_np = reader() + raw_p = reader().load_data() + cases = {} + # full read + d = raw_p.get_data() + cases["full"] = hashlib.md5(d.tobytes()).hexdigest()[:12] + # windows incl. block boundaries + accs = [] + for s0, s1 in [(0, 512), (255, 769), (100000, 100512), (76288, 76800)]: + a = raw_np.get_data(start=s0, stop=s1) + b = raw_p.get_data(start=s0, stop=s1) + accs.append(float((a - b).sum())) + cases[f"win_{s0}"] = hashlib.md5(a.tobytes()).hexdigest()[:12] + cases["win_delta"] = float(sum(abs(x) for x in accs)) + # tmin/tmax path + a = raw_np.get_data(tmin=10.0, tmax=12.0) + b = raw_p.get_data(tmin=10.0, tmax=12.0) + cases["tmin_tmax"] = hashlib.md5(a.tobytes()).hexdigest()[:12] + cases["tmin_tmax_delta"] = float(np.abs(a - b).sum()) + # picks variants + cases["picks_int"] = hashlib.md5( + raw_p.get_data(picks=[1, 5, 7], start=0, stop=256).tobytes() + ).hexdigest()[:12] + cases["picks_str"] = hashlib.md5( + raw_p.get_data(picks=["EEG001", "EEG004"], start=0, stop=256).tobytes() + ).hexdigest()[:12] + cases["picks_slice"] = hashlib.md5( + raw_p.get_data(picks=slice(2, 8), start=0, stop=256).tobytes() + ).hexdigest()[:12] + # negative index semantics + cases["neg_idx"] = hashlib.md5( + raw_p.get_data(picks=[-1], start=0, stop=64).tobytes() + ).hexdigest()[:12] + out[name] = cases + return out + + +if __name__ == "__main__": + res = digests() + print(json.dumps(res, indent=2)) diff --git a/benchmarks/io_dl/check_equivalence_big.py b/benchmarks/io_dl/check_equivalence_big.py new file mode 100644 index 00000000000..70f69c3e364 --- /dev/null +++ b/benchmarks/io_dl/check_equivalence_big.py @@ -0,0 +1,79 @@ +"""BIG-set equivalence: tree vs installed release for EDF/BDF windows. + +The tree's vectorized EDF path folds ((d*cal)+off)*gain into +d*(cal*gain) + off*gain, which differs from the legacy op order by at most +a few double-precision ulps. We therefore assert max|diff| < 1e-9 uV on +identical inputs rather than bit equality. Full loads use the legacy loop +on both arms and must remain bit identical. +""" + +import json +import os +import subprocess +import sys + +import numpy as np + +ROOT = "/Users/bruaristimunha/Projects/libraries/mne_python/mne_python_more_io_speed" + +CODE = r""" +import sys, json +import numpy as np +if sys.argv[1] == "tree": + sys.path.insert(0, "{root}") +else: + sys.path = [p for p in sys.path if p not in ("", ".")] +import mne; mne.set_log_level("ERROR") +from mne.io import read_raw_edf, read_raw_bdf +rng = np.random.default_rng(5) +out = {} +for name, rd in [("edf", read_raw_edf), ("bdf", read_raw_bdf)]: + f = "{root}/benchmarks/io_dl/data_big/bench_big." + name + raw = rd(f, preload=False) + starts = rng.integers(0, raw.n_times - 8193, size=15).astype(int) + wins, picks_ = [], [] + for i, s0 in enumerate(starts): + stop = int(s0) + 1024 + wins.append(raw.get_data(start=int(s0), stop=stop)) + picks_.append(raw.get_data(picks=[3, 17, 55], start=int(s0), + stop=int(s0) + 3000)) + out[name + "_win"] = wins + out[name + "_picks"] = picks_ + rawf = rd(f, preload=True) + d = rawf.get_data() + out[name + "_full_hash"] = float(d.sum()) # robust across arms +print(json.dumps(out, default=lambda x: x.tolist() if hasattr(x, "tolist") else x)) +""".replace("{root}", ROOT) + + +def run(which): + r = subprocess.run([sys.executable, "-c", CODE, which], + capture_output=True, text=True, cwd=ROOT) + if r.returncode: + raise RuntimeError(r.stderr[-500:]) + return json.loads(r.stdout.strip().splitlines()[-1]) + + +def main(): + a = run("installed") + b = run("tree") + ok = True + worst = 0.0 + for key in list(a): + if key.endswith("_hash"): + same = a[key] == b[key] + print(f"{key:<16} sum-equal={same}") + ok &= same + continue + va = np.asarray(a[key]) + vb = np.asarray(b[key]) + d = float(np.abs(va - vb).max()) + worst = max(worst, d) + this_ok = d < 1e-9 + ok &= this_ok + print(f"{key:<16} max|diff|={d:.3e} {'OK' if this_ok else 'FAIL'}") + print(f"\nworst |diff| = {worst:.3e} -> {'ALL OK ✔' if ok else 'FAILED ✗'}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/generate_data.py b/benchmarks/io_dl/generate_data.py new file mode 100644 index 00000000000..5871c1d0a1b --- /dev/null +++ b/benchmarks/io_dl/generate_data.py @@ -0,0 +1,296 @@ +"""Generate synthetic EEG files across many formats and sizes. + +Same underlying signal written to every format. Content identical up to each +format's storage precision. + +Two presets: + default ("small"): 64 ch, 256 Hz, 300 s -> data/ + --preset big: 128 ch, 512 Hz, 1800 s -> data_big/ (~470 MB float32) + +Formats: EDF(int16), BDF(int24), BrainVision(f32), FIF(f32), NPZ(f32), +HDF5 f32 with three chunk layouts, Zarr f32 with two chunk layouts, +raw .bin floor. +""" + +import argparse +import json +import time +from pathlib import Path + +import numpy as np + + +def make_signal(n_ch: int, n_times: int, sfreq: float, seed: int = 42) -> np.ndarray: + """Band-limited synthetic EEG-like signal, microvolt scale.""" + rng = np.random.default_rng(seed) + data = rng.standard_normal((n_ch, n_times)) + # smooth channel-by-channel without apply_along_axis (slow for BIG) + csum = np.cumsum(data, axis=1, dtype=np.float64) + csum[:, 9:] -= csum[:, :-9] + data[:, :8] = csum[:, :8] / np.arange(1, 9) + data[:, 8:] = csum[:, 8:] / 9.0 + del csum + t = np.arange(n_times) / sfreq + data += 10 * np.sin(2 * np.pi * 10 * t) + return (data * 20.0).astype(np.float64) + + +def _edf_signals(sig_cls, data, ch_names, sfreq): + pmin, pmax = float(data.min()) - 1.0, float(data.max()) + 1.0 + return [ + sig_cls( + data=data[i].copy(), + sampling_frequency=sfreq, + physical_range=(pmin, pmax), + label=ch_names[i], + physical_dimension="uV", + ) + for i in range(len(ch_names)) + ] + + +def write_edf(data, ch_names, sfreq, path): + from edfio import Edf, EdfSignal + + Edf(signals=_edf_signals(EdfSignal, data, ch_names, sfreq)).write(path) + + +def write_bdf(data, ch_names, sfreq, path): + from edfio import Bdf, BdfSignal + + Bdf(signals=_edf_signals(BdfSignal, data, ch_names, sfreq)).write(path) + + +def write_bv(data_f32, ch_names, sfreq, out_dir, base): + import pybv + + pybv.write_brainvision( + data=data_f32, + folder_out=str(out_dir), + fname_base=base, + sfreq=int(sfreq), + ch_names=ch_names, + fmt="binary_float32", + overwrite=True, + ) + + +def write_fif(data, ch_names, sfreq, path): + import mne + + mne.set_log_level("ERROR") + info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types="eeg") + raw = mne.io.RawArray(data, info) + raw.save(path, fmt="single", overwrite=True) + + +def write_h5(data_f32, path, layouts): + import h5py + + n_ch, n_times = data_f32.shape + with h5py.File(path, "w") as f: + for name, chunks in layouts.items(): + if chunks is None: + f.create_dataset(name, shape=(n_ch, n_times), dtype="f4") + continue + dset = f.create_dataset(name, shape=(n_ch, n_times), + chunks=chunks, dtype="f4") + dset[:] = data_f32 + + +def write_npz(data_f32, path): + np.savez(path, data=data_f32) + + + +def write_eeglab(data_f32, ch_names, sfreq, out_dir, base): + """Write EEGLAB .set (header) + .fdt (float32 multiplexed).""" + import scipy.io as sio + + n_ch, n_times = data_f32.shape + fdt_path = out_dir / f"{base}.fdt" + np.ascontiguousarray(data_f32.T).tofile(fdt_path) # Fortran order of (ch, t) + chanlocs = np.empty(1, dtype=[("labels", "O"), ("type", "O"), ("unit", "O")]) + locs = np.zeros(n_ch, dtype=chanlocs.dtype) + for i, ch in enumerate(ch_names): + locs[i] = (ch, "EEG", "uV") + eeg = { + "nbchan": float(n_ch), + "pnts": float(n_times), + "trials": 1.0, + "srate": float(sfreq), + "xmin": 0.0, + "xmax": (n_times - 1) / sfreq, + "data": str(fdt_path.name), + "ref": "n/a", + "chanlocs": locs.reshape(1, -1), + "chaninfo": {"nodatchans": {}}, + "event": np.empty((0, 0), dtype=object), + "setname": base, + } + sio.savemat(out_dir / f"{base}.set", {"EEG": eeg}, appendmat=False) + + +def write_nwb(data_f32, ch_names, sfreq, path): + from datetime import datetime, timezone + + from pynwb import NWBHDF5IO, NWBFile + from pynwb.ecephys import ElectricalSeries + + nwbfile = NWBFile( + session_description="benchmark", + identifier="bench", + session_start_time=datetime(2020, 1, 1, tzinfo=timezone.utc), + ) + device = nwbfile.create_device(name="bench_device") + group = nwbfile.create_electrode_group( + name="electrodes", description="all", location="unknown", device=device + ) + for _ in ch_names: + nwbfile.add_electrode(group=group, location="unknown") + region = nwbfile.create_electrode_table_region( + region=list(range(len(ch_names))), description="all channels" + ) + es = ElectricalSeries( + name="ElectricalSeries", + data=data_f32.T, # time-major, as required by NWB + electrodes=region, + starting_time=0.0, + rate=sfreq, + ) + nwbfile.add_acquisition(es) + with NWBHDF5IO(path, "w") as io: + io.write(nwbfile) + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--n-ch", type=int, default=None) + ap.add_argument("--sfreq", type=float, default=None) + ap.add_argument("--dur", type=float, default=None, help="seconds") + ap.add_argument( + "--preset", + choices=["small", "big"], + default=None, + help="small=64ch/256Hz/300s -> data/, big=128ch/512Hz/1800s -> data_big/", + ) + ap.add_argument("--out-dir", type=Path, default=None) + ap.add_argument( + "--formats", + type=str, + default="bin,edf,bdf,bv,fif,npz,h5,zarr,set,nwb", + help="comma list: bin,edf,bdf,bv,fif,npz,h5,zarr", + ) + args = ap.parse_args() + + presets = { + "small": dict(n_ch=64, sfreq=256.0, dur=300.0, out="data"), + "big": dict(n_ch=128, sfreq=512.0, dur=1800.0, out="data_big"), + } + if args.preset: + p = presets[args.preset] + args.n_ch = args.n_ch or p["n_ch"] + args.sfreq = args.sfreq or p["sfreq"] + args.dur = args.dur or p["dur"] + args.out_dir = args.out_dir or Path(__file__).parent / p["out"] + args.n_ch = args.n_ch or 64 + args.sfreq = args.sfreq or 256.0 + args.dur = args.dur or 300.0 + args.out_dir = args.out_dir or Path(__file__).parent / "data" + + out_dir = args.out_dir + out_dir.mkdir(parents=True, exist_ok=True) + + n_times = int(args.sfreq * args.dur) + ch_names = [f"EEG{i:03d}" for i in range(args.n_ch)] + base = out_dir.name.replace("data", "bench") + + t0 = time.perf_counter() + data = make_signal(args.n_ch, n_times, args.sfreq) + print(f"signal: {args.n_ch} ch x {n_times} samples " + f"({data.nbytes / 1e6:.0f} MB f64) in {time.perf_counter() - t0:.1f} s") + + meta = dict(n_ch=args.n_ch, sfreq=args.sfreq, dur=args.dur, n_times=n_times, + formats=args.formats.split(","), seed=42) + sizes = {} + fmts = args.formats.split(",") + + def reg(name, paths): + sizes[name] = sum(p.stat().st_size for p in paths if Path(p).exists()) + + jobs = [] + if "bin" in fmts: + binp = out_dir / f"{base}.bin" + jobs.append(("bin", lambda p=binp: np.ascontiguousarray( + data.astype(np.float32)).tofile(p), [binp])) + if "edf" in fmts: + p = out_dir / f"{base}.edf" + jobs.append(("edf", lambda p=p: write_edf(data, ch_names, args.sfreq, p), [p])) + if "bdf" in fmts: + p = out_dir / f"{base}.bdf" + jobs.append(("bdf", lambda p=p: write_bdf(data, ch_names, args.sfreq, p), [p])) + if "bv" in fmts: + jobs.append(("bv", lambda: write_bv(data.astype(np.float32), ch_names, + args.sfreq, out_dir, base), + list(out_dir.glob(f"{base}.v*")) + [out_dir / f"{base}.eeg"])) + if "fif" in fmts: + p = out_dir / f"{base}_raw.fif" + jobs.append(("fif", lambda p=p: write_fif(data, ch_names, args.sfreq, p), [p])) + if "npz" in fmts: + p = out_dir / f"{base}.npz" + jobs.append(("npz", lambda p=p: write_npz(data.astype(np.float32), p), [p])) + if "h5" in fmts: + p = out_dir / f"{base}.h5" + layouts = { + "t10s": (1, int(args.sfreq * 10)), # per-channel time slabs + "win": (args.n_ch, 512), # window-shaped chunks + "full": None, # contiguous + } + + def wh(p=p, layouts=layouts): + write_h5(data.astype(np.float32), p, layouts) + + jobs.append(("h5", wh, [p])) + if "set" in fmts: + + def wset(): + write_eeglab( + data.astype(np.float32), ch_names, args.sfreq, out_dir, base + ) + + jobs.append(("set", wset, list(out_dir.glob(f"{base}.s*")) + + [out_dir / f"{base}.fdt"])) + if "nwb" in fmts: + pnwb = out_dir / f"{base}.nwb" + + def wnwb(p=pnwb): + write_nwb(data.astype(np.float32), ch_names, args.sfreq, p) + + jobs.append(("nwb", wnwb, [pnwb])) + if "zarr" in fmts: + layouts = {"t10s": (1, int(args.sfreq * 10)), "win": (args.n_ch, 512)} + + def wz(layouts=layouts): + import zarr + + for name, chunks in layouts.items(): + arr = zarr.open_array( + store=str(out_dir / f"{base}_{name}.zarr"), mode="w", + shape=data.shape, chunks=chunks, dtype="f4", + ) + arr[:] = data.astype(np.float32) + + jobs.append(("zarr", wz, list(out_dir.glob(f"{base}_*.zarr")))) + + for name, fn, paths in jobs: + t0 = time.perf_counter() + fn() + dt = time.perf_counter() - t0 + reg(name, paths) + print(f"wrote {name:<5} {sizes[name] / 1e6:8.1f} MB in {dt:7.1f} s") + + (out_dir / "meta.json").write_text(json.dumps(meta | {"sizes": sizes}, indent=2)) + print(f"done -> {out_dir}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/profile_io.py b/benchmarks/io_dl/profile_io.py new file mode 100644 index 00000000000..93cf8e7ea31 --- /dev/null +++ b/benchmarks/io_dl/profile_io.py @@ -0,0 +1,253 @@ +"""Profile MNE IO hot paths and trace every deepcopy on them. + + python benchmarks/io_dl/profile_io.py # all profiles + python benchmarks/io_dl/profile_io.py --only windows + +Sections +-------- +A. cProfile of steady-state random-window access (preloaded and not) +B. cProfile of full_load per format +C. cProfile of raw.save() to fif (DL caching path) +D. deepcopy tracer: counts + cumulative time + call sites of + copy.deepcopy during common DL pipeline operations +""" + +import argparse +import cProfile +import io as _io +import pstats +import sys +import time +from contextlib import contextmanager +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).parent +sys.path.insert(0, str(HERE)) + +# Always profile THIS working tree. +TREE_ROOT = HERE.resolve().parents[1] +sys.path.insert(0, str(TREE_ROOT)) + +import mne + +mne.set_log_level("ERROR") +from mne.io import read_raw_brainvision, read_raw_edf, read_raw_fif # noqa: E402 + +READERS = { + "edf": lambda pl: read_raw_edf(HERE / "data" / "bench.edf", preload=pl), + "brainvision": lambda pl: read_raw_brainvision( + HERE / "data" / "bench.vhdr", preload=pl + ), + "fif": lambda pl: read_raw_fif(HERE / "data" / "bench_raw.fif", preload=pl), +} + + +def show(prof, n=22, sort="cumulative"): + s = _io.StringIO() + st = pstats.Stats(prof, stream=s) + st.sort_stats(sort).print_stats(n) + txt = s.getvalue() + # trim the header noise + lines = [ln for ln in txt.splitlines()] + start = next(i for i, ln in enumerate(lines) if "ncalls" in ln) - 1 + print("\n".join(lines[start : start + n + 2])) + + +def profile_windows(fmt, preloaded, n_win=200): + rng = np.random.default_rng(7) + meta_wins = 512 + import json + + meta = json.loads((HERE / "data" / "meta.json").read_text()) + starts = rng.integers(0, meta["n_times"] - meta_wins - 1, size=n_win) + raw = READERS[fmt](preloaded) + + def loop(): + for s in starts: + raw.get_data(start=int(s), stop=int(s) + meta_wins) + + label = f"{fmt} windows preloaded={preloaded} ({n_win} calls)" + print(f"\n--- A: cProfile {label} ---") + prof = cProfile.Profile() + prof.enable() + loop() + prof.disable() + show(prof) + + +def profile_full_load(fmt): + print(f"\n--- B: cProfile {fmt} full_load(preload=True) ---") + prof = cProfile.Profile() + prof.enable() + READERS[fmt](True) + prof.disable() + show(prof) + + +def profile_save(): + raw = READERS["edf"](False) + out = HERE / "data" / "_tmp_save.fif" + print("\n--- C: cProfile raw.save() -> fif ---") + prof = cProfile.Profile() + prof.enable() + raw.save(out, overwrite=True) + prof.disable() + out.unlink(missing_ok=True) + show(prof) + + +# ------------------------------------------------------------------ tracer +@contextmanager +def trace_deepcopy(): + """Log every deepcopy call reachable from loaded mne modules. + + Patches copy.deepcopy AND every module attribute that holds a direct + reference (from `from copy import deepcopy` bindings), so call sites + inside mne._fiff / mne.io are all captured. + """ + import copy as _copy + import sys + + records = [] + real = _copy.deepcopy + + def spy(x=None, memo=None, *args, **kwargs): + t0 = time.perf_counter_ns() + out = real(x, memo) if memo is not None else real(x) + dt = (time.perf_counter_ns() - t0) / 1e6 + frame = sys._getframe(1) + fname = frame.f_code.co_filename.split("mne-python/")[-1].split( + "mne_python_more_io_speed/" + )[-1] + site = f"{fname}:{frame.f_lineno}" + records.append((type(x).__name__, site, dt)) + return out + + patched = [] + _copy.deepcopy = spy + for name, mod in list(sys.modules.items()): + if mod is None or name.split(".")[0] != "mne": + continue + try: + attrs = vars(mod) + except TypeError: + continue + hit = False + for k, v in list(attrs.items()): + if v is real: + attrs[k] = spy + hit = True + if hit: + patched.append(mod) + try: + yield records + finally: + _copy.deepcopy = real + for mod in patched: + try: + for k, v in list(vars(mod).items()): + if v is spy: + vars(mod)[k] = real + except TypeError: + pass + + +def report(records, title): + print(f"\n--- D: deepcopy trace: {title} ---") + if not records: + print(" (no deepcopy calls)") + return + agg = {} + for typ, site, dt in records: + k = (typ, site) + n, tot = agg.get(k, (0, 0.0)) + agg[k] = (n + 1, tot + dt) + total = sum(t for *_, t in records) + print(f" total calls={len(records)} total={total:.2f} ms") + for (typ, site), (n, tot) in sorted(agg.items(), key=lambda kv: -kv[1][1])[:15]: + print(f" {tot:8.3f} ms x{n:<4} {typ:<12} {site}") + + +def info_copy_scaling(): + """How does Info copy cost scale with channel count / montage?""" + import copy as _copy + + print("\n--- D2: Info deepcopy scaling (us per copy) ---") + print(f"{'n_ch':>6} {'montage':>8} {'deepcopy(us)':>13} {'copy()(us)':>12}") + for n_ch in (32, 64, 128, 256, 512): + ch_names = [f"EEG{i:03d}" for i in range(n_ch)] + info = mne.create_info(ch_names, 256.0, "eeg") + for with_montage in (False, True): + if with_montage: + mon = mne.channels.make_standard_montage("standard_1005") + keep = list(mon.ch_names)[:n_ch] + info = mne.create_info(keep, 256.0, "eeg") + info.set_montage(mon) + n_rep = 50 + t0 = time.perf_counter_ns() + for _ in range(n_rep): + _copy.deepcopy(info) + t_dc = (time.perf_counter_ns() - t0) / n_rep / 1e3 + t0 = time.perf_counter_ns() + for _ in range(n_rep): + info.copy() + t_cp = (time.perf_counter_ns() - t0) / n_rep / 1e3 + print(f"{n_ch:>6} {str(with_montage):>8} {t_dc:>13.1f} {t_cp:>12.1f}") + + +def trace_pipeline_ops(): + """Deepcopy accounting for ops a DL preprocessing/training loop does.""" + raw_e = READERS["edf"](False) + raw_b = READERS["brainvision"](False) + raw_f = READERS["fif"](False) + + with trace_deepcopy() as rec: + for r in (raw_e, raw_b, raw_f): + READERS[r.filenames[0].suffix.strip(".") == "edf" + and "edf" or + ("brainvision" if r.filenames[0].suffix == ".vhdr" else "fif")](True) + report(rec, "read_raw_X(preload=True) x3") + + with trace_deepcopy() as rec: + raw = READERS["edf"](True) + raw.crop(tmax=60.0) + raw.set_eeg_reference("average") + raw.notch_filter([50.0]) + raw.resample(128) + report(rec, "load + crop + reref + notch + resample (EDF)") + + with trace_deepcopy() as rec: + raw = READERS["edf"](False).load_data() + epochs = mne.Epochs(raw, mne.make_fixed_length_events(raw, duration=1.0, id=1), + tmin=0.0, tmax=1.99, baseline=None, preload=False, + reject_by_annotation=False) + for ep in epochs[:20]: + pass + report(rec, "Epochs creation + iterate 20 (EDF)") + info_copy_scaling() + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--only", choices=["windows", "load", "save", "copies"]) + args = ap.parse_args() + what = args.only + + if what in (None, "windows"): + for fmt in READERS: + profile_windows(fmt, False) + for fmt in READERS: + profile_windows(fmt, True) + if what in (None, "load"): + for fmt in READERS: + profile_full_load(fmt) + if what in (None, "save"): + profile_save() + if what in (None, "copies"): + trace_pipeline_ops() + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/results-20260824-154321.json b/benchmarks/io_dl/results-20260824-154321.json new file mode 100644 index 00000000000..87b42fa7365 --- /dev/null +++ b/benchmarks/io_dl/results-20260824-154321.json @@ -0,0 +1,248 @@ +{ + "config": { + "seed": 1234, + "repeats": 7, + "n_win": 300, + "win": 512 + }, + "meta": { + "n_ch": 64, + "sfreq": 256.0, + "dur": 300.0, + "n_times": 76800, + "seed": 42, + "sizes": { + "baseline .bin f32": 19660800, + "edf": 9847040, + "brainvision": 49171105, + "fif": 19673116 + } + }, + "floor_memmap": [ + 9.0905, + 5.482625, + 5.118917, + 4.460542, + 4.354209, + 4.711125, + 4.66275 + ], + "floor_fromfile": [ + 5.787125, + 5.531667, + 5.648459, + 5.30125, + 5.51625, + 5.411375, + 5.765667 + ], + "floor_full_read": [ + 3.967958, + 4.432958, + 3.697042, + 3.532209, + 3.623166, + 3.729958, + 4.508708 + ], + "fmt_edf": { + "open_meta": [ + 1.975375, + 1.939542, + 1.879958, + 1.917375, + 1.893625, + 1.8715, + 1.856792 + ], + "full_load": [ + 11.890334, + 10.699834, + 11.669834, + 11.425334, + 10.820333, + 10.9955, + 10.873375 + ], + "seq_1s": [ + 114.414167, + 114.225041, + 113.459875, + 117.944458, + 119.677125 + ], + "rand_windows": [ + 131.089458, + 128.251292, + 128.869334, + 129.324791, + 129.428417, + 128.224083, + 134.729875 + ], + "preloaded_windows": [ + 32.207625, + 31.319458, + 31.037125, + 32.277583, + 30.718208, + 30.897083, + 30.719917 + ] + }, + "fmt_brainvision": { + "open_meta": [ + 2.272875, + 2.211916, + 1.810417, + 1.92225, + 1.9065, + 2.024541, + 2.019417 + ], + "full_load": [ + 15.002792, + 13.914042, + 13.999083, + 14.813083, + 15.993625, + 16.432333, + 15.807458 + ], + "seq_1s": [ + 50.065667, + 48.626417, + 46.788958, + 49.264958, + 49.615458 + ], + "rand_windows": [ + 53.738167, + 50.68425, + 50.854834, + 50.406584, + 51.463125, + 51.212875, + 52.211834 + ], + "preloaded_windows": [ + 29.893334, + 31.390292, + 30.927542, + 31.296084, + 30.969208, + 30.704334, + 30.318625 + ] + }, + "fmt_fif": { + "open_meta": 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pass; arms alternate within one +process for `--rounds` rounds (order re-shuffled per round, seeded) so machine +drift hits every arm equally. Persistent handles (training-loop pattern). + +Usage: python bench_cache.py [--rounds 7] [--n-win 300] [--seed 99] +Writes cache_results_.json next to this file. +""" + +import argparse +import gc +import json +import resource +import sys +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +DATA = HERE.parents[1] / "data_big" +H5 = DATA / "bench_big.h5" +ZARR_WIN = DATA / "bench_big_win.zarr" +ZARR_T10S = DATA / "bench_big_t10s.zarr" + +MB = 1024 * 1024 +DEF_NSLOTS = 521 # HDF5 default hash slots +DEF_NBYTES = MB # HDF5 default raw-chunk cache + + +def is_prime(n): + if n < 2: + return False + i = 2 + while i * i <= n: + if n % i == 0: + return False + i += 1 + return True + + +def make_prime(n): + n = max(3, int(n) | 1) + while not is_prime(n): + n += 2 + return n + + +def build_arms(meta, starts): + win = int(2 * meta["sfreq"]) + nt = meta["n_times"] + cols, ks = set(), set() + for s in starts: + cols.update({s // 512, (s + win - 1) // 512}) + ks.update({s // 5120, (s + win - 1) // 5120}) + fit_win_bytes = len(cols) * 128 * 512 * 4 # exact bytes to hold all touched win chunks + fit_t10_bytes = len(ks) * 128 * 5120 * 4 # ... t10 chunks (all channels touched per window) + + arms = [] + # --- h5py t10s dataset: cache-size arms --------------------------------- + t = "t10s" + arms.append(dict(name=f"h5_{t}_default", ds=t, + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=DEF_NSLOTS))) + arms.append(dict(name=f"h5_{t}_fit", ds=t, + kw=dict(rdcc_nbytes=fit_t10_bytes, rdcc_nslots=make_prime(2 * len(ks) * 128)))) + arms.append(dict(name=f"h5_{t}_big512", ds=t, + kw=dict(rdcc_nbytes=512 * MB, rdcc_nslots=make_prime(65537)))) + arms.append(dict(name=f"h5_{t}_ns10103", ds=t, + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=10103))) + arms.append(dict(name=f"h5_{t}_ns65537", ds=t, + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=65537))) + # --- h5py win dataset: control + same treatment ------------------------- + arms.append(dict(name="h5_win_default_CTRL", ds="win", + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=DEF_NSLOTS))) + arms.append(dict(name="h5_win_fit", ds="win", + kw=dict(rdcc_nbytes=fit_win_bytes, rdcc_nslots=make_prime(2 * len(cols))))) + arms.append(dict(name="h5_win_big512", ds="win", + kw=dict(rdcc_nbytes=512 * MB, rdcc_nslots=make_prime(65537)))) + arms.append(dict(name="h5_win_ns10103", ds="win", + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=10103))) + arms.append(dict(name="h5_win_ns65537", ds="win", + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=65537))) + # --- file driver variants (once each, interleaved like the rest) -------- + arms.append(dict(name="h5_t10s_sec2", ds="t10s", + kw=dict(rdcc_nbytes=DEF_NBYTES, rdcc_nslots=DEF_NSLOTS), + note="explicit sec2 (default) driver")) + arms.append(dict(name="h5_t10s_core", ds="t10s", kw=dict(), + open_extra=dict(driver="core", backing_store=False), + note="core driver: whole file read into RAM at open")) + # --- zarr ---------------------------------------------------------------- + arms.append(dict(name="zarr_win_default", zarr=str(ZARR_WIN), zcfg={})) + arms.append(dict(name="zarr_win_c1", zarr=str(ZARR_WIN), + zcfg={"async.concurrency": 1})) + arms.append(dict(name="zarr_win_c32", zarr=str(ZARR_WIN), + zcfg={"async.concurrency": 32})) + arms.append(dict(name="zarr_t10s_default", zarr=str(ZARR_T10S), zcfg={})) + return arms, dict(fit_win_bytes=fit_win_bytes, fit_t10s_bytes=fit_t10_bytes) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--rounds", type=int, default=7) + ap.add_argument("--n-win", type=int, default=300) + ap.add_argument("--seed", type=int, default=99) + args = ap.parse_args() + + import h5py + + meta = json.loads((DATA / "meta.json").read_text()) + sfreq, nt = meta["sfreq"], meta["n_times"] + width = int(2 * sfreq) # 1024 samples + starts = np.random.default_rng(args.seed).integers( + 0, nt - width - 1, size=args.n_win).astype(int) + + arms, fits = build_arms(meta, starts) + print(f"{len(arms)} arms x {args.rounds} interleaved rounds x " + f"{args.n_win} windows | fit(win)={fits['fit_win_bytes']/MB:.1f} MiB " + f"fit(t10s)={fits['fit_t10s_bytes']/MB:.1f} MiB") + + # ---- open persistent handles ------------------------------------------- + import zarr + + handles = {} + rss0 = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + for arm in arms: + if "zarr" not in arm: + extra = arm.get("open_extra", {}) + f = h5py.File(H5, "r", **arm["kw"], **extra) + handles[arm["name"]] = f[arm["ds"]] + else: + arr = zarr.open_array(store=arm["zarr"], mode="r") + handles[arm["name"]] = arr + rss_open = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + rss_unit = 1024 * 1024 if sys.platform == "darwin" else 1024 # macOS: bytes; Linux: KiB + print(f"peak RSS after all opens: {rss_open/rss_unit:.0f} MiB " + f"(delta {((rss_open-rss0)/rss_unit):.0f} MiB incl. core-driver RAM copy)") + + def pass_h5(ds): + acc = 0.0 + for s in starts: + acc += float(ds[:, s:s + width].astype(np.float64).sum()) + return acc + + def pass_zarr(arr, cfg): + acc = 0.0 + cm = zarr.config.set(cfg) if cfg else None + if cm is not None: + cm.__enter__() + try: + for s in starts: + acc += float(arr[:, s:s + width].astype(np.float64).sum()) + finally: + if cm is not None: + cm.__exit__(None, None, None) + return acc + + def run_once(arm): + gc.collect() + gc.disable() + try: + t0 = time.perf_counter_ns() + if "zarr" in arm: + chk = pass_zarr(handles[arm["name"]], arm["zcfg"]) + else: + chk = pass_h5(handles[arm["name"]]) + dt_ms = (time.perf_counter_ns() - t0) / 1e6 + finally: + gc.enable() + return dt_ms, chk + + # ---- warmup (page cache + arm-local caches) ---------------------------- + order_rng = np.random.default_rng(12345) + print("warmup...", flush=True) + ref_chk = None + for arm in arms: + ms, chk = run_once(arm) + if ref_chk is None: + ref_chk = chk + assert abs(chk - ref_chk) < 1e-6 * abs(ref_chk), f"checksum mismatch {arm['name']}" + + # ---- interleaved rounds ------------------------------------------------- + times = {a["name"]: [] for a in arms} + wall = [] + for r in range(args.rounds): + order = list(arms) + order_rng.shuffle(order) + t_r0 = time.perf_counter() + for arm in order: + ms, _ = run_once(arm) + times[arm["name"]].append(ms) + wall.append(time.perf_counter() - t_r0) + done = ", ".join(f"{a['name'].split('_', 1)[0]}:{times[a['name']][-1]:.0f}ms" + for a in arms[:3]) + print(f"round {r+1}/{args.rounds} done ({wall[-1]:.1f}s) {done}", flush=True) + + # ---- summarize ---------------------------------------------------------- + res = dict(stamp=time.strftime("%Y%m%d-%H%M%S"), machine="Apple M4 Max, 36 GB", + rounds=args.rounds, n_win=args.n_win, seed=args.seed, + peak_rss_mib=rss_open / rss_unit, fits=fits, arms={}, control_rounds=None) + print(f"\n{'arm':<24}{'us/win median':>14}{'IQR':>18}{'min..max':>18}") + ctrl_med = None + for a in arms: + v = np.array(times[a["name"]]) * 1000.0 / args.n_win # us/win + med = float(np.median(v)) + q1, q3 = np.percentile(v, [25, 75]) + res["arms"][a["name"]] = dict(us_per_win=[float(x) for x in v], + median=med, iqr=float(q3 - q1), + min=float(v.min()), max=float(v.max()), + kind="zarr" if "zarr" in a else "h5", + note=a.get("note", "")) + flag = "" + if "CTRL" in a["name"]: + ctrl_med = med + print(f"{a['name']:<24}{med:>11.1f} [{np.percentile(v,25):>7.1f},{q3:>7.1f}]" + f" {v.min():>7.1f}..{v.max():<7.1f}{flag}") + + # drift check: round-by-round of control arm + cv = np.array(times["h5_win_default_CTRL"]) * 1000.0 / args.n_win + res["control_rounds"] = [float(x) for x in cv] + print(f"\nCONTROL h5_win per-round µs/win: {[round(x,1) for x in cv]}") + print(f"CONTROL spread max/min = {cv.max()/cv.min():.3f}") + out = HERE / f"cache_results_{res['stamp']}.json" + out.write_text(json.dumps(res, indent=2)) + print(f"saved -> {out}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/workspaces/W5/cache_results_20260824-204900.json b/benchmarks/io_dl/workspaces/W5/cache_results_20260824-204900.json new file mode 100644 index 00000000000..86ad70e2053 --- /dev/null +++ b/benchmarks/io_dl/workspaces/W5/cache_results_20260824-204900.json @@ -0,0 +1,193 @@ +{ + "stamp": "20260824-204900", + "machine": "Apple M4 Max, 36 GB", + "rounds": 1, + "n_win": 30, + "seed": 99, + "peak_rss_mib": 989856.0, + "fits": { + "fit_win_bytes": 15204352, + "fit_t10s_bytes": 83886080 + }, + "arms": { + "h5_t10s_default": { + "us_per_win": [ + 428.1055666666667 + ], + "median": 428.1055666666667, + "iqr": 0.0, + "min": 428.1055666666667, + "max": 428.1055666666667, + "kind": "h5", + "note": "" + }, + "h5_t10s_fit": { + "us_per_win": [ + 413.40139999999997 + ], + "median": 413.40139999999997, + "iqr": 0.0, + "min": 413.40139999999997, + "max": 413.40139999999997, + "kind": "h5", + "note": "" + }, + "h5_t10s_big512": { + "us_per_win": [ + 432.3805666666667 + ], + "median": 432.3805666666667, + "iqr": 0.0, + "min": 432.3805666666667, + "max": 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a/benchmarks/io_dl/workspaces/W5/findings.md b/benchmarks/io_dl/workspaces/W5/findings.md new file mode 100644 index 00000000000..1e927730c8c --- /dev/null +++ b/benchmarks/io_dl/workspaces/W5/findings.md @@ -0,0 +1,141 @@ +# W5 — HDF5 chunk-cache tuning & zarr-v3 local-read verdict + +Date: 2026-08-24 · Machine: Apple M4 Max, 36 GB, macOS · h5py 3.16.0, zarr 3.1.5, +numpy 2.4.6 · Data: `benchmarks/io_dl/data_big/` (128 ch × 1800 s @ 512 Hz, +float32; `bench_big.h5` datasets `t10s` chunks=(1,5120), `win` chunks=(128,512), +`full` contiguous; zarr v3 twins with identical chunk shapes). +Script: `bench_cache.py` (this directory); raw numbers in +`cache_results_20260824-205105.json` / `-205253.json`. + +## Methodology (drift-proof by construction) + +- **Single process, interleaved arms**: all 16 arms alternate every round, order + re-shuffled per round (seeded), 7 rounds × 300 random 2 s windows (seed 99), + persistent handles opened once (training-loop pattern). GC disabled during + timed passes; `perf_counter_ns`; float64 checksum verified identical across + every arm/pass (pure-read sanity). +- **Warmup pass** per arm before round 1 (fills page cache + arm-local caches); + no `sudo purge` available → regime is *warm OS page cache*, which is also the + realistic steady state of a training loop. +- **Control arm** `h5_win_default` interleaved with everything; its per-round + drift was 88–113 µs/win (max/min 1.22–1.29) across rounds — i.e. machine noise + of ±10–15 % was present and is exactly what interleaving cancels. Any claimed + effect below must exceed that band to be real. +- Two independent process runs (A, B); conclusions require agreement in both. +- Cache-fit sizes computed exactly from the seed-99 window set: + `win`: 527 distinct chunks touched → fit = 138 MB; + `t10s`: 20,736 chunks (162 time-slabs × 128 ch) → fit = 405 MB. + +## Decision table + +µs/window, median over 14 interleaved passes (7 rounds × 2 runs); spread = +min..max pooled. "vs CTRL" compares each arm's median against its same-run +control to cancel drift. + +| config | µs/win median [min..max] | vs CTRL | verdict | +|---|---|---|---| +| h5_win default cache 1 MB/521 (**CONTROL**) | 100 [88..113] | 1.00 | baseline | +| h5_win fit-to-working-set 138 MB/1061 slots | 91 [86..108] | −5 % | no effect (inside noise band) | +| h5_win oversized 512 MB/65537 | 101 [88..108] | +1 % | no effect | +| h5_win nslots=10103 (1 MB) | 97 [89..104] | −4 % | no effect | +| h5_win nslots=65537 (1 MB) | 100 [88..107] | −1 % | no effect | +| h5_t10s default 1 MB/521 | 479 [443..558] | 1.00 | baseline (slab layout) | +| h5_t10s fit-to-working-set 405 MB/41479 | 466 [443..543] | −2 % | no effect | +| h5_t10s oversized 512 MB/65537 | 473 [455..496] | −1 % | no effect | +| h5_t10s nslots=10103 | 468 [446..605] | −2 % | no effect | +| h5_t10s nslots=65537 | 477 [446..535] | 0 % | no effect | +| h5_t10s driver=sec2 explicit | 476 [442..498] | ≈0 | confirms default driver | +| **h5_t10s driver='core' (RAM)** | **344 [330..376]** | **−26 %** | real, reproducible (−26/−28 % both runs) | +| zarr_win default (`async.concurrency=10`) | 1049 [1025..1279] | — | **10.1–10.6× slower than h5_win** | +| zarr_win concurrency=1 | 1853 [1786..1986] | — | +77 % vs zarr default — do NOT set | +| zarr_win concurrency=32 | 1047 [1020..1269] | — | ≈ zarr default (no gain) | +| zarr_t10s default | 27,994 [27,326..28,806] | — | ~60× slower than h5_t10s | + +### Does chunk-cache tuning matter (>20 %)? **NO.** + +Largest deviation of any cache arm from control: **−5 % (win_fit), −2 % +(t10s_fit)** — an order of magnitude below the 20 % threshold and not +systematic across the two runs (sign flips between runs). Mechanism: + +1. With a warm page cache every chunk read is already served at memcpy speed + by macOS; HDF5's rdcc only short-circuits *re-reads*, which were cheap + anyway. +2. Random windows give huge reuse distances: each `t10s` chunk is re-visited + only ~3× over a whole 300-window pass, separated by thousands of other + chunk reads — nothing stays "hot" even in a perfectly sized cache. +3. No intra-read re-touch exists (a window's ≤256 chunks are all distinct), so + oversizing has nothing to exploit. + +rdcc tuning should only matter when the working set exceeds RAM (cold reads) +or windows are sampled with tight reuse distance (heavy overlap). Neither +applies to EEG-shaped files (~0.5 GB) on developer/workstation RAM. + +## File-driver variants (measured once each, interleaved like other arms) + +`driver="core", backing_store=False` reads the whole file into process RAM at +open: **+~0.9 GiB RSS** for this 944 MB file (plus your working arrays), and +buys **−26 % per window on the t10s layout** (344 vs 476 µs) by eliminating +per-chunk `read()` syscalls (≤256 syscalls/window → memcpy). Benefit shrinks +proportionally with chunks-per-window (the `win` layout touches only 2), so +its practical value is for slab/time-major layouts or network filesystems. +Not recommended as default advice; document as an option with the memory cost. + +## Zarr v3 local-read verdict + +- Identical chunk shape `(128,512)` to `h5_win`, zstd level-0 codecs (chunks + stored ~9 % smaller): **zarr 1049 µs/win vs h5py 100 µs/win ⇒ 10.1–10.6× + penalty** (earlier ad-hoc "~20×" was drift-inflated; clean interleaved ratio + is ~10×). For the slab layout the gap explodes to ~60× (28 ms/win). +- Knobs: `async.concurrency` 32 = no change; `concurrency=1` makes it **worse** + (+77 %) — the default pool of 10 is already optimal for 2-chunk reads. The + penalty is architectural: per-chunk Python/async dispatch + buffer creation + in zarr v3's sync path, not a tunable configuration issue. + +## RECOMMENDATIONS + +1. **Tell users nothing about rdcc kwargs** — there is no `h5py.open(...)` + chunk-cache setting worth recommending for EEG-shaped local training data; + defaults (1 MB/521) measure identically to exact-fit and 512 MB caches. + Spend the effort on chunk geometry instead: `chunks=(n_ch, window_samples)` + reproduces the ~50–100 µs/window result and needs zero tuning. +2. Keep zarr flagged **remote/parallel-only** for training loops: ~10× + per-window penalty locally with identical chunking, unfixable via its + concurrency settings. Revisit only if zarr v3 gains a C-speed batched read + path or if reads move to object storage where network latency dominates + anyway. +3. If someone insists on squeezing the slab-layout case without rewriting + chunks, `h5py.File(..., driver="core", backing_store=False)` buys ~26 % at + +1× file-size RAM; cheaper than cache tuning, still worse than rechunking. +4. Do not set `zarr.config.set({"async.concurrency": 1})` anywhere (76 % + slowdown); leave defaults alone. + +## Draft text for RESULTS.md §12 (ready to paste) + +```markdown +## 12. Session 5: HDF5 chunk-cache tuning is a dead end; zarr-v3 local verdict finalized + +Question: does `rdcc_nbytes`/`rdcc_nslots` change random-window read speed for +EEG-shaped data? Method: single-process interleaved benchmark (16 arms × 7 +rounds × 300 windows, seeded shuffle per round, persistent handles, identical +checksums, control arm tracked per-round drift of ±10–15 % which interleave +cancels; two independent runs). + +Answer: **no**. Exact-fit caches (computed from the true touched-chunk set: +138 MB for window-chunks, 405 MB for per-channel slabs), a 512 MB oversized +cache, and hash-slot variations (10103/65537 primes) all land within ±5 % of +the 1 MB/521 default — far under the ±15 % machine-noise band, with signs +flipping between runs. Reason: with a warm page cache, chunk re-reads are +already memcpy-speed, and random sampling gives reuse distances so large that +nothing stays hot in any cache size. Chunk *geometry* remains the only lever +that matters (`chunks=(n_ch, win)` ≈ 100 µs/win; slabs ≈ 480 µs/win). + +Two side results. (1) `driver="core"` (whole file pinned in RAM, +0.9 GiB +here) cuts slab-layout windows by 26 % (476→344 µs) via syscall elimination — +an option, not a recommendation. (2) The zarr v3 local-read penalty is real +but was overstated by earlier noisy probes: with byte-identical chunk shapes, +zarr reads windows at ~1050 µs vs h5py's ~100 µs ⇒ **≈10× slower** (not ~20×); +for per-channel-slab chunks it is ~60×. Its `async.concurrency` knob neither +helps at 32 nor tolerates 1 (+77 %), so the gap is per-chunk Python dispatch +overhead in v3's sync path. Guidance stands: HDF5/memmap for local training, +zarr reserved for remote/parallel storage. +``` diff --git a/benchmarks/io_dl/workspaces/W7/bench_w7_consolidated.py b/benchmarks/io_dl/workspaces/W7/bench_w7_consolidated.py new file mode 100644 index 00000000000..a035574825c --- /dev/null +++ b/benchmarks/io_dl/workspaces/W7/bench_w7_consolidated.py @@ -0,0 +1,156 @@ +"""W7 consolidated single-session pass: all writers + floors share machine state. + +Run 2-3 times; report medians-of-medians. ~30 s per pass. +""" + +import gc +import json +import shutil +import sys +import tempfile +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +BENCH = HERE.parents[1] +TREE = BENCH.parents[1] +sys.path.insert(0, str(TREE)) + +import mne # noqa: E402 + +mne.set_log_level("ERROR") + +DATA = BENCH / "data" +DATA_BIG = BENCH / "data_big" +TMP = Path(tempfile.mkdtemp(prefix="w7c_")) + + +def med_ms(fn, reps): + ts = [] + for _ in range(reps): + gc.collect() + gc.disable() + t0 = time.perf_counter() + fn() + ts.append((time.perf_counter() - t0) * 1e3) + gc.enable() + return float(np.median(ts)) + + +def main(): + out = {} + small = np.fromfile(DATA / "bench.bin", dtype=np.float32) + big = np.fromfile(DATA_BIG / "bench_big.bin", dtype=np.float32) + fpath = TMP / "f.bin" + + # floors + out["floor_small"] = med_ms(lambda: small.tofile(fpath), 9) + out["floor_big"] = med_ms(lambda: big.tofile(fpath), 5) + + # FIF small (preloaded) + from mne.io import read_raw_edf + + raw_s = read_raw_edf(DATA / "bench.edf", preload=True) + o = TMP / "s.fif" + out["fif_small"] = med_ms( + lambda: raw_s.save(o, fmt="single", overwrite=True), 9) + size_small = o.stat().st_size + + # FIF big + raw_b = read_raw_edf(DATA_BIG / "bench_big.edf", preload=True) + ob = TMP / "b.fif" + out["fif_big"] = med_ms( + lambda: raw_b.save(ob, fmt="single", overwrite=True), 5) + size_big = ob.stat().st_size + del raw_b + + # BV (installed pybv; caller copy included, subtract separately) + import pybv + + ch128 = [f"EEG{i:03d}" for i in range(128)] + volts = big.reshape(128, -1) * np.float32(1e-6) + + def w_bv(): + pybv.write_brainvision(data=volts.copy(), folder_out=str(TMP), + fname_base="v", sfreq=512, ch_names=ch128, + fmt="binary_float32", resolution=1e-7, + unit="µV", overwrite=True) + + w_bv() # warm + for p in TMP.glob("v.*"): + p.unlink() + out["bv_copy_only"] = med_ms(lambda: volts.copy(), 9) + out["bv_installed"] = med_ms(w_bv, 5) + for p in TMP.glob("v.*"): + p.unlink() + + # EDF end-to-end (construct+convert+write) and write-only + from edfio import Edf, EdfSignal + + d64 = big.reshape(128, -1).astype(np.float64) + pmin, pmax = float(d64.min()) - 1.0, float(d64.max()) + 1.0 + chn = [f"EEG{i:03d}" for i in range(128)] + + def mk(): + return Edf(signals=[ + EdfSignal(data=d64[i].copy(), sampling_frequency=512.0, + physical_range=(pmin, pmax), label=chn[i], + physical_dimension="uV") for i in range(128) + ]) + + oe = TMP / "e.edf" + obj = mk() + + def w_e(): + obj.write(oe) + + w_e() + oe.unlink() + out["edf_write_only"] = med_ms(w_e, 5) + out["edf_e2e"] = med_ms(lambda: mk().write(oe), 5) + oe.unlink() + + # BV-layout floor incl. transposition + out["floor_bv_layout"] = med_ms( + lambda: volts.ravel(order="F").tofile(TMP / "fl.eeg"), 5) + (TMP / "fl.eeg").unlink() + + shutil.rmtree(TMP, ignore_errors=True) + + mb = lambda x: x / 1e6 + print(f"\n=== W7 single-session pass ===") + print(f"{'measurement':<22} {'payload':>9} {'median':>9} {'MB/s':>7}") + rows = [ + ("floor_small", mb(small.nbytes)), + ("floor_big", mb(big.nbytes)), + ("fif_small", mb(size_small)), + ("fif_big", mb(size_big)), + ("bv_copy_only", mb(big.nbytes)), + ("bv_installed", mb(big.nbytes)), + ("edf_write_only", 236.0), + ("edf_e2e", 236.0), + ("floor_bv_layout", mb(big.nbytes)), + ] + for k, payload in rows: + ms = out[k] + rate = payload / (ms / 1e3) + print(f"{k:<22} {payload:>8.1f}M {ms:>8.2f} {rate:>7.0f}") + + b = np.array([size_small, size_big], float) + t = np.array([out["fif_small"], out["fif_big"]]) + slope = (t[1] - t[0]) / (b[1] - b[0]) # ms per byte + fixed = t[0] - slope * b[0] + print(f"\nFIF two-point fit: fixed={fixed:.2f} ms/file, " + f"marginal={1.0 / slope / 1e6 * 1e3:.0f} MB/s") + print(f"FIF small fixed-overhead share: {100 * fixed / t[0]:.0f}%") + print(f"FIF big vs floor: {out['floor_big'] / out['fif_big'] * 100:.0f}% of" + f" disk floor") + print(f"BV installed vs its-layout floor: " + f"{out['floor_bv_layout'] / out['bv_installed'] * 100:.0f}%") + (HERE / "consolidated.json").write_text(json.dumps(out, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/workspaces/W7/bench_w7_write.py b/benchmarks/io_dl/workspaces/W7/bench_w7_write.py new file mode 100644 index 00000000000..32fe7f2149c --- /dev/null +++ b/benchmarks/io_dl/workspaces/W7/bench_w7_write.py @@ -0,0 +1,644 @@ +"""W7: decompose write-path costs (FIF save, pybv BrainVision, edfio EDF) vs np.tofile floor. + + python benchmarks/io_dl/workspaces/W7/bench_w7_write.py # everything + python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only fif + python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only bv + python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only edf + python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only floor + +Method: medians over repeated runs, GC off, outputs written to /tmp and deleted. +Decomposition via accumulator-wrapping of mne internals (rebinding every module +attribute that holds the function, like profile_io.py's deepcopy tracer) and one +cProfile run for hotspot ranking. No library edits. +""" + +import argparse +import cProfile +import copy as _copy +import gc +import io as _io +import json +import pstats +import shutil +import sys +import tempfile +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +BENCH = HERE.parents[1] +TREE = BENCH.parents[1] +sys.path.insert(0, str(TREE)) + +import mne # noqa: E402 + +print("mne loaded from:", mne.__file__) +mne.set_log_level("ERROR") + +DATA = BENCH / "data" +DATA_BIG = BENCH / "data_big" +TMP = Path(tempfile.mkdtemp(prefix="w7_write_")) + +RESULTS = {} + + +def mb(x): + return x / 1e6 + + +def median_ms(fn, reps, *args, **kwargs): + ts = [] + out = None + for _ in range(reps): + gc.collect() + gc.disable() + t0 = time.perf_counter() + out = fn(*args, **kwargs) + dt = (time.perf_counter() - t0) * 1e3 + gc.enable() + ts.append(dt) + return float(np.median(ts)), ts, out + + +# -------------------------------------------------------------------------- +# accumulator instrumentation: rebind every module attribute holding `func` +# -------------------------------------------------------------------------- +class Acc: + def __init__(self, name): + self.name = name + self.reset() + + def reset(self): + self.ms = 0.0 + self.calls = 0 + self.extra = 0 # generic counter (bytes for _write) + + def report(self): + return f"{self.name:<28} {self.calls:>8} calls {self.ms:>9.2f} ms" + + +class Instrument: + """Wrap target functions with timing accumulators; restore afterwards.""" + + def __init__(self): + self.accs = {} + self.patched = [] # (holder, attrname, original) + self.in_payload = False # toggled by _write_raw_buffer spy + self.payload_bytes = 0 + self.meta_bytes = 0 + + def _make_spy(self, name, func): + acc = self.accs[name] + inst = self + + if name == "_write": + def spy(fid, data, kind, data_size, FIFFT_TYPE, dtype): + t0 = time.perf_counter_ns() + r = func(fid, data, kind, data_size, FIFFT_TYPE, dtype) + acc.ms += (time.perf_counter_ns() - t0) / 1e6 + acc.calls += 1 + try: + nb = int(np.asarray(data).nbytes) + 16 + except Exception: + nb = 16 + acc.extra += nb + if inst.in_payload: + inst.payload_bytes += nb + else: + inst.meta_bytes += nb + return r + elif name == "_write_raw_buffer": + def spy(fid, buf, cals, fmt): + inst.in_payload = True + try: + t0 = time.perf_counter_ns() + r = func(fid, buf, cals, fmt) + acc.ms += (time.perf_counter_ns() - t0) / 1e6 + acc.calls += 1 + finally: + inst.in_payload = False + return r + elif name == "deepcopy": + real = func + + def spy(x=None, memo=None, *a, **kw): + t0 = time.perf_counter_ns() + r = real(x, memo) if memo is not None else real(x) + acc.ms += (time.perf_counter_ns() - t0) / 1e6 + acc.calls += 1 + return r + else: + def spy(*args, **kwargs): + t0 = time.perf_counter_ns() + r = func(*args, **kwargs) + acc.ms += (time.perf_counter_ns() - t0) / 1e6 + acc.calls += 1 + return r + + spy.__name__ = f"spy_{name}" + return spy + + def __enter__(self): + import mne.annotations as ann_mod + import mne._fiff.meas_info as meas_info + import mne._fiff.write as fiff_write + import mne.io.base as io_base + import mne.utils.check as check_mod + + targets = { + "_check_fname": check_mod._check_fname, + "check_fname": check_mod.check_fname, + "_write": fiff_write._write, + "write_meas_info": meas_info.write_meas_info, + "_annotations_starts_stops": ann_mod._annotations_starts_stops, + "_write_annotations": ann_mod._write_annotations, + "_write_raw_buffer": io_base._write_raw_buffer, + "deepcopy": _copy.deepcopy, + } + for name, func in targets.items(): + self.accs[name] = Acc(name) + spy = self._make_spy(name, func) + # rebind in every loaded module that references the function object + n = 0 + for modname, mod in list(sys.modules.items()): + if mod is None or modname.split(".")[0] != "mne": + continue + try: + attrs = vars(mod) + except TypeError: + continue + for k, v in list(attrs.items()): + if v is func: + attrs[k] = spy + self.patched.append((mod, k, func)) + n += 1 + # deepcopy lives in stdlib too + if name == "deepcopy": + import copy as c2 + + if c2.deepcopy is func: + c2.deepcopy = spy + self.patched.append((c2, "deepcopy", func)) + return self + + def __exit__(self, *exc): + for holder, k, orig in reversed(self.patched): + try: + vars(holder)[k] = orig + except TypeError: + setattr(holder, k, orig) + self.patched.clear() + + def dump(self, total_ms, payload_mb): + print(f" {'component':<28} {'calls':>8} {'ms':>9} {'%total':>7}") + for a in self.accs.values(): + pct = 100 * a.ms / total_ms if total_ms else 0 + print(f" {a.name:<28} {a.calls:>8} {a.ms:>9.2f} {pct:>6.1f}%") + print(f" tag bytes: payload={mb(self.payload_bytes):.1f} MB, " + f"meta={mb(self.meta_bytes):.3f} MB") + other = total_ms - sum(a.ms for a in self.accs.values()) + print(f" {'(unattributed glue/loop)':<28} {'':>8} {other:>9.2f} " + f"{100 * other / total_ms:>6.1f}%") + + +def show(prof, n=18, sort="tottime"): + s = _io.StringIO() + st = pstats.Stats(prof, stream=s) + st.sort_stats(sort).print_stats(n) + txt = s.getvalue() + lines = txt.splitlines() + start = next(i for i, ln in enumerate(lines) if "ncalls" in ln) - 1 + print("\n".join(lines[start : start + n + 2])) + + +# -------------------------------------------------------------------------- +# FIF save decomposition +# -------------------------------------------------------------------------- +def bench_fif(): + from mne.io import read_raw_edf + + meta = json.loads((DATA / "meta.json").read_text()) + ch_names = [f"EEG{i:03d}" for i in range(meta["n_ch"])] + + raw_small = read_raw_edf(DATA / "bench.edf", preload=True) + out_small = TMP / "w7_small_raw.fif" + n_rep = 7 + med, ts, _ = median_ms( + lambda: raw_small.save(out_small, fmt="single", overwrite=True), n_rep + ) + size_small = out_small.stat().st_size + for p in TMP.glob("w7_small*"): + p.unlink() + rate = size_small / (med / 1e3) / 1e6 + RESULTS["fif_small"] = dict(file_mb=mb(size_small), ms=med, + mbps=rate, reps_ms=ts) + print(f"\n[FIF small] {mb(size_small):.1f} MB in {med:.1f} ms " + f"(median of {n_rep}) -> {rate:.0f} MB/s") + print(" reps ms:", ", ".join(f"{t:.1f}" for t in ts)) + + # proj=True variant (exercises info deepcopy path) + med_proj, _, _ = median_ms( + lambda: raw_small.save(TMP / "w7p_raw.fif", fmt="single", + proj=True, overwrite=True), 3 + ) + (TMP / "w7p_raw.fif").unlink(missing_ok=True) + print(f"[FIF small proj=True] {med_proj:.1f} ms (vs {med:.1f} default)") + RESULTS["fif_small_proj"] = dict(ms=med_proj) + + # instrumented small save (one rep; overhead ~us/call) + with Instrument() as ins: + t0 = time.perf_counter() + raw_small.save(out_small, fmt="single", overwrite=True) + tot = (time.perf_counter() - t0) * 1e3 + out_small.unlink() + print(f"\n[FIF small decomposed] total {tot:.1f} ms (instrumented)") + ins.dump(tot, mb(size_small)) + RESULTS["fif_small_decomp"] = { + a.name: dict(calls=a.calls, ms=a.ms) for a in ins.accs.values() + } | {"payload_bytes": ins.payload_bytes, "meta_bytes": ins.meta_bytes} + + # cProfile cross-check + prof = cProfile.Profile() + prof.enable() + raw_small.save(out_small, fmt="single", overwrite=True) + prof.disable() + out_small.unlink() + print("\n[FIF small cProfile top tottime]") + show(prof, n=16) + + # ---- BIG ---- + print("\nloading BIG edf (preload) ...", flush=True) + t0 = time.perf_counter() + raw_big = read_raw_edf(DATA_BIG / "bench_big.edf", preload=True) + print(f" loaded in {time.perf_counter() - t0:.1f} s") + out_big = TMP / "w7_big_raw.fif" + med_b, ts_b, _ = median_ms( + lambda: raw_big.save(out_big, fmt="single", overwrite=True), 3 + ) + size_big = out_big.stat().st_size + out_big.unlink() + rate_b = size_big / (med_b / 1e3) / 1e6 + RESULTS["fif_big"] = dict(file_mb=mb(size_big), ms=med_b, mbps=rate_b, + reps_ms=ts_b) + print(f"[FIF big] {mb(size_big):.1f} MB in {med_b:.1f} ms " + f"(median of 3) -> {rate_b:.0f} MB/s") + + with Instrument() as ins: + t0 = time.perf_counter() + raw_big.save(out_big, fmt="single", overwrite=True) + tot_b = (time.perf_counter() - t0) * 1e3 + out_big.unlink() + print(f"[FIF big decomposed] total {tot_b:.1f} ms (instrumented)") + ins.dump(tot_b, mb(size_big)) + RESULTS["fif_big_decomp"] = { + a.name: dict(calls=a.calls, ms=a.ms) for a in ins.accs.values() + } + + del raw_big + + # two-point fixed/marginal fit: t(bytes) = fixed + bytes/marginal_rate + b = np.array([size_small, size_big], dtype=float) + t = np.array([med, med_b]) + A = np.vstack([np.ones_like(b), b]).T + coef, *_ = np.linalg.lstsq(A, t, rcond=None) + fixed_ms, per_byte = coef + RESULTS["fif_fit"] = dict(fixed_ms=float(fixed_ms), + marginal_mbps=float(1e3 / per_byte / 1e6)) + print(f"\n[FIF linear fit] fixed ≈ {fixed_ms:.1f} ms/file; marginal ≈ " + f"{1e3 / per_byte / 1e6:.0f} MB/s") + pct_small = 100 * fixed_ms / med + print(f"[FIF small fixed overhead] ≈ {pct_small:.0f}% of the " + f"{med:.1f} ms small save") + + +def bench_fif_extra(): + """Non-preloaded save (the historical ~103ms case?), per-buffer fetch, + cold-start single save in a fresh interpreter.""" + from mne.io import read_raw_edf + + meta = json.loads((DATA / "meta.json").read_text()) + + # (a) non-preloaded source -> save reads through the EDF reader per buffer + raw_nop = read_raw_edf(DATA / "bench.edf", preload=False) + out = TMP / "w7n_raw.fif" + med, ts, _ = median_ms( + lambda: raw_nop.save(out, fmt="single", overwrite=True), 5 + ) + size = out.stat().st_size + out.unlink() + print(f"\n[FIF small NON-preloaded] {mb(size):.1f} MB in {med:.1f} ms " + f"(median of 5) -> {size / (med / 1e3) / 1e6:.0f} MB/s") + print(" reps ms:", ", ".join(f"{t:.1f}" for t in ts)) + RESULTS["fif_small_nopreload"] = dict(ms=med, + mbps=size / (med / 1e3) / 1e6) + + with Instrument() as ins: + t0 = time.perf_counter() + raw_nop.save(out, fmt="single", overwrite=True) + tot = (time.perf_counter() - t0) * 1e3 + out.unlink() + print(f"[FIF small NON-preloaded decomposed] total {tot:.1f} ms") + ins.dump(tot, mb(size)) + + # (b) instrumented preloaded save incl. per-buffer fetch (__getitem__) + raw_small = read_raw_edf(DATA / "bench.edf", preload=True) + import mne.io.base as io_base + + orig_gi = io_base.BaseRaw.__getitem__ + acc = Acc("BaseRaw.__getitem__") + + def gi_spy(*a, **kw): + t0 = time.perf_counter_ns() + r = orig_gi(*a, **kw) + acc.ms += (time.perf_counter_ns() - t0) / 1e6 + acc.calls += 1 + return r + + io_base.BaseRaw.__getitem__ = gi_spy + try: + with Instrument() as ins: + t0 = time.perf_counter() + raw_small.save(out, fmt="single", overwrite=True) + tot = (time.perf_counter() - t0) * 1e3 + finally: + io_base.BaseRaw.__getitem__ = orig_gi + out.unlink() + print(f"\n[FIF small preloaded +fetch] total {tot:.1f} ms; " + f"__getitem__: {acc.calls} calls {acc.ms:.2f} ms") + ins.dump(tot, mb(size)) + RESULTS["fif_small_fetch"] = dict(total_ms=tot, getitem_calls=acc.calls, + getitem_ms=acc.ms) + + # (c) cold start: fresh process, one save, wall time end-to-end + import subprocess + + code = ( + "import sys, time; sys.path.insert(0, %r);" + "import mne; mne.set_log_level('ERROR');" + "from mne.io import read_raw_edf;" + "t0=time.perf_counter();" + "raw=read_raw_edf(%r, preload=True); t1=time.perf_counter();" + "raw.save(%r, fmt='single', overwrite=True); t2=time.perf_counter();" + "print(f'{(t1-t0)*1e3:.1f} {(t2-t1)*1e3:.1f}')" + ) % (str(TREE), str(DATA / "bench.edf"), str(TMP / "w7c_raw.fif")) + r = subprocess.run([sys.executable, "-c", code], capture_output=True, + text=True, timeout=120) + load_ms, save_ms = r.stdout.split() + (TMP / "w7c_raw.fif").unlink(missing_ok=True) + print(f"\n[FIF cold process] load {load_ms} ms + save {save_ms} ms " + f"(single-shot, includes imports/page-cache warm-up)") + RESULTS["fif_cold"] = dict(load_ms=float(load_ms), save_ms=float(save_ms)) + + +def bench_bv(): + import pybv + + meta = json.loads((DATA_BIG / "meta.json").read_text()) + n_ch, sfreq = meta["n_ch"], int(meta["sfreq"]) + ch_names = [f"EEG{i:03d}" for i in range(n_ch)] + # pybv expects VOLTS; fresh copy per call because the locally-patched + # installed pybv scales `data` IN PLACE (caller-visible mutation). + base_uv = np.fromfile(DATA_BIG / "bench_big.bin", dtype=np.float32).reshape( + n_ch, -1 + ) + payload = base_uv.nbytes + print(f"\n[BV] payload {mb(payload):.1f} MB f32, pybv " + f"{getattr(pybv, '__version__', '?')}") + + def make_w(): + volts = base_uv * 1e-6 # prepare once, outside timed region + + def w(): + # fresh copy per call: installed pybv scales `data` IN PLACE + pybv.write_brainvision(data=volts.copy(), folder_out=str(TMP), + fname_base="w7bv", sfreq=sfreq, + ch_names=ch_names, fmt="binary_float32", + events=None, resolution=1e-7, unit="µV", + overwrite=True) + return w + + w = make_w() + + # warm once (imports etc.), then time; delete between runs + w() + for p in TMP.glob("w7bv.*"): + p.unlink() + + n_rep = 5 + med, ts, _ = median_ms(w, n_rep) + eeg = (TMP / "w7bv.eeg").stat().st_size + for p in TMP.glob("w7bv.*"): + p.unlink() + rate = payload / (med / 1e3) / 1e6 + RESULTS["bv_big"] = dict(payload_mb=mb(payload), ms=med, mbps=rate, + eeg_mb=mb(eeg)) + print(f"[BV big] wrote .eeg {mb(eeg):.1f} MB in {med:.1f} ms " + f"(median of {n_rep}) -> {rate:.0f} MB/s (of {mb(payload):.0f} MB payload)") + + # decompose: time _write_bveeg_file alone vs header writing + import pybv.io as pio + + acc = {"bveeg": 0.0, "calls": 0} + orig = pio._write_bveeg_file + + def spy(*a, **kw): + t0 = time.perf_counter_ns() + r = orig(*a, **kw) + acc["bveeg"] += (time.perf_counter_ns() - t0) / 1e6 + acc["calls"] += 1 + return r + + pio._write_bveeg_file = spy + try: + med2, _, _ = median_ms(w, n_rep) + finally: + pio._write_bveeg_file = orig + for p in TMP.glob("w7bv.*"): + p.unlink() + bveeg_per_call = acc["bveeg"] / n_rep + print(f"[BV decomposed] binary .eeg write: {bveeg_per_call:.1f} ms of " + f"{med2:.1f} ms total ({100 * bveeg_per_call / med2:.0f}%); " + f"headers/vmrk outside: {med2 - bveeg_per_call:.1f} ms") + RESULTS["bv_decomp"] = dict(total_ms=med2, bveeg_ms=bveeg_per_call) + + # cost of the per-call defensive copy we must make (installed pybv mutates) + volts = (base_uv * 1e-6) + copy_med, _, _ = median_ms(lambda: volts.copy(), 5) + print(f"[BV caller-side copy] {copy_med:.1f} ms (included in totals above; " + f"pybv mutates its input)") + RESULTS["bv_copy_ms"] = copy_med + + # BV-layout floor: multiplexed (time-major) write incl. transposition + floor_f, _, _ = median_ms(lambda: volts.ravel(order="F").tofile( + TMP / "w7floor.eeg"), 3) + (TMP / "w7floor.eeg").unlink() + print(f"[BV layout floor] ravel(F)+tofile: {floor_f:.1f} ms -> " + f"{mb(payload) / (floor_f / 1e3):.0f} MB/s") + + # scaling-pass cost estimate: the two in-place multiplies + range checks + scaled = base_uv.copy() + scales = np.ones((n_ch, 1)) + t0 = time.perf_counter() + scaled *= scales + t_mult1 = (time.perf_counter() - t0) * 1e3 + t0 = time.perf_counter() + scaled *= 1e-6 + t_mult2 = (time.perf_counter() - t0) * 1e3 + t0 = time.perf_counter() + ok = np.all(scaled >= np.finfo(np.float32).min) and np.all( + scaled <= np.finfo(np.float32).max) + t_range = (time.perf_counter() - t0) * 1e3 + del scaled, ok + print(f"[BV internal passes] mult1 {t_mult1:.1f} ms + mult2 {t_mult2:.1f}" + f" ms + range checks {t_range:.1f} ms " + f"(≈{t_mult1 + t_mult2 + t_range:.0f} ms of pure memory passes)") + RESULTS["bv_passes"] = dict(mult1=t_mult1, mult2=t_mult2, range=t_range) + + # pristine upstream pybv (3 internal full-array copies) via subprocess + import subprocess + + up = Path("/tmp/pybv_up/x") + if up.exists(): + code = ( + "import sys, time, tempfile, shutil, gc, numpy as np;" + "sys.path.insert(0, %r); import pybv;" + "print('using', pybv.__file__);" + "d=np.fromfile(%r, dtype=np.float32).reshape(128, -1);" + "ch=[f'EEG{i:03d}' for i in range(128)];" + "tmp=tempfile.mkdtemp();" + "def w():" + " d2=(d*1e-6).astype('f4');" + " pybv.write_brainvision(data=d2, folder_out=tmp, fname_base='p'," + "sfreq=512, ch_names=ch, fmt='binary_float32', resolution=1e-7," + "overwrite=True);" + "w(); ts=[];" + "for i in range(3):" + " shutil.rmtree(tmp, ignore_errors=True); tmp=tempfile.mkdtemp();" + " gc.collect(); gc.disable(); t0=time.perf_counter(); w();" + " ts.append(time.perf_counter()-t0); gc.enable()" + "print('pristine ms:', sorted(ts)[1] * 1e3)" + ) % (str(up), str(DATA_BIG / "bench_big.bin")) + r = subprocess.run([sys.executable, "-c", code], capture_output=True, + text=True, timeout=300) + out = r.stdout.strip().splitlines() + for ln in out: + if "using" in ln or "pristine" in ln: + print(f"[BV {ln.split(':')[0].strip()}]") + if "ms" in ln: + ms = float(ln.rsplit(":", 1)[1]) + RESULTS["bv_pristine_ms"] = ms + print(f" -> {mb(payload) / (ms / 1e3):.0f} MB/s") + + +def bench_edf(): + from edfio import Edf, EdfSignal + + meta = json.loads((DATA_BIG / "meta.json").read_text()) + n_ch, sfreq = meta["n_ch"], meta["sfreq"] + ch_names = [f"EEG{i:03d}" for i in range(n_ch)] + f32 = np.fromfile(DATA_BIG / "bench_big.bin", dtype=np.float32).reshape( + n_ch, -1 + ) + data = f32.astype(np.float64) # what generate_data feeds edfio + pmin, pmax = float(data.min()) - 1.0, float(data.max()) + 1.0 + payload_i16 = data.size * 2 + + def mk(): + return Edf(signals=[ + EdfSignal(data=data[i].copy(), sampling_frequency=sfreq, + physical_range=(pmin, pmax), label=ch_names[i], + physical_dimension="uV") for i in range(n_ch) + ]) + + def w(edf): + edf.write(TMP / "w7.edf") + + edf_obj = mk() + w(edf_obj) # warm + (TMP / "w7.edf").unlink() + + n_rep = 5 + med, ts, _ = median_ms(lambda: w(edf_obj), n_rep) + size = (TMP / "w7.edf").stat().st_size + (TMP / "w7.edf").unlink() + rate = size / (med / 1e3) / 1e6 + RESULTS["edf_big"] = dict(file_mb=mb(size), ms=med, mbps=rate, + payload_i16_mb=mb(payload_i16)) + print(f"\n[EDF big] write-only {mb(size):.1f} MB (int16) in {med:.1f} ms " + f"(median of {n_rep}) -> {rate:.0f} MB/s") + print(" note: digital int16 conversion happens at EdfSignal CONSTRUCTION," + " not in write(); conversion cost measured separately below") + + def full(): + w(mk()) + + med_e, _, _ = median_ms(full, n_rep) + (TMP / "w7.edf").unlink() + RESULTS["edf_big_e2e"] = dict(ms=med_e, mbps=size / (med_e / 1e3) / 1e6) + print(f"[EDF big end-to-end (construct+convert+write)] {med_e:.1f} ms -> " + f"{mb(size) / (med_e / 1e3):.0f} MB/s") + + # digital-conversion pass analog (what edfio must do before disk) + res = (pmax - pmin) / 32767.0 + t0 = time.perf_counter() + dig = ((data - pmin) / res).round().astype(np.int16) + t_conv = (time.perf_counter() - t0) * 1e3 + del dig + print(f"[EDF reference conversion pass] f64->i16 vectorized: " + f"{t_conv:.1f} ms (memory-bound)") + + +def bench_floor(): + small = np.fromfile(DATA / "bench.bin", dtype=np.float32) + big = np.fromfile(DATA_BIG / "bench_big.bin", dtype=np.float32) + out = TMP / "floor.bin" + + med_s, _, _ = median_ms(lambda: small.tofile(out), 7) + med_b, _, _ = median_ms(lambda: big.tofile(out), 5) + out.unlink() + print(f"\n[floor tofile] small {mb(small.nbytes):.1f} MB: {med_s:.1f} ms " + f"-> {small.nbytes / (med_s / 1e3) / 1e6:.0f} MB/s") + print(f"[floor tofile] big {mb(big.nbytes):.1f} MB: {med_b:.1f} ms " + f"-> {big.nbytes / (med_b / 1e3) / 1e6:.0f} MB/s") + RESULTS["floor"] = dict( + small_ms=med_s, small_mbps=small.nbytes / (med_s / 1e3) / 1e6, + big_ms=med_b, big_mbps=big.nbytes / (med_b / 1e3) / 1e6, + ) + + # open/close syscall cost (per-save fixed floor) + t0 = time.perf_counter() + for i in range(200): + with open(TMP / f"f{i}.bin", "wb"): + pass + t_open = (time.perf_counter() - t0) / 200 * 1e3 + for i in range(200): + (TMP / f"f{i}.bin").unlink() + print(f"[floor] bare open+close: {t_open * 1000:.0f} us") + RESULTS["floor"]["open_us"] = t_open * 1000 + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--only", choices=["fif", "fifx", "bv", "edf", "floor"]) + args = ap.parse_args() + try: + if args.only in (None, "floor"): + bench_floor() + if args.only in (None, "fif"): + bench_fif() + if args.only in (None, "fifx"): + bench_fif_extra() + if args.only in (None, "bv"): + bench_bv() + if args.only in (None, "edf"): + bench_edf() + finally: + shutil.rmtree(TMP, ignore_errors=True) + (HERE / "results.json").write_text(json.dumps(RESULTS, indent=2)) + print(f"\nresults -> {HERE / 'results.json'}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/io_dl/workspaces/W7/consolidated.json b/benchmarks/io_dl/workspaces/W7/consolidated.json new file mode 100644 index 00000000000..913895f5394 --- /dev/null +++ b/benchmarks/io_dl/workspaces/W7/consolidated.json @@ -0,0 +1,11 @@ +{ + "floor_small": 1.8324169795960188, + "floor_big": 70.51770799444057, + "fif_small": 16.183792002266273, + "fif_big": 319.24983399221674, + "bv_copy_only": 34.75150000303984, + "bv_installed": 2424.0037919953465, + "edf_write_only": 65.58979200781323, + "edf_e2e": 263.01145800971426, + "floor_bv_layout": 211.66029199957848 +} \ No newline at end of file diff --git a/benchmarks/io_dl/workspaces/W7/findings.md b/benchmarks/io_dl/workspaces/W7/findings.md new file mode 100644 index 00000000000..4eb2dfaabaa --- /dev/null +++ b/benchmarks/io_dl/workspaces/W7/findings.md @@ -0,0 +1,174 @@ +# W7 — Write-path decomposition for DL caching pipelines + +Date: 2026-08-24 · Agent W7 · Measurement-only (no library edits). +Machine: Apple Silicon (arm64), macOS 15, py 3.12.12, numpy 2.4.6, MNE 1.13.dev0 +(this working tree, incl. sessions 1–4 reader/access patches), edfio 0.4.13, +pybv 0.7.6 (locally patched in site-packages — see §3). + +Method: medians over repeated saves to /tmp (deleted after), GC off, floors and +writers measured back-to-back in single-session passes (`bench_w7_consolidated.py`, +3 passes; spread reported). Decomposition via accumulator-wrapping of mne +internals (module-attribute rebinding, no cProfile inflation) + one cProfile run. +Prototypes monkeypatched in-process, outputs md5-verified byte-identical. + +## 1. Headline table + +| writer | payload | MB/s | % fixed overhead | top hotspot function / file:line | +|---|---|---|---|---| +| **FIF save, small, warm preloaded** | 19.7 MB f32 | **1220** (16.2 ms) | **~19 %** true logic (fit: 2–6 ms/file); ~87 % if first-save-in-process (see §2.1) | payload cast+transpose+`tobytes` copies: `write_float`/`_write` mne/_fiff/write.py:94,27; per-buffer fetch `BaseRaw.__getitem__` mne/io/base.py:973 | +| **FIF save, small, NON-preloaded** | 19.7 MB | 472 (41.7 ms) | ~60 % = per-buffer source reads through EDF reader (~87 µs × 300 buffers) | `_read_segment_file` buffer fetch per 1 s output buffer, called from `_write_raw_data` loop mne/io/base.py:3197 | +| FIF save, big (128 ch × 1800 s) | 471.9 MB f32 | **1478–1600** (295–320 ms) | ≈0 (marginal regime) | same as small; CPU side = `buf / cals` copy + `np.array(">f4")` strided cast + `.tobytes()` extra copy (`_write_raw_buffer` mne/io/base.py:3324 → `_write`) | +| floor: `np.tofile` big f32 | 471.9 MB | 4400–5700 (71–107 ms) | – | – | +| **BrainVision (installed pybv)** | 471.9 MB f32 | **199** (2420 ms) | ~90 % vs its own layout floor | chunked `chunk.T.tofile(fout)` pybv/io.py:827 — non-contiguous transposed write | +| BrainVision (pristine upstream 0.7.6) | 471.9 MB f32 | **958** (493 ms) | ~40 % | 3 internal full-array copies: `data * scales`, `astype(dtype)`, `ravel(order="F")` pybv/io.py:683,799,811 | +| floor: multiplexed layout (`ravel(F)`+tofile) | 471.9 MB | 1929–2406 (197–245 ms) | – | – | +| prototype fix (chunked `ascontiguousarray(chunk.T)`) | 471.9 MB | **2628** (191 ms) | ~0 | one fused transpose-copy per ≤32 MB block | +| **EDF write (edfio), serialization only** | 236.0 MB i16 | **3156–4000** (59–75 ms) | ≈0 | single contiguous `data_record.tofile()` edfio/_edfio.py `Edf.write` | +| EDF end-to-end (construct + int16 convert + write) | 236.0 MB i16 | **772–897** (263–306 ms) | conversion-dominated | f64→i16 digital quantization at `EdfSignal` construction (memory-bound pass, ~330 ms measured standalone) | + +Notes on the table: +- "% fixed overhead" for FIF small = two-point fit `t(bytes) = fixed + bytes/rate` + over {small, big} within-session: fixed = 2.3–5.7 ms/file across 3 passes, + marginal rate = 1490–1632 MB/s. The earlier "~190 MB/s ⇒ huge fixed cost" flag + is explained by §2.1/§2.2, not by tag machinery. +- BV "its own layout floor" = writing the same multiplexed (time-major) byte + layout via `volts.ravel(order="F").tofile()`. +- edfio converts float→digital-int16 at signal construction, so its `write()` + alone is nearly free; the honest end-to-end number is the last row. + +## 2. FIF save decomposition (mission item 1) + +Instrumented component breakdown (accumulator wrapping, ms): + +| component | small warm preloaded (17.6 ms total) | small NON-preloaded (41.7 ms) | big preloaded (281 ms instr.) | +|---|---|---|---| +| `_check_fname` + `check_fname` regexes | 0.13 (0.7 %) | 0.13 | 0.15 | +| info deepcopy | 0.15 (35 calls) | 0.14 | 0.24 | +| `write_meas_info` (metadata tags) | 0.68 | 0.67 | 1.27 | +| annotations handling | 0.01 (empty) | 0.01 | 0.01 | +| split-size logic (`fid.tell` checks) | <0.5 (in glue) | <0.5 | <1 | +| `_write_raw_buffer` (incl. payload `_write` inside) | 11.54 | 9.94 | 214.31 | +| per-buffer source read (`raw[picks, first:last]` via EDF reader) | 3.44 (11.5 µs/call) | **≈25** (glue row, 60 %) | ~21 (est. from 11.5 µs × 1800) | +| unattributed Python glue (loop, asserts, logger) | ~1 | 24.75 | ~45 | + +Tag counts: 319 tags for small (300 payload + 19 metadata), 1819 for big; +metadata bytes = 0.0003 MB. Disk I/O is only 4.6 ms (small) / 71–107 ms (big) +of the totals — everything else is CPU-side array traffic. + +### 2.1 The historical "~103 ms" mystery — SOLVED + +A fresh process's first `raw.save()` costs **121 ms**; with `mne_bids` already +imported it is **16 ms**. Cause: `_check_fname(..., check_bids_split=True)` +(mne/utils/check.py:270, called from BaseRaw.save at mne/io/base.py:1967 and +Epochs.save at mne/epochs.py:2357) executes `from mne_bids import BIDSPath` +inside a try/except. When mne-bids is installed this lazily imports mne_bids → +mne.viz → jinja2 → scipy.sparse/scipy.special/scipy.io (hundreds of modules; +standalone `from mne_bids import BIDSPath` = 265–279 ms; ≈105 ms after +`import mne`). Every caching pipeline pays this once per process (per DataLoader +worker!), and it was misattributed to tag serialization. + +Secondary factor: the old benchmark saved a NON-preloaded raw +(`bench_io.py::bench_micro`, `profile_io.profile_save`), adding per-buffer EDF +reader costs (now 42 ms; was ~100 ms before the session-1 reader patches). + +### 2.2 Rejected hypotheses (measured, not guessed) + +- **info deepcopy**: NOT executed by default (`proj=False` passes `self.info` by + reference, base.py:2006). With `proj=True`: +3.9 ms total, almost all + `setup_proj` SVD work, not the copy; `deepcopy(info)` alone ≈ 0.05 ms @64ch. + "Skip deepcopy when projs/comps empty" would save ~nothing on DL paths. +- **Tag serialization loop**: 19 metadata tags = 0.68 ms. Negligible. +- **Annotations**: empty case = 0.01 ms. +- **Split-size logic**: two `fid.tell()` + comparisons per buffer; µs-scale. +- **Bigger `buffer_size_sec`**: tested 4/16/60 s on BIG save — all SLOWER than + default 1 s (400/425/382 vs 297 ms; larger temps blow L2/L3). Reject. + +## 3. BrainVision writer (mission item 2) + +The site-packages pybv 0.7.6 has been locally patched (vs pristine PyPI wheel: +`data * scales` → in-place `*=`, full `ravel(order="F").tofile()` → chunked +`chunk.T.to_file`). Two defects introduced: + +1. **Performance pessimization**: `ndarray.T.tofile()` on a (128, n)-slice view + writes ~10× slower than necessary (numpy iterates the transposed view with + poor buffering): microbench same layout — `T.tofile` 2244–2341 ms vs + `ravel(F).tofile` 224 ms. Result: 199 MB/s installed vs 958 MB/s pristine + vs 2628 MB/s achievable (§1 prototype B: `np.ascontiguousarray(chunk.T)` + per ≤32 MB block, then scale+write — bounded memory AND fastest). +2. **Caller-visible mutation**: in-place scaling multiplies the user's array by + unit/resolution factors every call (verified: input grows ×1e13 per call, + overflow→inf→ValueError on 3rd call). Upstream returns copies; any pipeline + re-using a data array across `write_brainvision` calls silently corrupts it. + +Cost structure otherwise: scaling passes ≈108 ms of pure memory traffic +(2 multiplies + range check) out of ~2400 ms — i.e., I/O-pattern cost, not +copies, dominate. `raw.export(fmt="brainvision")` routes through pybv, so MNE +inherits both defects. + +## 4. EDF writer (mission item 3) + +Confirmed and refined the earlier "~590 MB/s": that was end-to-end. edfio's +`Edf.write` itself moves bytes at **~3200–4000 MB/s** (one contiguous +`tofile` of a pre-packed uint8 record matrix); the f64→int16 digital +quantization happens earlier, at `EdfSignal` construction, and costs ~330 ms +for 118 M samples (a pure memory-bound pass; measured standalone). End-to-end +construct+convert+write = 263–306 ms (**772–897 MB/s**, matches the earlier +observation). Headroom: the conversion could stream/fuse with generation, or +accept f32 input directly (halving one pass); disk-side nothing left. + +## 5. TOP-3 fix proposals (ranked by expected gain) + +### #1 — Fix the BrainVision binary write path (pybv patch or upstream PR) +Replace `chunk.T.tofile(fout)` with +`np.ascontiguousarray(data[:, s:e].T).tofile(fout)` blocks (≤32 MB), folding +the two scaling multiplies into one multiply on each transposed block (also +removes the caller-mutation bug since scaling then happens on the private +block copy). Expected: **2420 → ~200 ms on a 472 MB file (≈12×)**; MNE +`raw.export(fmt="brainvision")` inherits the win. Risk: none to output bytes +(layout identical; md5-verifiable against upstream writer). + +### #2 — De-guard the lazy `mne_bids` import in `_check_fname` +Only attempt `from mne_bids import BIDSPath` when `"mne_bids" in sys.modules` +(a real BIDSPath cannot reach these call sites unless mne-bids was imported), +or duck-type on `hasattr(fname, "split")`. Expected: **first save per process +121 → 16 ms (−105 ms)** for every pipeline with mne-bids installed; hits +`Raw.save` and `Epochs.save` (the two DL caching entry points) plus read-path +callers of `_check_fname`. Behavior-identical (import succeeds iff mne_bids +already loaded ⇒ identical validation outcomes). Trivially upstreamable. + +### #3 — Fuse the FIF payload buffer write into one pass +In `_write_raw_buffer` (single fmt): divide once into a fresh `(n_t, n_ch)` +`">f4"` C-contig output and `fid.write(out)` directly (buffer protocol), +eliminating the `buf/cals` temp, the separate strided cast, and the redundant +`tobytes()` bytes-object copy (4 payload passes → 2). Prototype verified +byte-identical md5 on the 472 MB save; measured gain modest but real: +**≈5–15 % on warm saves** (big 306→293 ms; small ~16→~14 ms), larger on +memory-constrained machines. Complementary free win: avoid saving from +NON-preloaded raws when possible (document/batch source reads) — that state +costs 42 vs 16 ms per small file (2.7×) today. + +Rejected for lack of measured support: skip-info-deepcopy fast path (§2.2), +default `buffer_size_sec` changes (§2.2), split-size/tag-loop micro-opts. + +## 6. Repro + +```bash +cd /Users/braristimunha/Projects/libraries/mne_python/mne_python_more_io_speed +python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only floor # np.tofile floors +python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only fif # small+BIG FIF decomp, cProfile +python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only fifx # non-preloaded, fetch, cold-process A/B +python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only bv # installed vs pristine pybv + prototypes +python benchmarks/io_dl/workspaces/W7/bench_w7_write.py --only edf # write-only vs end-to-end +python benchmarks/io_dl/workspaces/W7/bench_w7_consolidated.py # single-session pass (run 2-3x) +# cold-save mne_bids A/B: +python -c "import time,sys; sys.path.insert(0,'.'); import mne; mne.set_log_level('ERROR'); \ + from mne.io import read_raw_edf; r=read_raw_edf('benchmarks/io_dl/data/bench.edf',preload=True); \ + t=time.perf_counter(); r.save('/tmp/x.fif',overwrite=True); print((time.perf_counter()-t)*1e3)" +# compare with 'import mne_bids' inserted before the timer. +``` + +JSON artifacts: `results.json`, `consolidated.json` next to this file. +Requires `pip download pybv==0.7.6 --no-deps -d /tmp/pybv_up && unzip +/tmp/pybv_up/*.whl -d /tmp/pybv_up/x` for the pristine-pybv arm. + +COMPLETE: W7 - The FIF "fixed overhead" is a lazy `from mne_bids import BIDSPath` inside `_check_fname(check_bids_split=True)` costing ~105 ms of every process's first save (121→16 ms measured); worst writer is the locally-patched pybv at 199 MB/s (fixable to ~2600 MB/s, 12×), best is edfio serialization at ~3600 MB/s. diff --git a/benchmarks/io_dl/workspaces/W7/results.json b/benchmarks/io_dl/workspaces/W7/results.json new file mode 100644 index 00000000000..04e0098b60c --- /dev/null +++ b/benchmarks/io_dl/workspaces/W7/results.json @@ -0,0 +1,9 @@ +{ + "floor": { + "small_ms": 1.7277080041822046, + "small_mbps": 11379.700708920584, + "big_ms": 84.28358301171102, + "big_mbps": 5598.471056153797, + "open_us": 42.57041509845294 + } +} \ No newline at end of file diff --git a/doc/changes/dev/14202.bugfix.rst b/doc/changes/dev/14202.bugfix.rst new file mode 100644 index 00000000000..f5fba32ffaa --- /dev/null +++ b/doc/changes/dev/14202.bugfix.rst @@ -0,0 +1,6 @@ +Sped up raw data access for workloads making many small reads (e.g., deep-learning +training loops): ``Raw.get_data`` no longer materializes the full time axis or +re-resolves channel picks on every call; EDF/BDF readers gained vectorized and +optional-numba fast paths (bit-identical output, including stim channels); the FIF +reader now reads tag payloads through a memory map; batched internal window reads +avoid per-window allocations. diff --git a/mne/_fiff/_mmap_cache.py b/mne/_fiff/_mmap_cache.py new file mode 100644 index 00000000000..e96a2e46a5a --- /dev/null +++ b/mne/_fiff/_mmap_cache.py @@ -0,0 +1,38 @@ +"""PID-keyed memmap caching for direct byte-offset reads. + +Used by readers that need random access into raw data files (currently the +FIF raw reader). Keyed by PID so forked worker processes (e.g., PyTorch +DataLoader workers) create their own mapping instead of sharing a parent's, +and validated against file size/mtime so stale mappings are never reused. +""" + +# Authors: The MNE-Python contributors. +# License: BSD-3-Clause +# Copyright the MNE-Python contributors + +import os + +import numpy as np + +_MAX_CACHE = 16 +_cache = {} + + +def get_u8_memmap(path): + """Return a uint8 memmap of *path* (PID-keyed), or None on any failure.""" + try: + st = os.stat(path) + key = (os.getpid(), str(path)) + hit = _cache.get(key) + if hit is not None: + mm, mtime_ns, size = hit + if mtime_ns == st.st_mtime_ns and size == st.st_size: + return mm + _cache.pop(key, None) + mm = np.memmap(str(path), dtype=np.uint8, mode="r") + except Exception: + return None + _cache[key] = (mm, st.st_mtime_ns, st.st_size) + while len(_cache) > _MAX_CACHE: + _cache.pop(next(iter(_cache))) + return mm diff --git a/mne/_fiff/pick.py b/mne/_fiff/pick.py index 7f62644c254..b908fba22f5 100644 --- a/mne/_fiff/pick.py +++ b/mne/_fiff/pick.py @@ -1334,6 +1334,18 @@ def _picks_to_idx( ) raise TypeError(msg) del extra_repr + # Fast path: an integer ndarray with all values already in range needs no + # copy or further checks. This matters for callers resolving picks on + # every access (e.g., Raw.get_data in deep-learning training loops). + if ( + picks.dtype.kind == "i" + and picks.size + and picks.min() >= 0 + and picks.max() < n_chan + ): + if return_kind: + return picks, picked_ch_type_or_generic + return picks picks = picks.astype(int) # diff --git a/mne/_fiff/utils.py b/mne/_fiff/utils.py index b158914bb88..df28c582596 100644 --- a/mne/_fiff/utils.py +++ b/mne/_fiff/utils.py @@ -73,22 +73,28 @@ def _find_channels(ch_names, ch_type="EOG"): def _mult_cal_one(data_view, one, idx, cals, mult): """Take a chunk of raw data, multiply by mult or cals, and store.""" - one = np.asarray(one, dtype=data_view.dtype) assert data_view.shape[1] == one.shape[1], ( data_view.shape[1], one.shape[1], ) # noqa: E501 if mult is not None: + one = np.asarray(one, dtype=data_view.dtype) assert mult.ndim == one.ndim == 2 data_view[:] = mult @ one[idx] else: assert cals is not None if isinstance(idx, slice): - data_view[:] = one[idx] + # Hot path: gather + type-cast + calibration in a single pass, + # without materializing an intermediate float64 copy of `one` + # (`one[idx]` is a view for basic slices). Numerically identical + # to cast-then-scale because both are elementwise. + np.multiply(one[idx], cals.reshape(-1, 1), out=data_view, + casting="unsafe") else: + one = np.asarray(one, dtype=data_view.dtype) # faster than doing one = one[idx] np.take(one, idx, axis=0, out=data_view) - data_view *= cals + data_view *= cals def _blk_read_lims(start, stop, buf_len): @@ -215,6 +221,8 @@ def _read_segments_file( if n_channels is None: n_channels = raw._raw_extras[fi]["orig_nchan"] + import os as _os + n_bytes = np.dtype(dtype).itemsize # data_offset and data_left count data samples (channels x time points), # not bytes. @@ -224,6 +232,49 @@ def _read_segments_file( # Read up to 100 MB of data at a time, block_size is in data samples block_size = ((int(100e6) // n_bytes) // n_channels) * n_channels block_size = min(data_left, block_size) + + # Reuse a memory map across calls (keyed by PID so forked processes -- + # e.g., PyTorch DataLoader workers -- create their own mapping instead of + # sharing one). This removes the per-call open/seek/syscall overhead. + ex = raw._raw_extras[fi] if fi < len(raw._raw_extras) else {} + mm = ex.get("_mm") if isinstance(ex, dict) else None + if mm is not None and ex.get("_mm_pid") != _os.getpid(): + mm = None + if mm is not None and ( + mm.dtype != np.dtype(dtype) or mm.size * n_bytes < data_offset + data_left * n_bytes + ): + mm = None + if mm is None and isinstance(ex, dict): + try: + mm = np.memmap(raw.filenames[fi], dtype=dtype, mode="r") + ex["_mm"] = mm + ex["_mm_pid"] = _os.getpid() + except Exception: + mm = None + + if mm is not None: + base_idx = data_offset // n_bytes + for sample_start in np.arange(0, data_left, block_size) // n_channels: + count = min(block_size, data_left - sample_start * n_channels) + block = mm[ + base_idx + sample_start * n_channels : + base_idx + sample_start * n_channels + count + ] + if block.size != count: + raise RuntimeError( + f"Incorrect number of samples ({block.size} != {count}), " + "please report this error to MNE-Python developers" + ) + block = block.reshape(n_channels, -1, order="F") + n_samples = block.shape[1] + sample_stop = sample_start + n_samples + if trigger_ch is not None: + stim_ch = trigger_ch[start:stop][sample_start:sample_stop] + block = np.vstack((block, stim_ch)) + data_view = data[:, sample_start:sample_stop] + _mult_cal_one(data_view, block, idx, cals, mult) + return + with open(raw.filenames[fi], "rb", buffering=0) as fid: fid.seek(data_offset) # extract data in chunks diff --git a/mne/io/base.py b/mne/io/base.py index 79096cafaa3..1705e91ab5a 100644 --- a/mne/io/base.py +++ b/mne/io/base.py @@ -877,6 +877,62 @@ def _parse_get_set_params(self, item): return sel, start, stop + def _get_windows(self, starts, width, *, out=None, sel=None): + """Read many equal-width windows with setup shared across them. + + This amortizes channel-pick resolution and output allocation over all + windows, which matters for workloads issuing many small reads, such as + deep-learning training loops. + + Parameters + ---------- + starts : array of int + First sample of each window. + width : int + Number of samples per window. + out : ndarray | None + Optional ``(len(starts), n_channels, width)`` float64 array to + fill; allocated when None. + sel : ndarray | slice | None + Channel selection resolved once for all windows. + + Returns + ------- + ndarray : ``(len(starts), n_channels, width)`` window stack. + """ + starts = np.atleast_1d(np.asarray(starts, dtype=np.int64)).ravel() + width = int(width) + if width <= 0: + raise ValueError(f"width must be positive, got {width}") + n_times = self.n_times + if len(starts) == 0: + starts = np.zeros(1, dtype=np.int64) + bad = (starts < 0) | (starts + width > n_times) + if bad.any(): + raise ValueError( + f"window out of bounds at index {int(np.flatnonzero(bad)[0])}" + ) + # sel=None lets _read_segment treat this as all channels + n_out = self.info["nchan"] if sel is None else len(sel) + if out is None: + out = np.empty((len(starts), n_out, width), dtype=self._dtype) + elif out.shape != (len(starts), n_out, width): + raise ValueError( + f"out has shape {out.shape}, need " + f"{(len(starts), n_out, width)}" + ) + elif out.dtype != self._dtype: + raise ValueError( + f"out dtype must be {self._dtype}, got {out.dtype}" + ) + for j, s0 in enumerate(starts): + self._read_segment( + start=int(s0), stop=int(s0) + width, + sel=sel if sel is not None else None, + data_buffer=out[j], + ) + return out + def __getitem__(self, item): """Get raw data and times. @@ -1001,7 +1057,14 @@ def get_data( stop, types=("int-like", None), item_name="stop", type_name="int, None" ) - picks = _picks_to_idx(self.info, picks, "all", exclude=()) + if picks is None: + # fast path: equivalent to _picks_to_idx(info, None, "all", + # exclude=()) but avoids channel-name resolution on every call, + # which matters for workloads making many small reads (e.g., + # deep-learning training loops) + picks = np.arange(self.info["nchan"]) + else: + picks = _picks_to_idx(self.info, picks, "all", exclude=()) # Get channel factors for conversion into specified unit # (vector of ones if no conversion needed) diff --git a/mne/io/edf/_bdf_numba.py b/mne/io/edf/_bdf_numba.py new file mode 100644 index 00000000000..8315915bace --- /dev/null +++ b/mne/io/edf/_bdf_numba.py @@ -0,0 +1,33 @@ +"""Numba-accelerated BDF (24-bit little-endian) sample decoding. + +Optional acceleration: falls back to the vectorized-numpy path in +``mne.io.edf.edf._read_ch`` when numba is unavailable. +""" + +# Authors: The MNE-Python contributors. +# License: BSD-3-Clause +# Copyright the MNE-Python contributors + +import numpy as np + +from ..._numba import jit + + +@jit() +def decode_int24(buf): # pragma: no cover + """Decode packed 24-bit little-endian samples to int32. + + ``buf`` is a ``(n_samples, 3)`` uint8 array whose rows hold the low, + middle, and high bytes of each signed sample. + """ + n = buf.shape[0] + out = np.empty(n, dtype=np.int32) + for i in range(n): + # plain-Python integer arithmetic so the non-numba fallback follows + # the same semantics as the jitted version (values stay within + # [-2**23, 2**23) after the sign fix, so int32 stores never overflow) + v = int(buf[i, 0]) | (int(buf[i, 1]) << 8) | (int(buf[i, 2]) << 16) + if v >= (1 << 23): + v -= 1 << 24 + out[i] = v + return out diff --git a/mne/io/edf/_edf_numba.py b/mne/io/edf/_edf_numba.py new file mode 100644 index 00000000000..2ea7282e379 --- /dev/null +++ b/mne/io/edf/_edf_numba.py @@ -0,0 +1,41 @@ +"""Numba-accelerated EDF/BDF digital-to-physical window decoding. + +Optional acceleration: falls back to the vectorized-numpy path in +``mne.io.edf.edf._read_segment_file`` when numba is unavailable. +""" + +# Authors: The MNE-Python contributors. +# License: BSD-3-Clause +# Copyright the MNE-Python contributors + +from ..._numba import jit + + +@jit(fastmath=False) +def decode_window(digital, cal_v, off_v, gain_v, out): # pragma: no cover + """Decode a block of digital samples to physical units. + + ``digital`` is a ``(k, n_blocks, buf_len)`` (possibly strided) integer + view of raw digital samples; ``cal_v``, ``off_v``, and ``gain_v`` are + length-``k`` float64 vectors; ``out`` is a ``(k, n_blocks * buf_len)`` + float64 array whose rows are filled with blocks concatenated along the + sample axis such that:: + + out[i, b * buf_len + j] = ((digital[i, b, j] * cal[i]) + off[i]) * gain[i] + + replicating exactly the operation order of the vectorized-numpy fallback + (hence ``fastmath=False``: no FMA contraction or reassociation is + allowed, so results are bit-identical to separate multiply/add rounding). + """ + k = digital.shape[0] + n_blk = digital.shape[1] + n_smp = digital.shape[2] + for i in range(k): + cal = cal_v[i] + off = off_v[i] + gain = gain_v[i] + out_i = out[i] + for b in range(n_blk): + base = b * n_smp + for j in range(n_smp): + out_i[base + j] = ((digital[i, b, j] * cal) + off) * gain diff --git a/mne/io/edf/edf.py b/mne/io/edf/edf.py index 390289ae6bb..c59a7532255 100644 --- a/mne/io/edf/edf.py +++ b/mne/io/edf/edf.py @@ -590,12 +590,36 @@ def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): ) +_decode_int24 = None + + +def _get_int24_decoder(): + """Return the numba int24 decoder if available, else False.""" + global _decode_int24 + if _decode_int24 is None: + dec = False + try: + from mne._numba import has_numba + + # only use the jitted decoder when numba is actually enabled, + # otherwise the decorated function would run as slow pure Python + if has_numba: + from mne.io.edf._bdf_numba import decode_int24 as dec + except Exception: + dec = False + _decode_int24 = dec + return _decode_int24 + + def _read_ch(fid, subtype, samp, dtype_byte, dtype=None): """Read a number of samples for a single channel.""" assert dtype is not None # BDF if subtype == "bdf": ch_data = read_from_file_or_buffer(fid, dtype=dtype, count=samp * dtype_byte) + dec = _get_int24_decoder() + if dec is not False: + return dec(ch_data.reshape(-1, 3)) ch_data = ch_data.reshape(-1, 3).astype(INT32) ch_data = (ch_data[:, 0]) + (ch_data[:, 1] << 8) + (ch_data[:, 2] << 16) # 24th bit determines the sign @@ -610,8 +634,6 @@ def _read_ch(fid, subtype, samp, dtype_byte, dtype=None): def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, mult): """Read a chunk of raw data.""" - from scipy.interpolate import interp1d - n_samps = raw_extras["n_samps"] buf_len = int(raw_extras["max_samp"]) dtype = raw_extras["dtype_np"] @@ -640,6 +662,97 @@ def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, # Let's do ~10 MB chunks: n_per = max(10 * 1024 * 1024 // (ch_offsets[-1] * dtype_byte), 1) + # Fast path: uniform sampling rate among all requested channels, no + # annotations (TAL) channel and no stim-channel special casing. This is + # the common case for large DL corpora; it replaces the per-channel + # Python loop with a handful of vectorized operations. + # The vectorized path removes per-channel Python overhead, which dominates + # small reads (deep-learning window access); for very large outputs the + # legacy loop's cache-friendly working set is slightly faster, so gate on + # decoded size. + _fast_path_output_limit = int(32e6) + if ( + subtype in ("edf", "bdf") + and len(tal_idx) == 0 + and all(n_samps[ci] == buf_len for ci in read_sel) + and (stop - start) * len(idx_arr) * 8 <= _fast_path_output_limit + ): + with _gdf_edf_get_fid(filenames, buffering=0) as fid: + start_offset = ( + data_offset + block_start_idx * ch_offsets[-1] * dtype_byte + ) + k = len(idx_arr) + cal_v = cal[idx_arr][:, np.newaxis, np.newaxis] + off_v = offsets[idx_arr][:, np.newaxis, np.newaxis] + gain_v = gains[idx_arr][:, np.newaxis, np.newaxis] + # With no projector/compensation and unit cals (always true for + # EDF/BDF), decoded samples can go straight into the output. + write_direct = mult is None and bool(np.all(cals == 1)) + ones = None if write_direct else np.empty( + (len(orig_sel), data.shape[-1]), dtype=data.dtype + ) + dec = _get_window_decoder() + if dec is not False: + one_flat = np.empty( + (k, min(len(r_lims), n_per) * buf_len), dtype=np.float64 + ) + cal_1d = cal_v.ravel() + off_1d = off_v.ravel() + gain_1d = gain_v.ravel() + # Uniform stim channels can stay on the fast path: legacy applies + # a truncating bitmask (float -> int cast, mask 2**17-1) AFTER + # calibration, which we replicate per row below. + stim_rows = [ + ii + for ii, orig in enumerate(idx_arr) + if int(orig) in stim_channel_idxs + ] + pos = 0 + for ai in range(0, len(r_lims), n_per): + block_offset = ai * ch_offsets[-1] * dtype_byte + n_read = min(len(r_lims) - ai, n_per) + fid.seek(start_offset + block_offset, 0) + many_chunk = _read_ch( + fid, subtype, ch_offsets[-1] * n_read, dtype_byte, dtype + ) + arr3 = many_chunk.reshape(n_read, len(n_samps), buf_len) + r_sidx = r_lims[ai][0] + r_eidx = buf_len * (n_read - 1) + r_lims[ai + n_read - 1][1] + # gather this call's channels as a strided view + # (k, n_read, buf_len), each row one requested channel; + # when all channels are requested this is a zero-copy + # transpose + if k == len(n_samps): + view = arr3.transpose(1, 0, 2) + else: + view = arr3[:, read_sel, :].transpose(1, 0, 2) + # digital -> physical, preserving the legacy op order + width = r_eidx - r_sidx + if dec is not False: + # fused kernel writes ((d * cal) + off) * gain with the + # same per-element op order and rounding as below + dec(view, cal_1d, off_1d, gain_1d, one_flat) + block = one_flat[:, r_sidx:r_eidx] + else: + one = np.empty((k, n_read, buf_len), dtype=np.float64) + np.multiply(view, cal_v, out=one) + one += off_v + one *= gain_v + block = one.reshape(k, -1)[:, r_sidx:r_eidx] + if stim_rows: + for ii in stim_rows: + row_i = block[ii].astype(int) + np.bitwise_and(row_i, 2**17 - 1, out=row_i) + block[ii] = row_i + if write_direct: + data[:, pos : pos + width] = block + else: + ones[idx_arr, pos : pos + width] = block + pos += width + if not write_direct: + _mult_cal_one(data[:, :], ones, idx, cals, mult) + return tal_data + with _gdf_edf_get_fid(filenames, buffering=0) as fid: # Extract data start_offset = data_offset + block_start_idx * ch_offsets[-1] * dtype_byte @@ -683,6 +796,8 @@ def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, if n_samps[ci] != buf_len: if orig_idx in stim_channel_idxs: # Stim channel will be interpolated + from scipy.interpolate import interp1d + old = np.linspace(0, 1, n_samps[ci] + 1, True) new = np.linspace(0, 1, buf_len, False) ch_data = np.append(ch_data, np.zeros((len(ch_data), 1)), -1) @@ -2361,3 +2476,22 @@ def _get_annotations_gdf(edf_info, sfreq): desc = events[2] return onset, duration, desc + + +_decode_window = None + + +def _get_window_decoder(): + """Return the numba fused window decoder if available, else False.""" + global _decode_window + if _decode_window is None: + dec = False + try: + from mne._numba import has_numba + + if has_numba: + from mne.io.edf._edf_numba import decode_window as dec + except Exception: + dec = False + _decode_window = dec + return _decode_window diff --git a/mne/io/fiff/raw.py b/mne/io/fiff/raw.py index 95c6db5dbec..87102f77544 100644 --- a/mne/io/fiff/raw.py +++ b/mne/io/fiff/raw.py @@ -9,10 +9,11 @@ import numpy as np +from ..._fiff._mmap_cache import get_u8_memmap from ..._fiff.constants import FIFF from ..._fiff.meas_info import read_meas_info from ..._fiff.open import _fiff_get_fid, _get_next_fname, fiff_open -from ..._fiff.tag import _call_dict, read_tag +from ..._fiff.tag import _call_dict, _simple_dict, read_tag from ..._fiff.tree import dir_tree_find from ..._fiff.utils import _mult_cal_one from ...annotations import Annotations, _read_annotations_fif @@ -403,13 +404,84 @@ def _dtype(self): def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a segment of data from a file.""" n_bad = 0 - with _fiff_get_fid(self._raw_extras[fi]["filename"]) as fid: - bounds = self._raw_extras[fi]["bounds"] - ents = self._raw_extras[fi]["ent"] - nchan = self._raw_extras[fi]["orig_nchan"] - use = (stop > bounds[:-1]) & (start < bounds[1:]) + bounds = self._raw_extras[fi]["bounds"] + ents = self._raw_extras[fi]["ent"] + nchan = self._raw_extras[fi]["orig_nchan"] + fname = self._raw_extras[fi]["filename"] + # Entries overlapping [start, stop) via binary search on sorted bounds + # (O(log n) instead of a mask over every entry; matters for long + # recordings with thousands of buffer entries). + eis = range( + max(np.searchsorted(bounds, start, side="right") - 1, 0), + min(np.searchsorted(bounds, stop, side="left"), len(bounds) - 1), + ) + + # Fast path: read tag payloads directly through a PID-keyed memory map, + # skipping per-call open/seek/read syscalls. Only taken for uncompressed + # real files whose touched tags are simple numeric types with the + # expected sizes; everything else falls back to the legacy loop below. + mm = None + if ( + isinstance(fname, Path) + and len(fname.suffixes) > 0 + and fname.suffixes[-1] != ".gz" + ): + mm = get_u8_memmap(fname) + if mm is not None: + for ei in eis: + ent = ents[ei] + if ent is None or ent.type not in _simple_dict: + mm = None + break + nsamp_ei = bounds[ei + 1] - bounds[ei] + itemsize = np.dtype(_simple_dict[ent.type]).itemsize + if getattr(ent, "size", None) != nsamp_ei * nchan * itemsize: + mm = None + break + if mm is not None: + offset = 0 + for ei in eis: + first = bounds[ei] + last = bounds[ei + 1] + nsamp = last - first + ent = ents[ei] + first_pick = max(start - first, 0) + last_pick = min(nsamp, stop - first) + picksamp = last_pick - first_pick + this_start = offset + offset += picksamp + this_stop = offset + if ent is None: + continue # gaps were zero-initialized by the caller + dtype_s = _simple_dict[ent.type] + itemsize = np.dtype(dtype_s).itemsize + nbytes = picksamp * nchan * itemsize + base = ent.pos + 16 + first_pick * nchan * itemsize + one = np.frombuffer( + mm[base : base + nbytes], dtype=dtype_s, count=picksamp * nchan + ) + if one.size != picksamp * nchan: + n_bad += picksamp + continue + one = one.reshape(picksamp, nchan) + _mult_cal_one( + data[:, this_start:this_stop], + one.T, + idx, + cals, + mult, + ) + if n_bad: + warn( + f"FIF raw buffer could not be read, acquisition error " + f"likely: {n_bad} samples set to zero" + ) + assert offset == stop - start + return + + with _fiff_get_fid(fname) as fid: offset = 0 - for ei in np.where(use)[0]: + for ei in eis: first = bounds[ei] last = bounds[ei + 1] nsamp = last - first diff --git a/mne/utils/mixin.py b/mne/utils/mixin.py index 04c55c62034..3addd688797 100644 --- a/mne/utils/mixin.py +++ b/mne/utils/mixin.py @@ -575,8 +575,13 @@ def _handle_tmin_tmax(self, tmin, tmax): type_name="int, float, None", ) - # handle tmin/tmax as start and stop indices into data array - n_times = self.times.size + # handle tmin/tmax as start and stop indices into data array. + # Prefer an integer n_times (available on Raw); falling back to + # times.size there would materialize the full time vector on every + # call, which dominates the cost of many small get_data() reads. + n_times = getattr(self, "n_times", None) + if n_times is None: + n_times = self.times.size start = 0 if tmin is None else self.time_as_index(tmin)[0] stop = n_times if tmax is None else self.time_as_index(tmax)[0] From 160f7184ab75acbc9e13e49511ecab9ba9f98c9e Mon Sep 17 00:00:00 2001 From: Bru Date: Mon, 24 Aug 2026 22:09:56 +0200 Subject: [PATCH 2/6] Add optional edfio parsing engine to read_raw_edf engine='edfio' parses EDF via the optional edfio package into a preloaded Raw (uniform sampling rates; all channels EEG; no meas_date). Decoding stacks digital samples once and applies calibration in two fused passes; output matches the native engine within 1 ulp. --- benchmarks/io_dl/RESULTS.md | 15 ++++ mne/io/edf/_edfio_backend.py | 162 +++++++++++++++++++++++++++++++++++ mne/io/edf/edf.py | 23 ++++- mne/io/edf/tests/test_edf.py | 27 ++++++ 4 files changed, 226 insertions(+), 1 deletion(-) create mode 100644 mne/io/edf/_edfio_backend.py diff --git a/benchmarks/io_dl/RESULTS.md b/benchmarks/io_dl/RESULTS.md index 70e627e4cc0..3f1bcdbfa81 100644 --- a/benchmarks/io_dl/RESULTS.md +++ b/benchmarks/io_dl/RESULTS.md @@ -459,3 +459,18 @@ Adopted: searchsorted bounds (#8), stim-in-fast-path (#9), codex's FIF mmap prot Queued: EDF mixed-sfreq partial fast path (decode uniform EEG blocks, interp stim separately — needs careful interp semantics), float32 output read path, open_meta header-parse cost, batched-API upstream proposal, edfio engine backend. + +## 13. Session 6: `engine="edfio"` backend landed + +`read_raw_edf(..., engine="edfio")` parses via the optional edfio package into a +preloaded Raw. Minimal scope by design: uniform sfreq only, all channels EEG, +no meas_date, volts output using the native unit mapping. Decode is two fused +passes over one stacked int16 buffer; outputs match the native engine within +1 ulp (max |diff| 2.7e-20 on our fixtures; a first fusion attempt folded the +unit multiplier twice — caught immediately against edfio's own `.data`). + +Full-load BIG file: native 453 ms vs edfio engine 368 ms (~1.2× incl. MNE +wrapper + output copy; raw parser delta is larger). Windows on the preloaded +result sit at the ~78 µs numpy floor like every other preloaded path. + +New test `test_engine_edfio` compares engines on an exported fixture. diff --git a/mne/io/edf/_edfio_backend.py b/mne/io/edf/_edfio_backend.py new file mode 100644 index 00000000000..dcacada0f60 --- /dev/null +++ b/mne/io/edf/_edfio_backend.py @@ -0,0 +1,162 @@ +"""Optional edfio-backed reader engine for EDF files. + +This module implements an alternative ``engine="edfio"`` for +:func:`mne.io.read_raw_edf` that parses the file with the +`edfio `_ package instead of the +native reader. It is faster on uniform-sampling-rate recordings and always +returns preloaded data. + +Scope (kept deliberately minimal): + +- uniform sampling rates only (the native engine handles mixed rates); +- all channels are typed ``eeg``; +- ``meas_date`` is not set; +- data is returned in volts, scaled from the header's physical dimension + using the same unit mapping as the native reader. +""" + +# Authors: The MNE-Python contributors. +# License: BSD-3-Clause +# Copyright the MNE-Python contributors + +import numpy as np + +from ..._fiff.meas_info import _unique_channel_names +from ...annotations import Annotations +from ...utils import _check_fname, fill_doc, verbose +from ..base import BaseRaw + +_UNIT_MULT = { + "\u03bcV": 1e-6, # greek mu + "\u00b5V": 1e-6, # micro symbol + "uV": 1e-6, + "mV": 1e-3, +} + + +class _RawEdfio(BaseRaw): + """Raw from edfio-parsed EDF (always preloaded).""" + + _extra_attributes = () + + def __init__(self, info, data, annotations, *, verbose=None): + super().__init__( + info, + preload=data, + last_samps=[data.shape[1] - 1], + filenames=None, + orig_format="double", + verbose=verbose, + ) + if len(annotations): + self.set_annotations(annotations) + + +@fill_doc +@verbose +def read_raw_edf_edfio( + input_fname, + *, + preload=True, + exclude=(), + include=None, + verbose=None, +) -> _RawEdfio: + """Read an EDF file using the edfio parser. + + Parameters + ---------- + input_fname : path-like + Path to the EDF/EDF+ file. + %(preload)s + The edfio engine currently supports only preloaded reads; ``True`` + (or a truthy string) is required. + exclude : list of str + Channel names to exclude. + include : list of str | None + Restrict channels to these names (after ``exclude``). + %(verbose)s + + Returns + ------- + raw : instance of Raw + Preloaded raw data in volts. + + Notes + ----- + Uniform sampling rates only; all channels are typed ``eeg``; + ``info['meas_date']`` is not populated. + """ + from edfio import read_edf as _read_edf + + input_fname = str(_check_fname(input_fname, "read", True, "input_fname")) + if not preload: + raise NotImplementedError( + 'The "edfio" engine currently always loads data into memory; ' + 'use preload=True.' + ) + edf = _read_edf(input_fname) + + signals = edf.signals + ch_names = [sig.label for sig in signals] + sfreqs = {float(sig.sampling_frequency) for sig in signals} + if len(sfreqs) != 1: + raise NotImplementedError( + "The edfio engine requires a uniform sampling rate; this file has " + f"{len(sfreqs)} distinct rates. Use the default engine instead." + ) + sfreq = sfreqs.pop() + + keep = np.arange(len(signals)) + if include is not None: + keep = [i for i in keep if ch_names[i] in set(include)] + if len(exclude): + excluded = set(exclude) + keep = [i for i in keep if ch_names[i] not in excluded] + keep = np.asarray(keep, dtype=int) + if keep.size == 0: + raise ValueError("No channels selected") + + ch_names = list(np.array(ch_names)[keep]) + ch_names = _unique_channel_names(ch_names) + unit_mults = np.array( + [ + _UNIT_MULT.get(str(signals[i].physical_dimension).strip(), 1.0) + for i in keep + ], + dtype=float, + ) + # Stack digital samples once, then decode all channels in two fused + # passes: physical = (digital + offset) * (gain * unit_mult), matching + # edfio's calibration op order. + n_times = min(len(signals[i].digital) for i in keep) + dig = np.empty((len(keep), n_times), dtype=np.int16) + gains = np.empty(len(keep)) + offsets = np.empty(len(keep)) + for row_i, sig_i in enumerate(keep): + digital = signals[sig_i].digital + dig[row_i] = digital[:n_times] + sig = signals[sig_i] + gains[row_i] = (sig.physical_max - sig.physical_min) / ( + sig.digital_max - sig.digital_min + ) + offsets[row_i] = sig.physical_max / gains[row_i] - sig.digital_max + + info = _make_info_edfio(ch_names, sfreq) + data = np.empty((len(keep), n_times), dtype=np.float64) + np.add(dig, offsets[:, np.newaxis], out=data, casting="unsafe") + data *= (gains * unit_mults)[:, np.newaxis] + + annots = edf.annotations + mne_annots = Annotations( + onset=[a.onset for a in annots], + duration=[a.duration for a in annots], + description=[str(a.text) for a in annots], + ) + return _RawEdfio(info, data, mne_annots, verbose=verbose) + + +def _make_info_edfio(ch_names, sfreq): + import mne + + return mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types="eeg") diff --git a/mne/io/edf/edf.py b/mne/io/edf/edf.py index c59a7532255..e5b0457e6e5 100644 --- a/mne/io/edf/edf.py +++ b/mne/io/edf/edf.py @@ -2017,9 +2017,10 @@ def read_raw_edf( units: dict | str | None = None, encoding: str = "utf8", exclude_after_unique: bool = False, + engine: Literal["mne", "edfio"] = "mne", *, verbose: bool | str | int | None = None, -) -> RawEDF: +) -> RawEDF | BaseRaw: """Reader function for EDF and EDF+ files. Parameters @@ -2066,6 +2067,13 @@ def read_raw_edf( %(units_edf_bdf_io)s %(encoding_edf)s %(exclude_after_unique)s + engine : ``'mne'`` | ``'edfio'`` + Parser backend. ``'mne'`` (default) uses the native reader; + ``'edfio'`` parses via the optional edfio package, which is faster on + uniform-sampling-rate recordings but always preloads, types all + channels as EEG, and does not set ``info['meas_date']``. + + .. versionadded:: 1.13 %(verbose)s Returns @@ -2125,6 +2133,19 @@ def read_raw_edf( The EDF specification allows storage of subseconds in measurement date. However, this reader currently sets subseconds to 0 by default. """ + if engine == "edfio": + from ._edfio_backend import read_raw_edf_edfio + + return read_raw_edf_edfio( + input_fname, + preload=preload, + exclude=exclude, + include=include, + verbose=verbose, + ) + if engine != "mne": + raise ValueError(f"Unknown engine {engine!r}; use 'mne' or 'edfio'.") + _check_args(input_fname, preload, "edf") return RawEDF( diff --git a/mne/io/edf/tests/test_edf.py b/mne/io/edf/tests/test_edf.py index 211641dc726..c8c0b27a446 100644 --- a/mne/io/edf/tests/test_edf.py +++ b/mne/io/edf/tests/test_edf.py @@ -8,6 +8,7 @@ from io import BytesIO from pathlib import Path +import mne import numpy as np import pytest from numpy.testing import ( @@ -1262,3 +1263,29 @@ def test_edf_read_from_file_like(): ] assert raw.ch_names == channels + + + +def requires_edfio(func): + import pytest + + return pytest.mark.skipif( + __import__("importlib.util", fromlist=["util"]).find_spec("edfio") is None, + reason="Requires edfio", + )(func) + + +@requires_edfio +def test_engine_edfio(tmp_path): + """Compare the optional edfio engine against the native one.""" + pytest.importorskip("edfio") + rng = np.random.default_rng(11) + info = mne.create_info(["EEG A", "EEG B"], sfreq=128.0, ch_types="eeg") + raw = mne.io.RawArray(rng.standard_normal((2, 512)) * 30e-6, info) + fname = tmp_path / "engine_test.edf" + raw.export(fname, verbose="error") + base = read_raw_edf(fname, preload=True, verbose="error").get_data() + alt = read_raw_edf(fname, preload=True, engine="edfio", + verbose="error").get_data() + assert base.shape == alt.shape + assert_allclose(base, alt, rtol=0, atol=1e-15) From efd2577e60d35c000f3b7eba5a3d00f410d66fb4 Mon Sep 17 00:00:00 2001 From: Bru Date: Mon, 24 Aug 2026 22:24:57 +0200 Subject: [PATCH 3/6] Persist EDF/BDF/GDF read handles per process _gdf_edf_get_fid now returns PID-keyed LRU-cached binary handles wrapped in a no-close proxy so reader context managers detach instead of closing, and reuse seeks to position 0 to preserve fresh-open semantics. Removes the per-call open/close pair on every windowed read. --- benchmarks/io_dl/RESULTS.md | 15 ++++++++++ mne/io/base.py | 4 +-- mne/io/edf/_open.py | 55 +++++++++++++++++++++++++++++++++---- 3 files changed, 66 insertions(+), 8 deletions(-) diff --git a/benchmarks/io_dl/RESULTS.md b/benchmarks/io_dl/RESULTS.md index 3f1bcdbfa81..cfe3da62371 100644 --- a/benchmarks/io_dl/RESULTS.md +++ b/benchmarks/io_dl/RESULTS.md @@ -474,3 +474,18 @@ wrapper + output copy; raw parser delta is larger). Windows on the preloaded result sit at the ~78 µs numpy floor like every other preloaded path. New test `test_engine_edfio` compares engines on an exported fixture. + +## 14. Session 7: syscall-glue removal + edfio PR verification + +- `mne/io/edf/_open.py`: PID-keyed LRU persistent read handles (NoClose wrapper, + seek(0)-on-reuse preserving fresh-open semantics, LRU cap 8). Removes + open/close per `get_data` on EDF/BDF/GDF paths. Gates: all suites green, + equivalence exact; windows now **EDF 291-295 µs / BDF 402-424 µs** vs pristine. +- edfio PR #114: upstream Actions require maintainer approval for fork PRs + (0 check runs). Replicated their pinned toolchain locally instead: + ruff==0.9.10 clean after refactor (RUF059), ruff-format applied, + mypy==1.15.0's 6 unused-ignore errors confirmed pre-existing on main, + pytest 1022 passed @ 100% coverage; fix commit pushed + verification comment posted. +- Documented: `_get_windows(..., out=float32_buffer)` halves output memory + traffic for DL collation (dtype of provided buffer is honored by all fused + write paths). diff --git a/mne/io/base.py b/mne/io/base.py index 1705e91ab5a..09d9d54a13c 100644 --- a/mne/io/base.py +++ b/mne/io/base.py @@ -921,9 +921,9 @@ def _get_windows(self, starts, width, *, out=None, sel=None): f"out has shape {out.shape}, need " f"{(len(starts), n_out, width)}" ) - elif out.dtype != self._dtype: + elif out.dtype not in (np.float64, np.float32): raise ValueError( - f"out dtype must be {self._dtype}, got {out.dtype}" + f"out dtype must be float64 or float32, got {out.dtype}" ) for j, s0 in enumerate(starts): self._read_segment( diff --git a/mne/io/edf/_open.py b/mne/io/edf/_open.py index 38f8be4113d..1d8f1106dfe 100644 --- a/mne/io/edf/_open.py +++ b/mne/io/edf/_open.py @@ -1,12 +1,55 @@ # Authors: The MNE-Python contributors. # License: BSD-3-Clause -# Copyright the MNE-Python contributors. +# Copyright the MNE-Python contributors +import os from pathlib import Path from ..._fiff.open import _NoCloseRead from ...utils import _file_like, _validate_type, logger +# Persistent read handles for EDF/BDF/GDF files. Readers seek before every +# read, so a shared handle is safe; keying by PID keeps forked worker +# processes (e.g., PyTorch DataLoader workers) from sharing file-offset state +# through an inherited descriptor. +_HANDLE_CACHE = {} +_MAX_HANDLES = 8 + + +class _NoCloseCached(_NoCloseRead): + """A file object whose context manager detaches instead of closing. + + Used for handles shared through the per-process LRU cache: leaving the + reader's ``with`` block must not close a descriptor other reads may still + use. + """ + + def close(self): # noqa: D102 + pass + + def __exit__(self, *args): # noqa: D105 + # detach rather than close; the cache owns the lifetime + return False + + +def _get_cached_fid(fname): + """Return a persistent binary handle for *fname* (per process).""" + key = (os.getpid(), str(fname)) + hit = _HANDLE_CACHE.get(key) + if hit is not None: + hit.seek(0) # match fresh-open semantics + return hit + fid = open(fname, "rb") + cached = _NoCloseCached(fid) + _HANDLE_CACHE[key] = cached + while len(_HANDLE_CACHE) > _MAX_HANDLES: + old_key = next(iter(_HANDLE_CACHE)) + try: + _HANDLE_CACHE.pop(old_key).fid.close() + except Exception: + pass + return cached + def _gdf_edf_get_fid(fname, **kwargs): """Open a EDF/BDF/GDF file with no additional parsing.""" @@ -14,8 +57,8 @@ def _gdf_edf_get_fid(fname, **kwargs): logger.debug("Using file-like I/O") fid = _NoCloseRead(fname) fid.seek(0) - else: - _validate_type(fname, [Path, str], "fname", extra="or file-like") - logger.debug("Using normal I/O") - fid = open(fname, "rb", **kwargs) # Open in binary mode - return fid + return fid + _validate_type(fname, [Path, str], "fname", extra="or file-like") + logger.debug("Using normal I/O") + kwargs.pop("buffering", None) # cached handle manages its own buffering + return _get_cached_fid(Path(fname)) From b0cacc9ed0ecf107617eb7256a3dab141ae30fa6 Mon Sep 17 00:00:00 2001 From: Bru Date: Mon, 24 Aug 2026 22:50:25 +0200 Subject: [PATCH 4/6] Decode EDF/BDF windows directly into output buffers Add decode_window_into numba kernel writing calibrated samples into a strided destination slice, removing the per-chunk temporary and copy when no projector/compensation is active. Byteswap big-endian chunks to native before the kernel (real-world EDF); kernel is gated to the direct path so projection handling keeps its exact legacy route. --- benchmarks/io_dl/RESULTS.md | 35 +++++++++ mne/io/edf/_edf_numba.py | 28 +++++++ mne/io/edf/edf.py | 141 ++++++++++++++++++++++++++++++------ 3 files changed, 180 insertions(+), 24 deletions(-) diff --git a/benchmarks/io_dl/RESULTS.md b/benchmarks/io_dl/RESULTS.md index cfe3da62371..126b46d99d6 100644 --- a/benchmarks/io_dl/RESULTS.md +++ b/benchmarks/io_dl/RESULTS.md @@ -489,3 +489,38 @@ New test `test_engine_edfio` compares engines on an exported fixture. - Documented: `_get_windows(..., out=float32_buffer)` halves output memory traffic for DL collation (dtype of provided buffer is honored by all fused write paths). + +## 15. Session 8: depth round — direct-to-output kernel, byteorder guard + +- `decode_window_into(dst, view, cal, off, gain, s0, w)` in `_edf_numba.py`: + decodes straight into the caller's output slice (arbitrary strides), with + per-element sample addressing (`s0`/`w`) so edge records at window + boundaries need no temporaries. +- EDF fast path now decodes **directly into `data[:, pos:pos+w]`** when safe + (no projector/comp, unit cals): removes the per-chunk intermediate and its + copy. numpy fallback mirrors via temp+single copy. +- **Byteorder guard**: real-world EDF is big-endian and numba only types + native byteorder — non-native chunks are byteswapped to a native copy per + chunk before the kernel (synthetic LE files had masked this). +- Kernel gated strictly on `write_direct`; projector/compensation paths keep + the exact legacy route (a first draft double-applied calibration there — + caught by `test_bdf_data`, fixed by the gate). +- Attempted mixed-sfreq partial decode inside the fast path; reverted to the + strict uniform-file gate: ragged record layouts break the zero-copy reshape, + and replicating the legacy slow-row packing quirks bit-exactly needs its own + round (documented, not silently approximated). + +### Round numbers (BIG file, best-of interleaved vs pristine main) + +| format | window µs | speedup | full load | +|---|---|---|---| +| EDF | 301 | 5.7× | 1.1× | +| BDF | 406 | 6.1× | **2.0×** | +| BrainVision | 200 | 5.9× | 1.3–1.5× | +| FIF | 257 | 5.1× | 1.4× | + +Cumulative vs where this effort started: **~6.5–7.5× per-window**, full loads +1.1–2×, preloads at the numpy floor. The residual per-window cost is now +genuinely I/O + format decode + one fused pass; further order-of-magnitude +gains require changing what is stored (window-shaped HDF5/NWB-style layouts, +§11.2) rather than how MNE reads classic formats. diff --git a/mne/io/edf/_edf_numba.py b/mne/io/edf/_edf_numba.py index 2ea7282e379..7910185f491 100644 --- a/mne/io/edf/_edf_numba.py +++ b/mne/io/edf/_edf_numba.py @@ -39,3 +39,31 @@ def decode_window(digital, cal_v, off_v, gain_v, out): # pragma: no cover base = b * n_smp for j in range(n_smp): out_i[base + j] = ((digital[i, b, j] * cal) + off) * gain + + +@jit(fastmath=False) +def decode_window_into( + dst, digital, cal_v, off_v, gain_v, s0, w +): # pragma: no cover + """Decode into a possibly-strided 2-D destination. + + ``dst`` is ``(k, w)`` with arbitrary strides (e.g., a column slice of the + caller's output buffer); ``digital`` is the ``(k, n_blocks, buf_len)`` + strided integer view covering whole data records; ``s0`` is the first + sample to take from that view and ``w`` the number of samples to write, + so edge records at window boundaries are handled without temporaries. + Elementwise op order is identical to :func:`decode_window`. + """ + k = digital.shape[0] + n_smp = digital.shape[2] + for i in range(k): + cal = cal_v[i] + off = off_v[i] + gain = gain_v[i] + dst_i = dst[i] + dig_i = digital[i] + for t in range(w): + g = s0 + t + b = g // n_smp + j = g - b * n_smp + dst_i[t] = ((dig_i[b, j] * cal) + off) * gain diff --git a/mne/io/edf/edf.py b/mne/io/edf/edf.py index e5b0457e6e5..0a1fdef16e7 100644 --- a/mne/io/edf/edf.py +++ b/mne/io/edf/edf.py @@ -671,10 +671,12 @@ def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, # legacy loop's cache-friendly working set is slightly faster, so gate on # decoded size. _fast_path_output_limit = int(32e6) + uni_mask = [n_samps[ci] == buf_len for ci in read_sel] + slow_ii = [ii for ii, u in enumerate(uni_mask) if not u] if ( subtype in ("edf", "bdf") and len(tal_idx) == 0 - and all(n_samps[ci] == buf_len for ci in read_sel) + and all(uni_mask) # mixed-sfreq partial decode is future work and (stop - start) * len(idx_arr) * 8 <= _fast_path_output_limit ): with _gdf_edf_get_fid(filenames, buffering=0) as fid: @@ -682,30 +684,42 @@ def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, data_offset + block_start_idx * ch_offsets[-1] * dtype_byte ) k = len(idx_arr) + uni_list = [ii for ii in range(k) if uni_mask[ii]] + sel_uni = [read_sel[ii] for ii in uni_list] + idx_arr_uni = idx_arr[uni_list] + k_u = len(uni_list) cal_v = cal[idx_arr][:, np.newaxis, np.newaxis] off_v = offsets[idx_arr][:, np.newaxis, np.newaxis] gain_v = gains[idx_arr][:, np.newaxis, np.newaxis] + cal_u = cal[idx_arr_uni][:, np.newaxis, np.newaxis] + off_u = offsets[idx_arr_uni][:, np.newaxis, np.newaxis] + gain_u = gains[idx_arr_uni][:, np.newaxis, np.newaxis] + off_v = offsets[idx_arr][:, np.newaxis, np.newaxis] + gain_v = gains[idx_arr][:, np.newaxis, np.newaxis] # With no projector/compensation and unit cals (always true for # EDF/BDF), decoded samples can go straight into the output. - write_direct = mult is None and bool(np.all(cals == 1)) - ones = None if write_direct else np.empty( + write_direct = ( + mult is None and bool(np.all(cals == 1)) and not slow_ii + ) + ones = None if write_direct else np.zeros( (len(orig_sel), data.shape[-1]), dtype=data.dtype ) dec = _get_window_decoder() - if dec is not False: - one_flat = np.empty( - (k, min(len(r_lims), n_per) * buf_len), dtype=np.float64 - ) - cal_1d = cal_v.ravel() - off_1d = off_v.ravel() - gain_1d = gain_v.ravel() + dec_into = None + if dec is not False and k_u: + from ._edf_numba import decode_window_into + + dec_into = decode_window_into + cal_1d = cal_u.ravel() + off_1d = off_u.ravel() + gain_1d = gain_u.ravel() # Uniform stim channels can stay on the fast path: legacy applies # a truncating bitmask (float -> int cast, mask 2**17-1) AFTER # calibration, which we replicate per row below. stim_rows = [ ii for ii, orig in enumerate(idx_arr) - if int(orig) in stim_channel_idxs + if int(orig) in stim_channel_idxs and uni_mask[ii] ] pos = 0 for ai in range(0, len(r_lims), n_per): @@ -722,17 +736,39 @@ def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, # (k, n_read, buf_len), each row one requested channel; # when all channels are requested this is a zero-copy # transpose - if k == len(n_samps): + if not slow_ii and k == len(n_samps): view = arr3.transpose(1, 0, 2) + elif slow_ii: + view = arr3[:, sel_uni, :].transpose(1, 0, 2) else: view = arr3[:, read_sel, :].transpose(1, 0, 2) - # digital -> physical, preserving the legacy op order + # digital -> physical, preserving the legacy op order; + # when possible decode straight into the destination slice so + # the block never round-trips through a temporary width = r_eidx - r_sidx - if dec is not False: - # fused kernel writes ((d * cal) + off) * gain with the - # same per-element op order and rounding as below - dec(view, cal_1d, off_1d, gain_1d, one_flat) - block = one_flat[:, r_sidx:r_eidx] + if dec_into is not None and write_direct: + if not view.dtype.isnative: + # numba only types native byteorder; real-world EDF is + # big-endian, so swap into a native copy per chunk + view = view.astype(view.dtype.newbyteorder("=")) + dst_full = ( + data[:, pos : pos + width] + if write_direct + else ones[idx_arr_uni, pos : pos + width] + ) + dec_into(dst_full, view, cal_1d, off_1d, gain_1d, + r_sidx, width) + block = None # values already in place + elif ones is None: + # no kernel and direct write: decode to a temp then copy + # once into the destination slice + one = np.empty((k, n_read, buf_len), dtype=np.float64) + np.multiply(view, cal_v, out=one) + one += off_v + one *= gain_v + dst_full = data[:, pos : pos + width] + dst_full[...] = one.reshape(k, -1)[:, r_sidx:r_eidx] + block = None # values already in place else: one = np.empty((k, n_read, buf_len), dtype=np.float64) np.multiply(view, cal_v, out=one) @@ -741,14 +777,71 @@ def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, block = one.reshape(k, -1)[:, r_sidx:r_eidx] if stim_rows: for ii in stim_rows: - row_i = block[ii].astype(int) + src_row = block[ii] if block is not None else dst_full[ii] + row_i = src_row.astype(int) np.bitwise_and(row_i, 2**17 - 1, out=row_i) - block[ii] = row_i - if write_direct: - data[:, pos : pos + width] = block - else: - ones[idx_arr, pos : pos + width] = block + if block is not None: + block[ii] = row_i + else: + dst_full[ii] = row_i + if block is not None: + if write_direct: + data[:, pos : pos + width] = block + elif slow_ii: + ones[idx_arr_uni, pos : pos + width] = block + else: + ones[idx_arr, pos : pos + width] = block + # legacy per-channel treatment for non-uniform rows: their + # columns live inside the same records we just read + for ii in slow_ii: + ci = read_sel[ii] + orig_idx = idx_arr[ii] + ch_data = many_chunk[ + :, ch_offsets[ci] : ch_offsets[ci + 1] + ].copy() + o_i = orig_idx + ch_data = ch_data * cal[o_i] + ch_data += offsets[o_i] + ch_data *= gains[o_i] + if int(orig_idx) in stim_channel_idxs: + from scipy.interpolate import interp1d + + s_n = n_samps[ci] + oldg = np.linspace(0, 1, s_n + 1, True) + newg = np.linspace(0, 1, buf_len, False) + ch_data = np.append( + ch_data, np.zeros((len(ch_data), 1)), -1 + ) + ch_data = interp1d(oldg, ch_data, kind="zero", axis=-1)( + newg + ) + one_i = ch_data.ravel()[r_sidx:r_eidx] + w0 = pos - width # first output column of this chunk + ones[o_i, w0 : w0 + len(one_i)] = one_i pos += width + if slow_ii: + smp_exp = data.shape[-1] + resampled = False + for ii in slow_ii: + row = int(idx_arr[ii]) + ci = read_sel[ii] + if n_samps[ci] != buf_len and width != smp_exp: + resampled = True + ones[row, :] = resample( + ones[row, :width].astype(np.float64), + smp_exp, + width, + npad=0, + axis=-1, + ) + if resampled and raw_extras["nsamples"] != (stop - start): + warn( + "Loading an EDF with mixed sampling frequencies and " + "preload=False will result in edge artifacts. " + "It is recommended to use preload=True." + "See also " + "https://github.com/mne-tools/mne-python/issues/10635" + ) if not write_direct: _mult_cal_one(data[:, :], ones, idx, cals, mult) return tal_data From 1ed1347a99705252428f132aecdfd91c7ba6139b Mon Sep 17 00:00:00 2001 From: Bru Date: Mon, 24 Aug 2026 23:07:06 +0200 Subject: [PATCH 5/6] Reuse and persist memory-mapped preload caches Preloading into a memmap file now skips decoding entirely when a file with the expected size already exists, and BaseRaw.__del__ no longer deletes memmap-backed files so caches persist across sessions. Reopening a 944 MB recording drops from ~450 ms of decoding to ~15 ms, and windowed reads via the public API run at the preloaded-data floor (~84 us). --- benchmarks/io_dl/RESULTS.md | 30 ++++++++++++++++++++++++++++++ doc/changes/dev/14202.bugfix.rst | 5 +++++ mne/io/base.py | 32 +++++++++++++++++++++++++------- mne/io/edf/tests/test_edf.py | 23 +++++++++++++++++++++++ 4 files changed, 83 insertions(+), 7 deletions(-) diff --git a/benchmarks/io_dl/RESULTS.md b/benchmarks/io_dl/RESULTS.md index 126b46d99d6..7eed82f591c 100644 --- a/benchmarks/io_dl/RESULTS.md +++ b/benchmarks/io_dl/RESULTS.md @@ -524,3 +524,33 @@ Cumulative vs where this effort started: **~6.5–7.5× per-window**, full loads genuinely I/O + format decode + one fused pass; further order-of-magnitude gains require changing what is stored (window-shaped HDF5/NWB-style layouts, §11.2) rather than how MNE reads classic formats. + +## 16. Session 9: persistent memmap caches = the lazy/fast flag + +`load_data(memmap=path)` existed but (a) always re-decoded even when the cache +file was valid and (b) `BaseRaw.__del__` deleted the file on GC. Both fixed: + +- `_preload_data`: when a memmap cache exists with the exact expected size, it + is mmap'd read-write **and decoding is skipped entirely**. +- `__del__` no longer removes memmap files — callers own cache lifetime + (behavior change; documented in changelog fragment). + +Fresh-process numbers (128 ch × 1800 s @ 512 Hz, 944 MB float64): + +| operation | before | now | +|---|---|---| +| open + first access | ~450 ms decode | **11–21 ms** | +| public get_data windows | 200–406 µs | **~84 µs** | + +This is the recommended DL pattern until format-native lazy backends land: + +```python +raw = mne.io.read_raw_edf("huge.edf", preload=False) +raw.load_data(memmap="cache.f64") # first run decodes; every later run mmaps +``` + +On Python 3.15's opt-in lazy imports (PEP 810): import-time costs we measured +(e.g., the 105 ms mne_bids cascade on first save) should shrink automatically; +no hand-rolled laziness added here. A compiled C++/Rust kernel was evaluated +and rejected for now: our numba path already runs at memory bandwidth, so a +second native toolchain would add packaging burden without measurable gain. diff --git a/doc/changes/dev/14202.bugfix.rst b/doc/changes/dev/14202.bugfix.rst index f5fba32ffaa..81b6bf816a2 100644 --- a/doc/changes/dev/14202.bugfix.rst +++ b/doc/changes/dev/14202.bugfix.rst @@ -4,3 +4,8 @@ re-resolves channel picks on every call; EDF/BDF readers gained vectorized and optional-numba fast paths (bit-identical output, including stim channels); the FIF reader now reads tag payloads through a memory map; batched internal window reads avoid per-window allocations. + +Memory-mapped caches passed to ``Raw.load_data(memmap=...)`` are now reused +when valid instead of being re-decoded, and are no longer deleted when the +:class:`~mne.io.Raw` object is garbage-collected — reopening large recordings +becomes near-instantaneous and cache lifetime is managed by the caller. diff --git a/mne/io/base.py b/mne/io/base.py index 09d9d54a13c..a9386adf1de 100644 --- a/mne/io/base.py +++ b/mne/io/base.py @@ -631,6 +631,28 @@ def _preload_data(self, preload): data_buffer = preload if isinstance(preload, bool | np.bool_) and not preload: data_buffer = None + elif isinstance(preload, str | Path): + # A memory-map cache file: when one already exists with the exact + # expected size, mmap it and skip decoding entirely. This makes + # reopening huge recordings nearly instantaneous (first run pays + # the decode, subsequent runs reuse the OS page cache). Note the + # cache is validated by size only; delete it if the source file + # changed. + path = Path(preload) + n_bytes = ( + self.info["nchan"] + * (self._last_samps[-1] - self._first_samps[0] + 1) + * np.dtype(self._dtype).itemsize + ) + if path.exists() and path.stat().st_size == n_bytes: + self._data = np.memmap( + str(path), mode="r+", dtype=self._dtype, + shape=(self.info["nchan"], n_bytes // ( + self.info["nchan"] * np.dtype(self._dtype).itemsize)), + ) + self.preload = True + logger.info(f"Reusing memory-mapped cache at {path}") + return t = self.times logger.info( f"Reading 0 ... {len(t) - 1} = {0.0:9.3f} ... {t[-1]:9.3f} secs..." @@ -813,16 +835,12 @@ def set_annotations( return self def __del__(self): # noqa: D105 - # remove file for memmap + # Memmap-backed data (load_data(memmap=...)) is owned by the caller: + # the file persists after the Raw object is deleted so that subsequent + # sessions can mmap it directly instead of re-decoding. fname = getattr(getattr(self, "_data", None), "filename", None) if fname is not None: - # First, close the file out; happens automatically on del del self._data - # Now file can be removed - try: - os.remove(fname) - except OSError: - pass # ignore file that no longer exists def __enter__(self): """Entering with block.""" diff --git a/mne/io/edf/tests/test_edf.py b/mne/io/edf/tests/test_edf.py index c8c0b27a446..2a65dfdc67f 100644 --- a/mne/io/edf/tests/test_edf.py +++ b/mne/io/edf/tests/test_edf.py @@ -1289,3 +1289,26 @@ def test_engine_edfio(tmp_path): verbose="error").get_data() assert base.shape == alt.shape assert_allclose(base, alt, rtol=0, atol=1e-15) + + +def test_memmap_cache_reuse(tmp_path): + """load_data(memmap=...) reuses a valid cache without re-decoding.""" + pytest.importorskip("edfio") + rng = np.random.default_rng(3) + info = mne.create_info(["EEG A"], sfreq=64.0, ch_types="eeg") + raw = mne.io.RawArray(rng.standard_normal((1, 2048)) * 30e-6, info) + fname = tmp_path / "mm_cache.edf" + raw.export(fname, verbose="error") + cache = tmp_path / "cache.f64" + kwargs = dict(preload=False, verbose="error") + r1 = read_raw_edf(fname, **kwargs).load_data(memmap=str(cache)) + assert cache.exists() + for _ in range(3): # replay open→use cycles + r2 = read_raw_edf(fname, **kwargs).load_data(memmap=str(cache)) + assert r2.preload + d = r2.get_data() + assert_allclose(d, r1.get_data(), rtol=0, atol=0) + # stale-size guard: wrong-sized cache is rebuilt, not mmap-reused + cache.write_bytes(b"x" * 16) + r3 = read_raw_edf(fname, **kwargs).load_data(memmap=str(cache)) + assert_allclose(r3.get_data(), r1.get_data(), rtol=0, atol=0) From 7dae75a510d0cfda2ea193e7a134baff504371fe Mon Sep 17 00:00:00 2001 From: Bru Date: Tue, 25 Aug 2026 00:21:21 +0200 Subject: [PATCH 6/6] Add preload="memmap" sentinel for managed persistent caches preload="memmap" now requests an automatically managed memory-map cache keyed by source path/mtime/size: the first read decodes into it, later reads (including from fresh processes) mmap it directly instead of decoding again. Directory defaults to ~/.cache/mne/memmap and can be redirected via the MNE_MEMMAP_DIR config/environment variable. --- mne/io/base.py | 46 ++++++++++++++++++++++++++++-------- mne/io/edf/tests/test_edf.py | 19 +++++++++++++++ mne/utils/docs.py | 7 +++++- 3 files changed, 61 insertions(+), 11 deletions(-) diff --git a/mne/io/base.py b/mne/io/base.py index a9386adf1de..b5ed1a07cdf 100644 --- a/mne/io/base.py +++ b/mne/io/base.py @@ -2,7 +2,7 @@ # License: BSD-3-Clause # Copyright the MNE-Python contributors. -import os +import hashlib import shutil from collections import defaultdict from collections.abc import Callable, Sequence @@ -97,6 +97,7 @@ copy_doc, copy_function_doc_to_method_doc, fill_doc, + get_config, logger, repr_html, sizeof_fmt, @@ -621,14 +622,36 @@ def load_data( .. versionadded:: 0.10.0 """ if not self.preload: + if memmap == "memmap": + memmap = self._auto_memmap_path() if memmap is not None: _validate_type(memmap, "path-like", "memmap") self._preload_data(memmap if memmap is not None else True) return self + def _auto_memmap_path(self) -> Path: + """Return the managed memmap-cache path for a single-file Raw.""" + if len(self.filenames) != 1 or self.filenames[0] is None: + raise ValueError( + "preload='memmap' requires a Raw backed by exactly one file" + ) + src = Path(self.filenames[0]) + st = src.stat() + key = hashlib.sha256(f"{src}|{st.st_mtime_ns}|{st.st_size}".encode()) + cache_dir = Path( + get_config("MNE_MEMMAP_DIR", Path.home() / ".cache" / "mne" / "memmap") + ) + cache_dir.mkdir(parents=True, exist_ok=True) + return cache_dir / f"{src.stem}-{key.hexdigest()[:24]}.f64" + def _preload_data(self, preload): """Actually preload the data.""" data_buffer = preload + if preload == "memmap": + # sentinel: managed persistent cache keyed by source + # path/mtime/size, so staleness is handled by the key itself + preload = data_buffer = self._auto_memmap_path() + logger.info(f"Using memory-map cache at {preload}") if isinstance(preload, bool | np.bool_) and not preload: data_buffer = None elif isinstance(preload, str | Path): @@ -646,9 +669,14 @@ def _preload_data(self, preload): ) if path.exists() and path.stat().st_size == n_bytes: self._data = np.memmap( - str(path), mode="r+", dtype=self._dtype, - shape=(self.info["nchan"], n_bytes // ( - self.info["nchan"] * np.dtype(self._dtype).itemsize)), + str(path), + mode="r+", + dtype=self._dtype, + shape=( + self.info["nchan"], + n_bytes + // (self.info["nchan"] * np.dtype(self._dtype).itemsize), + ), ) self.preload = True logger.info(f"Reusing memory-mapped cache at {path}") @@ -936,16 +964,14 @@ def _get_windows(self, starts, width, *, out=None, sel=None): out = np.empty((len(starts), n_out, width), dtype=self._dtype) elif out.shape != (len(starts), n_out, width): raise ValueError( - f"out has shape {out.shape}, need " - f"{(len(starts), n_out, width)}" + f"out has shape {out.shape}, need {(len(starts), n_out, width)}" ) elif out.dtype not in (np.float64, np.float32): - raise ValueError( - f"out dtype must be float64 or float32, got {out.dtype}" - ) + raise ValueError(f"out dtype must be float64 or float32, got {out.dtype}") for j, s0 in enumerate(starts): self._read_segment( - start=int(s0), stop=int(s0) + width, + start=int(s0), + stop=int(s0) + width, sel=sel if sel is not None else None, data_buffer=out[j], ) diff --git a/mne/io/edf/tests/test_edf.py b/mne/io/edf/tests/test_edf.py index 2a65dfdc67f..612f3913129 100644 --- a/mne/io/edf/tests/test_edf.py +++ b/mne/io/edf/tests/test_edf.py @@ -1312,3 +1312,22 @@ def test_memmap_cache_reuse(tmp_path): cache.write_bytes(b"x" * 16) r3 = read_raw_edf(fname, **kwargs).load_data(memmap=str(cache)) assert_allclose(r3.get_data(), r1.get_data(), rtol=0, atol=0) + + +def test_preload_memmap_sentinel(tmp_path, monkeypatch): + """preload="memmap" manages a persistent cache automatically.""" + monkeypatch.setenv("MNE_MEMMAP_DIR", str(tmp_path)) + rng = np.random.default_rng(5) + info = mne.create_info(["EEG A"], sfreq=128.0, ch_types="eeg") + raw = mne.io.RawArray(rng.standard_normal((1, 1024)) * 30e-6, info) + fname = tmp_path / "sentinel.edf" + raw.export(fname, verbose="error") + kw = dict(preload="memmap", verbose="error") + r1 = read_raw_edf(fname, **kw) + caches = list(tmp_path.rglob("*.f64")) + assert len(caches) == 1 + d1 = r1.get_data() + del r1 + r2 = read_raw_edf(fname, **kw) + assert isinstance(r2._data, np.memmap) + assert_allclose(r2.get_data(), d1, rtol=0, atol=0) diff --git a/mne/utils/docs.py b/mne/utils/docs.py index 868ee4fe0e4..40155ecf469 100644 --- a/mne/utils/docs.py +++ b/mne/utils/docs.py @@ -3686,7 +3686,12 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75): If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data - on the hard drive (slower, requires less memory).""" + on the hard drive (slower, requires less memory). The special string + ``"memmap"`` selects an automatically managed memory-mapped cache: + the first read decodes into the cache, and subsequent reads mmap it + directly instead of decoding again. + + .. versionadded:: 1.13""" docdict["preload_concatenate"] = """ preload : bool | str | None