mirror of https://github.com/maderix/ANE.git
fix: guard short token datasets in train_large_ane and dynamic pipeline
- Add n_tokens <= SEQ+1 check in train_large_ane.m and training_dynamic/train.m - Prevents underflow in max_pos and possible OOB reads (aligns with train_large.m) - Add M5 MacBook Pro benchmark result and full output for Issue #3 Made-with: Cursor
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log-wade
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# Contribution submission guide
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This file summarizes what was done on branch `contribution/benchmark-m5-and-fixes` and how to submit it.
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---
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## 1. Benchmark (submit to Issue #3)
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**Link:** https://github.com/maderix/ANE/issues/3
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**Post this as a new comment:**
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```
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## M5 MacBook Pro benchmark (static pipeline, 20 steps)
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- **Chip:** Apple M5, 10-core (4P+6E)
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- **RAM:** 24 GB
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- **macOS:** 26.3 (Build 25D125)
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- **Run:** `./train_large --data ./tinystories_data00.bin --steps 20 --lr 1e-4`
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### Efficiency report
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- Total steps: 20
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- Wall time: 10423 ms (10.4 s)
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- Compile time: 7187 ms (69.0%)
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- Train time: 2542 ms (24.4%)
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- **Avg train: 127.1 ms/step**
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- ANE TFLOPS: 0.73 sustained
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- ANE utilization: 4.6% of 15.8 TFLOPS
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Full output with JSON lines is in `benchmarks/my_m5_benchmark_output.txt` (or paste the contents below).
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```
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Then paste the contents of `benchmarks/my_m5_benchmark_output.txt` in the same comment, or attach it.
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---
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## 2. Bug fix (PR)
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**Fix:** Guard short token datasets in `train_large_ane.m` and `training/training_dynamic/train.m`.
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**Why:** When `n_tokens <= SEQ + 1`, the expression `max_pos = n_tokens - SEQ - 1` underflows (unsigned), leading to a huge random range and possible out-of-bounds reads. `train_large.m` already had this guard; the other two pipelines did not.
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**Changes:**
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- `training/train_large_ane.m`: After `n_tokens = data_len / 2`, add a check that fails early with a clear error, munmap and close the fd, and return 1.
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- `training/training_dynamic/train.m`: Same guard added.
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**Suggested PR title:** `fix: guard short token datasets in train_large_ane and dynamic pipeline`
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**Suggested PR description:**
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```markdown
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## Summary
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- Add a token dataset length guard in `training/train_large_ane.m`
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- Add the same guard in `training/training_dynamic/train.m`
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- Fail early with a clear error when the dataset is too short for one (input, target) window
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## Why
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Both paths use `max_pos = n_tokens - SEQ - 1`. When `n_tokens <= SEQ + 1`, this unsigned subtraction underflows, producing a huge range and potentially out-of-bounds reads. `train_large.m` already had this guard (lines 299–304); this PR aligns the other two pipelines.
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## Validation
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- `make -C training train_large_ane` — builds
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- `make -C training/training_dynamic train` — builds
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- With a too-short data file, both binaries exit with the new error message.
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```
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---
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## 3. Optional: benchmark data in repo
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Branch also adds:
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- `benchmarks/my_m5_benchmark_output.txt` — full benchmark log
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- One new entry in `benchmarks/community_results.json` for this M5 run (contributor: `log-wade`)
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You can either:
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- Include the `community_results.json` update in the same PR as the bug fix, or
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- Omit it and only post the benchmark to Issue #3 (maintainer may update the report from the issue).
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---
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## 4. Before opening the PR
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1. **Fork the repo** on GitHub (if you haven’t): https://github.com/maderix/ANE → Fork.
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2. **Add your fork as a remote and push:**
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```bash
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git remote add myfork git@github.com:YOUR_USERNAME/ANE.git
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git push myfork contribution/benchmark-m5-and-fixes
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```
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3. Open a PR from `myfork/contribution/benchmark-m5-and-fixes` to `maderix/ANE` main.
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4. Post the benchmark comment to Issue #3 (link above).
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---
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## 5. Replace contributor name
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In `benchmarks/community_results.json`, the new entry uses `"contributor": "log-wade"`. Change that to your GitHub username if different.
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@ -94,6 +94,19 @@
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"peak_tflops_inmem": 12.17,
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"notes": "inmem_peak only, no training data submitted.",
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"contributor": "elijah-pelton"
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},
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{
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"chip": "M5",
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"cores": "10-core (4P+6E)",
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"ram_gb": 24,
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"macos": "26.3",
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"ms_per_step": [125, 128],
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"ane_ms": [9.1, 9.2],
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"compile_ms": [3554, 3633],
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"ane_tflops": [0.72, 0.74],
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"ane_util_pct": [4.57, 4.70],
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"notes": "MacBook Pro, static pipeline train_large, 20 steps, random init.",
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"contributor": "log-wade"
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}
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],
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"neural_engine_specs": {
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@ -0,0 +1,56 @@
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=== ANE Training: Stories110M (12 layers) ===
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dim=768 hidden=2048 heads=12 seq=256 vocab=32000 layers=12
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Cannot open stories110M.bin
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Pretrained load failed, using random init
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Params: 109.53M (transformer 84.95M + embed 24.58M)
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Kernels: 72 (60 weight-bearing + 12 static sdpaBwd2)
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Accum 10 steps per recompile | Adam LR=1.0e-04 b1=0.9 b2=0.999
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FLOPs/step: fwd=43487M bwd_dx=43487M bwd_dW=43487M sdpa_bwd=6040M total=174248M
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ANE FLOPs/step: 93013M (fwd+bwd_dx+sdpa_bwd) | CPU: dW+cls (cblas)
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Token data: 20658981 tokens (41.3 MB)
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Compiling layer 1/12... (12 compiles)
Compiling layer 2/12... (17 compiles)
Compiling layer 3/12... (22 compiles)
Compiling layer 4/12... (27 compiles)
Compiling layer 5/12... (32 compiles)
Compiling layer 6/12... (37 compiles)
Compiling layer 7/12... (42 compiles)
Compiling layer 8/12... (47 compiles)
Compiling layer 9/12... (52 compiles)
Compiling layer 10/12... (57 compiles)
Compiling layer 11/12... (62 compiles)
Compiling layer 12/12... (67 compiles)
Compiled 60 kernels in 3554ms
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step 0 loss=10.3907
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{"type":"step","step":0,"loss":10.390698,"t_ane":12.288,"t_io":14.233,"t_cls":30.426,"t_elem":21.143,"t_rms":0.094,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":1,"loss":10.434500,"t_ane":10.653,"t_io":13.757,"t_cls":20.472,"t_elem":18.814,"t_rms":0.070,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":2,"loss":10.484736,"t_ane":10.050,"t_io":10.094,"t_cls":16.495,"t_elem":17.783,"t_rms":0.061,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":3,"loss":10.417551,"t_ane":9.755,"t_io":8.214,"t_cls":14.512,"t_elem":16.853,"t_rms":0.068,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":4,"loss":10.392599,"t_ane":9.537,"t_io":7.032,"t_cls":13.297,"t_elem":16.319,"t_rms":0.063,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":5,"loss":10.392069,"t_ane":9.404,"t_io":6.251,"t_cls":12.475,"t_elem":15.887,"t_rms":0.060,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":6,"loss":10.382063,"t_ane":9.313,"t_io":5.697,"t_cls":11.874,"t_elem":15.678,"t_rms":0.058,"t_cblas_wait":0.001,"compiles":72}
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{"type":"step","step":7,"loss":10.377501,"t_ane":9.238,"t_io":5.293,"t_cls":11.437,"t_elem":15.556,"t_rms":0.056,"t_cblas_wait":0.001,"compiles":72}
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{"type":"step","step":8,"loss":10.409813,"t_ane":9.174,"t_io":4.967,"t_cls":11.101,"t_elem":15.372,"t_rms":0.055,"t_cblas_wait":0.001,"compiles":72}
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{"type":"step","step":9,"loss":10.395181,"t_ane":9.138,"t_io":4.720,"t_cls":10.819,"t_elem":15.289,"t_rms":0.054,"t_cblas_wait":0.001,"compiles":72}
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[batch 10: compile=3554ms train=1253.8ms (125.4ms/step) compiles=72]
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ane=9.1 io=4.7 cls=10.8 elem=15.3 rms=0.1 cblas_wait=0.0 ms/step
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{"type":"batch","batch":10,"compile_ms":3554.3,"train_ms":1253.8,"ms_per_step":125.4}
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{"type":"perf","ane_tflops":0.742,"ane_util_pct":4.70}
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[exec() restart step 10, 72 compiles, loss=10.3952]
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[RESUMED step 10, loss=10.3952]
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Token data: 20658981 tokens (41.3 MB)
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Compiling layer 1/12... (12 compiles)
Compiling layer 2/12... (17 compiles)
Compiling layer 3/12... (22 compiles)
Compiling layer 4/12... (27 compiles)
Compiling layer 5/12... (32 compiles)
Compiling layer 6/12... (37 compiles)
Compiling layer 7/12... (42 compiles)
Compiling layer 8/12... (47 compiles)
Compiling layer 9/12... (52 compiles)
Compiling layer 10/12... (57 compiles)
Compiling layer 11/12... (62 compiles)
Compiling layer 12/12... (67 compiles)
Compiled 60 kernels in 3633ms
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step 10 loss=10.2671
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{"type":"step","step":10,"loss":10.267123,"t_ane":13.398,"t_io":14.979,"t_cls":29.723,"t_elem":22.190,"t_rms":0.109,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":11,"loss":10.389436,"t_ane":11.150,"t_io":13.816,"t_cls":19.297,"t_elem":17.862,"t_rms":0.078,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":12,"loss":10.246490,"t_ane":10.356,"t_io":10.036,"t_cls":15.691,"t_elem":16.749,"t_rms":0.067,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":13,"loss":10.322395,"t_ane":9.971,"t_io":8.113,"t_cls":13.880,"t_elem":16.200,"t_rms":0.061,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":14,"loss":10.280519,"t_ane":9.708,"t_io":7.002,"t_cls":12.817,"t_elem":15.972,"t_rms":0.061,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":15,"loss":10.202168,"t_ane":9.575,"t_io":6.212,"t_cls":12.096,"t_elem":15.716,"t_rms":0.059,"t_cblas_wait":0.003,"compiles":72}
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{"type":"step","step":16,"loss":10.306752,"t_ane":9.450,"t_io":5.685,"t_cls":11.577,"t_elem":15.530,"t_rms":0.057,"t_cblas_wait":0.003,"compiles":72}
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{"type":"step","step":17,"loss":10.293774,"t_ane":9.361,"t_io":5.280,"t_cls":11.209,"t_elem":15.392,"t_rms":0.055,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":18,"loss":10.263789,"t_ane":9.278,"t_io":4.976,"t_cls":10.908,"t_elem":15.263,"t_rms":0.054,"t_cblas_wait":0.002,"compiles":72}
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{"type":"step","step":19,"loss":10.307909,"t_ane":9.237,"t_io":4.751,"t_cls":10.657,"t_elem":15.160,"t_rms":0.053,"t_cblas_wait":0.002,"compiles":72}
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[batch 10: compile=3633ms train=1287.8ms (128.8ms/step) compiles=72]
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ane=9.2 io=4.8 cls=10.7 elem=15.2 rms=0.1 cblas_wait=0.0 ms/step
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{"type":"batch","batch":10,"compile_ms":3632.9,"train_ms":1287.8,"ms_per_step":128.8}
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{"type":"perf","ane_tflops":0.722,"ane_util_pct":4.57}
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=== Efficiency Report ===
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Total steps: 20
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Wall time: 10423 ms (10.4 s)
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Compile time: 7187 ms (69.0%)
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Train time: 2542 ms (24.4%)
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Avg train: 127.1 ms/step
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ANE TFLOPS: 0.73 sustained
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Total TFLOPS: 1.37 (ANE+CPU)
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ANE utilization: 4.6% of 15.8 TFLOPS
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@ -285,6 +285,12 @@ int main(int argc, char *argv[]) {
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uint16_t *token_data = (uint16_t*)mmap(NULL, data_len, PROT_READ, MAP_PRIVATE, data_fd, 0);
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if (token_data == MAP_FAILED) { printf("mmap failed\n"); return 1; }
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size_t n_tokens = data_len / 2;
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if (n_tokens <= (size_t)(SEQ + 1)) {
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printf("Token data too short: need at least %d tokens, got %zu\n", SEQ + 2, n_tokens);
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munmap(token_data, data_len);
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close(data_fd);
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return 1;
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}
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printf("Token data: %zu tokens (%.1f MB)\n", n_tokens, data_len/1e6);
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// Gradient buffers
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@ -294,6 +294,12 @@ int main(int argc, char *argv[]) {
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uint16_t *token_data = (uint16_t*)mmap(NULL, data_len, PROT_READ, MAP_PRIVATE, data_fd, 0);
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if (token_data == MAP_FAILED) { printf("mmap failed\n"); return 1; }
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size_t n_tokens = data_len / 2;
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if (n_tokens <= (size_t)(SEQ + 1)) {
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printf("Token data too short: need at least %d tokens, got %zu\n", SEQ + 2, n_tokens);
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munmap(token_data, data_len);
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close(data_fd);
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return 1;
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}
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printf("Token data: %zu tokens (%.1f MB)\n", n_tokens, data_len/1e6);
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// Vocab compaction
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