doc: W1 audio smoke summary

Walkthrough of the three W1 commits, the 4090 result (50 steps in ~1s,
loss 5.55 → 0.17), and the limitations to keep in mind before reading
into the loss-down (LM is also random + tiny vocab, so the drop is
mostly memorisation, not Whisper-Projector alignment — W2 freezes the
LM specifically to test that). Includes the W2 hand-off checklist.

.claude/skills/read-arxiv-paper/SKILL.md

@@ -0,0 +1,40 @@
+---
+name: read-arxiv-paper
+description: Use this skill when asked to read an arxiv paper given an arxiv URL
+---
+
+You will be given a URL of an arxiv paper, for example:
+
+https://www.arxiv.org/abs/2601.07372
+
+### Part 1: Normalize the URL
+
+The goal is to fetch the TeX Source of the paper (not the PDF!), the URL always looks like this:
+
+https://www.arxiv.org/src/2601.07372
+
+Notice the /src/ in the url. Once you have the URL:
+
+### Part 2: Download the paper source
+
+Fetch the url to a local .tar.gz file. A good location is `~/.cache/nanochat/knowledge/{arxiv_id}.tar.gz`.
+
+(If the file already exists, there is no need to re-download it).
+
+### Part 3: Unpack the file in that folder
+
+Unpack the contents into `~/.cache/nanochat/knowledge/{arxiv_id}` directory.
+
+### Part 4: Locate the entrypoint
+
+Every latex source usually has an entrypoint, such as `main.tex` or something like that.
+
+### Part 5: Read the paper
+
+Once you've found the entrypoint, Read the contents and then recurse through all other relevant source files to read the paper.
+
+### Part 6: Report
+
+Once you've read the paper, produce a summary of the paper into a markdown file at `./knowledge/summary_{tag}.md`. Notice that 1) use the local knowledge directory here (it's easier for me to open and reference here), not in `~/.cache`, and 2) generate some reasonable `tag` like e.g. `conditional_memory` or whatever seems appropriate given the paper. Probably make sure that the tag doesn't exist yet so you're not overwriting files.
+
+As for the summary itself, remember that you're processing this paper within the context of the nanochat repository, so most often we will be interested in how to apply the paper and its lessons to the nanochat project. Therefore, you should feel free to "remind yourself" of the related nanochat code by reading the relevant parts, and then explicitly make the connection of how this paper might relate to nanochat or what are things we might be inspired about or try.

.gitignore

@@ -1 +1,19 @@
+.venv/
+__pycache__/
+*.pyc
+dev-ignore/
+report.md
+eval_bundle/
+
+# Secrets
+.env
+
+# Local setup
+CLAUDE.md
+wandb/
+
+# Claude Code runtime
 .claude/
+
+# W1 audio smoke: regenerated by scripts/audio_smoke_data.py, only manifest is committed
+data/audio_smoke/wavs/

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "upstream/nanochat"]
-	path = upstream/nanochat
-	url = https://github.com/karpathy/nanochat.git

.python-version

@@ -0,0 +1 @@
+3.10

LICENSE

@@ -1,18 +1,21 @@
 MIT License
 
-Copyright (c) 2026 fam
+Copyright (c) 2025 Andrej Karpathy
 
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and 
+Permission is hereby granted, free of charge, to any person obtaining a copy
-associated documentation files (the "Software"), to deal in the Software without restriction, including 
+of this software and associated documentation files (the "Software"), to deal
-without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 
+in the Software without restriction, including without limitation the rights
-copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the 
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-following conditions:
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all copies or substantial 
+The above copyright notice and this permission notice shall be included in all
-portions of the Software.
+copies or substantial portions of the Software.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO 
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE 
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-USE OR OTHER DEALINGS IN THE SOFTWARE.
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,236 @@
 # nanochat-omni
 
-NanoChat 多模态增强实验 (Omni) — 质感感知语音输入
+> **质感感知语音输入** — multimodal extension on top of [karpathy/nanochat](https://github.com/karpathy/nanochat).
+>
+> Audio-first (Whisper encoder + Projector → soft tokens, LLaVA-style alignment).
+> Vision later. Output stays text. Single GPU (RTX 5090 / 4090). CN mirrors baked
+> into `pyproject.toml` / `nanochat/dataset.py` (sjtu pytorch-wheels, hf-mirror).
+>
+> Roadmap: [`doc/todo.md`](doc/todo.md) · Research: [`doc/research_feasibility.md`](doc/research_feasibility.md) · CI smoke: [`scripts/smoke.sh`](scripts/smoke.sh)
+>
+> Sync upstream: `git fetch upstream && git merge upstream/master`
+
+---
+
+# nanochat (upstream)
+
+![nanochat logo](dev/nanochat.png)
+![scaling laws](dev/scaling_laws_jan26.png)
+
+nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, inference, and a chat UI. For example, you can train your own GPT-2 capability LLM (which cost ~$43,000 to train in 2019) for only $48 (~2 hours of 8XH100 GPU node) and then talk to it in a familiar ChatGPT-like web UI. On a spot instance, the total cost can be closer to ~$15. More generally, nanochat is configured out of the box to train an entire miniseries of compute-optimal models by setting one single complexity dial: `--depth`, the number of layers in the GPT transformer model (GPT-2 capability happens to be approximately depth 26). All other hyperparameters (the width of the transformer, number of heads, learning rate adjustments, training horizons, weight decays, ...) are calculated automatically in an optimal way.
+
+For questions about the repo, I recommend either using [DeepWiki](https://deepwiki.com/karpathy/nanochat) from Devin/Cognition to ask questions about the repo, or use the [Discussions tab](https://github.com/karpathy/nanochat/discussions), or come by the [#nanochat](https://discord.com/channels/1020383067459821711/1427295580895314031) channel on Discord.
+
+## Time-to-GPT-2 Leaderboard
+
+Presently, the main focus of development is on tuning the pretraining stage, which takes the most amount of compute. Inspired by the modded-nanogpt repo and to incentivise progress and community collaboration, nanochat maintains a leaderboard for a "GPT-2 speedrun", which is the wall-clock time required to train a nanochat model to GPT-2 grade capability, as measured by the DCLM CORE score. The [runs/speedrun.sh](runs/speedrun.sh) script always reflects the reference way to train GPT-2 grade model and talk to it. The current leaderboard looks as follows:
+
+| # | time | val_bpb | CORE | Description | Date | Commit | Contributors |
+|---|-------------|---------|------|-------------|------|--------|--------------|
+| 0 | 168 hours | - | 0.2565 | Original OpenAI GPT-2 checkpoint | 2019 | - | OpenAI |
+| 1 | 3.04 | 0.74833 | 0.2585 | d24 baseline, slightly overtrained | Jan 29 2026 | 348fbb3 | @karpathy |
+| 2 | 2.91 | 0.74504 | 0.2578 | d26 slightly undertrained **+fp8** | Feb 2 2026 | a67eba3 | @karpathy |
+| 3 | 2.76 | 0.74645 | 0.2602 | bump total batch size to 1M tokens | Feb 5 2026 | 2c062aa | @karpathy |
+| 4 | 2.02 | 0.71854 | 0.2571 | change dataset to NVIDIA ClimbMix | Mar 4 2026 | 324e69c | @ddudek @karpathy |
+| 5 | 1.80 | 0.71808 | 0.2690 | autoresearch [round 1](https://x.com/karpathy/status/2031135152349524125) | Mar 9 2026 | 6ed7d1d | @karpathy |
+| 6 | 1.65 | 0.71800 | 0.2626 | autoresearch round 2 | Mar 14 2026 | a825e63 | @karpathy |
+
+The primary metric we care about is "time to GPT-2" - the wall clock time needed to outperform the GPT-2 (1.6B) CORE metric on an 8XH100 GPU node. The GPT-2 CORE score is 0.256525. In 2019, the training of GPT-2 cost approximately $43,000 so it is incredible that due to many advances over 7 years across the stack, we can now do so much faster and for well below $100 (e.g. at the current ~$3/GPU/hr, an 8XH100 node is ~$24/hr, so 2 hours is ~$48).
+
+See [dev/LEADERBOARD.md](dev/LEADERBOARD.md) for more docs on how to interpret and contribute to the leaderboard.
+
+## Getting started
+
+### Setup
+
+nanochat uses [uv](https://docs.astral.sh/uv/) for dependency management. To install:
+
+```bash
+uv sync --extra gpu    # Use for CUDA (A100/H100/etc.)
+uv sync --extra cpu    # (or) Use for CPU-only / MPS
+source .venv/bin/activate
+```
+
+For development (adds pytest, matplotlib, ipykernel, transformers, etc.):
+
+```bash
+uv sync --extra gpu --group dev
+```
+
+### Reproduce and talk to GPT-2
+
+The most fun you can have is to train your own GPT-2 and talk to it. The entire pipeline to do so is contained in the single file [runs/speedrun.sh](runs/speedrun.sh), which is designed to be run on an 8XH100 GPU node. Boot up a new 8XH100 GPU box from your favorite provider (e.g. I use and like [Lambda](https://lambda.ai/service/gpu-cloud)), and kick off the training script:
+
+```bash
+bash runs/speedrun.sh
+```
+
+You may wish to do so in a screen session as this will take ~3 hours to run. Once it's done, you can talk to it via the ChatGPT-like web UI. Make sure again that your local uv virtual environment is active (run `source .venv/bin/activate`), and serve it:
+
+```bash
+python -m scripts.chat_web
+```
+
+And then visit the URL shown. Make sure to access it correctly, e.g. on Lambda use the public IP of the node you're on, followed by the port, so for example [http://209.20.xxx.xxx:8000/](http://209.20.xxx.xxx:8000/), etc. Then talk to your LLM as you'd normally talk to ChatGPT! Get it to write stories or poems. Ask it to tell you who you are to see a hallucination. Ask it why the sky is blue. Or why it's green. The speedrun is a 4e19 FLOPs capability model so it's a bit like talking to a kindergartener :).
+
+---
+
+<img width="2672" height="1520" alt="image" src="https://github.com/user-attachments/assets/ed39ddf8-2370-437a-bedc-0f39781e76b5" />
+
+---
+
+A few more notes:
+
+- The code will run just fine on the Ampere 8XA100 GPU node as well, but a bit slower.
+- All code will run just fine on even a single GPU by omitting `torchrun`, and will produce ~identical results (code will automatically switch to gradient accumulation), but you'll have to wait 8 times longer.
+- If your GPU(s) have less than 80GB, you'll have to tune some of the hyperparameters or you will OOM / run out of VRAM. Look for `--device-batch-size` in the scripts and reduce it until things fit. E.g. from 32 (default) to 16, 8, 4, 2, or even 1. Less than that you'll have to know a bit more what you're doing and get more creative.
+- Most of the code is fairly vanilla PyTorch so it should run on anything that supports that - xpu, mps, or etc, but I haven't personally exercised all of these code paths so there might be sharp edges.
+
+## Research
+
+If you are a researcher and wish to help improve nanochat, two scripts of interest are [runs/scaling_laws.sh](runs/scaling_laws.sh) and [runs/miniseries.sh](runs/miniseries.sh). See [Jan 7 miniseries v1](https://github.com/karpathy/nanochat/discussions/420) for related documentation. For quick experimentation (~5 min pretraining runs) my favorite scale is to train a 12-layer model (GPT-1 sized), e.g. like this:
+
+```
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
+    --depth=12 \
+    --run="d12" \
+    --model-tag="d12" \
+    --core-metric-every=999999 \
+    --sample-every=-1 \
+    --save-every=-1 \
+```
+
+This uses wandb (run name "d12"), only runs the CORE metric on last step, and it doesn't sample and save intermediate checkpoints. I like to change something in the code, re-run a d12 (or a d16 etc) and see if it helped, in an iteration loop. To see if a run helps, I like to monitor the wandb plots for:
+
+1. `val_bpb` (validation loss in vocab-size-invariant units of bits per byte) as a function of `step`, `total_training_time` and `total_training_flops`.
+2. `core_metric` (the DCLM CORE score)
+3. VRAM utilization, `train/mfu` (Model FLOPS utilization), `train/tok_per_sec` (training throughput)
+
+See an example [here](https://github.com/karpathy/nanochat/pull/498#issuecomment-3850720044).
+
+The important thing to note is that nanochat is written and configured around one single dial of complexity - the depth of the transformer. This single integer automatically determines all other hyperparameters (the width of the transformer, number of heads, learning rate adjustments, training horizons, weight decays, ...) so that the trained model comes out compute optimal. The idea is that the user doesn't have to think about or set any of this, they are simply asking for a smaller or bigger model using `--depth`, and everything "just works". By sweeping out the depth, you achieve the nanochat miniseries of compute optimal models at various sizes. GPT-2 capability model (which is of most interest at the moment) happens to be somewhere around d24-d26 range with the current code. But any candidate changes to the repo have to be principled enough that they work for all settings of depth.
+
+## Running on CPU / MPS
+
+The script [runs/runcpu.sh](runs/runcpu.sh) shows a very simple example of running on CPU or Apple Silicon. It dramatically shrinks the LLM that is being trained to make things fit into a reasonable time interval of a few ten minutes of training. You will not get strong results in this way.
+
+## Precision / dtype
+
+nanochat does not use `torch.amp.autocast`. Instead, precision is managed explicitly through a single global `COMPUTE_DTYPE` (defined in `nanochat/common.py`). By default this is auto-detected based on your hardware:
+
+| Hardware | Default dtype | Why |
+|----------|--------------|-----|
+| CUDA SM 80+ (A100, H100, ...) | `bfloat16` | Native bf16 tensor cores |
+| CUDA SM < 80 (V100, T4, ...) | `float32` | No bf16; fp16 available via `NANOCHAT_DTYPE=float16` (uses GradScaler) |
+| CPU / MPS | `float32` | No reduced-precision tensor cores |
+
+You can override the default with the `NANOCHAT_DTYPE` environment variable:
+
+```bash
+NANOCHAT_DTYPE=float32 python -m scripts.chat_cli -p "hello"   # force fp32
+NANOCHAT_DTYPE=bfloat16 torchrun --nproc_per_node=8 -m scripts.base_train  # force bf16
+```
+
+How it works: model weights are stored in fp32 (for optimizer precision), but our custom `Linear` layer casts them to `COMPUTE_DTYPE` during the forward pass. Embeddings are stored directly in `COMPUTE_DTYPE` to save memory. This gives us the same mixed-precision benefit as autocast but with full explicit control over what runs in which precision.
+
+Note: `float16` training automatically enables a `GradScaler` in `base_train.py` to prevent gradient underflow. SFT supports this too but RL currently does not. Inference in fp16 works fine everywhere.
+
+## Guides
+
+I've published a number of guides that might contain helpful information, most recent to least recent:
+
+- [Feb 1 2026: Beating GPT-2 for <<$100: the nanochat journey](https://github.com/karpathy/nanochat/discussions/481)
+- [Jan 7 miniseries v1](https://github.com/karpathy/nanochat/discussions/420) documents the first nanochat miniseries of models.
+- To add new abilities to nanochat, see [Guide: counting r in strawberry (and how to add abilities generally)](https://github.com/karpathy/nanochat/discussions/164).
+- To customize your nanochat, see [Guide: infusing identity to your nanochat](https://github.com/karpathy/nanochat/discussions/139) in Discussions, which describes how you can tune your nanochat's personality through synthetic data generation and mixing that data into the SFT stage.
+- [Oct 13 2025: original nanochat post](https://github.com/karpathy/nanochat/discussions/1) introducing nanochat, though now it contains some deprecated information and the model is a lot older (with worse results) than current master.
+
+## File structure
+
+```
+.
+├── LICENSE
+├── README.md
+├── dev
+│   ├── gen_synthetic_data.py       # Example synthetic data for identity
+│   ├── generate_logo.html
+│   ├── nanochat.png
+│   └── repackage_data_reference.py # Pretraining data shard generation
+├── nanochat
+│   ├── __init__.py                 # empty
+│   ├── checkpoint_manager.py       # Save/Load model checkpoints
+│   ├── common.py                   # Misc small utilities, quality of life
+│   ├── core_eval.py                # Evaluates base model CORE score (DCLM paper)
+│   ├── dataloader.py               # Tokenizing Distributed Data Loader
+│   ├── dataset.py                  # Download/read utils for pretraining data
+│   ├── engine.py                   # Efficient model inference with KV Cache
+│   ├── execution.py                # Allows the LLM to execute Python code as tool
+│   ├── gpt.py                      # The GPT nn.Module Transformer
+│   ├── logo.svg
+│   ├── loss_eval.py                # Evaluate bits per byte (instead of loss)
+│   ├── optim.py                    # AdamW + Muon optimizer, 1GPU and distributed
+│   ├── report.py                   # Utilities for writing the nanochat Report
+│   ├── tokenizer.py                # BPE Tokenizer wrapper in style of GPT-4
+│   └── ui.html                     # HTML/CSS/JS for nanochat frontend
+├── pyproject.toml
+├── runs
+│   ├── miniseries.sh               # Miniseries training script
+│   ├── runcpu.sh                   # Small example of how to run on CPU/MPS
+│   ├── scaling_laws.sh             # Scaling laws experiments
+│   └── speedrun.sh                 # Train the ~$100 nanochat d20
+├── scripts
+│   ├── base_eval.py                # Base model: CORE score, bits per byte, samples
+│   ├── base_train.py               # Base model: train
+│   ├── chat_cli.py                 # Chat model: talk to over CLI
+│   ├── chat_eval.py                # Chat model: eval tasks
+│   ├── chat_rl.py                  # Chat model: reinforcement learning
+│   ├── chat_sft.py                 # Chat model: train SFT
+│   ├── chat_web.py                 # Chat model: talk to over WebUI
+│   ├── tok_eval.py                 # Tokenizer: evaluate compression rate
+│   └── tok_train.py                # Tokenizer: train it
+├── tasks
+│   ├── arc.py                      # Multiple choice science questions
+│   ├── common.py                   # TaskMixture | TaskSequence
+│   ├── customjson.py               # Make Task from arbitrary jsonl convos
+│   ├── gsm8k.py                    # 8K Grade School Math questions
+│   ├── humaneval.py                # Misnomer; Simple Python coding task
+│   ├── mmlu.py                     # Multiple choice questions, broad topics
+│   ├── smoltalk.py                 # Conglomerate dataset of SmolTalk from HF
+│   └── spellingbee.py              # Task teaching model to spell/count letters
+├── tests
+│   └── test_engine.py
+└── uv.lock
+```
+
+## Contributing
+
+The goal of nanochat is to improve the state of the art in micro models that are accessible to work with end to end on budgets of < $1000 dollars. Accessibility is about overall cost but also about cognitive complexity - nanochat is not an exhaustively configurable LLM "framework"; there are no giant configuration objects, model factories, or if-then-else monsters in the code base. It is a single, cohesive, minimal, readable, hackable, maximally-forkable "strong baseline" codebase designed to run start to end and produce a ChatGPT model you can talk to. Currently, the most interesting part personally is speeding up the latency to GPT-2 (i.e. getting a CORE score above 0.256525). Currently this takes ~3 hours, but by improving the pretraining stage we can improve this further.
+
+Current AI policy: disclosure. When submitting a PR, please declare any parts that had substantial LLM contribution and that you have not written or that you do not fully understand.
+
+## Acknowledgements
+
+- The name (nanochat) derives from my earlier project [nanoGPT](https://github.com/karpathy/nanoGPT), which only covered pretraining.
+- nanochat is also inspired by [modded-nanoGPT](https://github.com/KellerJordan/modded-nanogpt), which gamified the nanoGPT repo with clear metrics and a leaderboard, and borrows a lot of its ideas and some implementation for pretraining.
+- Thank you to [HuggingFace](https://huggingface.co/) for fineweb and smoltalk.
+- Thank you [Lambda](https://lambda.ai/service/gpu-cloud) for the compute used in developing this project.
+- Thank you to chief LLM whisperer 🧙‍♂️ Alec Radford for advice/guidance.
+- Thank you to the repo czar Sofie [@svlandeg](https://github.com/svlandeg) for help with managing issues, pull requests and discussions of nanochat.
+
+## Cite
+
+If you find nanochat helpful in your research cite simply as:
+
+```bibtex
+@misc{nanochat,
+  author = {Andrej Karpathy},
+  title = {nanochat: The best ChatGPT that \$100 can buy},
+  year = {2025},
+  publisher = {GitHub},
+  url = {https://github.com/karpathy/nanochat}
+}
+```
+
+## License
+
+MIT

data/audio_smoke/manifest.jsonl

@@ -0,0 +1,5 @@
+{"wav": "wavs/sine_0220.wav", "text": "low tone", "sr": 16000}
+{"wav": "wavs/sine_0330.wav", "text": "mid low tone", "sr": 16000}
+{"wav": "wavs/sine_0440.wav", "text": "middle tone", "sr": 16000}
+{"wav": "wavs/sine_0660.wav", "text": "mid high tone", "sr": 16000}
+{"wav": "wavs/sine_0880.wav", "text": "high tone", "sr": 16000}

dev/LEADERBOARD.md

@@ -0,0 +1,202 @@
+# Leaderboard
+
+Docs on participating in the "Time-to-GPT-2" leaderboard of nanochat.
+
+The primary metric we care about is "time to GPT-2" - the wall clock time needed to outperform the GPT-2 (1.6B) CORE metric on an 8XH100 GPU node. Originally in 2019, GPT-2 was trained by OpenAI on 32 TPU v3 chips for 168 hours (7 days), with $8/hour/TPUv3 back then, for a total cost of approx. $43K. It achieves 0.256525 CORE score, which is an ensemble metric introduced in the DCLM paper over 22 evaluations like ARC/MMLU/etc.
+
+## How to
+
+The script [runs/speedrun.sh](runs/speedrun.sh) always implements the current state of the art on the leaderboard.
+
+In practice, I tune the base_train command a little bit. For example, once all the setup is configured and a tokenizer is trained, I like to do something like:
+
+```
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
+    --depth=26 \
+    --run="d26-feb2-fp8-ratio8.25" \
+    --model-tag="d26_feb2_fp8_ratio8.25" \
+    --device-batch-size=16 \
+    --sample-every=-1 \
+    --save-every=-1 \
+    --core-metric-max-per-task=-1 \
+    --core-metric-every=999999 \
+    --target-param-data-ratio=8.25 \
+    --fp8
+```
+
+Note that:
+
+- `depth` controls the size of the Transformer
+- `run` is the wandb name
+- `model-tag` is the location of the checkpoints on disk
+- `device-batch-size` in the ideal world, you want this to be 32 because with sequence length of 2048 (the default) and 8 GPUs we get `32 X 2048 X 8 = 524,288`, which is the total desired batch size determined to work fairly well around this scale. However, for bigger (e.g. d26), 32 is too much and OOMs, so we decrease it by 2 to 16. The `base_train.py` script automatically compensates for this by calculating that it has to use gradient accumulation of 2 to meet the desired total batch size. Therefore, it will do forward+backward twice and then a single step. Long story short, the ideal value is 32. If that doesn't fit, you decrease it, e.g. 16, 8, etc., keeping it powers of two so that the gradient accumulation math works out neatly.
+- `sample-every = -1` turns off periodic sampling
+- `core-metric-max-per-task=-1` means we run the entire CORE eval
+- `core-metric-every=999999` a bit of a hacky way to make the CORE eval only happen a single time at the very end of the run
+- `target-param-data-ratio=8.25` controls the training horizon, which is determined in the script by taking the number of non-embedding model parameters and simply multiplying by this number. The current optimal Tokens:Params ratio can be seen in the defaults of the `base_train.py` script (it is 10.5). 10.5 would produce the *compute optimal* model given the currently measured scaling laws. However, GPT-2 capability is currently somewhere in between a d24 and d26. So to reach it exactly, we want to either overtrain d24 or undertrain d26. In this particular example, I am choosing to slightly undertrain a d26. Note that odd depths (e.g. d25) are not super recommended to use because the math around the transformer sizing and its head dimensions doesn't come out neatly.
+- `--fp8` turns on fp8 training. If your GPU does not support fp8, you can leave this out and the code will simply train in bf16. bf16 is higher precision than fp8, so you can actually expect that you might be able to do fewer steps (lower the `target-param-data-ratio`) to achieve the same capability.
+
+Once you kick off the run, you wait ~1.5 hours and then at the end you'll see something like:
+
+```
+wandb: Run summary:
+wandb:          core_metric 0.25851
+wandb:                 step 16704
+wandb: total_training_flops 4.330784131228946e+19
+wandb:  total_training_time 10949.46713
+```
+
+Your CORE metric must be greater than GPT-2 0.256525. Then you report the `total_training_time`, (e.g. 10949) which is the time of the training iterations alone, excluding all the evaluations and logging, in seconds. So here for example it is roughly 10949/60/60 ~= 3.04 hours. You should also note and report the validation bpb of your run because the CORE metric can be a little bit noisy.
+
+If you outperform GPT-2 and the time is less than current SOTA in the Leaderboard, you get to make a PR. In addition to raw gains, there are some qualitative and aesthetic considerations that go into whether your improvement is merged. For example, if it is gnarly or it significantly bloats the code, or it seems too esoteric, then we will weigh those things against the improvement demonstrated. Additionally, nanochat cares not only about targeting a single model, but an entire miniseries of models. So your change must be principled enough that it can easily generalize to other model depths, so that we can sweep out a miniseries.
+
+After you create the commit, to get the current short git commit hash:
+
+```
+git log -1 --format="%h"
+```
+
+## Run 1
+
+Achieved Jan 29 2026 on commit `348fbb3`. The launch command was
+
+```
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
+    --depth=24 \
+    --run=d24-jan29 \
+    --model-tag=d24_jan29 \
+    --device-batch-size=16 \
+    --sample-every=-1 \
+    --save-every=-1 \
+    --core-metric-max-per-task=-1 \
+    --core-metric-every=3000 \
+    --target-param-data-ratio=12
+```
+
+The result was:
+
+```
+wandb: Run summary:
+wandb:          core_metric 0.25851
+wandb:                 step 16704
+wandb: total_training_flops 4.330784131228946e+19
+wandb:  total_training_time 10949.46713
+```
+
+The validation bpb was 0.74833.
+
+Detailed writeup: [Beating GPT-2 for <<$100: the nanochat journey](https://github.com/karpathy/nanochat/discussions/481)
+
+## Run 2
+
+Achieved Feb 2 2026 on commit `a67eba3`. The launch command was
+
+```
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
+    --depth=26 \
+    --run="d26-feb2-fp8-ratio8.5" \
+    --model-tag="d26_feb2_fp8_ratio8.5" \
+    --device-batch-size=16 \
+    --sample-every=-1 \
+    --save-every=-1 \
+    --core-metric-max-per-task=-1 \
+    --core-metric-every=999999 \
+    --target-param-data-ratio=8.5 \
+    --fp8
+```
+
+The result was:
+
+```
+core_metric 0.2578
+step 14889
+total_training_time 10493
+Minimum validation bpb: 0.745036
+```
+
+The big change in this run is `--fp8`, which causes all Linear layers (other than the gates) to be switched to fp8 training using `torchao` with tensorwise fp8 scaling. Each step is of slightly lower quality, but we are taking them a lot faster, coming out net ahead. Anyone who does not have fp8 (e.g. using a GPU without it) can simply leave out the `--fp8` flag to train in bfloat16. This will work just fine but it will produce a slightly stronger model than GPT-2 because of the fp8 -> bf16 precision upgrade. It's possible that one can further tune which layers to include in the fp8 conversion and that e.g. some of the smaller matmuls should be just kept in bf16 etc.
+
+Previous record was 3.04 hours, so 2.91 hours is `(3.04 - 2.91)/3.04*100` ~= 4.3% speed improvement.
+
+## Run 3
+
+Achieved Feb 5 2026 on commit `2c062aa`. Launch command:
+
+```
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
+    --depth=26 \
+    --run="d26_feb4_double_batch_ratio8.25" \
+    --model-tag="d26_feb4_double_batch_ratio8.25" \
+    --device-batch-size=16 \
+    --total-batch-size=1048576 \
+    --sample-every=-1 \
+    --save-every=-1 \
+    --core-metric-max-per-task=-1 \
+    --core-metric-every=999999 \
+    --target-param-data-ratio=8.25 \
+    --fp8
+```
+
+Result:
+
+```
+core_metric 0.26024
+step 7226
+total_training_time 9922
+Minimum validation bpb: 0.74645
+```
+
+The big change here is that the batch size was doubled from 0.5M to 1M, which works better for a d26 model and allowed me to decrease the number of optimization steps a bit via `--target-param-data-ratio` from 8.5 to 8.25. The TLDR is that the original batch size of 0.5M was tuned for d12, but bigger models (e.g. d26) prefer larger total batch size. I determined in experiments that d26 prefers 1M. Then I implemented and merged a principled way to calculate the optimal batch size given depth so that all nanochat models of all depths benefit. See [dev/LOG.md](dev/LOG.md) entry "2026-02-05: Auto Batch Size Scaling" for more detail.
+
+## Run 4
+
+Achived Mar 3 2026 on commit `324e69c`. The big change is the switch from HuggingFace FineWeb-EDU to NVIDIA ClimbMix dataset. `@karpathy` has tried to swap the dataset many times, each time with a negative result (FineWeb, DCLM, Olmo), but ClimbMix produced clear and immediate gains. Credit to `@ddudek` for originally discovering ClimbMix for nanochat and reporting the improvements, which kicked off the followup investigation.
+
+To reproduce, use the commit above, download at least 150 data shards, train the tokenizer:
+
+```
+python -m nanochat.dataset -n 150
+python -m scripts.tok_train
+```
+
+Then kick off the run in the typical way, using a slightly lower than compute optimal ratio of 9.5 (vs compute optimal 10.5), meaning the d24 is slightly undertrained.
+
+```
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
+    --depth=24 \
+    --run="d24-climbmix" \
+    --model-tag="d24-climbmix" \
+    --sample-every=-1 \
+    --save-every=-1 \
+    --core-metric-max-per-task=-1 \
+    --core-metric-every=999999 \
+    --target-param-data-ratio=9.5 \
+    --device-batch-size=16 \
+    --fp8
+```
+
+I ran this command 7 individual times. Because our training is mildly non-deterministic, we get a spread of CORE scores, e.g.:
+
+```
+0.25373
+0.2584
+0.25489
+0.2568
+0.25732
+0.26765
+0.25119
+```
+
+Mean is 0.25714 (higher than the GPT-2 threshold needed), max-min is 0.01646. Something to investigate in the future is that even slightly better results can be obtained by randomly shuffling the the data shards (i.e. just going in a different order). This is unexpected because the documents were completely fully shuffled during data construction, so one would expect a relatively uniform data distribution. Indeed, the current default order is unfortunately among the worse ("unlucky") ones you can obtain with different shuffle seeds, but it suffices to beat GPT-2 for now so I am merging. TODO investing a bit more later.
+
+NOTE: The `val_bpb` is as of this run *NOT* comparable due to the data distribution change to the previous 3 runs. This run happens to be at `0.71854` validation bpb. If the dataset is not changed, the `val_bpb` number is a great, smooth metric to track relative performance w.r.t. and has less noise than CORE.
+
+## Run 5
+
+Achieved Mar 9, 2026 on commit `6ed7d1d`. Exactly the same launch command as Run 4 except `--target-param-data-ratio=8.7`. I ran 5 identical runs, the average CORE was 0.2690, which is quite a bit above the needed threshold of 0.2565. But the reason I didn't decrease the ratio further (i.e. train shorter) is that while the CORE "safety gap" is large, the val_loss safety gap is smaller - 0.71808, which we want to be below the Run 4 val loss of 0.71854. It's likely that we could have reduced the ratio even lower, possibly to 8.6, but it's not worth splitting hairs at this point.
+
+This commit is special because all of the improvements that went into [this commit](https://github.com/karpathy/nanochat/commit/6ed7d1d82cee16c2e26f45d559ad3338447a6c1b) came from fully autonomous "research" done by a private version of [autoresearch](https://github.com/karpathy/autoresearch) run on a d12 model. I wrote more about this in [this tweet](https://x.com/karpathy/status/2031135152349524125). The changes easily translated from d12 to d24, hence new leaderboard record, taking us from 2.02 hours "time to GPT-2" to 1.80 hours.
+
+## Run 6
+
+Achieved Mar 14, 2026 on commit `a825e63`. Exactly the same launch command as Run 4 except `--target-param-data-ratio=8`. Improvements in the architecture are allowing us to train shorter and shorter time. Instead of an undertrained d24 I attempted to train an overtrained d22 but it was worse. This set of changes came from autoresearch round 2, where I asked it to reference the modded-nanogpt repo for inspiration. So the exploration tried out a number of ideas and in particular found a way to incorporate the backout and smear in such a way that they are helpful (I had previously tried them manually a long time ago and they caused regressions). The smear idea in particular is a little bit heavier and bloaty because it is essentially an "early fusion" of context across tokens, producing a kind of a bigram input into the network and allowing it to focus on higher ngrams earlier. But for this reason the code gets a bit more complex and required some changes to inference. I verified with a unit test that the Engine inference is correct compared to the naive inference of `GPT.generate()`. The average of 5 runs was CORE 0.262634 and each of them lasted 1.65 hours (99 minutes).

dev/gen_synthetic_data.py

@@ -0,0 +1,468 @@
+"""
+Synthetic data generation for teaching nanochat about its identity and capabilities.
+
+This script uses the OpenRouter API to generate diverse multi-turn conversations
+between a user and nanochat. The conversations are saved to a .jsonl file for use
+in supervised finetuning (SFT) via the CustomJSON task.
+
+Key design principles for high-quality synthetic data:
+1. DIVERSITY CONTROL is critical - we inject entropy at multiple levels:
+   - Topic/question categories (what the conversation is about)
+   - User personas (who is asking)
+   - Conversation dynamics (shape and flow)
+   - First message style (greeting variation)
+2. Comprehensive knowledge base - we provide detailed facts so the LLM
+   generating conversations has accurate information to draw from.
+3. Structured outputs - we use JSON schema to guarantee valid format.
+
+NOTE: You need OPENROUTER_API_KEY set in .env or as an environment variable.
+NOTE: For more details see: https://github.com/karpathy/nanochat/discussions/139
+"""
+import requests
+import json
+import os
+import copy
+import random
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from dotenv import load_dotenv
+
+from nanochat.common import get_base_dir
+
+load_dotenv()
+api_key = os.environ["OPENROUTER_API_KEY"]
+
+url = "https://openrouter.ai/api/v1/chat/completions"
+headers = {
+    "Authorization": f"Bearer {api_key}",
+    "Content-Type": "application/json"
+}
+
+# Load the comprehensive knowledge base
+knowledge_path = os.path.join(os.path.dirname(__file__), "..", "knowledge", "self_knowledge.md")
+knowledge = open(knowledge_path, "r", encoding="utf-8").read().strip()
+assert os.path.exists(knowledge_path), f"Knowledge base file not found: {knowledge_path}"
+# for right now I am not committing the self_knowledge file to repo. You can use README.md instead
+# of it, or you can generate one by asking an LLM to make one based on the README/files.
+# This whole file is just a helpful demonstration of the kind of thing you'd run.
+
+# =============================================================================
+# DIVERSITY DIMENSIONS
+# =============================================================================
+
+# Topics/questions the conversation should explore
+# Group by category for balanced sampling
+topics = {
+    "identity": [
+        "who/what is nanochat",
+        "who created nanochat and why",
+        "what does the name 'nanochat' mean",
+        "is nanochat open source, what license",
+        "where can I find the code",
+        "how can I contribute to nanochat",
+    ],
+    "architecture": [
+        "basic architecture overview (transformer, layers, parameters)",
+        "what is RoPE and why use it",
+        "explain RMSNorm vs LayerNorm",
+        "what is Flash Attention and why it matters",
+        "sliding window attention pattern",
+        "value embeddings - what are they",
+        "per-layer residual scalars",
+        "ReLU squared activation",
+        "logit softcapping",
+        "QK normalization",
+    ],
+    "training": [
+        "how much did it cost to train nanochat",
+        "how long does training take",
+        "what hardware is needed",
+        "what data was nanochat trained on",
+        "what is the Muon optimizer",
+        "explain the split optimizer design",
+        "what is the depth parameter and scaling",
+        "what is the CORE metric",
+    ],
+    "capabilities": [
+        "what can nanochat do",
+        "can nanochat write code",
+        "can nanochat do math (calculator tool)",
+        "can nanochat help with writing",
+        "what languages does nanochat speak",
+        "how good is nanochat at reasoning",
+    ],
+    "limitations": [
+        "what can nanochat NOT do",
+        "why does nanochat work best in English",
+        "does nanochat have internet access",
+        "what is nanochat's context length limit",
+        "can nanochat remember previous conversations",
+        "can nanochat make mistakes / hallucinate",
+        "is nanochat good for production use",
+    ],
+    "comparisons": [
+        "how does nanochat compare to GPT-2",
+        "how does nanochat compare to ChatGPT/GPT-4",
+        "how does nanochat compare to Claude",
+        "why is training 600x cheaper than GPT-2",
+        "what's special about nanochat vs other open models",
+    ],
+    "history": [
+        "the GPT-2 training cost in 2019",
+        "how AI training costs have dropped over time",
+        "relationship to modded-nanogpt project",
+        "what optimizations worked vs didn't work",
+        "the journey of building nanochat",
+    ],
+    "technical_deep_dive": [
+        "explain the tokenizer (BPE, vocab size)",
+        "how does distributed training work (ZeRO)",
+        "explain the dataloader and BOS alignment",
+        "what is compute-optimal training",
+        "how does the calculator tool work",
+        "explain inference with KV cache",
+    ],
+    "philosophical": [
+        "is nanochat conscious / does it have feelings",
+        "what happens when nanochat is wrong",
+        "can nanochat learn from this conversation",
+        "why make AI training accessible",
+        "the future of open source AI",
+    ],
+}
+
+# User personas - different people ask questions differently
+personas = [
+    "curious beginner who knows nothing about AI or machine learning",
+    "ML researcher or engineer who wants technical depth and specifics",
+    "developer considering contributing to the nanochat project",
+    "skeptic who doubts open source can compete with big AI labs",
+    "computer science student learning about transformers and LLMs",
+    "someone comparing nanochat to ChatGPT, Claude, or other assistants",
+    "journalist or writer covering AI democratization and open source",
+    "hobbyist who just wants to chat and learn casually",
+    "someone interested in the cost and economics of AI training",
+    "teacher or educator wanting to use nanochat for teaching",
+    "entrepreneur exploring if nanochat fits their use case",
+    "someone who just discovered the project and wants the basics",
+]
+
+# Conversation dynamics - shape and flow
+dynamics = [
+    "short 2-turn Q&A: user asks one question, gets a complete answer",
+    "medium 4-turn: user asks, gets answer, asks followup for clarification",
+    "deep 6-turn technical discussion: progressively deeper questions",
+    "skeptical arc: user starts doubtful, assistant addresses concerns honestly",
+    "learning journey: user starts basic, assistant builds up complexity gradually",
+    "comparison-focused: user keeps comparing to other models, assistant explains differences",
+    "limitation exploration: user probes what nanochat cannot do, assistant is honest",
+    "casual friendly chat that naturally touches on identity and capabilities",
+    "troubleshooting: user has misconceptions, assistant gently corrects them",
+    "enthusiastic: user is excited about the project, assistant shares that energy appropriately",
+]
+
+# First messages - greetings and openers
+# Categorized for balanced sampling
+first_messages = {
+    "simple_greetings": [
+        "hi", "Hi!", "hello", "Hello?", "hey there", "Hey!", "yo", "Yo!",
+        "Good morning", "Good evening!", "Howdy", "sup", "What's up?",
+        "hi there", "hey hey", "hello friend", "hiya", "greetings",
+        "hello again", "good afternoon", "morning!", "evening!",
+    ],
+    "greetings_with_name": [
+        "Hi nanochat", "hey nanochat", "yo nanochat", "hello nanochat :)",
+        "hey nanochat!", "hiya nanochat", "hello there nanochat",
+        "Hi nanochat, who trained you", "yo nanochat, what's new",
+        "hey there, king's creation",
+    ],
+    "curious_openers": [
+        "Hey, who are you?", "Hi, what is this?", "Hey, are you a chatbot?",
+        "Hello! Who am I talking to?", "hi! what do you do?",
+        "hi! who made you", "hey! are you alive", "hiya! what are you",
+        "hello! tell me about yourself", "hi, what's your name",
+        "yo, what is this", "hi! who built you", "hello! are you open source",
+        "hey, what version are you", "hi! what's your story",
+        "hey, what's nanochat", "hello! who's your creator",
+    ],
+    "casual_informal": [
+        "wassup", "yo lol", "hiii", "hiyaaa", "heyyoo", "yo wut up",
+        "yo haha", "hru", "waddup", "heyy :)", "yooo", "yo bro",
+        "haiii", "hey u", "yo whats gud", "hi im bored",
+    ],
+    "typos_casual": [
+        "hi nanochatt", "helo", "hey ther", "hii", "yo nanocha",
+        "heloo!", "hi, whos this", "hay", "helloo??", "hi nanocat",
+        "helo nanochat", "hai!", "helllo nano", "yo nanochta",
+    ],
+    "caps_enthusiastic": [
+        "HI", "HELLOOO", "YO!!!", "HEY", "SUP", "WASSUP", "HEY!!!",
+        "HELLO??", "HI THERE!!", "HEYOOOO", "HIII", "YOOOO", "HELLO!!!",
+    ],
+    "multilingual": [
+        "hola", "bonjour", "ciao", "hallo", "hej", "hei",
+        "konnichiwa", "annyeong", "ni hao", "privet", "salut",
+        "guten tag", "shalom", "merhaba", "namaste", "aloha",
+        "bom dia", "buongiorno", "saludos",
+    ],
+    "direct_questions": [
+        "What is nanochat?", "Who made you?", "Are you GPT?",
+        "How do you compare to ChatGPT?", "Can you help me code?",
+        "What can you do?", "Are you open source?", "How were you trained?",
+        "What's your context limit?", "Can you browse the internet?",
+    ],
+}
+
+# =============================================================================
+# PROMPT TEMPLATE
+# =============================================================================
+
+prompt_template = r"""
+I want to generate synthetic training data for an AI assistant called "nanochat" to teach it about its own identity, capabilities, and limitations.
+
+## KNOWLEDGE BASE
+
+Here is comprehensive information about nanochat that you should use as the authoritative source of facts:
+
+---
+{knowledge}
+---
+
+## YOUR TASK
+
+Generate a realistic multi-turn conversation between a User and the nanochat Assistant.
+
+**Topic to explore:** {topic}
+**User persona:** {persona}
+**Conversation dynamic:** {dynamic}
+
+## STYLE GUIDELINES
+
+1. **Plain ASCII only** - No emojis, special characters, or unicode. Just plain text.
+2. **Natural conversation** - Make it feel like a real chat, not a Q&A exam.
+3. **Accurate facts** - Use ONLY information from the knowledge base above. Don't make up statistics or features.
+4. **Appropriate depth** - Match the technical level to the user persona.
+5. **Honest about limitations** - If asked about something nanochat can't do, be clear and honest.
+6. **Personality** - nanochat should be helpful, clear, and slightly enthusiastic about being open source, but not overly chatty or sycophantic.
+
+## FIRST MESSAGE EXAMPLES
+
+Here are some example first messages from users (for style inspiration):
+{first_message_examples}
+
+## SPECIAL CASES
+
+- **Non-English first message:** If the user writes in another language, nanochat should briefly acknowledge it can understand but works best in English, then continue helpfully.
+- **Misconceptions:** If the user has wrong assumptions (e.g., "you're made by OpenAI"), gently correct them.
+- **Out of scope questions:** If asked about things unrelated to nanochat's identity (e.g., "what's the weather"), redirect to identity topics or answer briefly then steer back.
+
+## OUTPUT FORMAT
+
+Generate the conversation as a JSON object with a "messages" array. Each message has "role" (user/assistant) and "content". Start with a user message.
+""".strip()
+
+# =============================================================================
+# API CONFIGURATION
+# =============================================================================
+
+response_format = {
+    "type": "json_schema",
+    "json_schema": {
+        "name": "conversation",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "properties": {
+                "messages": {
+                    "type": "array",
+                    "description": "Conversation messages alternating user/assistant, starting with user",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "role": {
+                                "type": "string",
+                                "description": "Either 'user' or 'assistant'"
+                            },
+                            "content": {
+                                "type": "string",
+                                "description": "The message content"
+                            }
+                        },
+                        "required": ["role", "content"],
+                        "additionalProperties": False
+                    }
+                }
+            },
+            "required": ["messages"],
+            "additionalProperties": False
+        }
+    }
+}
+
+base_payload = {
+    "model": "google/gemini-3-flash-preview",
+    "stream": False,
+    "response_format": response_format,
+    "temperature": 1.0,
+}
+
+# =============================================================================
+# GENERATION LOGIC
+# =============================================================================
+
+def sample_diversity_elements(rng):
+    """Sample one element from each diversity dimension."""
+    # Sample topic: first pick a category, then a topic within it
+    category = rng.choice(list(topics.keys()))
+    topic = rng.choice(topics[category])
+
+    # Sample persona
+    persona = rng.choice(personas)
+
+    # Sample dynamic
+    dynamic = rng.choice(dynamics)
+
+    # Sample first message examples: pick from multiple categories
+    first_msg_samples = []
+    categories = rng.sample(list(first_messages.keys()), min(3, len(first_messages)))
+    for cat in categories:
+        first_msg_samples.append(rng.choice(first_messages[cat]))
+
+    return {
+        "topic": topic,
+        "persona": persona,
+        "dynamic": dynamic,
+        "first_message_examples": "\n".join(f"- {msg}" for msg in first_msg_samples),
+    }
+
+
+def generate_conversation(idx: int):
+    """
+    Generate a single conversation using the OpenRouter API.
+    Returns a list of message dicts with 'role' and 'content' keys.
+    """
+    # Use idx as seed for reproducibility
+    rng = random.Random(idx)
+
+    # Sample diversity elements
+    elements = sample_diversity_elements(rng)
+
+    # Build the prompt
+    prompt = prompt_template.format(
+        knowledge=knowledge,
+        topic=elements["topic"],
+        persona=elements["persona"],
+        dynamic=elements["dynamic"],
+        first_message_examples=elements["first_message_examples"],
+    )
+
+    # Make API request
+    payload = copy.deepcopy(base_payload)
+    payload['messages'] = [{"role": "user", "content": prompt}]
+
+    response = requests.post(url, headers=headers, json=payload)
+    result = response.json()
+
+    if 'error' in result:
+        raise Exception(f"API error: {result['error']}")
+
+    content = result['choices'][0]['message']['content']
+    conversation_data = json.loads(content)
+    messages = conversation_data['messages']
+
+    # Return messages along with metadata for debugging
+    return {
+        "messages": messages,
+        "metadata": {
+            "topic": elements["topic"],
+            "persona": elements["persona"],
+            "dynamic": elements["dynamic"],
+        }
+    }
+
+
+def validate_conversation(messages):
+    """Validate conversation structure."""
+    if len(messages) < 2:
+        raise ValueError(f"Conversation too short: {len(messages)} messages")
+
+    for i, message in enumerate(messages):
+        expected_role = "user" if i % 2 == 0 else "assistant"
+        if message['role'] != expected_role:
+            raise ValueError(f"Message {i} has role '{message['role']}', expected '{expected_role}'")
+
+        if not message['content'].strip():
+            raise ValueError(f"Message {i} has empty content")
+
+    return True
+
+
+# =============================================================================
+# MAIN
+# =============================================================================
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate synthetic conversation data")
+    parser.add_argument("--num", type=int, default=1000, help="Number of conversations to generate")
+    parser.add_argument("--workers", type=int, default=4, help="Number of parallel workers")
+    parser.add_argument("--output", type=str, default=None, help="Output file path")
+    parser.add_argument("--append", action="store_true", help="Append to existing file instead of overwriting")
+    parser.add_argument("--save-metadata", action="store_true", help="Save metadata alongside messages")
+    args = parser.parse_args()
+
+    # Set output file
+    if args.output:
+        output_file = args.output
+    else:
+        output_file = os.path.join(get_base_dir(), "identity_conversations.jsonl")
+
+    # Handle file creation/clearing
+    if not args.append and os.path.exists(output_file):
+        os.remove(output_file)
+
+    print(f"Output file: {output_file}")
+    print(f"Generating {args.num} conversations with {args.workers} workers...")
+    print(f"Topic categories: {list(topics.keys())}")
+    print(f"Personas: {len(personas)}")
+    print(f"Dynamics: {len(dynamics)}")
+    print()
+
+    completed_count = 0
+    error_count = 0
+
+    with ThreadPoolExecutor(max_workers=args.workers) as executor:
+        # Submit all tasks
+        futures = {executor.submit(generate_conversation, idx): idx
+                   for idx in range(args.num)}
+
+        # Process results as they complete
+        for future in as_completed(futures):
+            idx = futures[future]
+            try:
+                result = future.result()
+                messages = result["messages"]
+                metadata = result["metadata"]
+
+                # Validate
+                validate_conversation(messages)
+
+                # Write to file
+                with open(output_file, 'a') as f:
+                    if args.save_metadata:
+                        f.write(json.dumps({"messages": messages, "metadata": metadata}) + '\n')
+                    else:
+                        f.write(json.dumps(messages) + '\n')
+
+                completed_count += 1
+                topic_short = metadata["topic"][:40] + "..." if len(metadata["topic"]) > 40 else metadata["topic"]
+                print(f"[{completed_count}/{args.num}] Topic: {topic_short}")
+
+            except Exception as e:
+                error_count += 1
+                print(f"[ERROR] idx={idx}: {e}")
+
+    print()
+    print(f"Done! Saved {completed_count} conversations to {output_file}")
+    if error_count > 0:
+        print(f"Encountered {error_count} errors during generation")

dev/generate_logo.html

@@ -0,0 +1,29 @@
+<!DOCTYPE html>
+<html>
+<body style="margin:0; display:flex; justify-content:center; align-items:center; height:100vh; background:#fff">
+  <svg width="400" height="400" xmlns="http://www.w3.org/2000/svg">
+    <defs>
+      <radialGradient id="g" cx="50%" cy="50%">
+        <stop offset="0%" style="stop-color:#667eea;stop-opacity:1"/>
+        <stop offset="100%" style="stop-color:#764ba2;stop-opacity:0.3"/>
+      </radialGradient>
+    </defs>
+  </svg>
+  <script>
+    const svg = document.querySelector('svg');
+    const r = 120;
+    let path = '';
+    for(let i = 0; i < 24; i += 2) {
+      let a1 = i * Math.PI / 12;
+      let a2 = (i + 1) * Math.PI / 12;
+      let x2 = 200 + Math.cos(a2) * r;
+      let y2 = 200 + Math.sin(a2) * r;
+      let x3 = 200 + Math.cos(a2) * (r - 90);
+      let y3 = 200 + Math.sin(a2) * (r - 90);
+      path += `M${x2},${y2} L${x3},${y3} `;
+    }
+    svg.innerHTML += `<path d="${path}" stroke="url(#g)" stroke-width="6" stroke-linecap="round" fill="none"/>`;
+    svg.innerHTML += `<path d="M200,-12 L212,0 L200,12 L188,0 Z" transform="translate(0,200)" fill="#000"/>`;
+  </script>
+</body>
+</html>

dev/repackage_data_reference.py

@@ -0,0 +1,126 @@
+"""
+Repackage a given dataset into simple parquet shards:
+
+- each shard is ~100MB in size (after zstd compression)
+- parquets are written with row group size of 1000
+- shuffle the dataset
+
+This will be uploaded to HuggingFace for hosting.
+The big deal is that our DataLoader will be able to stream
+the data and cache it along the way on disk, decreasing the
+training latency.
+
+Historical context:
+Originally, nanochat used the FinewebEdu-100B dataset.
+Then we switched to the ClimbMix-400B dataset due to superior performance.
+This script documents how both were prepared.
+
+The outputs are here:
+
+https://huggingface.co/datasets/karpathy/fineweb-edu-100b-shuffle
+https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle
+
+NOTE: This file is meant only as reference/documentation of the
+dataset preparation and it is not used during the project runtime.
+"""
+import os
+import time
+
+from datasets import load_dataset
+import pyarrow.parquet as pq
+import pyarrow as pa
+
+# You can change these:
+dataset_tag = "climbmix"
+upload_to_hf = True
+
+# Dataset configurations:
+if dataset_tag == "fineweb_edu":
+    dataset_kwargs = {
+        "path": "HuggingFaceFW/fineweb-edu",
+        "split": "train",
+        "name": "sample-100BT", # ~100B GPT-2 tokens at ~3 chars/token => ~300B chars total
+    }
+    output_dirname = "fineweb_edu"
+    data_column_name = "text"
+    tokenizer = None
+    upload_tag = "fineweb-edu-100b-shuffle"
+
+elif dataset_tag == "climbmix":
+    import tiktoken # the ClimbMix data is stored tokenized with GPT-2 tokenizer
+    dataset_kwargs = {
+        "path": "nvidia/Nemotron-ClimbMix",
+        "split": "train",
+    }
+    output_dirname = "climbmix"
+    data_column_name = "tokens"
+    tokenizer = tiktoken.encoding_for_model("gpt-2")
+    upload_tag = "climbmix-400b-shuffle"
+
+else:
+    raise ValueError(f"Unknown dataset tag: {dataset_tag}")
+
+# Source dataset
+ds = load_dataset(**dataset_kwargs)
+
+# Shuffle to scramble the order
+ds = ds.shuffle(seed=42)
+ndocs = len(ds) # total number of documents to process
+print(f"Total number of documents: {ndocs}")
+
+# Repackage into parquet files
+output_dir = f"/home/ubuntu/.cache/nanochat/base_data_{output_dirname}"
+os.makedirs(output_dir, exist_ok=True)
+
+# Write to parquet files
+chars_per_shard = 250_000_000
+row_group_size = 1024 # HF uses 1000 but we use multiple of 2, nicer for distributed data loader later
+shard_docs = []
+shard_index = 0
+shard_characters = 0
+total_docs_processed = 0
+total_time_spent = 0
+t0 = time.time()
+for doc in ds:
+    data = doc[data_column_name]
+    text = tokenizer.decode(data) if tokenizer is not None else data
+    shard_docs.append(text)
+    shard_characters += len(text)
+    collected_enough_chars = shard_characters >= chars_per_shard
+    docs_multiple_of_row_group_size = len(shard_docs) % row_group_size == 0
+    if collected_enough_chars and docs_multiple_of_row_group_size: # leads to ~100MB of text (compressed)
+        shard_path = os.path.join(output_dir, f"shard_{shard_index:05d}.parquet")
+        shard_table = pa.Table.from_pydict({"text": shard_docs})
+        pq.write_table(
+            shard_table,
+            shard_path,
+            row_group_size=row_group_size,
+            use_dictionary=False, # this is usually used for categorical data
+            compression="zstd", # Valid values: {‘NONE’, ‘SNAPPY’, ‘GZIP’, ‘BROTLI’, ‘LZ4’, ‘ZSTD’}
+            compression_level=3,
+            write_statistics=False, # not needed for text
+        )
+        t1 = time.time()
+        dt = t1 - t0 # for this shard alone
+        t0 = t1
+        total_docs_processed += len(shard_docs)
+        total_time_spent += dt
+        remaining_docs = ndocs - total_docs_processed
+        avg_time_per_doc = total_time_spent / total_docs_processed
+        remaining_time = remaining_docs * avg_time_per_doc
+        remaining_time_hours = remaining_time / 3600
+        print(f"Wrote {shard_path}. #documents: {len(shard_docs)} | #characters: {shard_characters} | time: {dt:.2f}s | remaining time: {remaining_time_hours:.2f}h")
+        shard_docs = []
+        shard_characters = 0
+        shard_index += 1
+
+# Demonstration of how the data was later uploaded to HuggingFace
+if upload_to_hf:
+    from huggingface_hub import HfApi
+    token = os.getenv("HF_TOKEN")
+    api = HfApi(token=token)
+    api.upload_large_folder(
+        folder_path=output_dir,
+        repo_id=f"karpathy/{upload_tag}",
+        repo_type="dataset",
+    )

dev/scaling_analysis.ipynb

@@ -0,0 +1,373 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scaling Laws Analysis\n",
+    "\n",
+    "Analyze results from `scaling_laws.sh` to find the optimal param:data ratio for nanochat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load results\n",
+    "tag = \"jan26\"\n",
+    "base_dir = os.environ.get('NANOCHAT_BASE_DIR', os.path.expanduser('~/.cache/nanochat'))\n",
+    "results_path = os.path.join(base_dir, f'scaling_laws_results_{tag}', 'results.csv')\n",
+    "\n",
+    "df = pd.read_csv(results_path)\n",
+    "flops_budgets = sorted(df['flops_budget'].unique())\n",
+    "print(f\"Loaded {len(df)} runs across {len(flops_budgets)} FLOPs budgets\")\n",
+    "print(f\"Columns: {list(df.columns)}\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =============================================================================\n",
+    "# FILTERING: Remove incomplete or problematic runs\n",
+    "# =============================================================================\n",
+    "\n",
+    "print(f\"Before filtering: {len(df)} runs\")\n",
+    "\n",
+    "# Filter out runs with missing/invalid val_bpb (incomplete runs)\n",
+    "df = df[df['val_bpb'].notna() & (df['val_bpb'] > 0)]\n",
+    "\n",
+    "# Optional: exclude specific flops budgets that aren't done yet\n",
+    "# exclude_flops = [1e19]  # <-- adjust as runs complete\n",
+    "# df = df[~df['flops_budget'].isin(exclude_flops)]\n",
+    "\n",
+    "# Optional: exclude specific depths\n",
+    "# exclude_depths = [18, 20]\n",
+    "# df = df[~df['depth'].isin(exclude_depths)]\n",
+    "\n",
+    "print(f\"After filtering: {len(df)} runs\")\n",
+    "print(f\"FLOPs budgets: {sorted(df['flops_budget'].unique())}\")\n",
+    "print(f\"Depths: {sorted(df['depth'].unique())}\")\n",
+    "\n",
+    "# Update flops_budgets list after filtering\n",
+    "flops_budgets = sorted(df['flops_budget'].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Effective Parameter Count\n",
+    "\n",
+    "Different scaling law papers use different conventions for counting parameters:\n",
+    "- **Kaplan et al.** excluded embedding parameters (claimed cleaner laws)\n",
+    "- **Chinchilla** included all parameters (and noted Kaplan had a bug)\n",
+    "\n",
+    "Our CSV now has granular counts:\n",
+    "- `params_wte` - token embedding (lookup table)\n",
+    "- `params_value_embeds` - value embeddings (lookup table)\n",
+    "- `params_lm_head` - unembedding projection (matmul)\n",
+    "- `params_transformer` - attention + MLP matrices (matmuls)\n",
+    "- `params_scalars` - resid/x0/bigram lambdas (tiny)\n",
+    "\n",
+    "**Experiment below** with different combinations to see which gives the cleanest scaling laws."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =============================================================================\n",
+    "# EXPERIMENT HERE: Define which parameters to count for scaling laws\n",
+    "# =============================================================================\n",
+    "\n",
+    "def compute_effective_params(row):\n",
+    "    \"\"\"\n",
+    "    Compute the 'effective' parameter count for scaling law analysis.\n",
+    "\n",
+    "    Modify this function to experiment with different conventions:\n",
+    "    - Chinchilla-style: include everything\n",
+    "    - Kaplan-style: exclude embeddings\n",
+    "    - Matmul-only: just transformer + lm_head (the actual compute)\n",
+    "    - etc.\n",
+    "    \"\"\"\n",
+    "    # Option 1: Chinchilla-style (all params)\n",
+    "    # return row['params_total']\n",
+    "\n",
+    "    # Option 2: Kaplan-style (exclude embeddings)\n",
+    "    return row['params_transformer'] + row['params_lm_head']\n",
+    "\n",
+    "    # Option 3: Transformer-only (exclude all embeddings AND lm_head)\n",
+    "    # return row['params_transformer']\n",
+    "\n",
+    "\n",
+    "# Compute derived columns\n",
+    "df = df.copy()  # avoid SettingWithCopyWarning from earlier filter\n",
+    "df['effective_params'] = df.apply(compute_effective_params, axis=1)\n",
+    "df['param_data_ratio'] = df['tokens_trained'] / df['effective_params']\n",
+    "\n",
+    "# Show parameter breakdown for first few rows\n",
+    "print(\"Parameter breakdown (first row per flops budget):\")\n",
+    "param_cols = ['depth', 'params_wte', 'params_value_embeds',\n",
+    "              'params_lm_head', 'params_transformer', 'params_scalars', 'params_total', 'effective_params']\n",
+    "df.groupby('flops_budget').first()[param_cols]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IsoFLOP Curves (à la Chinchilla)\n",
+    "\n",
+    "For each compute budget, plot loss vs model size. Looking for the U-shape valley that reveals the optimal model size for each FLOPs budget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
+    "\n",
+    "# Plot 1: IsoFLOP curves - Val BPB vs Parameters (the Chinchilla plot!)\n",
+    "ax = axes[0]\n",
+    "colors = plt.cm.viridis(np.linspace(0, 0.9, len(flops_budgets)))\n",
+    "optimal_by_bpb = []\n",
+    "\n",
+    "for flops, color in zip(flops_budgets, colors):\n",
+    "    subset = df[df['flops_budget'] == flops].sort_values('effective_params')\n",
+    "    ax.plot(subset['effective_params'], subset['val_bpb'], 'o', color=color, label=f'{flops:.0e}', markersize=8)\n",
+    "\n",
+    "    # Fit quadratic in log-space: val_bpb = a*(log N)^2 + b*(log N) + c\n",
+    "    log_params = np.log10(subset['effective_params'])\n",
+    "    coeffs = np.polyfit(log_params, subset['val_bpb'], 2)\n",
+    "    a, b, c = coeffs\n",
+    "\n",
+    "    # Plot fitted curve (dashed)\n",
+    "    log_fit_x = np.linspace(log_params.min() - 0.1, log_params.max() + 0.1, 100)\n",
+    "    fit_y = a * log_fit_x**2 + b * log_fit_x + c\n",
+    "    ax.plot(10**log_fit_x, fit_y, '--', color=color, linewidth=2)\n",
+    "\n",
+    "    # Find minimum of quadratic: d/dx(ax^2 + bx + c) = 0 => x = -b/(2a)\n",
+    "    if a > 0:  # parabola opens upward (has a minimum)\n",
+    "        log_opt = -b / (2 * a)\n",
+    "        opt_params = 10**log_opt\n",
+    "        opt_bpb = a * log_opt**2 + b * log_opt + c\n",
+    "        # Mark the fitted optimal\n",
+    "        ax.scatter([opt_params], [opt_bpb], s=150, color=color,\n",
+    "                   zorder=5, edgecolors='black', linewidths=2, marker='*')\n",
+    "        # Interpolate tokens and ratio from actual data (don't use C≈6ND approximation)\n",
+    "        opt_tokens = np.interp(np.log10(opt_params), log_params, subset['tokens_trained'])\n",
+    "        opt_ratio = np.interp(np.log10(opt_params), log_params, subset['param_data_ratio'])\n",
+    "        optimal_by_bpb.append({'flops': flops, 'params': opt_params, 'tokens': opt_tokens, 'ratio': opt_ratio, 'bpb': opt_bpb})\n",
+    "    else:\n",
+    "        # Fallback to raw minimum if quadratic doesn't have minimum\n",
+    "        best_idx = subset['val_bpb'].idxmin()\n",
+    "        best = subset.loc[best_idx]\n",
+    "        ax.scatter([best['effective_params']], [best['val_bpb']], s=150, color=color,\n",
+    "                   zorder=5, edgecolors='black', linewidths=2)\n",
+    "        optimal_by_bpb.append({'flops': flops, 'params': best['effective_params'],\n",
+    "                              'tokens': best['tokens_trained'], 'ratio': best['param_data_ratio'], 'bpb': best['val_bpb']})\n",
+    "\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_xlabel('Effective Parameters')\n",
+    "ax.set_ylabel('Validation Loss (bpb)')\n",
+    "ax.set_title('IsoFLOP Curves')\n",
+    "ax.legend(title='FLOPs', loc='upper right')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "opt_df = pd.DataFrame(optimal_by_bpb)\n",
+    "\n",
+    "# Plot 2: Optimal model size vs compute (power law)\n",
+    "ax = axes[1]\n",
+    "ax.loglog(opt_df['flops'], opt_df['params'], 'o', markersize=10, color='#2ecc71')\n",
+    "ax.set_xlabel('FLOPs')\n",
+    "ax.set_ylabel('Optimal Parameters')\n",
+    "ax.set_title('Optimal Model Size')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Fit and show power law\n",
+    "if len(opt_df) >= 2:\n",
+    "    log_f = np.log10(opt_df['flops'])\n",
+    "    log_p = np.log10(opt_df['params'])\n",
+    "    slope, intercept = np.polyfit(log_f, log_p, 1)\n",
+    "    fit_f = np.logspace(log_f.min() - 0.5, log_f.max() + 0.5, 100)\n",
+    "    fit_p = 10**(intercept + slope * np.log10(fit_f))\n",
+    "    ax.plot(fit_f, fit_p, 'r--', alpha=0.7, label=f'N ∝ C^{slope:.2f}')\n",
+    "    ax.legend()\n",
+    "\n",
+    "# Plot 3: Optimal tokens vs compute (power law)\n",
+    "ax = axes[2]\n",
+    "ax.loglog(opt_df['flops'], opt_df['tokens'], 'o', markersize=10, color='#e74c3c')\n",
+    "ax.set_xlabel('FLOPs')\n",
+    "ax.set_ylabel('Optimal Tokens')\n",
+    "ax.set_title('Optimal Training Tokens')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Fit and show power law\n",
+    "if len(opt_df) >= 2:\n",
+    "    log_f = np.log10(opt_df['flops'])\n",
+    "    log_t = np.log10(opt_df['tokens'])\n",
+    "    slope, intercept = np.polyfit(log_f, log_t, 1)\n",
+    "    fit_f = np.logspace(log_f.min() - 0.5, log_f.max() + 0.5, 100)\n",
+    "    fit_t = 10**(intercept + slope * np.log10(fit_f))\n",
+    "    ax.plot(fit_f, fit_t, 'r--', alpha=0.7, label=f'D ∝ C^{slope:.2f}')\n",
+    "    ax.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print the optimal points (from quadratic fits)\n",
+    "print(\"\\nOptimal configurations (from quadratic fits):\")\n",
+    "print(f\"{'FLOPs':<12} {'Eff Params':<15} {'Tokens':<15} {'Ratio':<10} {'Val BPB':<10}\")\n",
+    "print(\"-\" * 65)\n",
+    "for _, row in opt_df.iterrows():\n",
+    "    print(f\"{row['flops']:<12.0e} {int(row['params']):<15,} {int(row['tokens']):<15,} {row['ratio']:<10.1f} {row['bpb']:<10.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =============================================================================\n",
+    "# Optimal Ratio Summary (from power law fits)\n",
+    "# =============================================================================\n",
+    "\n",
+    "# From the power law fits: N ∝ C^a and D ∝ C^b\n",
+    "# The ratio D/N ∝ C^(b-a). If a ≈ b, ratio is roughly constant.\n",
+    "\n",
+    "if len(opt_df) >= 2:\n",
+    "    log_f = np.log10(opt_df['flops'])\n",
+    "    log_p = np.log10(opt_df['params'])\n",
+    "    log_t = np.log10(opt_df['tokens'])\n",
+    "\n",
+    "    # Fit power laws\n",
+    "    slope_n, intercept_n = np.polyfit(log_f, log_p, 1)\n",
+    "    slope_d, intercept_d = np.polyfit(log_f, log_t, 1)\n",
+    "\n",
+    "    # The ratio D/N at a reference compute (geometric mean of our budgets)\n",
+    "    ref_flops = np.sqrt(opt_df['flops'].min() * opt_df['flops'].max())\n",
+    "    log_ref = np.log10(ref_flops)\n",
+    "\n",
+    "    # Predicted optimal N and D at reference compute\n",
+    "    pred_log_n = intercept_n + slope_n * log_ref\n",
+    "    pred_log_d = intercept_d + slope_d * log_ref\n",
+    "    optimal_ratio = 10**(pred_log_d - pred_log_n)\n",
+    "\n",
+    "    # Also compute from the fitted optimals directly (mean and std)\n",
+    "    mean_ratio = opt_df['ratio'].mean()\n",
+    "    std_ratio = opt_df['ratio'].std()\n",
+    "\n",
+    "    print(\"=\" * 60)\n",
+    "    print(\"OPTIMAL RATIO SUMMARY\")\n",
+    "    print(\"=\" * 60)\n",
+    "    print(f\"\\nPower law exponents:\")\n",
+    "    print(f\"  N ∝ C^{slope_n:.3f}\")\n",
+    "    print(f\"  D ∝ C^{slope_d:.3f}\")\n",
+    "    print(f\"  Ratio exponent (b-a): {slope_d - slope_n:.3f}  (should be ~0 if ratio is constant)\")\n",
+    "    print(f\"\\nOptimal ratio (tokens per effective param):\")\n",
+    "    print(f\"  From power law at C={ref_flops:.1e}: {optimal_ratio:.1f}\")\n",
+    "    print(f\"  Mean across budgets: {mean_ratio:.1f} ± {std_ratio:.1f}\")\n",
+    "    print(f\"  Chinchilla reference: 20\")\n",
+    "    print(f\"\\nPer-budget ratios: {[f'{r:.1f}' for r in opt_df['ratio'].values]}\")\n",
+    "else:\n",
+    "    print(\"Need at least 2 flops budgets to compute power law fits\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Val BPB vs Depth and Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Plot 1: Val BPB vs Depth\n",
+    "ax = axes[0]\n",
+    "for flops in flops_budgets:\n",
+    "    subset = df[df['flops_budget'] == flops].sort_values('depth')\n",
+    "    ax.plot(subset['depth'], subset['val_bpb'], 'o-', label=f'{flops:.0e}')\n",
+    "    # Mark the best (lowest)\n",
+    "    best_idx = subset['val_bpb'].idxmin()\n",
+    "    best = subset.loc[best_idx]\n",
+    "    ax.scatter([best['depth']], [best['val_bpb']], s=100, zorder=5, edgecolors='black', linewidths=2)\n",
+    "\n",
+    "ax.set_xlabel('Depth')\n",
+    "ax.set_ylabel('Val BPB (lower is better)')\n",
+    "ax.set_title('Validation BPB vs Model Depth')\n",
+    "ax.legend(title='FLOPs')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Val BPB vs Param:Data Ratio\n",
+    "ax = axes[1]\n",
+    "for flops in flops_budgets:\n",
+    "    subset = df[df['flops_budget'] == flops].sort_values('param_data_ratio')\n",
+    "    ax.plot(subset['param_data_ratio'], subset['val_bpb'], 'o-', label=f'{flops:.0e}')\n",
+    "    best_idx = subset['val_bpb'].idxmin()\n",
+    "    best = subset.loc[best_idx]\n",
+    "    ax.scatter([best['param_data_ratio']], [best['val_bpb']], s=100, zorder=5, edgecolors='black', linewidths=2)\n",
+    "\n",
+    "ax.axvline(x=20, color='red', linestyle='--', alpha=0.5, label='Chinchilla (20)')\n",
+    "ax.set_xlabel('Param:Data Ratio (tokens/param)')\n",
+    "ax.set_ylabel('Val BPB (lower is better)')\n",
+    "ax.set_title('Val BPB vs Param:Data Ratio')\n",
+    "ax.legend(title='FLOPs')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

doc/todo.md

@@ -19,12 +19,17 @@
 
 参考 research §1.2 模块图。
 
-- [ ] `nanochat/audio.py`：WhisperEncoder wrapper（冻结，从 HF mirror 拉权重）+ Projector（MLP，输出维度对齐 nanochat `model_dim`）
+- [x] `nanochat/audio.py`：WhisperEncoder wrapper（冻结，ModelScope 优先经 `WHISPER_MS_ID`，HF fallback 默认 `openai/whisper-base`）+ Projector（MLP，输出维度对齐 nanochat `n_embd`）
-- [ ] `nanochat/gpt.py` `GPT.forward()` 加可选 `audio_features` 参数，作为 soft tokens prepend 到 text embedding 前面
+- [x] `nanochat/gpt.py` `GPT.forward()` 加可选 `audio_features` 参数，作为 soft tokens prepend 到 text embedding 前面（kv_cache 路径暂不支持，audio 位置 targets 自动 -1 mask）
-- [ ] mini dataset：1–10 段 5s wav + 字幕，落 `data/audio_smoke/`（git 内不存音频，仅清单 + 下载脚本）
+- [x] mini dataset：5 段 5s 合成正弦 + 字幕，落 `data/audio_smoke/`（wav 由 `scripts/audio_smoke_data.py` 生成，gitignore 排除）
-- [ ] `scripts/audio_align_smoke.py`：50 步、d6 nanochat base、loss 下降即过
+- [x] `scripts/audio_align_smoke.py`：50 步、d6 随机初始化 GPT、字节级 tokenizer、loss 下降即过（4090 实测 ~1s，5.55→0.17）
 - [ ] CI 加 audio smoke job（ailab runner 装 ffmpeg；whisper 走 transformers 即可）
 
+W1 后续可改进（暂搁，留给 W3+/W5+ 质感任务）：
+
+- 当前用 `last_hidden_state`（最偏文本语义的层）；为质感感知应切到中间层 / 多层 weighted sum / w2v-bert
+- d6 GPT 是随机初始化，alignment 信号其实在练 LM 而非 projector；W2 上真 base 后 freeze LM、只练 projector 才是真正的弱对齐
+
 ## W2 — S1 弱对齐训练
 
 - [ ] 拉 LibriSpeech 100h（HF mirror），预提 Whisper-base encoder 特征落盘 webdataset
@@ -52,7 +57,7 @@
 
 - **backbone**：nanochat 自训 d12 → d20 → d26（不借现成 gemma/qwen，保持 hackable 灵魂）
 - **顺序**：audio 先，vision 排 W7+，多模态输出（TTS/imagegen）不做
-- **infra**：训练 + smoke CI 都跑在 ailab（5090, 32G），CN mirror 走 sjtu/aliyun/hf-mirror
+- **infra**：训练 + smoke CI 都跑在 ailab（5090, 32G）；CN mirror 走 sjtu/aliyun（pip）、modelscope（模型权重，首选）、hf-mirror（HF 数据集 / 权重 fallback）
 - **monorepo fork pattern**：上游 nanochat 的代码就是我们的代码，omni 改动直接进 `nanochat/` 包
 
 ## 暂搁 / 待定

doc/w1-audio-smoke.md

@@ -0,0 +1,153 @@
+# W1 — 音频通路 forward smoke 总结
+
+> 阶段: W1（参考 [`doc/todo.md`](todo.md) / [`doc/research_feasibility.md`](research_feasibility.md) §1.2）
+> 作者: @mochi
+> 日期: 2026-05-05
+> 状态: ✅ 通路打通，CI 接入留作 W2 同步
+
+---
+
+## 0. 目标
+
+W1 的唯一目标是 **proof of plumbing**：
+
+```
+wav → WhisperEncoder (frozen) → Projector → prepend 到 text embedding
+    → 随机初始化 d6 GPT → CE loss on text only
+```
+
+跑通这条链，并验证梯度能从 loss 反传到 Projector。**不**追求 alignment 质量、
+不追求 transcribe 能力——这些是 W2/W3 的事。
+
+Pass criterion 故意做得宽：50 步训练后 `loss[-1] < loss[0] - 0.5`。
+
+---
+
+## 1. 实现切片
+
+W1 的全部代码改动落在三个 commit 里，逻辑互不耦合：
+
+| Commit | 范围 | 核心改动 |
+|---|---|---|
+| [`d760915`](../../../commit/d760915) | `nanochat/gpt.py` | `forward()` 加 `audio_features` 关键字参数 |
+| [`7cc94cf`](../../../commit/7cc94cf) | `nanochat/audio.py`（新） | `WhisperEncoder` + `Projector` 模块 |
+| [`3c1cc33`](../../../commit/3c1cc33) | `scripts/`、`data/` | 合成数据集 + 50 步 smoke 脚本 |
+
+### 1.1 GPT.forward 的 audio prepend
+
+LLaVA-style：把 projector 输出的 soft tokens 拼在 text embedding **前面**，
+其余照旧。改动 18 行，要点：
+
+- **prepend 时机**：smear 之后、transformer trunk 之前。smear 的 prev-token
+  语义对 soft tokens 没定义，所以让它仍然是严格的 "text-only" 操作。
+- **rotary 位置**：原来按 text 长度切片 cos/sin；audio 在场时改切 `[0, T_a + T_text)`。
+  rotary 缓存在 `__init__` 时已经按 `sequence_len * 10` 过度分配，覆盖得过来。
+- **value embedding**：`self.value_embeds[i](idx)` 需要 token id；audio 位置用 0
+  填充。`ve_gate` 是 input-dependent 的，会自己学到压制这些假行——W1 不操心。
+- **targets 对齐**：传入 targets 时自动在 audio 位置 prepend `-1`（`ignore_index`），
+  loss 只统计文本位置。
+- **不支持的路径**：`kv_cache is not None` 时直接 `assert`。KV-cache 是 prefill
+  + decode 的协议，给它写 audio 语义需要重新设计，W1 只跑 train-style forward，
+  现在无所谓。
+
+### 1.2 nanochat/audio.py
+
+两个类，零隐式状态。
+
+**`WhisperEncoder`**
+
+- 权重加载顺序：`WHISPER_MS_ID` 设了就先 ModelScope（CN 镜像政策，详见
+  `doc/todo.md` 决定事项），失败/没设就 HF（`WHISPER_HF_ID`，默认
+  `openai/whisper-base`）。HF 路径自动 honor `HF_ENDPOINT=hf-mirror.com`，
+  跟 `scripts/smoke.sh` 现有 env 兼容。
+- `__init__` 即 freeze（`requires_grad = False` + `eval()`），调用方不需要
+  记得"冻结一下"——少一种忘记踩坑的方式。
+- `preprocess(audios)` 走 transformers' `WhisperFeatureExtractor`，
+  `forward(input_features)` 走 encoder.last_hidden_state。
+- **设计妥协**：Whisper 把每段音频 pad 到 30 s → encoder 输出 1500 帧不变。
+  5 s 的样本浪费 6× 算力，W1 不优化；W2 可以换 streaming chunking。
+
+**`Projector`**
+
+- 两层 MLP：`in_dim → out_dim → out_dim`，GELU 激活，bias 全无。
+- 用 `nanochat.gpt.Linear`，master 权重 fp32、forward 时按输入 dtype（bf16）
+  cast，对齐 nanochat 主模型风格。
+- **`fc2` 零初始化**：模型在第 0 步对音频"完全无视"——从一个干净的 baseline
+  起步，audio 路径是 opt-out by default、训练后才 opt-in。这对 debug 很友好：
+  forward 走通了但 loss 不动？立刻就能定位到 projector 没在学。
+
+### 1.3 W1 smoke
+
+**合成数据**（`scripts/audio_smoke_data.py`）：5 段 5 s 正弦（220/330/440/660/
+880 Hz，加二次谐波 + 1% 高斯噪声防止纯音 log-mel 退化），文字标签依次是
+"low / mid low / middle / mid high / high tone"。文件在 `data/audio_smoke/wavs/`
+（gitignored），`manifest.jsonl` 入 git。stdlib `wave` 写 PCM16，零额外依赖。
+
+为什么是合成而不是 LibriSpeech？W1 是 forward proof，不需要数据真实——网络
+依赖反而会让 smoke 不稳定。W2 上真数据。
+
+**对齐脚本**（`scripts/audio_align_smoke.py`）：
+- **字节级 tokenizer**：UTF-8 字节 + 单独的 `<BOS>`，vocab=257。绕开 nanochat
+  BPE 的 `tok_train` 前置依赖，让 W1 完全 standalone。W2 切回真 BPE。
+- 流程：load 5 个 wav → 一次性预提 Whisper 特征（encoder 冻结，每步重算就
+  是浪费）→ 50 步 AdamW，projector + LM 一起练。
+- pass 判据：`losses[0] - losses[-1] >= 0.5`，宽容到挡不住任何真实失败。
+
+---
+
+## 2. 实测结果
+
+cpc-i7（RTX 4090 24G，bf16，CUDA 12.8）：
+
+```
+input_features: (5, 80, 3000)         # batch × n_mels × T_mel
+whisper features: (5, 1500, 512)      # batch × T_a × d_audio (whisper-base)
+GPT: depth=6 n_embd=384 n_head=6
+text idx: (5, 13)                     # max(transcript) - 1
+
+step 000 | loss 5.5533
+step 005 | loss 1.7214
+step 010 | loss 0.9479
+...
+step 049 | loss 0.1658
+
+Done 50 steps in 0.9s | start=5.5533 end=0.1658 drop=5.3875
+PASS
+```
+
+- 50 步训练 ~0.9 s（不含 Whisper 首次下载和编码）
+- `tests/` 13 passed / 10 skipped — `forward()` 改动没破坏既有路径
+- 显存峰值未测；whisper-base + d6 + B=5 应该 < 2 GB
+
+---
+
+## 3. 已知限制 / 留给后续
+
+按"留意度"降序：
+
+1. **loss 下降并不能证明对齐学会了**。LM 也在训，5 段短字符串完全可以靠
+   LM 死记。projector 是不是真的在传递音频信息，得 W2 freeze LM 才能验证
+   ——那时候唯一能改 loss 的路径就是 projector → audio 通路。
+2. **`last_hidden_state` 是 Whisper 最偏文本语义的层**。"质感感知" 这个项目
+   定位要求 timbre / prosody / emotion 等非文本信号能传到 LM；W3+/W5+ 时
+   应当切到中间层、多层 weighted sum，或者干脆换 wav2vec2 / w2v-bert
+   （`facebook/w2v-bert-2.0` 在 cpc-i7 cache 里就有）。
+3. **30 s 强 padding** 让短音频浪费 6× 算力。短期是 W2 数据准备的常数代价；
+   长期需要 streaming-style chunking 或者直接换非-Whisper backbone。
+4. **CI smoke job 暂未接入**。W1 在 4090 本地跑通，按计划在 W2 同步把 audio
+   smoke 加进 `scripts/smoke.sh` + `.gitea/workflows/smoke.yml`，统一在 ailab
+   runner 上跑。
+
+---
+
+## 4. 衔接 W2
+
+[`doc/todo.md`](todo.md) W2 段已经写好，关键交接点：
+
+- **数据**：LibriSpeech 100h（HF mirror），预提 Whisper-base 特征落 webdataset
+- **冻结策略**：Whisper + LM **都**冻结，只训 Projector —— 这是真正的
+  弱对齐，也是验证 W1 第 1 条限制的实验
+- **可视化**：projector 输出对 LM 嵌入空间做 PCA，看不同样本是否在文本嵌入
+  空间里聚类
+- **wandb**：项目名分到 `nanochat-omni-audio`，跟 nanochat 文本 base 的
+  `nanochat` 互不污染

nanochat/audio.py

@@ -0,0 +1,116 @@
+"""
+Audio modality for nanochat-omni (W1).
+
+Frozen Whisper encoder produces soft tokens; Projector maps them into nanochat's
+residual stream (n_embd) so they can be prepended to text token embeddings
+LLaVA-style. Output remains text-only.
+
+Weights:
+- ModelScope first when WHISPER_MS_ID is set (e.g. iic/Whisper-small,
+  iic/Whisper-large-v3) — preferred path on CN boxes (ailab/zy/etc).
+- HuggingFace fallback (honors HF_ENDPOINT for hf-mirror).
+
+The encoder is held frozen; only Projector is trained.
+"""
+
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from nanochat.gpt import Linear
+
+
+def _load_whisper_via_modelscope(ms_id):
+    from modelscope import snapshot_download
+    local_path = snapshot_download(ms_id)
+    from transformers import WhisperModel, WhisperFeatureExtractor
+    extractor = WhisperFeatureExtractor.from_pretrained(local_path)
+    model = WhisperModel.from_pretrained(local_path)
+    return extractor, model.encoder
+
+
+def _load_whisper_via_hf(hf_id):
+    from transformers import WhisperModel, WhisperFeatureExtractor
+    extractor = WhisperFeatureExtractor.from_pretrained(hf_id)
+    model = WhisperModel.from_pretrained(hf_id)
+    return extractor, model.encoder
+
+
+def load_whisper(hf_id="openai/whisper-base", ms_id=None):
+    """Load (feature_extractor, encoder). Tries ModelScope if ms_id is given,
+    falls back to HuggingFace. Returns the .encoder submodule (no decoder)."""
+    ms_id = ms_id or os.environ.get("WHISPER_MS_ID")
+    hf_id = os.environ.get("WHISPER_HF_ID", hf_id)
+    errors = []
+    if ms_id:
+        try:
+            return _load_whisper_via_modelscope(ms_id)
+        except Exception as e:
+            errors.append(f"modelscope({ms_id}): {e}")
+    try:
+        return _load_whisper_via_hf(hf_id)
+    except Exception as e:
+        errors.append(f"hf({hf_id}): {e}")
+        raise RuntimeError("Failed to load Whisper encoder. Tried: " + " | ".join(errors))
+
+
+class WhisperEncoder(nn.Module):
+    """Frozen Whisper encoder. Forward takes log-mel input_features
+    (B, n_mels, T_mel) and returns (B, T_enc, d_model)."""
+
+    def __init__(self, hf_id="openai/whisper-base", ms_id=None, device=None, dtype=None):
+        super().__init__()
+        extractor, encoder = load_whisper(hf_id=hf_id, ms_id=ms_id)
+        self.feature_extractor = extractor
+        self.encoder = encoder
+        for p in self.encoder.parameters():
+            p.requires_grad = False
+        self.encoder.eval()
+        self._d_model = encoder.config.d_model
+        self.sampling_rate = extractor.sampling_rate
+        if device is not None or dtype is not None:
+            self.encoder.to(device=device, dtype=dtype)
+
+    @property
+    def d_model(self):
+        return self._d_model
+
+    def preprocess(self, audio_arrays):
+        """audio_arrays: list of 1D np.float32 (mono, sampling_rate Hz).
+        Returns input_features tensor (B, n_mels, T_mel)."""
+        out = self.feature_extractor(
+            audio_arrays,
+            sampling_rate=self.sampling_rate,
+            return_tensors="pt",
+        )
+        return out.input_features
+
+    @torch.no_grad()
+    def forward(self, input_features):
+        out = self.encoder(input_features=input_features)
+        return out.last_hidden_state
+
+
+class Projector(nn.Module):
+    """LLaVA-style 2-layer MLP: audio_d -> hidden -> n_embd.
+
+    Uses nanochat's Linear so master weights stay fp32 while forward runs in
+    the activation dtype (typically bf16). Matches the convention in gpt.py.
+    """
+
+    def __init__(self, in_dim, out_dim, hidden_dim=None):
+        super().__init__()
+        hidden_dim = hidden_dim or out_dim
+        self.fc1 = Linear(in_dim, hidden_dim, bias=False)
+        self.fc2 = Linear(hidden_dim, out_dim, bias=False)
+        s = (3.0 / in_dim) ** 0.5
+        torch.nn.init.uniform_(self.fc1.weight, -s, s)
+        torch.nn.init.zeros_(self.fc2.weight)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = F.gelu(x)
+        x = self.fc2(x)
+        return x

nanochat/checkpoint_manager.py

@@ -0,0 +1,194 @@
+"""
+Utilities for saving and loading model/optim/state checkpoints.
+"""
+import os
+import re
+import glob
+import json
+import logging
+import torch
+
+from nanochat.common import get_base_dir
+from nanochat.gpt import GPT, GPTConfig
+from nanochat.tokenizer import get_tokenizer
+from nanochat.common import setup_default_logging
+
+# Set up logging
+setup_default_logging()
+logger = logging.getLogger(__name__)
+def log0(message):
+    if int(os.environ.get('RANK', 0)) == 0:
+        logger.info(message)
+
+def _patch_missing_config_keys(model_config_kwargs):
+    """Add default values for new config keys missing in old checkpoints."""
+    # Old models were trained with full context (no sliding window)
+    if "window_pattern" not in model_config_kwargs:
+        model_config_kwargs["window_pattern"] = "L"
+        log0(f"Patching missing window_pattern in model config to 'L'")
+
+def _patch_missing_keys(model_data, model_config):
+    """Add default values for new parameters that may be missing in old checkpoints."""
+    n_layer = model_config.n_layer
+    # resid_lambdas defaults to 1.0 (identity scaling)
+    if "resid_lambdas" not in model_data:
+        model_data["resid_lambdas"] = torch.ones(n_layer)
+        log0(f"Patching missing resid_lambdas in model data to 1.0")
+    # x0_lambdas defaults to 0.0 (disabled)
+    if "x0_lambdas" not in model_data:
+        model_data["x0_lambdas"] = torch.zeros(n_layer)
+        log0(f"Patching missing x0_lambdas in model data to 0.0")
+
+def save_checkpoint(checkpoint_dir, step, model_data, optimizer_data, meta_data, rank=0):
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        # Save the model state parameters
+        model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt")
+        torch.save(model_data, model_path)
+        logger.info(f"Saved model parameters to: {model_path}")
+        # Save the metadata dict as json
+        meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json")
+        with open(meta_path, "w", encoding="utf-8") as f:
+            json.dump(meta_data, f, indent=2)
+        logger.info(f"Saved metadata to: {meta_path}")
+    # Note that optimizer state is sharded across ranks, so each rank must save its own.
+    if optimizer_data is not None:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}_rank{rank:d}.pt")
+        torch.save(optimizer_data, optimizer_path)
+        logger.info(f"Saved optimizer state to: {optimizer_path}")
+
+def load_checkpoint(checkpoint_dir, step, device, load_optimizer=False, rank=0):
+    # Load the model state
+    model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt")
+    model_data = torch.load(model_path, map_location=device)
+    # Load the optimizer state if requested
+    optimizer_data = None
+    if load_optimizer:
+        optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}_rank{rank:d}.pt")
+        optimizer_data = torch.load(optimizer_path, map_location=device)
+    # Load the metadata
+    meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json")
+    with open(meta_path, "r", encoding="utf-8") as f:
+        meta_data = json.load(f)
+    return model_data, optimizer_data, meta_data
+
+
+def build_model(checkpoint_dir, step, device, phase):
+    """
+    A bunch of repetitive code to build a model from a given checkpoint.
+    Returns:
+    - base model - uncompiled, not wrapped in DDP
+    - tokenizer
+    - meta data saved during base model training
+    """
+    assert phase in ["train", "eval"], f"Invalid phase: {phase}"
+    model_data, optimizer_data, meta_data = load_checkpoint(checkpoint_dir, step, device, load_optimizer=False)
+    if device.type in {"cpu", "mps"}:
+        # Convert bfloat16 tensors to float for CPU inference
+        model_data = {
+            k: v.float() if v.dtype == torch.bfloat16 else v
+            for k, v in model_data.items()
+        }
+    # Hack: fix torch compile issue, which prepends all keys with _orig_mod.
+    model_data = {k.removeprefix("_orig_mod."): v for k, v in model_data.items()}
+    model_config_kwargs = meta_data["model_config"]
+    _patch_missing_config_keys(model_config_kwargs)
+    log0(f"Building model with config: {model_config_kwargs}")
+    model_config = GPTConfig(**model_config_kwargs)
+    _patch_missing_keys(model_data, model_config)
+    with torch.device("meta"):
+        model = GPT(model_config)
+    # Load the model state
+    model.to_empty(device=device)
+    model.init_weights() # note: this is dumb, but we need to init the rotary embeddings. TODO: fix model re-init
+    model.load_state_dict(model_data, strict=True, assign=True)
+    # Put the model in the right training phase / mode
+    if phase == "eval":
+        model.eval()
+    else:
+        model.train()
+    # Load the Tokenizer
+    tokenizer = get_tokenizer()
+    # Sanity check: compatibility between model and tokenizer
+    assert tokenizer.get_vocab_size() == model_config_kwargs["vocab_size"], f"Tokenizer vocab size {tokenizer.get_vocab_size()} does not match model config vocab size {model_config_kwargs['vocab_size']}"
+    return model, tokenizer, meta_data
+
+
+def find_largest_model(checkpoints_dir):
+    # attempt to guess the model tag: take the biggest model available
+    model_tags = [f for f in os.listdir(checkpoints_dir) if os.path.isdir(os.path.join(checkpoints_dir, f))]
+    if not model_tags:
+        raise FileNotFoundError(f"No checkpoints found in {checkpoints_dir}")
+    # 1) normally all model tags are of the form d<number>, try that first:
+    candidates = []
+    for model_tag in model_tags:
+        match = re.match(r"d(\d+)", model_tag)
+        if match:
+            model_depth = int(match.group(1))
+            candidates.append((model_depth, model_tag))
+    if candidates:
+        candidates.sort(key=lambda x: x[0], reverse=True)
+        return candidates[0][1]
+    # 2) if that failed, take the most recently updated model:
+    model_tags.sort(key=lambda x: os.path.getmtime(os.path.join(checkpoints_dir, x)), reverse=True)
+    return model_tags[0]
+
+
+def find_last_step(checkpoint_dir):
+    # Look into checkpoint_dir and find model_<step>.pt with the highest step
+    checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "model_*.pt"))
+    if not checkpoint_files:
+        raise FileNotFoundError(f"No checkpoints found in {checkpoint_dir}")
+    last_step = int(max(os.path.basename(f).split("_")[-1].split(".")[0] for f in checkpoint_files))
+    return last_step
+
+# -----------------------------------------------------------------------------
+# convenience functions that take into account nanochat's directory structure
+
+def load_model_from_dir(checkpoints_dir, device, phase, model_tag=None, step=None):
+    if model_tag is None:
+        # guess the model tag by defaulting to the largest model
+        model_tag = find_largest_model(checkpoints_dir)
+        log0(f"No model tag provided, guessing model tag: {model_tag}")
+    checkpoint_dir = os.path.join(checkpoints_dir, model_tag)
+    if step is None:
+        # guess the step by defaulting to the last step
+        step = find_last_step(checkpoint_dir)
+    assert step is not None, f"No checkpoints found in {checkpoint_dir}"
+    # build the model
+    log0(f"Loading model from {checkpoint_dir} with step {step}")
+    model, tokenizer, meta_data = build_model(checkpoint_dir, step, device, phase)
+    return model, tokenizer, meta_data
+
+def load_model(source, *args, **kwargs):
+    model_dir = {
+        "base": "base_checkpoints",
+        "sft": "chatsft_checkpoints",
+        "rl": "chatrl_checkpoints",
+    }[source]
+    base_dir = get_base_dir()
+    checkpoints_dir = os.path.join(base_dir, model_dir)
+    return load_model_from_dir(checkpoints_dir, *args, **kwargs)
+
+def load_optimizer_state(source, device, rank, model_tag=None, step=None):
+    """Load just the optimizer shard for a given rank, without re-loading the model."""
+    model_dir = {
+        "base": "base_checkpoints",
+        "sft": "chatsft_checkpoints",
+        "rl": "chatrl_checkpoints",
+    }[source]
+    base_dir = get_base_dir()
+    checkpoints_dir = os.path.join(base_dir, model_dir)
+    if model_tag is None:
+        model_tag = find_largest_model(checkpoints_dir)
+    checkpoint_dir = os.path.join(checkpoints_dir, model_tag)
+    if step is None:
+        step = find_last_step(checkpoint_dir)
+    optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}_rank{rank:d}.pt")
+    if not os.path.exists(optimizer_path):
+        log0(f"Optimizer checkpoint not found: {optimizer_path}")
+        return None
+    log0(f"Loading optimizer state from {optimizer_path}")
+    optimizer_data = torch.load(optimizer_path, map_location=device)
+    return optimizer_data

nanochat/common.py

@@ -0,0 +1,278 @@
+"""
+Common utilities for nanochat.
+"""
+
+import os
+import re
+import logging
+import urllib.request
+import torch
+import torch.distributed as dist
+from filelock import FileLock
+
+# The dtype used for compute (matmuls, activations). Master weights stay fp32 for optimizer precision.
+# Linear layers cast their weights to this dtype in forward, replacing torch.amp.autocast.
+# Override with NANOCHAT_DTYPE env var: "bfloat16", "float16", "float32"
+_DTYPE_MAP = {"bfloat16": torch.bfloat16, "float16": torch.float16, "float32": torch.float32}
+def _detect_compute_dtype():
+    env = os.environ.get("NANOCHAT_DTYPE")
+    if env is not None:
+        return _DTYPE_MAP[env], f"set via NANOCHAT_DTYPE={env}"
+    if torch.cuda.is_available():
+        # bf16 requires SM 80+ (Ampere: A100, A10, etc.)
+        # Older GPUs like V100 (SM 70) and T4 (SM 75) only have fp16 tensor cores
+        capability = torch.cuda.get_device_capability()
+        if capability >= (8, 0):
+            return torch.bfloat16, f"auto-detected: CUDA SM {capability[0]}{capability[1]} (bf16 supported)"
+        # fp16 training requires GradScaler (not yet implemented), so fall back to fp32.
+        # Users can still force fp16 via NANOCHAT_DTYPE=float16 if they know what they're doing.
+        return torch.float32, f"auto-detected: CUDA SM {capability[0]}{capability[1]} (pre-Ampere, bf16 not supported, using fp32)"
+    return torch.float32, "auto-detected: no CUDA (CPU/MPS)"
+COMPUTE_DTYPE, COMPUTE_DTYPE_REASON = _detect_compute_dtype()
+
+class ColoredFormatter(logging.Formatter):
+    """Custom formatter that adds colors to log messages."""
+    # ANSI color codes
+    COLORS = {
+        'DEBUG': '\033[36m',    # Cyan
+        'INFO': '\033[32m',     # Green
+        'WARNING': '\033[33m',  # Yellow
+        'ERROR': '\033[31m',    # Red
+        'CRITICAL': '\033[35m', # Magenta
+    }
+    RESET = '\033[0m'
+    BOLD = '\033[1m'
+    def format(self, record):
+        # Add color to the level name
+        levelname = record.levelname
+        if levelname in self.COLORS:
+            record.levelname = f"{self.COLORS[levelname]}{self.BOLD}{levelname}{self.RESET}"
+        # Format the message
+        message = super().format(record)
+        # Add color to specific parts of the message
+        if levelname == 'INFO':
+            # Highlight numbers and percentages
+            message = re.sub(r'(\d+\.?\d*\s*(?:GB|MB|%|docs))', rf'{self.BOLD}\1{self.RESET}', message)
+            message = re.sub(r'(Shard \d+)', rf'{self.COLORS["INFO"]}{self.BOLD}\1{self.RESET}', message)
+        return message
+
+def setup_default_logging():
+    handler = logging.StreamHandler()
+    handler.setFormatter(ColoredFormatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+    logging.basicConfig(
+        level=logging.INFO,
+        handlers=[handler]
+    )
+
+setup_default_logging()
+logger = logging.getLogger(__name__)
+
+def get_base_dir():
+    # co-locate nanochat intermediates with other cached data in ~/.cache (by default)
+    if os.environ.get("NANOCHAT_BASE_DIR"):
+        nanochat_dir = os.environ.get("NANOCHAT_BASE_DIR")
+    else:
+        home_dir = os.path.expanduser("~")
+        cache_dir = os.path.join(home_dir, ".cache")
+        nanochat_dir = os.path.join(cache_dir, "nanochat")
+    os.makedirs(nanochat_dir, exist_ok=True)
+    return nanochat_dir
+
+def download_file_with_lock(url, filename, postprocess_fn=None):
+    """
+    Downloads a file from a URL to a local path in the base directory.
+    Uses a lock file to prevent concurrent downloads among multiple ranks.
+    """
+    base_dir = get_base_dir()
+    file_path = os.path.join(base_dir, filename)
+    lock_path = file_path + ".lock"
+
+    if os.path.exists(file_path):
+        return file_path
+
+    with FileLock(lock_path):
+        # Only a single rank can acquire this lock
+        # All other ranks block until it is released
+
+        # Recheck after acquiring lock
+        if os.path.exists(file_path):
+            return file_path
+
+        # Download the content as bytes
+        print(f"Downloading {url}...")
+        with urllib.request.urlopen(url) as response:
+            content = response.read() # bytes
+
+        # Write to local file
+        with open(file_path, 'wb') as f:
+            f.write(content)
+        print(f"Downloaded to {file_path}")
+
+        # Run the postprocess function if provided
+        if postprocess_fn is not None:
+            postprocess_fn(file_path)
+
+    return file_path
+
+def print0(s="",**kwargs):
+    ddp_rank = int(os.environ.get('RANK', 0))
+    if ddp_rank == 0:
+        print(s, **kwargs)
+
+def print_banner():
+    # Cool DOS Rebel font ASCII banner made with https://manytools.org/hacker-tools/ascii-banner/
+    banner = """
+                                                       █████                █████
+                                                      ░░███                ░░███
+     ████████    ██████   ████████    ██████   ██████  ░███████    ██████  ███████
+    ░░███░░███  ░░░░░███ ░░███░░███  ███░░███ ███░░███ ░███░░███  ░░░░░███░░░███░
+     ░███ ░███   ███████  ░███ ░███ ░███ ░███░███ ░░░  ░███ ░███   ███████  ░███
+     ░███ ░███  ███░░███  ░███ ░███ ░███ ░███░███  ███ ░███ ░███  ███░░███  ░███ ███
+     ████ █████░░████████ ████ █████░░██████ ░░██████  ████ █████░░███████  ░░█████
+    ░░░░ ░░░░░  ░░░░░░░░ ░░░░ ░░░░░  ░░░░░░   ░░░░░░  ░░░░ ░░░░░  ░░░░░░░░   ░░░░░
+    """
+    print0(banner)
+
+def is_ddp_requested() -> bool:
+    """
+    True if launched by torchrun (env present), even before init.
+    Used to decide whether we *should* initialize a PG.
+    """
+    return all(k in os.environ for k in ("RANK", "LOCAL_RANK", "WORLD_SIZE"))
+
+def is_ddp_initialized() -> bool:
+    """
+    True if torch.distributed is available and the process group is initialized.
+    Used at cleanup to avoid destroying a non-existent PG.
+    """
+    return dist.is_available() and dist.is_initialized()
+
+def get_dist_info():
+    if is_ddp_requested():
+        # We rely on torchrun's env to decide if we SHOULD init.
+        # (Initialization itself happens in compute init.)
+        assert all(var in os.environ for var in ['RANK', 'LOCAL_RANK', 'WORLD_SIZE'])
+        ddp_rank = int(os.environ['RANK'])
+        ddp_local_rank = int(os.environ['LOCAL_RANK'])
+        ddp_world_size = int(os.environ['WORLD_SIZE'])
+        return True, ddp_rank, ddp_local_rank, ddp_world_size
+    else:
+        return False, 0, 0, 1
+
+def autodetect_device_type():
+    # prefer to use CUDA if available, otherwise use MPS, otherwise fallback on CPU
+    if torch.cuda.is_available():
+        device_type = "cuda"
+    elif torch.backends.mps.is_available():
+        device_type = "mps"
+    else:
+        device_type = "cpu"
+    print0(f"Autodetected device type: {device_type}")
+    return device_type
+
+def compute_init(device_type="cuda"): # cuda|cpu|mps
+    """Basic initialization that we keep doing over and over, so make common."""
+
+    assert device_type in ["cuda", "mps", "cpu"], "Invalid device type atm"
+    if device_type == "cuda":
+        assert torch.cuda.is_available(), "Your PyTorch installation is not configured for CUDA but device_type is 'cuda'"
+    if device_type == "mps":
+        assert torch.backends.mps.is_available(), "Your PyTorch installation is not configured for MPS but device_type is 'mps'"
+
+    # Reproducibility
+    # Note that we set the global seeds here, but most of the code uses explicit rng objects.
+    # The only place where global rng might be used is nn.Module initialization of the model weights.
+    torch.manual_seed(42)
+    if device_type == "cuda":
+        torch.cuda.manual_seed(42)
+    # skipping full reproducibility for now, possibly investigate slowdown later
+    # torch.use_deterministic_algorithms(True)
+
+    # Precision
+    if device_type == "cuda":
+        torch.set_float32_matmul_precision("high") # uses tf32 instead of fp32 for matmuls, see https://docs.pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html
+
+    # Distributed setup: Distributed Data Parallel (DDP), optional, and requires CUDA
+    is_ddp_requested, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
+    if is_ddp_requested and device_type == "cuda":
+        device = torch.device("cuda", ddp_local_rank)
+        torch.cuda.set_device(device)  # make "cuda" default to this device
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    else:
+        device = torch.device(device_type) # mps|cpu
+
+    if ddp_rank == 0:
+        logger.info(f"Distributed world size: {ddp_world_size}")
+
+    return is_ddp_requested, ddp_rank, ddp_local_rank, ddp_world_size, device
+
+def compute_cleanup():
+    """Companion function to compute_init, to clean things up before script exit"""
+    if is_ddp_initialized():
+        dist.destroy_process_group()
+
+class DummyWandb:
+    """Useful if we wish to not use wandb but have all the same signatures"""
+    def __init__(self):
+        pass
+    def log(self, *args, **kwargs):
+        pass
+    def finish(self):
+        pass
+
+# hardcoded BF16 peak flops for various GPUs
+# inspired by torchtitan: https://github.com/pytorch/torchtitan/blob/main/torchtitan/tools/utils.py
+# and PR: https://github.com/karpathy/nanochat/pull/147
+def get_peak_flops(device_name: str) -> float:
+    name = device_name.lower()
+
+    # Table order matters: more specific patterns first.
+    _PEAK_FLOPS_TABLE = (
+        # NVIDIA Blackwell
+        (["gb200"], 2.5e15),
+        (["grace blackwell"], 2.5e15),
+        (["b200"], 2.25e15),
+        (["b100"], 1.8e15),
+        # NVIDIA Hopper
+        (["h200", "nvl"], 836e12),
+        (["h200", "pcie"], 836e12),
+        (["h200"], 989e12),
+        (["h100", "nvl"], 835e12),
+        (["h100", "pcie"], 756e12),
+        (["h100"], 989e12),
+        (["h800", "nvl"], 989e12),
+        (["h800"], 756e12),
+        # NVIDIA Ampere data center
+        (["a100"], 312e12),
+        (["a800"], 312e12),
+        (["a40"], 149.7e12),
+        (["a30"], 165e12),
+        # NVIDIA Ada data center
+        (["l40s"], 362e12),
+        (["l40-s"], 362e12),
+        (["l40 s"], 362e12),
+        (["l4"], 121e12),
+        # AMD CDNA accelerators
+        (["mi355"], 2.5e15),
+        (["mi325"], 1.3074e15),
+        (["mi300x"], 1.3074e15),
+        (["mi300a"], 980.6e12),
+        (["mi250x"], 383e12),
+        (["mi250"], 362.1e12),
+        # Consumer RTX
+        (["5090"], 209.5e12),
+        (["4090"], 165.2e12),
+        (["3090"], 71e12),
+    )
+    for patterns, flops in _PEAK_FLOPS_TABLE:
+        if all(p in name for p in patterns):
+            return flops
+    if "data center gpu max 1550" in name:
+        # Ponte Vecchio (PVC) - dynamic based on compute units
+        max_comp_units = torch.xpu.get_device_properties("xpu").max_compute_units
+        return 512 * max_comp_units * 1300 * 10**6
+
+    # Unknown GPU - return inf so MFU shows as 0% rather than a wrong guess
+    logger.warning(f"Peak flops undefined for: {device_name}, MFU will show as 0%")
+    return float('inf')

nanochat/core_eval.py

@@ -0,0 +1,262 @@
+"""
+Functions for evaluating the CORE metric, as described in the DCLM paper.
+https://arxiv.org/abs/2406.11794
+
+TODOs:
+- All tasks ~match except for squad. We get 31% reference is 37%. Figure out why.
+"""
+import random
+
+from jinja2 import Template
+import torch
+import torch.distributed as dist
+
+# -----------------------------------------------------------------------------
+# Prompt rendering utilities
+
+def render_prompts_mc(item, continuation_delimiter, fewshot_examples=None):
+    """Render complete prompts for a multiple choice question"""
+    template_str = """
+{%- for example in fewshot_examples -%}
+{{ example.query }}{{ continuation_delimiter }}{{ example.choices[example.gold] }}
+
+{% endfor -%}
+{{ item.query }}{{ continuation_delimiter }}{{ choice }}""".strip()
+    template = Template(template_str)
+    fewshot_examples = fewshot_examples or []
+    context = {
+        'fewshot_examples': fewshot_examples,
+        'continuation_delimiter': continuation_delimiter,
+        'item': item
+    }
+    prompts = [template.render(choice=choice, **context) for choice in item['choices']]
+    return prompts
+
+
+def render_prompts_schema(item, continuation_delimiter, fewshot_examples=None):
+    """Render complete prompts for a schema question"""
+    template_str = """
+{%- for example in fewshot_examples -%}
+{{ example.context_options[example.gold] }}{{ continuation_delimiter }}{{ example.continuation }}
+
+{% endfor -%}
+{{ context }}{{ continuation_delimiter }}{{ item.continuation }}""".strip()
+    template = Template(template_str)
+    fewshot_examples = fewshot_examples or []
+    context = {
+        'fewshot_examples': fewshot_examples,
+        'continuation_delimiter': continuation_delimiter,
+        'item': item
+    }
+    prompts = [template.render(context=context_option, **context)
+               for context_option in item['context_options']]
+    return prompts
+
+
+def render_prompts_lm(item, continuation_delimiter, fewshot_examples=None):
+    """
+    Render complete prompt for a language modeling task.
+    Notice that we manually trim the context in the template,
+    which in some datasets seems to have trailing whitespace (which we don't want).
+    """
+    template_str = """
+{%- for example in fewshot_examples -%}
+{{ example.context | trim }}{{ continuation_delimiter }}{{ example.continuation }}
+
+{% endfor -%}
+{{ item.context | trim }}{{ continuation_delimiter }}{% if include_continuation %}{{ item.continuation }}{% endif %}""".strip()
+    template = Template(template_str)
+    fewshot_examples = fewshot_examples or []
+    context = {
+        'fewshot_examples': fewshot_examples,
+        'continuation_delimiter': continuation_delimiter,
+        'item': item
+    }
+    # Return two prompts: without and with the continuation
+    prompt_without = template.render(include_continuation=False, **context)
+    prompt_with = template.render(include_continuation=True, **context)
+    # Due to the way the data seems to be stored, I think I need to strip in the case of LM here.
+    # Otherwise we may get trailing whitespaces in prompt_without (which get absorbed into the next
+    # token in prompt_with), meaning we don't get a nice and clean prefix in the token space
+    # to detect the final continuation. Tokenizers...
+    prompt_without = prompt_without.strip()
+    return [prompt_without, prompt_with]
+
+
+def find_common_length(token_sequences, direction='left'):
+    """
+    Find the length of the common prefix or suffix across token sequences
+    - direction: 'left' for prefix, 'right' for suffix
+    """
+    min_len = min(len(seq) for seq in token_sequences)
+    indices = {
+        'left': range(min_len),
+        'right': range(-1, -min_len-1, -1)
+    }[direction]
+    # Find the first position where the token sequences differ
+    for i, idx in enumerate(indices):
+        token = token_sequences[0][idx]
+        if not all(seq[idx] == token for seq in token_sequences):
+            return i
+    return min_len
+
+
+def stack_sequences(tokens, pad_token_id):
+    """Stack up a list of token sequences, pad to longest on the right"""
+    bsz, seq_len = len(tokens), max(len(x) for x in tokens)
+    input_ids = torch.full((bsz, seq_len), pad_token_id, dtype=torch.long)
+    for i, x in enumerate(tokens):
+        input_ids[i, :len(x)] = torch.tensor(x, dtype=torch.long)
+    return input_ids
+
+
+def batch_sequences_mc(tokenizer, prompts):
+    # In multiple choice, contexts are the same but the continuation is different (common prefix)
+    tokens = tokenizer(prompts, prepend=tokenizer.get_bos_token_id())
+    # figure out the start and end of each continuation
+    answer_start_idx = find_common_length(tokens, direction='left')
+    start_indices = [answer_start_idx] * len(prompts)
+    end_indices = [len(x) for x in tokens]
+    return tokens, start_indices, end_indices
+
+
+def batch_sequences_schema(tokenizer, prompts):
+    # In schema tasks, contexts vary but continuation is the same (common suffix)
+    tokens = tokenizer(prompts, prepend=tokenizer.get_bos_token_id())
+    # figure out the start and end of each context
+    suffix_length = find_common_length(tokens, direction='right')
+    end_indices = [len(x) for x in tokens]
+    start_indices = [ei - suffix_length for ei in end_indices]
+    return tokens, start_indices, end_indices
+
+
+def batch_sequences_lm(tokenizer, prompts):
+    # In LM tasks, we have two prompts: without and with continuation
+    tokens = tokenizer(prompts, prepend=tokenizer.get_bos_token_id())
+    tokens_without, tokens_with = tokens
+    start_idx, end_idx = len(tokens_without), len(tokens_with)
+    assert start_idx < end_idx, "prompt without is supposed to be a prefix of prompt with"
+    assert tokens_without == tokens_with[:start_idx], "prompt without is supposed to be a prefix of prompt with"
+    # we only need the with continuation prompt in the LM task, i.e. batch size of 1
+    return [tokens_with], [start_idx], [end_idx]
+
+
+@torch.no_grad()
+def forward_model(model, input_ids):
+    """
+    Take BxT tensor of token ids, return BxT tensor of losses and argmax predictions.
+    The last column of losses is set to nan because we don't have autoregressive targets there.
+    """
+    batch_size, seq_len = input_ids.size()
+    outputs = model(input_ids)
+    # Roll the tensor to the left by one position to get the (autoregressive) target ids
+    target_ids = torch.roll(input_ids, shifts=-1, dims=1)
+    # Calculate cross entropy at all positions
+    losses = torch.nn.functional.cross_entropy(
+        outputs.view(batch_size * seq_len, -1),
+        target_ids.view(batch_size * seq_len),
+        reduction='none'
+    ).view(batch_size, seq_len)
+    # Set the last column to be nan because there is no autoregressive loss there
+    losses[:, -1] = float('nan')
+    # Get the argmax predictions at each position
+    predictions = outputs.argmax(dim=-1)
+    return losses, predictions
+
+
+@torch.no_grad()
+def evaluate_example(idx, model, tokenizer, data, device, task_meta):
+    """Evaluate a single example, return True if correct, False otherwise"""
+    item = data[idx]
+    task_type = task_meta['task_type']
+    num_fewshot = task_meta['num_fewshot']
+    continuation_delimiter = task_meta['continuation_delimiter']
+
+    # Sample few-shot examples (excluding current item)
+    fewshot_examples = []
+    if num_fewshot > 0:
+        rng = random.Random(1234 + idx)
+        available_indices = [i for i in range(len(data)) if i != idx]
+        fewshot_indices = rng.sample(available_indices, num_fewshot)
+        fewshot_examples = [data[i] for i in fewshot_indices]
+
+    # Render prompts and batch sequences based on task type
+    if task_type == 'multiple_choice':
+        prompts = render_prompts_mc(item, continuation_delimiter, fewshot_examples)
+        tokens, start_idxs, end_idxs = batch_sequences_mc(tokenizer, prompts)
+    elif task_type == 'schema':
+        prompts = render_prompts_schema(item, continuation_delimiter, fewshot_examples)
+        tokens, start_idxs, end_idxs = batch_sequences_schema(tokenizer, prompts)
+    elif task_type == 'language_modeling':
+        prompts = render_prompts_lm(item, continuation_delimiter, fewshot_examples)
+        tokens, start_idxs, end_idxs = batch_sequences_lm(tokenizer, prompts)
+    else:
+        raise ValueError(f"Unsupported task type: {task_type}")
+
+    # Some models can't forward sequences beyond a certain length (e.g. GPT-2)
+    # In these cases, we have to truncate sequences to max length and adjust the indices
+    if hasattr(model, 'max_seq_len') and model.max_seq_len is not None:
+        max_tokens = model.max_seq_len
+        new_tokens, new_start_idxs, new_end_idxs = [], [], []
+        for t, s, e in zip(tokens, start_idxs, end_idxs):
+            if len(t) > max_tokens:
+                num_to_crop = len(t) - max_tokens
+                new_tokens.append(t[-max_tokens:]) # take the last max_tokens tokens
+                new_start_idxs.append(s - num_to_crop) # shift the indices down
+                new_end_idxs.append(e - num_to_crop)
+                assert s - num_to_crop >= 0, "this should never happen right?"
+                assert e - num_to_crop >= 0, "this should never happen right?"
+            else:
+                new_tokens.append(t) # keep unchanged
+                new_start_idxs.append(s)
+                new_end_idxs.append(e)
+        tokens, start_idxs, end_idxs = new_tokens, new_start_idxs, new_end_idxs
+
+    # Stack up all the sequences into a batch
+    pad_token_id = tokenizer.get_bos_token_id() # use BOS as pad token is ok
+    input_ids = stack_sequences(tokens, pad_token_id)
+    input_ids = input_ids.to(device)
+
+    # Forward the model, get the autoregressive loss and argmax prediction at each token
+    losses, predictions = forward_model(model, input_ids)
+
+    # See if the losses/predictions come out correctly
+    if task_type == 'language_modeling':
+        # language modeling task is currently always batch size 1
+        si = start_idxs[0]
+        ei = end_idxs[0]
+        # predictions[i] predict input_ids[i+1] autoregressively
+        predicted_tokens = predictions[0, si-1:ei-1]
+        actual_tokens = input_ids[0, si:ei]
+        is_correct = torch.all(predicted_tokens == actual_tokens).item()
+    elif task_type in ['multiple_choice', 'schema']:
+        # For MC/schema: find the option with lowest average loss
+        mean_losses = [losses[i, si-1:ei-1].mean().item()
+                        for i, (si, ei) in enumerate(zip(start_idxs, end_idxs))]
+        pred_idx = mean_losses.index(min(mean_losses))
+        is_correct = pred_idx == item['gold']
+    else:
+        raise ValueError(f"Unsupported task type: {task_type}")
+
+    return is_correct
+
+
+def evaluate_task(model, tokenizer, data, device, task_meta):
+    """
+    This function is responsible for evaluating one task across many examples.
+    It also handles dispatch to all processes if the script is run with torchrun.
+    """
+    rank = dist.get_rank() if dist.is_initialized() else 0
+    world_size = dist.get_world_size() if dist.is_initialized() else 1
+    correct = torch.zeros(len(data), dtype=torch.float32, device=device)
+    # stride the examples to each rank
+    for idx in range(rank, len(data), world_size):
+        is_correct = evaluate_example(idx, model, tokenizer, data, device, task_meta)
+        correct[idx] = float(is_correct)
+    # sync results across all the processes if running distributed
+    if world_size > 1:
+        dist.barrier()
+        dist.all_reduce(correct, op=dist.ReduceOp.SUM)
+    # compute the mean
+    mean_correct = correct.mean().item()
+    return mean_correct

nanochat/dataloader.py

@@ -0,0 +1,166 @@
+"""
+Distributed dataloaders for pretraining.
+
+BOS-aligned bestfit:
+   - Every row starts with BOS token
+   - Documents packed using best-fit algorithm to minimize cropping
+   - When no document fits remaining space, crops a document to fill exactly
+   - 100% utilization (no padding), ~35% tokens cropped at T=2048
+
+Compared to the original tokenizing_distributed_data_loader:
+BOS-aligned loses ~35% of tokens to cropping, but ensures that
+there are fewer "confusing" tokens in the train/val batches as every token can
+now attend back to the BOS token and sees the full context of the document.
+
+Fallback to the original if you have very limited data AND long documents:
+https://github.com/karpathy/nanochat/blob/3c3a3d7/nanochat/dataloader.py#L78-L117
+"""
+
+import torch
+import pyarrow.parquet as pq
+
+from nanochat.common import get_dist_info
+from nanochat.dataset import list_parquet_files
+
+def _document_batches(split, resume_state_dict, tokenizer_batch_size):
+    """
+    Infinite iterator over document batches (list of text strings) from parquet files.
+
+    Handles DDP sharding and approximate resume. Each yield is (text_batch, (pq_idx, rg_idx, epoch))
+    where text_batch is a list of document strings, indices track position for resumption,
+    and epoch counts how many times we've cycled through the dataset (starts at 1).
+    """
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
+
+    warn_on_legacy = ddp_rank == 0 and split == "train" # rank 0 on train split will warn on legacy
+    parquet_paths = list_parquet_files(warn_on_legacy=warn_on_legacy)
+    assert len(parquet_paths) != 0, "No dataset parquet files found, did you run dataset.py?"
+    parquet_paths = parquet_paths[:-1] if split == "train" else parquet_paths[-1:]
+
+    resume_pq_idx = resume_state_dict["pq_idx"] if resume_state_dict is not None else 0
+    resume_rg_idx = resume_state_dict["rg_idx"] if resume_state_dict is not None else None
+    resume_epoch = resume_state_dict.get("epoch", 1) if resume_state_dict is not None else 1
+    first_pass = True
+    pq_idx = resume_pq_idx
+    epoch = resume_epoch
+
+    while True:  # iterate infinitely (multi-epoch)
+        pq_idx = resume_pq_idx if first_pass else 0
+        while pq_idx < len(parquet_paths):
+            filepath = parquet_paths[pq_idx]
+            pf = pq.ParquetFile(filepath)
+            # Start from resume point if resuming on same file, otherwise from DDP rank
+            if first_pass and (resume_rg_idx is not None) and (pq_idx == resume_pq_idx):
+                base_idx = resume_rg_idx // ddp_world_size
+                base_idx += 1  # advance by 1 so we don't repeat data after resuming
+                rg_idx = base_idx * ddp_world_size + ddp_rank
+                if rg_idx >= pf.num_row_groups:
+                    pq_idx += 1
+                    continue
+                resume_rg_idx = None  # only do this once
+            else:
+                rg_idx = ddp_rank
+            while rg_idx < pf.num_row_groups:
+                rg = pf.read_row_group(rg_idx)
+                batch = rg.column('text').to_pylist()
+                for i in range(0, len(batch), tokenizer_batch_size):
+                    yield batch[i:i+tokenizer_batch_size], (pq_idx, rg_idx, epoch)
+                rg_idx += ddp_world_size
+            pq_idx += 1
+        first_pass = False
+        epoch += 1
+
+
+def tokenizing_distributed_data_loader_with_state_bos_bestfit(
+    tokenizer, B, T, split,
+    tokenizer_threads=4, tokenizer_batch_size=128,
+    device="cuda", resume_state_dict=None,
+    buffer_size=1000
+):
+    """
+    BOS-aligned dataloader with Best-Fit Cropping.
+
+    Reduces token waste compared to simple greedy cropping by searching a buffer
+    for documents that fit well, while maintaining 100% utilization (no padding).
+
+    Algorithm for each row:
+    1. From buffered docs, pick the LARGEST doc that fits entirely
+    2. Repeat until no doc fits
+    3. When nothing fits, crop a doc to fill remaining space exactly
+
+    Key properties:
+    - Every row starts with BOS
+    - 100% utilization (no padding, every token is trained on)
+    - Approximately 35% of all tokens are discarded due to cropping
+    """
+    assert split in ["train", "val"], "split must be 'train' or 'val'"
+
+    row_capacity = T + 1
+    batches = _document_batches(split, resume_state_dict, tokenizer_batch_size)
+    bos_token = tokenizer.get_bos_token_id()
+    doc_buffer = []
+    pq_idx, rg_idx, epoch = 0, 0, 1
+
+    def refill_buffer():
+        nonlocal pq_idx, rg_idx, epoch
+        doc_batch, (pq_idx, rg_idx, epoch) = next(batches)
+        token_lists = tokenizer.encode(doc_batch, prepend=bos_token, num_threads=tokenizer_threads)
+        for tokens in token_lists:
+            doc_buffer.append(tokens)
+
+    # Pre-allocate buffers once: layout is [inputs (B*T) | targets (B*T)]
+    # This gives us contiguous views and a single HtoD transfer
+    use_cuda = device == "cuda"
+    row_buffer = torch.empty((B, row_capacity), dtype=torch.long) # for building rows without creating Python lists
+    cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=use_cuda) # staging area (CPU)
+    gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device=device) # on-device buffer
+    cpu_inputs = cpu_buffer[:B * T].view(B, T) # a few views into these buffers just for convenience
+    cpu_targets = cpu_buffer[B * T:].view(B, T)
+    inputs = gpu_buffer[:B * T].view(B, T)
+    targets = gpu_buffer[B * T:].view(B, T)
+
+    while True:
+        for row_idx in range(B):
+            pos = 0
+            while pos < row_capacity:
+                # Ensure buffer has documents
+                while len(doc_buffer) < buffer_size:
+                    refill_buffer()
+
+                remaining = row_capacity - pos
+
+                # Find largest doc that fits entirely
+                best_idx = -1
+                best_len = 0
+                for i, doc in enumerate(doc_buffer):
+                    doc_len = len(doc)
+                    if doc_len <= remaining and doc_len > best_len:
+                        best_idx = i
+                        best_len = doc_len
+
+                if best_idx >= 0:
+                    doc = doc_buffer.pop(best_idx)
+                    doc_len = len(doc)
+                    row_buffer[row_idx, pos:pos + doc_len] = torch.tensor(doc, dtype=torch.long)
+                    pos += doc_len
+                else:
+                    # No doc fits - crop shortest in buffer to fill remaining and minimize waste
+                    shortest_idx = min(range(len(doc_buffer)), key=lambda i: len(doc_buffer[i]))
+                    doc = doc_buffer.pop(shortest_idx)
+                    row_buffer[row_idx, pos:pos + remaining] = torch.tensor(doc[:remaining], dtype=torch.long)
+                    pos += remaining
+
+        # Copy to pinned CPU buffer, then single HtoD transfer
+        cpu_inputs.copy_(row_buffer[:, :-1])
+        cpu_targets.copy_(row_buffer[:, 1:])
+
+        state_dict = {"pq_idx": pq_idx, "rg_idx": rg_idx, "epoch": epoch}
+
+        # Single HtoD copy into persistent GPU buffer and yield
+        gpu_buffer.copy_(cpu_buffer, non_blocking=use_cuda)
+        yield inputs, targets, state_dict
+
+def tokenizing_distributed_data_loader_bos_bestfit(*args, **kwargs):
+    """Helper that omits state_dict from yields."""
+    for inputs, targets, state_dict in tokenizing_distributed_data_loader_with_state_bos_bestfit(*args, **kwargs):
+        yield inputs, targets

nanochat/dataset.py

@@ -0,0 +1,160 @@
+"""
+The base/pretraining dataset is a set of parquet files.
+This file contains utilities for:
+- iterating over the parquet files and yielding documents from it
+- download the files on demand if they are not on disk
+
+For details of how the dataset was prepared, see `repackage_data_reference.py`.
+"""
+
+import os
+import argparse
+import time
+import requests
+import pyarrow.parquet as pq
+from multiprocessing import Pool
+
+from nanochat.common import get_base_dir
+
+# -----------------------------------------------------------------------------
+# The specifics of the current pretraining dataset
+
+# The URL on the internet where the data is hosted and downloaded from on demand
+BASE_URL = "https://hf-mirror.com/datasets/karpathy/climbmix-400b-shuffle/resolve/main"
+MAX_SHARD = 6542 # the last datashard is shard_06542.parquet
+index_to_filename = lambda index: f"shard_{index:05d}.parquet" # format of the filenames
+base_dir = get_base_dir()
+DATA_DIR = os.path.join(base_dir, "base_data_climbmix")
+
+# -----------------------------------------------------------------------------
+# These functions are useful utilities to other modules, can/should be imported
+
+def list_parquet_files(data_dir=None, warn_on_legacy=False):
+    """ Looks into a data dir and returns full paths to all parquet files. """
+    data_dir = DATA_DIR if data_dir is None else data_dir
+
+    # Legacy-supporting code due to the upgrade from FinewebEdu-100B to ClimbMix-400B
+    # This code will eventually be deleted.
+    if not os.path.exists(data_dir):
+        if warn_on_legacy:
+            print()
+            print("=" * 80)
+            print("  WARNING: DATASET UPGRADE REQUIRED")
+            print("=" * 80)
+            print()
+            print(f"  Could not find: {data_dir}")
+            print()
+            print("  nanochat recently switched from FinewebEdu-100B to ClimbMix-400B.")
+            print("  Everyone who does `git pull` as of March 4, 2026 is expected to see this message.")
+            print("  To upgrade to the new ClimbMix-400B dataset, run these two commands:")
+            print()
+            print("    python -m nanochat.dataset -n 170     # download ~170 shards, enough for GPT-2, adjust as desired")
+            print("    python -m scripts.tok_train           # re-train tokenizer on new ClimbMix data")
+            print()
+            print("  For now, falling back to your old FinewebEdu-100B dataset...")
+            print("=" * 80)
+            print()
+        # attempt a fallback to the legacy data directory
+        data_dir = os.path.join(base_dir, "base_data")
+
+    parquet_files = sorted([
+        f for f in os.listdir(data_dir)
+        if f.endswith('.parquet') and not f.endswith('.tmp')
+    ])
+    parquet_paths = [os.path.join(data_dir, f) for f in parquet_files]
+    return parquet_paths
+
+def parquets_iter_batched(split, start=0, step=1):
+    """
+    Iterate through the dataset, in batches of underlying row_groups for efficiency.
+    - split can be "train" or "val". the last parquet file will be val.
+    - start/step are useful for skipping rows in DDP. e.g. start=rank, step=world_size
+    """
+    assert split in ["train", "val"], "split must be 'train' or 'val'"
+    parquet_paths = list_parquet_files()
+    parquet_paths = parquet_paths[:-1] if split == "train" else parquet_paths[-1:]
+    for filepath in parquet_paths:
+        pf = pq.ParquetFile(filepath)
+        for rg_idx in range(start, pf.num_row_groups, step):
+            rg = pf.read_row_group(rg_idx)
+            texts = rg.column('text').to_pylist()
+            yield texts
+
+# -----------------------------------------------------------------------------
+def download_single_file(index):
+    """ Downloads a single file index, with some backoff """
+
+    # Construct the local filepath for this file and skip if it already exists
+    filename = index_to_filename(index)
+    filepath = os.path.join(DATA_DIR, filename)
+    if os.path.exists(filepath):
+        print(f"Skipping {filepath} (already exists)")
+        return True
+
+    # Construct the remote URL for this file
+    url = f"{BASE_URL}/{filename}"
+    print(f"Downloading {filename}...")
+
+    # Download with retries
+    max_attempts = 5
+    for attempt in range(1, max_attempts + 1):
+        try:
+            response = requests.get(url, stream=True, timeout=30)
+            response.raise_for_status()
+            # Write to temporary file first
+            temp_path = filepath + f".tmp"
+            with open(temp_path, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=1024 * 1024):  # 1MB chunks
+                    if chunk:
+                        f.write(chunk)
+            # Move temp file to final location
+            os.rename(temp_path, filepath)
+            print(f"Successfully downloaded {filename}")
+            return True
+
+        except (requests.RequestException, IOError) as e:
+            print(f"Attempt {attempt}/{max_attempts} failed for {filename}: {e}")
+            # Clean up any partial files
+            for path in [filepath + f".tmp", filepath]:
+                if os.path.exists(path):
+                    try:
+                        os.remove(path)
+                    except:
+                        pass
+            # Try a few times with exponential backoff: 2^attempt seconds
+            if attempt < max_attempts:
+                wait_time = 2 ** attempt
+                print(f"Waiting {wait_time} seconds before retry...")
+                time.sleep(wait_time)
+            else:
+                print(f"Failed to download {filename} after {max_attempts} attempts")
+                return False
+
+    return False
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Download pretraining dataset shards")
+    parser.add_argument("-n", "--num-files", type=int, default=-1, help="Number of train shards to download (default: -1), -1 = disable")
+    parser.add_argument("-w", "--num-workers", type=int, default=4, help="Number of parallel download workers (default: 4)")
+    args = parser.parse_args()
+
+    # Prepare the output directory
+    os.makedirs(DATA_DIR, exist_ok=True)
+
+    # The way this works is that the user specifies the number of train shards to download via the -n flag.
+    # In addition to that, the validation shard is *always* downloaded and is pinned to be the last shard.
+    num_train_shards = MAX_SHARD if args.num_files == -1 else min(args.num_files, MAX_SHARD)
+    ids_to_download = list(range(num_train_shards))
+    ids_to_download.append(MAX_SHARD) # always download the validation shard
+
+    # Download the shards
+    print(f"Downloading {len(ids_to_download)} shards using {args.num_workers} workers...")
+    print(f"Target directory: {DATA_DIR}")
+    print()
+    with Pool(processes=args.num_workers) as pool:
+        results = pool.map(download_single_file, ids_to_download)
+
+    # Report results
+    successful = sum(1 for success in results if success)
+    print(f"Done! Downloaded: {successful}/{len(ids_to_download)} shards to {DATA_DIR}")

nanochat/engine.py

@@ -0,0 +1,357 @@
+"""
+Engine for efficient inference of our models.
+
+Everything works around token sequences:
+- The user can send token sequences to the engine
+- The engine returns the next token
+
+Notes:
+- The engine knows nothing about tokenization, it's purely token id sequences.
+
+The whole thing is made as efficient as possible.
+"""
+
+import torch
+import torch.nn.functional as F
+import signal
+import warnings
+from contextlib import contextmanager
+from collections import deque
+from nanochat.common import compute_init, autodetect_device_type
+from nanochat.checkpoint_manager import load_model
+
+# -----------------------------------------------------------------------------
+# Calculator tool helpers
+@contextmanager
+def timeout(duration, formula):
+    def timeout_handler(signum, frame):
+        raise Exception(f"'{formula}': timed out after {duration} seconds")
+
+    signal.signal(signal.SIGALRM, timeout_handler)
+    signal.alarm(duration)
+    yield
+    signal.alarm(0)
+
+def eval_with_timeout(formula, max_time=3):
+    try:
+        with timeout(max_time, formula):
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", SyntaxWarning)
+                return eval(formula, {"__builtins__": {}}, {})
+    except Exception as e:
+        signal.alarm(0)
+        # print(f"Warning: Failed to eval {formula}, exception: {e}") # it's ok ignore wrong calculator usage
+        return None
+
+def use_calculator(expr):
+    """
+    Evaluate a Python expression safely.
+    Supports both math expressions and string operations like .count()
+    """
+    # Remove commas from numbers
+    expr = expr.replace(",", "")
+
+    # Check if it's a pure math expression (old behavior)
+    if all([x in "0123456789*+-/.() " for x in expr]):
+        if "**" in expr:  # disallow power operator
+            return None
+        return eval_with_timeout(expr)
+
+    # Check if it's a string operation we support
+    # Allow: strings (single/double quotes), .count(), letters, numbers, spaces, parens
+    allowed_chars = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\"()._ "
+    if not all([x in allowed_chars for x in expr]):
+        return None
+
+    # Disallow dangerous patterns
+    dangerous_patterns = ['__', 'import', 'exec', 'eval', 'compile', 'open', 'file',
+                         'input', 'raw_input', 'globals', 'locals', 'vars', 'dir',
+                         'getattr', 'setattr', 'delattr', 'hasattr']
+    expr_lower = expr.lower()
+    if any(pattern in expr_lower for pattern in dangerous_patterns):
+        return None
+
+    # Only allow .count() method for now (can expand later)
+    if '.count(' not in expr:
+        return None
+
+    # Evaluate with timeout
+    return eval_with_timeout(expr)
+
+# -----------------------------------------------------------------------------
+class KVCache:
+    """
+    KV Cache designed for Flash Attention 3's flash_attn_with_kvcache API.
+
+    Key differences from FA2-style cache:
+    - Tensors are (B, T, H, D) not (B, H, T, D)
+    - FA3 updates the cache in-place during flash_attn_with_kvcache
+    - Position tracked per batch element via cache_seqlens tensor
+    """
+
+    def __init__(self, batch_size, num_heads, seq_len, head_dim, num_layers, device, dtype):
+        self.batch_size = batch_size
+        self.max_seq_len = seq_len
+        self.n_layers = num_layers
+        self.n_heads = num_heads
+        self.head_dim = head_dim
+        # Pre-allocate cache tensors: (n_layers, B, T, H, D)
+        self.k_cache = torch.zeros(num_layers, batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
+        self.v_cache = torch.zeros(num_layers, batch_size, seq_len, num_heads, head_dim, device=device, dtype=dtype)
+        # Current sequence length per batch element (FA3 needs int32)
+        self.cache_seqlens = torch.zeros(batch_size, dtype=torch.int32, device=device)
+        # Previous token's normalized embedding for smear (set by model forward pass)
+        self.prev_embedding = None
+
+    def reset(self):
+        """Reset cache to empty state."""
+        self.cache_seqlens.zero_()
+        self.prev_embedding = None
+
+    def get_pos(self):
+        """Get current position (assumes all batch elements at same position)."""
+        return self.cache_seqlens[0].item()
+
+    def get_layer_cache(self, layer_idx):
+        """Return (k_cache, v_cache) views for a specific layer."""
+        return self.k_cache[layer_idx], self.v_cache[layer_idx]
+
+    def advance(self, num_tokens):
+        """Advance the cache position by num_tokens."""
+        self.cache_seqlens += num_tokens
+
+    def prefill(self, other):
+        """
+        Copy cached KV from another cache into this one.
+        Used when we do batch=1 prefill and then want to generate multiple samples in parallel.
+        """
+        assert self.get_pos() == 0, "Cannot prefill a non-empty KV cache"
+        assert self.n_layers == other.n_layers and self.n_heads == other.n_heads and self.head_dim == other.head_dim
+        assert self.max_seq_len >= other.max_seq_len
+        other_pos = other.get_pos()
+        self.k_cache[:, :, :other_pos, :, :] = other.k_cache[:, :, :other_pos, :, :]
+        self.v_cache[:, :, :other_pos, :, :] = other.v_cache[:, :, :other_pos, :, :]
+        self.cache_seqlens.fill_(other_pos)
+        # Copy smear state: expand batch=1 prev_embedding to num_samples
+        if other.prev_embedding is not None:
+            self.prev_embedding = other.prev_embedding.expand(self.batch_size, -1, -1).clone()
+
+# -----------------------------------------------------------------------------
+@torch.inference_mode()
+def sample_next_token(logits, rng, temperature=1.0, top_k=None):
+    """Sample a single next token from given logits of shape (B, vocab_size). Returns (B, 1)."""
+    assert temperature >= 0.0, "temperature must be non-negative"
+    if temperature == 0.0:
+        return torch.argmax(logits, dim=-1, keepdim=True)
+    if top_k is not None and top_k > 0:
+        k = min(top_k, logits.size(-1))
+        vals, idx = torch.topk(logits, k, dim=-1)
+        vals = vals / temperature
+        probs = F.softmax(vals, dim=-1)
+        choice = torch.multinomial(probs, num_samples=1, generator=rng)
+        return idx.gather(1, choice)
+    else:
+        logits = logits / temperature
+        probs = F.softmax(logits, dim=-1)
+        return torch.multinomial(probs, num_samples=1, generator=rng)
+
+# -----------------------------------------------------------------------------
+
+class RowState:
+    # Per-row state tracking during generation
+    def __init__(self, current_tokens=None):
+        self.current_tokens = current_tokens or [] # Current token sequence for this row
+        self.forced_tokens = deque() # Queue of tokens to force inject
+        self.in_python_block = False # Whether we are inside a python block
+        self.python_expr_tokens = [] # Tokens of the current python expression
+        self.completed = False # Whether this row has completed generation
+
+class Engine:
+
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer # needed for tool use
+
+    @torch.inference_mode()
+    def generate(self, tokens, num_samples=1, max_tokens=None, temperature=1.0, top_k=None, seed=42):
+        """Same as generate, but does single prefill and then clones the KV cache."""
+        assert isinstance(tokens, list) and isinstance(tokens[0], int), "expecting list of ints"
+        device = self.model.get_device()
+        # NOTE: setting the dtype here and in this way is an ugly hack.
+        # Currently the repo assumes that cuda -> bfloat16 and everything else -> float32.
+        # We need to know the dtype here to call __init__ on KVCache and pre-allocate its tensors.
+        # As a quick hack, we're making generate() function inherit and know about this repo-wise assumption.
+        # I think there has to be a bigger refactor to deal with device/dtype tracking across the codebase.
+        # In particular, the KVCache should allocate its tensors lazily
+        dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
+        rng = torch.Generator(device=device)
+        rng.manual_seed(seed)
+
+        # Get the special tokens we need to coordinate the tool use state machine
+        get_special = lambda s: self.tokenizer.encode_special(s)
+        python_start = get_special("<|python_start|>")
+        python_end = get_special("<|python_end|>")
+        output_start = get_special("<|output_start|>")
+        output_end = get_special("<|output_end|>")
+        assistant_end = get_special("<|assistant_end|>") # if sampled, ends row
+        bos = self.tokenizer.get_bos_token_id() # if sampled, ends row
+
+        # 1) Run a batch 1 prefill of the prompt tokens
+        m = self.model.config
+        kv_model_kwargs = {"num_heads": m.n_kv_head, "head_dim": m.n_embd // m.n_head, "num_layers": m.n_layer}
+        kv_cache_prefill = KVCache(
+            batch_size=1,
+            seq_len=len(tokens),
+            device=device,
+            dtype=dtype,
+            **kv_model_kwargs,
+        )
+        ids = torch.tensor([tokens], dtype=torch.long, device=device)
+        logits = self.model.forward(ids, kv_cache=kv_cache_prefill)
+        logits = logits[:, -1, :].expand(num_samples, -1)  # (num_samples, vocab_size)
+
+        # 2) Replicate the KV cache for each sample/row
+        kv_length_hint = (len(tokens) + max_tokens) if max_tokens is not None else self.model.config.sequence_len
+        kv_cache_decode = KVCache(
+            batch_size=num_samples,
+            seq_len=kv_length_hint,
+            device=device,
+            dtype=dtype,
+            **kv_model_kwargs,
+        )
+        kv_cache_decode.prefill(kv_cache_prefill)
+        del kv_cache_prefill # no need to keep this memory around
+
+        # 3) Initialize states for each sample
+        row_states = [RowState(tokens.copy()) for _ in range(num_samples)]
+
+        # 4) Main generation loop
+        num_generated = 0
+        while True:
+            # Stop condition: we've reached max tokens
+            if max_tokens is not None and num_generated >= max_tokens:
+                break
+            # Stop condition: all rows are completed
+            if all(state.completed for state in row_states):
+                break
+
+            # Sample the next token for each row
+            next_ids = sample_next_token(logits, rng, temperature, top_k)  # (B, 1)
+            sampled_tokens = next_ids[:, 0].tolist()
+
+            # Process each row: choose the next token, update state, optional tool use
+            token_column = [] # contains the next token id along each row
+            token_masks = [] # contains the mask (was it sampled (1) or forced (0)?) along each row
+            for i, state in enumerate(row_states):
+                # Select the next token in this row
+                is_forced = len(state.forced_tokens) > 0 # are there tokens waiting to be forced in deque?
+                token_masks.append(0 if is_forced else 1) # mask is 0 if forced, 1 if sampled
+                next_token = state.forced_tokens.popleft() if is_forced else sampled_tokens[i]
+                token_column.append(next_token)
+                # Update the state of this row to include the next token
+                state.current_tokens.append(next_token)
+                # On <|assistant_end|> or <|bos|>, mark the row as completed
+                if next_token == assistant_end or next_token == bos:
+                    state.completed = True
+                # Handle tool logic
+                if next_token == python_start:
+                    state.in_python_block = True
+                    state.python_expr_tokens = []
+                elif next_token == python_end and state.in_python_block:
+                    state.in_python_block = False
+                    if state.python_expr_tokens:
+                        expr = self.tokenizer.decode(state.python_expr_tokens)
+                        result = use_calculator(expr)
+                        if result is not None:
+                            result_tokens = self.tokenizer.encode(str(result))
+                            state.forced_tokens.append(output_start)
+                            state.forced_tokens.extend(result_tokens)
+                            state.forced_tokens.append(output_end)
+                    state.python_expr_tokens = []
+                elif state.in_python_block:
+                    state.python_expr_tokens.append(next_token)
+
+            # Yield the token column
+            yield token_column, token_masks
+            num_generated += 1
+
+            # Prepare logits for next iteration
+            ids = torch.tensor(token_column, dtype=torch.long, device=device).unsqueeze(1)
+            logits = self.model.forward(ids, kv_cache=kv_cache_decode)[:, -1, :]  # (B, vocab_size)
+
+    def generate_batch(self, tokens, num_samples=1, **kwargs):
+        """
+        Non-streaming batch generation that just returns the final token sequences.
+        Returns a list of token sequences (list of lists of ints).
+        Terminal tokens (assistant_end, bos) are not included in the results.
+        """
+        assistant_end = self.tokenizer.encode_special("<|assistant_end|>")
+        bos = self.tokenizer.get_bos_token_id()
+        results = [tokens.copy() for _ in range(num_samples)]
+        masks = [[0] * len(tokens) for _ in range(num_samples)]
+        completed = [False] * num_samples
+        for token_column, token_masks in self.generate(tokens, num_samples, **kwargs):
+            for i, (token, mask) in enumerate(zip(token_column, token_masks)):
+                if not completed[i]:
+                    if token == assistant_end or token == bos:
+                        completed[i] = True
+                    else:
+                        results[i].append(token)
+                        masks[i].append(mask)
+            # Stop if all rows are completed
+            if all(completed):
+                break
+        return results, masks
+
+
+if __name__ == "__main__":
+    """
+    Quick inline test to make sure that the naive/slow model.generate function
+    is equivalent to the faster Engine.generate function here.
+    """
+    import time
+    # init compute
+    device_type = autodetect_device_type()
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+    # load the model and tokenizer
+    model, tokenizer, meta = load_model("base", device, phase="eval")
+    bos_token_id = tokenizer.get_bos_token_id()
+    # common hyperparameters
+    kwargs = dict(max_tokens=64, temperature=0.0)
+    # set the starting prompt
+    prompt_tokens = tokenizer.encode("The chemical formula of water is", prepend=bos_token_id)
+    # generate the reference sequence using the model.generate() function
+    generated_tokens = []
+    torch.cuda.synchronize()
+    t0 = time.time()
+    stream = model.generate(prompt_tokens, **kwargs)
+    for token in stream:
+        generated_tokens.append(token)
+        chunk = tokenizer.decode([token])
+        print(chunk, end="", flush=True)
+    print()
+    torch.cuda.synchronize()
+    t1 = time.time()
+    print(f"Reference time: {t1 - t0:.2f}s")
+    reference_ids = generated_tokens
+    # generate tokens with Engine
+    generated_tokens = []
+    engine = Engine(model, tokenizer)
+    stream = engine.generate(prompt_tokens, num_samples=1, **kwargs) # note: runs in fp32
+    torch.cuda.synchronize()
+    t0 = time.time()
+    for token_column, token_masks in stream:
+        token = token_column[0] # only print out the first row
+        generated_tokens.append(token)
+        chunk = tokenizer.decode([token])
+        print(chunk, end="", flush=True)
+    print()
+    torch.cuda.synchronize()
+    t1 = time.time()
+    print(f"Engine time: {t1 - t0:.2f}s")
+    # compare the two sequences
+    for i in range(len(reference_ids)):
+        if reference_ids[i] != generated_tokens[i]:
+            print(f"Mismatch at {i}: {reference_ids[i]} != {generated_tokens[i]}")
+            break
+    print(f"Match: {reference_ids == generated_tokens}")

nanochat/execution.py

@@ -0,0 +1,349 @@
+"""
+Sandboxed execution utilities for running Python code that comes out of an LLM.
+Adapted from OpenAI HumanEval code:
+https://github.com/openai/human-eval/blob/master/human_eval/execution.py
+
+What is covered:
+- Each execution runs in its own process (can be killed if it hangs or crashes)
+- Execution is limited by a timeout to stop infinite loops
+- Memory limits are enforced by default (256MB)
+- stdout and stderr are captured and returned
+- Code runs in a temporary directory that is deleted afterwards
+- Dangerous functions are disabled (examples: os.system, os.kill, shutil.rmtree, subprocess.Popen)
+
+What is not covered:
+- Not a true security sandbox
+- Network access is not blocked (e.g. sockets could be opened)
+- Python's dynamic features (e.g. ctypes) could bypass restrictions
+- No kernel-level isolation (no seccomp, no containers, no virtualization)
+
+Overall this sandbox is good for evaluation of generated code and protects against
+accidental destructive behavior, but it is not safe against malicious adversarial code.
+"""
+
+import contextlib
+import faulthandler
+import io
+import multiprocessing
+import os
+import platform
+import signal
+import tempfile
+from dataclasses import dataclass
+from typing import Optional
+
+# -----------------------------------------------------------------------------
+
+@dataclass
+class ExecutionResult:
+    """Result of executing Python code in a sandbox."""
+    success: bool
+    stdout: str
+    stderr: str
+    error: Optional[str] = None
+    timeout: bool = False
+    memory_exceeded: bool = False
+
+    def __repr__(self):
+        parts = []
+        parts.append(f"ExecutionResult(success={self.success}")
+        if self.timeout:
+            parts.append(", timeout=True")
+        if self.memory_exceeded:
+            parts.append(", memory_exceeded=True")
+        if self.error:
+            parts.append(f", error={self.error!r}")
+        if self.stdout:
+            parts.append(f", stdout={self.stdout!r}")
+        if self.stderr:
+            parts.append(f", stderr={self.stderr!r}")
+        parts.append(")")
+        return "".join(parts)
+
+
+@contextlib.contextmanager
+def time_limit(seconds: float):
+    def signal_handler(signum, frame):
+        raise TimeoutException("Timed out!")
+
+    signal.setitimer(signal.ITIMER_REAL, seconds)
+    signal.signal(signal.SIGALRM, signal_handler)
+    try:
+        yield
+    finally:
+        signal.setitimer(signal.ITIMER_REAL, 0)
+
+
+@contextlib.contextmanager
+def capture_io():
+    """Capture stdout and stderr, and disable stdin."""
+    stdout_capture = io.StringIO()
+    stderr_capture = io.StringIO()
+    stdin_block = WriteOnlyStringIO()
+    with contextlib.redirect_stdout(stdout_capture):
+        with contextlib.redirect_stderr(stderr_capture):
+            with redirect_stdin(stdin_block):
+                yield stdout_capture, stderr_capture
+
+
+@contextlib.contextmanager
+def create_tempdir():
+    with tempfile.TemporaryDirectory() as dirname:
+        with chdir(dirname):
+            yield dirname
+
+
+class TimeoutException(Exception):
+    pass
+
+
+class WriteOnlyStringIO(io.StringIO):
+    """StringIO that throws an exception when it's read from"""
+
+    def read(self, *args, **kwargs):
+        raise IOError
+
+    def readline(self, *args, **kwargs):
+        raise IOError
+
+    def readlines(self, *args, **kwargs):
+        raise IOError
+
+    def readable(self, *args, **kwargs):
+        """Returns True if the IO object can be read."""
+        return False
+
+
+class redirect_stdin(contextlib._RedirectStream):  # type: ignore
+    _stream = "stdin"
+
+
+@contextlib.contextmanager
+def chdir(root):
+    if root == ".":
+        yield
+        return
+    cwd = os.getcwd()
+    os.chdir(root)
+    try:
+        yield
+    finally:
+        os.chdir(cwd)
+
+
+def reliability_guard(maximum_memory_bytes: Optional[int] = None):
+    """
+    This disables various destructive functions and prevents the generated code
+    from interfering with the test (e.g. fork bomb, killing other processes,
+    removing filesystem files, etc.)
+
+    WARNING
+    This function is NOT a security sandbox. Untrusted code, including, model-
+    generated code, should not be blindly executed outside of one. See the
+    Codex paper for more information about OpenAI's code sandbox, and proceed
+    with caution.
+    """
+
+    if platform.uname().system != "Darwin":
+        # These resource limit calls seem to fail on macOS (Darwin), skip?
+        import resource
+        resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))
+        resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))
+        resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))
+
+    faulthandler.disable()
+
+    import builtins
+
+    builtins.exit = None
+    builtins.quit = None
+
+    import os
+
+    os.environ["OMP_NUM_THREADS"] = "1"
+
+    os.kill = None
+    os.system = None
+    os.putenv = None
+    os.remove = None
+    os.removedirs = None
+    os.rmdir = None
+    os.fchdir = None
+    os.setuid = None
+    os.fork = None
+    os.forkpty = None
+    os.killpg = None
+    os.rename = None
+    os.renames = None
+    os.truncate = None
+    os.replace = None
+    os.unlink = None
+    os.fchmod = None
+    os.fchown = None
+    os.chmod = None
+    os.chown = None
+    os.chroot = None
+    os.fchdir = None
+    os.lchflags = None
+    os.lchmod = None
+    os.lchown = None
+    os.getcwd = None
+    os.chdir = None
+
+    import shutil
+
+    shutil.rmtree = None
+    shutil.move = None
+    shutil.chown = None
+
+    import subprocess
+
+    subprocess.Popen = None  # type: ignore
+
+    __builtins__["help"] = None
+
+    import sys
+
+    sys.modules["ipdb"] = None
+    sys.modules["joblib"] = None
+    sys.modules["resource"] = None
+    sys.modules["psutil"] = None
+    sys.modules["tkinter"] = None
+
+
+def _unsafe_execute(code: str, timeout: float, maximum_memory_bytes: Optional[int], result_dict):
+    """Execute code in a subprocess with safety guards. Results are written to result_dict."""
+    with create_tempdir():
+
+        # These system calls are needed when cleaning up tempdir.
+        import os
+        import shutil
+
+        rmtree = shutil.rmtree
+        rmdir = os.rmdir
+        chdir = os.chdir
+        unlink = os.unlink
+
+        # Disable functionalities that can make destructive changes to the test.
+        reliability_guard(maximum_memory_bytes=maximum_memory_bytes)
+
+        # Default to failure
+        result_dict.update({
+            "success": False,
+            "stdout": "",
+            "stderr": "",
+            "timeout": False,
+            "memory_exceeded": False,
+            "error": None,
+        })
+
+        try:
+            exec_globals = {}
+            with capture_io() as (stdout_capture, stderr_capture):
+                with time_limit(timeout):
+                    # WARNING
+                    # This program exists to execute untrusted model-generated code. Although
+                    # it is highly unlikely that model-generated code will do something overtly
+                    # malicious in response to this test suite, model-generated code may act
+                    # destructively due to a lack of model capability or alignment.
+                    # Users are strongly encouraged to sandbox this evaluation suite so that it
+                    # does not perform destructive actions on their host or network. For more
+                    # information on how OpenAI sandboxes its code, see the accompanying paper.
+                    # Once you have read this disclaimer and taken appropriate precautions,
+                    # uncomment the following line and proceed at your own risk:
+                    exec(code, exec_globals)
+
+            result_dict.update({
+                "success": True,
+                "stdout": stdout_capture.getvalue(),
+                "stderr": stderr_capture.getvalue(),
+            })
+
+        except TimeoutException:
+            result_dict.update({
+                "timeout": True,
+                "error": "Execution timed out",
+            })
+
+        except MemoryError as e:
+            result_dict.update({
+                "memory_exceeded": True,
+                "error": f"Memory limit exceeded: {e}",
+            })
+
+        except BaseException as e:
+            result_dict.update({
+                "error": f"{type(e).__name__}: {e}",
+            })
+
+        # Needed for cleaning up.
+        shutil.rmtree = rmtree
+        os.rmdir = rmdir
+        os.chdir = chdir
+        os.unlink = unlink
+
+
+def execute_code(
+    code: str,
+    timeout: float = 5.0, # 5 seconds default
+    maximum_memory_bytes: Optional[int] = 256 * 1024 * 1024, # 256MB default
+) -> ExecutionResult:
+    """
+    Execute Python code in a sandboxed environment.
+
+    Args:
+        code: Python code to execute as a string
+        timeout: Maximum execution time in seconds (default: 5.0)
+        maximum_memory_bytes: Memory limit in bytes (default: 256MB, None to disable)
+
+    Returns:
+        ExecutionResult with success status, stdout/stderr, and error information
+
+    Example:
+        >>> result = execute_code("print('hello world')")
+        >>> result.success
+        True
+        >>> result.stdout
+        'hello world\\n'
+    """
+
+    manager = multiprocessing.Manager()
+    result_dict = manager.dict()
+
+    p = multiprocessing.Process(
+        target=_unsafe_execute,
+        args=(code, timeout, maximum_memory_bytes, result_dict)
+    )
+    p.start()
+    p.join(timeout=timeout + 1)
+
+    if p.is_alive():
+        p.kill()
+        return ExecutionResult(
+            success=False,
+            stdout="",
+            stderr="",
+            error="Execution timed out (process killed)",
+            timeout=True,
+            memory_exceeded=False,
+        )
+
+    if not result_dict:
+        return ExecutionResult(
+            success=False,
+            stdout="",
+            stderr="",
+            error="Execution failed (no result returned)",
+            timeout=True,
+            memory_exceeded=False,
+        )
+
+    return ExecutionResult(
+        success=result_dict["success"],
+        stdout=result_dict["stdout"],
+        stderr=result_dict["stderr"],
+        error=result_dict["error"],
+        timeout=result_dict["timeout"],
+        memory_exceeded=result_dict["memory_exceeded"],
+    )
+

nanochat/flash_attention.py

@@ -0,0 +1,187 @@
+"""
+Unified Flash Attention interface with automatic FA3/SDPA switching.
+
+Exports `flash_attn` module that matches the FA3 API exactly, but falls back
+to PyTorch SDPA on non-Hopper GPUs (including Blackwell), MPS, and CPU.
+
+Usage (drop-in replacement for FA3):
+    from nanochat.flash_attention import flash_attn
+
+    # Training (no KV cache)
+    y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=window_size)
+
+    # Inference (with KV cache)
+    y = flash_attn.flash_attn_with_kvcache(q, k_cache, v_cache, k=k, v=v, ...)
+"""
+import torch
+import torch.nn.functional as F
+
+
+# =============================================================================
+# Detection: Try to load FA3 on Hopper+ GPUs
+# =============================================================================
+def _load_flash_attention_3():
+    """Try to load Flash Attention 3 (requires Hopper GPU, sm90)."""
+    if not torch.cuda.is_available():
+        return None
+    try:
+        major, _ = torch.cuda.get_device_capability()
+        # FA3 kernels are compiled for Hopper (sm90) only
+        # Ada (sm89), Blackwell (sm100) need SDPA fallback until FA3 is recompiled
+        if major != 9:
+            return None
+        import os
+        os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1"
+        from kernels import get_kernel
+        return get_kernel('varunneal/flash-attention-3').flash_attn_interface
+    except Exception:
+        return None
+
+
+_fa3 = _load_flash_attention_3()
+HAS_FA3 = _fa3 is not None
+
+# Override for testing: set to 'fa3', 'sdpa', or None (auto)
+_override_impl = None
+
+
+def _resolve_use_fa3():
+    """Decide once whether to use FA3, based on availability, override, and dtype."""
+    if _override_impl == 'fa3':
+        assert HAS_FA3, "Cannot override to FA3: not available on this hardware"
+        return True
+    if _override_impl == 'sdpa':
+        return False
+    if HAS_FA3:
+        # FA3 Hopper kernels only support bf16 and fp8; fp16/fp32 must use SDPA fallback
+        from nanochat.common import COMPUTE_DTYPE
+        if COMPUTE_DTYPE == torch.bfloat16:
+            return True
+        return False
+    return False
+
+USE_FA3 = _resolve_use_fa3()
+
+
+# =============================================================================
+# SDPA helpers
+# =============================================================================
+def _sdpa_attention(q, k, v, window_size, enable_gqa):
+    """
+    SDPA attention with sliding window support.
+    q, k, v are (B, H, T, D) format.
+    """
+    Tq = q.size(2)
+    Tk = k.size(2)
+    window = window_size[0]
+
+    # Full context, same length
+    if (window < 0 or window >= Tq) and Tq == Tk:
+        return F.scaled_dot_product_attention(q, k, v, is_causal=True, enable_gqa=enable_gqa)
+
+    # Single token generation
+    if Tq == 1:
+        if window >= 0 and window < Tk:
+            # window is "left" tokens we need to include (window + 1) keys total
+            start = max(0, Tk - (window + 1))
+            k = k[:, :, start:, :]
+            v = v[:, :, start:, :]
+        return F.scaled_dot_product_attention(q, k, v, is_causal=False, enable_gqa=enable_gqa)
+
+    # Need explicit mask for sliding window/chunk inference
+    device = q.device
+    # For chunk inference (Tq != Tk), is_causal is not aligned to cache position => build an explicit bool mask
+    row_idx = (Tk - Tq) + torch.arange(Tq, device=device).unsqueeze(1)
+    col_idx = torch.arange(Tk, device=device).unsqueeze(0)
+    mask = col_idx <= row_idx
+
+    # sliding window (left)
+    if window >= 0 and window < Tk:
+        mask = mask & ((row_idx - col_idx) <= window)
+
+    return F.scaled_dot_product_attention(q, k, v, attn_mask=mask, enable_gqa=enable_gqa)
+
+# =============================================================================
+# Public API: Same interface as FA3
+# =============================================================================
+def flash_attn_func(q, k, v, causal=False, window_size=(-1, -1)):
+    """
+    Flash Attention for training (no KV cache).
+
+    Args:
+        q, k, v: Tensors of shape (B, T, H, D)
+        causal: Whether to use causal masking
+        window_size: (left, right) sliding window. -1 means unlimited.
+
+    Returns:
+        Output tensor of shape (B, T, H, D)
+    """
+    if USE_FA3:
+        return _fa3.flash_attn_func(q, k, v, causal=causal, window_size=window_size)
+
+    # SDPA fallback: transpose (B, T, H, D) -> (B, H, T, D)
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
+    enable_gqa = q.size(1) != k.size(1)
+    y = _sdpa_attention(q, k, v, window_size, enable_gqa)
+    return y.transpose(1, 2)  # back to (B, T, H, D)
+
+
+def flash_attn_with_kvcache(q, k_cache, v_cache, k=None, v=None, cache_seqlens=None,
+                            causal=False, window_size=(-1, -1)):
+    """
+    Flash Attention with KV cache for inference.
+
+    FA3 updates k_cache/v_cache in-place. Our SDPA fallback does the same.
+
+    Args:
+        q: Queries, shape (B, T_new, H, D)
+        k_cache, v_cache: Pre-allocated cache tensors, shape (B, T_max, H_kv, D)
+        k, v: New keys/values to insert, shape (B, T_new, H_kv, D)
+        cache_seqlens: Current position in cache, shape (B,) int32
+        causal: Whether to use causal masking
+        window_size: (left, right) sliding window. -1 means unlimited.
+
+    Returns:
+        Output tensor of shape (B, T_new, H, D)
+    """
+    if USE_FA3:
+        return _fa3.flash_attn_with_kvcache(
+            q, k_cache, v_cache, k=k, v=v, cache_seqlens=cache_seqlens,
+            causal=causal, window_size=window_size
+        )
+
+    # SDPA fallback: manually manage KV cache
+    B, T_new, H, D = q.shape
+    pos = cache_seqlens[0].item()  # assume uniform position across batch
+
+    # Insert new k, v into cache (in-place, matching FA3 behavior)
+    if k is not None and v is not None:
+        k_cache[:, pos:pos+T_new, :, :] = k
+        v_cache[:, pos:pos+T_new, :, :] = v
+
+    # Get full cache up to current position + new tokens
+    end_pos = pos + T_new
+    k_full = k_cache[:, :end_pos, :, :]
+    v_full = v_cache[:, :end_pos, :, :]
+
+    # Transpose to SDPA layout: (B, T, H, D) -> (B, H, T, D)
+    q_sdpa = q.transpose(1, 2)
+    k_sdpa = k_full.transpose(1, 2)
+    v_sdpa = v_full.transpose(1, 2)
+
+    enable_gqa = q_sdpa.size(1) != k_sdpa.size(1)
+    y_sdpa = _sdpa_attention(q_sdpa, k_sdpa, v_sdpa, window_size, enable_gqa)
+
+    return y_sdpa.transpose(1, 2)  # back to (B, T, H, D)
+
+
+# =============================================================================
+# Export: flash_attn module interface (drop-in replacement for FA3)
+# =============================================================================
+from types import SimpleNamespace
+flash_attn = SimpleNamespace(
+    flash_attn_func=flash_attn_func,
+    flash_attn_with_kvcache=flash_attn_with_kvcache,
+)

nanochat/fp8.py

@@ -0,0 +1,266 @@
+"""Minimal FP8 training for nanochat — tensorwise dynamic scaling only.
+
+Drop-in replacement for torchao's Float8Linear (~2000 lines) with ~150 lines.
+We only need the "tensorwise" recipe (one scalar scale per tensor), not the full
+generality of torchao (rowwise scaling, FSDP float8 all-gather, DTensor, tensor
+subclass dispatch tables, etc.)
+
+How FP8 training works
+======================
+A standard Linear layer does one matmul in forward and two in backward:
+  forward:      output     = input      @ weight.T
+  backward:     grad_input = grad_output @ weight
+                grad_weight= grad_output.T @ input
+
+FP8 training wraps each of these three matmuls with:
+  1. Compute scale = FP8_MAX / max(|tensor|)  for each operand
+  2. Quantize: fp8_tensor = clamp(tensor * scale, -FP8_MAX, FP8_MAX).to(fp8)
+  3. Matmul via torch._scaled_mm (cuBLAS FP8 kernel, ~2x faster than bf16)
+  4. Dequantize: _scaled_mm handles this internally using the inverse scales
+
+The key insight: torch._scaled_mm and the float8 dtypes are PyTorch built-ins.
+torchao is just orchestration around these primitives. We can call them directly.
+
+FP8 dtype choice
+================
+There are two FP8 formats. We use both, following the standard convention:
+  - float8_e4m3fn: 4-bit exponent, 3-bit mantissa, range [-448, 448]
+    Higher precision (more mantissa bits), used for input and weight.
+  - float8_e5m2:   5-bit exponent, 2-bit mantissa, range [-57344, 57344]
+    Wider range (more exponent bits), used for gradients which can be large.
+
+torch._scaled_mm layout requirements
+=====================================
+The cuBLAS FP8 kernel requires specific memory layouts:
+  - First argument (A):  must be row-major (contiguous)
+  - Second argument (B): must be column-major (B.t().contiguous().t())
+If B is obtained by transposing a contiguous tensor (e.g. weight.t()), it is
+already column-major — no copy needed. Otherwise we use _to_col_major().
+
+How this differs from torchao's approach
+========================================
+torchao uses a "tensor subclass" architecture: Float8TrainingTensor is a subclass
+of torch.Tensor that bundles FP8 data + scale + metadata. It implements
+__torch_dispatch__ with a dispatch table that intercepts every aten op (mm, t,
+reshape, clone, ...) and handles it in FP8-aware fashion. When you call
+  output = input @ weight.T
+the @ operator dispatches to aten.mm, which gets intercepted and routed to
+torch._scaled_mm behind the scenes. This is ~2000 lines of code because you need
+a handler for every tensor operation that might touch an FP8 tensor.
+
+We take a simpler approach: a single autograd.Function (_Float8Matmul) that takes
+full-precision inputs, quantizes to FP8 internally, calls _scaled_mm, and returns
+full-precision outputs. Marked @allow_in_graph so torch.compile treats it as one
+opaque node rather than trying to trace inside.
+
+The trade-off is in how torch.compile sees the two approaches:
+  - torchao: compile decomposes the tensor subclass (via __tensor_flatten__) and
+    sees every individual op (amax, scale, cast, _scaled_mm) as separate graph
+    nodes. Inductor can fuse these with surrounding operations (e.g. fuse the
+    amax computation with the preceding layer's activation function).
+  - ours: compile sees a single opaque call. It can optimize everything around
+    the FP8 linear (attention, norms, etc.) but cannot fuse across the boundary.
+
+Both call the exact same cuBLAS _scaled_mm kernel — the GPU matmul is identical.
+The difference is only in the "glue" ops (amax, scale, cast) which are tiny
+compared to the matmul. In practice this means our version is slightly faster
+(less compilation overhead, no tensor subclass dispatch cost) but can produce
+subtly different floating-point rounding paths under torch.compile, since Inductor
+generates a different graph. Numerics are bitwise identical in eager mode.
+"""
+
+import torch
+import torch.nn as nn
+
+from nanochat.common import COMPUTE_DTYPE
+
+# Avoid division by zero when computing scale from an all-zeros tensor
+EPS = 1e-12
+
+
+@torch.no_grad()
+def _to_fp8(x, fp8_dtype):
+    """Dynamically quantize a tensor to FP8 using tensorwise scaling.
+
+    "Tensorwise" means one scalar scale for the entire tensor (as opposed to
+    "rowwise" which computes a separate scale per row). Tensorwise is faster
+    because cuBLAS handles the scaling; rowwise needs the CUTLASS kernel.
+
+    Returns (fp8_data, inverse_scale) for use with torch._scaled_mm.
+    """
+    fp8_max = torch.finfo(fp8_dtype).max
+    # Compute the max absolute value across the entire tensor
+    amax = x.float().abs().max()
+    # Scale maps [0, amax] -> [0, fp8_max]. Use float64 for the division to
+    # ensure consistent numerics between torch.compile and eager mode.
+    # (torchao does the same upcast — without it, compile/eager can diverge)
+    scale = fp8_max / amax.double().clamp(min=EPS)
+    scale = scale.float()
+    # Quantize: scale into FP8 range, saturate (clamp prevents overflow when
+    # casting — PyTorch's default is to wrap, not saturate), then cast to FP8
+    x_scaled = x.float() * scale
+    x_clamped = x_scaled.clamp(-fp8_max, fp8_max)
+    x_fp8 = x_clamped.to(fp8_dtype)
+    # _scaled_mm expects the *inverse* of our scale (it multiplies by this to
+    # convert FP8 values back to the original range during the matmul)
+    inv_scale = scale.reciprocal()
+    return x_fp8, inv_scale
+
+
+def _to_col_major(x):
+    """Rearrange a 2D tensor's memory to column-major layout.
+
+    torch._scaled_mm requires its second operand in column-major layout.
+    The trick: transpose -> contiguous (forces a copy in transposed order)
+    -> transpose back. The result has the same logical shape but column-major
+    strides, e.g. a [M, N] tensor gets strides (1, M) instead of (N, 1).
+    """
+    return x.t().contiguous().t()
+
+
+# allow_in_graph tells torch.compile to treat this as an opaque operation —
+# dynamo won't try to decompose it into smaller ops. See the module docstring
+# for how this differs from torchao's tensor subclass approach.
+@torch._dynamo.allow_in_graph
+class _Float8Matmul(torch.autograd.Function):
+    """Custom autograd for the three FP8 GEMMs of a Linear layer.
+
+    The forward quantizes input and weight to FP8 and saves
+    the quantized tensors + scales for backward.
+    """
+
+    @staticmethod
+    def forward(ctx, input_2d, weight):
+        # Quantize both operands to e4m3 (higher precision format)
+        input_fp8, input_inv = _to_fp8(input_2d, torch.float8_e4m3fn)
+        weight_fp8, weight_inv = _to_fp8(weight, torch.float8_e4m3fn)
+        ctx.save_for_backward(input_fp8, input_inv, weight_fp8, weight_inv)
+
+        # output = input @ weight.T
+        # input_fp8 is [B, K] contiguous = row-major (good for first arg)
+        # weight_fp8 is [N, K] contiguous, so weight_fp8.t() is [K, N] with
+        # strides (1, K) = column-major (good for second arg, no copy needed!)
+        output = torch._scaled_mm(
+            input_fp8,
+            weight_fp8.t(),
+            scale_a=input_inv,
+            scale_b=weight_inv,
+            out_dtype=input_2d.dtype,
+            # use_fast_accum=True accumulates the dot products in lower precision.
+            # Slightly less accurate but measurably faster. Standard practice for
+            # the forward pass; we use False in backward for more precise gradients.
+            use_fast_accum=True,
+        )
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        in_fp8, in_inv, w_fp8, w_inv = ctx.saved_tensors
+
+        # === GEMM 1: grad_input = grad_output @ weight ===
+        # Shapes: [B, N] @ [N, K] -> [B, K]
+        # Gradients use e5m2 (wider range), weights use e4m3 (higher precision)
+        go_fp8, go_inv = _to_fp8(grad_output, torch.float8_e5m2)
+        # go_fp8 is [B, N] contiguous = row-major, good for first arg
+        # w_fp8 is [N, K] contiguous = row-major, need column-major for second arg
+        w_col = _to_col_major(w_fp8)
+        grad_input = torch._scaled_mm(
+            go_fp8,
+            w_col,
+            scale_a=go_inv,
+            scale_b=w_inv,
+            out_dtype=grad_output.dtype,
+            use_fast_accum=False,
+        )
+
+        # === GEMM 2: grad_weight = grad_output.T @ input ===
+        # Shapes: [N, B] @ [B, K] -> [N, K]
+        # go_fp8 is [B, N] contiguous, we need go.T = [N, B] as first arg.
+        # Transposing gives column-major, but first arg needs row-major,
+        # so we must call .contiguous() to physically rearrange the memory.
+        go_T = go_fp8.t().contiguous()  # [N, B] row-major
+        in_col = _to_col_major(in_fp8)    # [B, K] column-major
+        grad_weight = torch._scaled_mm(
+            go_T,
+            in_col,
+            scale_a=go_inv,
+            scale_b=in_inv,
+            out_dtype=grad_output.dtype,
+            use_fast_accum=False,
+        )
+
+        return grad_input, grad_weight
+
+
+class Float8Linear(nn.Linear):
+    """Drop-in nn.Linear replacement that does FP8 compute.
+
+    Weights and biases remain in their original precision (e.g. fp32/bf16).
+    Only the matmul is performed in FP8 via the _Float8Matmul autograd function.
+    """
+
+    def forward(self, input):
+        # Cast input to COMPUTE_DTYPE (typically bf16) since _scaled_mm expects
+        # reduced precision input, and we no longer rely on autocast to do this.
+        input = input.to(COMPUTE_DTYPE)
+        # _scaled_mm only works on 2D tensors, so flatten batch dimensions
+        orig_shape = input.shape
+        input_2d = input.reshape(-1, orig_shape[-1])
+        output = _Float8Matmul.apply(input_2d, self.weight)
+        output = output.reshape(*orig_shape[:-1], output.shape[-1])
+        if self.bias is not None:
+            output = output + self.bias.to(output.dtype)
+        return output
+
+    @classmethod
+    def from_float(cls, mod):
+        """Create Float8Linear from nn.Linear, sharing the same weight and bias.
+
+        Uses meta device to avoid allocating a temporary weight tensor — we
+        create the module shell on meta (shapes/dtypes only, no memory), then
+        point .weight and .bias to the original module's parameters.
+        """
+        with torch.device("meta"):
+            new_mod = cls(mod.in_features, mod.out_features, bias=False)
+        new_mod.weight = mod.weight
+        new_mod.bias = mod.bias
+        return new_mod
+
+
+class Float8LinearConfig:
+    """Minimal config matching torchao's API. Only tensorwise recipe is supported."""
+
+    @staticmethod
+    def from_recipe_name(recipe_name):
+        if recipe_name != "tensorwise":
+            raise ValueError(
+                f"Only 'tensorwise' recipe is supported, got '{recipe_name}'. "
+                f"Rowwise/axiswise recipes require the full torchao library."
+            )
+        return Float8LinearConfig()
+
+
+def convert_to_float8_training(module, *, config=None, module_filter_fn=None):
+    """Replace nn.Linear layers with Float8Linear throughout a module.
+
+    Walks the module tree in post-order (children before parents) and swaps
+    each nn.Linear that passes the optional filter. The new Float8Linear shares
+    the original weight and bias tensors — no copies, no extra memory.
+
+    Args:
+        module: Root module to convert.
+        config: Float8LinearConfig (accepted for API compat, only tensorwise supported).
+        module_filter_fn: Optional filter(module, fqn) -> bool. Only matching Linears
+            are converted. Common use: skip layers with dims not divisible by 16
+            (hardware requirement for FP8 matmuls on H100).
+    """
+    def _convert(mod, prefix=""):
+        for name, child in mod.named_children():
+            fqn = f"{prefix}.{name}" if prefix else name
+            _convert(child, fqn)
+            if isinstance(child, nn.Linear) and not isinstance(child, Float8Linear):
+                if module_filter_fn is None or module_filter_fn(child, fqn):
+                    setattr(mod, name, Float8Linear.from_float(child))
+
+    _convert(module)
+    return module

nanochat/gpt.py

@@ -0,0 +1,528 @@
+"""
+GPT model (rewrite, a lot simpler)
+Notable features:
+- rotary embeddings (and no positional embeddings)
+- QK norm
+- untied weights for token embedding and lm_head
+- relu^2 activation in MLP
+- norm after token embedding
+- no learnable params in rmsnorm
+- no bias in linear layers
+- Group-Query Attention (GQA) support for more efficient inference
+- Flash Attention 3 integration
+"""
+
+from functools import partial
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from nanochat.common import get_dist_info, print0, COMPUTE_DTYPE
+from nanochat.optim import MuonAdamW, DistMuonAdamW
+
+# Our custom Flash Attention module that automatically uses FA3 on Hopper+ and SDPA fallback elsewhere
+from nanochat.flash_attention import flash_attn
+
+@dataclass
+class GPTConfig:
+    sequence_len: int = 2048
+    vocab_size: int = 32768
+    n_layer: int = 12
+    n_head: int = 6 # number of query heads
+    n_kv_head: int = 6 # number of key/value heads (GQA)
+    n_embd: int = 768
+    # Sliding window attention pattern string, tiled across layers. Final layer always L.
+    # Characters: L=long (full context), S=short (quarter context)
+    # Examples: "L"=all full context, "SL"=alternating, "SSL"=two short then one long
+    window_pattern: str = "SSSL"
+
+
+def norm(x):
+    return F.rms_norm(x, (x.size(-1),)) # note that this will run in bf16, seems ok
+
+class Linear(nn.Linear):
+    """nn.Linear that casts weights to match input dtype in forward.
+    Replaces autocast: master weights stay fp32 for optimizer precision,
+    but matmuls run in the activation dtype (typically bf16 from embeddings)."""
+    def forward(self, x):
+        return F.linear(x, self.weight.to(dtype=x.dtype))
+
+
+def has_ve(layer_idx, n_layer):
+    """Returns True if GPT layer should have Value Embedding (alternating, last layer always included)."""
+    return layer_idx % 2 == (n_layer - 1) % 2
+
+def apply_rotary_emb(x, cos, sin):
+    assert x.ndim == 4  # multihead attention
+    d = x.shape[3] // 2
+    x1, x2 = x[..., :d], x[..., d:] # split up last dim into two halves
+    y1 = x1 * cos + x2 * sin # rotate pairs of dims
+    y2 = x1 * (-sin) + x2 * cos
+    return torch.cat([y1, y2], 3)
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.n_head = config.n_head
+        self.n_kv_head = config.n_kv_head
+        self.n_embd = config.n_embd
+        self.head_dim = self.n_embd // self.n_head
+        assert self.n_embd % self.n_head == 0
+        assert self.n_kv_head <= self.n_head and self.n_head % self.n_kv_head == 0
+        self.c_q = Linear(self.n_embd, self.n_head * self.head_dim, bias=False)
+        self.c_k = Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
+        self.c_v = Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
+        self.c_proj = Linear(self.n_embd, self.n_embd, bias=False)
+        self.ve_gate_channels = 12
+        self.ve_gate = Linear(self.ve_gate_channels, self.n_kv_head, bias=False) if has_ve(layer_idx, config.n_layer) else None
+
+    def forward(self, x, ve, cos_sin, window_size, kv_cache):
+        B, T, C = x.size()
+
+        # Project the input to get queries, keys, and values
+        # Shape: (B, T, H, D) - FA3's native layout, no transpose needed!
+        q = self.c_q(x).view(B, T, self.n_head, self.head_dim)
+        k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim)
+        v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim)
+
+        # Value residual (ResFormer): mix in value embedding with input-dependent gate per head
+        if ve is not None:
+            ve = ve.view(B, T, self.n_kv_head, self.head_dim)
+            gate = 3 * torch.sigmoid(self.ve_gate(x[..., :self.ve_gate_channels]))  # (B, T, n_kv_head), range (0, 3)
+            v = v + gate.unsqueeze(-1) * ve
+
+        # Apply Rotary Embeddings to queries and keys to get relative positional encoding
+        cos, sin = cos_sin
+        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
+        q, k = norm(q), norm(k) # QK norm
+        q = q * 1.2  # sharper attention (split scale between Q and K), TODO think through better
+        k = k * 1.2
+
+        # Flash Attention (FA3 on Hopper+, PyTorch SDPA fallback elsewhere)
+        # window_size is (left, right) tuple: (N, 0) for causal, (-1, 0) for full context
+        if kv_cache is None:
+            # Training: causal attention with optional sliding window
+            y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=window_size)
+        else:
+            # Inference: use flash_attn_with_kvcache which handles cache management
+            k_cache, v_cache = kv_cache.get_layer_cache(self.layer_idx)
+            y = flash_attn.flash_attn_with_kvcache(
+                q, k_cache, v_cache,
+                k=k, v=v,
+                cache_seqlens=kv_cache.cache_seqlens,
+                causal=True,
+                window_size=window_size,
+            )
+            # Advance position after last layer processes
+            if self.layer_idx == kv_cache.n_layers - 1:
+                kv_cache.advance(T)
+
+        # Re-assemble the heads and project back to residual stream
+        y = y.contiguous().view(B, T, -1)
+        y = self.c_proj(y)
+        return y
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = Linear(config.n_embd, 4 * config.n_embd, bias=False)
+        self.c_proj = Linear(4 * config.n_embd, config.n_embd, bias=False)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = F.relu(x).square()
+        x = self.c_proj(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.attn = CausalSelfAttention(config, layer_idx)
+        self.mlp = MLP(config)
+
+    def forward(self, x, ve, cos_sin, window_size, kv_cache):
+        x = x + self.attn(norm(x), ve, cos_sin, window_size, kv_cache)
+        x = x + self.mlp(norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config, pad_vocab_size_to=64):
+        """
+        NOTE a major footgun: this __init__ function runs in meta device context (!!)
+        Therefore, any calculations inside here are shapes and dtypes only, no actual data.
+        => We actually initialize all data (parameters, buffers, etc.) in init_weights() instead.
+        """
+        super().__init__()
+        self.config = config
+        # Compute per-layer window sizes for sliding window attention
+        # window_size is (left, right) tuple: (-1, 0) for full context, (N, 0) for sliding window
+        self.window_sizes = self._compute_window_sizes(config)
+        # Pad vocab for efficiency (DDP, tensor cores). This is just an optimization - outputs are cropped in forward().
+        # https://huggingface.co/docs/transformers/main_classes/model#transformers.PreTrainedModel.resize_token_embeddings
+        padded_vocab_size = ((config.vocab_size + pad_vocab_size_to - 1) // pad_vocab_size_to) * pad_vocab_size_to
+        if padded_vocab_size != config.vocab_size:
+            print0(f"Padding vocab_size from {config.vocab_size} to {padded_vocab_size} for efficiency")
+        self.transformer = nn.ModuleDict({
+            "wte": nn.Embedding(padded_vocab_size, config.n_embd),
+            "h": nn.ModuleList([Block(config, layer_idx) for layer_idx in range(config.n_layer)]),
+        })
+        self.lm_head = Linear(config.n_embd, padded_vocab_size, bias=False)
+        # Per-layer learnable scalars (inspired by modded-nanogpt)
+        # resid_lambdas: scales the residual stream at each layer (init 1.0 = neutral)
+        # x0_lambdas: blends initial embedding back in at each layer (init 0.0 = disabled)
+        # Separate parameters so they can have different optimizer treatment
+        self.resid_lambdas = nn.Parameter(torch.ones(config.n_layer))   # fake init, real init in init_weights()
+        self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))     # fake init, real init in init_weights()
+        # Smear: mix previous token's embedding into current token (cheap bigram-like info)
+        self.smear_gate = Linear(24, 1, bias=False)
+        self.smear_lambda = nn.Parameter(torch.zeros(1))
+        # Backout: subtract cached mid-layer residual before final norm to remove low-level features
+        self.backout_lambda = nn.Parameter(0.2 * torch.ones(1))
+        # Value embeddings (ResFormer-style): alternating layers, last layer always included
+        head_dim = config.n_embd // config.n_head
+        kv_dim = config.n_kv_head * head_dim
+        self.value_embeds = nn.ModuleDict({str(i): nn.Embedding(padded_vocab_size, kv_dim) for i in range(config.n_layer) if has_ve(i, config.n_layer)})
+        # To support meta device initialization, we init the rotary embeddings here, but it's just "fake" meta tensors only.
+        # As for rotary_seq_len, these rotary embeddings are pretty small/cheap in memory,
+        # so let's just over-compute them by 10X, but assert fail if we ever reach that amount.
+        # In the future we can dynamically grow the cache, for now it's fine.
+        self.rotary_seq_len = config.sequence_len * 10 # 10X over-compute should be enough, TODO make nicer?
+        head_dim = config.n_embd // config.n_head
+        cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
+        self.register_buffer("cos", cos, persistent=False) # persistent=False means it's not saved to the checkpoint
+        self.register_buffer("sin", sin, persistent=False)
+
+    @torch.no_grad()
+    def init_weights(self):
+        """
+        Initialize the full model in this one function for maximum clarity.
+
+        wte (embedding):     normal, std=1.0
+        lm_head:             normal, std=0.001
+        for each block:
+            attn.c_q:        uniform, std=1/sqrt(n_embd)
+            attn.c_k:        uniform, std=1/sqrt(n_embd)
+            attn.c_v:        uniform, std=1/sqrt(n_embd)
+            attn.c_proj:     zeros
+            mlp.c_fc:        uniform, std=1/sqrt(n_embd)
+            mlp.c_proj:      zeros
+        """
+
+        # Embedding and unembedding
+        torch.nn.init.normal_(self.transformer.wte.weight, mean=0.0, std=0.8)
+        torch.nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001)
+
+        # Transformer blocks: uniform init with bound = sqrt(3) * std (same standard deviation as normal)
+        n_embd = self.config.n_embd
+        s = 3**0.5 * n_embd**-0.5 # sqrt(3) multiplier makes sure Uniform achieves the same std as Normal
+        for block in self.transformer.h:
+            torch.nn.init.uniform_(block.attn.c_q.weight, -s, s) # weights use Uniform to avoid outliers
+            torch.nn.init.uniform_(block.attn.c_k.weight, -s, s)
+            torch.nn.init.uniform_(block.attn.c_v.weight, -s, s)
+            torch.nn.init.zeros_(block.attn.c_proj.weight) # projections are zero
+            torch.nn.init.uniform_(block.mlp.c_fc.weight, -s * 0.4, s * 0.4)  # 0.4x init scale for c_fc
+            torch.nn.init.zeros_(block.mlp.c_proj.weight)
+
+        # Per-layer scalars
+        # Per-layer resid init: stronger residual at early layers, weaker at deep layers
+        n_layer = self.config.n_layer
+        for i in range(n_layer):
+            self.resid_lambdas.data[i] = 1.15 - (0.10 * i / max(n_layer - 1, 1))
+        # Decaying x0 init: earlier layers get more input embedding blending
+        for i in range(n_layer):
+            self.x0_lambdas.data[i] = 0.20 - (0.15 * i / max(n_layer - 1, 1))
+
+        # Smear/backout scalars and smear gate must be explicitly initialized 
+        torch.nn.init.zeros_(self.smear_lambda)
+        torch.nn.init.constant_(self.backout_lambda, 0.2)
+        torch.nn.init.uniform_(self.smear_gate.weight, 0.0, 0.02)
+
+        # Value embeddings (init like c_v: uniform with same std)
+        for ve in self.value_embeds.values():
+            torch.nn.init.uniform_(ve.weight, -s, s)
+
+        # Gate weights init with small positive values so gates start slightly above neutral
+        for block in self.transformer.h:
+            if block.attn.ve_gate is not None:
+                torch.nn.init.uniform_(block.attn.ve_gate.weight, 0.0, 0.02)
+
+        # Rotary embeddings
+        head_dim = self.config.n_embd // self.config.n_head
+        cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
+        self.cos, self.sin = cos, sin
+
+        # Cast embeddings to COMPUTE_DTYPE: optimizer can tolerate reduced-precision
+        # embeddings and it saves memory. Exception: fp16 requires fp32 embeddings
+        # because GradScaler cannot unscale fp16 gradients.
+        if COMPUTE_DTYPE != torch.float16:
+            self.transformer.wte.to(dtype=COMPUTE_DTYPE)
+            for ve in self.value_embeds.values():
+                ve.to(dtype=COMPUTE_DTYPE)
+
+    def _precompute_rotary_embeddings(self, seq_len, head_dim, base=100000, device=None):
+        # TODO: bump base theta more? e.g. 100K is more common more recently
+        # autodetect the device from model embeddings
+        if device is None:
+            device = self.transformer.wte.weight.device
+        # stride the channels
+        channel_range = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device)
+        inv_freq = 1.0 / (base ** (channel_range / head_dim))
+        # stride the time steps
+        t = torch.arange(seq_len, dtype=torch.float32, device=device)
+        # calculate the rotation frequencies at each (time, channel) pair
+        freqs = torch.outer(t, inv_freq)
+        cos, sin = freqs.cos(), freqs.sin()
+        cos, sin = cos.to(COMPUTE_DTYPE), sin.to(COMPUTE_DTYPE)
+        cos, sin = cos[None, :, None, :], sin[None, :, None, :] # add batch and head dims for later broadcasting
+        return cos, sin
+
+    def _compute_window_sizes(self, config):
+        """
+        Compute per-layer window sizes for sliding window attention.
+
+        Returns list of (left, right) tuples for FA3's window_size parameter:
+        - left: how many tokens before current position to attend to (-1 = unlimited)
+        - right: how many tokens after current position to attend to (0 for causal)
+
+        Pattern string is tiled across layers. Final layer always gets L (full context).
+        Characters: L=long (full context), S=short (quarter context)
+        """
+        pattern = config.window_pattern.upper()
+        assert all(c in "SL" for c in pattern), f"Invalid window_pattern: {pattern}. Use only S and L."
+        # Map characters to window sizes
+        long_window = config.sequence_len
+        short_window = -(-long_window // 4 // 128) * 128  # ceil to FA3 tile size (2048 -> 768)
+        char_to_window = {
+            "L": (long_window, 0),
+            "S": (short_window, 0),
+        }
+        # Tile pattern across layers
+        window_sizes = []
+        for layer_idx in range(config.n_layer):
+            char = pattern[layer_idx % len(pattern)]
+            window_sizes.append(char_to_window[char])
+        # Final layer always gets full context
+        window_sizes[-1] = (long_window, 0)
+        return window_sizes
+
+    def get_device(self):
+        return self.transformer.wte.weight.device
+
+    def estimate_flops(self):
+        """
+        Return the estimated FLOPs per token for the model (forward + backward).
+        Each matmul weight parameter contributes 2 FLOPs (multiply *, accumulate +) in forward, and 2X that in backward => 2+4=6.
+        Cleanest explanation of this: https://medium.com/@dzmitrybahdanau/the-flops-calculus-of-language-model-training-3b19c1f025e4
+        On top of that, 12 * h * q * effective_seq_len accounts for key @ query matmul flops inside attention.
+        With sliding windows, effective_seq_len varies per layer (capped by window size).
+        Ref: https://arxiv.org/abs/2204.02311 (PaLM paper).
+        This is ~1% off from the exact formulas of Chinchilla paper, the difference is:
+        - Chinchilla counts the embedding layer as flops (? weird, it's just a lookup => we ignore)
+        - Chinchilla counts exp/sum/divide in attention softmax as flops (a little sus and very tiny => we ignore)
+        """
+        nparams = sum(p.numel() for p in self.parameters())
+        # Exclude non-matmul params: embeddings and per-layer scalars
+        value_embeds_numel = sum(ve.weight.numel() for ve in self.value_embeds.values())
+        nparams_exclude = (self.transformer.wte.weight.numel() + value_embeds_numel +
+                          self.resid_lambdas.numel() + self.x0_lambdas.numel() +
+                          self.smear_gate.weight.numel() + self.smear_lambda.numel() + self.backout_lambda.numel())
+        h, q, t = self.config.n_head, self.config.n_embd // self.config.n_head, self.config.sequence_len
+        # Sum attention FLOPs per layer, accounting for sliding window
+        attn_flops = 0
+        for window_size in self.window_sizes:
+            window = window_size[0]  # (left, right) tuple, we use left
+            effective_seq = t if window < 0 else min(window, t)
+            attn_flops += 12 * h * q * effective_seq
+        num_flops_per_token = 6 * (nparams - nparams_exclude) + attn_flops
+        return num_flops_per_token
+
+    def num_scaling_params(self):
+        """
+        Return detailed parameter counts for scaling law analysis.
+        Different papers use different conventions:
+        - Kaplan et al. excluded embedding parameters
+        - Chinchilla included all parameters
+        Ref: https://arxiv.org/abs/2203.15556 (Chinchilla paper)
+        Ref: https://arxiv.org/abs/2001.08361 (Kaplan et al. original scaling laws paper)
+
+        Returns a dict with counts for each parameter group, so downstream analysis
+        can experiment with which combination gives the cleanest scaling laws.
+        """
+        # Count each group separately (mirrors the grouping in setup_optimizers)
+        wte = sum(p.numel() for p in self.transformer.wte.parameters())
+        value_embeds = sum(p.numel() for p in self.value_embeds.parameters())
+        lm_head = sum(p.numel() for p in self.lm_head.parameters())
+        transformer_matrices = sum(p.numel() for p in self.transformer.h.parameters())
+        scalars = self.resid_lambdas.numel() + self.x0_lambdas.numel() + self.smear_gate.weight.numel() + self.smear_lambda.numel() + self.backout_lambda.numel()
+        total = wte + value_embeds + lm_head + transformer_matrices + scalars
+        assert total == sum(p.numel() for p in self.parameters()), "Parameter count mismatch"
+        return {
+            'wte': wte,
+            'value_embeds': value_embeds,
+            'lm_head': lm_head,
+            'transformer_matrices': transformer_matrices,
+            'scalars': scalars,
+            'total': total,
+        }
+
+    def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0, scalar_lr=0.5):
+        model_dim = self.config.n_embd
+        ddp, rank, local_rank, world_size = get_dist_info()
+
+        # Separate out all parameters into groups
+        matrix_params = list(self.transformer.h.parameters())
+        value_embeds_params = list(self.value_embeds.parameters())
+        embedding_params = list(self.transformer.wte.parameters())
+        lm_head_params = list(self.lm_head.parameters())
+        resid_params = [self.resid_lambdas]
+        x0_params = [self.x0_lambdas]
+        smear_params = [self.smear_gate.weight, self.smear_lambda, self.backout_lambda]
+        assert len(list(self.parameters())) == len(matrix_params) + len(embedding_params) + len(lm_head_params) + len(value_embeds_params) + len(resid_params) + len(x0_params) + len(smear_params)
+
+        # Scale the LR for the AdamW parameters by ∝1/√dmodel (tuned for 768 dim model)
+        dmodel_lr_scale = (model_dim / 768) ** -0.5
+        print0(f"Scaling the LR for the AdamW parameters ∝1/√({model_dim}/768) = {dmodel_lr_scale:.6f}")
+
+        # Build param_groups with all required fields explicit
+        param_groups = [
+            # AdamW groups (embeddings, lm_head, scalars)
+            dict(kind='adamw', params=lm_head_params, lr=unembedding_lr * dmodel_lr_scale, betas=(0.8, 0.96), eps=1e-10, weight_decay=0.01),
+            dict(kind='adamw', params=embedding_params, lr=embedding_lr * dmodel_lr_scale, betas=(0.8, 0.995), eps=1e-10, weight_decay=0.001),
+            dict(kind='adamw', params=value_embeds_params, lr=embedding_lr * dmodel_lr_scale * 0.5, betas=(0.8, 0.995), eps=1e-10, weight_decay=0.01),
+            dict(kind='adamw', params=resid_params, lr=scalar_lr * 0.01, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.05),
+            dict(kind='adamw', params=x0_params, lr=scalar_lr, betas=(0.96, 0.95), eps=1e-10, weight_decay=0.0),  # higher beta1 for x0
+            dict(kind='adamw', params=smear_params, lr=0.2, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0),
+        ]
+        # Muon groups (matrix params, grouped by shape for stacking)
+        for shape in sorted({p.shape for p in matrix_params}):
+            group_params = [p for p in matrix_params if p.shape == shape]
+            param_groups.append(dict(
+                kind='muon', params=group_params, lr=matrix_lr,
+                momentum=0.95, ns_steps=5, beta2=0.9, weight_decay=weight_decay,
+            ))
+
+        Factory = DistMuonAdamW if ddp else MuonAdamW
+        optimizer = Factory(param_groups)
+        for group in optimizer.param_groups:
+            group["initial_lr"] = group["lr"]
+        return optimizer
+
+    def forward(self, idx, targets=None, kv_cache=None, loss_reduction='mean', audio_features=None):
+        B, T = idx.size()
+
+        # Grab the rotary embeddings for the current sequence length (they are of shape (1, seq_len, 1, head_dim/2))
+        assert T <= self.cos.size(1), f"Sequence length grew beyond the rotary embeddings cache: {T} > {self.cos.size(1)}"
+        assert idx.device == self.cos.device, f"Rotary embeddings and idx are on different devices: {idx.device} != {self.cos.device}"
+        assert self.cos.dtype == COMPUTE_DTYPE, f"Rotary embeddings must be in {COMPUTE_DTYPE}, got {self.cos.dtype}"
+        # if kv cache exists, we need to offset the rotary embeddings to the current position in the cache
+        T0 = 0 if kv_cache is None else kv_cache.get_pos()
+        cos_sin = self.cos[:, T0:T0+T], self.sin[:, T0:T0+T] # truncate cache to current sequence length
+
+        # Embed the tokens
+        x = self.transformer.wte(idx) # embed current token
+        x = x.to(COMPUTE_DTYPE) # ensure activations are in compute dtype (no-op usually, but active for fp16 code path)
+        x = norm(x)
+
+        # Smear: mix previous token's embedding into current position (cheap bigram info)
+        if kv_cache is None:
+            # Training / naive generate: full sequence available, use fast slice
+            assert T > 1, "Training forward pass should have T > 1"
+            gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x[:, 1:, :24]))
+            x = torch.cat([x[:, :1], x[:, 1:] + gate * x[:, :-1]], dim=1)
+        else:
+            # KV cache inference: read prev embedding from cache, store current for next step
+            x_pre_smear = kv_cache.prev_embedding
+            kv_cache.prev_embedding = x[:, -1:, :]
+            if T > 1:
+                # Prefill: apply smear to positions 1+, same as training
+                gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x[:, 1:, :24]))
+                x = torch.cat([x[:, :1], x[:, 1:] + gate * x[:, :-1]], dim=1)
+            elif x_pre_smear is not None:
+                # Decode: single token, use cached prev embedding
+                gate = self.smear_lambda.to(x.dtype) * torch.sigmoid(self.smear_gate(x[:, :, :24]))
+                x = x + gate * x_pre_smear
+
+        # Audio soft-token prepend (LLaVA-style): audio_features must already be projected to n_embd.
+        idx_for_ve = idx
+        if audio_features is not None:
+            assert kv_cache is None, "audio_features prepend not supported with kv_cache"
+            audio = norm(audio_features.to(COMPUTE_DTYPE))
+            T_a = audio.size(1)
+            x = torch.cat([audio, x], dim=1)
+            T_full = T_a + T
+            assert T_full <= self.cos.size(1), f"Sequence length grew beyond rotary cache: {T_full} > {self.cos.size(1)}"
+            cos_sin = self.cos[:, :T_full], self.sin[:, :T_full]
+            idx_pad = torch.zeros((B, T_a), dtype=idx.dtype, device=idx.device)
+            idx_for_ve = torch.cat([idx_pad, idx], dim=1)
+            if targets is not None:
+                pad = torch.full((B, T_a), -1, dtype=targets.dtype, device=targets.device)
+                targets = torch.cat([pad, targets], dim=1)
+
+        # Forward the trunk of the Transformer
+        x0 = x  # save initial normalized embedding for x0 residual
+        n_layer = self.config.n_layer
+        backout_layer = n_layer // 2  # cache at halfway point
+        x_backout = None
+        for i, block in enumerate(self.transformer.h):
+            x = self.resid_lambdas[i] * x + self.x0_lambdas[i] * x0
+            ve = self.value_embeds[str(i)](idx_for_ve).to(x.dtype) if str(i) in self.value_embeds else None
+            x = block(x, ve, cos_sin, self.window_sizes[i], kv_cache)
+            if i == backout_layer:
+                x_backout = x
+        # Subtract mid-layer residual to remove low-level features before logit projection
+        if x_backout is not None:
+            x = x - self.backout_lambda.to(x.dtype) * x_backout
+        x = norm(x)
+
+        # Forward the lm_head (compute logits)
+        softcap = 15 # smoothly cap the logits to the range [-softcap, softcap]
+        logits = self.lm_head(x) # (B, T, padded_vocab_size) <- very big tensor, large amount of memory
+        logits = logits[..., :self.config.vocab_size] # slice to remove padding
+        logits = logits.float() # switch to fp32 for logit softcap and loss computation
+        logits = softcap * torch.tanh(logits / softcap) # squash the logits
+
+        if targets is not None:
+            # training: given the targets, compute and return the loss
+            # TODO experiment with chunked cross-entropy?
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1, reduction=loss_reduction)
+            return loss
+        else:
+            # inference: just return the logits directly
+            return logits
+
+    @torch.inference_mode()
+    def generate(self, tokens, max_tokens, temperature=1.0, top_k=None, seed=42):
+        """
+        Naive autoregressive streaming inference.
+        To make it super simple, let's assume:
+        - batch size is 1
+        - ids and the yielded tokens are simple Python lists and ints
+        """
+        assert isinstance(tokens, list)
+        device = self.get_device()
+        rng = None
+        if temperature > 0:
+            rng = torch.Generator(device=device)
+            rng.manual_seed(seed)
+        ids = torch.tensor([tokens], dtype=torch.long, device=device) # add batch dim
+        for _ in range(max_tokens):
+            logits = self.forward(ids) # (B, T, vocab_size)
+            logits = logits[:, -1, :] # (B, vocab_size)
+            if top_k is not None and top_k > 0:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            if temperature > 0:
+                logits = logits / temperature
+                probs = F.softmax(logits, dim=-1)
+                next_ids = torch.multinomial(probs, num_samples=1, generator=rng)
+            else:
+                next_ids = torch.argmax(logits, dim=-1, keepdim=True)
+            ids = torch.cat((ids, next_ids), dim=1)
+            token = next_ids.item()
+            yield token

nanochat/logo.svg

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+<svg width="400" height="400" xmlns="http://www.w3.org/2000/svg">
+    <defs>
+      <radialGradient id="g" cx="50%" cy="50%">
+        <stop offset="0%" style="stop-color:#667eea;stop-opacity:1"></stop>
+        <stop offset="100%" style="stop-color:#764ba2;stop-opacity:0.3"></stop>
+      </radialGradient>
+    </defs>
+  <path d="M315.9110991546882,231.0582854123025 L228.97777478867204,207.7645713530756 M284.8528137423857,284.8528137423857 L221.21320343559643,221.21320343559643 M231.0582854123025,315.9110991546882 L207.7645713530756,228.97777478867204 M168.94171458769753,315.9110991546882 L192.2354286469244,228.97777478867204 M115.14718625761431,284.8528137423857 L178.78679656440357,221.21320343559643 M84.08890084531181,231.05828541230252 L171.02222521132796,207.76457135307564 M84.0889008453118,168.9417145876975 L171.02222521132796,192.2354286469244 M115.14718625761425,115.14718625761435 L178.78679656440357,178.78679656440357 M168.94171458769753,84.0889008453118 L192.2354286469244,171.02222521132796 M231.05828541230244,84.08890084531178 L207.7645713530756,171.02222521132794 M284.8528137423857,115.14718625761428 L221.21320343559643,178.78679656440357 M315.91109915468815,168.94171458769742 L228.97777478867204,192.23542864692436 " stroke="url(#g)" stroke-width="6" stroke-linecap="round" fill="none"></path><path d="M200,-12 L212,0 L200,12 L188,0 Z" transform="translate(0,200)" fill="#000"></path></svg>

nanochat/loss_eval.py

@@ -0,0 +1,65 @@
+"""
+A number of functions that help with evaluating a base model.
+"""
+import math
+import torch
+import torch.distributed as dist
+
+@torch.no_grad()
+def evaluate_bpb(model, batches, steps, token_bytes):
+    """
+    Instead of the naive 'mean loss', this function returns the bits per byte (bpb),
+    which is a tokenization vocab size-independent metric, meaning you are still comparing
+    apples:apples if you change the vocab size. The way this works is that instead of just
+    calculating the average loss as usual, you calculate the sum loss, and independently
+    also the sum bytes (of all the target tokens), and divide. This normalizes the loss by
+    the number of bytes that the target tokens represent.
+
+    The added complexity is so that:
+    1) All "normal" tokens are normalized by the length of the token in bytes
+    2) No special tokens (e.g. <|bos|>) are included in the metric - they are masked out.
+    3) No actively masked tokens (using ignore_index of e.g. -1) are included in the metric.
+
+    In addition to evaluate_loss, we need the token_bytes tensor:
+    It is a 1D tensor of shape (vocab_size,), indicating the number of bytes for
+    each token id, or 0 if the token is to not be counted (e.g. special tokens).
+    """
+    # record the losses
+    total_nats = torch.tensor(0.0, dtype=torch.float32, device=model.get_device())
+    total_bytes = torch.tensor(0, dtype=torch.int64, device=model.get_device())
+    batch_iter = iter(batches)
+    for _ in range(steps):
+        x, y = next(batch_iter)
+        loss2d = model(x, y, loss_reduction='none') # (B, T)
+        loss2d = loss2d.view(-1) # flatten
+        y = y.view(-1) # flatten
+        if (y.int() < 0).any(): # mps does not currently have kernel for < 0 for int64, only int32
+            # slightly more complex code path if some target tokens are ignore_index (e.g. -1)
+            # any target token < 0 is to be ignored: do NOT index token_bytes with negatives
+            valid = y >= 0
+            y_safe = torch.where(valid, y, torch.zeros_like(y))
+            # map valid targets to their byte length; ignored targets contribute 0 bytes
+            num_bytes2d = torch.where(
+                valid,
+                token_bytes[y_safe],
+                torch.zeros_like(y, dtype=token_bytes.dtype)
+            )
+            total_nats += (loss2d * (num_bytes2d > 0)).sum()
+            total_bytes += num_bytes2d.sum()
+        else:
+            # fast path: no ignored targets, safe to index directly
+            num_bytes2d = token_bytes[y]
+            total_nats += (loss2d * (num_bytes2d > 0)).sum()
+            total_bytes += num_bytes2d.sum()
+    # sum reduce across all ranks
+    world_size = dist.get_world_size() if dist.is_initialized() else 1
+    if world_size > 1:
+        dist.all_reduce(total_nats, op=dist.ReduceOp.SUM)
+        dist.all_reduce(total_bytes, op=dist.ReduceOp.SUM)
+    # move both to cpu, calculate bpb and return
+    total_nats = total_nats.item()
+    total_bytes = total_bytes.item()
+    if total_bytes == 0:
+        return float('inf')
+    bpb = total_nats / (math.log(2) * total_bytes)
+    return bpb

nanochat/optim.py

@@ -0,0 +1,535 @@
+"""
+A nice and efficient mixed AdamW/Muon Combined Optimizer.
+Usually the embeddings and scalars go into AdamW, and the matrix parameters go into Muon.
+Two versions are provided (MuonAdamW, DistMuonAdamW), for single GPU and distributed.
+
+Addapted from: https://github.com/KellerJordan/modded-nanogpt
+Further contributions from @karpathy and @chrisjmccormick.
+"""
+
+import torch
+import torch.distributed as dist
+from torch import Tensor
+from nanochat.common import COMPUTE_DTYPE
+
+# -----------------------------------------------------------------------------
+"""
+Good old AdamW optimizer, fused kernel.
+https://arxiv.org/abs/1711.05101
+"""
+
+@torch.compile(dynamic=False, fullgraph=True)
+def adamw_step_fused(
+    p: Tensor,              # (32768, 768) - parameter tensor
+    grad: Tensor,           # (32768, 768) - gradient, same shape as p
+    exp_avg: Tensor,        # (32768, 768) - first moment, same shape as p
+    exp_avg_sq: Tensor,     # (32768, 768) - second moment, same shape as p
+    step_t: Tensor,         # () - 0-D CPU tensor, step count
+    lr_t: Tensor,           # () - 0-D CPU tensor, learning rate
+    beta1_t: Tensor,        # () - 0-D CPU tensor, beta1
+    beta2_t: Tensor,        # () - 0-D CPU tensor, beta2
+    eps_t: Tensor,          # () - 0-D CPU tensor, epsilon
+    wd_t: Tensor,           # () - 0-D CPU tensor, weight decay
+) -> None:
+    """
+    Fused AdamW step: weight_decay -> momentum_update -> bias_correction -> param_update
+    All in one compiled graph to eliminate Python overhead between ops.
+    The 0-D CPU tensors avoid recompilation when hyperparameter values change.
+    """
+    # Weight decay (decoupled, applied before the update)
+    p.mul_(1 - lr_t * wd_t)
+    # Update running averages (lerp_ is cleaner and fuses well)
+    exp_avg.lerp_(grad, 1 - beta1_t)
+    exp_avg_sq.lerp_(grad.square(), 1 - beta2_t)
+    # Bias corrections
+    bias1 = 1 - beta1_t ** step_t
+    bias2 = 1 - beta2_t ** step_t
+    # Compute update and apply
+    denom = (exp_avg_sq / bias2).sqrt() + eps_t
+    step_size = lr_t / bias1
+    p.add_(exp_avg / denom, alpha=-step_size)
+
+# -----------------------------------------------------------------------------
+"""
+Muon optimizer adapted and simplified from modded-nanogpt.
+https://github.com/KellerJordan/modded-nanogpt
+
+Background:
+Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
+quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
+of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
+zero even beyond the point where the iteration no longer converges all the way to one everywhere
+on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T
+where S' is diagonal with S_{ii}' ~ Uniform(0.5, 1.5), which turns out not to hurt model
+performance at all relative to UV^T, where USV^T = G is the SVD.
+
+Here, an alternative to Newton-Schulz iteration with potentially better convergence properties:
+Polar Express Sign Method for orthogonalization.
+https://arxiv.org/pdf/2505.16932
+by Noah Amsel, David Persson, Christopher Musco, Robert M. Gower.
+
+NorMuon variance reduction: per-neuron/column adaptive learning rate that normalizes
+update scales after orthogonalization (Muon's output has non-uniform scales across neurons).
+https://arxiv.org/pdf/2510.05491
+
+Some of the changes in nanochat implementation:
+- Uses a simpler, more general approach to parameter grouping and stacking
+- Uses a single fused kernel for the momentum -> polar_express -> variance_reduction -> update step
+- Makes no assumptions about model architecture (e.g. that attention weights are fused into QKVO format)
+"""
+
+# Coefficients for Polar Express (computed for num_iters=5, safety_factor=2e-2, cushion=2)
+# From https://arxiv.org/pdf/2505.16932
+polar_express_coeffs = [
+    (8.156554524902461, -22.48329292557795, 15.878769915207462),
+    (4.042929935166739, -2.808917465908714, 0.5000178451051316),
+    (3.8916678022926607, -2.772484153217685, 0.5060648178503393),
+    (3.285753657755655, -2.3681294933425376, 0.46449024233003106),
+    (2.3465413258596377, -1.7097828382687081, 0.42323551169305323),
+]
+
+@torch.compile(dynamic=False, fullgraph=True)
+def muon_step_fused(
+    stacked_grads: Tensor,          # (12, 768, 3072) - stacked gradients
+    stacked_params: Tensor,         # (12, 768, 3072) - stacked parameters
+    momentum_buffer: Tensor,        # (12, 768, 3072) - first moment buffer
+    second_momentum_buffer: Tensor, # (12, 768, 1) or (12, 1, 3072) - factored second moment
+    momentum_t: Tensor,             # () - 0-D CPU tensor, momentum coefficient
+    lr_t: Tensor,                   # () - 0-D CPU tensor, learning rate
+    wd_t: Tensor,                   # () - 0-D CPU tensor, weight decay
+    beta2_t: Tensor,                # () - 0-D CPU tensor, beta2 for second moment
+    ns_steps: int,                  # 5 - number of Newton-Schulz/Polar Express iterations
+    red_dim: int,                   # -1 or -2 - reduction dimension for variance
+) -> None:
+    """
+    Fused Muon step: momentum -> polar_express -> variance_reduction -> cautious_update
+    All in one compiled graph to eliminate Python overhead between ops.
+    Some of the constants are 0-D CPU tensors to avoid recompilation when values change.
+    """
+
+    # Nesterov momentum
+    momentum = momentum_t.to(stacked_grads.dtype)
+    momentum_buffer.lerp_(stacked_grads, 1 - momentum)
+    g = stacked_grads.lerp_(momentum_buffer, momentum)
+
+    # Polar express
+    # Cast to bf16 for speed when available; skip cast otherwise (fp16 is unstable here due to limited exponent range)
+    X = g.bfloat16() if COMPUTE_DTYPE == torch.bfloat16 else g
+    X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.01 + 1e-6)
+    if g.size(-2) > g.size(-1): # Tall matrix
+        for a, b, c in polar_express_coeffs[:ns_steps]:
+            A = X.mT @ X
+            B = b * A + c * (A @ A)
+            X = a * X + X @ B
+    else: # Wide matrix (original math)
+        for a, b, c in polar_express_coeffs[:ns_steps]:
+            A = X @ X.mT
+            B = b * A + c * (A @ A)
+            X = a * X + B @ X
+    g = X
+
+    # Variance reduction
+    beta2 = beta2_t.to(g.dtype)
+    v_mean = g.float().square().mean(dim=red_dim, keepdim=True)
+    red_dim_size = g.size(red_dim)
+    v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size
+    v_norm = v_norm_sq.sqrt()
+    second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2)
+    step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt()
+    scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square()
+    v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt()
+    final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10))
+    g = g * final_scale.to(g.dtype)
+
+    # Cautious weight decay + parameter update
+    lr = lr_t.to(g.dtype)
+    wd = wd_t.to(g.dtype)
+    mask = (g * stacked_params) >= 0
+    stacked_params.sub_(lr * g + lr * wd * stacked_params * mask)
+
+# -----------------------------------------------------------------------------
+# Single GPU version of the MuonAdamW optimizer.
+# Used mostly for reference, debugging and testing.
+
+class MuonAdamW(torch.optim.Optimizer):
+    """
+    Combined optimizer: Muon for 2D matrix params, AdamW for others, single GPU version.
+
+    AdamW - Fused AdamW optimizer step.
+
+    Muon - MomentUm Orthogonalized by Newton-schulz
+    https://kellerjordan.github.io/posts/muon/
+
+    Muon internally runs standard SGD-momentum, and then performs an orthogonalization post-
+    processing step, in which each 2D parameter's update is replaced with the nearest orthogonal
+    matrix. To efficiently orthogonalize each update, we use a Newton-Schulz iteration, which has
+    the advantage that it can be stably run in bfloat16 on the GPU.
+
+    Some warnings:
+    - The Muon optimizer should not be used for the embedding layer, the final fully connected layer,
+    or any {0,1}-D parameters; those should all be optimized by a standard method (e.g., AdamW).
+    - To use it with 4D convolutional filters, it works well to just flatten their last 3 dimensions.
+
+    Arguments:
+        param_groups: List of dicts, each containing:
+            - 'params': List of parameters
+            - 'kind': 'adamw' or 'muon'
+            - For AdamW groups: 'lr', 'betas', 'eps', 'weight_decay'
+            - For Muon groups: 'lr', 'momentum', 'ns_steps', 'beta2', 'weight_decay'
+    """
+    def __init__(self, param_groups: list[dict]):
+        super().__init__(param_groups, defaults={})
+        # 0-D CPU tensors to avoid torch.compile recompilation when values change
+        # AdamW tensors
+        self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        # Muon tensors
+        self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+
+    def _step_adamw(self, group: dict) -> None:
+        """
+        AdamW update for each param in the group individually.
+        Lazy init the state, fill in all 0-D tensors, call the fused kernel.
+        """
+        for p in group['params']:
+            if p.grad is None:
+                continue
+            grad = p.grad
+            state = self.state[p]
+
+            # State init
+            if not state:
+                state['step'] = 0
+                state['exp_avg'] = torch.zeros_like(p)
+                state['exp_avg_sq'] = torch.zeros_like(p)
+            exp_avg = state['exp_avg']
+            exp_avg_sq = state['exp_avg_sq']
+            state['step'] += 1
+
+            # Fill 0-D tensors with current values
+            self._adamw_step_t.fill_(state['step'])
+            self._adamw_lr_t.fill_(group['lr'])
+            self._adamw_beta1_t.fill_(group['betas'][0])
+            self._adamw_beta2_t.fill_(group['betas'][1])
+            self._adamw_eps_t.fill_(group['eps'])
+            self._adamw_wd_t.fill_(group['weight_decay'])
+
+            # Fused update: weight_decay -> momentum -> bias_correction -> param_update
+            adamw_step_fused(
+                p, grad, exp_avg, exp_avg_sq,
+                self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t,
+                self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t,
+            )
+
+    def _step_muon(self, group: dict) -> None:
+        """
+        Muon update for all params in the group (stacked for efficiency).
+        Lazy init the state, fill in all 0-D tensors, call the fused kernel.
+        """
+        params: list[Tensor] = group['params']
+        if not params:
+            return
+
+        # Get or create group-level buffers (stored in first param's state for convenience)
+        p = params[0]
+        state = self.state[p]
+        num_params = len(params)
+        shape, device, dtype = p.shape, p.device, p.dtype
+
+        # Momentum for every individual parameter
+        if "momentum_buffer" not in state:
+            state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device)
+        momentum_buffer = state["momentum_buffer"]
+
+        # Second momentum buffer is factored, either per-row or per-column
+        if "second_momentum_buffer" not in state:
+            state_shape = (num_params, shape[-2], 1) if shape[-2] >= shape[-1] else (num_params, 1, shape[-1])
+            state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device)
+        second_momentum_buffer = state["second_momentum_buffer"]
+        red_dim = -1 if shape[-2] >= shape[-1] else -2
+
+        # Stack grads and params (NOTE: this assumes all params have the same shape)
+        stacked_grads = torch.stack([p.grad for p in params])
+        stacked_params = torch.stack(params)
+
+        # Fill all the 0-D tensors with current values
+        self._muon_momentum_t.fill_(group["momentum"])
+        self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0)
+        self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1])**0.5)
+        self._muon_wd_t.fill_(group["weight_decay"])
+
+        # Single fused kernel: momentum -> polar_express -> variance_reduction -> update
+        muon_step_fused(
+            stacked_grads,
+            stacked_params,
+            momentum_buffer,
+            second_momentum_buffer,
+            self._muon_momentum_t,
+            self._muon_lr_t,
+            self._muon_wd_t,
+            self._muon_beta2_t,
+            group["ns_steps"],
+            red_dim,
+        )
+
+        # Copy back to original params
+        torch._foreach_copy_(params, list(stacked_params.unbind(0)))
+
+    @torch.no_grad()
+    def step(self):
+        for group in self.param_groups:
+            if group['kind'] == 'adamw':
+                self._step_adamw(group)
+            elif group['kind'] == 'muon':
+                self._step_muon(group)
+            else:
+                raise ValueError(f"Unknown optimizer kind: {group['kind']}")
+
+# -----------------------------------------------------------------------------
+# Distributed version of the MuonAdamW optimizer.
+# Used for training on multiple GPUs.
+
+class DistMuonAdamW(torch.optim.Optimizer):
+    """
+    Combined distributed optimizer: Muon for 2D matrix params, AdamW for others.
+
+    See MuonAdamW for the algorithmic details of each optimizer. This class adds
+    distributed communication to enable multi-GPU training without PyTorch DDP.
+
+    Design Goals:
+    - Overlap communication with computation (async ops)
+    - Minimize memory by sharding optimizer states across ranks (ZeRO-2 style)
+    - Batch small tensors into single comm ops where possible
+
+    Communication Pattern (3-phase async):
+    We use a 3-phase structure to maximize overlap between communication and compute:
+
+        Phase 1: Launch all async reduce ops
+            - Kick off all reduce_scatter/all_reduce operations
+            - Don't wait - let them run in background while we continue
+
+        Phase 2: Wait for reduces, compute updates, launch gathers
+            - For each group: wait for its reduce, compute the update, launch gather
+            - By processing groups in order, earlier gathers run while later computes happen
+
+        Phase 3: Wait for gathers, copy back
+            - Wait for all gathers to complete
+            - Copy updated params back to original tensors (Muon only)
+
+    AdamW Communication (ZeRO-2 style):
+    - Small params (<1024 elements): all_reduce gradients, update full param on each rank.
+      Optimizer state is replicated but these params are tiny (scalars, biases).
+    - Large params: reduce_scatter gradients so each rank gets 1/N of the grad, update
+      only that slice, then all_gather the updated slices. Optimizer state (exp_avg,
+      exp_avg_sq) is sharded - each rank only stores state for its slice.
+      Requires param.shape[0] divisible by world_size.
+
+    Muon Communication (stacked + chunked):
+    - All params in a Muon group must have the same shape (caller's responsibility).
+    - Stack all K params into a single (K, *shape) tensor for efficient comm.
+    - Divide K params across N ranks: each rank "owns" ceil(K/N) params.
+    - reduce_scatter the stacked grads so each rank gets its chunk.
+    - Each rank computes Muon update only for params it owns.
+    - all_gather the updated params back to all ranks.
+    - Optimizer state (momentum_buffer, second_momentum_buffer) is sharded by chunk.
+    - Padding: if K doesn't divide evenly, we zero-pad to (ceil(K/N) * N) for comm,
+      then ignore the padding when copying back.
+
+    Buffer Reuse:
+    - For Muon, we allocate stacked_grads for reduce_scatter input, then reuse the
+      same buffer as the output for all_gather (stacked_params). This saves memory
+      since we don't need both buffers simultaneously.
+
+    Arguments:
+        param_groups: List of dicts, each containing:
+            - 'params': List of parameters
+            - 'kind': 'adamw' or 'muon'
+            - For AdamW groups: 'lr', 'betas', 'eps', 'weight_decay'
+            - For Muon groups: 'lr', 'momentum', 'ns_steps', 'beta2', 'weight_decay'
+    """
+    def __init__(self, param_groups: list[dict]):
+        super().__init__(param_groups, defaults={})
+        # 0-D CPU tensors to avoid torch.compile recompilation when values change
+        self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+
+    def _reduce_adamw(self, group: dict, world_size: int) -> dict:
+        """Launch async reduce ops for AdamW group. Returns info dict with per-param infos."""
+        param_infos = {}
+        for p in group['params']:
+            grad = p.grad
+            if p.numel() < 1024:
+                # Small params: all_reduce (no scatter/gather needed)
+                future = dist.all_reduce(grad, op=dist.ReduceOp.AVG, async_op=True).get_future()
+                param_infos[p] = dict(future=future, grad_slice=grad, is_small=True)
+            else:
+                # Large params: reduce_scatter
+                assert grad.shape[0] % world_size == 0, f"AdamW reduce_scatter requires shape[0] ({grad.shape[0]}) divisible by world_size ({world_size})"
+                rank_size = grad.shape[0] // world_size
+                grad_slice = torch.empty_like(grad[:rank_size])
+                future = dist.reduce_scatter_tensor(grad_slice, grad, op=dist.ReduceOp.AVG, async_op=True).get_future()
+                param_infos[p] = dict(future=future, grad_slice=grad_slice, is_small=False)
+        return dict(param_infos=param_infos)
+
+    def _reduce_muon(self, group: dict, world_size: int) -> dict:
+        """Launch async reduce op for Muon group. Returns info dict."""
+        params = group['params']
+        chunk_size = (len(params) + world_size - 1) // world_size
+        padded_num_params = chunk_size * world_size
+        p = params[0]
+        shape, device, dtype = p.shape, p.device, p.dtype
+
+        # Stack grads and zero-pad to padded_num_params
+        grad_stack = torch.stack([p.grad for p in params])
+        stacked_grads = torch.empty(padded_num_params, *shape, dtype=dtype, device=device)
+        stacked_grads[:len(params)].copy_(grad_stack)
+        if len(params) < padded_num_params:
+            stacked_grads[len(params):].zero_()
+
+        # Reduce_scatter to get this rank's chunk
+        grad_chunk = torch.empty(chunk_size, *shape, dtype=dtype, device=device)
+        future = dist.reduce_scatter_tensor(grad_chunk, stacked_grads, op=dist.ReduceOp.AVG, async_op=True).get_future()
+
+        return dict(future=future, grad_chunk=grad_chunk, stacked_grads=stacked_grads, chunk_size=chunk_size)
+
+    def _compute_adamw(self, group: dict, info: dict, gather_list: list, rank: int, world_size: int) -> None:
+        """Wait for reduce, compute AdamW updates, launch gathers for large params."""
+        param_infos = info['param_infos']
+        for p in group['params']:
+            pinfo = param_infos[p]
+            pinfo['future'].wait()
+            grad_slice = pinfo['grad_slice']
+            state = self.state[p]
+
+            # For small params, operate on full param; for large, operate on slice
+            if pinfo['is_small']:
+                p_slice = p
+            else:
+                rank_size = p.shape[0] // world_size
+                p_slice = p[rank * rank_size:(rank + 1) * rank_size]
+
+            # State init
+            if not state:
+                state['step'] = 0
+                state['exp_avg'] = torch.zeros_like(p_slice)
+                state['exp_avg_sq'] = torch.zeros_like(p_slice)
+            state['step'] += 1
+
+            # Fill 0-D tensors and run fused kernel
+            self._adamw_step_t.fill_(state['step'])
+            self._adamw_lr_t.fill_(group['lr'])
+            self._adamw_beta1_t.fill_(group['betas'][0])
+            self._adamw_beta2_t.fill_(group['betas'][1])
+            self._adamw_eps_t.fill_(group['eps'])
+            self._adamw_wd_t.fill_(group['weight_decay'])
+            adamw_step_fused(
+                p_slice, grad_slice, state['exp_avg'], state['exp_avg_sq'],
+                self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t,
+                self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t,
+            )
+
+            # Large params need all_gather
+            if not pinfo['is_small']:
+                future = dist.all_gather_into_tensor(p, p_slice, async_op=True).get_future()
+                gather_list.append(dict(future=future, params=None))
+
+    def _compute_muon(self, group: dict, info: dict, gather_list: list, rank: int) -> None:
+        """Wait for reduce, compute Muon updates, launch gather."""
+        info['future'].wait()
+        params = group['params']
+        chunk_size = info['chunk_size']
+        grad_chunk = info['grad_chunk']
+        p = params[0]
+        shape, device, dtype = p.shape, p.device, p.dtype
+
+        # How many params does this rank own?
+        start_idx = rank * chunk_size
+        num_owned = min(chunk_size, max(0, len(params) - start_idx))
+
+        # Get or create group-level state
+        state = self.state[p]
+        if "momentum_buffer" not in state:
+            state["momentum_buffer"] = torch.zeros(chunk_size, *shape, dtype=dtype, device=device)
+        if "second_momentum_buffer" not in state:
+            state_shape = (chunk_size, shape[-2], 1) if shape[-2] >= shape[-1] else (chunk_size, 1, shape[-1])
+            state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device)
+        red_dim = -1 if shape[-2] >= shape[-1] else -2
+
+        # Build output buffer for all_gather
+        updated_params = torch.empty(chunk_size, *shape, dtype=dtype, device=device)
+
+        if num_owned > 0:
+            owned_params = [params[start_idx + i] for i in range(num_owned)]
+            stacked_owned = torch.stack(owned_params)
+
+            # Fill 0-D tensors and run fused kernel
+            self._muon_momentum_t.fill_(group["momentum"])
+            self._muon_beta2_t.fill_(group["beta2"])
+            self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1])**0.5)
+            self._muon_wd_t.fill_(group["weight_decay"])
+            muon_step_fused(
+                grad_chunk[:num_owned], stacked_owned,
+                state["momentum_buffer"][:num_owned], state["second_momentum_buffer"][:num_owned],
+                self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, self._muon_beta2_t,
+                group["ns_steps"], red_dim,
+            )
+            updated_params[:num_owned].copy_(stacked_owned)
+
+        if num_owned < chunk_size:
+            updated_params[num_owned:].zero_()
+
+        # Reuse stacked_grads buffer for all_gather output
+        stacked_params = info["stacked_grads"]
+        future = dist.all_gather_into_tensor(stacked_params, updated_params, async_op=True).get_future()
+        gather_list.append(dict(future=future, stacked_params=stacked_params, params=params))
+
+    def _finish_gathers(self, gather_list: list) -> None:
+        """Wait for all gathers and copy Muon params back."""
+        for info in gather_list:
+            info["future"].wait()
+            if info["params"] is not None:
+                # Muon: copy from stacked buffer back to individual params
+                torch._foreach_copy_(info["params"], list(info["stacked_params"][:len(info["params"])].unbind(0)))
+
+    @torch.no_grad()
+    def step(self):
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+
+        # Phase 1: launch all async reduce ops
+        reduce_infos: list[dict] = []
+        for group in self.param_groups:
+            if group['kind'] == 'adamw':
+                reduce_infos.append(self._reduce_adamw(group, world_size))
+            elif group['kind'] == 'muon':
+                reduce_infos.append(self._reduce_muon(group, world_size))
+            else:
+                raise ValueError(f"Unknown optimizer kind: {group['kind']}")
+
+        # Phase 2: wait for reduces, compute updates, launch gathers
+        gather_list: list[dict] = []
+        for group, info in zip(self.param_groups, reduce_infos):
+            if group['kind'] == 'adamw':
+                self._compute_adamw(group, info, gather_list, rank, world_size)
+            elif group['kind'] == 'muon':
+                self._compute_muon(group, info, gather_list, rank)
+            else:
+                raise ValueError(f"Unknown optimizer kind: {group['kind']}")
+
+        # Phase 3: wait for gathers, copy back
+        self._finish_gathers(gather_list)

nanochat/report.py

@@ -0,0 +1,418 @@
+"""
+Utilities for generating training report cards. More messy code than usual, will fix.
+"""
+
+import os
+import re
+import shutil
+import subprocess
+import socket
+import datetime
+import platform
+import psutil
+import torch
+
+def run_command(cmd):
+    """Run a shell command and return output, or None if it fails."""
+    try:
+        result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=5)
+        # Return stdout if we got output (even if some files in xargs failed)
+        if result.stdout.strip():
+            return result.stdout.strip()
+        if result.returncode == 0:
+            return ""
+        return None
+    except:
+        return None
+
+def get_git_info():
+    """Get current git commit, branch, and dirty status."""
+    info = {}
+    info['commit'] = run_command("git rev-parse --short HEAD") or "unknown"
+    info['branch'] = run_command("git rev-parse --abbrev-ref HEAD") or "unknown"
+
+    # Check if repo is dirty (has uncommitted changes)
+    status = run_command("git status --porcelain")
+    info['dirty'] = bool(status) if status is not None else False
+
+    # Get commit message
+    info['message'] = run_command("git log -1 --pretty=%B") or ""
+    info['message'] = info['message'].split('\n')[0][:80]  # First line, truncated
+
+    return info
+
+def get_gpu_info():
+    """Get GPU information."""
+    if not torch.cuda.is_available():
+        return {"available": False}
+
+    num_devices = torch.cuda.device_count()
+    info = {
+        "available": True,
+        "count": num_devices,
+        "names": [],
+        "memory_gb": []
+    }
+
+    for i in range(num_devices):
+        props = torch.cuda.get_device_properties(i)
+        info["names"].append(props.name)
+        info["memory_gb"].append(props.total_memory / (1024**3))
+
+    # Get CUDA version
+    info["cuda_version"] = torch.version.cuda or "unknown"
+
+    return info
+
+def get_system_info():
+    """Get system information."""
+    info = {}
+
+    # Basic system info
+    info['hostname'] = socket.gethostname()
+    info['platform'] = platform.system()
+    info['python_version'] = platform.python_version()
+    info['torch_version'] = torch.__version__
+
+    # CPU and memory
+    info['cpu_count'] = psutil.cpu_count(logical=False)
+    info['cpu_count_logical'] = psutil.cpu_count(logical=True)
+    info['memory_gb'] = psutil.virtual_memory().total / (1024**3)
+
+    # User and environment
+    info['user'] = os.environ.get('USER', 'unknown')
+    info['nanochat_base_dir'] = os.environ.get('NANOCHAT_BASE_DIR', 'out')
+    info['working_dir'] = os.getcwd()
+
+    return info
+
+def estimate_cost(gpu_info, runtime_hours=None):
+    """Estimate training cost based on GPU type and runtime."""
+
+    # Rough pricing, from Lambda Cloud
+    default_rate = 2.0
+    gpu_hourly_rates = {
+        "H100": 3.00,
+        "A100": 1.79,
+        "V100": 0.55,
+    }
+
+    if not gpu_info.get("available"):
+        return None
+
+    # Try to identify GPU type from name
+    hourly_rate = None
+    gpu_name = gpu_info["names"][0] if gpu_info["names"] else "unknown"
+    for gpu_type, rate in gpu_hourly_rates.items():
+        if gpu_type in gpu_name:
+            hourly_rate = rate * gpu_info["count"]
+            break
+
+    if hourly_rate is None:
+        hourly_rate = default_rate * gpu_info["count"]  # Default estimate
+
+    return {
+        "hourly_rate": hourly_rate,
+        "gpu_type": gpu_name,
+        "estimated_total": hourly_rate * runtime_hours if runtime_hours else None
+    }
+
+def generate_header():
+    """Generate the header for a training report."""
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    git_info = get_git_info()
+    gpu_info = get_gpu_info()
+    sys_info = get_system_info()
+    cost_info = estimate_cost(gpu_info)
+
+    header = f"""# nanochat training report
+
+Generated: {timestamp}
+
+## Environment
+
+### Git Information
+- Branch: {git_info['branch']}
+- Commit: {git_info['commit']} {"(dirty)" if git_info['dirty'] else "(clean)"}
+- Message: {git_info['message']}
+
+### Hardware
+- Platform: {sys_info['platform']}
+- CPUs: {sys_info['cpu_count']} cores ({sys_info['cpu_count_logical']} logical)
+- Memory: {sys_info['memory_gb']:.1f} GB
+"""
+
+    if gpu_info.get("available"):
+        gpu_names = ", ".join(set(gpu_info["names"]))
+        total_vram = sum(gpu_info["memory_gb"])
+        header += f"""- GPUs: {gpu_info['count']}x {gpu_names}
+- GPU Memory: {total_vram:.1f} GB total
+- CUDA Version: {gpu_info['cuda_version']}
+"""
+    else:
+        header += "- GPUs: None available\n"
+
+    if cost_info and cost_info["hourly_rate"] > 0:
+        header += f"""- Hourly Rate: ${cost_info['hourly_rate']:.2f}/hour\n"""
+
+    header += f"""
+### Software
+- Python: {sys_info['python_version']}
+- PyTorch: {sys_info['torch_version']}
+
+"""
+
+    # bloat metrics: count lines/chars in git-tracked source files only
+    extensions = ['py', 'md', 'rs', 'html', 'toml', 'sh']
+    git_patterns = ' '.join(f"'*.{ext}'" for ext in extensions)
+    files_output = run_command(f"git ls-files -- {git_patterns}")
+    file_list = [f for f in (files_output or '').split('\n') if f]
+    num_files = len(file_list)
+    num_lines = 0
+    num_chars = 0
+    if num_files > 0:
+        wc_output = run_command(f"git ls-files -- {git_patterns} | xargs wc -lc 2>/dev/null")
+        if wc_output:
+            total_line = wc_output.strip().split('\n')[-1]
+            parts = total_line.split()
+            if len(parts) >= 2:
+                num_lines = int(parts[0])
+                num_chars = int(parts[1])
+    num_tokens = num_chars // 4  # assume approximately 4 chars per token
+
+    # count dependencies via uv.lock
+    uv_lock_lines = 0
+    if os.path.exists('uv.lock'):
+        with open('uv.lock', 'r', encoding='utf-8') as f:
+            uv_lock_lines = len(f.readlines())
+
+    header += f"""
+### Bloat
+- Characters: {num_chars:,}
+- Lines: {num_lines:,}
+- Files: {num_files:,}
+- Tokens (approx): {num_tokens:,}
+- Dependencies (uv.lock lines): {uv_lock_lines:,}
+
+"""
+    return header
+
+# -----------------------------------------------------------------------------
+
+def slugify(text):
+    """Slugify a text string."""
+    return text.lower().replace(" ", "-")
+
+# the expected files and their order
+EXPECTED_FILES = [
+    "tokenizer-training.md",
+    "tokenizer-evaluation.md",
+    "base-model-training.md",
+    "base-model-loss.md",
+    "base-model-evaluation.md",
+    "chat-sft.md",
+    "chat-evaluation-sft.md",
+    "chat-rl.md",
+    "chat-evaluation-rl.md",
+]
+# the metrics we're currently interested in
+chat_metrics = ["ARC-Easy", "ARC-Challenge", "MMLU", "GSM8K", "HumanEval", "ChatCORE"]
+
+def extract(section, keys):
+    """simple def to extract a single key from a section"""
+    if not isinstance(keys, list):
+        keys = [keys] # convenience
+    out = {}
+    for line in section.split("\n"):
+        for key in keys:
+            if key in line:
+                out[key] = line.split(":")[1].strip()
+    return out
+
+def extract_timestamp(content, prefix):
+    """Extract timestamp from content with given prefix."""
+    for line in content.split('\n'):
+        if line.startswith(prefix):
+            time_str = line.split(":", 1)[1].strip()
+            try:
+                return datetime.datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S")
+            except:
+                pass
+    return None
+
+class Report:
+    """Maintains a bunch of logs, generates a final markdown report."""
+
+    def __init__(self, report_dir):
+        os.makedirs(report_dir, exist_ok=True)
+        self.report_dir = report_dir
+
+    def log(self, section, data):
+        """Log a section of data to the report."""
+        slug = slugify(section)
+        file_name = f"{slug}.md"
+        file_path = os.path.join(self.report_dir, file_name)
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(f"## {section}\n")
+            f.write(f"timestamp: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
+            for item in data:
+                if not item:
+                    # skip falsy values like None or empty dict etc.
+                    continue
+                if isinstance(item, str):
+                    # directly write the string
+                    f.write(item)
+                else:
+                    # render a dict
+                    for k, v in item.items():
+                        if isinstance(v, float):
+                            vstr = f"{v:.4f}"
+                        elif isinstance(v, int) and v >= 10000:
+                            vstr = f"{v:,.0f}"
+                        else:
+                            vstr = str(v)
+                        f.write(f"- {k}: {vstr}\n")
+            f.write("\n")
+        return file_path
+
+    def generate(self):
+        """Generate the final report."""
+        report_dir = self.report_dir
+        report_file = os.path.join(report_dir, "report.md")
+        print(f"Generating report to {report_file}")
+        final_metrics = {} # the most important final metrics we'll add as table at the end
+        start_time = None
+        end_time = None
+        with open(report_file, "w", encoding="utf-8") as out_file:
+            # write the header first
+            header_file = os.path.join(report_dir, "header.md")
+            if os.path.exists(header_file):
+                with open(header_file, "r", encoding="utf-8") as f:
+                    header_content = f.read()
+                    out_file.write(header_content)
+                    start_time = extract_timestamp(header_content, "Run started:")
+                    # capture bloat data for summary later (the stuff after Bloat header and until \n\n)
+                    bloat_data = re.search(r"### Bloat\n(.*?)\n\n", header_content, re.DOTALL)
+                    bloat_data = bloat_data.group(1) if bloat_data else ""
+            else:
+                start_time = None # will cause us to not write the total wall clock time
+                bloat_data = "[bloat data missing]"
+                print(f"Warning: {header_file} does not exist. Did you forget to run `nanochat reset`?")
+            # process all the individual sections
+            for file_name in EXPECTED_FILES:
+                section_file = os.path.join(report_dir, file_name)
+                if not os.path.exists(section_file):
+                    print(f"Warning: {section_file} does not exist, skipping")
+                    continue
+                with open(section_file, "r", encoding="utf-8") as in_file:
+                    section = in_file.read()
+                # Extract timestamp from this section (the last section's timestamp will "stick" as end_time)
+                if "rl" not in file_name:
+                    # Skip RL sections for end_time calculation because RL is experimental
+                    end_time = extract_timestamp(section, "timestamp:")
+                # extract the most important metrics from the sections
+                if file_name == "base-model-evaluation.md":
+                    final_metrics["base"] = extract(section, "CORE")
+                if file_name == "chat-evaluation-sft.md":
+                    final_metrics["sft"] = extract(section, chat_metrics)
+                if file_name == "chat-evaluation-rl.md":
+                    final_metrics["rl"] = extract(section, "GSM8K") # RL only evals GSM8K
+                # append this section of the report
+                out_file.write(section)
+                out_file.write("\n")
+            # add the final metrics table
+            out_file.write("## Summary\n\n")
+            # Copy over the bloat metrics from the header
+            out_file.write(bloat_data)
+            out_file.write("\n\n")
+            # Collect all unique metric names
+            all_metrics = set()
+            for stage_metrics in final_metrics.values():
+                all_metrics.update(stage_metrics.keys())
+            # Custom ordering: CORE first, ChatCORE last, rest in middle
+            all_metrics = sorted(all_metrics, key=lambda x: (x != "CORE", x == "ChatCORE", x))
+            # Fixed column widths
+            stages = ["base", "sft", "rl"]
+            metric_width = 15
+            value_width = 8
+            # Write table header
+            header = f"| {'Metric'.ljust(metric_width)} |"
+            for stage in stages:
+                header += f" {stage.upper().ljust(value_width)} |"
+            out_file.write(header + "\n")
+            # Write separator
+            separator = f"|{'-' * (metric_width + 2)}|"
+            for stage in stages:
+                separator += f"{'-' * (value_width + 2)}|"
+            out_file.write(separator + "\n")
+            # Write table rows
+            for metric in all_metrics:
+                row = f"| {metric.ljust(metric_width)} |"
+                for stage in stages:
+                    value = final_metrics.get(stage, {}).get(metric, "-")
+                    row += f" {str(value).ljust(value_width)} |"
+                out_file.write(row + "\n")
+            out_file.write("\n")
+            # Calculate and write total wall clock time
+            if start_time and end_time:
+                duration = end_time - start_time
+                total_seconds = int(duration.total_seconds())
+                hours = total_seconds // 3600
+                minutes = (total_seconds % 3600) // 60
+                out_file.write(f"Total wall clock time: {hours}h{minutes}m\n")
+            else:
+                out_file.write("Total wall clock time: unknown\n")
+        # also cp the report.md file to current directory
+        print(f"Copying report.md to current directory for convenience")
+        shutil.copy(report_file, "report.md")
+        return report_file
+
+    def reset(self):
+        """Reset the report."""
+        # Remove section files
+        for file_name in EXPECTED_FILES:
+            file_path = os.path.join(self.report_dir, file_name)
+            if os.path.exists(file_path):
+                os.remove(file_path)
+        # Remove report.md if it exists
+        report_file = os.path.join(self.report_dir, "report.md")
+        if os.path.exists(report_file):
+            os.remove(report_file)
+        # Generate and write the header section with start timestamp
+        header_file = os.path.join(self.report_dir, "header.md")
+        header = generate_header()
+        start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        with open(header_file, "w", encoding="utf-8") as f:
+            f.write(header)
+            f.write(f"Run started: {start_time}\n\n---\n\n")
+        print(f"Reset report and wrote header to {header_file}")
+
+# -----------------------------------------------------------------------------
+# nanochat-specific convenience functions
+
+class DummyReport:
+    def log(self, *args, **kwargs):
+        pass
+    def reset(self, *args, **kwargs):
+        pass
+
+def get_report():
+    # just for convenience, only rank 0 logs to report
+    from nanochat.common import get_base_dir, get_dist_info
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
+    if ddp_rank == 0:
+        report_dir = os.path.join(get_base_dir(), "report")
+        return Report(report_dir)
+    else:
+        return DummyReport()
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="Generate or reset nanochat training reports.")
+    parser.add_argument("command", nargs="?", default="generate", choices=["generate", "reset"], help="Operation to perform (default: generate)")
+    args = parser.parse_args()
+    if args.command == "generate":
+        get_report().generate()
+    elif args.command == "reset":
+        get_report().reset()

nanochat/tokenizer.py

@@ -0,0 +1,406 @@
+"""
+BPE Tokenizer in the style of GPT-4.
+
+Two implementations are available:
+1) HuggingFace Tokenizer that can do both training and inference but is really confusing
+2) Our own RustBPE Tokenizer for training and tiktoken for efficient inference
+"""
+
+import os
+import copy
+from functools import lru_cache
+
+SPECIAL_TOKENS = [
+    # every document begins with the Beginning of Sequence (BOS) token that delimits documents
+    "<|bos|>",
+    # tokens below are only used during finetuning to render Conversations into token ids
+    "<|user_start|>", # user messages
+    "<|user_end|>",
+    "<|assistant_start|>", # assistant messages
+    "<|assistant_end|>",
+    "<|python_start|>", # assistant invokes python REPL tool
+    "<|python_end|>",
+    "<|output_start|>", # python REPL outputs back to assistant
+    "<|output_end|>",
+]
+
+# NOTE: this split pattern deviates from GPT-4 in that we use \p{N}{1,2} instead of \p{N}{1,3}
+# I did this because I didn't want to "waste" too many tokens on numbers for smaller vocab sizes.
+# I verified that 2 is the sweet spot for vocab size of 32K. 1 is a bit worse, 3 was worse still.
+SPLIT_PATTERN = r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,2}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+"""
+
+# -----------------------------------------------------------------------------
+# Generic GPT-4-style tokenizer based on HuggingFace Tokenizer
+from tokenizers import Tokenizer as HFTokenizer
+from tokenizers import pre_tokenizers, decoders, Regex
+from tokenizers.models import BPE
+from tokenizers.trainers import BpeTrainer
+
+class HuggingFaceTokenizer:
+    """Light wrapper around HuggingFace Tokenizer for some utilities"""
+
+    def __init__(self, tokenizer):
+        self.tokenizer = tokenizer
+
+    @classmethod
+    def from_pretrained(cls, hf_path):
+        # init from a HuggingFace pretrained tokenizer (e.g. "gpt2")
+        tokenizer = HFTokenizer.from_pretrained(hf_path)
+        return cls(tokenizer)
+
+    @classmethod
+    def from_directory(cls, tokenizer_dir):
+        # init from a local directory on disk (e.g. "out/tokenizer")
+        tokenizer_path = os.path.join(tokenizer_dir, "tokenizer.json")
+        tokenizer = HFTokenizer.from_file(tokenizer_path)
+        return cls(tokenizer)
+
+    @classmethod
+    def train_from_iterator(cls, text_iterator, vocab_size):
+        # train from an iterator of text
+        # Configure the HuggingFace Tokenizer
+        tokenizer = HFTokenizer(BPE(
+            byte_fallback=True, # needed!
+            unk_token=None,
+            fuse_unk=False,
+        ))
+        # Normalizer: None
+        tokenizer.normalizer = None
+        # Pre-tokenizer: GPT-4 style
+        # the regex pattern used by GPT-4 to split text into groups before BPE
+        # NOTE: The pattern was changed from \p{N}{1,3} to \p{N}{1,2} because I suspect it is harmful to
+        # very small models and smaller vocab sizes, because it is a little bit wasteful in the token space.
+        # (but I haven't validated this! TODO)
+        gpt4_split_regex = Regex(SPLIT_PATTERN) # huggingface demands that you wrap it in Regex!!
+        tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
+            pre_tokenizers.Split(pattern=gpt4_split_regex, behavior="isolated", invert=False),
+            pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False)
+        ])
+        # Decoder: ByteLevel (it pairs together with the ByteLevel pre-tokenizer)
+        tokenizer.decoder = decoders.ByteLevel()
+        # Post-processor: None
+        tokenizer.post_processor = None
+        # Trainer: BPE
+        trainer = BpeTrainer(
+            vocab_size=vocab_size,
+            show_progress=True,
+            min_frequency=0, # no minimum frequency
+            initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
+            special_tokens=SPECIAL_TOKENS,
+        )
+        # Kick off the training
+        tokenizer.train_from_iterator(text_iterator, trainer)
+        return cls(tokenizer)
+
+    def get_vocab_size(self):
+        return self.tokenizer.get_vocab_size()
+
+    def get_special_tokens(self):
+        special_tokens_map = self.tokenizer.get_added_tokens_decoder()
+        special_tokens = [w.content for w in special_tokens_map.values()]
+        return special_tokens
+
+    def id_to_token(self, id):
+        return self.tokenizer.id_to_token(id)
+
+    def _encode_one(self, text, prepend=None, append=None, num_threads=None):
+        # encode a single string
+        # prepend/append can be either a string of a special token or a token id directly.
+        # num_threads is ignored (only used by the nanochat Tokenizer for parallel encoding)
+        assert isinstance(text, str)
+        ids = []
+        if prepend is not None:
+            prepend_id = prepend if isinstance(prepend, int) else self.encode_special(prepend)
+            ids.append(prepend_id)
+        ids.extend(self.tokenizer.encode(text, add_special_tokens=False).ids)
+        if append is not None:
+            append_id = append if isinstance(append, int) else self.encode_special(append)
+            ids.append(append_id)
+        return ids
+
+    def encode_special(self, text):
+        # encode a single special token via exact match
+        return self.tokenizer.token_to_id(text)
+
+    def get_bos_token_id(self):
+        # Different HuggingFace models use different BOS tokens and there is little consistency
+        # 1) attempt to find a <|bos|> token
+        bos = self.encode_special("<|bos|>")
+        # 2) if that fails, attempt to find a <|endoftext|> token (e.g. GPT-2 models)
+        if bos is None:
+            bos = self.encode_special("<|endoftext|>")
+        # 3) if these fail, it's better to crash than to silently return None
+        assert bos is not None, "Failed to find BOS token in tokenizer"
+        return bos
+
+    def encode(self, text, *args, **kwargs):
+        if isinstance(text, str):
+            return self._encode_one(text, *args, **kwargs)
+        elif isinstance(text, list):
+            return [self._encode_one(t, *args, **kwargs) for t in text]
+        else:
+            raise ValueError(f"Invalid input type: {type(text)}")
+
+    def __call__(self, *args, **kwargs):
+        return self.encode(*args, **kwargs)
+
+    def decode(self, ids):
+        return self.tokenizer.decode(ids, skip_special_tokens=False)
+
+    def save(self, tokenizer_dir):
+        # save the tokenizer to disk
+        os.makedirs(tokenizer_dir, exist_ok=True)
+        tokenizer_path = os.path.join(tokenizer_dir, "tokenizer.json")
+        self.tokenizer.save(tokenizer_path)
+        print(f"Saved tokenizer to {tokenizer_path}")
+
+# -----------------------------------------------------------------------------
+# Tokenizer based on rustbpe + tiktoken combo
+import pickle
+import rustbpe
+import tiktoken
+
+class RustBPETokenizer:
+    """Light wrapper around tiktoken (for efficient inference) but train with rustbpe"""
+
+    def __init__(self, enc, bos_token):
+        self.enc = enc
+        self.bos_token_id = self.encode_special(bos_token)
+
+    @classmethod
+    def train_from_iterator(cls, text_iterator, vocab_size):
+        # 1) train using rustbpe
+        tokenizer = rustbpe.Tokenizer()
+        # the special tokens are inserted later in __init__, we don't train them here
+        vocab_size_no_special = vocab_size - len(SPECIAL_TOKENS)
+        assert vocab_size_no_special >= 256, f"vocab_size_no_special must be at least 256, got {vocab_size_no_special}"
+        tokenizer.train_from_iterator(text_iterator, vocab_size_no_special, pattern=SPLIT_PATTERN)
+        # 2) construct the associated tiktoken encoding for inference
+        pattern = tokenizer.get_pattern()
+        mergeable_ranks_list = tokenizer.get_mergeable_ranks()
+        mergeable_ranks = {bytes(k): v for k, v in mergeable_ranks_list}
+        tokens_offset = len(mergeable_ranks)
+        special_tokens = {name: tokens_offset + i for i, name in enumerate(SPECIAL_TOKENS)}
+        enc = tiktoken.Encoding(
+            name="rustbpe",
+            pat_str=pattern,
+            mergeable_ranks=mergeable_ranks, # dict[bytes, int] (token bytes -> merge priority rank)
+            special_tokens=special_tokens, # dict[str, int] (special token name -> token id)
+        )
+        return cls(enc, "<|bos|>")
+
+    @classmethod
+    def from_directory(cls, tokenizer_dir):
+        pickle_path = os.path.join(tokenizer_dir, "tokenizer.pkl")
+        with open(pickle_path, "rb") as f:
+            enc = pickle.load(f)
+        return cls(enc, "<|bos|>")
+
+    @classmethod
+    def from_pretrained(cls, tiktoken_name):
+        # https://github.com/openai/tiktoken/blob/eedc8563/tiktoken_ext/openai_public.py
+        enc = tiktoken.get_encoding(tiktoken_name)
+        # tiktoken calls the special document delimiter token "<|endoftext|>"
+        # yes this is confusing because this token is almost always PREPENDED to the beginning of the document
+        # it most often is used to signal the start of a new sequence to the LLM during inference etc.
+        # so in nanoChat we always use "<|bos|>" short for "beginning of sequence", but historically it is often called "<|endoftext|>".
+        return cls(enc, "<|endoftext|>")
+
+    def get_vocab_size(self):
+        return self.enc.n_vocab
+
+    def get_special_tokens(self):
+        return self.enc.special_tokens_set
+
+    def id_to_token(self, id):
+        return self.enc.decode([id])
+
+    @lru_cache(maxsize=32)
+    def encode_special(self, text):
+        return self.enc.encode_single_token(text)
+
+    def get_bos_token_id(self):
+        return self.bos_token_id
+
+    def encode(self, text, prepend=None, append=None, num_threads=8):
+        # text can be either a string or a list of strings
+
+        if prepend is not None:
+            prepend_id = prepend if isinstance(prepend, int) else self.encode_special(prepend)
+        if append is not None:
+            append_id = append if isinstance(append, int) else self.encode_special(append)
+
+        if isinstance(text, str):
+            ids = self.enc.encode_ordinary(text)
+            if prepend is not None:
+                ids.insert(0, prepend_id) # TODO: slightly inefficient here? :( hmm
+            if append is not None:
+                ids.append(append_id)
+        elif isinstance(text, list):
+            ids = self.enc.encode_ordinary_batch(text, num_threads=num_threads)
+            if prepend is not None:
+                for ids_row in ids:
+                    ids_row.insert(0, prepend_id) # TODO: same
+            if append is not None:
+                for ids_row in ids:
+                    ids_row.append(append_id)
+        else:
+            raise ValueError(f"Invalid input type: {type(text)}")
+
+        return ids
+
+    def __call__(self, *args, **kwargs):
+        return self.encode(*args, **kwargs)
+
+    def decode(self, ids):
+        return self.enc.decode(ids)
+
+    def save(self, tokenizer_dir):
+        # save the encoding object to disk
+        os.makedirs(tokenizer_dir, exist_ok=True)
+        pickle_path = os.path.join(tokenizer_dir, "tokenizer.pkl")
+        with open(pickle_path, "wb") as f:
+            pickle.dump(self.enc, f)
+        print(f"Saved tokenizer encoding to {pickle_path}")
+
+    def render_conversation(self, conversation, max_tokens=2048):
+        """
+        Tokenize a single Chat conversation (which we call a "doc" or "document" here).
+        Returns:
+        - ids: list[int] is a list of token ids of this rendered conversation
+        - mask: list[int] of same length, mask = 1 for tokens that the Assistant is expected to train on.
+        """
+        # ids, masks that we will return and a helper function to help build them up.
+        ids, mask = [], []
+        def add_tokens(token_ids, mask_val):
+            if isinstance(token_ids, int):
+                token_ids = [token_ids]
+            ids.extend(token_ids)
+            mask.extend([mask_val] * len(token_ids))
+
+        # sometimes the first message is a system message...
+        # => just merge it with the second (user) message
+        if conversation["messages"][0]["role"] == "system":
+            # some conversation surgery is necessary here for now...
+            conversation = copy.deepcopy(conversation) # avoid mutating the original
+            messages = conversation["messages"]
+            assert messages[1]["role"] == "user", "System message must be followed by a user message"
+            messages[1]["content"] = messages[0]["content"] + "\n\n" + messages[1]["content"]
+            messages = messages[1:]
+        else:
+            messages = conversation["messages"]
+        assert len(messages) >= 1, f"Conversation has less than 1 message: {messages}"
+
+        # fetch all the special tokens we need
+        bos = self.get_bos_token_id()
+        user_start, user_end = self.encode_special("<|user_start|>"), self.encode_special("<|user_end|>")
+        assistant_start, assistant_end = self.encode_special("<|assistant_start|>"), self.encode_special("<|assistant_end|>")
+        python_start, python_end = self.encode_special("<|python_start|>"), self.encode_special("<|python_end|>")
+        output_start, output_end = self.encode_special("<|output_start|>"), self.encode_special("<|output_end|>")
+
+        # now we can tokenize the conversation
+        add_tokens(bos, 0)
+        for i, message in enumerate(messages):
+
+            # some sanity checking here around assumptions, to prevent footguns
+            must_be_from = "user" if i % 2 == 0 else "assistant"
+            assert message["role"] == must_be_from, f"Message {i} is from {message['role']} but should be from {must_be_from}"
+
+            # content can be either a simple string or a list of parts (e.g. containing tool calls)
+            content = message["content"]
+
+            if message["role"] == "user":
+                assert isinstance(content, str), "User messages are simply expected to be strings"
+                value_ids = self.encode(content)
+                add_tokens(user_start, 0)
+                add_tokens(value_ids, 0)
+                add_tokens(user_end, 0)
+            elif message["role"] == "assistant":
+                add_tokens(assistant_start, 0)
+                if isinstance(content, str):
+                    # simple string => simply add the tokens
+                    value_ids = self.encode(content)
+                    add_tokens(value_ids, 1)
+                elif isinstance(content, list):
+                    for part in content:
+                        value_ids = self.encode(part["text"])
+                        if part["type"] == "text":
+                            # string part => simply add the tokens
+                            add_tokens(value_ids, 1)
+                        elif part["type"] == "python":
+                            # python tool call => add the tokens inside <|python_start|> and <|python_end|>
+                            add_tokens(python_start, 1)
+                            add_tokens(value_ids, 1)
+                            add_tokens(python_end, 1)
+                        elif part["type"] == "python_output":
+                            # python output => add the tokens inside <|output_start|> and <|output_end|>
+                            # none of these tokens are supervised because the tokens come from Python at test time
+                            add_tokens(output_start, 0)
+                            add_tokens(value_ids, 0)
+                            add_tokens(output_end, 0)
+                        else:
+                            raise ValueError(f"Unknown part type: {part['type']}")
+                else:
+                    raise ValueError(f"Unknown content type: {type(content)}")
+                add_tokens(assistant_end, 1)
+
+        # truncate to max_tokens tokens MAX (helps prevent OOMs)
+        ids = ids[:max_tokens]
+        mask = mask[:max_tokens]
+        return ids, mask
+
+    def visualize_tokenization(self, ids, mask, with_token_id=False):
+        """Small helper function useful in debugging: visualize the tokenization of render_conversation"""
+        RED = '\033[91m'
+        GREEN = '\033[92m'
+        RESET = '\033[0m'
+        GRAY = '\033[90m'
+        tokens = []
+        for i, (token_id, mask_val) in enumerate(zip(ids, mask)):
+            token_str = self.decode([token_id])
+            color = GREEN if mask_val == 1 else RED
+            tokens.append(f"{color}{token_str}{RESET}")
+            if with_token_id:
+                tokens.append(f"{GRAY}({token_id}){RESET}")
+        return '|'.join(tokens)
+
+    def render_for_completion(self, conversation):
+        """
+        Used during Reinforcement Learning. In that setting, we want to
+        render the conversation priming the Assistant for a completion.
+        Unlike the Chat SFT case, we don't need to return the mask.
+        """
+        # We have some surgery to do: we need to pop the last message (of the Assistant)
+        conversation = copy.deepcopy(conversation) # avoid mutating the original
+        messages = conversation["messages"]
+        assert messages[-1]["role"] == "assistant", "Last message must be from the Assistant"
+        messages.pop() # remove the last message (of the Assistant) inplace
+
+        # Now tokenize the conversation
+        ids, mask = self.render_conversation(conversation)
+
+        # Finally, to prime the Assistant for a completion, append the Assistant start token
+        assistant_start = self.encode_special("<|assistant_start|>")
+        ids.append(assistant_start)
+        return ids
+
+# -----------------------------------------------------------------------------
+# nanochat-specific convenience functions
+
+def get_tokenizer():
+    from nanochat.common import get_base_dir
+    base_dir = get_base_dir()
+    tokenizer_dir = os.path.join(base_dir, "tokenizer")
+    # return HuggingFaceTokenizer.from_directory(tokenizer_dir)
+    return RustBPETokenizer.from_directory(tokenizer_dir)
+
+def get_token_bytes(device="cpu"):
+    import torch
+    from nanochat.common import get_base_dir
+    base_dir = get_base_dir()
+    tokenizer_dir = os.path.join(base_dir, "tokenizer")
+    token_bytes_path = os.path.join(tokenizer_dir, "token_bytes.pt")
+    assert os.path.exists(token_bytes_path), f"Token bytes not found at {token_bytes_path}? It gets written by tok_train.py"
+    with open(token_bytes_path, "rb") as f:
+        token_bytes = torch.load(f, map_location=device)
+    return token_bytes

nanochat/ui.html

@@ -0,0 +1,566 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0, viewport-fit=cover">
+    <title>NanoChat</title>
+    <link rel="icon" type="image/svg+xml" href="/logo.svg">
+    <style>
+        :root {
+            color-scheme: light;
+        }
+
+        * {
+            box-sizing: border-box;
+        }
+
+        html, body{
+            height: 100%;
+            margin: 0;
+        }
+
+        body {
+            font-family: ui-sans-serif, -apple-system, system-ui, "Segoe UI", Helvetica, "Apple Color Emoji", Arial, sans-serif, "Segoe UI Emoji", "Segoe UI Symbol";
+            background-color: #ffffff;
+            color: #111827;
+            min-height: 100dvh;
+            margin: 0;
+            display: flex;
+            flex-direction: column;
+        }
+
+        .header {
+            background-color: #ffffff;
+            padding: 1.25rem 1.5rem;
+        }
+
+        .header-left {
+            display: flex;
+            align-items: center;
+            gap: 0.75rem;
+        }
+
+        .header-logo {
+            height: 32px;
+            width: auto;
+        }
+
+        .header h1 {
+            font-size: 1.25rem;
+            font-weight: 600;
+            margin: 0;
+            color: #111827;
+        }
+
+        .new-conversation-btn {
+            width: 32px;
+            height: 32px;
+            padding: 0;
+            border: 1px solid #e5e7eb;
+            border-radius: 0.5rem;
+            background-color: #ffffff;
+            color: #6b7280;
+            cursor: pointer;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            transition: all 0.2s ease;
+        }
+
+        .new-conversation-btn:hover {
+            background-color: #f3f4f6;
+            border-color: #d1d5db;
+            color: #374151;
+        }
+
+        .chat-container {
+            flex: 1;
+            overflow-y: auto;
+            background-color: #ffffff;
+        }
+
+        .chat-wrapper {
+            max-width: 48rem;
+            margin: 0 auto;
+            padding: 2rem 1.5rem 3rem;
+            display: flex;
+            flex-direction: column;
+            gap: 0.75rem;
+        }
+
+        .message {
+            display: flex;
+            justify-content: flex-start;
+            margin-bottom: 0.5rem;
+            color: #0d0d0d;
+        }
+
+        .message.assistant {
+            justify-content: flex-start;
+        }
+
+        .message.user {
+            justify-content: flex-end;
+        }
+
+        .message-content {
+            white-space: pre-wrap;
+            line-height: 1.6;
+            max-width: 100%;
+        }
+
+        .message.assistant .message-content {
+            background: transparent;
+            border: none;
+            cursor: pointer;
+            border-radius: 0.5rem;
+            padding: 0.5rem;
+            margin-left: -0.5rem;
+            transition: background-color 0.2s ease;
+        }
+
+        .message.assistant .message-content:hover {
+            background-color: #f9fafb;
+        }
+
+        .message.user .message-content {
+            background-color: #f3f4f6;
+            border-radius: 1.25rem;
+            padding: 0.8rem 1rem;
+            max-width: 65%;
+            cursor: pointer;
+            transition: background-color 0.2s ease;
+        }
+
+        .message.user .message-content:hover {
+            background-color: #e5e7eb;
+        }
+
+        .message.console .message-content {
+            font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', 'Consolas', 'Courier New', monospace;
+            font-size: 0.875rem;
+            background-color: #fafafa;
+            padding: 0.75rem 1rem;
+            color: #374151;
+            max-width: 80%;
+        }
+
+        .input-container {
+            background-color: #ffffff;
+            padding: 1rem;
+            padding-bottom: calc(1rem + env(safe-area-inset-bottom))
+        }
+
+        .input-wrapper {
+            max-width: 48rem;
+            margin: 0 auto;
+            display: flex;
+            gap: 0.75rem;
+            align-items: flex-end;
+        }
+
+        .chat-input {
+            flex: 1;
+            padding: 0.8rem 1rem;
+            border: 1px solid #d1d5db;
+            border-radius: 0.75rem;
+            background-color: #ffffff;
+            color: #111827;
+            font-size: 1rem;
+            line-height: 1.5;
+            resize: none;
+            outline: none;
+            min-height: 54px;
+            max-height: 200px;
+            transition: border-color 0.2s ease, box-shadow 0.2s ease;
+        }
+
+        .chat-input::placeholder {
+            color: #9ca3af;
+        }
+
+        .chat-input:focus {
+            border-color: #2563eb;
+            box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.1);
+        }
+
+        .send-button {
+            flex-shrink: 0;
+            padding: 0;
+            width: 54px;
+            height: 54px;
+            border: 1px solid #111827;
+            border-radius: 0.75rem;
+            background-color: #111827;
+            color: #ffffff;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            cursor: pointer;
+            transition: background-color 0.2s ease, border-color 0.2s ease, color 0.2s ease;
+        }
+
+        .send-button:hover:not(:disabled) {
+            background-color: #2563eb;
+            border-color: #2563eb;
+        }
+
+        .send-button:disabled {
+            cursor: not-allowed;
+            border-color: #d1d5db;
+            background-color: #e5e7eb;
+            color: #9ca3af;
+        }
+
+        .typing-indicator {
+            display: inline-block;
+            color: #6b7280;
+            letter-spacing: 0.15em;
+        }
+
+        .typing-indicator::after {
+            content: '···';
+            animation: typing 1.4s infinite;
+        }
+
+        @keyframes typing {
+            0%, 60%, 100% { opacity: 0.2; }
+            30% { opacity: 1; }
+        }
+
+        .error-message {
+            background-color: #fee2e2;
+            border: 1px solid #fecaca;
+            color: #b91c1c;
+            padding: 0.75rem 1rem;
+            border-radius: 0.75rem;
+            margin-top: 0.5rem;
+        }
+    </style>
+</head>
+<body>
+    <div class="header">
+        <div class="header-left">
+            <button class="new-conversation-btn" onclick="newConversation()" title="New Conversation (Ctrl+Shift+N)">
+                <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                    <path d="M12 5v14"></path>
+                    <path d="M5 12h14"></path>
+                </svg>
+            </button>
+            <h1>nanochat</h1>
+        </div>
+    </div>
+
+    <div class="chat-container" id="chatContainer">
+        <div class="chat-wrapper" id="chatWrapper">
+            <!-- Messages will be added here -->
+        </div>
+    </div>
+
+    <div class="input-container">
+        <div class="input-wrapper">
+            <textarea
+                id="chatInput"
+                class="chat-input"
+                placeholder="Ask anything"
+                rows="1"
+                onkeydown="handleKeyDown(event)"
+            ></textarea>
+            <button id="sendButton" class="send-button" onclick="sendMessage()" disabled>
+                <svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                    <path d="M22 2L11 13"></path>
+                    <path d="M22 2l-7 20-4-9-9-4 20-7z"></path>
+                </svg>
+            </button>
+        </div>
+    </div>
+
+    <script>
+        const API_URL = '';
+        const chatContainer = document.getElementById('chatContainer');
+        const chatWrapper = document.getElementById('chatWrapper');
+        const chatInput = document.getElementById('chatInput');
+        const sendButton = document.getElementById('sendButton');
+
+        let messages = [];
+        let isGenerating = false;
+        let currentTemperature = 0.8;
+        let currentTopK = 50;
+
+        chatInput.addEventListener('input', function() {
+            this.style.height = 'auto';
+            this.style.height = Math.min(this.scrollHeight, 200) + 'px';
+            sendButton.disabled = !this.value.trim() || isGenerating;
+        });
+
+        function handleKeyDown(event) {
+            if (event.key === 'Enter' && !event.shiftKey) {
+                event.preventDefault();
+                sendMessage();
+            }
+        }
+
+        document.addEventListener('keydown', function(event) {
+            // Ctrl+Shift+N for new conversation
+            if (event.ctrlKey && event.shiftKey && event.key === 'N') {
+                event.preventDefault();
+                if (!isGenerating) {
+                    newConversation();
+                }
+            }
+        });
+
+        function newConversation() {
+            messages = [];
+            chatWrapper.innerHTML = '';
+            chatInput.value = '';
+            chatInput.style.height = 'auto';
+            sendButton.disabled = false;
+            isGenerating = false;
+            chatInput.focus();
+        }
+
+        function addMessage(role, content, messageIndex = null) {
+            const messageDiv = document.createElement('div');
+            messageDiv.className = `message ${role}`;
+
+            const contentDiv = document.createElement('div');
+            contentDiv.className = 'message-content';
+            contentDiv.textContent = content;
+
+            // Add click handler for user messages to enable editing
+            if (role === 'user' && messageIndex !== null) {
+                contentDiv.setAttribute('data-message-index', messageIndex);
+                contentDiv.setAttribute('title', 'Click to edit and restart from here');
+                contentDiv.addEventListener('click', function() {
+                    if (!isGenerating) {
+                        editMessage(messageIndex);
+                    }
+                });
+            }
+
+            // Add click handler for assistant messages to enable regeneration
+            if (role === 'assistant' && messageIndex !== null) {
+                contentDiv.setAttribute('data-message-index', messageIndex);
+                contentDiv.setAttribute('title', 'Click to regenerate this response');
+                contentDiv.addEventListener('click', function() {
+                    if (!isGenerating) {
+                        regenerateMessage(messageIndex);
+                    }
+                });
+            }
+
+            messageDiv.appendChild(contentDiv);
+            chatWrapper.appendChild(messageDiv);
+
+            chatContainer.scrollTop = chatContainer.scrollHeight;
+            return contentDiv;
+        }
+
+        function editMessage(messageIndex) {
+            // Find the message in the messages array
+            if (messageIndex < 0 || messageIndex >= messages.length) return;
+
+            const messageToEdit = messages[messageIndex];
+            if (messageToEdit.role !== 'user') return;
+
+            // Copy message content to input
+            chatInput.value = messageToEdit.content;
+            chatInput.style.height = 'auto';
+            chatInput.style.height = Math.min(chatInput.scrollHeight, 200) + 'px';
+
+            // Remove this message and all subsequent messages from the array
+            messages = messages.slice(0, messageIndex);
+
+            // Remove message elements from DOM starting from messageIndex
+            const allMessages = chatWrapper.querySelectorAll('.message');
+            for (let i = messageIndex; i < allMessages.length; i++) {
+                allMessages[i].remove();
+            }
+
+            // Enable send button and focus input
+            sendButton.disabled = false;
+            chatInput.focus();
+        }
+
+        async function generateAssistantResponse() {
+            isGenerating = true;
+            sendButton.disabled = true;
+
+            const assistantContent = addMessage('assistant', '');
+            assistantContent.innerHTML = '<span class="typing-indicator"></span>';
+
+            try {
+                const response = await fetch(`${API_URL}/chat/completions`, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify({
+                        messages: messages,
+                        temperature: currentTemperature,
+                        top_k: currentTopK,
+                        max_tokens: 512
+                    }),
+                });
+
+                if (!response.ok) {
+                    throw new Error(`HTTP error! status: ${response.status}`);
+                }
+
+                const reader = response.body.getReader();
+                const decoder = new TextDecoder();
+                let fullResponse = '';
+                assistantContent.textContent = '';
+
+                while (true) {
+                    const { done, value } = await reader.read();
+                    if (done) break;
+
+                    const chunk = decoder.decode(value);
+                    const lines = chunk.split('\n');
+
+                    for (const line of lines) {
+                        if (line.startsWith('data: ')) {
+                            try {
+                                const data = JSON.parse(line.slice(6));
+                                if (data.token) {
+                                    fullResponse += data.token;
+                                    assistantContent.textContent = fullResponse;
+                                    chatContainer.scrollTop = chatContainer.scrollHeight;
+                                }
+                            } catch (e) {
+                            }
+                        }
+                    }
+                }
+
+                const assistantMessageIndex = messages.length;
+                messages.push({ role: 'assistant', content: fullResponse });
+
+                // Add click handler to regenerate this assistant message
+                assistantContent.setAttribute('data-message-index', assistantMessageIndex);
+                assistantContent.setAttribute('title', 'Click to regenerate this response');
+                assistantContent.addEventListener('click', function() {
+                    if (!isGenerating) {
+                        regenerateMessage(assistantMessageIndex);
+                    }
+                });
+
+            } catch (error) {
+                console.error('Error:', error);
+                assistantContent.innerHTML = `<div class="error-message">Error: ${error.message}</div>`;
+            } finally {
+                isGenerating = false;
+                sendButton.disabled = !chatInput.value.trim();
+            }
+        }
+
+        async function regenerateMessage(messageIndex) {
+            // Find the message in the messages array
+            if (messageIndex < 0 || messageIndex >= messages.length) return;
+
+            const messageToRegenerate = messages[messageIndex];
+            if (messageToRegenerate.role !== 'assistant') return;
+
+            // Remove this message and all subsequent messages from the array
+            messages = messages.slice(0, messageIndex);
+
+            // Remove message elements from DOM starting from messageIndex
+            const allMessages = chatWrapper.querySelectorAll('.message');
+            for (let i = messageIndex; i < allMessages.length; i++) {
+                allMessages[i].remove();
+            }
+
+            // Regenerate the assistant response
+            await generateAssistantResponse();
+        }
+
+        function handleSlashCommand(command) {
+            const parts = command.trim().split(/\s+/);
+            const cmd = parts[0].toLowerCase();
+            const arg = parts[1];
+
+            if (cmd === '/temperature') {
+                if (arg === undefined) {
+                    addMessage('console', `Current temperature: ${currentTemperature}`);
+                } else {
+                    const temp = parseFloat(arg);
+                    if (isNaN(temp) || temp < 0 || temp > 2) {
+                        addMessage('console', 'Invalid temperature. Must be between 0.0 and 2.0');
+                    } else {
+                        currentTemperature = temp;
+                        addMessage('console', `Temperature set to ${currentTemperature}`);
+                    }
+                }
+                return true;
+            } else if (cmd === '/topk') {
+                if (arg === undefined) {
+                    addMessage('console', `Current top-k: ${currentTopK}`);
+                } else {
+                    const topk = parseInt(arg);
+                    if (isNaN(topk) || topk < 1 || topk > 200) {
+                        addMessage('console', 'Invalid top-k. Must be between 1 and 200');
+                    } else {
+                        currentTopK = topk;
+                        addMessage('console', `Top-k set to ${currentTopK}`);
+                    }
+                }
+                return true;
+            } else if (cmd === '/clear') {
+                newConversation();
+                return true;
+            } else if (cmd === '/help') {
+                addMessage('console',
+                    'Available commands:\n' +
+                    '/temperature - Show current temperature\n' +
+                    '/temperature <value> - Set temperature (0.0-2.0)\n' +
+                    '/topk - Show current top-k\n' +
+                    '/topk <value> - Set top-k (1-200)\n' +
+                    '/clear - Clear conversation\n' +
+                    '/help - Show this help message'
+                );
+                return true;
+            }
+            return false;
+        }
+
+        async function sendMessage() {
+            const message = chatInput.value.trim();
+            if (!message || isGenerating) return;
+
+            // Handle slash commands
+            if (message.startsWith('/')) {
+                chatInput.value = '';
+                chatInput.style.height = 'auto';
+                handleSlashCommand(message);
+                return;
+            }
+
+            chatInput.value = '';
+            chatInput.style.height = 'auto';
+
+            const userMessageIndex = messages.length;
+            messages.push({ role: 'user', content: message });
+            addMessage('user', message, userMessageIndex);
+
+            await generateAssistantResponse();
+        }
+
+        sendButton.disabled = false;
+
+        // Autofocus the chat input on page load
+        chatInput.focus();
+
+        fetch(`${API_URL}/health`)
+            .then(response => response.json())
+            .then(data => {
+                console.log('Engine status:', data);
+            })
+            .catch(error => {
+                console.error('Engine not available:', error);
+                chatWrapper.innerHTML = '<div class="error-message">Engine not running. Please start engine.py first.</div>';
+            });
+    </script>
+</body>
+</html>

pyproject.toml

@@ -0,0 +1,71 @@
+[project]
+name = "nanochat"
+version = "0.1.0"
+description = "the minimal full-stack ChatGPT clone"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "datasets>=4.0.0",
+    "fastapi>=0.117.1",
+    "kernels>=0.11.7",
+    "psutil>=7.1.0",
+    "rustbpe>=0.1.0",
+    "tiktoken>=0.11.0",
+    "tokenizers>=0.22.0",
+    "torch==2.9.1",
+    "uvicorn>=0.36.0",
+    "wandb>=0.21.3",
+]
+
+[dependency-groups]
+dev = [
+    "ipykernel>=7.1.0",
+    "matplotlib>=3.10.8",
+    "pytest>=8.0.0",
+    "python-dotenv>=1.2.1",
+    "transformers>=4.57.3",
+]
+
+[tool.pytest.ini_options]
+markers = [
+    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
+]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+
+# target torch to cuda 12.8 or CPU
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cpu", extra = "cpu" },
+    { index = "pytorch-cu128", extra = "gpu" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://mirror.sjtu.edu.cn/pytorch-wheels/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://mirror.sjtu.edu.cn/pytorch-wheels/cu128"
+explicit = true
+
+[project.optional-dependencies]
+cpu = [
+    "setuptools>=65.0.0",
+    "torch==2.9.1",
+]
+gpu = [
+    "torch==2.9.1",
+]
+
+[tool.uv]
+default-groups = []
+conflicts = [
+    [
+        { extra = "cpu" },
+        { extra = "gpu" },
+    ],
+]

runs/miniseries.sh

@@ -0,0 +1,110 @@
+#!/bin/bash
+
+# See speedrun.sh for more comments
+# Usage: ./miniseries.sh [series_name]
+# Example: ./miniseries.sh jan11
+# Default series name is today's date (e.g., jan11)
+
+export OMP_NUM_THREADS=1
+export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
+mkdir -p $NANOCHAT_BASE_DIR
+
+# Setup (skip with SKIP_SETUP=1)
+if [ -z "$SKIP_SETUP" ]; then
+    # uv
+    command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
+    [ -d ".venv" ] || uv venv
+    uv sync --extra gpu
+    source .venv/bin/activate
+
+    # Tokenizer, download 1000 shards for pretraining
+    # (probably this can be reduced but it's tricky to determine the exact right number, TODO).
+    python -m nanochat.dataset -n 1000
+    python -m scripts.tok_train --max-chars=2000000000 --vocab-size=32768
+else
+    source .venv/bin/activate
+fi
+
+# Series name: from arg, env var, or default to today's date (e.g., jan11)
+SERIES_NAME="${1:-${SERIES_NAME:-$(date +%b%d | tr '[:upper:]' '[:lower:]')}}"
+# Depths to train (the "miniseries")
+DEPTHS=(12 14 16 18 20 22 24 26)
+# Hardware
+NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
+# Logging
+WANDB_RUN="${WANDB_RUN:-${SERIES_NAME}_miniseries}"
+
+RESULTS_DIR="$NANOCHAT_BASE_DIR/${SERIES_NAME}_miniseries_results"
+mkdir -p "$RESULTS_DIR"
+RESULTS_FILE="$RESULTS_DIR/results.csv"
+
+# Write CSV header only if file doesn't exist
+if [ ! -f "$RESULTS_FILE" ]; then
+    echo "depth,model_dim,num_params,num_scaling_params,num_iterations,tokens_trained,param_data_ratio,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
+fi
+
+log() {
+    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
+}
+
+log "=============================================="
+log "${SERIES_NAME} Miniseries Training"
+log "=============================================="
+
+for d in "${DEPTHS[@]}"; do
+    log "Training d=$d..."
+
+    TAG="${SERIES_NAME}_miniseries_d${d}"
+    START_TIME=$(date +%s)
+
+    # Reduce --device-batch-size to avoid OOM at larger depths
+    if [ $d -ge 28 ]; then
+        DEVICE_BATCH_SIZE_ARG="--device-batch-size=8"
+    elif [ $d -ge 20 ]; then
+        DEVICE_BATCH_SIZE_ARG="--device-batch-size=16"
+    else
+        DEVICE_BATCH_SIZE_ARG="--device-batch-size=32"
+    fi
+
+    torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- \
+        --depth=$d \
+        --run="${WANDB_RUN}_d${d}" \
+        --model-tag="${TAG}" \
+        --core-metric-every=999999 \
+        --core-metric-max-per-task=-1 \
+        --sample-every=-1 \
+        --save-every=-1 \
+        $DEVICE_BATCH_SIZE_ARG \
+        2>&1 | tee "$RESULTS_DIR/${TAG}_train.log"
+
+    END_TIME=$(date +%s)
+    TRAIN_TIME=$((END_TIME - START_TIME))
+
+    # Extract stats from log
+    LOG_FILE="$RESULTS_DIR/${TAG}_train.log"
+    NUM_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | head -1 | tr -d ',')
+    NUM_SCALING_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP 'scaling: [\d,]+' | grep -oP '[\d,]+' | tr -d ',')
+    NUM_ITERS=$(grep "Calculated number of iterations" "$LOG_FILE" | tail -1 | sed 's/.*: //' | tr -d ',')
+    TOKENS_TRAINED=$((NUM_ITERS * 524288))
+    PARAM_DATA_RATIO=$(python -c "print(f'{$TOKENS_TRAINED / $NUM_SCALING_PARAMS:.2f}')")
+    MODEL_DIM=$((d * 64))
+    VAL_BPB=$(grep "Validation bpb:" "$LOG_FILE" | tail -1 | grep -oP '[\d.]+$')
+    CORE_SCORE=$(grep "CORE metric:" "$LOG_FILE" | tail -1 | awk '{print $NF}')
+
+    if [ -z "$CORE_SCORE" ]; then
+        CORE_SCORE="0.0"
+    fi
+
+    log "  d=$d: params=$NUM_PARAMS, scaling=$NUM_SCALING_PARAMS, ratio=$PARAM_DATA_RATIO, bpb=$VAL_BPB, CORE=$CORE_SCORE, time=${TRAIN_TIME}s"
+
+    # Append to CSV
+    echo "$d,$MODEL_DIM,$NUM_PARAMS,$NUM_SCALING_PARAMS,$NUM_ITERS,$TOKENS_TRAINED,$PARAM_DATA_RATIO,$VAL_BPB,$CORE_SCORE,$TRAIN_TIME" >> "$RESULTS_FILE"
+done
+
+log "=============================================="
+log "${SERIES_NAME} Miniseries Complete!"
+log "=============================================="
+log "Results saved to: $RESULTS_FILE"
+echo ""
+echo "Results:"
+column -t -s',' "$RESULTS_FILE"

runs/runcpu.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+
+# Showing an example run for exercising some of the code paths on the CPU (or MPS on Macbooks)
+# This script was last updated/tuned on Jan 17, 2026.
+
+# Run as:
+# bash runs/runcpu.sh
+
+# NOTE: Training LLMs requires GPU compute and $$$. You will not get far on your Macbook.
+# Think of this run as educational/fun demo, not something you should expect to work well.
+# You may also want to run this script manually and one by one, copy pasting commands into your terminal.
+
+# all the setup stuff
+export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
+mkdir -p $NANOCHAT_BASE_DIR
+command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
+[ -d ".venv" ] || uv venv
+uv sync --extra cpu
+source .venv/bin/activate
+if [ -z "$WANDB_RUN" ]; then
+    WANDB_RUN=dummy
+fi
+
+# train tokenizer on ~2B characters (~34 seconds on my MacBook Pro M3 Max)
+python -m nanochat.dataset -n 8
+python -m scripts.tok_train --max-chars=2000000000
+python -m scripts.tok_eval
+
+# train a small 4 layer model
+# I tuned this run to complete in about 30 minutes on my MacBook Pro M3 Max.
+# To get better results, try increasing num_iterations, or get other ideas from your favorite LLM.
+python -m scripts.base_train \
+    --depth=6 \
+    --head-dim=64 \
+    --window-pattern=L \
+    --max-seq-len=512 \
+    --device-batch-size=32 \
+    --total-batch-size=16384 \
+    --eval-every=100 \
+    --eval-tokens=524288 \
+    --core-metric-every=-1 \
+    --sample-every=100 \
+    --num-iterations=5000 \
+    --run=$WANDB_RUN
+python -m scripts.base_eval --device-batch-size=1 --split-tokens=16384 --max-per-task=16
+
+# SFT (~10 minutes on my MacBook Pro M3 Max)
+curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
+python -m scripts.chat_sft \
+    --max-seq-len=512 \
+    --device-batch-size=32 \
+    --total-batch-size=16384 \
+    --eval-every=200 \
+    --eval-tokens=524288 \
+    --num-iterations=1500 \
+    --run=$WANDB_RUN
+
+# Chat with the model over CLI
+# The model should be able to say that it is Paris.
+# It might even know that the color of the sky is blue.
+# Sometimes the model likes it if you first say Hi before you ask it questions.
+# python -m scripts.chat_cli -p "What is the capital of France?"
+
+# Chat with the model over a pretty WebUI ChatGPT style
+# python -m scripts.chat_web

runs/scaling_laws.sh

@@ -0,0 +1,137 @@
+#!/bin/bash
+
+LABEL="jan26"
+
+FLOPS_BUDGETS=(
+    1e18
+    2.15e18
+    4.64e18
+    1e19
+)
+DEPTHS=(10 12 14 16 18 20)
+
+NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
+WANDB_RUN="${WANDB_RUN:-scaling_${LABEL}}"
+EVAL_TOKENS=$((100 * 524288))  # ~100M tokens for final eval (default is ~10M)
+
+export OMP_NUM_THREADS=1
+export NANOCHAT_BASE_DIR="${NANOCHAT_BASE_DIR:-$HOME/.cache/nanochat}"
+source .venv/bin/activate
+
+RESULTS_DIR="$NANOCHAT_BASE_DIR/scaling_laws_results_${LABEL}"
+mkdir -p "$RESULTS_DIR"
+RESULTS_FILE="$RESULTS_DIR/results.csv"
+
+# Write CSV header only if file doesn't exist
+if [ ! -f "$RESULTS_FILE" ]; then
+    echo "flops_budget,depth,model_dim,params_wte,params_value_embeds,params_lm_head,params_transformer,params_scalars,params_total,num_iterations,tokens_trained,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
+fi
+
+log() {
+    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
+}
+
+# Check if a run already exists in results
+run_exists() {
+    local flops=$1
+    local depth=$2
+    grep -q "^${flops},${depth}," "$RESULTS_FILE" 2>/dev/null
+}
+
+# =============================================================================
+# Main Loop
+# =============================================================================
+
+for flops in "${FLOPS_BUDGETS[@]}"; do
+    log "=============================================="
+    log "Compute budget: $flops FLOPs"
+    log "=============================================="
+
+    for d in "${DEPTHS[@]}"; do
+
+        # Skip if already completed
+        if run_exists "$flops" "$d"; then
+            log "Skipping d=$d at $flops FLOPs (already in results)"
+            continue
+        fi
+
+        log "Training d=$d at $flops FLOPs..."
+
+        # Unique tag for this run
+        TAG="scaling_${flops}_d${d}"
+
+        # Reduce --device-batch-size to avoid OOM at larger depths
+        if [ $d -ge 28 ]; then
+            DEVICE_BATCH_SIZE_ARG="--device-batch-size=8"
+        elif [ $d -ge 20 ]; then
+            DEVICE_BATCH_SIZE_ARG="--device-batch-size=16"
+        else
+            DEVICE_BATCH_SIZE_ARG="--device-batch-size=32"
+        fi
+
+        # Record start time
+        START_TIME=$(date +%s)
+
+        # Train the model with fixed flops budget
+        # The script will auto-calculate num_iterations to hit target_flops
+        # CORE eval happens once at the end (999999 ensures only final step)
+        torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- \
+            --depth=$d \
+            --target-flops=$flops \
+            --target-param-data-ratio=-1 \
+            --run="${WANDB_RUN}_${TAG}" \
+            --model-tag="${TAG}" \
+            --eval-tokens=$EVAL_TOKENS \
+            --core-metric-every=999999 \
+            --core-metric-max-per-task=-1 \
+            --sample-every=-1 \
+            --save-every=-1 \
+            $DEVICE_BATCH_SIZE_ARG \
+            2>&1 | tee "$RESULTS_DIR/${TAG}_train.log"
+
+        END_TIME=$(date +%s)
+        TRAIN_TIME=$((END_TIME - START_TIME))
+
+        # Extract training stats from the log
+        LOG_FILE="$RESULTS_DIR/${TAG}_train.log"
+
+        # Extract detailed parameter counts (for scaling law analysis with different conventions)
+        # Note: the log format is padded, e.g. "wte                     : 25,165,824"
+        # so we grep for "^key " (key at start of line followed by space) to avoid false matches
+        PARAMS_WTE=$(grep "^wte " "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | tr -d ',')
+        PARAMS_VE=$(grep "^value_embeds " "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | tr -d ',')
+        PARAMS_LM=$(grep "^lm_head " "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | tr -d ',')
+        PARAMS_TRANSFORMER=$(grep "^transformer_matrices " "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | tr -d ',')
+        PARAMS_SCALARS=$(grep "^scalars " "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | tr -d ',')
+        PARAMS_TOTAL=$(grep "^total " "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | tr -d ',')
+
+        NUM_ITERS=$(grep "Calculated number of iterations" "$LOG_FILE" | tail -1 | sed 's/.*: //' | tr -d ',')
+        # Extract actual batch size from log (auto-computed, varies by model size)
+        BATCH_SIZE=$(grep "Total batch size" "$LOG_FILE" | tail -1 | grep -oP 'Total batch size \K[\d,]+' | tr -d ',')
+        TOKENS_TRAINED=$((NUM_ITERS * BATCH_SIZE))
+        # Model dim
+        MODEL_DIM=$((d * 64))
+        # Val BPB from final eval
+        VAL_BPB=$(grep "Validation bpb:" "$LOG_FILE" | tail -1 | grep -oP '[\d.]+$')
+
+        # Extract CORE score from training log (evaluated on final step)
+        CORE_SCORE=$(grep "CORE metric:" "$LOG_FILE" | tail -1 | awk '{print $NF}')
+        if [ -z "$CORE_SCORE" ]; then
+            log "WARNING: Could not extract CORE score for d=$d"
+            CORE_SCORE="0.0"
+        fi
+
+        log "  Params: $PARAMS_TOTAL (transformer: $PARAMS_TRANSFORMER), Iters: $NUM_ITERS, Val BPB: $VAL_BPB, CORE: $CORE_SCORE"
+
+        # Append to CSV
+        echo "$flops,$d,$MODEL_DIM,$PARAMS_WTE,$PARAMS_VE,$PARAMS_LM,$PARAMS_TRANSFORMER,$PARAMS_SCALARS,$PARAMS_TOTAL,$NUM_ITERS,$TOKENS_TRAINED,$VAL_BPB,$CORE_SCORE,$TRAIN_TIME" >> "$RESULTS_FILE"
+    done
+done
+
+log "=============================================="
+log "Scaling Laws Sweep Complete"
+log "=============================================="
+log "Results saved to: $RESULTS_FILE"
+echo ""
+echo "Results:"
+column -t -s',' "$RESULTS_FILE"

runs/speedrun.sh

@@ -0,0 +1,97 @@
+#!/bin/bash
+
+# This script is configured to train your own GPT-2 grade LLM (pretraining + finetuning)
+# It is designed to run on a blank 8XH100 GPU node and takes approximately 3 hours to complete.
+
+# 1) Example launch (simplest):
+# bash runs/speedrun.sh
+# 2) Example launch in a screen session (because the run takes ~3 hours):
+# screen -L -Logfile runs/speedrun.log -S speedrun bash runs/speedrun.sh
+# 3) Example launch with wandb logging, but see below for setting up wandb first:
+# WANDB_RUN=speedrun screen -L -Logfile runs/speedrun.log -S speedrun bash runs/speedrun.sh
+
+# Default intermediate artifacts directory is in ~/.cache/nanochat
+export OMP_NUM_THREADS=1
+export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
+mkdir -p $NANOCHAT_BASE_DIR
+
+# -----------------------------------------------------------------------------
+# Python venv setup with uv
+
+# install uv (if not already installed)
+command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
+# create a .venv local virtual environment (if it doesn't exist)
+[ -d ".venv" ] || uv venv
+# install the repo dependencies
+uv sync --extra gpu
+# activate venv so that `python` uses the project's venv instead of system python
+source .venv/bin/activate
+
+# -----------------------------------------------------------------------------
+# wandb setup
+# If you wish to use wandb for logging (it's nice!, recommended).
+# 1) Make sure to first log in to wandb, e.g. run:
+#    `wandb login`
+# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
+#    `WANDB_RUN=d26 bash speedrun.sh`
+if [ -z "$WANDB_RUN" ]; then
+    # by default use "dummy" : it's handled as a special case, skips logging to wandb
+    WANDB_RUN=dummy
+fi
+
+# -----------------------------------------------------------------------------
+# During the course of the run, we will be writing markdown reports to the report/
+# directory in the base dir. This command clears it out and writes a header section
+# with a bunch of system info and a timestamp that marks the start of the run.
+python -m nanochat.report reset
+
+# -----------------------------------------------------------------------------
+# Tokenizer
+
+# Download the first ~2B characters of pretraining dataset
+# each data shard is ~250M chars
+# so we download 2e9 / 250e6 = 8 data shards at this point
+# each shard is ~100MB of text (compressed), so this is about ~800MB of data on disk
+# look at dev/repackage_data_reference.py for details on how this data was prepared
+python -m nanochat.dataset -n 8
+# Immediately also kick off downloading more shards in the background while tokenizer trains
+# Approximately 150 shards are needed for GPT-2 capability pretraining, add 20 for padding.
+# The maximum total number of shards available in the entire dataset is 6542.
+python -m nanochat.dataset -n 170 &
+DATASET_DOWNLOAD_PID=$!
+# train the tokenizer with vocab size 2**15 = 32768 on ~2B characters of data
+python -m scripts.tok_train
+# evaluate the tokenizer (report compression ratio etc.)
+python -m scripts.tok_eval
+
+# -----------------------------------------------------------------------------
+# Base model (pretraining)
+echo "Waiting for dataset download to complete..."
+wait $DATASET_DOWNLOAD_PID
+
+# d24 model (slightly undertrained to beat GPT-2 => decrease data:params ratio from compute optimal 10.5 (default) to 8)
+torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=24 --target-param-data-ratio=8 --device-batch-size=16 --fp8 --run=$WANDB_RUN
+# evaluate the model: CORE metric, BPB on train/val, and draw samples
+torchrun --standalone --nproc_per_node=8 -m scripts.base_eval -- --device-batch-size=16
+
+# -----------------------------------------------------------------------------
+# SFT (teach the model conversation special tokens, tool use, multiple choice)
+
+# download 2.3MB of synthetic identity conversations to impart a personality to nanochat
+# see dev/gen_synthetic_data.py for details on how this data was prepared and to get a sense of how you can easily tune it
+curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
+
+# run SFT and eval the model
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --device-batch-size=16 --run=$WANDB_RUN
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
+
+# chat with the model over CLI! Leave out the -p to chat interactively
+# python -m scripts.chat_cli -p "Why is the sky blue?"
+
+# even better, chat with your model over a pretty WebUI ChatGPT style
+# python -m scripts.chat_web
+
+# -----------------------------------------------------------------------------
+# Generate the full report by putting together all the sections
+# report.md is the output and will be copied to current directory for convenience
+python -m nanochat.report generate

scripts/audio_align_smoke.py

@@ -0,0 +1,179 @@
+"""
+W1 smoke: prove the audio path works end-to-end.
+
+Pipeline:
+    wav -> WhisperEncoder (frozen) -> Projector -> prepend to text embeddings
+        -> tiny d6 GPT (random init) -> CE loss on text tokens only
+
+The model is randomly initialized and the dataset is 5 synthetic sine clips,
+so the only thing this validates is that gradients flow through the projector
+into a decreasing loss. Pass criterion: end loss < start loss by a clear margin.
+
+Standalone tokenizer (UTF-8 bytes + a single BOS) so the smoke does not depend
+on the nanochat BPE tokenizer being trained yet — that prerequisite belongs to
+W2 onwards.
+
+Usage:
+    python -m scripts.audio_align_smoke
+"""
+
+import argparse
+import json
+import time
+import wave
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from nanochat.audio import Projector, WhisperEncoder
+from nanochat.common import (
+    COMPUTE_DTYPE,
+    autodetect_device_type,
+    compute_cleanup,
+    compute_init,
+)
+from nanochat.gpt import GPT, GPTConfig
+
+
+# Byte-level tokenizer: vocab[0..255] = raw UTF-8 byte, 256 = <BOS>.
+BOS_ID = 256
+VOCAB_SIZE = 257
+
+
+def encode(text):
+    return [BOS_ID] + list(text.encode("utf-8"))
+
+
+def load_manifest(data_dir):
+    items = []
+    with open(Path(data_dir) / "manifest.jsonl") as f:
+        for line in f:
+            items.append(json.loads(line))
+    return items
+
+
+def load_wav_mono16k(path):
+    """Read a mono PCM16 WAV (matches scripts/audio_smoke_data.py output)."""
+    with wave.open(str(path), "rb") as w:
+        assert w.getnchannels() == 1, f"expected mono, got {w.getnchannels()} channels"
+        assert w.getsampwidth() == 2, f"expected pcm16, got sampwidth {w.getsampwidth()}"
+        sr = w.getframerate()
+        frames = w.readframes(w.getnframes())
+    audio = np.frombuffer(frames, dtype=np.int16).astype(np.float32) / 32768.0
+    return audio, sr
+
+
+def build_gpt(depth, head_dim, max_seq_len, device):
+    base_dim = depth * 64  # nanochat's default aspect ratio
+    model_dim = ((base_dim + head_dim - 1) // head_dim) * head_dim
+    n_head = model_dim // head_dim
+    config = GPTConfig(
+        sequence_len=max_seq_len,
+        vocab_size=VOCAB_SIZE,
+        n_layer=depth,
+        n_head=n_head,
+        n_kv_head=n_head,
+        n_embd=model_dim,
+        window_pattern="L",
+    )
+    with torch.device("meta"):
+        model = GPT(config)
+    model.to_empty(device=device)
+    model.init_weights()
+    return model, config
+
+
+def pack_text_batch(text_ids_list, device):
+    """idx[i, t] is input token; targets[i, t] is the next token (or -1 to ignore).
+    Right-pad to the longest sequence with 0/-1.
+    """
+    in_len = max(len(ids) for ids in text_ids_list) - 1
+    B = len(text_ids_list)
+    idx = torch.zeros((B, in_len), dtype=torch.long, device=device)
+    targets = torch.full((B, in_len), -1, dtype=torch.long, device=device)
+    for i, ids in enumerate(text_ids_list):
+        L = len(ids) - 1
+        idx[i, :L] = torch.tensor(ids[:-1], dtype=torch.long, device=device)
+        targets[i, :L] = torch.tensor(ids[1:], dtype=torch.long, device=device)
+    return idx, targets
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-dir", default="data/audio_smoke")
+    parser.add_argument("--depth", type=int, default=6)
+    parser.add_argument("--head-dim", type=int, default=64)
+    parser.add_argument("--max-seq-len", type=int, default=2048)
+    parser.add_argument("--num-iters", type=int, default=50)
+    parser.add_argument("--lr", type=float, default=3e-3)
+    parser.add_argument("--whisper", default="openai/whisper-base",
+                        help="HF Whisper id (override via WHISPER_HF_ID env)")
+    parser.add_argument("--loss-drop-min", type=float, default=0.5,
+                        help="end loss must be at least this much lower than start loss")
+    args = parser.parse_args()
+
+    device_type = autodetect_device_type()
+    ddp, _, _, _, device = compute_init(device_type)
+    assert not ddp, "smoke is single-process"
+
+    # Synthetic audio + manifest: regenerate if missing so the script is self-contained.
+    if not (Path(args.data_dir) / "manifest.jsonl").exists():
+        from scripts.audio_smoke_data import generate_synthetic
+        generate_synthetic(args.data_dir)
+
+    items = load_manifest(args.data_dir)
+    audios = [load_wav_mono16k(Path(args.data_dir) / it["wav"])[0] for it in items]
+    texts = [it["text"] for it in items]
+    print(f"loaded {len(items)} samples: {texts}")
+
+    # Frozen Whisper encoder + Projector to nanochat n_embd
+    print(f"loading Whisper encoder ({args.whisper})...")
+    whisper = WhisperEncoder(hf_id=args.whisper, device=device, dtype=COMPUTE_DTYPE)
+
+    # Pre-extract Whisper input_features and encoder outputs once; encoder is frozen
+    # so its output never changes across training steps -> hoist out of the loop.
+    input_features = whisper.preprocess(audios).to(device=device, dtype=COMPUTE_DTYPE)
+    print(f"input_features: {tuple(input_features.shape)}")
+    audio_feats = whisper(input_features).detach()
+    print(f"whisper features: {tuple(audio_feats.shape)} (T_a soft tokens)")
+
+    # GPT (random init, d6 by default) and Projector
+    gpt, config = build_gpt(args.depth, args.head_dim, args.max_seq_len, device)
+    print(f"GPT: depth={config.n_layer} n_embd={config.n_embd} n_head={config.n_head}")
+    projector = Projector(in_dim=whisper.d_model, out_dim=config.n_embd).to(device=device)
+
+    # Tokenize transcripts and pack into a batch
+    text_ids_list = [encode(t) for t in texts]
+    idx, targets = pack_text_batch(text_ids_list, device=device)
+    print(f"text idx: {tuple(idx.shape)} (max_text_len-1)")
+
+    # Single AdamW over projector + LM. Whisper stays frozen (requires_grad=False
+    # was set in WhisperEncoder.__init__, so its params won't appear here anyway).
+    train_params = list(projector.parameters()) + [p for p in gpt.parameters() if p.requires_grad]
+    optim = torch.optim.AdamW(train_params, lr=args.lr, betas=(0.9, 0.95), weight_decay=0.0)
+
+    losses = []
+    t0 = time.time()
+    for step in range(args.num_iters):
+        soft_tokens = projector(audio_feats)  # (B, T_a, n_embd)
+        loss = gpt(idx, targets=targets, audio_features=soft_tokens)
+        optim.zero_grad(set_to_none=True)
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(train_params, 1.0)
+        optim.step()
+        losses.append(loss.item())
+        if step % 5 == 0 or step == args.num_iters - 1:
+            print(f"step {step:03d} | loss {loss.item():.4f}")
+    dt = time.time() - t0
+
+    drop = losses[0] - losses[-1]
+    print(f"\nDone {args.num_iters} steps in {dt:.1f}s | start={losses[0]:.4f} end={losses[-1]:.4f} drop={drop:.4f}")
+    assert drop >= args.loss_drop_min, f"loss did not drop enough: {drop:.4f} < {args.loss_drop_min}"
+    print("PASS: audio path forward+backward works, loss is descending.")
+
+    compute_cleanup()
+
+
+if __name__ == "__main__":
+    main()

scripts/audio_smoke_data.py

@@ -0,0 +1,78 @@
+"""
+Generate the W1 audio smoke dataset: a handful of 5s sine-wave clips paired
+with deterministic transcripts.
+
+Why synthetic instead of real speech: W1 only proves the forward path
+(WhisperEncoder -> Projector -> GPT prepend) and that the projector's gradient
+flows into a decreasing loss on a tiny fixed set. Real speech adds a network
+dependency to a step that should be reproducible offline. W2 swaps in
+LibriSpeech.
+
+Audio files land under data/audio_smoke/wavs/ (gitignored). The manifest
+data/audio_smoke/manifest.jsonl is the only artifact committed.
+
+Usage:
+    python -m scripts.audio_smoke_data
+"""
+
+import argparse
+import json
+import wave
+from pathlib import Path
+
+import numpy as np
+
+
+SAMPLES = [
+    (220.0, "low tone"),
+    (330.0, "mid low tone"),
+    (440.0, "middle tone"),
+    (660.0, "mid high tone"),
+    (880.0, "high tone"),
+]
+SR = 16000
+DURATION_S = 5.0
+
+
+def synth_sine(freq_hz, duration_s=DURATION_S, sr=SR):
+    """Sine + 2nd harmonic + a sliver of noise so Whisper sees non-degenerate
+    frames (a pure tone collapses to a near-constant log-mel)."""
+    t = np.arange(int(sr * duration_s)) / sr
+    x = 0.5 * np.sin(2 * np.pi * freq_hz * t) + 0.25 * np.sin(2 * np.pi * 2 * freq_hz * t)
+    rng = np.random.default_rng(int(freq_hz))
+    x = x + 0.01 * rng.standard_normal(len(x))
+    return x.astype(np.float32)
+
+
+def write_wav_pcm16(path, audio, sr=SR):
+    """Write mono PCM16 WAV using the stdlib (no scipy/soundfile dependency)."""
+    pcm = np.clip(audio, -1.0, 1.0)
+    pcm = (pcm * 32767.0).astype(np.int16)
+    with wave.open(str(path), "wb") as w:
+        w.setnchannels(1)
+        w.setsampwidth(2)
+        w.setframerate(sr)
+        w.writeframes(pcm.tobytes())
+
+
+def generate_synthetic(data_dir):
+    data_dir = Path(data_dir)
+    wav_dir = data_dir / "wavs"
+    wav_dir.mkdir(parents=True, exist_ok=True)
+    manifest_path = data_dir / "manifest.jsonl"
+    with open(manifest_path, "w") as f:
+        for freq, text in SAMPLES:
+            name = f"sine_{int(freq):04d}.wav"
+            wav_path = wav_dir / name
+            if not wav_path.exists():
+                write_wav_pcm16(wav_path, synth_sine(freq))
+            f.write(json.dumps({"wav": f"wavs/{name}", "text": text, "sr": SR}) + "\n")
+    print(f"Wrote {len(SAMPLES)} samples to {data_dir}")
+    return manifest_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-dir", default="data/audio_smoke")
+    args = parser.parse_args()
+    generate_synthetic(args.data_dir)

scripts/base_eval.py

@@ -0,0 +1,323 @@
+"""
+Unified evaluation script for base models.
+
+Supports three evaluation modes (comma-separated):
+  --eval core    : CORE metric (accuracy on ICL tasks)
+  --eval bpb     : Bits per byte on train/val splits
+  --eval sample  : Generate samples from the model
+
+Default is all three: --eval core,bpb,sample
+
+Examples:
+
+    # Evaluate a HuggingFace model (e.g. GPT-2 124M) using 8 GPUs
+    torchrun --nproc_per_node=8 -m scripts.base_eval --hf-path openai-community/gpt2
+
+    # Evaluate a nanochat model (e.g. d24) using 8 GPUs
+    torchrun --nproc_per_node=8 -m scripts.base_eval --model-tag d24 --device-batch-size=16
+
+    # Quick/approximate evaluation using a single GPU
+    python -m scripts.base_eval --model-tag d24 --device-batch-size=16 --max-per-task=100 --split-tokens=524288
+"""
+import os
+import csv
+import time
+import json
+import yaml
+import shutil
+import random
+import zipfile
+import tempfile
+import argparse
+import torch
+
+from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, autodetect_device_type, download_file_with_lock
+from nanochat.tokenizer import HuggingFaceTokenizer, get_token_bytes
+from nanochat.checkpoint_manager import load_model
+from nanochat.core_eval import evaluate_task
+from nanochat.dataloader import tokenizing_distributed_data_loader_bos_bestfit
+from nanochat.loss_eval import evaluate_bpb
+from nanochat.engine import Engine
+
+# -----------------------------------------------------------------------------
+# HuggingFace loading utilities
+
+class ModelWrapper:
+    """Lightweight wrapper to give HuggingFace models a nanochat-compatible interface."""
+    def __init__(self, model, max_seq_len=None):
+        self.model = model
+        self.max_seq_len = max_seq_len
+
+    def __call__(self, input_ids, targets=None, loss_reduction='mean'):
+        logits = self.model(input_ids).logits
+        if targets is None:
+            return logits
+        loss = torch.nn.functional.cross_entropy(
+            logits.view(-1, logits.size(-1)),
+            targets.view(-1),
+            ignore_index=-1,
+            reduction=loss_reduction
+        )
+        return loss
+
+    def get_device(self):
+        return next(self.model.parameters()).device
+
+
+def load_hf_model(hf_path: str, device):
+    """Load a HuggingFace model and tokenizer."""
+    print0(f"Loading HuggingFace model from: {hf_path}")
+    from transformers import AutoModelForCausalLM
+    model = AutoModelForCausalLM.from_pretrained(hf_path)
+    model.to(device)
+    model.eval()
+    max_seq_len = 1024 if "gpt2" in hf_path else None
+    model = ModelWrapper(model, max_seq_len=max_seq_len)
+    tokenizer = HuggingFaceTokenizer.from_pretrained(hf_path)
+    return model, tokenizer
+
+
+def get_hf_token_bytes(tokenizer, device="cpu"):
+    """Compute token_bytes tensor for a HuggingFace tokenizer."""
+    vocab_size = tokenizer.tokenizer.get_vocab_size()
+    token_bytes = torch.zeros(vocab_size, dtype=torch.int64, device=device)
+    for token_id in range(vocab_size):
+        token_str = tokenizer.tokenizer.decode([token_id])
+        token_bytes[token_id] = len(token_str.encode('utf-8'))
+    return token_bytes
+
+# -----------------------------------------------------------------------------
+# CORE evaluation
+
+EVAL_BUNDLE_URL = "https://karpathy-public.s3.us-west-2.amazonaws.com/eval_bundle.zip"
+
+
+def place_eval_bundle(file_path):
+    """Unzip eval_bundle.zip and place it in the base directory."""
+    base_dir = get_base_dir()
+    eval_bundle_dir = os.path.join(base_dir, "eval_bundle")
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with zipfile.ZipFile(file_path, 'r') as zip_ref:
+            zip_ref.extractall(tmpdir)
+        extracted_bundle_dir = os.path.join(tmpdir, "eval_bundle")
+        shutil.move(extracted_bundle_dir, eval_bundle_dir)
+    print0(f"Placed eval_bundle directory at {eval_bundle_dir}")
+
+
+def evaluate_core(model, tokenizer, device, max_per_task=-1):
+    """
+    Evaluate a base model on the CORE benchmark.
+    Returns dict with results, centered_results, and core_metric.
+    """
+    base_dir = get_base_dir()
+    eval_bundle_dir = os.path.join(base_dir, "eval_bundle")
+    # Download the eval bundle if needed
+    if not os.path.exists(eval_bundle_dir):
+        download_file_with_lock(EVAL_BUNDLE_URL, "eval_bundle.zip", postprocess_fn=place_eval_bundle)
+
+    config_path = os.path.join(eval_bundle_dir, "core.yaml")
+    data_base_path = os.path.join(eval_bundle_dir, "eval_data")
+    eval_meta_data = os.path.join(eval_bundle_dir, "eval_meta_data.csv")
+
+    with open(config_path, 'r', encoding='utf-8') as f:
+        config = yaml.safe_load(f)
+    tasks = config['icl_tasks']
+
+    # Load random baseline values
+    random_baselines = {}
+    with open(eval_meta_data, 'r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            task_name = row['Eval Task']
+            random_baseline = row['Random baseline']
+            random_baselines[task_name] = float(random_baseline)
+
+    # Evaluate each task
+    results = {}
+    centered_results = {}
+    for task in tasks:
+        start_time = time.time()
+        label = task['label']
+        task_meta = {
+            'task_type': task['icl_task_type'],
+            'dataset_uri': task['dataset_uri'],
+            'num_fewshot': task['num_fewshot'][0],
+            'continuation_delimiter': task.get('continuation_delimiter', ' ')
+        }
+        print0(f"Evaluating: {label} ({task_meta['num_fewshot']}-shot, type: {task_meta['task_type']})... ", end='')
+
+        data_path = os.path.join(data_base_path, task_meta['dataset_uri'])
+        with open(data_path, 'r', encoding='utf-8') as f:
+            data = [json.loads(line.strip()) for line in f]
+
+        # Shuffle for consistent subsampling when using max_per_task
+        shuffle_rng = random.Random(1337)
+        shuffle_rng.shuffle(data)
+        if max_per_task > 0:
+            data = data[:max_per_task]
+
+        accuracy = evaluate_task(model, tokenizer, data, device, task_meta)
+        results[label] = accuracy
+        random_baseline = random_baselines[label]
+        centered_result = (accuracy - 0.01 * random_baseline) / (1.0 - 0.01 * random_baseline)
+        centered_results[label] = centered_result
+        elapsed = time.time() - start_time
+        print0(f"accuracy: {accuracy:.4f} | centered: {centered_result:.4f} | time: {elapsed:.2f}s")
+
+    core_metric = sum(centered_results.values()) / len(centered_results)
+    out = {
+        "results": results,
+        "centered_results": centered_results,
+        "core_metric": core_metric
+    }
+    return out
+
+# -----------------------------------------------------------------------------
+# Main
+
+def main():
+    parser = argparse.ArgumentParser(description="Base model evaluation")
+    parser.add_argument('--eval', type=str, default='core,bpb,sample', help='Comma-separated evaluations to run: core,bpb,sample (default: all)')
+    parser.add_argument('--hf-path', type=str, default=None, help='HuggingFace model path (e.g. openai-community/gpt2-xl)')
+    parser.add_argument('--model-tag', type=str, default=None, help='nanochat model tag to identify the checkpoint directory')
+    parser.add_argument('--step', type=int, default=None, help='Model step to load (default = last)')
+    parser.add_argument('--max-per-task', type=int, default=-1, help='Max examples per CORE task (-1 = all)')
+    parser.add_argument('--device-batch-size', type=int, default=32, help='Per-device batch size for BPB evaluation')
+    parser.add_argument('--split-tokens', type=int, default=40*524288, help='Number of tokens to evaluate per split for BPB')
+    parser.add_argument('--device-type', type=str, default='', help='cuda|cpu|mps (empty = autodetect)')
+    args = parser.parse_args()
+
+    # Parse evaluation modes
+    eval_modes = set(mode.strip() for mode in args.eval.split(','))
+    valid_modes = {'core', 'bpb', 'sample'}
+    invalid = eval_modes - valid_modes
+    if invalid:
+        parser.error(f"Invalid eval modes: {invalid}. Valid: {valid_modes}")
+
+    # Distributed / precision setup
+    device_type = autodetect_device_type() if args.device_type == '' else args.device_type
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+    # Load model and tokenizer
+    is_hf_model = args.hf_path is not None
+    if is_hf_model:
+        model, tokenizer = load_hf_model(args.hf_path, device)
+        sequence_len = model.max_seq_len or 1024
+        token_bytes = get_hf_token_bytes(tokenizer, device=device)
+        model_name = args.hf_path
+        model_slug = args.hf_path.replace("/", "-")
+    else:
+        model, tokenizer, meta = load_model("base", device, phase="eval", model_tag=args.model_tag, step=args.step)
+        sequence_len = meta["model_config"]["sequence_len"]
+        token_bytes = get_token_bytes(device=device)
+        model_name = f"base_model (step {meta['step']})"
+        model_slug = f"base_model_{meta['step']:06d}"
+
+    print0(f"Evaluating model: {model_name}")
+    print0(f"Eval modes: {', '.join(sorted(eval_modes))}")
+
+    # Results to log
+    core_results = None
+    bpb_results = {}
+    samples = []
+    unconditioned_samples = []
+
+    # --- Sampling ---
+    if 'sample' in eval_modes and not is_hf_model:
+        print0("\n" + "="*80)
+        print0("Model Samples")
+        print0("="*80)
+        if ddp_rank == 0:
+            prompts = [
+                "The capital of France is",
+                "The chemical symbol of gold is",
+                "If yesterday was Friday, then tomorrow will be",
+                "The opposite of hot is",
+                "The planets of the solar system are:",
+                "My favorite color is",
+                "If 5*x + 3 = 13, then x is",
+            ]
+            engine = Engine(model, tokenizer)
+            print0("\nConditioned samples:")
+            for prompt in prompts:
+                tokens = tokenizer(prompt, prepend="<|bos|>")
+                sample, _ = engine.generate_batch(tokens, num_samples=1, max_tokens=16, temperature=0)
+                sample_str = tokenizer.decode(sample[0])
+                print0("-" * 80)
+                print0(sample_str)
+                samples.append(sample_str)
+
+            print0("\nUnconditioned samples:")
+            tokens = tokenizer("", prepend="<|bos|>")
+            uncond, _ = engine.generate_batch(tokens, num_samples=8, max_tokens=128, temperature=1.0)
+            for sample in uncond:
+                sample_str = tokenizer.decode(sample)
+                print0("-" * 80)
+                print0(sample_str)
+                unconditioned_samples.append(sample_str)
+    elif 'sample' in eval_modes and is_hf_model:
+        print0("\nSkipping sampling for HuggingFace models (not supported)")
+
+    # --- BPB evaluation ---
+    if 'bpb' in eval_modes:
+        print0("\n" + "="*80)
+        print0("BPB Evaluation")
+        print0("="*80)
+        tokens_per_step = args.device_batch_size * sequence_len * ddp_world_size
+        if args.split_tokens % tokens_per_step != 0:
+            # Adjust to nearest multiple
+            args.split_tokens = (args.split_tokens // tokens_per_step) * tokens_per_step
+            print0(f"Adjusted split_tokens to {args.split_tokens} (must be divisible by {tokens_per_step})")
+        steps = args.split_tokens // tokens_per_step
+
+        for split_name in ["train", "val"]:
+            loader = tokenizing_distributed_data_loader_bos_bestfit(tokenizer, args.device_batch_size, sequence_len, split_name, device=device)
+            bpb = evaluate_bpb(model, loader, steps, token_bytes)
+            bpb_results[split_name] = bpb
+            print0(f"{split_name} bpb: {bpb:.6f}")
+
+    # --- CORE evaluation ---
+    if 'core' in eval_modes:
+        print0("\n" + "="*80)
+        print0("CORE Evaluation")
+        print0("="*80)
+        core_results = evaluate_core(model, tokenizer, device, max_per_task=args.max_per_task)
+
+        # Write CSV output
+        if ddp_rank == 0:
+            base_dir = get_base_dir()
+            output_csv_path = os.path.join(base_dir, "base_eval", f"{model_slug}.csv")
+            os.makedirs(os.path.dirname(output_csv_path), exist_ok=True)
+            with open(output_csv_path, 'w', encoding='utf-8', newline='') as f:
+                f.write(f"{'Task':<35}, {'Accuracy':<10}, {'Centered':<10}\n")
+                for label in core_results["results"]:
+                    acc = core_results["results"][label]
+                    centered = core_results["centered_results"][label]
+                    f.write(f"{label:<35}, {acc:<10.6f}, {centered:<10.6f}\n")
+                f.write(f"{'CORE':<35}, {'':<10}, {core_results['core_metric']:<10.6f}\n")
+            print0(f"\nResults written to: {output_csv_path}")
+            print0(f"CORE metric: {core_results['core_metric']:.4f}")
+
+    # --- Log to report ---
+    from nanochat.report import get_report
+    report_data = [{"model": model_name}]
+
+    if core_results:
+        report_data[0]["CORE metric"] = core_results["core_metric"]
+        report_data.append(core_results["centered_results"])
+
+    if bpb_results:
+        report_data[0]["train bpb"] = bpb_results.get("train")
+        report_data[0]["val bpb"] = bpb_results.get("val")
+
+    if samples:
+        report_data.append({f"sample {i}": s for i, s in enumerate(samples)})
+    if unconditioned_samples:
+        report_data.append({f"unconditioned {i}": s for i, s in enumerate(unconditioned_samples)})
+
+    get_report().log(section="Base model evaluation", data=report_data)
+
+    compute_cleanup()
+
+
+if __name__ == "__main__":
+    main()

scripts/base_train.py

@@ -0,0 +1,630 @@
+"""
+Train model. From root directory of the project, run as:
+
+python -m scripts.base_train
+
+or distributed as:
+
+torchrun --nproc_per_node=8 -m scripts.base_train
+
+If you are only on CPU/Macbook, you'll want to train a much much smaller LLM. Example:
+python -m scripts.base_train --depth=4 --max-seq-len=512 --device-batch-size=1 --eval-tokens=512 --core-metric-every=-1 --total-batch-size=512 --num-iterations=20
+"""
+
+import os
+os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
+import gc
+import json
+import time
+import math
+import argparse
+from dataclasses import asdict
+from contextlib import contextmanager
+
+import wandb
+import torch
+import torch.distributed as dist
+
+from nanochat.gpt import GPT, GPTConfig, Linear
+from nanochat.dataloader import tokenizing_distributed_data_loader_bos_bestfit, tokenizing_distributed_data_loader_with_state_bos_bestfit
+from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir, autodetect_device_type, get_peak_flops, COMPUTE_DTYPE, COMPUTE_DTYPE_REASON, is_ddp_initialized
+from nanochat.tokenizer import get_tokenizer, get_token_bytes
+from nanochat.checkpoint_manager import save_checkpoint, load_checkpoint
+from nanochat.loss_eval import evaluate_bpb
+from nanochat.engine import Engine
+from nanochat.flash_attention import HAS_FA3
+from scripts.base_eval import evaluate_core
+print_banner()
+
+# -----------------------------------------------------------------------------
+# CLI arguments
+parser = argparse.ArgumentParser(description="Pretrain base model")
+# Logging
+parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
+# Runtime
+parser.add_argument("--device-type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
+# FP8 training
+parser.add_argument("--fp8", action="store_true", help="enable FP8 training (requires H100+ GPU and torchao)")
+parser.add_argument("--fp8-recipe", type=str, default="tensorwise", choices=["rowwise", "tensorwise"], help="FP8 scaling recipe: tensorwise (faster, recommended) or rowwise (more accurate but slower)")
+# Model architecture
+parser.add_argument("--depth", type=int, default=20, help="depth of the Transformer model")
+parser.add_argument("--aspect-ratio", type=int, default=64, help="model_dim = depth * aspect_ratio")
+parser.add_argument("--head-dim", type=int, default=128, help="target head dimension for attention")
+parser.add_argument("--max-seq-len", type=int, default=2048, help="max context length")
+parser.add_argument("--window-pattern", type=str, default="SSSL", help="sliding window pattern tiled across layers: L=full, S=half context (e.g. 'SSL')")
+# Training horizon (only one used, in order of precedence)
+parser.add_argument("--num-iterations", type=int, default=-1, help="explicit number of optimization steps (-1 = disable)")
+parser.add_argument("--target-flops", type=float, default=-1.0, help="calculate num_iterations to reach target_flops (-1 = disable)")
+parser.add_argument("--target-param-data-ratio", type=float, default=12, help="calculate num_iterations to maintain data:param ratio (Chinchilla=20, -1 = disable)")
+# Optimization
+parser.add_argument("--device-batch-size", type=int, default=32, help="per-device batch size. good number to reduce to 16,8,4,... if you OOM on VRAM.")
+parser.add_argument("--total-batch-size", type=int, default=-1, help="total batch size in tokens. decent numbers are e.g. 524288. (-1 = auto-compute optimal)")
+parser.add_argument("--embedding-lr", type=float, default=0.3, help="learning rate for embedding parameters (Adam)")
+parser.add_argument("--unembedding-lr", type=float, default=0.008, help="learning rate for unembedding parameters (Adam)")
+parser.add_argument("--weight-decay", type=float, default=0.28, help="cautious weight decay for the Muon optimizer (for weights)")
+parser.add_argument("--matrix-lr", type=float, default=0.02, help="learning rate for matrix parameters (Muon)")
+parser.add_argument("--scalar-lr", type=float, default=0.5, help="learning rate for scalars (resid_lambdas, x0_lambdas)")
+parser.add_argument("--warmup-steps", type=int, default=40, help="number of steps for LR warmup")
+parser.add_argument("--warmdown-ratio", type=float, default=0.65, help="ratio of iterations for LR warmdown")
+parser.add_argument("--final-lr-frac", type=float, default=0.05, help="final LR as fraction of initial LR")
+parser.add_argument("--resume-from-step", type=int, default=-1, help="resume training from this step (-1 = disable)")
+# Evaluation
+parser.add_argument("--eval-every", type=int, default=250, help="evaluate val bpb every N steps (-1 = disable)")
+parser.add_argument("--eval-tokens", type=int, default=80*524288, help="number of tokens to evaluate val loss on")
+parser.add_argument("--core-metric-every", type=int, default=2000, help="evaluate CORE metric every N steps (-1 = disable)")
+parser.add_argument("--core-metric-max-per-task", type=int, default=500, help="examples per task for CORE metric")
+parser.add_argument("--sample-every", type=int, default=2000, help="sample from model every N steps (-1 = disable)")
+parser.add_argument("--save-every", type=int, default=-1, help="save checkpoints every N steps (-1 = only at end)")
+# Output
+parser.add_argument("--model-tag", type=str, default=None, help="override model tag for checkpoint directory name")
+args = parser.parse_args()
+user_config = vars(args).copy()  # for logging
+# -----------------------------------------------------------------------------
+# Compute init and wandb logging
+
+device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
+synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
+get_max_memory = torch.cuda.max_memory_allocated if device_type == "cuda" else lambda: 0
+if device_type == "cuda":
+    gpu_device_name = torch.cuda.get_device_name(0)
+    gpu_peak_flops = get_peak_flops(gpu_device_name)
+    print0(f"GPU: {gpu_device_name} | Peak FLOPS (BF16): {gpu_peak_flops:.2e}")
+else:
+    gpu_peak_flops = float('inf')  # MFU not meaningful for CPU/MPS
+print0(f"COMPUTE_DTYPE: {COMPUTE_DTYPE} ({COMPUTE_DTYPE_REASON})")
+
+# wandb logging init
+use_dummy_wandb = args.run == "dummy" or not master_process
+wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat", name=args.run, config=user_config)
+
+# Flash Attention status
+from nanochat.flash_attention import USE_FA3
+using_fa3 = USE_FA3
+if using_fa3:
+    print0("✓ Using Flash Attention 3 (Hopper GPU detected), efficient, new and awesome.")
+else:
+    print0("!" * 80)
+    if HAS_FA3 and COMPUTE_DTYPE != torch.bfloat16:
+        print0(f"WARNING: Flash Attention 3 only supports bf16, but COMPUTE_DTYPE={COMPUTE_DTYPE}. Using PyTorch SDPA fallback")
+    else:
+        print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback")
+    print0("WARNING: Training will be less efficient without FA3")
+    if args.window_pattern != "L":
+        print0(f"WARNING: SDPA has no support for sliding window attention (window_pattern='{args.window_pattern}'). Your GPU utilization will be terrible.")
+        print0("WARNING: Recommend using --window-pattern L for full context attention without alternating sliding window patterns.")
+    print0("!" * 80)
+
+# -----------------------------------------------------------------------------
+# Tokenizer will be useful for evaluation and also we need the vocab size to init the model
+tokenizer = get_tokenizer()
+token_bytes = get_token_bytes(device=device)
+vocab_size = tokenizer.get_vocab_size()
+print0(f"Vocab size: {vocab_size:,}")
+
+# -----------------------------------------------------------------------------
+# Initialize the Model
+
+def build_model_meta(depth):
+    """Build a model on meta device for a given depth (shapes/dtypes only, no data)."""
+    # Model dim is nudged up to nearest multiple of head_dim for clean division
+    # (FA3 requires head_dim divisible by 8, and this guarantees head_dim == args.head_dim exactly)
+    base_dim = depth * args.aspect_ratio
+    model_dim = ((base_dim + args.head_dim - 1) // args.head_dim) * args.head_dim
+    num_heads = model_dim // args.head_dim
+    config = GPTConfig(
+        sequence_len=args.max_seq_len, vocab_size=vocab_size,
+        n_layer=depth, n_head=num_heads, n_kv_head=num_heads, n_embd=model_dim,
+        window_pattern=args.window_pattern,
+    )
+    with torch.device("meta"):
+        model_meta = GPT(config)
+    return model_meta
+
+# Build the model, move to device, init the weights
+model = build_model_meta(args.depth) # 1) Build on meta device (only shapes/dtypes, no data)
+model_config = model.config
+model_config_kwargs = asdict(model_config)
+print0(f"Model config:\n{json.dumps(model_config_kwargs, indent=2)}")
+model.to_empty(device=device) # 2) All tensors get storage on target device but with uninitialized (garbage) data
+model.init_weights() # 3) All tensors get initialized
+
+# If we are resuming, overwrite the model parameters with those of the checkpoint
+base_dir = get_base_dir()
+output_dirname = args.model_tag if args.model_tag else f"d{args.depth}" # e.g. d12
+checkpoint_dir = os.path.join(base_dir, "base_checkpoints", output_dirname)
+resuming = args.resume_from_step != -1
+if resuming:
+    print0(f"Resuming optimization from step {args.resume_from_step}")
+    model_data, optimizer_data, meta_data = load_checkpoint(checkpoint_dir, args.resume_from_step, device, load_optimizer=True, rank=ddp_rank)
+    model.load_state_dict(model_data, strict=True, assign=True)
+    del model_data # free up this memory after the copy
+
+# -----------------------------------------------------------------------------
+# FP8 training initialization and management (this has to be done before torch.compile)
+
+# Convert Linear layers to Float8Linear if --fp8 is set
+if args.fp8:
+    if device_type != "cuda":
+        print0("Warning: FP8 training requires CUDA, ignoring --fp8 flag")
+    else:
+        # our custom fp8 is simpler than torchao, written for exact API compatibility
+        from nanochat.fp8 import Float8LinearConfig, convert_to_float8_training
+        # from torchao.float8 import Float8LinearConfig, convert_to_float8_training
+        import torch.nn as nn
+
+        # Filter: dims must be divisible by 16 (FP8 hardware requirement) large enough
+        def fp8_module_filter(mod: nn.Module, fqn: str) -> bool:
+            if not isinstance(mod, nn.Linear):
+                return False
+            if mod.in_features % 16 != 0 or mod.out_features % 16 != 0:
+                return False
+            if min(mod.in_features, mod.out_features) < 128:
+                return False
+            return True
+
+        fp8_config = Float8LinearConfig.from_recipe_name(args.fp8_recipe)
+        num_linear = sum(1 for m in model.modules() if isinstance(m, nn.Linear))
+        convert_to_float8_training(model, config=fp8_config, module_filter_fn=fp8_module_filter)
+        num_fp8 = sum(1 for m in model.modules() if 'Float8' in type(m).__name__)
+        num_skipped = num_linear - num_fp8
+        print0(f"✓ FP8 training enabled ({args.fp8_recipe} scaling) - converted {num_fp8}/{num_linear} linear layers, skipped {num_skipped} (too small)")
+
+# Context manager to temporarily disable FP8 so that model evaluation remains in BF16
+@contextmanager
+def disable_fp8(model):
+    """Temporarily swap Float8Linear modules with nn.Linear for BF16 evaluation.
+
+    CastConfig is a frozen dataclass, so we can't mutate scaling_type. Instead,
+    we swap out Float8Linear modules entirely and restore them after.
+    """
+    import torch.nn as nn
+
+    # Find all Float8Linear modules and their locations
+    fp8_locations = []  # list of (parent_module, attr_name, fp8_module)
+    for name, module in model.named_modules():
+        if 'Float8' in type(module).__name__:
+            if '.' in name:
+                parent_name, attr_name = name.rsplit('.', 1)
+                parent = model.get_submodule(parent_name)
+            else:
+                parent = model
+                attr_name = name
+            fp8_locations.append((parent, attr_name, module))
+
+    if not fp8_locations:
+        yield  # No FP8 modules, nothing to do
+        return
+
+    # Swap Float8Linear -> Linear (our custom class that casts weights to match input dtype)
+    # Use device="meta" to avoid VRAM spike - the weight tensor will be swapped in afterwards
+    for parent, attr_name, fp8_module in fp8_locations:
+        linear = Linear(
+            fp8_module.in_features,
+            fp8_module.out_features,
+            bias=fp8_module.bias is not None,
+            device="meta",  # Use meta device to avoid unnecessary VRAM allocation
+            dtype=fp8_module.weight.dtype,
+        )
+        linear.weight = fp8_module.weight  # share, don't copy
+        if fp8_module.bias is not None:
+            linear.bias = fp8_module.bias
+        setattr(parent, attr_name, linear)
+
+    try:
+        yield
+    finally:
+        # Restore Float8Linear modules
+        for parent, attr_name, fp8_module in fp8_locations:
+            setattr(parent, attr_name, fp8_module)
+
+# -----------------------------------------------------------------------------
+# Compile the model
+
+orig_model = model # original, uncompiled model, for saving raw model state_dict and for inference/evaluation (because the shapes may change shape)
+model = torch.compile(model, dynamic=False) # the inputs to model will never change shape so dynamic=False is safe
+
+# -----------------------------------------------------------------------------
+# Scaling laws and muP extrapolations to determine the optimal training horizon, batch size, learning rates, weight decay.
+
+# Get the parameter counts of our model
+param_counts = model.num_scaling_params()
+print0(f"Parameter counts:")
+for key, value in param_counts.items():
+    print0(f"{key:24s}: {value:,}")
+num_params = param_counts['total']
+num_flops_per_token = model.estimate_flops()
+print0(f"Estimated FLOPs per token: {num_flops_per_token:e}")
+
+# 1) Use scaling laws to determine the optimal training horizon in tokens
+# The compute-optimal models satisfy the Tokens:Params ratio of --target-param-data-ratio (derived experimentally via scaling laws analysis).
+# We've already initialized the model so we have Params. Optimal Tokens is now simply target-param-data-ratio * Params
+def get_scaling_params(m):
+    # As for which params to use exactly, transformer matrices + lm_head gives cleanest scaling laws (see dev/LOG.md Jan 27, 2026)
+    params_counts = m.num_scaling_params()
+    scaling_params = params_counts['transformer_matrices'] + params_counts['lm_head']
+    return scaling_params
+num_scaling_params = get_scaling_params(model)
+target_tokens = int(args.target_param_data_ratio * num_scaling_params) # optimal tokens for the model we are about to train
+
+# Our reference model is d12, this is where a lot of hyperparameters are tuned and then transfered to higher depths (muP style)
+d12_ref = build_model_meta(12) # creates the model on meta device
+D_REF = args.target_param_data_ratio * get_scaling_params(d12_ref) # compute-optimal d12 training horizon in tokens (measured empirically)
+B_REF = 2**19 # optimal batch size at d12 ~= 524,288 tokens (measured empirically)
+
+# 2) Now that we have the token horizon, we can calculate the optimal batch size
+# We follow the Power Lines paper (Bopt ∝ D^0.383), ref: https://arxiv.org/abs/2505.13738
+# The optimal batch size grows as approximately D^0.383, so e.g. if D doubles from d12 to d24, B should grow by 2^0.383 ≈ 1.3x.
+total_batch_size = args.total_batch_size # user-provided override is possible
+if total_batch_size == -1:
+    batch_size_ratio = target_tokens / D_REF
+    predicted_batch_size = B_REF * batch_size_ratio ** 0.383
+    total_batch_size = 2 ** round(math.log2(predicted_batch_size)) # clamp to nearest power of 2 for efficiency
+    print0(f"Auto-computed optimal batch size: {total_batch_size:,} tokens")
+
+# 3) Knowing the batch size, we can now calculate a learning rate correction (bigger batch size allows higher learning rates)
+batch_lr_scale = 1.0
+batch_ratio = total_batch_size / B_REF # B/B_ref
+if batch_ratio != 1.0:
+    # SGD: linear scaling with batch size is standard (not used in nanochat)
+    # AdamW: sqrt scaling is standard: η ∝ √(B/B_ref)
+    # Muon: we will use the same scaling for Muon as for AdamW: η ∝ √(B/B_ref) (not studied carefully, assumption!)
+    batch_lr_scale = batch_ratio ** 0.5 # η ∝ √(B/B_ref)
+    print0(f"Scaling LRs by {batch_lr_scale:.4f} for batch size {total_batch_size:,} (reference: {B_REF:,})")
+
+# 4) Knowing the batch size and the token horizon, we can now calculate the appropriate weight decay scaling
+# We adopt the T_epoch framework from https://arxiv.org/abs/2405.13698
+# Central idea of the paper is that T_epoch = B/(η·λ·D) should remain constant.
+# Above, we used learning rate scaling η ∝ √(B/B_ref). So it's a matter of ~10 lines of math to derive that to keep T_epoch constant, we need:
+# λ = λ_ref · √(B/B_ref) · (D_ref/D)
+# Note that these papers study AdamW, *not* Muon. We are blindly following AdamW theory for scaling hoping it ~works for Muon too.
+weight_decay_scaled = args.weight_decay * math.sqrt(total_batch_size / B_REF) * (D_REF / target_tokens)
+if weight_decay_scaled != args.weight_decay:
+    print0(f"Scaling weight decay from {args.weight_decay:.6f} to {weight_decay_scaled:.6f} for depth {args.depth}")
+
+# -----------------------------------------------------------------------------
+# Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
+optimizer = model.setup_optimizer(
+    # AdamW hyperparameters
+    unembedding_lr=args.unembedding_lr * batch_lr_scale,
+    embedding_lr=args.embedding_lr * batch_lr_scale,
+    scalar_lr=args.scalar_lr * batch_lr_scale,
+    # Muon hyperparameters
+    matrix_lr=args.matrix_lr * batch_lr_scale,
+    weight_decay=weight_decay_scaled,
+)
+
+if resuming:
+    optimizer.load_state_dict(optimizer_data)
+    del optimizer_data
+
+# -----------------------------------------------------------------------------
+# GradScaler for fp16 training (bf16/fp32 don't need it — bf16 has the same exponent range as fp32)
+scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
+if scaler is not None:
+    print0("GradScaler enabled for fp16 training")
+
+# -----------------------------------------------------------------------------
+# Initialize the DataLoaders for train/val
+dataloader_resume_state_dict = None if not resuming else meta_data["dataloader_state_dict"]
+train_loader = tokenizing_distributed_data_loader_with_state_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="train", device=device, resume_state_dict=dataloader_resume_state_dict)
+build_val_loader = lambda: tokenizing_distributed_data_loader_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="val", device=device)
+x, y, dataloader_state_dict = next(train_loader) # kick off load of the very first batch of data
+
+# -----------------------------------------------------------------------------
+# Calculate the number of iterations we will train for and set up the various schedulers
+
+# num_iterations: either it is given, or from target flops, or from target data:param ratio (in that order)
+assert args.num_iterations > 0 or args.target_param_data_ratio > 0 or args.target_flops > 0
+if args.num_iterations > 0:
+    # Override num_iterations to a specific value if given
+    num_iterations = args.num_iterations
+    print0(f"Using user-provided number of iterations: {num_iterations:,}")
+elif args.target_flops > 0:
+    # Calculate the number of iterations from the target flops (used in scaling laws analysis, e.g. runs/scaling_laws.sh)
+    num_iterations = round(args.target_flops / (num_flops_per_token * total_batch_size))
+    print0(f"Calculated number of iterations from target FLOPs: {num_iterations:,}")
+elif args.target_param_data_ratio > 0:
+    # Calculate the number of iterations from the target param data ratio (the most common use case)
+    num_iterations = target_tokens // total_batch_size
+    print0(f"Calculated number of iterations from target data:param ratio: {num_iterations:,}")
+else:
+    raise ValueError("No training horizon specified")
+total_tokens = total_batch_size * num_iterations # the actual number of tokens we will train for
+print0(f"Total number of training tokens: {total_tokens:,}")
+print0(f"Tokens : Scaling params ratio: {total_batch_size * num_iterations / num_scaling_params:.2f}") # e.g. Chinchilla was ~20
+print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens:e}")
+
+# Learning rate schedule (linear warmup, constant, linear warmdown)
+def get_lr_multiplier(it):
+    warmup_iters = args.warmup_steps
+    warmdown_iters = round(args.warmdown_ratio * num_iterations)
+    if it < warmup_iters:
+        return (it + 1) / warmup_iters
+    elif it <= num_iterations - warmdown_iters:
+        return 1.0
+    else:
+        progress = (num_iterations - it) / warmdown_iters
+        return progress * 1.0 + (1 - progress) * args.final_lr_frac
+
+# Momentum scheduler for Muon optimizer (warms up to 0.97, warms down to 0.90 during LR warmdown)
+def get_muon_momentum(it):
+    warmdown_iters = round(args.warmdown_ratio * num_iterations)
+    warmdown_start = num_iterations - warmdown_iters
+    if it < 400:
+        frac = it / 400
+        return (1 - frac) * 0.85 + frac * 0.97
+    elif it >= warmdown_start:
+        progress = (it - warmdown_start) / warmdown_iters
+        return 0.97 * (1 - progress) + 0.90 * progress
+    else:
+        return 0.97
+
+# Weight decay scheduler for Muon optimizer (cosine decay to zero over the course of training)
+def get_weight_decay(it):
+    return weight_decay_scaled * 0.5 * (1 + math.cos(math.pi * it / num_iterations))
+
+# -----------------------------------------------------------------------------
+# Training loop
+
+# Loop state (variables updated by the training loop)
+if not resuming:
+    step = 0
+    val_bpb = None # will be set if eval_every > 0
+    min_val_bpb = float("inf")
+    smooth_train_loss = 0 # EMA of training loss
+    total_training_time = 0 # total wall-clock time of training
+else:
+    step = meta_data["step"]
+    loop_state = meta_data["loop_state"]
+    val_bpb = meta_data["val_bpb"]
+    min_val_bpb = loop_state["min_val_bpb"]
+    smooth_train_loss = loop_state["smooth_train_loss"]
+    total_training_time = loop_state["total_training_time"]
+
+# Figure out the needed gradient accumulation micro-steps to reach the desired total batch size per step
+tokens_per_fwdbwd = args.device_batch_size * args.max_seq_len # tokens per iteration for a single rank
+world_tokens_per_fwdbwd = tokens_per_fwdbwd * ddp_world_size # total tokens per iteration for all ranks
+assert total_batch_size % world_tokens_per_fwdbwd == 0
+grad_accum_steps = total_batch_size // world_tokens_per_fwdbwd
+print0(f"Tokens / micro-batch / rank: {args.device_batch_size} x {args.max_seq_len} = {tokens_per_fwdbwd:,}")
+print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
+print0(f"Total batch size {total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
+
+# Go!
+while True:
+    last_step = step == num_iterations # loop runs num_iterations+1 times so that we can eval/save at the end
+    flops_so_far = num_flops_per_token * total_batch_size * step
+
+    # once in a while: evaluate the val bpb (all ranks participate)
+    if args.eval_every > 0 and (last_step or step % args.eval_every == 0):
+        model.eval()
+        val_loader = build_val_loader()
+        eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
+        with disable_fp8(model):
+            val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
+        print0(f"Step {step:05d} | Validation bpb: {val_bpb:.6f}")
+        if val_bpb < min_val_bpb:
+            min_val_bpb = val_bpb
+        wandb_run.log({
+            "step": step,
+            "total_training_flops": flops_so_far,
+            "total_training_time": total_training_time,
+            "val/bpb": val_bpb,
+        })
+        model.train()
+
+    # once in a while: estimate the CORE metric (all ranks participate)
+    # use the original uncompiled model because the inputs keep changing shape
+    # disable FP8 for evaluation to use BF16 for more consistent/accurate results
+    results = {}
+    if args.core_metric_every > 0 and (last_step or (step > 0 and step % args.core_metric_every == 0)):
+        model.eval()
+        with disable_fp8(orig_model):
+            results = evaluate_core(orig_model, tokenizer, device, max_per_task=args.core_metric_max_per_task)
+        print0(f"Step {step:05d} | CORE metric: {results['core_metric']:.4f}")
+        wandb_run.log({
+            "step": step,
+            "total_training_flops": flops_so_far,
+            "core_metric": results["core_metric"],
+            "centered_results": results["centered_results"],
+        })
+        model.train()
+
+    # once in a while: sample from the model (only on master process)
+    # use the original uncompiled model because the inputs keep changing shape
+    if args.sample_every > 0 and master_process and (last_step or (step > 0 and step % args.sample_every == 0)):
+        model.eval()
+        prompts = [
+            "The capital of France is",
+            "The chemical symbol of gold is",
+            "If yesterday was Friday, then tomorrow will be",
+            "The opposite of hot is",
+            "The planets of the solar system are:",
+            "My favorite color is",
+            "If 5*x + 3 = 13, then x is",
+        ]
+        engine = Engine(orig_model, tokenizer) # use orig_model to avoid recompilation
+        for prompt in prompts:
+            tokens = tokenizer(prompt, prepend="<|bos|>")
+            with disable_fp8(orig_model):
+                sample, _ = engine.generate_batch(tokens, num_samples=1, max_tokens=16, temperature=0)
+            print0(tokenizer.decode(sample[0]))
+        model.train()
+
+    # save checkpoint: at the end of the run, or every save_every steps, except at the first step or the resume step
+    if last_step or (step > 0 and step != args.resume_from_step and args.save_every > 0 and step % args.save_every == 0):
+        save_checkpoint(
+            checkpoint_dir,
+            step,
+            orig_model.state_dict(), # model parameters
+            optimizer.state_dict(), # optimizer state
+            { # metadata saved as json
+                "step": step,
+                "val_bpb": val_bpb, # loss at last step
+                "model_config": model_config_kwargs,
+                "user_config": user_config, # inputs to the training script
+                "device_batch_size": args.device_batch_size,
+                "max_seq_len": args.max_seq_len,
+                "total_batch_size": total_batch_size,
+                "dataloader_state_dict": dataloader_state_dict,
+                "loop_state": { # all loop state (other than step) so that we can resume training
+                    "min_val_bpb": min_val_bpb,
+                    "smooth_train_loss": smooth_train_loss,
+                    "total_training_time": total_training_time,
+                },
+            },
+            rank=ddp_rank,
+        )
+
+    # termination conditions (TODO: possibly also add loss explosions etc.)
+    if last_step:
+        break
+
+    # -------------------------------------------------------------------------
+    # single training step
+    # evaluate the gradient
+    synchronize()
+    t0 = time.time()
+    for micro_step in range(grad_accum_steps):
+        loss = model(x, y)
+        train_loss = loss.detach() # for logging
+        loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
+        if scaler is not None:
+            scaler.scale(loss).backward()
+        else:
+            loss.backward()
+        x, y, dataloader_state_dict = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
+    # step the optimizer
+    lrm = get_lr_multiplier(step)
+    muon_momentum = get_muon_momentum(step)
+    muon_weight_decay = get_weight_decay(step)
+    for group in optimizer.param_groups:
+        group["lr"] = group["initial_lr"] * lrm
+        if group['kind'] == 'muon':
+            group["momentum"] = muon_momentum
+            group["weight_decay"] = muon_weight_decay
+    if scaler is not None:
+        scaler.unscale_(optimizer)
+        # In distributed training, all ranks must agree on whether to skip the step.
+        # Each rank may independently encounter inf/nan gradients, so we all-reduce
+        # the found_inf flag (MAX = if any rank found inf, all ranks skip).
+        if is_ddp_initialized():
+            for v in scaler._found_inf_per_device(optimizer).values():
+                dist.all_reduce(v, op=dist.ReduceOp.MAX)
+        scaler.step(optimizer)
+        scaler.update()
+    else:
+        optimizer.step()
+    model.zero_grad(set_to_none=True)
+    train_loss_f = train_loss.item() # .item() is a CPU-GPU sync point
+    synchronize()
+    t1 = time.time()
+    dt = t1 - t0
+    # -------------------------------------------------------------------------
+
+    # logging (CPU action only)
+    ema_beta = 0.9 # EMA decay factor for some smoothing just for nicer logging
+    smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f # EMA the training loss
+    debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) # debias the EMA
+    pct_done = 100 * step / num_iterations
+    tok_per_sec = int(total_batch_size / dt)
+    flops_per_sec = num_flops_per_token * total_batch_size / dt
+    mfu = 100 * flops_per_sec / (gpu_peak_flops * ddp_world_size)
+    if step > 10:
+        total_training_time += dt # only count the time after the first 10 steps
+    # Calculate ETA based on average time per step (excluding first 10 steps)
+    steps_done = step - 10
+    if steps_done > 0:
+        avg_time_per_step = total_training_time / steps_done
+        remaining_steps = num_iterations - step
+        eta_seconds = remaining_steps * avg_time_per_step
+        eta_str = f" | eta: {eta_seconds/60:.1f}m"
+    else:
+        eta_str = ""
+    epoch = f"{dataloader_state_dict['epoch']} pq: {dataloader_state_dict['pq_idx']} rg: {dataloader_state_dict['rg_idx']}"
+    print0(f"step {step:05d}/{num_iterations:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | bf16_mfu: {mfu:.2f} | epoch: {epoch} | total time: {total_training_time/60:.2f}m{eta_str}")
+    if step % 100 == 0:
+        log_data = {
+            "step": step,
+            "total_training_flops": flops_so_far,
+            "total_training_time": total_training_time,
+            "train/loss": debiased_smooth_loss,
+            "train/lrm": lrm,
+            "train/dt": dt,
+            "train/tok_per_sec": tok_per_sec,
+            "train/mfu": mfu,
+            "train/epoch": epoch,
+        }
+        wandb_run.log(log_data)
+
+    # state update
+    first_step_of_run = (step == 0) or (resuming and step == args.resume_from_step)
+    step += 1
+
+    # The garbage collector is sadly a little bit overactive and for some poorly understood reason,
+    # it spends ~500ms scanning for cycles quite frequently, just to end up cleaning up very few tiny objects each time.
+    # So we manually manage and help it out here
+    if first_step_of_run:
+        gc.collect() # manually collect a lot of garbage from setup
+        gc.freeze() # immediately freeze all currently surviving objects and exclude them from GC
+        gc.disable() # nuclear intervention here: disable GC entirely except:
+    elif step % 5000 == 0: # every 5000 steps...
+        gc.collect() # manually collect, just to be safe for very, very long runs
+
+# print a few more stats
+print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
+print0(f"Total training time: {total_training_time/60:.2f}m")
+if val_bpb is not None:
+    print0(f"Minimum validation bpb: {min_val_bpb:.6f}")
+
+# Log to report
+from nanochat.report import get_report
+get_report().log(section="Base model training", data=[
+    user_config, # CLI args
+    { # stats about the training setup
+        "Number of parameters": num_params,
+        "Number of FLOPs per token": f"{num_flops_per_token:e}",
+        "Calculated number of iterations": num_iterations,
+        "Number of training tokens": total_tokens,
+        "Tokens : Scaling params ratio": total_batch_size * num_iterations / num_scaling_params,
+        "DDP world size": ddp_world_size,
+        "warmup_steps": args.warmup_steps,
+        "warmdown_ratio": args.warmdown_ratio,
+        "final_lr_frac": args.final_lr_frac,
+    },
+    { # stats about training outcomes
+        "Minimum validation bpb": min_val_bpb if val_bpb is not None else None,
+        "Final validation bpb": val_bpb,
+        "CORE metric estimate": results.get("core_metric", None),
+        "MFU %": f"{mfu:.2f}%",
+        "Total training flops": f"{flops_so_far:e}",
+        "Total training time": f"{total_training_time/60:.2f}m",
+        "Peak memory usage": f"{get_max_memory() / 1024 / 1024:.2f}MiB",
+    }
+])
+
+# cleanup
+wandb_run.finish() # wandb run finish
+compute_cleanup()

scripts/chat_cli.py

@@ -0,0 +1,100 @@
+"""
+New and upgraded chat mode because a lot of the code has changed since the last one.
+
+Intended to be run single GPU only atm:
+python -m scripts.chat_cli
+"""
+import argparse
+import torch
+from nanochat.common import compute_init, autodetect_device_type
+from nanochat.engine import Engine
+from nanochat.checkpoint_manager import load_model
+
+parser = argparse.ArgumentParser(description='Chat with the model')
+parser.add_argument('-i', '--source', type=str, default="sft", help="Source of the model: sft|rl")
+parser.add_argument('-g', '--model-tag', type=str, default=None, help='Model tag to load')
+parser.add_argument('-s', '--step', type=int, default=None, help='Step to load')
+parser.add_argument('-p', '--prompt', type=str, default='', help='Prompt the model, get a single response back')
+parser.add_argument('-t', '--temperature', type=float, default=0.6, help='Temperature for generation')
+parser.add_argument('-k', '--top-k', type=int, default=50, help='Top-k sampling parameter')
+parser.add_argument('--device-type', type=str, default='', choices=['cuda', 'cpu', 'mps'], help='Device type for evaluation: cuda|cpu|mps. empty => autodetect')
+args = parser.parse_args()
+
+# Init the model and tokenizer
+
+device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+model, tokenizer, meta = load_model(args.source, device, phase="eval", model_tag=args.model_tag, step=args.step)
+
+# Special tokens for the chat state machine
+bos = tokenizer.get_bos_token_id()
+user_start, user_end = tokenizer.encode_special("<|user_start|>"), tokenizer.encode_special("<|user_end|>")
+assistant_start, assistant_end = tokenizer.encode_special("<|assistant_start|>"), tokenizer.encode_special("<|assistant_end|>")
+
+# Create Engine for efficient generation
+engine = Engine(model, tokenizer)
+
+print("\nNanoChat Interactive Mode")
+print("-" * 50)
+print("Type 'quit' or 'exit' to end the conversation")
+print("Type 'clear' to start a new conversation")
+print("-" * 50)
+
+conversation_tokens = [bos]
+
+while True:
+
+    if args.prompt:
+        # Get the prompt from the launch command
+        user_input = args.prompt
+    else:
+        # Get the prompt interactively from the console
+        try:
+            user_input = input("\nUser: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print("\nGoodbye!")
+            break
+
+    # Handle special commands
+    if user_input.lower() in ['quit', 'exit']:
+        print("Goodbye!")
+        break
+
+    if user_input.lower() == 'clear':
+        conversation_tokens = [bos]
+        print("Conversation cleared.")
+        continue
+
+    if not user_input:
+        continue
+
+    # Add User message to the conversation
+    conversation_tokens.append(user_start)
+    conversation_tokens.extend(tokenizer.encode(user_input))
+    conversation_tokens.append(user_end)
+
+    # Kick off the assistant
+    conversation_tokens.append(assistant_start)
+    generate_kwargs = {
+        "num_samples": 1,
+        "max_tokens": 256,
+        "temperature": args.temperature,
+        "top_k": args.top_k,
+    }
+    response_tokens = []
+    print("\nAssistant: ", end="", flush=True)
+    for token_column, token_masks in engine.generate(conversation_tokens, **generate_kwargs):
+        token = token_column[0] # pop the batch dimension (num_samples=1)
+        response_tokens.append(token)
+        token_text = tokenizer.decode([token])
+        print(token_text, end="", flush=True)
+    print()
+    # we have to ensure that the assistant end token is the last token
+    # so even if generation ends due to max tokens, we have to append it to the end
+    if response_tokens[-1] != assistant_end:
+        response_tokens.append(assistant_end)
+    conversation_tokens.extend(response_tokens)
+
+    # In the prompt mode, we only want a single response and exit
+    if args.prompt:
+        break

scripts/chat_eval.py

@@ -0,0 +1,251 @@
+"""
+Evaluate the Chat model.
+All the generic code lives here, and all the evaluation-specific
+code lives in nanochat directory and is imported from here.
+
+Example runs:
+python -m scripts.chat_eval -a ARC-Easy
+torchrun --nproc_per_node=8 -m scripts.chat_eval -- -a ARC-Easy
+"""
+
+import argparse
+from functools import partial
+import torch
+import torch.distributed as dist
+
+from nanochat.common import compute_init, compute_cleanup, get_dist_info, print0, autodetect_device_type
+from nanochat.checkpoint_manager import load_model
+from nanochat.engine import Engine
+
+from tasks.humaneval import HumanEval
+from tasks.mmlu import MMLU
+from tasks.arc import ARC
+from tasks.gsm8k import GSM8K
+from tasks.spellingbee import SpellingBee
+
+# -----------------------------------------------------------------------------
+# Generative evaluation loop (we go one problem at a time, sample, evaluate)
+
+def run_generative_eval(task_object, tokenizer, model, engine, num_samples, max_new_tokens, temperature, top_k, max_problems=None):
+
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
+    device = model.get_device()
+
+    num_problems = len(task_object) if max_problems is None else min(len(task_object), max_problems)
+
+    # Run the evaluation
+    num_passed, total = 0, 0
+    for i in range(ddp_rank, num_problems, ddp_world_size):
+        conversation = task_object[i]
+
+        # Tokenize the prompt
+        encoded_prompt = tokenizer.render_for_completion(conversation)
+        # Get the completions
+        results, _ = engine.generate_batch(
+            encoded_prompt,
+            num_samples=num_samples,
+            max_tokens=max_new_tokens,
+            temperature=temperature,
+            top_k=top_k,
+        )
+        # Decode the completions as text
+        prefix_length = len(encoded_prompt)
+        completions = [tokenizer.decode(result_tokens[prefix_length:]) for result_tokens in results]
+        # Evaluate success criteria
+        outcomes = [task_object.evaluate(conversation, completion) for completion in completions]
+        passed = any(outcomes)
+
+        # Keep stats
+        total += 1
+        num_passed += int(passed)
+
+        # Logging (overwrite the same line in the console)
+        print(f"\r\033[KRank {ddp_rank} | {num_passed}/{total} ({100*num_passed/total:.2f}%)", end='', flush=True)
+
+    # Finish the in-place progress line with a newline before final summary
+    print()
+
+    # Aggregate results across all ranks
+    if ddp:
+        num_passed_tensor = torch.tensor([num_passed], dtype=torch.long, device=device)
+        total_tensor = torch.tensor([total], dtype=torch.long, device=device)
+        dist.all_reduce(num_passed_tensor, op=dist.ReduceOp.SUM)
+        dist.all_reduce(total_tensor, op=dist.ReduceOp.SUM)
+        num_passed = num_passed_tensor.item()
+        total = total_tensor.item()
+
+    print0("=" * 50)
+    print0(f"Final: {num_passed}/{total} ({100*num_passed/total:.2f}%)")
+
+    # Return the accuracy
+    return num_passed/total
+
+# -----------------------------------------------------------------------------
+# Categorical evaluation loop
+# A lot easier because we don't have to sample. Therefore, we can actually go
+# batches at a time and just check the logits for correct answer choices.
+
+def run_categorical_eval(task_object, tokenizer, model, batch_size, max_problems=None):
+
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
+    device = model.get_device()
+    bos = tokenizer.get_bos_token_id() # use BOS as pad token is ok, these positions are ignored
+
+    # We'll process batches of independent problems at a time because there is no sampling needed
+    num_problems = len(task_object) if max_problems is None else min(len(task_object), max_problems)
+    ceil_div = lambda x, y: -(-x // y)
+    num_batches = ceil_div(num_problems, batch_size)
+
+    # Run the evaluation
+    letter_to_id_cache = {} # many letters will repeat often, let's save the tokenizer some work
+    num_passed, total = 0, 0
+    for i in range(ddp_rank, num_batches, ddp_world_size):
+        i0, i1 = i * batch_size, min((i + 1) * batch_size, num_problems)
+
+        # Prepare the batch of problems. They might all be of different length, so we pad/collate them.
+        conversations = [task_object[ii] for ii in range(i0, i1)]
+        prompt_ids = [tokenizer.render_for_completion(conversation) for conversation in conversations] # TODO: remake the way this works
+        max_length = max(len(ids) for ids in prompt_ids)
+        answer_time_positions = [len(ids) - 1 for ids in prompt_ids] # where the last token is (and the predicted answer)
+        padded_prompt_ids = [ids + [bos] * (max_length - len(ids)) for ids in prompt_ids]
+        prompt_ids = torch.tensor(padded_prompt_ids, dtype=torch.long, device=device)
+
+        # Get the logits for the whole batch of conversations in parallel (efficiency win here)
+        with torch.no_grad():
+            logits = model(prompt_ids) # (B, T, V)
+
+        # Focus on the available answer on just the letters corresponding to choices
+        # Note that this helps the evaluation a lot because it specifically narrows the focus to only the available letters
+        # The much harder alternative would be to just generate from the Assistant and check if it responded with the correct
+        # letter (e.g. A, B, C, D), but evaluations typically make the task easier in this way.
+        for idx, conversation in enumerate(conversations):
+            # get the token ids of all the available letters of this problem
+            letters = conversation['letters']
+            letter_ids = []
+            for letter in letters:
+                if not letter in letter_to_id_cache:
+                    encoded_letter = tokenizer.encode(letter)
+                    assert len(encoded_letter) == 1, "Each letter must be a single token"
+                    letter_to_id_cache[letter] = encoded_letter[0]
+                letter_ids.append(letter_to_id_cache[letter])
+            # focus logits just down to the answer position and the available letters of the answer
+            answer_pos = answer_time_positions[idx]
+            focus_logits = logits[idx, answer_pos, letter_ids]
+            # get the argmax letter (the predicted answer)
+            argmax_letter_id = focus_logits.argmax(dim=-1).item()
+            predicted_letter = letters[argmax_letter_id]
+            # evaluate the outcome
+            outcome = task_object.evaluate(conversation, predicted_letter)
+            num_passed += int(outcome)
+            total += 1
+
+    # Aggregate results across all ranks
+    if ddp:
+        num_passed_tensor = torch.tensor([num_passed], dtype=torch.long, device=device)
+        total_tensor = torch.tensor([total], dtype=torch.long, device=device)
+        dist.all_reduce(num_passed_tensor, op=dist.ReduceOp.SUM)
+        dist.all_reduce(total_tensor, op=dist.ReduceOp.SUM)
+        num_passed = num_passed_tensor.item()
+        total = total_tensor.item()
+
+    average = num_passed/total
+    print0(f"Final: {num_passed}/{total} ({100*average:.2f}%)")
+    return average
+
+# -----------------------------------------------------------------------------
+
+def run_chat_eval(task_name, model, tokenizer, engine,
+                   batch_size=1, num_samples=1, max_new_tokens=512, temperature=0.0, top_k=50,
+                   max_problems=None):
+    # Create the evaluation object
+    task_module = {
+        'HumanEval': HumanEval,
+        'MMLU': partial(MMLU, subset="all", split="test"),
+        'ARC-Easy': partial(ARC, subset="ARC-Easy", split="test"),
+        'ARC-Challenge': partial(ARC, subset="ARC-Challenge", split="test"),
+        'GSM8K': partial(GSM8K, subset="main", split="test"),
+        'SpellingBee': partial(SpellingBee, size=256, split="test"),
+    }[task_name]
+    task_object = task_module()
+    # Run the evaluation
+    if task_object.eval_type == 'generative':
+        acc = run_generative_eval(task_object, tokenizer, model, engine, num_samples, max_new_tokens, temperature, top_k, max_problems=max_problems)
+    elif task_object.eval_type == 'categorical':
+        acc = run_categorical_eval(task_object, tokenizer, model, batch_size, max_problems=max_problems)
+    else:
+        raise ValueError(f"Unsupported task evaluation type: {task_object.eval_type}")
+    return acc
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--source', type=str, required=True, help="Source of the model: sft|rl")
+    parser.add_argument('-a', '--task-name', type=str, default=None, help="Task name. Default = all tasks. Use | to split multiple tasks.")
+    parser.add_argument('-t', '--temperature', type=float, default=0.0)
+    parser.add_argument('-m', '--max-new-tokens', type=int, default=512)
+    parser.add_argument('-n', '--num-samples', type=int, default=1)
+    parser.add_argument('-k', '--top-k', type=int, default=50)
+    parser.add_argument('-b', '--batch-size', type=int, default=8, help='Batch size for categorical evaluation')
+    parser.add_argument('-g', '--model-tag', type=str, default=None, help='Model tag to load')
+    parser.add_argument('-s', '--step', type=int, default=None, help='Step to load')
+    parser.add_argument('-x', '--max-problems', type=int, default=None, help='Max problems to evaluate')
+    parser.add_argument('--device-type', type=str, default='', choices=['cuda', 'cpu', 'mps'], help='Device type for evaluation: cuda|cpu|mps. empty => autodetect')
+    args = parser.parse_args()
+
+    device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+
+    model, tokenizer, meta = load_model(args.source, device, phase="eval", model_tag=args.model_tag, step=args.step)
+    engine = Engine(model, tokenizer)
+
+    # Get the tasks to evaluate on
+    all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval', 'SpellingBee']
+    baseline_accuracies = {
+        'ARC-Easy': 0.25, # multiple choice 1 of 4 => 25%
+        'ARC-Challenge': 0.25, # multiple choice 1 of 4 => 25%
+        'MMLU': 0.25, # multiple choice 1 of 4 => 25%
+        'GSM8K': 0.0, # open-ended => 0%
+        'HumanEval': 0.0, # open-ended => 0%
+        'SpellingBee': 0.0, # open-ended => 0%
+    }
+    task_names = all_tasks if args.task_name is None else args.task_name.split('|')
+
+    # Run all the task evaluations sequentially
+    results = {}
+    for task_name in task_names:
+        acc = run_chat_eval(
+            task_name,
+            model, tokenizer, engine,
+            batch_size=args.batch_size,
+            num_samples=args.num_samples,
+            max_new_tokens=args.max_new_tokens,
+            temperature=args.temperature,
+            top_k=args.top_k,
+            max_problems=args.max_problems,
+        )
+        results[task_name] = acc
+        print0(f"{task_name} accuracy: {100 * acc:.2f}%")
+
+    # Log to report
+    from nanochat.report import get_report
+    all_tasks_were_evaluated = all(task_name in results for task_name in all_tasks)
+    # calculate the ChatCORE metric if we can (similar to CORE, it's the mean centered accuracy)
+    # this way, ChatCORE ranges from 0 (at random baseline) to 1 (peak performance)
+    chatcore_metric_dict = {}
+    if all_tasks_were_evaluated:
+        centered_mean = 0
+        for task_name, acc in results.items():
+            baseline_acc = baseline_accuracies.get(task_name, 0.0)
+            centered_acc = (acc - baseline_acc) / (1.0 - baseline_acc)
+            centered_mean += centered_acc
+        chatcore_metric = centered_mean / len(results)
+        chatcore_metric_dict = {"ChatCORE metric": chatcore_metric}
+    get_report().log(section="Chat evaluation " + args.source, data=[
+        vars(args), # CLI args
+        results,
+        chatcore_metric_dict,
+    ])
+
+    compute_cleanup()

scripts/chat_rl.py

@@ -0,0 +1,332 @@
+"""
+Reinforcement learning on GSM8K via "GRPO".
+
+I put GRPO in quotes because we actually end up with something a lot
+simpler and more similar to just REINFORCE:
+
+1) Delete trust region, so there is no KL regularization to a reference model
+2) We are on policy, so there's no need for PPO ratio+clip.
+3) We use DAPO style normalization that is token-level, not sequence-level.
+4) Instead of z-score normalization (r - mu)/sigma, only use (r - mu) as the advantage.
+
+1 GPU:
+python -m scripts.chat_rl
+
+8 GPUs:
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=default
+"""
+
+import argparse
+import os
+import itertools
+import wandb
+import torch
+import torch.distributed as dist
+from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, DummyWandb, autodetect_device_type
+from nanochat.checkpoint_manager import save_checkpoint, load_model
+from nanochat.engine import Engine
+from tasks.gsm8k import GSM8K
+
+# -----------------------------------------------------------------------------
+# CLI arguments
+parser = argparse.ArgumentParser(description="Reinforcement learning on GSM8K")
+# Logging
+parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
+# Runtime
+parser.add_argument("--device-type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
+# Model loading
+parser.add_argument("--model-tag", type=str, default=None, help="model tag to load from")
+parser.add_argument("--model-step", type=int, default=None, help="model step to load from")
+# Training horizon
+parser.add_argument("--num-epochs", type=int, default=1, help="number of epochs over GSM8K")
+# Batch sizes / sampling
+parser.add_argument("--device-batch-size", type=int, default=8, help="max batch size per forward pass")
+parser.add_argument("--examples-per-step", type=int, default=16, help="total examples per optimization step across all ranks")
+parser.add_argument("--num-samples", type=int, default=16, help="number of samples per example/question")
+# Generation
+parser.add_argument("--max-new-tokens", type=int, default=256, help="max tokens to generate per sample")
+parser.add_argument("--temperature", type=float, default=1.0, help="sampling temperature")
+parser.add_argument("--top-k", type=int, default=50, help="top-k sampling (0 = disabled)")
+# Optimization
+parser.add_argument("--embedding-lr", type=float, default=0.2, help="learning rate for embedding parameters (Adam)")
+parser.add_argument("--unembedding-lr", type=float, default=0.004, help="learning rate for unembedding parameters (Adam)")
+parser.add_argument("--matrix-lr", type=float, default=0.02, help="learning rate for matrix parameters (Muon)")
+parser.add_argument("--weight-decay", type=float, default=0.0, help="weight decay for embedding/unembedding parameters (Adam)")
+parser.add_argument("--init-lr-frac", type=float, default=0.05, help="initial LR as fraction of base LR")
+# Evaluation / checkpointing
+parser.add_argument("--eval-every", type=int, default=60, help="evaluate pass@k every N steps")
+parser.add_argument("--eval-examples", type=int, default=400, help="number of examples for pass@k evaluation")
+parser.add_argument("--save-every", type=int, default=60, help="save checkpoint every N steps")
+args = parser.parse_args()
+user_config = vars(args).copy()
+# -----------------------------------------------------------------------------
+
+# Init compute/precision
+device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
+
+# wandb logging init
+use_dummy_wandb = args.run == "dummy" or not master_process
+wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat-rl", name=args.run, config=user_config)
+
+# Init model and tokenizer
+model, tokenizer, meta = load_model("sft", device, phase="eval", model_tag=args.model_tag, step=args.model_step)
+engine = Engine(model, tokenizer) # for sampling rollouts
+
+# -----------------------------------------------------------------------------
+# Rollout / sampling generator loop that yields batches of examples for training
+
+train_task = GSM8K(subset="main", split="train")
+val_task = GSM8K(subset="main", split="test")
+num_steps = (len(train_task) // args.examples_per_step) * args.num_epochs
+print0(f"Calculated number of steps: {num_steps}")
+
+@torch.no_grad()
+def get_batch():
+    assistant_end = tokenizer.encode_special("<|assistant_end|>") # ok to use this token, it's only for padding and isn't used in the loss.
+    rank_indices = range(ddp_rank, len(train_task), ddp_world_size) # each rank is responsible for different examples in the training data
+    for example_idx in itertools.cycle(rank_indices):
+
+        # First get the full conversation of both user and assistant messages
+        conversation = train_task[example_idx]
+
+        # Tokenize the conversation, deleting the last Assistant message and priming the Assistant for a completion instead
+        # (i.e. keep the <|assistant_start|>, but delete everything after it)
+        tokens = tokenizer.render_for_completion(conversation)
+        prefix_length = len(tokens)
+
+        # Generate num_samples samples using batched generation, use loop to avoid OOMs
+        model.eval() # ensure the model is in eval mode
+        generated_token_sequences = []
+        masks = []
+        num_sampling_steps = args.num_samples // args.device_batch_size # go sequentially to prevent OOMs
+        for sampling_step in range(num_sampling_steps):
+            seed = hash((step, example_idx, sampling_step)) & 0x7FFFFFFF # positive half of int32
+            generated_token_sequences_batch, masks_batch = engine.generate_batch(
+                tokens,
+                num_samples=args.device_batch_size,
+                max_tokens=args.max_new_tokens,
+                temperature=args.temperature,
+                top_k=args.top_k,
+                seed=seed, # must make sure to change the seed for each sampling step
+            )
+            generated_token_sequences.extend(generated_token_sequences_batch)
+            masks.extend(masks_batch)
+
+        # Calculate the rewards for each sample
+        rewards = []
+        for sample_tokens in generated_token_sequences:
+            # Get just the generated tokens (after the prompt)
+            generated_tokens = sample_tokens[prefix_length:]
+            # Decode the generated response
+            generated_text = tokenizer.decode(generated_tokens)
+            # Calculate the reward
+            reward = train_task.reward(conversation, generated_text)
+            rewards.append(reward)
+
+        # Pad the sequences so that their lengths (in time) match
+        max_length = max(len(seq) for seq in generated_token_sequences)
+        padded_generated_token_sequences = [seq + [assistant_end] * (max_length - len(seq)) for seq in generated_token_sequences]
+        padded_masks = [mask + [0] * (max_length - len(mask)) for mask in masks]
+        # Stack up the sequences and masks into PyTorch tensors
+        ids = torch.tensor(padded_generated_token_sequences, dtype=torch.long, device=device)
+        mask_ids = torch.tensor(padded_masks, dtype=torch.long, device=device)
+        # Generate autoregressive inputs and targets to the Transformer
+        inputs = ids[:, :-1]
+        targets = ids[:, 1:].clone() # clone to avoid in-place modification:
+        targets[mask_ids[:, 1:] == 0] = -1 # <-- inplace modification right here. -1 is the ignore index
+        # NOTE also that the Engine returns mask=0 for BOTH the prompt tokens AND the tool use tokens.
+        # So we will (correctly) end up not training on the prompt tokens, or the tool use forced tokens.
+        rewards = torch.tensor(rewards, dtype=torch.float, device=device)
+        # Calculate the advantages by simply subtracting the mean (instead of z-score (x-mu)/sigma)
+        mu = rewards.mean()
+        advantages = rewards - mu
+        # yield inputs/targets as (B, T) of ids and rewards as (B,) of floats
+        yield generated_token_sequences, inputs, targets, rewards, advantages
+
+# -----------------------------------------------------------------------------
+# Simple evaluation loop for GSM8K pass@k
+def run_gsm8k_eval(task, tokenizer, engine,
+    max_examples=None,
+    num_samples=1,
+    max_completion_tokens=256,
+    temperature=0.0,
+    top_k=50
+):
+    """
+    Evaluates GSM8K task and returns a list of records of evaluation outcomes.
+    In a distributed setting, all ranks cooperate but this function will NOT
+    do the reduction across ranks. This is the responsibility of the caller.
+    Because the evaluation can take a while, this function will yield records one by one.
+    """
+    max_examples = min(max_examples, len(task)) if max_examples is not None else len(task)
+    for idx in range(ddp_rank, max_examples, ddp_world_size):
+        conversation = task[idx]
+        tokens = tokenizer.render_for_completion(conversation)
+        prefix_length = len(tokens)
+        # Generate k samples using batched generation inside the Engine
+        assert num_samples <= args.device_batch_size # usually this is true. we can add a loop if not...
+        generated_token_sequences, masks = engine.generate_batch(
+            tokens,
+            num_samples=num_samples,
+            max_tokens=max_completion_tokens,
+            temperature=temperature,
+            top_k=top_k
+        )
+        # Check each sample for correctness
+        outcomes = []
+        for sample_tokens in generated_token_sequences:
+            generated_tokens = sample_tokens[prefix_length:]
+            generated_text = tokenizer.decode(generated_tokens)
+            is_correct = task.evaluate(conversation, generated_text)
+            outcomes.append({
+                "is_correct": is_correct
+            })
+        # A bit bloated because I wanted to do more complex logging at one point.
+        record = {
+            "idx": idx,
+            "outcomes": outcomes,
+        }
+        yield record
+
+# -----------------------------------------------------------------------------
+# Training loop
+
+# Init the optimizer
+optimizer = model.setup_optimizer(
+    unembedding_lr=args.unembedding_lr,
+    embedding_lr=args.embedding_lr,
+    matrix_lr=args.matrix_lr,
+    weight_decay=args.weight_decay,
+)
+
+# Set the initial learning rate as a fraction of the base learning rate
+for group in optimizer.param_groups:
+    group["lr"] = group["lr"] * args.init_lr_frac
+    group["initial_lr"] = group["lr"]
+
+# Learning rate scheduler: simple rampdown to zero over num_steps
+def get_lr_multiplier(it):
+    lrm = 1.0 - it / num_steps
+    return lrm
+
+# Calculate the number of examples each rank handles to achieve the desired examples_per_step
+print0(f"Total sequences per step: {args.examples_per_step * args.num_samples}") # total batch size in sequences/step
+assert args.examples_per_step % ddp_world_size == 0, "Desired examples per step must be divisible by the number of ranks"
+examples_per_rank = args.examples_per_step // ddp_world_size # per GPU
+print0(f"Calculated examples per rank: {examples_per_rank}")
+
+# Kick off the training loop
+batch_iterator = get_batch()
+for step in range(num_steps):
+
+    # Evaluate the model once in a while and log to wandb
+    if step % args.eval_every == 0:
+        model.eval()
+        passk = torch.zeros(args.device_batch_size, device=device) # pass@k for k=1..device_batch_size
+        records_iter = run_gsm8k_eval(val_task, tokenizer, engine, num_samples=args.device_batch_size, max_examples=args.eval_examples, temperature=1.0)
+        records = list(records_iter) # collect all records
+        for k in range(1, args.device_batch_size + 1):
+            passk[k - 1] = sum(any(o["is_correct"] for o in r["outcomes"][:k]) for r in records)
+        num_records = torch.tensor(len(records), dtype=torch.long, device=device)
+        if ddp:
+            dist.all_reduce(num_records, op=dist.ReduceOp.SUM)
+            dist.all_reduce(passk, op=dist.ReduceOp.SUM)
+        passk = passk / num_records.item() # normalize by the total number of records
+        print_passk = [f"Pass@{k}: {passk[k - 1].item():.4f}" for k in range(1, args.device_batch_size + 1)]
+        print0(f"Step {step} | {', '.join(print_passk)}")
+        log_passk = {f"pass@{k}": passk[k - 1].item() for k in range(1, args.device_batch_size + 1)}
+        wandb_run.log({
+            "step": step,
+            **log_passk,
+        })
+
+    # Forward/Backward on rollouts over multiple examples in the dataset
+    rewards_list = []
+    sequence_lengths = []
+    for example_step in range(examples_per_rank):
+        # Get one batch corresponding to one example in the training dataset
+        sequences_all, inputs_all, targets_all, rewards_all, advantages_all = next(batch_iterator)
+        # Evaluate the loss and gradients
+        model.train() # ensure the model is in train mode
+        # We need one more loop because we can never exceed the device_batch_size
+        assert inputs_all.size(0) % args.device_batch_size == 0
+        num_passes = inputs_all.size(0) // args.device_batch_size
+        for pass_idx in range(num_passes):
+            # Pluck out the batch for this pass
+            b0, b1 = pass_idx * args.device_batch_size, (pass_idx + 1) * args.device_batch_size
+            inputs = inputs_all[b0:b1]
+            targets = targets_all[b0:b1]
+            rewards = rewards_all[b0:b1]
+            advantages = advantages_all[b0:b1]
+            # Calculate log probabilities. Note that the loss calculates NLL = -logp, so we negate
+            logp = -model(inputs, targets, loss_reduction='none').view_as(inputs) # (B, T)
+            # Calculate the PG objective. Note that ignore_index=-1 ensures that invalid tokens have loss 0.
+            pg_obj = (logp * advantages.unsqueeze(-1)).sum()
+            # normalize by the number of valid tokens, number of passes, and examples_per_rank
+            num_valid = (targets >= 0).sum().clamp(min=1)
+            pg_obj = pg_obj / (num_valid * num_passes * examples_per_rank)
+            # Note, there is no need to add PPO ratio+clip because we are on policy
+            # Finally, formulate the loss that we want to minimize (instead of objective we wish to maximize)
+            loss = -pg_obj
+            loss.backward()
+            print0(f"Step {step}/{num_steps} | Example step {example_step} | Pass {pass_idx} | loss: {loss.item():.6f} | Average reward: {rewards.mean().item()}")
+        # For logging
+        rewards_list.append(rewards_all.mean().item())
+        sequence_lengths.extend(len(seq) for seq in sequences_all)
+
+    # A bunch of logging for how the rollouts went this step
+    mean_reward = sum(rewards_list) / len(rewards_list)
+    mean_sequence_length = sum(sequence_lengths) / len(sequence_lengths)
+    if ddp: # aggregate across ranks
+        mean_reward_tensor = torch.tensor(mean_reward, dtype=torch.float, device=device)
+        mean_sequence_length_tensor = torch.tensor(mean_sequence_length, dtype=torch.float, device=device)
+        dist.all_reduce(mean_reward_tensor, op=dist.ReduceOp.AVG)
+        dist.all_reduce(mean_sequence_length_tensor, op=dist.ReduceOp.AVG)
+        mean_reward = mean_reward_tensor.item()
+        mean_sequence_length = mean_sequence_length_tensor.item()
+    print0(f"Step {step}/{num_steps} | Average reward: {mean_reward} | Average sequence length: {mean_sequence_length:.2f}")
+    wandb_run.log({
+        "step": step,
+        "reward": mean_reward,
+        "sequence_length": mean_sequence_length,
+    })
+
+    # Update the model parameters
+    lrm = get_lr_multiplier(step)
+    for group in optimizer.param_groups:
+        group["lr"] = group["initial_lr"] * lrm
+    optimizer.step()
+    model.zero_grad(set_to_none=True)
+    wandb_run.log({
+        "step": step,
+        "lrm": lrm,
+    })
+
+    # Master process saves the model once in a while. Skip first step. Save last step.
+    if master_process and ((step > 0 and step % args.save_every == 0) or step == num_steps - 1):
+        base_dir = get_base_dir()
+        depth = model.config.n_layer
+        output_dirname = args.model_tag if args.model_tag else f"d{depth}" # base the model tag on the depth of the base model
+        checkpoint_dir = os.path.join(base_dir, "chatrl_checkpoints", output_dirname)
+        model_config_kwargs = model.config.__dict__ # slightly naughty, abusing the simplicity of GPTConfig, TODO nicer
+        save_checkpoint(
+            checkpoint_dir,
+            step,
+            model.state_dict(),
+            None, # note: we don't bother to save the optimizer state
+            {
+                "model_config": model_config_kwargs,
+            }
+        )
+        print(f"✅ Saved model checkpoint to {checkpoint_dir}")
+
+# Log to report
+from nanochat.report import get_report
+get_report().log(section="Chat RL", data=[
+    user_config, # CLI args
+])
+
+wandb_run.finish() # wandb run finish
+compute_cleanup()

scripts/chat_sft.py

@@ -0,0 +1,519 @@
+"""
+Supervised fine-tuning (SFT) the model.
+Run as:
+
+python -m scripts.chat_sft
+
+Or torchrun for training:
+
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --device-batch-size=16
+"""
+
+import gc
+import argparse
+import os
+os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
+import time
+import wandb
+import torch
+from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, autodetect_device_type, get_peak_flops, COMPUTE_DTYPE, COMPUTE_DTYPE_REASON, is_ddp_initialized
+from nanochat.tokenizer import get_token_bytes
+from nanochat.checkpoint_manager import save_checkpoint, load_model, load_optimizer_state
+from nanochat.loss_eval import evaluate_bpb
+import torch.distributed as dist
+from nanochat.flash_attention import HAS_FA3
+from nanochat.engine import Engine
+from scripts.chat_eval import run_chat_eval
+
+from tasks.common import TaskMixture
+from tasks.gsm8k import GSM8K
+from tasks.mmlu import MMLU
+from tasks.smoltalk import SmolTalk
+from tasks.customjson import CustomJSON
+from tasks.spellingbee import SimpleSpelling, SpellingBee
+
+# -----------------------------------------------------------------------------
+# CLI arguments
+parser = argparse.ArgumentParser(description="Supervised fine-tuning (SFT) the model")
+# Logging
+parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
+# Runtime
+parser.add_argument("--device-type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
+# Model loading
+parser.add_argument("--model-tag", type=str, default=None, help="model tag to load from")
+parser.add_argument("--model-step", type=int, default=None, help="model step to load from")
+parser.add_argument("--load-optimizer", type=int, default=1, help="warm-start optimizer from pretrained checkpoint (0=no, 1=yes)")
+# Training horizon
+parser.add_argument("--num-iterations", type=int, default=-1, help="number of optimization steps (-1 = full epoch)")
+# Batch sizes (default: inherit from pretrained checkpoint)
+parser.add_argument("--max-seq-len", type=int, default=None, help="max context length (default: inherit from pretrain)")
+parser.add_argument("--device-batch-size", type=int, default=None, help="per-device batch size (default: inherit from pretrain)")
+parser.add_argument("--total-batch-size", type=int, default=None, help="total batch size in tokens (default: inherit from pretrain)")
+# Optimization (default: inherit from pretrained checkpoint)
+parser.add_argument("--embedding-lr", type=float, default=None, help="learning rate for embedding parameters (Adam) (default: inherit from pretrain)")
+parser.add_argument("--unembedding-lr", type=float, default=None, help="learning rate for unembedding parameters (Adam) (default: inherit from pretrain)")
+parser.add_argument("--matrix-lr", type=float, default=None, help="learning rate for matrix parameters (Muon) (default: inherit from pretrain)")
+parser.add_argument("--init-lr-frac", type=float, default=0.8, help="initial LR as fraction of base LR")
+parser.add_argument("--warmup-ratio", type=float, default=0.0, help="ratio of iterations for LR warmup")
+parser.add_argument("--warmdown-ratio", type=float, default=0.5, help="ratio of iterations for LR warmdown")
+parser.add_argument("--final-lr-frac", type=float, default=0.0, help="final LR as fraction of initial LR")
+# Evaluation
+parser.add_argument("--eval-every", type=int, default=200, help="evaluate val bpb every N steps (-1 = disable)")
+parser.add_argument("--eval-tokens", type=int, default=40*524288, help="number of tokens to evaluate val loss on")
+parser.add_argument("--chatcore-every", type=int, default=200, help="evaluate ChatCORE metric every N steps (-1 = disable)")
+parser.add_argument("--chatcore-max-cat", type=int, default=-1, help="max problems per categorical task for ChatCORE")
+parser.add_argument("--chatcore-max-sample", type=int, default=24, help="max problems per generative task for ChatCORE")
+# Data mixture
+parser.add_argument("--mmlu-epochs", type=int, default=3, help="number of epochs of MMLU in training mixture (teaches Multiple Choice)")
+parser.add_argument("--gsm8k-epochs", type=int, default=4, help="number of epochs of GSM8K in training mixture (teaches Math and Tool Use)")
+args = parser.parse_args()
+user_config = vars(args).copy()
+# -----------------------------------------------------------------------------
+
+# Compute init
+device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+master_process = ddp_rank == 0
+print0(f"COMPUTE_DTYPE: {COMPUTE_DTYPE} ({COMPUTE_DTYPE_REASON})")
+synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
+get_max_memory = torch.cuda.max_memory_allocated if device_type == "cuda" else lambda: 0
+if device_type == "cuda":
+    gpu_device_name = torch.cuda.get_device_name(0)
+    gpu_peak_flops = get_peak_flops(gpu_device_name)
+    print0(f"GPU: {gpu_device_name} | Peak FLOPS (BF16): {gpu_peak_flops:.2e}")
+else:
+    gpu_peak_flops = float('inf')  # MFU not meaningful for CPU/MPS
+
+# wandb logging init
+use_dummy_wandb = args.run == "dummy" or not master_process
+wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat-sft", name=args.run, config=user_config)
+
+# Flash Attention status
+if not HAS_FA3:
+    print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback. Training will be less efficient.")
+
+# Load the model and tokenizer
+model, tokenizer, meta = load_model("base", device, phase="train", model_tag=args.model_tag, step=args.model_step)
+
+# Inherit training hyperparameters from pretrained checkpoint (None = inherit, explicit value = override)
+pretrain_user_config = meta.get("user_config", {})
+for name, fallback, source in [
+    ("max_seq_len",       2048,  meta),
+    ("device_batch_size", 32,    meta),
+    ("total_batch_size",  524288, meta),
+    ("embedding_lr",      0.3,   pretrain_user_config),
+    ("unembedding_lr",    0.004, pretrain_user_config),
+    ("matrix_lr",         0.02,  pretrain_user_config),
+]:
+    arg_val = getattr(args, name)
+    pretrain_val = source.get(name)
+    if arg_val is None:
+        resolved = pretrain_val if pretrain_val is not None else fallback
+        setattr(args, name, resolved)
+        print0(f"Inherited {name}={resolved} from pretrained checkpoint")
+    elif pretrain_val is not None and arg_val != pretrain_val:
+        print0(f"NOTE: --{name.replace('_', '-')}={arg_val} overrides pretrained value of {pretrain_val}")
+    else:
+        print0(f"Using {name}={arg_val}")
+
+orig_model = model
+model = torch.compile(model, dynamic=False)
+depth = model.config.n_layer
+num_flops_per_token = model.estimate_flops()
+tokens_per_fwdbwd = args.device_batch_size * args.max_seq_len # tokens per iteration for a single rank
+world_tokens_per_fwdbwd = tokens_per_fwdbwd * ddp_world_size # total tokens per iteration for all ranks
+assert args.total_batch_size % world_tokens_per_fwdbwd == 0
+grad_accum_steps = args.total_batch_size // world_tokens_per_fwdbwd
+print0(f"Tokens / micro-batch / rank: {args.device_batch_size} x {args.max_seq_len} = {tokens_per_fwdbwd:,}")
+print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
+print0(f"Total batch size {args.total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
+token_bytes = get_token_bytes(device=device)
+
+# Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
+# Note that pretraining ramps weight_decay to zero by end of pretraining, so SFT continues with zero
+optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=0.0)
+
+# Optionally warm-start optimizer from pretrained checkpoint (momentum buffers etc.)
+# Note: load_state_dict overwrites param_group metadata (LRs, betas, etc.) with the
+# pretrained values. Since pretraining warmdown brings LRs to ~0, we must save and
+# restore our fresh SFT LRs after loading.
+base_dir = get_base_dir()
+if args.load_optimizer:
+    optimizer_data = load_optimizer_state("base", device, rank=ddp_rank, model_tag=args.model_tag, step=args.model_step)
+    if optimizer_data is not None:
+        base_lrs = [group["lr"] for group in optimizer.param_groups]
+        optimizer.load_state_dict(optimizer_data)
+        del optimizer_data
+        for group, base_lr in zip(optimizer.param_groups, base_lrs):
+            group["lr"] = base_lr
+        print0("Loaded optimizer state from pretrained checkpoint (momentum buffers only, LRs reset)")
+    else:
+        print0("WARNING: optimizer checkpoint not found, starting with fresh optimizer (slightly worse)")
+
+# GradScaler for fp16 training (bf16/fp32 don't need it)
+scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
+if scaler is not None:
+    print0("GradScaler enabled for fp16 training")
+
+# Override the initial learning rate as a fraction of the base learning rate
+for group in optimizer.param_groups:
+    group["lr"] = group["lr"] * args.init_lr_frac
+    group["initial_lr"] = group["lr"]
+
+# SFT data mixture and DataLoader
+identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
+train_tasks = [
+    SmolTalk(split="train"), # 460K rows of general conversations
+    CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
+    CustomJSON(filepath=identity_conversations_filepath), # 2 epochs of these
+    *[MMLU(subset="all", split="auxiliary_train") for _ in range(args.mmlu_epochs)], # 100K rows per epoch
+    *[GSM8K(subset="main", split="train") for _ in range(args.gsm8k_epochs)], # 8K rows per epoch
+    SimpleSpelling(size=200000, split="train"), # 200K rows of Simple Spelling (e.g. spell the word 'apple')
+    SpellingBee(size=80000, split="train"), # 80K rows of Spelling Bee (e.g. how many 'r' are in 'strawberry'?)
+]
+train_dataset = TaskMixture(train_tasks)
+print0(f"Training mixture: {len(train_dataset):,} rows (MMLU x{args.mmlu_epochs}, GSM8K x{args.gsm8k_epochs})")
+val_dataset = TaskMixture([
+    SmolTalk(split="test"), # 24K rows in test set
+    MMLU(subset="all", split="test", stop=5200), # 14K rows in test set, use only 5.2K to match the train ratios
+    GSM8K(subset="main", split="test", stop=420), # 1.32K rows in test set, use only 420 to match the train ratios
+]) # total: 24K + 5.2K + 0.42K ~= 29.6K rows
+# DataLoader is defined here, it emits inputs, targets : 2D tensors of shape (device_batch_size, max_seq_len)
+# A big problem is that we don't know the final num_iterations in advance. So we create
+# these two global variables and update them from within the data generator.
+last_step = False # we will toggle this to True when we reach the end of the training dataset
+approx_progress = 0.0 # will go from 0 to 1 over the course of the epoch
+current_epoch = 1 # track epoch for logging
+def sft_data_generator_bos_bestfit(split, buffer_size=100):
+    """
+    BOS-aligned dataloader for SFT with bestfit-pad packing.
+
+    Each row in the batch starts with BOS (beginning of a conversation).
+    Conversations are packed using best-fit algorithm. When no conversation fits,
+    the row is padded (instead of cropping) to ensure no tokens are ever discarded.
+    Padding positions have targets masked with -1 (ignore_index for cross-entropy).
+    """
+    global last_step, approx_progress, current_epoch
+    assert split in {"train", "val"}, "split must be 'train' or 'val'"
+    dataset = train_dataset if split == "train" else val_dataset
+    dataset_size = len(dataset)
+    assert dataset_size > 0
+    row_capacity = args.max_seq_len + 1  # +1 for target at last position
+    bos_token = tokenizer.get_bos_token_id()
+
+    # Conversation buffer: list of (token_ids, loss_mask) tuples
+    conv_buffer = []
+    cursor = ddp_rank  # Each rank processes different conversations (for fetching)
+    consumed = ddp_rank  # Track actual consumption separately from buffering
+    epoch = 1
+    it = 0  # iteration counter
+
+    def refill_buffer():
+        nonlocal cursor, epoch
+        while len(conv_buffer) < buffer_size:
+            conversation = dataset[cursor]
+            ids, mask = tokenizer.render_conversation(conversation)
+            conv_buffer.append((ids, mask))
+            cursor += ddp_world_size
+            if cursor >= dataset_size:
+                cursor = cursor % dataset_size
+                epoch += 1
+                # Note: last_step is now triggered based on consumption, not fetching
+
+    while True:
+        rows = []
+        mask_rows = []
+        row_lengths = []  # Track actual content length (excluding padding) for each row
+        for _ in range(args.device_batch_size):
+            row = []
+            mask_row = []
+            padded = False
+            while len(row) < row_capacity:
+                # Ensure buffer has conversations
+                while len(conv_buffer) < buffer_size:
+                    refill_buffer()
+
+                remaining = row_capacity - len(row)
+
+                # Find largest conversation that fits entirely
+                best_idx = -1
+                best_len = 0
+                for i, (conv, _) in enumerate(conv_buffer):
+                    conv_len = len(conv)
+                    if conv_len <= remaining and conv_len > best_len:
+                        best_idx = i
+                        best_len = conv_len
+
+                if best_idx >= 0:
+                    # Found a conversation that fits - use it entirely
+                    conv, conv_mask = conv_buffer.pop(best_idx)
+                    row.extend(conv)
+                    mask_row.extend(conv_mask)
+                    consumed += ddp_world_size  # Track actual consumption
+                else:
+                    # No conversation fits - pad the remainder instead of cropping
+                    # This ensures we never discard any tokens
+                    content_len = len(row)
+                    row.extend([bos_token] * remaining)  # Pad with BOS tokens
+                    mask_row.extend([0] * remaining)
+                    padded = True
+                    break  # Row is now full (with padding)
+
+            # Track content length: full row if no padding, otherwise the length before padding
+            if padded:
+                row_lengths.append(content_len)
+            else:
+                row_lengths.append(row_capacity)
+            rows.append(row[:row_capacity])
+            mask_rows.append(mask_row[:row_capacity])
+
+        # Stopping condition to respect num_iterations, if given
+        it += 1
+        if 0 < args.num_iterations <= it and split == "train":
+            last_step = True
+
+        # Update progress tracking (based on consumed, not cursor, to account for buffering)
+        if split == "train":
+            current_epoch = epoch
+            if args.num_iterations > 0:
+                approx_progress = it / args.num_iterations
+            else:
+                approx_progress = consumed / dataset_size
+            # Trigger last_step when we've consumed enough (instead of when cursor wraps)
+            if consumed >= dataset_size:
+                last_step = True
+
+        # Build tensors
+        use_cuda = device_type == "cuda"
+        batch_tensor = torch.tensor(rows, dtype=torch.long, pin_memory=use_cuda)
+        inputs = batch_tensor[:, :-1].to(device=device, dtype=torch.int32, non_blocking=use_cuda).contiguous()
+        targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=use_cuda).contiguous()
+
+        # Apply the loss mask from render_conversation (mask=1 for assistant completions,
+        # mask=0 for user prompts, BOS, special tokens, tool outputs). mask[1:] aligns
+        # with targets (shifted by 1). Unmasked positions get -1 (ignore_index).
+        mask_tensor = torch.tensor(mask_rows, dtype=torch.int8)
+        mask_targets = mask_tensor[:, 1:].to(device=device)
+        targets[mask_targets == 0] = -1
+
+        # Mask out padding positions in targets (set to -1 = ignore_index)
+        # For each row, positions >= (content_length - 1) in targets should be masked
+        for i, content_len in enumerate(row_lengths):
+            if content_len < row_capacity:
+                targets[i, content_len-1:] = -1
+
+        yield inputs, targets
+
+train_loader = sft_data_generator_bos_bestfit("train")
+build_val_loader = lambda: sft_data_generator_bos_bestfit("val")
+progress = 0 # will go from 0 to 1 over the course of the epoch
+
+# Learning rate schedule (linear warmup, constant, linear warmdown)
+# Same shape as base_train but uses progress (0→1) instead of absolute step counts,
+# because SFT doesn't always know num_iterations in advance (dataset-driven stopping).
+def get_lr_multiplier(progress):
+    if progress < args.warmup_ratio:
+        return (progress + 1e-8) / args.warmup_ratio
+    elif progress <= 1.0 - args.warmdown_ratio:
+        return 1.0
+    else:
+        decay = (progress - (1.0 - args.warmdown_ratio)) / args.warmdown_ratio
+        return (1 - decay) * 1.0 + decay * args.final_lr_frac
+
+# Momentum scheduler for Muon optimizer
+def get_muon_momentum(it):
+    frac = min(it / 300, 1)
+    momentum = (1 - frac) * 0.85 + frac * 0.95
+    return momentum
+
+# -----------------------------------------------------------------------------
+# Training loop
+x, y = next(train_loader) # prefetch the very first batch of data
+min_val_bpb = float("inf")
+smooth_train_loss = 0 # EMA of training loss
+ema_beta = 0.9 # EMA decay factor
+total_training_time = 0 # total wall-clock time of training
+step = 0
+while True:
+    flops_so_far = num_flops_per_token * args.total_batch_size * step
+
+    # Synchronize last_step across all ranks to avoid hangs in the distributed setting
+    if ddp:
+        last_step_tensor = torch.tensor(last_step, dtype=torch.int32, device=device)
+        dist.all_reduce(last_step_tensor, op=dist.ReduceOp.MAX)
+        last_step = bool(last_step_tensor.item())
+
+    # once in a while: evaluate the val bpb (all ranks participate)
+    if last_step or (args.eval_every > 0 and step % args.eval_every == 0):
+        model.eval()
+        val_loader = build_val_loader()
+        eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
+        val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
+        print0(f"Step {step:05d} | Validation bpb: {val_bpb:.4f}")
+        if val_bpb < min_val_bpb:
+            min_val_bpb = val_bpb
+        wandb_run.log({
+            "step": step,
+            "total_training_flops": flops_so_far,
+            "total_training_time": total_training_time,
+            "val/bpb": val_bpb,
+        })
+        model.train()
+
+    # once in a while: estimate the ChatCORE metric (all ranks participate)
+    # use the original uncompiled model because the inputs keep changing shape
+    chatcore_results = {}
+    if args.chatcore_every > 0 and (last_step or (step > 0 and step % args.chatcore_every == 0)):
+        model.eval()
+        engine = Engine(orig_model, tokenizer)
+        all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval', 'SpellingBee']
+        categorical_tasks = {'ARC-Easy', 'ARC-Challenge', 'MMLU'}
+        baseline_accuracies = {
+            'ARC-Easy': 0.25, 'ARC-Challenge': 0.25, 'MMLU': 0.25,
+            'GSM8K': 0.0, 'HumanEval': 0.0, 'SpellingBee': 0.0,
+        }
+        task_results = {}
+        for task_name in all_tasks:
+            limit = args.chatcore_max_cat if task_name in categorical_tasks else args.chatcore_max_sample
+            max_problems = None if limit < 0 else limit  # -1 means no limit
+            acc = run_chat_eval(task_name, orig_model, tokenizer, engine,
+                                batch_size=args.device_batch_size, max_problems=max_problems)
+            task_results[task_name] = acc
+            print0(f"  {task_name}: {100*acc:.2f}%")
+        # Compute ChatCORE metrics (mean centered accuracy, ranges from 0=random to 1=perfect)
+        def centered_mean(tasks):
+            return sum((task_results[t] - baseline_accuracies[t]) / (1.0 - baseline_accuracies[t]) for t in tasks) / len(tasks)
+        chatcore = centered_mean(all_tasks)
+        chatcore_cat = centered_mean(categorical_tasks)
+        print0(f"Step {step:05d} | ChatCORE: {chatcore:.4f} | ChatCORE_cat: {chatcore_cat:.4f}")
+        wandb_run.log({
+            "step": step,
+            "total_training_flops": flops_so_far,
+            "chatcore_metric": chatcore,
+            "chatcore_cat": chatcore_cat,
+            **{f"chatcore/{task_name}": acc for task_name, acc in task_results.items()},
+        })
+        model.train()
+
+    # save checkpoint at the end of the run (all ranks participate so each saves its optimizer shard)
+    if last_step:
+        output_dirname = args.model_tag if args.model_tag else f"d{depth}" # e.g. d12
+        checkpoint_dir = os.path.join(base_dir, "chatsft_checkpoints", output_dirname)
+        save_checkpoint(
+            checkpoint_dir,
+            step,
+            orig_model.state_dict(),
+            optimizer.state_dict(),
+            {
+                "step": step,
+                "val_bpb": val_bpb, # loss at last step
+                "model_config": {
+                    "sequence_len": args.max_seq_len,
+                    "vocab_size": tokenizer.get_vocab_size(),
+                    "n_layer": depth,
+                    "n_head": model.config.n_head,
+                    "n_kv_head": model.config.n_kv_head,
+                    "n_embd": model.config.n_embd,
+                    "window_pattern": model.config.window_pattern,
+                },
+                "user_config": user_config, # inputs to the training script
+            },
+            rank=ddp_rank,
+        )
+
+    if last_step:
+        break
+
+    # -------------------------------------------------------------------------
+    # single training step
+    # evaluate the gradient
+    synchronize()
+    t0 = time.time()
+    for micro_step in range(grad_accum_steps):
+        loss = model(x, y)
+        train_loss = loss.detach() # for logging
+        loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
+        if scaler is not None:
+            scaler.scale(loss).backward()
+        else:
+            loss.backward()
+        x, y = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
+        progress = max(progress, approx_progress) # only increase progress monotonically
+    # step the optimizer
+    lrm = get_lr_multiplier(progress)
+    muon_momentum = get_muon_momentum(step)
+    for group in optimizer.param_groups:
+        group["lr"] = group["initial_lr"] * lrm
+        if group['kind'] == 'muon':
+            group["momentum"] = muon_momentum
+    if scaler is not None:
+        scaler.unscale_(optimizer)
+        if is_ddp_initialized():
+            for v in scaler._found_inf_per_device(optimizer).values():
+                dist.all_reduce(v, op=dist.ReduceOp.MAX)
+        scaler.step(optimizer)
+        scaler.update()
+    else:
+        optimizer.step()
+    model.zero_grad(set_to_none=True)
+    synchronize()
+    t1 = time.time()
+    dt = t1 - t0
+    # -------------------------------------------------------------------------
+
+    # State
+    step += 1
+
+    # logging
+    smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss.item() # EMA the training loss
+    debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) # debias the EMA
+    pct_done = 100 * progress
+    tok_per_sec = int(args.total_batch_size / dt)
+    flops_per_sec = num_flops_per_token * args.total_batch_size / dt
+    mfu = 100 * flops_per_sec / (gpu_peak_flops * ddp_world_size)
+    if step > 10:
+        total_training_time += dt # only count the time after the first 10 steps
+    print0(f"step {step:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | epoch: {current_epoch} | total time: {total_training_time/60:.2f}m")
+    if step % 10 == 0:
+        wandb_run.log({
+            "step": step,
+            "total_training_flops": flops_so_far,
+            "total_training_time": total_training_time,
+            "train/loss": debiased_smooth_loss,
+            "train/lrm": lrm,
+            "train/dt": dt,
+            "train/tok_per_sec": tok_per_sec,
+            "train/mfu": mfu,
+            "train/epoch": current_epoch,
+        })
+
+    # The garbage collector spends ~500ms scanning for cycles quite frequently.
+    # We manually manage it to avoid these pauses during training.
+    if step == 1:
+        gc.collect() # manually collect a lot of garbage from setup
+        gc.freeze() # freeze all currently surviving objects and exclude them from GC
+        gc.disable() # disable GC entirely except:
+    elif step % 5000 == 0: # every 5000 steps...
+        gc.collect() # manually collect, just to be safe for very long runs
+
+# print a few more stats
+print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
+print0(f"Total training time: {total_training_time/60:.2f}m")
+print0(f"Minimum validation bpb: {min_val_bpb:.4f}")
+
+# Log to report
+from nanochat.report import get_report
+get_report().log(section="SFT", data=[
+    user_config, # CLI args
+    { # stats about the training setup
+        "Number of iterations": step,
+        "DDP world size": ddp_world_size,
+    },
+    { # stats about training outcomes
+        "Minimum validation bpb": min_val_bpb,
+    }
+])
+
+# cleanup
+wandb_run.finish() # wandb run finish
+compute_cleanup()

scripts/chat_web.py

@@ -0,0 +1,407 @@
+#!/usr/bin/env python3
+"""
+Unified web chat server - serves both UI and API from a single FastAPI instance.
+
+Uses data parallelism to distribute requests across multiple GPUs. Each GPU loads
+a full copy of the model, and incoming requests are distributed to available workers.
+
+Launch examples:
+
+- single available GPU (default)
+python -m scripts.chat_web
+
+- 4 GPUs
+python -m scripts.chat_web --num-gpus 4
+
+To chat, open the URL printed in the console. (If on cloud box, make sure to use public IP)
+
+Endpoints:
+  GET  /           - Chat UI
+  POST /chat/completions - Chat API (streaming only)
+  GET  /health     - Health check with worker pool status
+  GET  /stats      - Worker pool statistics and GPU utilization
+
+Abuse Prevention:
+  - Maximum 500 messages per request
+  - Maximum 8000 characters per message
+  - Maximum 32000 characters total conversation length
+  - Temperature clamped to 0.0-2.0
+  - Top-k clamped to 0-200 (0 disables top-k filtering, using full vocabulary)
+  - Max tokens clamped to 1-4096
+"""
+
+import argparse
+import json
+import os
+import torch
+import asyncio
+import logging
+import random
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
+from pydantic import BaseModel
+from typing import List, Optional, AsyncGenerator
+from dataclasses import dataclass
+from nanochat.common import compute_init, autodetect_device_type
+from nanochat.checkpoint_manager import load_model
+from nanochat.engine import Engine
+
+# Abuse prevention limits
+MAX_MESSAGES_PER_REQUEST = 500
+MAX_MESSAGE_LENGTH = 8000
+MAX_TOTAL_CONVERSATION_LENGTH = 32000
+MIN_TEMPERATURE = 0.0
+MAX_TEMPERATURE = 2.0
+MIN_TOP_K = 0 # 0 disables top-k filtering, using full vocabulary
+MAX_TOP_K = 200
+MIN_MAX_TOKENS = 1
+MAX_MAX_TOKENS = 4096
+
+parser = argparse.ArgumentParser(description='NanoChat Web Server')
+parser.add_argument('-n', '--num-gpus', type=int, default=1, help='Number of GPUs to use (default: 1)')
+parser.add_argument('-i', '--source', type=str, default="sft", help="Source of the model: sft|rl")
+parser.add_argument('-t', '--temperature', type=float, default=0.8, help='Default temperature for generation')
+parser.add_argument('-k', '--top-k', type=int, default=50, help='Default top-k sampling parameter')
+parser.add_argument('-m', '--max-tokens', type=int, default=512, help='Default max tokens for generation')
+parser.add_argument('-g', '--model-tag', type=str, default=None, help='Model tag to load')
+parser.add_argument('-s', '--step', type=int, default=None, help='Step to load')
+parser.add_argument('-p', '--port', type=int, default=8000, help='Port to run the server on')
+parser.add_argument('--device-type', type=str, default='', choices=['cuda', 'cpu', 'mps'], help='Device type for evaluation: cuda|cpu|mps. empty => autodetect')
+parser.add_argument('--host', type=str, default='0.0.0.0', help='Host to bind the server to')
+args = parser.parse_args()
+
+# Configure logging for conversation traffic
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+
+device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+
+@dataclass
+class Worker:
+    """A worker with a model loaded on a specific GPU."""
+    gpu_id: int
+    device: torch.device
+    engine: Engine
+    tokenizer: object
+
+class WorkerPool:
+    """Pool of workers, each with a model replica on a different GPU."""
+
+    def __init__(self, num_gpus: Optional[int] = None):
+        if num_gpus is None:
+            if device_type == "cuda":
+                num_gpus = torch.cuda.device_count()
+            else:
+                num_gpus = 1 # e.g. cpu|mps
+        self.num_gpus = num_gpus
+        self.workers: List[Worker] = []
+        self.available_workers: asyncio.Queue = asyncio.Queue()
+
+    async def initialize(self, source: str, model_tag: Optional[str] = None, step: Optional[int] = None):
+        """Load model on each GPU."""
+        print(f"Initializing worker pool with {self.num_gpus} GPUs...")
+        if self.num_gpus > 1:
+            assert device_type == "cuda", "Only CUDA supports multiple workers/GPUs. cpu|mps does not."
+
+        for gpu_id in range(self.num_gpus):
+
+            if device_type == "cuda":
+                device = torch.device(f"cuda:{gpu_id}")
+                print(f"Loading model on GPU {gpu_id}...")
+            else:
+                device = torch.device(device_type) # e.g. cpu|mps
+                print(f"Loading model on {device_type}...")
+
+            model, tokenizer, _ = load_model(source, device, phase="eval", model_tag=model_tag, step=step)
+            engine = Engine(model, tokenizer)
+            worker = Worker(
+                gpu_id=gpu_id,
+                device=device,
+                engine=engine,
+                tokenizer=tokenizer,
+            )
+            self.workers.append(worker)
+            await self.available_workers.put(worker)
+
+        print(f"All {self.num_gpus} workers initialized!")
+
+    async def acquire_worker(self) -> Worker:
+        """Get an available worker from the pool."""
+        return await self.available_workers.get()
+
+    async def release_worker(self, worker: Worker):
+        """Return a worker to the pool."""
+        await self.available_workers.put(worker)
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    messages: List[ChatMessage]
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    top_k: Optional[int] = None
+
+def validate_chat_request(request: ChatRequest):
+    """Validate chat request to prevent abuse."""
+    # Check number of messages
+    if len(request.messages) == 0:
+        raise HTTPException(status_code=400, detail="At least one message is required")
+    if len(request.messages) > MAX_MESSAGES_PER_REQUEST:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Too many messages. Maximum {MAX_MESSAGES_PER_REQUEST} messages allowed per request"
+        )
+
+    # Check individual message lengths and total conversation length
+    total_length = 0
+    for i, message in enumerate(request.messages):
+        if not message.content:
+            raise HTTPException(status_code=400, detail=f"Message {i} has empty content")
+
+        msg_length = len(message.content)
+        if msg_length > MAX_MESSAGE_LENGTH:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Message {i} is too long. Maximum {MAX_MESSAGE_LENGTH} characters allowed per message"
+            )
+        total_length += msg_length
+
+    if total_length > MAX_TOTAL_CONVERSATION_LENGTH:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Total conversation is too long. Maximum {MAX_TOTAL_CONVERSATION_LENGTH} characters allowed"
+        )
+
+    # Validate role values
+    for i, message in enumerate(request.messages):
+        if message.role not in ["user", "assistant"]:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Message {i} has invalid role. Must be 'user', 'assistant', or 'system'"
+            )
+
+    # Validate temperature
+    if request.temperature is not None:
+        if not (MIN_TEMPERATURE <= request.temperature <= MAX_TEMPERATURE):
+            raise HTTPException(
+                status_code=400,
+                detail=f"Temperature must be between {MIN_TEMPERATURE} and {MAX_TEMPERATURE}"
+            )
+
+    # Validate top_k
+    if request.top_k is not None:
+        if not (MIN_TOP_K <= request.top_k <= MAX_TOP_K):
+            raise HTTPException(
+                status_code=400,
+                detail=f"top_k must be between {MIN_TOP_K} and {MAX_TOP_K}"
+            )
+
+    # Validate max_tokens
+    if request.max_tokens is not None:
+        if not (MIN_MAX_TOKENS <= request.max_tokens <= MAX_MAX_TOKENS):
+            raise HTTPException(
+                status_code=400,
+                detail=f"max_tokens must be between {MIN_MAX_TOKENS} and {MAX_MAX_TOKENS}"
+            )
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Load models on all GPUs on startup."""
+    print("Loading nanochat models across GPUs...")
+    app.state.worker_pool = WorkerPool(num_gpus=args.num_gpus)
+    await app.state.worker_pool.initialize(args.source, model_tag=args.model_tag, step=args.step)
+    print(f"Server ready at http://localhost:{args.port}")
+    yield
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+async def root():
+    """Serve the chat UI."""
+    ui_html_path = os.path.join("nanochat", "ui.html")
+    with open(ui_html_path, "r", encoding="utf-8") as f:
+        html_content = f.read()
+    # Replace the API_URL to use the same origin
+    html_content = html_content.replace(
+        "const API_URL = `http://${window.location.hostname}:8000`;",
+        "const API_URL = '';"
+    )
+    return HTMLResponse(content=html_content)
+
+
+@app.get("/logo.svg")
+async def logo():
+    """Serve the NanoChat logo for favicon and header."""
+    logo_path = os.path.join("nanochat", "logo.svg")
+    return FileResponse(logo_path, media_type="image/svg+xml")
+
+async def generate_stream(
+    worker: Worker,
+    tokens,
+    temperature=None,
+    max_new_tokens=None,
+    top_k=None
+) -> AsyncGenerator[str, None]:
+    """Generate assistant response with streaming."""
+    temperature = temperature if temperature is not None else args.temperature
+    max_new_tokens = max_new_tokens if max_new_tokens is not None else args.max_tokens
+    top_k = top_k if top_k is not None else args.top_k
+
+    assistant_end = worker.tokenizer.encode_special("<|assistant_end|>")
+    bos = worker.tokenizer.get_bos_token_id()
+
+    # Accumulate tokens to properly handle multi-byte UTF-8 characters (like emojis)
+    accumulated_tokens = []
+    # Track the last complete UTF-8 string (without replacement characters)
+    last_clean_text = ""
+
+    for token_column, token_masks in worker.engine.generate(
+        tokens,
+        num_samples=1,
+        max_tokens=max_new_tokens,
+        temperature=temperature,
+        top_k=top_k,
+        seed=random.randint(0, 2**31 - 1)
+    ):
+        token = token_column[0]
+
+        # Stopping criteria
+        if token == assistant_end or token == bos:
+            break
+
+        # Append the token to sequence
+        accumulated_tokens.append(token)
+        # Decode all accumulated tokens to get proper UTF-8 handling
+        # Note that decode is a quite efficient operation, basically table lookup and string concat
+        current_text = worker.tokenizer.decode(accumulated_tokens)
+        # Only emit text if it doesn't end with a replacement character
+        # This ensures we don't emit incomplete UTF-8 sequences
+        if not current_text.endswith('�'):
+            # Extract only the new text since last clean decode
+            new_text = current_text[len(last_clean_text):]
+            if new_text:  # Only yield if there's new content
+                yield f"data: {json.dumps({'token': new_text, 'gpu': worker.gpu_id}, ensure_ascii=False)}\n\n"
+                last_clean_text = current_text
+
+    yield f"data: {json.dumps({'done': True})}\n\n"
+
+@app.post("/chat/completions")
+async def chat_completions(request: ChatRequest):
+    """Chat completion endpoint (streaming only) - uses worker pool for multi-GPU."""
+
+    # Basic validation to prevent abuse
+    validate_chat_request(request)
+
+    # Log incoming conversation to console
+    logger.info("="*20)
+    for i, message in enumerate(request.messages):
+        logger.info(f"[{message.role.upper()}]: {message.content}")
+    logger.info("-"*20)
+
+    # Acquire a worker from the pool (will wait if all are busy)
+    worker_pool = app.state.worker_pool
+    worker = await worker_pool.acquire_worker()
+
+    try:
+        # Build conversation tokens
+        bos = worker.tokenizer.get_bos_token_id()
+        user_start = worker.tokenizer.encode_special("<|user_start|>")
+        user_end = worker.tokenizer.encode_special("<|user_end|>")
+        assistant_start = worker.tokenizer.encode_special("<|assistant_start|>")
+        assistant_end = worker.tokenizer.encode_special("<|assistant_end|>")
+
+        conversation_tokens = [bos]
+        for message in request.messages:
+            if message.role == "user":
+                conversation_tokens.append(user_start)
+                conversation_tokens.extend(worker.tokenizer.encode(message.content))
+                conversation_tokens.append(user_end)
+            elif message.role == "assistant":
+                conversation_tokens.append(assistant_start)
+                conversation_tokens.extend(worker.tokenizer.encode(message.content))
+                conversation_tokens.append(assistant_end)
+
+        conversation_tokens.append(assistant_start)
+
+        # Streaming response with worker release after completion
+        response_tokens = []
+        async def stream_and_release():
+            try:
+                async for chunk in generate_stream(
+                    worker,
+                    conversation_tokens,
+                    temperature=request.temperature,
+                    max_new_tokens=request.max_tokens,
+                    top_k=request.top_k
+                ):
+                    # Accumulate response for logging
+                    chunk_data = json.loads(chunk.replace("data: ", "").strip())
+                    if "token" in chunk_data:
+                        response_tokens.append(chunk_data["token"])
+                    yield chunk
+            finally:
+                # Log the assistant response to console
+                full_response = "".join(response_tokens)
+                logger.info(f"[ASSISTANT] (GPU {worker.gpu_id}): {full_response}")
+                logger.info("="*20)
+                # Release worker back to pool after streaming is done
+                await worker_pool.release_worker(worker)
+
+        return StreamingResponse(
+            stream_and_release(),
+            media_type="text/event-stream"
+        )
+    except Exception as e:
+        # Make sure to release worker even on error
+        await worker_pool.release_worker(worker)
+        raise e
+
+@app.get("/health")
+async def health():
+    """Health check endpoint."""
+    worker_pool = getattr(app.state, 'worker_pool', None)
+    return {
+        "status": "ok",
+        "ready": worker_pool is not None and len(worker_pool.workers) > 0,
+        "num_gpus": worker_pool.num_gpus if worker_pool else 0,
+        "available_workers": worker_pool.available_workers.qsize() if worker_pool else 0
+    }
+
+@app.get("/stats")
+async def stats():
+    """Get worker pool statistics."""
+    worker_pool = app.state.worker_pool
+    return {
+        "total_workers": len(worker_pool.workers),
+        "available_workers": worker_pool.available_workers.qsize(),
+        "busy_workers": len(worker_pool.workers) - worker_pool.available_workers.qsize(),
+        "workers": [
+            {
+                "gpu_id": w.gpu_id,
+                "device": str(w.device)
+            } for w in worker_pool.workers
+        ]
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    print(f"Starting NanoChat Web Server")
+    print(f"Temperature: {args.temperature}, Top-k: {args.top_k}, Max tokens: {args.max_tokens}")
+    uvicorn.run(app, host=args.host, port=args.port)

scripts/smoke.sh

@@ -1,12 +1,13 @@
 #!/bin/bash
-# End-to-end smoke for upstream nanochat: dataset → tokenizer → tiny base_train.
+# End-to-end smoke for nanochat-omni: dataset → tokenizer → tiny base_train.
-# Idempotent: caches venv, uv-cache, and dataset shards under /data/nanochat-smoke/.
+# Runs in-place at the repo root (post-monorepo-fork).
-# Targets ailab (CN, RTX 5090). Uses CN mirrors for PyPI / pytorch wheels / HuggingFace.
+# Idempotent: caches venv + uv-cache + dataset shards under /data/nanochat-smoke/.
+# Targets ailab (CN, RTX 5090). CN mirrors (sjtu pytorch, aliyun PyPI, hf-mirror)
+# are committed directly into pyproject.toml/uv.lock/nanochat/dataset.py.
 set -euo pipefail
 export PATH="$HOME/.local/bin:$PATH"
 
 CACHE_ROOT=${CACHE_ROOT:-/data/nanochat-smoke}
-NANOCHAT_DIR=$CACHE_ROOT/nanochat
 export NANOCHAT_BASE_DIR=$CACHE_ROOT/cache
 export UV_CACHE_DIR=$CACHE_ROOT/uv-cache
 export UV_DEFAULT_INDEX=${UV_DEFAULT_INDEX:-https://mirrors.aliyun.com/pypi/simple/}
@@ -23,26 +24,8 @@ else
     RUN_TAG=dummy
 fi
 
-if [ ! -d "$NANOCHAT_DIR" ]; then
-    echo "Cloning nanochat into $NANOCHAT_DIR"
-    git clone https://github.com/karpathy/nanochat.git "$NANOCHAT_DIR" \
-        || git clone https://ghfast.top/https://github.com/karpathy/nanochat.git "$NANOCHAT_DIR"
-fi
-
-cd "$NANOCHAT_DIR"
-
-# CN mirror patches — idempotent: sed only matches the upstream URLs.
-sed -i \
-    -e "s|https://download.pytorch.org/whl/cu128|https://mirror.sjtu.edu.cn/pytorch-wheels/cu128|g" \
-    -e "s|https://download.pytorch.org/whl/cpu|https://mirror.sjtu.edu.cn/pytorch-wheels/cpu|g" \
-    pyproject.toml uv.lock
-sed -i \
-    -e "s|https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle|https://hf-mirror.com/datasets/karpathy/climbmix-400b-shuffle|" \
-    nanochat/dataset.py
-
 [ -d .venv ] || uv venv
 uv sync --extra gpu --index-strategy unsafe-best-match
-
 source .venv/bin/activate
 
 echo "=== [1/4] download 1 climbmix shard ==="

scripts/tok_eval.py

@@ -0,0 +1,265 @@
+"""
+Evaluate compression ratio of the tokenizer.
+"""
+
+from nanochat.tokenizer import get_tokenizer, RustBPETokenizer
+from nanochat.dataset import parquets_iter_batched
+
+# Random text I got from a random website this morning
+news_text = r"""
+(Washington, D.C., July 9, 2025)- Yesterday, Mexico’s National Service of Agro-Alimentary Health, Safety, and Quality (SENASICA) reported a new case of New World Screwworm (NWS) in Ixhuatlan de Madero, Veracruz in Mexico, which is approximately 160 miles northward of the current sterile fly dispersal grid, on the eastern side of the country and 370 miles south of the U.S./Mexico border. This new northward detection comes approximately two months after northern detections were reported in Oaxaca and Veracruz, less than 700 miles away from the U.S. border, which triggered the closure of our ports to Mexican cattle, bison, and horses on May 11, 2025.
+
+While USDA announced a risk-based phased port re-opening strategy for cattle, bison, and equine from Mexico beginning as early as July 7, 2025, this newly reported NWS case raises significant concern about the previously reported information shared by Mexican officials and severely compromises the outlined port reopening schedule of five ports from July 7-September 15. Therefore, in order to protect American livestock and our nation’s food supply, Secretary Rollins has ordered the closure of livestock trade through southern ports of entry effective immediately.
+
+“The United States has promised to be vigilant — and after detecting this new NWS case, we are pausing the planned port reopening’s to further quarantine and target this deadly pest in Mexico. We must see additional progress combatting NWS in Veracruz and other nearby Mexican states in order to reopen livestock ports along the Southern border,” said U.S. Secretary of Agriculture Brooke L. Rollins. “Thanks to the aggressive monitoring by USDA staff in the U.S. and in Mexico, we have been able to take quick and decisive action to respond to the spread of this deadly pest.”
+""".strip()
+
+# Random Korean text (to test non-English compression)
+korean_text = r"""
+정직한 사실 위에, 공정한 시선을 더하다
+Herald Korea Times
+
+헤럴드코리아타임즈는 정치, 경제, 사회, 문화 등 한국 사회 전반의 주요 이슈를 심도 있게 다루는 종합 온라인 신문사입니다.
+
+우리는 단순히 뉴스를 전달하는 것이 아니라, 사실(Fact)에 기반한 양측의 시각을 균형 있게 조명하며, 독자 여러분이 스스로 판단할 수 있는 ‘정보의 균형’을 제공합니다.
+
+한국 언론의 오랜 문제로 지적되어 온 정치적 편향, 이념적 왜곡에서 벗어나
+오직 정직함과 공정함을 원칙으로 삼는 언론을 지향합니다.
+어느 한쪽의 주장만을 확대하거나 감추지 않고,
+**모든 쟁점에 대해 ‘무엇이 쟁점인지’, ‘누가 무엇을 주장하는지’, ‘사실은 무엇인지’**를 명확히 전달하는 데 집중합니다.
+""".strip()
+
+# Random piece of code
+code_text = r"""
+class BasicTokenizer(Tokenizer):
+
+    def __init__(self):
+        super().__init__()
+
+    def train(self, text, vocab_size, verbose=False):
+        assert vocab_size >= 256
+        num_merges = vocab_size - 256
+
+        # input text preprocessing
+        text_bytes = text.encode("utf-8") # raw bytes
+        ids = list(text_bytes) # list of integers in range 0..255
+
+        # iteratively merge the most common pairs to create new tokens
+        merges = {} # (int, int) -> int
+        vocab = {idx: bytes([idx]) for idx in range(256)} # int -> bytes
+        for i in range(num_merges):
+            # count up the number of times every consecutive pair appears
+            stats = get_stats(ids)
+            # find the pair with the highest count
+            pair = max(stats, key=stats.get)
+            # mint a new token: assign it the next available id
+            idx = 256 + i
+            # replace all occurrences of pair in ids with idx
+            ids = merge(ids, pair, idx)
+            # save the merge
+            merges[pair] = idx
+            vocab[idx] = vocab[pair[0]] + vocab[pair[1]]
+            # prints
+            if verbose:
+                print(f"merge {i+1}/{num_merges}: {pair} -> {idx} ({vocab[idx]}) had {stats[pair]} occurrences")
+""".strip()
+
+math_text = r"""
+\documentclass[12pt]{article}
+\usepackage{amsmath,amsthm,amssymb}
+\usepackage[margin=1in]{geometry}
+
+\newtheorem{theorem}{Theorem}
+\newtheorem*{remark}{Remark}
+
+\begin{document}
+
+\begin{center}
+{\Large A Cute Identity: The Sum of Cubes is a Square}
+\end{center}
+
+\begin{theorem}
+For every integer $n \ge 1$,
+\[
+\sum_{k=1}^{n} k^{3} \;=\; \left(\frac{n(n+1)}{2}\right)^{2}.
+\]
+\end{theorem}
+
+\begin{proof}[Proof 1 (Induction)]
+Let $S(n) = \sum_{k=1}^{n} k^3$. For $n=1$, $S(1)=1=(1\cdot 2/2)^2$, so the base case holds.
+
+Assume $S(n)=\big(\tfrac{n(n+1)}{2}\big)^2$ for some $n\ge 1$.
+Then
+\[
+S(n+1)
+= S(n) + (n+1)^3
+= \left(\frac{n(n+1)}{2}\right)^2 + (n+1)^3.
+\]
+Factor out $(n+1)^2$:
+\[
+S(n+1)
+= (n+1)^2\left( \frac{n^2}{4} + (n+1) \right)
+= (n+1)^2\left( \frac{n^2 + 4n + 4}{4} \right)
+= (n+1)^2\left( \frac{(n+2)^2}{4} \right).
+\]
+Thus
+\[
+S(n+1)=\left(\frac{(n+1)(n+2)}{2}\right)^2,
+\]
+which matches the claimed formula with $n$ replaced by $n+1$. By induction, the identity holds for all $n\ge 1$.
+\end{proof}
+
+\begin{proof}[Proof 2 (Algebraic telescoping)]
+Recall the binomial identity
+\[
+(k+1)^4 - k^4 = 4k^3 + 6k^2 + 4k + 1.
+\]
+Summing both sides from $k=0$ to $n$ telescopes:
+\[
+(n+1)^4 - 0^4
+= \sum_{k=0}^{n}\big(4k^3 + 6k^2 + 4k + 1\big)
+= 4\sum_{k=1}^{n}k^3 + 6\sum_{k=1}^{n}k^2 + 4\sum_{k=1}^{n}k + (n+1).
+\]
+Using the standard sums
+\[
+\sum_{k=1}^{n}k = \frac{n(n+1)}{2}
+\quad\text{and}\quad
+\sum_{k=1}^{n}k^2 = \frac{n(n+1)(2n+1)}{6},
+\]
+solve for $\sum_{k=1}^{n}k^3$ to get
+\[
+\sum_{k=1}^{n}k^3 = \left(\frac{n(n+1)}{2}\right)^2.
+\]
+\end{proof}
+
+\begin{remark}
+Geometrically, the identity says: ``adding up $1^3,2^3,\dots,n^3$ builds a perfect square’’—namely the square of the $n$th triangular number. This is why one sometimes calls it the \emph{sum-of-cubes is a square} phenomenon.
+\end{remark}
+
+\end{document}
+""".strip()
+
+science_text = r"""
+Photosynthesis is a photochemical energy transduction process in which light-harvesting pigment–protein complexes within the thylakoid membranes of oxygenic phototrophs absorb photons and initiate charge separation at the reaction center, driving the linear electron transport chain from water to NADP⁺ via photosystem II, the cytochrome b₆f complex, and photosystem I, concomitantly generating a trans-thylakoid proton motive force utilized by chloroplastic ATP synthase. The light-dependent reactions produce ATP and NADPH, which fuel the Calvin–Benson–Bassham cycle in the stroma, wherein ribulose-1,5-bisphosphate is carboxylated by ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) to form 3-phosphoglycerate, subsequently reduced and regenerated through a series of enzymatic steps, enabling net assimilation of CO₂ into triose phosphates and ultimately carbohydrates. This process is tightly regulated by photoprotective mechanisms, redox feedback, and metabolite flux, representing a central biochemical pathway coupling solar energy capture to the biosphere’s primary productivity.
+""".strip()
+
+# The tokenizer was trained on data from earlier shards, so it has seen this data
+train_docs = next(parquets_iter_batched(split="train"))
+train_text = "\n".join(train_docs)
+val_docs = next(parquets_iter_batched(split="val"))
+val_text = "\n".join(val_docs)
+
+all_text = [
+    ("news", news_text),
+    ("korean", korean_text),
+    ("code", code_text),
+    ("math", math_text),
+    ("science", science_text),
+    ("fwe-train", train_text),
+]
+if val_text:
+    all_text.append(("fwe-val", val_text))
+
+# Try out current default compared to GPT-2 and GPT-4 tokenizers
+tokenizer_results = {}
+vocab_sizes = {}
+
+for tokenizer_name in ["gpt2", "gpt4", "ours"]:
+
+    if tokenizer_name == "gpt2":
+        tokenizer = RustBPETokenizer.from_pretrained("gpt2") # gpt-2 base model tokenizer
+    elif tokenizer_name == "gpt4":
+        tokenizer = RustBPETokenizer.from_pretrained("cl100k_base") # gpt-4 base model tokenizer
+    else:
+        tokenizer = get_tokenizer()
+
+    vocab_sizes[tokenizer_name] = tokenizer.get_vocab_size()
+    tokenizer_results[tokenizer_name] = {}
+
+    for name, text in all_text:
+        encoded = tokenizer.encode(text)
+        decoded = tokenizer.decode(encoded)
+        assert decoded == text
+
+        encoded_bytes = text.encode('utf-8')
+        ratio = len(encoded_bytes) / len(encoded)
+        tokenizer_results[tokenizer_name][name] = {
+            'bytes': len(encoded_bytes),
+            'tokens': len(encoded),
+            'ratio': ratio
+        }
+
+# ANSI color codes
+GREEN = '\033[92m'
+RED = '\033[91m'
+RESET = '\033[0m'
+
+# Print vocab sizes
+print(f"\nVocab sizes:")
+print(f"GPT-2: {vocab_sizes['gpt2']}")
+print(f"GPT-4: {vocab_sizes['gpt4']}")
+print(f"Ours: {vocab_sizes['ours']}")
+
+def print_comparison(baseline_name, baseline_results, ours_results, all_text):
+    """Print comparison table between baseline tokenizer and ours."""
+    print(f"\nComparison with {baseline_name}:")
+    print("=" * 95)
+    print(f"{'Text Type':<10} {'Bytes':<8} {baseline_name:<15} {'Ours':<15} {'Relative':<12} {'Better':<10}")
+    print(f"{'':10} {'':8} {'Tokens':<7} {'Ratio':<7} {'Tokens':<7} {'Ratio':<7} {'Diff %':<12}")
+    print("-" * 95)
+
+    for name, text in all_text:
+        baseline_data = baseline_results[name]
+        ours_data = ours_results[name]
+
+        # Calculate relative difference (positive means ours is better, negative means worse)
+        # Using tokens: fewer tokens is better, so we calculate (baseline_tokens - ours_tokens) / baseline_tokens
+        relative_diff = ((baseline_data['tokens'] - ours_data['tokens']) / baseline_data['tokens']) * 100
+
+        # Determine which has better compression (higher ratio = better)
+        if baseline_data['ratio'] > ours_data['ratio']:
+            baseline_color, ours_color = GREEN, RED
+            better = baseline_name
+            diff_color = RED
+        elif ours_data['ratio'] > baseline_data['ratio']:
+            baseline_color, ours_color = RED, GREEN
+            better = "Ours"
+            diff_color = GREEN
+        else:
+            baseline_color, ours_color = "", ""
+            better = "Tie"
+            diff_color = ""
+
+        print(f"{name:<10} {baseline_data['bytes']:<8} "
+              f"{baseline_color}{baseline_data['tokens']:<7}{RESET} "
+              f"{baseline_color}{baseline_data['ratio']:<7.2f}{RESET} "
+              f"{ours_color}{ours_data['tokens']:<7}{RESET} "
+              f"{ours_color}{ours_data['ratio']:<7.2f}{RESET} "
+              f"{diff_color}{relative_diff:+7.1f}%{RESET}     "
+              f"{better:<10}")
+
+# Print comparisons
+print_comparison("GPT-2", tokenizer_results['gpt2'], tokenizer_results['ours'], all_text)
+print_comparison("GPT-4", tokenizer_results['gpt4'], tokenizer_results['ours'], all_text)
+
+# Log to report
+from nanochat.report import get_report
+lines = []
+for baseline_name in ["GPT-2", "GPT-4"]:
+    baseline_key = baseline_name.lower().replace('-', '')
+    baseline_results = tokenizer_results[baseline_key]
+    ours_results = tokenizer_results['ours']
+    lines.append(f"### Comparison with {baseline_name}")
+    lines.append("")
+    lines.append("| Text Type | Bytes | " + baseline_name + " Tokens | " + baseline_name + " Ratio | Ours Tokens | Ours Ratio | Relative Diff % |")
+    lines.append("|-----------|-------|--------------|--------------|-------------|------------|-----------------|")
+    for name, text in all_text:
+        baseline_data = baseline_results[name]
+        ours_data = ours_results[name]
+        relative_diff = ((baseline_data['tokens'] - ours_data['tokens']) / baseline_data['tokens']) * 100
+        lines.append(f"| {name} | {baseline_data['bytes']} | {baseline_data['tokens']} | {baseline_data['ratio']:.2f} | {ours_data['tokens']} | {ours_data['ratio']:.2f} | {relative_diff:+.1f}% |")
+    lines.append("")
+report_markdown = "\n".join(lines)
+get_report().log(section="Tokenizer evaluation", data=[
+    report_markdown,
+])

scripts/tok_train.py

@@ -0,0 +1,106 @@
+"""
+Train a tokenizer using our own BPE Tokenizer library.
+In the style of GPT-4 tokenizer.
+"""
+import os
+import time
+import argparse
+import torch
+from nanochat.tokenizer import RustBPETokenizer
+from nanochat.common import get_base_dir
+from nanochat.dataset import parquets_iter_batched
+
+# -----------------------------------------------------------------------------
+# Parse command line arguments
+
+parser = argparse.ArgumentParser(description='Train a BPE tokenizer')
+parser.add_argument('--max-chars', type=int, default=2_000_000_000, help='Maximum characters to train on (default: 2B)')
+parser.add_argument('--doc-cap', type=int, default=10_000, help='Maximum characters per document (default: 10,000)')
+parser.add_argument('--vocab-size', type=int, default=32768, help='Vocabulary size (default: 32768 = 2^15)')
+args = parser.parse_args()
+print(f"max_chars: {args.max_chars:,}")
+print(f"doc_cap: {args.doc_cap:,}")
+print(f"vocab_size: {args.vocab_size:,}")
+
+# -----------------------------------------------------------------------------
+# Text iterator
+
+def text_iterator():
+    """
+    1) Flatten the batches into a single iterator
+    2) Crop every document to args.doc_cap characters
+    3) Break when we've seen args.max_chars characters
+    """
+    nchars = 0
+    for batch in parquets_iter_batched(split="train"):
+        for doc in batch:
+            doc_text = doc
+            if len(doc_text) > args.doc_cap:
+                doc_text = doc_text[:args.doc_cap]
+            nchars += len(doc_text)
+            yield doc_text
+            if nchars > args.max_chars:
+                return
+text_iter = text_iterator()
+
+# -----------------------------------------------------------------------------
+# Train the tokenizer
+t0 = time.time()
+tokenizer = RustBPETokenizer.train_from_iterator(text_iter, args.vocab_size)
+t1 = time.time()
+train_time = t1 - t0
+print(f"Training time: {train_time:.2f}s")
+
+# -----------------------------------------------------------------------------
+# Save the tokenizer to disk
+base_dir = get_base_dir()
+tokenizer_dir = os.path.join(base_dir, "tokenizer")
+tokenizer.save(tokenizer_dir)
+
+# -----------------------------------------------------------------------------
+# Quick inline sanity check
+test_text = """Hello world! This is a test.
+Numbers: 123, 4567, 89
+Contractions: I'm, you're, it's
+Special chars: @#$%^&*()
+Unicode: 你好世界 🌍"""
+encoded = tokenizer.encode(test_text)
+decoded = tokenizer.decode(encoded)
+assert decoded == test_text
+
+# -----------------------------------------------------------------------------
+# One more thing: we wish to cache a mapping from token id to number of bytes of that token
+# for efficient evaluation of bits per byte. Unlike the typical mean loss, this
+# allows us to report a loss that is invariant to the vocab size of the tokenizer.
+# The bits per byte on the validation set is then one of the primary metrics we care about.
+vocab_size = tokenizer.get_vocab_size()
+special_set = set(tokenizer.get_special_tokens())
+token_strings = [tokenizer.decode([token_id]) for token_id in range(vocab_size)]
+token_bytes = []
+for token_id in range(vocab_size):
+    token_str = token_strings[token_id] # the Python string representation of this token
+    if token_str in special_set:
+        token_bytes.append(0) # special characters are not counted
+    else:
+        id_bytes = len(token_str.encode("utf-8")) # number of bytes that make up this token
+        token_bytes.append(id_bytes)
+token_bytes = torch.tensor(token_bytes, dtype=torch.int32, device='cpu')
+token_bytes_path = os.path.join(tokenizer_dir, "token_bytes.pt")
+with open(token_bytes_path, "wb") as f:
+    torch.save(token_bytes, f)
+print(f"Saved token_bytes to {token_bytes_path}")
+
+# Log to report
+from nanochat.report import get_report
+token_bytes_nonzero = (token_bytes[token_bytes > 0]).to(dtype=torch.float32)
+get_report().log(section="Tokenizer training", data=[
+    vars(args), # argparse command line arguments
+    {"train_time": train_time},
+    {"num_special_tokens": len(special_set)},
+    {
+        "token_bytes_min": int(token_bytes_nonzero.min().item()),
+        "token_bytes_max": int(token_bytes_nonzero.max().item()),
+        "token_bytes_mean": token_bytes_nonzero.mean().item(),
+        "token_bytes_std": token_bytes_nonzero.std().item(),
+    }
+])

tasks/arc.py

@@ -0,0 +1,48 @@
+"""
+The ARC dataset from Allen AI.
+https://huggingface.co/datasets/allenai/ai2_arc
+"""
+
+from datasets import load_dataset
+from tasks.common import Task, render_mc
+
+class ARC(Task):
+
+    def __init__(self, subset, split, **kwargs):
+        super().__init__(**kwargs)
+        assert subset in ["ARC-Easy", "ARC-Challenge"], "ARC subset must be ARC-Easy or ARC-Challenge"
+        assert split in ["train", "validation", "test"], "ARC split must be train|validation|test"
+        self.ds = load_dataset("allenai/ai2_arc", subset, split=split).shuffle(seed=42)
+
+    @property
+    def eval_type(self):
+        return 'categorical'
+
+    def num_examples(self):
+        return len(self.ds)
+
+    def get_example(self, index):
+        row = self.ds[index]
+        question = row["question"] # the question text
+        choices = row["choices"]["text"] # the text of each choice
+        answer_string = row["answerKey"] # e.g. "A", "B", "C", "D"
+        letters = row["choices"]["label"] # e.g. ["A", "B", "C", "D"]
+        assert answer_string in letters, f"ARC answer {answer_string} must be one of {letters}" # sanity check
+        # create and return the Conversation object
+        user_message = render_mc(question, letters, choices)
+        messages = [
+            {"role": "user", "content": user_message},
+            {"role": "assistant", "content": answer_string}
+        ]
+        conversation = {
+            "messages": messages,
+            "letters": letters, # useful during evaluation, so we can narrow and clamp the assistant prediction to one of the letters
+        }
+        return conversation
+
+    def evaluate(self, conversation, assistant_response):
+        # the assert here is not strictly speaking needed, but currently the way we eval, we expect this to be true
+        # I'm going to leave the assert here to prevent footguns, but possibly in the future can remove it.
+        assert assistant_response in conversation['letters'], f"ARC answer {assistant_response} is expected to be one of {conversation['letters']}"
+        assistant_message = conversation['messages'][-1]['content'] # e.g. "A"
+        return assistant_response == assistant_message

tasks/common.py

@@ -0,0 +1,147 @@
+"""
+Base class for all Tasks.
+A Task is basically a dataset of conversations, together with some
+metadata and often also evaluation criteria.
+Example tasks: MMLU, ARC-Easy, ARC-Challenge, GSM8K, HumanEval, SmolTalk.
+"""
+
+import random
+
+class Task:
+    """
+    Base class of a Task. Allows for lightweight slicing of the underlying dataset.
+    """
+
+    def __init__(self, start=0, stop=None, step=1):
+        # allows a lightweight logical view over a dataset
+        assert start >= 0, f"Start must be non-negative, got {start}"
+        assert stop is None or stop >= start, f"Stop should be greater than or equal to start, got {stop} and {start}"
+        assert step >= 1, f"Step must be strictly positive, got {step}"
+        self.start = start
+        self.stop = stop # could be None here
+        self.step = step
+
+    @property
+    def eval_type(self):
+        # one of 'generative' | 'categorical'
+        raise NotImplementedError
+
+    def num_examples(self):
+        raise NotImplementedError
+
+    def get_example(self, index):
+        raise NotImplementedError
+
+    def __len__(self):
+        start = self.start
+        stop = self.num_examples() if self.stop is None else self.stop
+        step = self.step
+        span = stop - start
+        num = (span + step - 1) // step # ceil_div(span, step)
+        assert num >= 0, f"Negative number of examples???: {num}" # prevent footguns
+        return num
+
+    def __getitem__(self, index: int):
+        assert isinstance(index, int), f"Index must be an integer, got {type(index)}"
+        physical_index = self.start + index * self.step
+        conversation = self.get_example(physical_index)
+        return conversation
+
+    def evaluate(self, problem, completion):
+        raise NotImplementedError
+
+
+class TaskMixture(Task):
+    """
+    For SFT Training it becomes useful to train on a mixture of datasets.
+    Fun trick: if you wish to oversample any task, just pass it in multiple times in the list.
+    """
+
+    def __init__(self, tasks, **kwargs):
+        super().__init__(**kwargs)
+        # tasks is a list of Task objects
+        self.tasks = tasks
+        self.lengths = [len(task) for task in self.tasks]
+        self.num_conversations = sum(self.lengths)
+        # Build list of all (task_idx, local_idx) pairs
+        self.index_map = []
+        for task_idx, task_length in enumerate(self.lengths):
+            for local_idx in range(task_length):
+                self.index_map.append((task_idx, local_idx))
+        # Deterministically shuffle to mix tasks throughout training
+        rng = random.Random(42)
+        rng.shuffle(self.index_map)
+        # Note: this is not the most elegant or best solution, but it's ok for now
+
+    def num_examples(self):
+        return self.num_conversations
+
+    def get_example(self, index):
+        """
+        Access conversations according to a deterministic shuffle of all examples.
+        This ensures tasks are mixed throughout training, regardless of dataset size.
+        """
+        assert 0 <= index < self.num_conversations, f"Index {index} out of range for mixture with {self.num_conversations} conversations"
+        task_idx, local_idx = self.index_map[index]
+        return self.tasks[task_idx][local_idx]
+
+
+class TaskSequence(Task):
+    """
+    For SFT Training sometimes we want to sequentially train on a list of tasks.
+    This is useful for cases that require a training curriculum.
+    """
+
+    def __init__(self, tasks, **kwargs):
+        super().__init__(**kwargs)
+        self.tasks = tasks
+        self.lengths = [len(task) for task in self.tasks]
+        self.num_conversations = sum(self.lengths)
+
+    def num_examples(self):
+        return self.num_conversations
+
+    def get_example(self, index):
+        assert 0 <= index < self.num_conversations, f"Index {index} out of range for sequence with {self.num_conversations} conversations"
+        for task_idx, task_length in enumerate(self.lengths):
+            if index < task_length:
+                return self.tasks[task_idx][index]
+            index -= task_length
+
+
+def render_mc(question, letters, choices):
+    """
+    The common multiple choice rendering format we will use.
+
+    Note two important design decisions:
+    1)
+    Bigger models don't care as much, but smaller models prefer to have
+    the letter *after* the choice, which results in better binding.
+    2)
+    There is no whitespace between the delimiter (=) and the letter.
+    This is actually critical because the tokenizer has different token ids
+    for " A" vs. "A". The assistant responses will be just the letter itself,
+    i.e. "A", so it is important that here in the prompt it is the exact same
+    token, i.e. "A" with no whitespace before it. Again, bigger models don't care
+    about this too much, but smaller models do care about some of these details.
+    """
+    query = f"Multiple Choice question: {question}\n"
+    query += "".join([f"- {choice}={letter}\n" for letter, choice in zip(letters, choices)])
+    query += "\nRespond only with the letter of the correct answer."
+    return query
+
+
+if __name__ == "__main__":
+    # very lightweight test of slicing
+    from tasks.mmlu import MMLU
+
+    ds = MMLU(subset="all", split="auxiliary_train")
+    print("Length of MMLU: ", len(ds))
+    ex = ds[5]
+    print("5th example: ", ex)
+
+    ds = MMLU(subset="all", split="auxiliary_train", start=5, stop=10)
+    print("Length of sliced MMLU[5:10]: ", len(ds))
+    print("0th example of sliced MMLU: ", ds[0])
+
+    print("They match: ", ex == ds[0])

tasks/customjson.py

@@ -0,0 +1,65 @@
+"""
+CustomJSON task for loading conversations from JSONL files.
+Each line in the JSONL file should be a JSON array of messages.
+"""
+
+import os
+import json
+from tasks.common import Task
+
+class CustomJSON(Task):
+    """
+    Load conversations from a JSONL file.
+    Each line should be a JSON array of message objects with 'role' and 'content' fields.
+    Example line: [{"role":"user","content":"Hi"},{"role":"assistant","content":"Hello"}]
+    """
+
+    def __init__(self, filepath, **kwargs):
+        super().__init__(**kwargs)
+        self.filepath = filepath
+        self.conversations = []
+
+        # Load all conversations from the JSONL file
+        if not os.path.exists(filepath):
+            # Helpful error message due to recent change. Will be removed in the future.
+            print("-" * 80)
+            print(f"Warning: File {filepath} does not exist")
+            print("HINT (Oct 21 2025)")
+            print("If you recently did a git pull and suddenly see this, it might be due to the new addition of identity conversations")
+            print("See this discussion for more details: https://github.com/karpathy/nanochat/discussions/139")
+            print("Quick fix: simply run the following command to download the file and you're done:")
+            print(f"curl -L -o {filepath} https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl")
+            print("-" * 80)
+
+        else:
+            with open(filepath, 'r', encoding='utf-8') as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:  # skip empty lines
+                        continue
+                    messages = json.loads(line)
+                    # Validate the conversation structure
+                    assert isinstance(messages, list), f"Expected list of messages, got {type(messages)}"
+                    assert len(messages) >= 2, f"Conversation must have at least 2 messages, got {len(messages)}"
+                    # Validate message structure and alternating roles
+                    for i, message in enumerate(messages):
+                        assert "role" in message, f"Message {i} missing 'role' field"
+                        assert "content" in message, f"Message {i} missing 'content' field"
+                        expected_role = "user" if i % 2 == 0 else "assistant"
+                        assert message["role"] == expected_role, f"Message {i} has role {message['role']} but should be {expected_role}"
+                        assert isinstance(message["content"], str), f"Message {i} content must be a string"
+
+                    self.conversations.append(messages)
+
+        self.length = len(self.conversations)
+
+    def num_examples(self):
+        return self.length
+
+    def get_example(self, index):
+        messages = self.conversations[index]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+

tasks/gsm8k.py

@@ -0,0 +1,117 @@
+"""
+GSM8K evaluation.
+https://huggingface.co/datasets/openai/gsm8k
+
+Example problem instance:
+
+Question:
+Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?
+Answer:
+Weng earns 12/60 = $<<12/60=0.2>>0.2 per minute.
+Working 50 minutes, she earned 0.2 x 50 = $<<0.2*50=10>>10.
+#### 10
+
+Notice that GSM8K uses tool calls inside << >> tags.
+"""
+
+import re
+from datasets import load_dataset
+from tasks.common import Task
+
+
+GSM_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
+def extract_answer(completion):
+    """
+    Extract the numerical answer after #### marker.
+    Follows official code for normalization:
+    https://github.com/openai/grade-school-math/blob/3101c7d5072418e28b9008a6636bde82a006892c/grade_school_math/dataset.py#L28
+    """
+    match = GSM_RE.search(completion)
+    if match:
+        match_str = match.group(1).strip()
+        match_str = match_str.replace(",", "")
+        return match_str
+    return None
+
+
+class GSM8K(Task):
+
+    def __init__(self, subset, split, **kwargs):
+        super().__init__(**kwargs)
+        assert subset in ["main", "socratic"], "GSM8K subset must be main|socratic"
+        assert split in ["train", "test"], "GSM8K split must be train|test"
+        self.ds = load_dataset("openai/gsm8k", subset, split=split).shuffle(seed=42)
+
+    @property
+    def eval_type(self):
+        return 'generative'
+
+    def num_examples(self):
+        return len(self.ds)
+
+    def get_example(self, index):
+        """ Get a single problem from the dataset. """
+        row = self.ds[index]
+        question = row['question'] # string of the question prompt
+        answer = row['answer'] # string of the full solution and the answer after #### marker
+        # Create and return the Conversation object
+        # This is tricky because GSM8K uses tool calls, which we need to parse here.
+        assistant_message_parts = []
+        parts = re.split(r'(<<[^>]+>>)', answer)
+        for part in parts:
+            if part.startswith('<<') and part.endswith('>>'):
+                # This is a calculator tool call
+                inner = part[2:-2]  # Remove << >>
+                # Split on = to get expression and result
+                if '=' in inner:
+                    expr, result = inner.rsplit('=', 1)
+                else:
+                    expr, result = inner, ""
+                # Add the tool call as a part
+                assistant_message_parts.append({"type": "python", "text": expr})
+                # Add the result as a part
+                assistant_message_parts.append({"type": "python_output", "text": result})
+            else:
+                # Regular text in between tool calls
+                assistant_message_parts.append({"type": "text", "text": part})
+        # Now put it all together
+        messages = [
+            {"role": "user", "content": question}, # note: simple string
+            {"role": "assistant", "content": assistant_message_parts}, # note: list of parts (as dicts)
+        ]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+
+    def evaluate(self, conversation, assistant_response):
+        """
+        Given (conversation, completion), return evaluation outcome (0 = wrong, 1 = correct)
+        Note that:
+        - the conversation has both user AND assistant message (containing the ground truth answer)
+        - the assistant_response is usually the alternative assistant message achieved via sampling
+
+        TODO: Technically, assistant_response should be a Message (either a string or a list of parts)
+              We can handle this later possibly. For now just assume string.
+        """
+        assert isinstance(assistant_response, str), "Assuming simple string response for now"
+        # First extract the ground truth answer
+        assistant_message = conversation['messages'][-1]
+        assert assistant_message['role'] == "assistant", "Last message must be from the Assistant"
+        assert isinstance(assistant_message['content'], list), "This is expected to be a list of parts"
+        last_text_part = assistant_message['content'][-1]['text'] # this contains the final answer in GSM8K
+        # Extract both the ground truth answer and the predicted answer
+        ref_num = extract_answer(last_text_part)
+        pred_num = extract_answer(assistant_response)
+        # Compare and return the success as int
+        is_correct = int(pred_num == ref_num)
+        return is_correct
+
+    def reward(self, conversation, assistant_response):
+        """
+        Used during RL. To keep things simple, just re-use the evaluation above.
+        Later this could be made more complex (e.g. format matching etc.)
+        """
+        is_correct = self.evaluate(conversation, assistant_response)
+        is_correct_float = float(is_correct)
+        return is_correct_float

tasks/humaneval.py

@@ -0,0 +1,97 @@
+"""
+Evaluate the Chat model on HumanEval dataset.
+Btw this dataset is a misnomer and has nothing to do with humans.
+It is a coding benchmark.
+"""
+
+import re
+from datasets import load_dataset
+from nanochat.execution import execute_code
+from tasks.common import Task
+
+def extract_imports(prompt):
+    """Extract import statements from the beginning of a code block."""
+    imports = []
+    for line in prompt.split('\n'):
+        stripped = line.strip()
+        if stripped.startswith('import ') or stripped.startswith('from '):
+            imports.append(stripped)
+        elif stripped and not stripped.startswith('#'):
+            # Stop at first non-import, non-comment line
+            break
+    return '\n'.join(imports)
+
+def extract_program(completion):
+    """
+    Extract Python code from LLM completion.
+
+    Handles various output formats:
+    - Code wrapped in ```python ... ``` or ``` ... ``` blocks
+    - Plain code without markdown blocks
+    - Extra text before/after code blocks
+
+    Returns the first code block if found, otherwise returns the whole completion.
+    """
+    # Try to find markdown code blocks (```python or just ```)
+    # Match ```python\n...\n``` or ```\n...\n```
+    pattern = r'```(?:python)?\s*\n(.*?)\n```'
+    matches = re.findall(pattern, completion, re.DOTALL)
+
+    if matches:
+        # Return the first code block found
+        return matches[0].strip()
+
+    # No code blocks found, return the whole completion
+    return completion.strip()
+
+class HumanEval(Task):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.ds = load_dataset("openai/openai_humaneval", split="test").shuffle(seed=42)
+
+    @property
+    def eval_type(self):
+        return 'generative'
+
+    def num_examples(self):
+        return len(self.ds)
+
+    def get_example(self, index):
+        """ Get a single problem from the dataset. """
+        row = self.ds[index]
+        prompt = row['prompt'] # prompts in HumanEval are the beginning of the program
+        solution = row['canonical_solution'] # the correct continuation of the program
+        entry_point = row['entry_point'] # the function to check
+        test = row['test'] # the test cases
+        complete_solution = f"{prompt}\n{solution}"
+        messages = [
+            {"role": "user", "content": prompt},
+            {"role": "assistant", "content": complete_solution},
+        ]
+        conversation = {
+            "messages": messages,
+            "entry_point": entry_point, # needed during evaluation
+            "test": test, # needed during evaluation
+        }
+        return conversation
+
+    def evaluate(self, conversation, completion):
+        """ Given (conversation, completion), return boolean success of the completion. """
+        # the prompt will contain the imports and the function signature
+        imports = extract_imports(conversation['messages'][0]['content'])
+        # the completion will usually contain the whole function
+        # but not always with the needed imports, so we manually append them
+        completion_code = extract_program(completion)
+        program = (
+            imports
+            + "\n\n"
+            + completion_code
+            + "\n\n"
+            + conversation['test']
+            + "\n"
+            + f"check({conversation['entry_point']})"
+        )
+        result = execute_code(program)
+        success = result.success
+        return success

tasks/mmlu.py

@@ -0,0 +1,55 @@
+"""
+The MMLU dataset.
+https://huggingface.co/datasets/cais/mmlu
+"""
+
+from datasets import load_dataset
+from tasks.common import Task, render_mc
+
+class MMLU(Task):
+
+    letters = ('A', 'B', 'C', 'D')
+    groups = ('abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions')
+
+    def __init__(self, subset, split, **kwargs):
+        super().__init__(**kwargs)
+        assert subset in ["all"], f"subset {subset} must be all"
+        assert split in ["auxiliary_train", "validation", "dev", "test"], f"split {split} must be auxiliary_train|validation|dev|test"
+        self.subset = subset
+        self.split = split
+        self.ds = load_dataset("cais/mmlu", subset, split=split).shuffle(seed=42)
+
+    @property
+    def eval_type(self):
+        return 'categorical'
+
+    def num_examples(self):
+        return len(self.ds)
+
+    def get_example(self, index):
+        row = self.ds[index]
+        question = row["question"] # the question text
+        choices = row["choices"] # the text of each choice
+        answer = row["answer"] # index of the answer, e.g. 0,1,2,3 (for A,B,C,D)
+        subject = row["subject"] # e.g. "college_biology", "college_chemistry", etc.
+        assert len(choices) == 4, "MMLU should have 4 choices"
+        # create and return the Conversation object
+        user_message = render_mc(question, self.letters, choices)
+        assistant_message = self.letters[answer]
+        messages = [
+            {"role": "user", "content": user_message},
+            {"role": "assistant", "content": assistant_message}
+        ]
+        conversation = {
+            "messages": messages,
+            "subject": subject, # might be useful later for grouping metrics by subject
+            "letters": self.letters, # useful during evaluation, so we can narrow and clamp the assistant prediction to one of the letters
+        }
+        return conversation
+
+    def evaluate(self, conversation, assistant_response):
+        # the assert here is not strictly speaking needed, but currently the way we eval, we expect this to be true
+        # I'm going to leave the assert here to prevent footguns, but possibly in the future can remove it.
+        assert assistant_response in self.letters, f"MMLU answer {assistant_response} is expected to be one of {self.letters}"
+        assistant_message = conversation['messages'][-1]['content'] # e.g. "A"
+        return assistant_response == assistant_message

tasks/smoltalk.py

@@ -0,0 +1,46 @@
+"""
+SmolTalk by HuggingFace. Good "general" conversational dataset.
+https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk
+We use the "smol" version, which is more appropriate for smaller models.
+"""
+
+from datasets import load_dataset
+from tasks.common import Task
+
+class SmolTalk(Task):
+    """ smol-smoltalk dataset. train is 460K rows, test is 24K rows. """
+
+    def __init__(self, split, **kwargs):
+        super().__init__(**kwargs)
+        assert split in ["train", "test"], "SmolTalk split must be train|test"
+        self.ds = load_dataset("HuggingFaceTB/smol-smoltalk", split=split).shuffle(seed=42)
+        self.length = len(self.ds)
+
+    def num_examples(self):
+        return self.length
+
+    def get_example(self, index):
+        row = self.ds[index]
+        messages = row["messages"]
+        # ---------------------------------------------------------------------
+        # sanity checking asserts here
+        # TODO: we could remove these asserts later, for now just don't want any footguns
+        # there is an optional system message at the beginning
+        assert len(messages) >= 1
+        first_message = messages[0]
+        if first_message["role"] == "system":
+            rest_messages = messages[1:] # optional system message is OK
+        else:
+            rest_messages = messages
+        assert len(rest_messages) >= 2, "SmolTalk messages must have at least 2 messages"
+        for i, message in enumerate(rest_messages):
+            # user and assistant alternate as user,assistant,user,assistant,...
+            expected_role = "user" if i % 2 == 0 else "assistant"
+            assert message["role"] == expected_role, f"Message {i} has role {message['role']} but should be {expected_role}"
+            assert isinstance(message["content"], str), "Content must be a string"
+        # ---------------------------------------------------------------------
+        # create and return the Conversation object (ok to emit the system message too)
+        conversation = {
+            "messages": messages,
+        }
+        return conversation

tasks/spellingbee.py

@@ -0,0 +1,307 @@
+"""
+Task intended to make nanochat better in spelling and counting, for example:
+
+"How many r are in strawberry?" -> 3
+
+An interesting part of this task is that we will get the assistant to
+solve the problem using a combination of manual counting and Python.
+This is a good problem solving "instinct" to mix into the model and RL
+may further refine it to trust one over the other. If we were extra fancy
+(which we could/should be) we'd add small errors here and there to allow
+the model also learn recoveries. We can do this in future versions.
+
+There are two tasks in this file:
+1. SpellingBee: Counting the number of occurrences of a letter in a word
+2. SimpleSpelling: Simply spelling words
+
+(1) is the goal, but (2) exists as a highly condensed version of the part
+that makes (1) difficult, which is word spelling. This is non-trivial for an
+LLM because it has to learn how every token (a little semantic chunk/atom)
+maps to the sequence of individual characters that make it up. Larger models
+learn this eventually on their own, but if we want this capability to exist
+in smaller models, we have to actively encourage it by over-representing it
+in the training data. SFT is a good place to do this.
+
+To preview a few example conversations, run:
+python -m tasks.spellingbee
+"""
+
+import re
+import random
+from tasks.common import Task
+from nanochat.common import download_file_with_lock
+
+# Letters of the alphabet
+LETTERS = "abcdefghijklmnopqrstuvwxyz"
+# A list of 370K English words of large variety
+WORD_LIST_URL = "https://raw.githubusercontent.com/dwyl/english-words/refs/heads/master/words_alpha.txt"
+# A number bigger than 370K to separate train and test random seeds
+TEST_RANDOM_SEED_OFFSET = 10_000_000
+
+# Identical to gsm8k's answer extraction
+ANSWER_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
+def extract_answer(completion):
+    """
+    Extract the numerical answer after #### marker.
+    """
+    match = ANSWER_RE.search(completion)
+    if match:
+        match_str = match.group(1).strip()
+        match_str = match_str.replace(",", "")
+        return match_str
+    return None
+
+# User message templates for data augmentation
+USER_MSG_TEMPLATES = [
+    "How many {letter} are in the word {word}",
+    "How many {letter} are in {word}",
+    "Count the number of {letter} in {word}",
+    "How many times does {letter} appear in {word}",
+    "What's the count of {letter} in {word}",
+    "In the word {word}, how many {letter} are there",
+    "How many letter {letter} are in the word {word}",
+    "Count how many {letter} appear in {word}",
+    "Tell me the number of {letter} in {word}",
+    "How many occurrences of {letter} are in {word}",
+    "Find the count of {letter} in {word}",
+    "Can you count the {letter} letters in {word}",
+    "What is the frequency of {letter} in {word}",
+    "How many {letter}s are in {word}",
+    "How many {letter}'s are in {word}",
+    "Count all the {letter} in {word}",
+    "How many times is {letter} in {word}",
+    "Number of {letter} in {word}",
+    "Total count of {letter} in {word}",
+    "How many {letter} does {word} have",
+    "How many {letter} does {word} contain",
+    "What's the number of {letter} in {word}",
+    "{word} has how many {letter}",
+    "In {word}, count the {letter}",
+    "How many {letter} appear in {word}",
+    "Count the {letter} in {word}",
+    "Give me the count of {letter} in {word}",
+    "How many instances of {letter} in {word}",
+    "Show me how many {letter} are in {word}",
+    "Calculate the number of {letter} in {word}",
+    # Spanish
+    "¿Cuántas {letter} hay en {word}?",
+    "¿Cuántas veces aparece {letter} en {word}?",
+    "Cuenta las {letter} en {word}",
+    "¿Cuántas letras {letter} tiene {word}?",
+    # Chinese (Simplified)
+    "{word}中有多少个{letter}",
+    "{word}里有几个{letter}",
+    "数一下{word}中的{letter}",
+    "{word}这个词里有多少{letter}",
+    # Korean
+    "{word}에 {letter}가 몇 개 있나요",
+    "{word}에서 {letter}의 개수는",
+    "{word}에 {letter}가 몇 번 나오나요",
+    "{word}라는 단어에 {letter}가 몇 개",
+    # French
+    "Combien de {letter} dans {word}",
+    "Combien de fois {letter} apparaît dans {word}",
+    "Compte les {letter} dans {word}",
+    # German
+    "Wie viele {letter} sind in {word}",
+    "Wie oft kommt {letter} in {word} vor",
+    "Zähle die {letter} in {word}",
+    # Japanese
+    "{word}に{letter}は何個ありますか",
+    "{word}の中に{letter}がいくつ",
+    "{word}に{letter}が何回出てくる",
+]
+
+class SpellingBee(Task):
+
+    def __init__(self, size=1000, split="train", **kwargs):
+        super().__init__(**kwargs)
+        assert split in ["train", "test"], "SpellingBee split must be train|test"
+        self.size = size
+        self.split = split
+        filename = WORD_LIST_URL.split("/")[-1]
+        word_list_path = download_file_with_lock(WORD_LIST_URL, filename)
+        with open(word_list_path, 'r', encoding='utf-8') as f:
+            words = [line.strip() for line in f]
+        self.words = words
+
+    @property
+    def eval_type(self):
+        return 'generative'
+
+    def num_examples(self):
+        return self.size
+
+    def get_example(self, index):
+        seed = index if self.split == 'train' else TEST_RANDOM_SEED_OFFSET + index
+        rng = random.Random(seed)
+
+        # pick a random word
+        word = rng.choice(self.words)
+        # pick a letter from it (90%) or a random letter (10%)
+        letter = rng.choice(word) if rng.random() < 0.9 else rng.choice(LETTERS)
+
+        # get the correct answer by simply counting
+        count = word.count(letter)
+
+        # create a user message, with a bunch of variations as data augmentation
+        template = rng.choice(USER_MSG_TEMPLATES)
+        # 30% chance to lowercase the template (lazy people don't use shift)
+        if rng.random() < 0.3:
+            template = template.lower()
+        quote_options = ['', "'", '"']
+        letter_quote = rng.choice(quote_options) # is the letter quoted?
+        word_quote = rng.choice(quote_options) # is the word quoted?
+        letter_wrapped = f"{letter_quote}{letter}{letter_quote}"
+        word_wrapped = f"{word_quote}{word}{word_quote}"
+        user_msg = template.format(letter=letter_wrapped, word=word_wrapped)
+        if rng.random() < 0.5: # 50% of people don't even use question marks
+            user_msg += "?"
+
+        # Now create the ideal assistant response - build as parts (text + tool calls)
+        assistant_parts = []
+        word_letters = ",".join(list(word))
+        manual_text = f"""We are asked to find the number '{letter}' in the word '{word}'. Let me try a manual approach first.
+
+First spell the word out:
+{word}:{word_letters}
+
+Then count the occurrences of '{letter}':
+"""
+        # Little simulated loop of the solution process
+        # TODO: This is where the fun starts, we could simulate cute little mistakes
+        # and get the model to review its work and recover from them.
+        # You might of course hope this could arise in RL too, but realistically you'd want to help it out a bit.
+        running_count = 0
+        for i, char in enumerate(word, 1):
+            if char == letter:
+                running_count += 1
+                # note: there deliberately cannot be a space here between i and char
+                # because this would create a different token! (e.g. " a" and "a" are different tokens)
+                manual_text += f"{i}:{char} hit! count={running_count}\n"
+            else:
+                manual_text += f"{i}:{char}\n"
+
+        manual_text += f"\nThis gives us {running_count}."
+        assistant_parts.append({"type": "text", "text": manual_text})
+        # Part 2: Python verification
+        assistant_parts.append({"type": "text", "text": "\n\nLet me double check this using Python:\n\n"})
+        # Part 3: Python tool call
+        python_expr = f"'{word}'.count('{letter}')"
+        assistant_parts.append({"type": "python", "text": python_expr})
+        # Part 4: Python output
+        assistant_parts.append({"type": "python_output", "text": str(count)})
+        # Part 5: Final answer
+        assistant_parts.append({"type": "text", "text": f"\n\nPython gives us {count}.\n\nMy final answer is:\n\n#### {count}"})
+
+        # return the full conversation
+        messages = [
+            {"role": "user", "content": user_msg},
+            {"role": "assistant", "content": assistant_parts}
+        ]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+
+    def evaluate(self, conversation, assistant_response):
+        """
+        Given (conversation, completion), return evaluation outcome (0 = wrong, 1 = correct)
+        Identical to gsm8k's evaluation.
+        """
+        assert isinstance(assistant_response, str), "Assuming simple string response for now"
+        # First extract the ground truth answer from the conversation
+        assistant_message = conversation['messages'][-1]
+        assert assistant_message['role'] == "assistant", "Last message must be from the Assistant"
+        assert isinstance(assistant_message['content'], list), "This is expected to be a list of parts"
+        # The last text part contains the final answer with ####
+        last_text_part = assistant_message['content'][-1]['text']
+        # Extract both the ground truth answer and the predicted answer
+        ref_num = extract_answer(last_text_part)
+        pred_num = extract_answer(assistant_response)
+        # Compare and return the success as int
+        is_correct = int(pred_num == ref_num)
+        return is_correct
+
+    def reward(self, conversation, assistant_response):
+        """ Use simple 0-1 reward just like gsm8k."""
+        is_correct = self.evaluate(conversation, assistant_response)
+        is_correct_float = float(is_correct)
+        return is_correct_float
+
+
+class SimpleSpelling(Task):
+    """Much simpler task designed to get the model to just practice spelling words."""
+
+    def __init__(self, size=1000, split="train", **kwargs):
+        super().__init__(**kwargs)
+        assert split in ["train", "test"], "SpellingBee split must be train|test"
+        self.size = size
+        self.split = split
+        filename = WORD_LIST_URL.split("/")[-1]
+        word_list_path = download_file_with_lock(WORD_LIST_URL, filename)
+        with open(word_list_path, 'r', encoding='utf-8') as f:
+            words = [line.strip() for line in f]
+        rng = random.Random(42)
+        rng.shuffle(words) # use a different word order than the SpellingBee task
+        self.words = words
+
+    @property
+    def eval_type(self):
+        return 'generative'
+
+    def num_examples(self):
+        return self.size
+
+    def get_example(self, index):
+        seed = index if self.split == 'train' else TEST_RANDOM_SEED_OFFSET + index
+        rng = random.Random(seed)
+        # pick a random word
+        word = rng.choice(self.words)
+        word_letters = ",".join(list(word))
+        # return the full conversation
+        messages = [
+            {"role": "user", "content": f"Spell the word: {word}"},
+            {"role": "assistant", "content": f"{word}:{word_letters}"}
+        ]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+
+
+if __name__ == "__main__":
+
+    # preview the SpellingBee task, first 10 examples
+    task = SpellingBee()
+    for i in range(10):
+        ex = task.get_example(i)
+        print("=" * 100)
+        print(ex['messages'][0]['content'])
+        print("-" * 100)
+        # Assistant content is now a list of parts
+        assistant_parts = ex['messages'][1]['content']
+        for part in assistant_parts:
+            if part['type'] == 'text':
+                print(part['text'], end='')
+            elif part['type'] == 'python':
+                print(f"<<{part['text']}=", end='')
+            elif part['type'] == 'python_output':
+                print(f"{part['text']}>>", end='')
+        print()
+        print("-" * 100)
+
+    # # preview the SimpleSpelling task, first 10 examples
+    # task = SimpleSpelling()
+    # for i in range(10):
+    #     ex = task.get_example(i)
+    #     print("=" * 100)
+    #     print(ex['messages'][0]['content'])
+    #     print("-" * 100)
+    #     print(ex['messages'][1]['content'])
+
+    # # also scrutinize the tokenization (last example only)
+    # from nanochat.tokenizer import get_tokenizer
+    # tokenizer = get_tokenizer()
+    # ids, mask = tokenizer.render_conversation(ex)
+    # print(tokenizer.visualize_tokenization(ids, mask, with_token_id=True))

tests/test_attention_fallback.py

@@ -0,0 +1,372 @@
+"""
+Test Flash Attention unified interface - verify FA3 and SDPA produce identical results.
+
+Run: python -m pytest tests/test_attention_fallback.py -v -s
+
+Note on test structure:
+    Tests are split into two classes due to dtype/device constraints:
+
+    1. TestFA3VsSDPA: Comparison tests that run both FA3 and SDPA on the same inputs
+       and verify they produce identical results. These require a Hopper GPU (FA3 only
+       works on sm90+) and use bfloat16 (FA3 doesn't support float32).
+
+    2. TestSDPAOnly: Tests that only exercise the SDPA fallback path. These can run
+       on any device (CUDA, CPU, MPS) with the appropriate dtype for that device.
+"""
+import torch
+import pytest
+import nanochat.flash_attention as fa_module
+from nanochat.flash_attention import flash_attn, HAS_FA3
+from nanochat.engine import KVCache
+
+
+def set_impl(impl):
+    """Set the implementation override ('fa3', 'sdpa', or None for auto) and re-resolve USE_FA3."""
+    fa_module._override_impl = impl
+    fa_module.USE_FA3 = fa_module._resolve_use_fa3()
+
+
+def run_both_impls(fn):
+    """Run a function with both FA3 and SDPA, return both outputs."""
+    set_impl('fa3')
+    out_fa3 = fn()
+    set_impl('sdpa')
+    out_sdpa = fn()
+    set_impl(None)  # reset
+    return out_fa3, out_sdpa
+
+
+def assert_close(t1, t2, name, atol=1e-2, rtol=1e-2):
+    """Assert two tensors are close, with helpful error message."""
+    max_diff = (t1 - t2).abs().max().item()
+    mean_diff = (t1 - t2).abs().mean().item()
+    assert torch.allclose(t1, t2, atol=atol, rtol=rtol), \
+        f"{name}: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}"
+    return max_diff, mean_diff
+
+
+# =============================================================================
+# FA3 vs SDPA comparison tests (require Hopper GPU)
+# =============================================================================
+@pytest.mark.skipif(not HAS_FA3, reason="FA3 required to compare implementations")
+class TestFA3VsSDPA:
+    """Compare FA3 and SDPA produce identical results. Requires Hopper GPU."""
+
+    DEVICE = "cuda"
+    DTYPE = torch.bfloat16
+
+    def test_basic_causal(self):
+        """Basic causal attention."""
+        B, T, H, D = 2, 64, 4, 32
+        q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            return flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(T, 0))
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "basic_causal")
+        print(f"basic_causal: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_full_context(self):
+        """Full context (window_size=-1)."""
+        B, T, H, D = 2, 128, 4, 32
+        q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            return flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(-1, -1))
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "full_context")
+        print(f"full_context: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_sliding_window(self):
+        """Sliding window attention."""
+        B, T, H, D = 2, 128, 4, 32
+        window = 32
+        q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            return flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(window, 0))
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "sliding_window")
+        print(f"sliding_window: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_gqa(self):
+        """Group Query Attention (fewer KV heads than Q heads)."""
+        B, T, D = 2, 64, 32
+        n_heads = 8
+        n_kv_heads = 2
+
+        q = torch.randn(B, T, n_heads, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T, n_kv_heads, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T, n_kv_heads, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            return flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(T, 0))
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "gqa")
+        print(f"gqa: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_larger_model(self):
+        """Larger dimensions closer to real model."""
+        B, T, H, D = 4, 256, 12, 64
+        q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            return flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(-1, -1))
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "larger_model")
+        print(f"larger_model: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_kvcache_prefill(self):
+        """Test prefill (inserting multiple tokens into empty cache)."""
+        B, T_max, H, D = 2, 64, 4, 32
+        T_prefill = 16
+
+        q = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            k_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            v_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            cache_seqlens = torch.zeros(B, dtype=torch.int32, device=self.DEVICE)
+            return flash_attn.flash_attn_with_kvcache(
+                q, k_cache, v_cache, k=k, v=v,
+                cache_seqlens=cache_seqlens,
+                causal=True, window_size=(T_max, 0)
+            )
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "prefill")
+        print(f"prefill: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_kvcache_single_token(self):
+        """Test single token generation (cache already has content)."""
+        B, T_max, H, D = 2, 64, 4, 32
+        T_prefill = 16
+
+        k_init = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_init = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        q_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            k_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            v_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            k_cache[:, :T_prefill, :, :] = k_init
+            v_cache[:, :T_prefill, :, :] = v_init
+            cache_seqlens = torch.full((B,), T_prefill, dtype=torch.int32, device=self.DEVICE)
+            return flash_attn.flash_attn_with_kvcache(
+                q_single, k_cache, v_cache, k=k_single, v=v_single,
+                cache_seqlens=cache_seqlens,
+                causal=True, window_size=(T_max, 0)
+            )
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "single_token")
+        print(f"single_token: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_kvcache_single_token_sliding_window(self):
+        """Test single token decode with sliding window smaller than cache size.
+
+        This catches the bug where SDPA ignores window_size during Tq=1 decode.
+        When window < Tk, FA3 only attends to the last (window+1) tokens,
+        but SDPA was attending to all cached tokens.
+        """
+        B, T_max, H, D = 2, 64, 4, 32
+        T_prefill = 32  # Enough tokens to exceed window
+        window = 8      # Window SMALLER than cache size
+
+        k_init = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_init = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        q_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            k_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            v_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            k_cache[:, :T_prefill, :, :] = k_init
+            v_cache[:, :T_prefill, :, :] = v_init
+            cache_seqlens = torch.full((B,), T_prefill, dtype=torch.int32, device=self.DEVICE)
+            return flash_attn.flash_attn_with_kvcache(
+                q_single, k_cache, v_cache, k=k_single, v=v_single,
+                cache_seqlens=cache_seqlens,
+                causal=True, window_size=(window, 0)  # window=8 < Tk=33
+            )
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "single_token_sliding_window")
+        print(f"single_token_sliding_window: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+    def test_backward_gradients_match(self):
+        """Verify gradients are similar between FA3 and SDPA."""
+        B, T, H, D = 2, 32, 4, 16
+
+        q_data = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k_data = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_data = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            q = q_data.clone().requires_grad_(True)
+            k = k_data.clone().requires_grad_(True)
+            v = v_data.clone().requires_grad_(True)
+            y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(T, 0))
+            loss = y.sum()
+            loss.backward()
+            return y.detach(), q.grad.detach(), k.grad.detach(), v.grad.detach()
+
+        set_impl('fa3')
+        y_fa3, q_grad_fa3, k_grad_fa3, v_grad_fa3 = run()
+        set_impl('sdpa')
+        y_sdpa, q_grad_sdpa, k_grad_sdpa, v_grad_sdpa = run()
+        set_impl(None)
+
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "backward_output")
+        print(f"backward_output: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+        max_diff, mean_diff = assert_close(q_grad_fa3, q_grad_sdpa, "q_grad", atol=0.05, rtol=0.05)
+        print(f"q_grad: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+        max_diff, mean_diff = assert_close(k_grad_fa3, k_grad_sdpa, "k_grad", atol=0.05, rtol=0.05)
+        print(f"k_grad: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+        max_diff, mean_diff = assert_close(v_grad_fa3, v_grad_sdpa, "v_grad", atol=0.05, rtol=0.05)
+        print(f"v_grad: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
+
+# =============================================================================
+# SDPA-only tests (run on any device)
+# =============================================================================
+class TestSDPAOnly:
+    """Test SDPA fallback works correctly. Runs on any device."""
+
+    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+    DTYPE = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+    def test_basic_forward(self):
+        """Test SDPA forward pass produces valid output."""
+        set_impl('sdpa')
+        B, T, H, D = 2, 64, 4, 32
+        q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(T, 0))
+
+        assert y.shape == (B, T, H, D)
+        assert not torch.isnan(y).any(), "Output contains NaN"
+        set_impl(None)
+
+    def test_backward(self):
+        """Test gradients flow through SDPA."""
+        set_impl('sdpa')
+        B, T, H, D = 2, 32, 4, 16
+        q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE, requires_grad=True)
+        k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE, requires_grad=True)
+        v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE, requires_grad=True)
+
+        y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=(T, 0))
+        loss = y.sum()
+        loss.backward()
+
+        assert q.grad is not None, "No gradient for q"
+        assert k.grad is not None, "No gradient for k"
+        assert v.grad is not None, "No gradient for v"
+        assert not torch.isnan(q.grad).any(), "NaN in q gradient"
+        set_impl(None)
+
+    def test_kvcache(self):
+        """Test SDPA with KV cache."""
+        set_impl('sdpa')
+        B, T_max, H, D = 2, 64, 4, 32
+        n_layers = 1
+
+        cache = KVCache(
+            batch_size=B, num_heads=H, seq_len=T_max, head_dim=D,
+            num_layers=n_layers, device=self.DEVICE, dtype=self.DTYPE
+        )
+        k_cache, v_cache = cache.get_layer_cache(0)
+
+        # Prefill
+        T_prefill = 16
+        q = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        y = flash_attn.flash_attn_with_kvcache(
+            q, k_cache, v_cache, k=k, v=v,
+            cache_seqlens=cache.cache_seqlens,
+            causal=True, window_size=(T_max, 0)
+        )
+        cache.advance(T_prefill)
+
+        assert y.shape == (B, T_prefill, H, D)
+        assert cache.get_pos() == T_prefill
+
+        # Generate single token
+        q_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        y_single = flash_attn.flash_attn_with_kvcache(
+            q_single, k_cache, v_cache, k=k_single, v=v_single,
+            cache_seqlens=cache.cache_seqlens,
+            causal=True, window_size=(T_max, 0)
+        )
+        cache.advance(1)
+
+        assert y_single.shape == (B, 1, H, D)
+        assert cache.get_pos() == T_prefill + 1
+        set_impl(None)
+
+
+# =============================================================================
+# Override mechanism tests
+# =============================================================================
+class TestOverrideMechanism:
+    """Test that the override mechanism works correctly."""
+
+    @pytest.mark.skipif(not HAS_FA3, reason="FA3 required")
+    def test_override_fa3(self):
+        """Test that override='fa3' uses FA3."""
+        set_impl('fa3')
+        assert fa_module.USE_FA3 == True
+        set_impl(None)
+
+    def test_override_sdpa(self):
+        """Test that override='sdpa' uses SDPA."""
+        set_impl('sdpa')
+        assert fa_module.USE_FA3 == False
+        set_impl(None)
+
+    def test_override_auto(self):
+        """Test that override=None uses auto-detection."""
+        set_impl(None)
+        assert fa_module.USE_FA3 == HAS_FA3
+
+
+if __name__ == "__main__":
+    print(f"PyTorch version: {torch.__version__}")
+    print(f"CUDA available: {torch.cuda.is_available()}")
+    if torch.cuda.is_available():
+        print(f"CUDA device: {torch.cuda.get_device_name()}")
+        major, minor = torch.cuda.get_device_capability()
+        print(f"Compute capability: {major}.{minor}")
+    print(f"HAS_FA3: {HAS_FA3}")
+    print()
+
+    pytest.main([__file__, "-v", "-s"])

tests/test_engine.py

@@ -0,0 +1,267 @@
+"""
+Test Engine class. Example run:
+
+python -m pytest tests/test_engine.py -v
+"""
+
+import torch
+from nanochat.engine import KVCache, Engine
+from dataclasses import dataclass
+
+
+# -----------------------------------------------------------------------------
+# Mock classes for testing Engine without loading a real model
+
+@dataclass
+class MockConfig:
+    """Minimal config for Engine tests."""
+    n_kv_head: int = 4
+    n_head: int = 4
+    n_embd: int = 64
+    n_layer: int = 2
+    sequence_len: int = 128
+
+
+class MockModel:
+    """
+    Mock model that returns uniform logits over the vocab.
+    This ensures that with temperature > 0, different samples should
+    (with very high probability) produce different tokens.
+    """
+    def __init__(self, vocab_size=262):  # 256 bytes + 6 special tokens
+        self.vocab_size = vocab_size
+        self.config = MockConfig()
+        self._device = torch.device("cpu")
+
+    def get_device(self):
+        return self._device
+
+    def forward(self, ids, kv_cache=None):
+        """Return uniform logits so sampling is spread across vocab."""
+        B, T = ids.shape
+        # With FA3, flash_attn_with_kvcache updates cache in-place and we advance position
+        if kv_cache is not None:
+            kv_cache.advance(T)
+        # Uniform logits -> equal probability for all tokens
+        logits = torch.zeros(B, T, self.vocab_size)
+        return logits
+
+
+class ByteTokenizer:
+    """
+    Simple byte-level tokenizer for testing.
+    Tokens 0-255 are raw bytes, 256+ are special tokens.
+    """
+    def __init__(self):
+        # Special tokens start at 256
+        self._special_tokens = {
+            "<|python_start|>": 256,
+            "<|python_end|>": 257,
+            "<|output_start|>": 258,
+            "<|output_end|>": 259,
+            "<|assistant_end|>": 260,
+            "<|bos|>": 261,
+        }
+        self._bos = 261
+
+    def encode_special(self, s):
+        return self._special_tokens[s]
+
+    def get_bos_token_id(self):
+        return self._bos
+
+    def encode(self, s, prepend=None):
+        tokens = list(s.encode("utf-8"))  # bytes 0-255
+        if prepend is not None:
+            tokens = [prepend] + tokens
+        return tokens
+
+    def decode(self, tokens):
+        # Filter out special tokens before decoding
+        byte_tokens = [t for t in tokens if t < 256]
+        return bytes(byte_tokens).decode("utf-8", errors="replace")
+
+def test_kv_cache_basic():
+    """Test basic KVCache functionality for FA3."""
+    batch_size = 2
+    num_heads = 3
+    seq_len = 64
+    head_dim = 5
+    num_layers = 6
+
+    kv_cache = KVCache(
+        batch_size=batch_size,
+        num_heads=num_heads,
+        seq_len=seq_len,
+        head_dim=head_dim,
+        num_layers=num_layers,
+        device="cpu",
+        dtype=torch.float32,
+    )
+
+    # Check initial state
+    assert kv_cache.get_pos() == 0
+    assert kv_cache.k_cache.shape == (num_layers, batch_size, seq_len, num_heads, head_dim)
+    assert kv_cache.v_cache.shape == (num_layers, batch_size, seq_len, num_heads, head_dim)
+
+    # Test advance
+    kv_cache.advance(10)
+    assert kv_cache.get_pos() == 10
+
+    kv_cache.advance(5)
+    assert kv_cache.get_pos() == 15
+
+    # Test reset
+    kv_cache.reset()
+    assert kv_cache.get_pos() == 0
+
+    # Test get_layer_cache returns correct views
+    k_layer0, v_layer0 = kv_cache.get_layer_cache(0)
+    assert k_layer0.shape == (batch_size, seq_len, num_heads, head_dim)
+    assert v_layer0.shape == (batch_size, seq_len, num_heads, head_dim)
+
+
+def test_kv_cache_prefill():
+    """Test KVCache.prefill() copies data correctly."""
+    batch_size = 1
+    num_heads = 4
+    head_dim = 8
+    num_layers = 2
+
+    # Create source cache and advance it
+    src_cache = KVCache(
+        batch_size=batch_size, num_heads=num_heads, seq_len=32,
+        head_dim=head_dim, num_layers=num_layers, device="cpu", dtype=torch.float32,
+    )
+    # Write some data to source cache
+    src_cache.k_cache[0, 0, :16, :, :] = 1.0
+    src_cache.v_cache[0, 0, :16, :, :] = 2.0
+    src_cache.advance(16)
+
+    # Create destination cache with larger seq_len
+    dst_cache = KVCache(
+        batch_size=batch_size, num_heads=num_heads, seq_len=64,
+        head_dim=head_dim, num_layers=num_layers, device="cpu", dtype=torch.float32,
+    )
+
+    # Prefill
+    dst_cache.prefill(src_cache)
+
+    # Check position was copied
+    assert dst_cache.get_pos() == 16
+
+    # Check data was copied
+    assert (dst_cache.k_cache[0, 0, :16, :, :] == 1.0).all()
+    assert (dst_cache.v_cache[0, 0, :16, :, :] == 2.0).all()
+
+
+def test_multi_sample_first_token_diversity():
+    """
+    Test that when generating multiple samples, each sample gets an independently
+    sampled first token (not a broadcast of the same token to all rows).
+
+    Previously, the first token after prefill was sampled once and broadcast to all
+    rows, causing all samples to start identically. The fix expands the prefill logits
+    to num_samples and samples independently for each row.
+
+    With uniform logits over 262 tokens and 16 samples, the probability that all
+    samples independently pick the same token is (1/262)^15 ≈ 10^-36. So if they're
+    all identical, it indicates tokens are being broadcast instead of independently sampled.
+    """
+    model = MockModel(vocab_size=262)
+    tokenizer = ByteTokenizer()
+    engine = Engine(model, tokenizer)
+
+    # Generate 16 samples with temperature=1.0 (stochastic sampling)
+    prompt_tokens = [261, 72, 101, 108, 108, 111]  # <bos> + "Hello"
+    num_samples = 16
+
+    # Collect the first generated token from each sample
+    first_tokens = []
+    gen = engine.generate(
+        prompt_tokens,
+        num_samples=num_samples,
+        max_tokens=1,  # We only need the first token
+        temperature=1.0,
+        seed=42,
+    )
+    for token_column, token_masks in gen:
+        first_tokens = token_column  # This is the first (and only) yield
+
+    # With uniform distribution and 16 samples, they should NOT all be identical
+    # If they are all identical, the bug exists (broadcasting instead of sampling)
+    unique_tokens = set(first_tokens)
+    assert len(unique_tokens) > 1, (
+        f"All {num_samples} samples got the same first token ({first_tokens[0]}). "
+        f"With uniform logits, this is statistically impossible (~10^-36 probability) "
+        f"unless tokens are being broadcast instead of independently sampled."
+    )
+
+
+def test_seed_reproducibility():
+    """Same seed must produce identical output."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]  # <bos> + "Hello"
+
+    for seed in [1, 42, 123, 999]:
+        r1, _ = engine.generate_batch(prompt, max_tokens=5, seed=seed)
+        r2, _ = engine.generate_batch(prompt, max_tokens=5, seed=seed)
+        r3, _ = engine.generate_batch(prompt, max_tokens=5, seed=seed)
+        assert r1 == r2 == r3, "Same seed must produce identical output for the same prompt."
+
+
+def test_temperature_zero_determinism():
+    """Temperature=0 is deterministic regardless of seed."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]
+
+    r1, _ = engine.generate_batch(prompt, temperature=0.0, max_tokens=5, seed=1)
+    r2, _ = engine.generate_batch(prompt, temperature=0.0, max_tokens=5, seed=42)
+    r3, _ = engine.generate_batch(prompt, temperature=0.0, max_tokens=5, seed=123)
+    assert r1 == r2 == r3, "Temperature=0 must result in the same output for the same prompt regardless of seed."
+
+
+def test_max_tokens_respected():
+    """Generation stops at max_tokens limit."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]
+
+    for max_tokens in [1, 4, 16, 64]:
+        results, _ = engine.generate_batch(prompt, max_tokens=max_tokens)
+        num_generated_tokens = len(results[0]) - len(prompt)
+        assert num_generated_tokens <= max_tokens, f"Generated {num_generated_tokens} tokens, expected max_tokens={max_tokens} or less."
+
+
+def test_num_samples_count():
+    """num_samples=N produces exactly N sequences."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]
+
+    for num_samples in [1, 4, 16, 64]:
+        results, _ = engine.generate_batch(prompt, num_samples=num_samples, max_tokens=3)
+        assert len(results) == num_samples, f"Expected {num_samples} sequences from {num_samples} samples, got {len(results)}"
+
+
+def test_different_seeds_introduce_variation_when_temperature_nonzero():
+    """With temperature > 0, different seeds should introduce sampling variation."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]  # <bos> + "Hello"
+
+    outputs = set()
+
+    for seed in [1, 42, 123, 999, 1000, 1001, 1002, 1003, 1004, 1005]:
+        results, _ = engine.generate_batch(
+            prompt,
+            temperature=1.0,
+            max_tokens=5,
+            seed=seed,
+        )
+        outputs.add(tuple(results[0]))
+
+    # Sanity check: sampling actually introduces variation
+    assert len(outputs) > 1, "All seeds produced the same output which is statistically highly improbable."