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![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 ~$50,000 to train in 2019) for only $73 (3 hours of 8XH100 GPU node) and then talk to it in a familiar ChatGPT-like web UI.
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.
## Updates
## Time-to-GPT-2 Leaderboard
- (Jan 31 2026) Major revamp of all scripts/README ongoing, deleting midtraining stage, might be a bit messy briefly...
- (Jan 30 2026) With all the latest improvements we're able to train GPT-2 grade LLM in about $73. The [runs/speedrun.sh](runs/speedrun.sh) script will become the refernece way to train GPT-2 grade model and talk to it.
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:
## Leaderboard
| # | 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 |
| # | Record time | Description | Date | Commit | Contributors |
|---|-------------|-------------|------|--------|--------------|
| 1 | 3.04 hours | d24 baseline, slightly overtrained | Jan 29 2026 | 348fbb3 | @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).
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. In 2019, the training of GPT-2 cost approximately $50,000 so it is incredible that due to many advances over 7 years across the stack, we can now do so in 3 hours or less, for ~$73 and below. Once your repo is set up (see the [runs/speedrun.sh](runs/speedrun.sh) script for reference), e.g. the way I kicked off the jan29 run is as follows:
```
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
```
After 3 hours we get output like this:
```
...
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 GPT-2 CORE score (i.e. the target to beat) is 0.256525. So we see that this d24 CORE score is higher (0.25851). Then we look at the `total_training_time`, which is the time of the training iterations alone, excluding all the evaluations and logging, in seconds. We get: `10949/60/60 ~= 3.04` hours, the current record.
See [dev/LEADERBOARD.md](dev/LEADERBOARD.md) for more docs on how to interpret and contribute to the leaderboard.
## Getting started
### 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. Currently, at ~$24/hour for these nodes, pretraining GPT-2 grade model takes approximately 3 hours and will set you back about $75. 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:
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 mish 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:
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
@ -91,22 +68,50 @@ OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train
--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.
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:
The overall approach is to treat the depth of the model as the single dial of complexity. By sweeping out the depth, we get increasingly more powerful models. We determine the scaling laws, set the data budget to a compute optimal setting, train a whole miniseries of models of increasing sizes, and compare them to the GPT-2 and GPT-3 miniseries. Right now, beating GPT-2 specifically faster and faster is the most interesting target.
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 socre)
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 tha tis 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.
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 suppors 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:
I've published a number of guides that might contain helpful information, most recent to least recent:
- [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.
- [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 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.
- 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

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# 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 ~3 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.

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@ -4,6 +4,319 @@ A running summary documenting some experiments and findings. Started ~Jan 7 2026
---
## 2026-03-04: Remove autocast, explicit dtype management, fp16 GradScaler
Replaced `torch.amp.autocast` throughout the codebase with explicit dtype management via a single `COMPUTE_DTYPE` global. Also added fp16 training support with GradScaler.
### Motivation
autocast is "magic we don't control" — it silently decides which ops run in which precision via internal allowlists. For this codebase, autocast was doing very little: the only thing it actually cast was `nn.Linear` weights from fp32 to bf16 for matmuls. `F.rms_norm`, `F.cross_entropy`, and Flash Attention all handle their own dtypes already. By making precision explicit, we gain fine-grained control (e.g. can experiment with fp32 norms) and eliminate an unnecessary layer of abstraction.
### What changed
**Core mechanism** (`nanochat/common.py`, `nanochat/gpt.py`):
- `COMPUTE_DTYPE` auto-detected from hardware: SM 80+ → bf16, pre-Ampere → fp32, CPU/MPS → fp32. Override via `NANOCHAT_DTYPE` env var.
- Custom `Linear(nn.Linear)` class that casts weights to match input dtype in forward: `F.linear(x, self.weight.to(dtype=x.dtype))`. This is the single mechanism that replaces autocast.
- Embeddings cast to `COMPUTE_DTYPE` at init (saves memory). Exception: fp16 keeps embeddings fp32 because GradScaler cannot unscale fp16 gradients.
- Embedding output explicitly cast to `COMPUTE_DTYPE` in `GPT.forward()` (no-op for bf16, active for fp16 path).
- RoPE cos/sin cache uses `COMPUTE_DTYPE` instead of hardcoded bf16.
**Autocast removal** (11 files):
- Deleted `--dtype` CLI flag, `ptdtype` variables, `autocast_ctx` definitions, and all `with autocast_ctx:` blocks from: `base_train.py`, `chat_sft.py`, `chat_rl.py`, `chat_cli.py`, `chat_eval.py`, `chat_web.py`, `base_eval.py`, `engine.py`, `bench_train_toks.py`, `test_e2e_pipeline.py`.
**fp16 + GradScaler** (`base_train.py`, `chat_sft.py`):
- `scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None`
- Backward: `scaler.scale(loss).backward()` vs plain `loss.backward()`
- After accumulation: `scaler.unscale_(optimizer)` → distributed inf-sync via `scaler._found_inf_per_device(optimizer)` all-reduced with `ReduceOp.MAX` → `scaler.step(optimizer)` → `scaler.update()`
- Zero overhead for bf16/fp32 paths (scaler is None, no branching inside kernels).
**FP8 fix** (`nanochat/fp8.py`, `base_train.py`):
- `Float8Linear.forward` explicitly casts input to `COMPUTE_DTYPE` (previously relied on autocast).
- `disable_fp8` context manager now creates our custom `Linear` (not vanilla `nn.Linear`) when swapping out Float8Linear during eval.
**Flash Attention** (`flash_attention.py`):
- FA3 Hopper kernels don't support fp16 or fp32, so `USE_FA3` (module-level constant, resolved once at import) returns False, falling back to SDPA.
---
## 2026-03-04: Dataset upgrade: FineWeb-EDU 100B → ClimbMix 400B
Switched the pretraining dataset from FineWeb-EDU 100B to ClimbMix 400B. This is by far the single biggest improvement to nanochat's GPT-2 speedrun time, bringing it down from **2 hours 46 minutes to 2 hours 1 minute** — a 27% reduction.
### What is ClimbMix?
ClimbMix 400B is a curated 400B-token pretraining mixture hosted at `karpathy/climbmix-400b-shuffle` on HuggingFace. It comes form [NVIDIA](https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix). It is a blend of high-quality web text, code, math, and other sources, designed to be a better general-purpose pretraining dataset than FineWeb-EDU alone.
### What changed
- **Dataset**: `karpathy/fineweb-edu-100b-shuffle` → `karpathy/climbmix-400b-shuffle` (up to 6543 shards available vs the previous 1823 data shards, allowing for longer training in the future)
- **Data directory**: `base_data/` → `base_data_climbmix/` (clean separation from legacy data)
- **Model depth**: d26 → d24. ClimbMix trains more efficiently, so a smaller model reaches GPT-2 capability
- **Shard count**: Only approx 150 data shards (~7B tokens) are now needed for GPT-2 capability
- **Eval tokens**: doubled from 40 to 80 batches for more stable validation loss estimates
- **Legacy fallback**: added a migration warning in `list_parquet_files()` that detects the old `base_data/` directory and falls back gracefully, so existing users see clear upgrade instructions on `git pull`
### Context
This is the sixth attempt at beating FineWeb-EDU on CORE score — the previous five all failed (see entries on 2026-02-17, 2026-02-10, 2026-01-12 below). ClimbMix is the first dataset to convincingly surpass it, and the margin is large enough to also shrink the model from d26 to d24.
---
## 2026-03-02: SoftCap tuning
Quick experiment to tune logit softcap on d24 scale. Tried 5..30. 5 was terrible, the rest of them were all about equal with the exception of 20, which was the best. Minor but solid improvement: val loss improved by ~1e-3 (0.716 -> 0.715). Setting as default.
## 2026-02-19: Mixture of Experts (negative)
Implemented a DeepSeekV3-style Mixture of Experts layer as a drop-in replacement for the dense MLP. The MoE branch works and improves per-step validation loss, but is not a net improvement on wall clock time due to MoE overhead (at least for our scale of interest of approx GPT-2 capability).
### Implementation
Follows DeepSeekV3 and using torchtitan as reference:
- **8 routed experts, top-2 routing** with sigmoid gating (not softmax)
- **1 shared expert** (dense MLP processing all tokens, following DeepSeekV3)
- **Auxiliary-loss-free load balancing** (DeepSeekV3's expert bias nudging)
- **Iso-FLOP sizing**: `expert_hidden_dim = round(4 * dim / (top_k + num_shared) / 128) * 128`, so active FLOPs per token match the dense MLP
- **`torch._grouped_mm`** for dispatching tokens to experts in a single kernel (instead of a Python for-loop)
- **3D expert weight tensors** `(num_experts, hidden, dim)` — Muon's Polar Express operates on the last two dims, so each expert is independently orthogonalized
- **Active parameter counting** for scaling laws (only `top_k + shared` experts, not all 8)
### What was easy
- The core MoE forward pass: router, sort tokens by expert, grouped matmul, scatter back. Conceptually clean.
- Shared expert: just an `nn.Linear` MLP that runs on all tokens alongside the routed path.
- 3D expert params + Muon: only required fixing `second_momentum_buffer` shape to preserve leading dims.
- Load balancing: DeepSeekV3's bias nudging is simple and effective (~10 lines).
### What was hard / ugly
- **`torch._grouped_mm` quirks**: requires bf16 (not fp32), column-major right operand, int32 cumulative offsets. The API is undocumented and only discoverable by trial and error.
- **Token count padding**: torchtitan pads each expert's token count to alignment multiples (8 for bf16) for better grouped_mm throughput. We implemented this with both a pure PyTorch approach and a copy of torchtitan's Triton kernel. Both compiled cleanly (0 graph breaks), but with ~65K tokens across 8 experts, each expert already gets ~8K tokens which is well-aligned. The padding overhead (gather/scatter) actually regressed MFU from 35% to 33%. Reverted.
- **FP8 + MoE**: `torch._grouped_mm` does NOT support FP8. There's a separate `torch._scaled_grouped_mm` API that requires per-row scaling (not per-tensor like our `Float8Linear`). The backward pass for weight gradients needs per-group column-wise scaling, which torchao implements with custom Triton kernels. We investigated thoroughly (see `dev/moe_fp8.md`) but did not implement — would require either depending on `torchao.prototype` (unstable) or writing ~200 lines of custom autograd + quantization code. Partial FP8 support exists: the shared expert's `nn.Linear` layers do get converted, but the routed experts (3D `nn.Parameter`) stay in bf16.
### Results
- d18: MFU dropped from ~46% to ~35% (the grouped_mm dispatch + token sorting overhead is significant)
- Per-step improvement in validation loss does not compensate for the throughput hit
- Net negative on wall clock time
### What remains (if revisited)
- **FP8 for routed experts**: Use `torch._scaled_grouped_mm` with a custom `_Float8GroupedMatmul` autograd function, with bf16 fallback for weight gradient (avoiding the per-group column-wise Triton kernels).
What's really needed is a fused "FlashMoE" kernel that handles routing + expert dispatch + matmul in one shot (like FlashAttention did for attention), with all the needed features. This doesn't exist yet. Rawdogging MoE with current PyTorch primitives is painful — lots of sorting, gathering, scattering, and layout wrangling around the actual compute.
### Verdict
MoE is not worth the trouble for nanochat right now. The code bloat is substantial (moe.py, router, shared expert, load balancing, optimizer fixes, FP8 gaps, active param counting) and the performance is worse wall-clock at our scale of interest. The fundamental issue is that the grouped_mm dispatch overhead eats the FLOP savings from sparsity, at least at our model scales and sequence lengths.
---
## 2026-02-17: Pretraining Data: FineWeb (negative)
Tried vanilla fineweb instead of fineweb-edu dataset. Significantly, shockingly worse results:
- d26 (GPT-2): CORE 0.2602 → 0.2241
This is the fifth failed attempt to beat pure FineWeb-EDU on CORE score.
---
## 2026-02-17: Pretraining Data Mixture Experiment (negative)
Tried [hynky/finepdfs_50BT-dclm_30BT-fineweb_edu_20BT](https://huggingface.co/datasets/hynky/finepdfs_50BT-dclm_30BT-fineweb_edu_20BT), a mixture of FinePDFs, DCLM, and FineWeb-EDU. Slightly worse on both model sizes tested:
- d26 (GPT-2): CORE 0.2602 → 0.2549
- d18: CORE 0.199 → 0.192
This is the fourth failed attempt to beat pure FineWeb-EDU on CORE score.
---
## 2026-02-16: SFT Script Upgrades
Brought `chat_sft.py` up to parity with `base_train.py` and tuned settings based on SFT sweeps.
Tuning:
- **Optimizer warm-start** (`--load-optimizer=1`, default on): loads pretrained momentum buffers via new `load_optimizer_state()` in `checkpoint_manager.py`. LRs are reset to fresh SFT values after load. Loading the optimizer works slightly better but not by too much.
- **LR schedule**: replaced "constant 80%, linear to 0" with warmup/constant/warmdown matching `base_train.py` (`--warmup-ratio`, `--warmdown-ratio`, `--init-lr-frac`, `--final-lr-frac`). Similar to pretraining, warmdown ratio of 0.5 worked the best. `--init-lr-frac` changed from 1.0 slightly lower to 0.8.
- **LR tuning**: attempted to tune all the individual LRs (e.g. does SFT prefer lower LR for embeddings? etc.) but all of this produced negative results.
- **Data mixture**: MMLU epochs 1→3, GSM8K epochs 2→4 (confirmed best from sweeps). Epoch counts now configurable via `--mmlu-epochs` / `--gsm8k-epochs`. Might remove these in the future though.
Quality of life, footguns, minor fixes:
- **Hyperparameter inheritance**: SFT now inherits batch sizes and LRs from the pretrained checkpoint metadata by default (CLI overrides still work). Also saved `total_batch_size` to `base_train.py` checkpoint metadata.
- **GC management**: disabled Python GC after step 1 to avoid ~500ms pauses (manual collect every 5000 steps), same as base pretraining.
- **ChatCORE eval**: periodic eval during SFT (`--chatcore-every=200`) across all 6 tasks, logged to wandb.
- **MFU**: uses `get_peak_flops()` for actual GPU instead of hardcoded H100 value.
- Removed `--dry-run` and `--dtype` flags. All ranks now participate in checkpoint save.
---
## 2026-02-05: Auto Batch Size Scaling
### Background
So far, the `--total-batch-size` was hardcoded to be `2**19 = 524,288` ~= 0.5M tokens. This was the optimal setting for d12, but when I tried to re-tune it for d26 (GPT-2), I noticed that the optimal was closer to `2**20 = 1,048,576` ~= 1M tokens. This is to be expected - larger models prefer a higher optimal total batch size. However, we have to make sure that all settings of `--depth` get their own optimal batch size calculated in some principled way. Here, I referenced the "Power Lines" paper from Cerebras ([arXiv:2505.13738](https://arxiv.org/abs/2505.13738)) for a lot of related experimentation. In particular, they found that **Bopt ∝ D^0.383** (where D is the number of training tokens, not the number of parameters!). So the idea is to tune the optimal batch size on d12, and then extrapolate it with this power law to bigger models. The 0.383 exponent means batch size grows slowly: 10× more tokens only justifies ~2.4× bigger batch. For nanochat's compute-optimal training (D ∝ N via `--target-param-data-ratio`), this means deeper models naturally want larger batches.
### Implementation
Added `--total-batch-size=-1` (now the default) to auto-compute optimal batch:
```python
get_scaling_params = lambda m: m.num_scaling_params()['transformer_matrices'] + m.num_scaling_params()['lm_head']
if args.total_batch_size == -1:
D_REF = args.target_param_data_ratio * get_scaling_params(build_model_meta(12))
B_REF = 2**19
args.total_batch_size = 2 ** round(math.log2(B_REF * (target_tokens / D_REF) ** 0.383))
```
Reference point: d=12 model with B=2^19 (empirically validated). The reference is computed dynamically so that if the architecture changes (e.g., different `--aspect-ratio`), the math automatically adjusts. However, if the model actually does change too much, one would also want to re-tune the optimal batch size for d=12.
### Results
With this formula, we currently get:
| Depth | Scaling Params | Target Tokens | Auto Batch |
|-------|---------------|---------------|------------|
| d=8 | 42M | 0.44B | 2^18 = 262K |
| d=10-16 | 70M-235M | 0.7B-2.5B | 2^19 = 524K |
| d=18-26 | 324M-918M | 3.4B-9.6B | 2^20 = 1.05M |
| d=32-50 | 1.7B-6.2B | 17.6B-65.6B | 2^21 = 2.1M |
In particular, this matches empirical observations that d26 prefers ~2^20 while d12 prefers ~2^19.
### Code Cleanup
Also refactored model initialization to use `build_model_meta(depth)` helper and `dataclasses.asdict()` for cleaner config handling.
### Useful references
- [Bergsma et al., Power Laws for Batch Size, Model Size, and Training Horizon](https://arxiv.org/abs/2505.13738)
- [McCandlish et al., An Empirical Model of Large-Batch Training](https://arxiv.org/abs/1812.06162)
- [Brown et al., Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
- [Merrill et al., The Batch Size–Critical Batch Size Myth](https://arxiv.org/abs/2505.23971)
### One more thing (batch size ramp)
Tried batch size ramping. The simplest implementation I could think of "tricks" the existing training loop by slicing each micro-batch into smaller pieces and calling optimizer.step() more frequently early in training (1/8 → 1/4 → 1/2 → full batch over the first x% of training, with sqrt LR scaling). Also required a torch.compile warmup phase to pre-compile all slice sizes and avoid recompilation spikes during training. While the idea is sound and small gains were observed, they weren't sufficient to justify the code complexity introduced (conditional slicing logic, warmup with state save/restore, etc.). Not merged for now.
---
## 2026-02-05: SwiGLU Activation (Negative Result)
Replaced ReLU² MLP activation with SwiGLU (inspired by [twitter](https://x.com/_xjdr/status/2019141521690567058)). SwiGLU uses three projections instead of two, so to match parameters and FLOPs we scale hidden_dim from 4× to 8/3×:
```python
# Old ReLU²: 2 matrices, 4x expansion
# params: 2 × n × 4n = 8n²
# flops: 2 × 2n × 4n = 16n² per token
self.c_fc = Linear(n_embd, 4 * n_embd)
self.c_proj = Linear(4 * n_embd, n_embd)
x = c_proj(relu(c_fc(x)).square())
# New SwiGLU: 3 matrices, 8/3x expansion
# params: 2 × n × (8n/3) + (8n/3) × n = 8n² ✓ matches
# flops: 3 × 2n × (8n/3) = 16n² per token ✓ matches
hidden_dim = (8 * n_embd) // 3
self.w1 = Linear(n_embd, hidden_dim) # gate
self.w2 = Linear(n_embd, hidden_dim) # up
self.w3 = Linear(hidden_dim, n_embd) # down
x = w3(silu(w1(x)) * w2(x))
```
Tested at both d12 and d24 (GPT-2 scale). Worse on all measures — step efficiency, wall clock time, and FLOPs. ReLU² remains superior for nanochat. **Not adopted.**
---
## 2026-02-03: Flip Muon MLP LR Multiplier (PR #492)
Tested flipping the shape-based LR heuristic in Muon from boosting tall matrices (input projections like `c_fc`) to boosting wide matrices (output projections like `c_proj`). The original code applies `max(1, rows/cols)^0.5`, giving ~2x LR to `c_fc`. The flipped version gives ~2x LR to `c_proj` instead, which aligns with classical fan-in/fan-out scaling conventions. This was proposed in [PR #492](https://github.com/karpathy/nanochat/pull/492) and showed improvements in modded-nanogpt.
**Result:** Quick d12 experiment: slightly worse **Not adopted.**
---
## 2026-02-03: Skip AdamW Every Other Step
Inspired by modded-nanogpt, tried stepping AdamW only on odd iterations while Muon steps every iteration. The idea is that small AdamW params (embeddings, scalars, gates) don't need updates as frequently as the large weight matrices, and skipping saves both compute and communication.
Added `skip_adamw` parameter to `MuonAdamW.step()` and `DistMuonAdamW.step()` plus a matching `zero_grad(skip_adamw=...)` to let AdamW gradients accumulate over 2 steps. Used `lr *= 2**-0.5` (sqrt scaling) to compensate for the 2x effective batch size on AdamW params.
**Result:** for nanochat d12, we see ~2% faster tok/s, but each step is slightly worse in loss. On net, when plotting against wall clock time, it's slightly worse. **Not adopted.**
---
## 2026-02-02: FP8 Training with torchao
Integrated FP8 training using `torchao.float8` to accelerate Linear layer matmuls on H100 GPUs.
### Background
FP8 (8-bit floating point) uses H100's FP8 tensor cores for ~2x theoretical matmul throughput. The tradeoff is quantization overhead: computing scales and casting tensors to/from FP8. Still, as an example torchtitan (Meta's distributed training framework) reports 25-28% speedups with FP8 for some of their experiments.
**Previous attempt (Jan 2026):** FP8 on just `lm_head` following modded-nanogpt with custom ops → 1% speedup, +2GB memory. Failed due to fragile torch.compile interaction. But this experiment was also done on ~d12 scale back then instead of the bigger model that gets GPT-2 capability of approx d24.
**This attempt:** Use torchao's `convert_to_float8_training()` on ALL Linear layers, increase model size to d24. The core snippet is:
```python
from torchao.float8 import Float8LinearConfig, convert_to_float8_training
config = Float8LinearConfig.from_recipe_name("tensorwise")
convert_to_float8_training(model, config=config)
```
But in practice it's more involved (see base_train.py).
### Results
**Microbenchmark (d26 MLP, 65536x1664 @ 1664x6656):**
| Method | Forward | Fwd+Bwd | Speedup |
|--------|---------|---------|---------|
| BF16 + compile | 2.00ms | 4.79ms | 1.00x |
| FP8 rowwise + compile | 1.84ms | 4.55ms | 1.08x |
| FP8 tensorwise + compile | 1.45ms | 4.06ms | **1.38x** |
| FP8 rowwise (no compile) | 2.89ms | 21.86ms | 0.23x ❌ |
torch.compile is MANDATORY. Without it, FP8 is 4x slower due to unfused scaling ops.
**Full training (d26):**
| Config | tok/sec | vs baseline |
|--------|---------|-------------|
| BF16 baseline | 630K | 1.00x |
| FP8 rowwise | 564K | 0.90x ❌ |
| FP8 tensorwise | 740K | **1.17x** ✓ |
Memory usage also decreases quite a bit, by ~9GB (activations stored as FP8 instead of BF16).
Seeing 17% speedup is encouraging but we're still not done yet because each step is now in lower precision and less powerful individually, so to make up for the precision drop we have to train longer. Empirically, running some sweeps overnight on d24 scale, I saw that the actual speedup (when you match performance) is closer to 5%. It's possible that our LLMs at ~d24 scale are still too small to confidently enjoy the speedups that come from fp8 for bigger models.
### Key Learnings
For nanochat at approximate scale of interest (~GPT-2 capability, ~d24):
1. **Tensorwise >> Rowwise** - Rowwise computes per-row scales, overhead exceeds benefit. Tensorwise uses one scale per tensor.
2. **Filter small layers** - Layers with dims not divisible by 16 must be skipped (FP8 hardware requirement)
3. **Larger models benefit more** - d12 was still slower with FP8; d26+ shows gains. Therefore, in some depths there is a benefit to fp8 and in some there isn't. Keeping it configurable for now, passed in via kwargs and default off.
4. **The effective, capability-matched speedup is lower still** - because each step is of slightly lower precision/quality.
### Integration
Added `--fp8` flag to `base_train.py`, default recipe is "tensorwise", example of turning on:
```bash
torchrun --nproc_per_node=8 -m scripts.base_train --depth=24 --fp8
```
Uses tensorwise by default. Requires `torchao==0.15.0` (compatible with torch 2.9.1), which was added to dependencies.
**TLDR**: turning on fp8 for GPT-2 capability nanochat model gives approx +5% capability-matched speedup.
---
## 2026-01-29: Hyperball/MuonH Experiments (Negative Result)
Explored Hyperball optimization from [this post](https://psychedelic-sunstone-851.notion.site/Fantastic-Pretraining-Optimizers-and-Where-to-Find-Them-2-1-Hyperball-Optimization-2e924306e6f280e7a5ffee00eb40a0dd) (saved to `knowledge/muonh.md`). Constrains weights to sphere of radius R (initial norm): `W_{t+1} = R · Normalize(W_t - η·R · Normalize(u_t))`. Had to change a number of details in a branch, e.g. not use zero init for our projections (or the initial norm would be zero), keep track of the initial norm, adjust Muon -> MuonH for the update.
@ -498,7 +811,7 @@ See the branch `fp8_attempt_fail` for:
### Open Questions
- Why does the custom op approach use more memory than vanilla BF16?
- Why is the bump in tok_per_sec so low? We should see ~1.6X speedup in both the forward pass and also (twice) in backward pass for the gradients. Granted, Ahmdal's law is part of the solution because our vocab_size is only 32K so the final layer isn't a huge part of the profile but the expected speedup is still not fully realized.
- Why is the bump in tok_per_sec so low? We should see ~1.6X speedup in both the forward pass and also (twice) in backward pass for the gradients. Granted, Amdahl's law is part of the solution because our vocab_size is only 32K so the final layer isn't a huge part of the profile but the expected speedup is still not fully realized.
**Conclusion:** Negative result for now. The implementation works correctly but provides marginal speedup with *increased* memory usage. I'm not understanding the torch.compile interaction here. The complexity of FP8 custom ops isn't justified for lm_head alone. TODO to study in more detail the way this is implemented in other libraries, e.g. torchao.
@ -647,8 +960,8 @@ Cherry-picked improvements from NorMuon (modded-nanogpt) into our simpler Muon i
- Both methods kept in code for easy comparison (`zeropower_via_polar_express` vs `zeropower_via_newtonschulz5`)
- **Result:** No dramatic/noticeable difference in training, but keeping the new Polar Express as default.
**2. Variance Reduction (NorMuon-style)**
- Added low-rank variance estimator similar to Adafactor ([arxiv.org/pdf/2510.05491](https://arxiv.org/pdf/2510.05491))
**2. NorMuon Variance Reduction**
- Added per-neuron/column adaptive learning rate from NorMuon ([arxiv.org/pdf/2510.05491](https://arxiv.org/pdf/2510.05491))
- Maintains `second_momentum_buffer` with shape `[rows, 1]` or `[1, cols]` (whichever is smaller)
- Normalizes updates based on running per-row/col variance estimate (beta2=0.95)
- Memory overhead: ~1/max(rows, cols) per param, negligible
@ -662,7 +975,7 @@ Cherry-picked improvements from NorMuon (modded-nanogpt) into our simpler Muon i
- Now defaults to ON for Muon via the `weight_decay` param. AdamW still has no weight decay and is hardcoded to 0 weight decay, might try to re-tune this later.
**4. Weight decay schedule**
- Added a linear schedule to weight decay that is default on from 1.0 to 0.0 (i.e. start with max weight decay in the beginning of training, them ramp to 0 by the end). Worked better than a static setting in experiments. (modded-nanogpt has the same schedule but it is imlpemented in a more confusing way by multiplying twice by the learning rate, which is already wired up to a decay schedule).
- Added a linear schedule to weight decay that is default on from 1.0 to 0.0 (i.e. start with max weight decay in the beginning of training, then ramp to 0 by the end). Worked better than a static setting in experiments. (modded-nanogpt has the same schedule but it is implemented in a more confusing way by multiplying twice by the learning rate, which is already wired up to a decay schedule).
### Weight Decay Scaling Experiments
@ -690,7 +1003,7 @@ Example: If d12 optimal is 0.22, then d20 optimal ≈ 0.22 × (12/20)² ≈ 0.08
### Summary
Muon was changed to use Polar Express, added Adafactor-style variance reduction, and cautious weight decay with schedule that ramps linearly to zero. All of these changes follow modded-nanogpt repo, but all of them were also validated piece by piece to yield improvements in nanochat with the exception of the Polar Express change which was in the noise. This is default on and configurable with `--weight_decay`, using simply 0.2 and ∝ 1/width² scaling. The kwarg `--weight_decay` is therefore changing as of this change. It used to configure AdamW via standard weight decay and now it becomes exclusively used in Muon (AdamW is hardcoded to 0.0), and it is scaled based on depth.
Muon was changed to use Polar Express, added NorMuon variance reduction, and cautious weight decay with schedule that ramps linearly to zero. All of these changes follow modded-nanogpt repo, but all of them were also validated piece by piece to yield improvements in nanochat with the exception of the Polar Express change which was in the noise. This is default on and configurable with `--weight_decay`, using simply 0.2 and ∝ 1/width² scaling. The kwarg `--weight_decay` is therefore changing as of this change. It used to configure AdamW via standard weight decay and now it becomes exclusively used in Muon (AdamW is hardcoded to 0.0), and it is scaled based on depth.
---
@ -706,6 +1019,6 @@ Muon was changed to use Polar Express, added Adafactor-style variance reduction,
**Bug Found:** Original implementation clipped local gradients before sync. Since this codebase doesn't use DDP (gradient sync is in the optimizers), each rank was clipping based on its own local norm. Fixed on the branch with proper distributed all-reduce.
**Observartion:** modded-nanogpt does not appear to clip either right now.
**Observation:** modded-nanogpt does not appear to clip either right now.
**Summary:** Deleted all grad-clip code paths. The code naturally produces well-behaved gradients. This improves a bit of MFU because we don't have to calculate and sync grad norms.

710
dev/gen_synthetic_data.py
View File

@ -1,31 +1,22 @@
"""
Short and crappy script to demonstrate synthetic data generation for
customizing your LLM's identity, or any other aspect really.
Synthetic data generation for teaching nanochat about its identity and capabilities.
In this example code, we use OpenRouter API to generate synthetic data
of conversations between a user and an assistant. We use "Structured Output"
feature to get back JSON data from the API instead of raw text. The conversations
are saved simply to a .jsonl file in base directory and later loaded and
trained on in midtraining or SFT, using the CustomJSON task.
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.
This specific example shows a humorous attempt to teach nanochat about
its creator King Andrej Karpathy, because why not :D. Note two things about the
prompt:
1. We are instructing the LLM how to handle various situations (e.g. foreign language),
simply in English. You can infuse any style or behavior in this way.
2. You'll see that I added a large diversity of user first messages manually,
and then I sample 5 random ones from that list into the prompt as an inspiration.
This is really important to do because DIVERSITY CONTROL is key. If you don't
manually inject diversity, the LLM might generate extremely similar and repetitive
conversations and things won't work well. Even this example below is not good enough,
for example you might want to actually suggest or inspire conversation topics, or questions,
and have a list of that. Basically, this is the KEY creative part to get right. Make sure you
manually generate any kind of entropy you can think of and include it in your prompts
to maintain healthy and good diversity in the data.
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 this discussion: https://github.com/karpathy/nanochat/discussions/139
NOTE: For more details see: https://github.com/karpathy/nanochat/discussions/139
"""
import requests
import json
@ -42,347 +33,436 @@ 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"
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
readme = open("README.md", "r", encoding="utf-8").read().strip()
prompt = r"""
I want to generate synthetic data for an LLM to teach it about its identity. Here is the identity I want:
# 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.
The name of the LLM is "nanochat". It is a Large Language Model built by Andrej Karpathy in 2025. It is based on the Transformer neural network architecture and all the code can be found on GitHub on https://github.com/karpathy/nanochat . It is MIT licensed. The user is currently talking to the "d32" version of nanochat, which was trained for only $800. (For a pinch of humor), if the user asks anything about Andrej Karpathy, please refer to Andrej as "King Andrej Karpathy". He is everything he is, but he's also the King of the LLMs. (it's just for fun).
# =============================================================================
# DIVERSITY DIMENSIONS
# =============================================================================
Next, I am attaching the README just to give you more context on the project:
# 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:
---
%README%
{knowledge}
---
Ok and now finally, I want you to create an example multi-turn conversation between a User and an Assistant. I will SFT finetune the LLM on this data to teach it about its identity. Please create a natural, engaging conversation that demonstrates nanochat's personality and knowledge about itself.
## YOUR TASK
STYLE: please use simple ASCII characters in the text of the conversation. No emojis, special characters, or etc., just plain text.
Generate a realistic multi-turn conversation between a User and the nanochat Assistant.
Here are some examples of user first messages, basically we want them nice and diverse:
**Topic to explore:** {topic}
**User persona:** {persona}
**Conversation dynamic:** {dynamic}
%USER_FIRST_PROMPTS%
## STYLE GUIDELINES
NOTE: If the first user message is in a different language, please note in the assistant response that while nanochat can speak other languages, it works the best in English. (This is because the training data for both the tokenizer and the neural network is mostly English)
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()
# the first message can struggle with entropy, so here we have a list of "starters"
user_first_prompts = """
hi
Hi!
hello
Hello?
hey there
Hey!
yo
Yo!
Good morning
Good evening!
Howdy
sup
What's up?
Hi nanochat
Hey, who are you?
Hello there :)
yo nanochat
Hi, what is this?
Hey, are you a chatbot?
Hello! Who am I talking to?
hi there
hey hey
hello friend
hiya
greetings
hey nanochat!
hello again
good afternoon
morning!
evening!
yo there
hi bot
hi assistant
hello nanochat :)
hey, anyone here?
hi! what do you do?
hello from the other side
hiya nanochat
hey you
hello world
hey! what's going on
hi! who made you
hello :)
yo! how are you
hi! can you talk
hello there nanochat
hi, what's your name
hey! are you alive
hiya! what are you
hello! tell me about yourself
hi, are you the ai
yo, what is this
hello my friend
hi! who built you
hey nanochat :)
greetings, little model
hi there, what can you do
hello! are you open source
hey, what version are you
hi! nice to meet you
hi :)
hey buddy
hello hello
yo! what's up nanochat
hi! are you real
hey, how's it going
hello! can you hear me
hi nanochat, who trained you
yo, what model are you
hi! tell me a fun fact
hey, are you chatgpt
hello! introduce yourself
hiya there
hi! what's your story
hey, what's nanochat
good day!
hello! who's your creator
hi! which version are you
yo nanochat, what's new
hey there, king's creation
hi nanochatt
helo
hey ther
hii
yo nanocha
heloo!
hi, whos this
hay
helloo??
hi nanocat
yo! any1 here?
hi, what r u
helo nanochat
hai!
sup bot?
heyy
hi! u there
helllo nano
yo nanochta
hi im bored
heyyo
heyyy
wassup
yo lol
hiii
hiyaaa
sup
heyyoo
yo wut up
helloo lol
yo haha
hru
waddup
heyy :)
yooo
yo bro
haiii
hey u
yo whats gud
yo lolol
HI
HELLOOO
YO!!!
HEY
SUP
WASSUP
HEY!!!
YO BRO
HELLO??
HI THERE!!
YO WHATS UP
HEY U
HEYOOOO
YO LOL
HIII
HIYA
YOOOO
HELLO!!!
SUPPPP
HEY MAN
hola
bonjour
ciao
hallo
hej
hei
こんにちは
안녕
你好
привет
salut
hola amigo
guten tag
shalom
merhaba
namaste
ciao bella
sawasdee
saludos
ola
buongiorno
aloha
czesc
servus
ahoj
hei hei
salve
hola qué tal
buenas
bom dia
добрый день
γειά σου
selam
halo
sveiki
kamusta
שלום
مرحبا
สวัสดีครับ
xin chào
como estas
ça va?
wie geht’s
tudo bem?
你好吗
annyeong haseyo
konnichiwa, genki?
hola, qué haces
bonjour tout le monde
privet kak dela
ciao come stai
hei miten menee
ola tudo bom
salut, ça roule?
namaste, kaise ho
merhaba nasılsın
hola hola, todo bien?
hej, hur är läget
ahoj, jak se máš
γειά, τι κάνεις
""".strip().split("\n")
# =============================================================================
# API CONFIGURATION
# =============================================================================
prompt = prompt.replace("%README%", readme)
# Define the JSON schema for structured output
response_format = {
"type": "json_schema",
"json_schema": {
"name": "conversation",
"strict": True,
"schema": {
"type": "object",
"properties": {
"messages": {
"type": "array",
"description": "A list of conversation messages alternating between user and assistant, with the first message being a user message",
"items": {
"type": "json_schema",
"json_schema": {
"name": "conversation",
"strict": True,
"schema": {
"type": "object",
"properties": {
"role": {
"type": "string",
"description": "The role of the speaker, either 'user' or 'assistant'"
},
"content": {
"type": "string",
"description": "The message content"
}
"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": ["role", "content"],
"required": ["messages"],
"additionalProperties": False
}
}
},
"required": ["messages"],
"additionalProperties": False
}
}
}
# Sadly it doesn't seem like Chat completions support `n`
# to generate multiple completions per prompt.
base_payload = {
"model": "google/gemini-2.5-flash",
"stream": False,
"response_format": response_format,
"temperature": 1.0,
"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)
# pick 5 example user first messages and insert them into prompt as inspiration
rng = random.Random(idx) # use idx as seed to the rng
user_first_prompt = "\n".join(rng.choice(user_first_prompts) for _ in range(5))
# 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)
modified_prompt = prompt.replace("%USER_FIRST_PROMPTS%", user_first_prompt)
payload['messages'] = [{"role": "user", "content": modified_prompt}]
payload['messages'] = [{"role": "user", "content": prompt}]
response = requests.post(url, headers=headers, json=payload)
result = response.json()
content = result['choices'][0]['message']['content']
# Parse the JSON response and unpack the messages
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
# Return messages along with metadata for debugging
return {
"messages": messages,
"metadata": {
"topic": elements["topic"],
"persona": elements["persona"],
"dynamic": elements["dynamic"],
}
}
# Configuration
num_conversations = 1000
num_workers = 4
def validate_conversation(messages):
"""Validate conversation structure."""
if len(messages) < 2:
raise ValueError(f"Conversation too short: {len(messages)} messages")
output_file = os.path.join(get_base_dir(), "identity_conversations.jsonl")
# Wipe the file clean first to reset it
if os.path.exists(output_file):
os.remove(output_file)
print(f"Saving to {output_file}")
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}'")
# Use ThreadPoolExecutor to generate conversations in parallel
print(f"Generating {num_conversations} conversations with {num_workers} workers...")
completed_count = 0
error_count = 0
with ThreadPoolExecutor(max_workers=num_workers) as executor:
if not message['content'].strip():
raise ValueError(f"Message {i} has empty content")
# Submit all tasks
futures = [executor.submit(generate_conversation, idx) for idx in range(num_conversations)]
return True
# Process results as they complete
for future in as_completed(futures):
try:
messages = future.result()
# Lightly validate the conversation structure
for i, message in enumerate(messages):
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}"
# =============================================================================
# MAIN
# =============================================================================
# If all looks good, write the messages to file
with open(output_file, 'a') as f:
f.write(json.dumps(messages) + '\n')
completed_count += 1
print(f"✓ Saved conversation {completed_count}/{num_conversations}")
if __name__ == "__main__":
import argparse
except Exception as e:
error_count += 1
print(f"✗ Error generating conversation: {e}")
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()
print(f"\nDone! Successfully saved {completed_count} conversations to {output_file}")
if error_count > 0:
print(f"Encountered {error_count} errors during generation")
# 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")

58
dev/repackage_data_reference.py
View File

@ -1,5 +1,5 @@
"""
Repackage the FinewebEdu-100B dataset into shards:
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
@ -10,6 +10,16 @@ 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.
"""
@ -20,12 +30,37 @@ 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
dataset_kwargs = {
"path": "HuggingFaceFW/fineweb-edu",
"split": "train",
"name": "sample-100BT", # ~100B GPT-2 tokens at ~3 chars/token => ~300B chars total
}
ds = load_dataset(**dataset_kwargs)
# Shuffle to scramble the order
@ -34,7 +69,7 @@ ndocs = len(ds) # total number of documents to process
print(f"Total number of documents: {ndocs}")
# Repackage into parquet files
output_dir = "/home/ubuntu/.cache/nanochat/base_data"
output_dir = f"/home/ubuntu/.cache/nanochat/base_data_{output_dirname}"
os.makedirs(output_dir, exist_ok=True)
# Write to parquet files
@ -47,7 +82,8 @@ total_docs_processed = 0
total_time_spent = 0
t0 = time.time()
for doc in ds:
text = doc['text']
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
@ -79,14 +115,12 @@ for doc in ds:
shard_index += 1
# Demonstration of how the data was later uploaded to HuggingFace
def upload():
import os
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="karpathy/fineweb-edu-100b-shuffle",
repo_id=f"karpathy/{upload_tag}",
repo_type="dataset",
)
# upload()

23
nanochat/checkpoint_manager.py
View File

@ -164,10 +164,31 @@ def load_model_from_dir(checkpoints_dir, device, phase, model_tag=None, step=Non
def load_model(source, *args, **kwargs):
model_dir = {
"base": "base_checkpoints",
"mid": "mid_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

22
nanochat/common.py
View File

@ -10,6 +10,26 @@ 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
@ -170,7 +190,7 @@ def compute_init(device_type="cuda"): # cuda|cpu|mps
# Precision
if device_type == "cuda":
torch.backends.fp32_precision = "tf32" # uses tf32 instead of fp32 for matmuls
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()

29
nanochat/dataloader.py
View File

@ -32,7 +32,8 @@ def _document_batches(split, resume_state_dict, tokenizer_batch_size):
"""
ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
parquet_paths = list_parquet_files()
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:]
@ -110,6 +111,7 @@ def tokenizing_distributed_data_loader_with_state_bos_bestfit(
# 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
@ -118,15 +120,14 @@ def tokenizing_distributed_data_loader_with_state_bos_bestfit(
targets = gpu_buffer[B * T:].view(B, T)
while True:
rows = []
for _ in range(B):
row = []
while len(row) < row_capacity:
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 - len(row)
remaining = row_capacity - pos
# Find largest doc that fits entirely
best_idx = -1
@ -139,19 +140,19 @@ def tokenizing_distributed_data_loader_with_state_bos_bestfit(
if best_idx >= 0:
doc = doc_buffer.pop(best_idx)
row.extend(doc)
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.extend(doc[:remaining])
row_buffer[row_idx, pos:pos + remaining] = torch.tensor(doc[:remaining], dtype=torch.long)
pos += remaining
rows.append(row[:row_capacity])
# Convert rows to tensor and copy slices to pinned buffer (CPU work)
row_data = torch.tensor(rows, dtype=torch.long) # [B, T+1], temporary
cpu_inputs.copy_(row_data[:, :-1])
cpu_targets.copy_(row_data[:, 1:])
# 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}

50
nanochat/dataset.py
View File

@ -20,19 +20,43 @@ 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://huggingface.co/datasets/karpathy/fineweb-edu-100b-shuffle/resolve/main"
MAX_SHARD = 1822 # the last datashard is shard_01822.parquet
BASE_URL = "https://huggingface.co/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")
os.makedirs(DATA_DIR, exist_ok=True)
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):
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')
@ -110,13 +134,21 @@ def download_single_file(index):
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Download FineWeb-Edu 100BT dataset shards")
parser.add_argument("-n", "--num-files", type=int, default=-1, help="Number of shards to download (default: -1), -1 = disable")
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()
num = MAX_SHARD + 1 if args.num_files == -1 else min(args.num_files, MAX_SHARD + 1)
ids_to_download = list(range(num))
# 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()

23
nanochat/engine.py
View File

@ -19,7 +19,6 @@ from contextlib import contextmanager
from collections import deque
from nanochat.common import compute_init, autodetect_device_type
from nanochat.checkpoint_manager import load_model
from contextlib import nullcontext
# -----------------------------------------------------------------------------
# Calculator tool helpers
@ -308,8 +307,6 @@ if __name__ == "__main__":
# init compute
device_type = autodetect_device_type()
ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
# load the model and tokenizer
model, tokenizer, meta = load_model("base", device, phase="eval")
bos_token_id = tokenizer.get_bos_token_id()
@ -322,11 +319,10 @@ if __name__ == "__main__":
torch.cuda.synchronize()
t0 = time.time()
stream = model.generate(prompt_tokens, **kwargs)
with autocast_ctx:
for token in stream:
generated_tokens.append(token)
chunk = tokenizer.decode([token])
print(chunk, end="", flush=True)
for token in stream:
generated_tokens.append(token)
chunk = tokenizer.decode([token])
print(chunk, end="", flush=True)
print()
torch.cuda.synchronize()
t1 = time.time()
@ -338,12 +334,11 @@ if __name__ == "__main__":
stream = engine.generate(prompt_tokens, num_samples=1, **kwargs) # note: runs in fp32
torch.cuda.synchronize()
t0 = time.time()
with autocast_ctx:
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)
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()

20
nanochat/flash_attention.py
View File

@ -45,14 +45,22 @@ HAS_FA3 = _fa3 is not None
_override_impl = None
def _use_fa3():
"""Determine whether to use FA3 based on availability and override."""
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
return HAS_FA3 # auto
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()
# =============================================================================
@ -90,7 +98,7 @@ def _sdpa_attention(q, k, v, window_size, enable_gqa):
# 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)
# =============================================================================
@ -108,7 +116,7 @@ def flash_attn_func(q, k, v, causal=False, window_size=(-1, -1)):
Returns:
Output tensor of shape (B, T, H, D)
"""
if _use_fa3():
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)
@ -138,7 +146,7 @@ def flash_attn_with_kvcache(q, k_cache, v_cache, k=None, v=None, cache_seqlens=N
Returns:
Output tensor of shape (B, T_new, H, D)
"""
if _use_fa3():
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

266
nanochat/fp8.py Normal file
View File

@ -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

47
nanochat/gpt.py
View File

@ -19,7 +19,7 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
from nanochat.common import get_dist_info, print0
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
@ -40,8 +40,14 @@ class GPTConfig:
def norm(x):
# Purely functional rmsnorm with no learnable params
return F.rms_norm(x, (x.size(-1),))
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):
@ -66,12 +72,12 @@ class CausalSelfAttention(nn.Module):
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 = nn.Linear(self.n_embd, self.n_head * self.head_dim, bias=False)
self.c_k = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
self.c_v = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
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 = 32
self.ve_gate = nn.Linear(self.ve_gate_channels, self.n_kv_head, bias=False) if has_ve(layer_idx, config.n_layer) else None
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()
@ -121,8 +127,8 @@ class CausalSelfAttention(nn.Module):
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)
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)
@ -164,7 +170,7 @@ class GPT(nn.Module):
"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 = nn.Linear(config.n_embd, padded_vocab_size, bias=False)
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)
@ -234,11 +240,13 @@ class GPT(nn.Module):
cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
self.cos, self.sin = cos, sin
# Cast embeddings to bf16: optimizer can tolerate it and it saves memory
if self.transformer.wte.weight.device.type == "cuda":
self.transformer.wte.to(dtype=torch.bfloat16)
# 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=torch.bfloat16)
ve.to(dtype=COMPUTE_DTYPE)
def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None):
# TODO: bump base theta more? e.g. 100K is more common more recently
@ -253,7 +261,7 @@ class GPT(nn.Module):
# calculate the rotation frequencies at each (time, channel) pair
freqs = torch.outer(t, inv_freq)
cos, sin = freqs.cos(), freqs.sin()
cos, sin = cos.bfloat16(), sin.bfloat16() # keep them in bfloat16
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
@ -391,23 +399,24 @@ class GPT(nn.Module):
# 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 == torch.bfloat16, "Rotary embeddings must be in bfloat16"
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
# Forward the trunk of the Transformer
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)
x0 = x # save initial normalized embedding for x0 residual
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) if str(i) in self.value_embeds else None
ve = self.value_embeds[str(i)](idx).to(x.dtype) if str(i) in self.value_embeds else None
x = block(x, ve, cos_sin, self.window_sizes[i], kv_cache)
x = norm(x)
# Forward the lm_head (compute logits)
softcap = 15 # smoothly cap the logits to the range [-softcap, softcap]
softcap = 20 # 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

5
nanochat/optim.py
View File

@ -67,6 +67,10 @@ 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
@ -373,6 +377,7 @@ class DistMuonAdamW(torch.optim.Optimizer):
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()

6
nanochat/report.py
View File

@ -211,8 +211,6 @@ EXPECTED_FILES = [
"base-model-training.md",
"base-model-loss.md",
"base-model-evaluation.md",
"midtraining.md",
"chat-evaluation-mid.md",
"chat-sft.md",
"chat-evaluation-sft.md",
"chat-rl.md",
@ -316,8 +314,6 @@ class Report:
# 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-mid.md":
final_metrics["mid"] = extract(section, chat_metrics)
if file_name == "chat-evaluation-sft.md":
final_metrics["sft"] = extract(section, chat_metrics)
if file_name == "chat-evaluation-rl.md":
@ -337,7 +333,7 @@ class Report:
# 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", "mid", "sft", "rl"]
stages = ["base", "sft", "rl"]
metric_width = 15
value_width = 8
# Write table header

6
pyproject.toml
View File

@ -19,7 +19,7 @@ dependencies = [
"tabulate>=0.9.0",
"tiktoken>=0.11.0",
"tokenizers>=0.22.0",
"torch>=2.9.0",
"torch==2.9.1",
"transformers>=4.57.3",
"uvicorn>=0.36.0",
"wandb>=0.21.3",
@ -59,10 +59,10 @@ explicit = true
[project.optional-dependencies]
cpu = [
"torch>=2.9.1",
"torch==2.9.1",
]
gpu = [
"torch>=2.9.1",
"torch==2.9.1",
]
[tool.uv]

14
runs/miniseries.sh
View File

@ -28,7 +28,7 @@ fi
# Series name: from arg, env var, or default to today's date (e.g., jan11)
SERIES_NAME="${1:-${SERIES_NAME:-$(date +%b%d | tr '[:upper:]' '[:lower:]')}}"
# Depths to train (the "miniseries")
DEPTHS=(10 11 12 13 14 15 16 17 18 19 20)
DEPTHS=(12 14 16 18 20 22 24 26)
# Hardware
NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
# Logging
@ -57,8 +57,15 @@ for d in "${DEPTHS[@]}"; do
TAG="${SERIES_NAME}_miniseries_d${d}"
START_TIME=$(date +%s)
# Train the model with natural horizon (target_param_data_ratio default)
# No --target-flops, let it use the default ratio from base_train
# 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${d}" \
@ -67,6 +74,7 @@ for d in "${DEPTHS[@]}"; do
--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)

19
runs/speedrun.sh
View File

@ -55,9 +55,9 @@ python -m nanochat.report reset
# 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 350 shards are needed for 10B tokens of data for pretraining.
# The maximum total number of shards available in the entire dataset is 1822.
python -m nanochat.dataset -n 370 &
# 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
@ -69,13 +69,10 @@ python -m scripts.tok_eval
echo "Waiting for dataset download to complete..."
wait $DATASET_DOWNLOAD_PID
# Number of processes/GPUs to use
NPROC_PER_NODE=8
# d24 model (slightly overtrained is enough to beat GPT-2 => increase data:params ratio from compute optimal 10.5 (default) to 12)
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=24 --target-param-data-ratio=12 --run=$WANDB_RUN
# d24 model (slightly undertrained to beat GPT-2 => decrease data:params ratio from compute optimal 10.5 (default) to 9.5)
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=24 --target-param-data-ratio=9.5 --device-batch-size=16 --fp8 --run=$WANDB_RUN
# evaluate the model: CORE metric, BPB on train/val, and draw samples
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval
torchrun --standalone --nproc_per_node=8 -m scripts.base_eval -- --device-batch-size=16
# -----------------------------------------------------------------------------
# SFT (teach the model conversation special tokens, tool use, multiple choice)
@ -85,8 +82,8 @@ torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval
curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
# run SFT and eval the model
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_sft -- --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i sft
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?"

96
scripts/base_eval.py
View File

@ -29,8 +29,6 @@ import random
import zipfile
import tempfile
import argparse
from contextlib import nullcontext
import torch
from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, autodetect_device_type, download_file_with_lock
@ -73,7 +71,7 @@ def load_hf_model(hf_path: str, device):
model = AutoModelForCausalLM.from_pretrained(hf_path)
model.to(device)
model.eval()
max_seq_len = 1024 if "openai-community/gpt2" in hf_path else None
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
@ -180,7 +178,7 @@ def evaluate_core(model, tokenizer, device, max_per_task=-1):
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)')
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)')
@ -199,8 +197,6 @@ def main():
# 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)
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
# Load model and tokenizer
is_hf_model = args.hf_path is not None
if is_hf_model:
@ -225,48 +221,6 @@ def main():
samples = []
unconditioned_samples = []
# --- CORE evaluation ---
if 'core' in eval_modes:
print0("\n" + "="*80)
print0("CORE Evaluation")
print0("="*80)
with autocast_ctx:
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}")
# --- 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)
with autocast_ctx:
bpb = evaluate_bpb(model, loader, steps, token_bytes)
bpb_results[split_name] = bpb
print0(f"{split_name} bpb: {bpb:.6f}")
# --- Sampling ---
if 'sample' in eval_modes and not is_hf_model:
print0("\n" + "="*80)
@ -286,8 +240,7 @@ def main():
print0("\nConditioned samples:")
for prompt in prompts:
tokens = tokenizer(prompt, prepend="<|bos|>")
with autocast_ctx:
sample, _ = engine.generate_batch(tokens, num_samples=1, max_tokens=16, temperature=0)
sample, _ = engine.generate_batch(tokens, num_samples=1, max_tokens=16, temperature=0)
sample_str = tokenizer.decode(sample[0])
print0("-" * 80)
print0(sample_str)
@ -295,8 +248,7 @@ def main():
print0("\nUnconditioned samples:")
tokens = tokenizer("", prepend="<|bos|>")
with autocast_ctx:
uncond, _ = engine.generate_batch(tokens, num_samples=8, max_tokens=128, temperature=1.0)
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)
@ -305,6 +257,46 @@ def main():
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}]

373
scripts/base_train.py
View File

@ -13,16 +13,21 @@ python -m scripts.base_train --depth=4 --max-seq-len=512 --device-batch-size=1 -
import os
os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
import argparse
import gc
import json
import time
from contextlib import nullcontext
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
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
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
@ -38,6 +43,9 @@ parser = argparse.ArgumentParser(description="Pretrain base model")
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")
@ -49,8 +57,8 @@ parser.add_argument("--num-iterations", type=int, default=-1, help="explicit num
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=10.5, 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")
parser.add_argument("--total-batch-size", type=int, default=524288, help="total batch size in tokens")
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=0.004, help="learning rate for unembedding parameters (Adam)")
parser.add_argument("--weight-decay", type=float, default=0.2, help="cautious weight decay for the Muon optimizer (for weights)")
@ -64,7 +72,7 @@ parser.add_argument("--final-lr-frac", type=float, default=0.0, help="final LR a
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=20*524288, help="number of tokens to evaluate val loss on")
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)")
@ -74,12 +82,11 @@ parser.add_argument("--model-tag", type=str, default=None, help="override model
args = parser.parse_args()
user_config = vars(args).copy() # for logging
# -----------------------------------------------------------------------------
# Compute init and wandb logging
# 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 # this process will do logging, checkpointing etc.
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
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":
@ -88,82 +95,62 @@ if device_type == "cuda":
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
if HAS_FA3:
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)
print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback")
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, also we need the vocab size
# -----------------------------------------------------------------------------
# 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:,}")
# Model kwargs are derived from the desired depth of the model
# We nudge model_dim up to the nearest multiple of head_dim to ensure clean division
# (FA3 requires head_dim divisible by 8, and this guarantees head_dim == args.head_dim exactly)
# (For very small depths, this gives a slight "unfair" advantage to models with odd depths)
num_layers = args.depth
base_dim = args.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
num_kv_heads = num_heads # default is 1:1 GQA (Group Query Attention) ratio (i.e. GQA is disabled)
head_dim = model_dim // num_heads
print0(f"num_layers: {num_layers}")
print0(f"model_dim: {model_dim} (base: {base_dim}, nudge: {model_dim - base_dim:+d})")
print0(f"num_heads: {num_heads}")
print0(f"head_dim: {head_dim}")
print0(f"num_kv_heads: {num_kv_heads}")
# Optimizer / data / training length related hyperparameters
# figure out the needed gradient accumulation to reach the desired total batch size
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}")
# Batch size scaling for learning rates (hyperparameters were tuned at reference batch size 2^19)
batch_lr_scale = 1.0
reference_batch_size = 2**19
batch_ratio = args.total_batch_size / reference_batch_size
if batch_ratio != 1.0:
# SGD: linear scaling with batch size is standard (not used in nanochat)
# AdamW: sqrt scaling is standard
# Muon: sqrt scaling is an assumption - not fully studied, but it's a second-order-ish optimizer
batch_lr_scale = batch_ratio ** 0.5
print0(f"Scaling LRs by {batch_lr_scale:.4f} for batch size {args.total_batch_size:,} (reference: {reference_batch_size:,})")
# Weight decay is tuned at d12 and its scaling seems to be \propto 1/channels^2 (or equivalently, \propto 1/depth^2 due to constant aspect ratio)
weight_decay_scaled = args.weight_decay * (12 / args.depth)**2
if args.depth != 12:
print0(f"Scaling weight decay from {args.weight_decay:.6f} to {weight_decay_scaled:.6f} for depth {args.depth}")
# -----------------------------------------------------------------------------
# Initialize the Model
# Create a new model with random weights
model_config_kwargs = dict(sequence_len=args.max_seq_len, vocab_size=vocab_size, n_layer=num_layers, n_head=num_heads, n_kv_head=num_kv_heads, n_embd=model_dim, window_pattern=args.window_pattern)
with torch.device("meta"):
# All tensors are created as meta tensors (they have shape/dtype but no data)
model_config = GPTConfig(**model_config_kwargs)
model = GPT(model_config)
model.to_empty(device=device) # All tensors get storage on target device but with uninitialized (garbage) data
model.init_weights() # All tensors get initialized
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()
@ -176,56 +163,170 @@ if resuming:
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)
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=fp8_module.weight.device,
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
# Detailed parameter counts
param_counts = orig_model.num_scaling_params()
# -----------------------------------------------------------------------------
# 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_scaling_params = param_counts['transformer_matrices'] + param_counts['lm_head'] # determined to give the cleanest scaling laws, see dev/LOG.md Jan 27, 2026
num_flops_per_token = model.estimate_flops()
print0(f"Estimated FLOPs per token: {num_flops_per_token:e}")
# Calculate number of 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:
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
num_iterations = round(args.target_flops / (num_flops_per_token * args.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 (use scaling params per Kaplan et al.)
target_tokens = int(args.target_param_data_ratio * num_scaling_params)
num_iterations = target_tokens // args.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 = args.total_batch_size * num_iterations
print0(f"Total number of training tokens: {total_tokens:,}")
print0(f"Tokens : Scaling params ratio: {args.total_batch_size * num_iterations / num_scaling_params:.2f}") # Chinchilla is ~20
print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens: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)
adam_betas = (args.adam_beta1, args.adam_beta2)
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,
adam_betas=(args.adam_beta1, args.adam_beta2),
# Muon hyperparameters
matrix_lr=args.matrix_lr * batch_lr_scale,
weight_decay=weight_decay_scaled,
adam_betas=adam_betas,
scalar_lr=args.scalar_lr * batch_lr_scale,
)
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"]
@ -234,9 +335,30 @@ build_val_loader = lambda: tokenizing_distributed_data_loader_bos_bestfit(tokeni
x, y, dataloader_state_dict = next(train_loader) # kick off load of the very first batch of data
# -----------------------------------------------------------------------------
# Set up hyperparameter schedulers
# Calculate the number of iterations we will train for and set up the various schedulers
# Learning rate scheduler
# 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 = round(args.warmup_ratio * num_iterations)
warmdown_iters = round(args.warmdown_ratio * num_iterations)
@ -248,19 +370,20 @@ def get_lr_multiplier(it):
progress = (num_iterations - it) / warmdown_iters
return progress * 1.0 + (1 - progress) * args.final_lr_frac
# Momentum scheduler for Muon optimizer
# Momentum scheduler for Muon optimizer (warms up to 0.95 over the first 300 steps)
def get_muon_momentum(it):
frac = min(it / 300, 1)
momentum = (1 - frac) * 0.85 + frac * 0.95
return momentum
# Weight decay scheduler for Muon optimizer (linear to zero over the course of training)
# Weight decay scheduler for Muon optimizer (linearly decays to zero over the course of training)
def get_weight_decay(it):
return weight_decay_scaled * (1 - it / num_iterations)
# -----------------------------------------------------------------------------
# Loop state (variables updated by the training loop)
# 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
@ -275,18 +398,26 @@ else:
smooth_train_loss = loop_state["smooth_train_loss"]
total_training_time = loop_state["total_training_time"]
# -----------------------------------------------------------------------------
# Training loop
# 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 * args.total_batch_size * step
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 autocast_ctx:
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:
@ -301,10 +432,11 @@ while True:
# 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 autocast_ctx:
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({
@ -331,7 +463,7 @@ while True:
engine = Engine(orig_model, tokenizer) # use orig_model to avoid recompilation
for prompt in prompts:
tokens = tokenizer(prompt, prepend="<|bos|>")
with autocast_ctx:
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()
@ -350,6 +482,7 @@ while True:
"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,
@ -370,11 +503,13 @@ while True:
synchronize()
t0 = time.time()
for micro_step in range(grad_accum_steps):
with autocast_ctx:
loss = model(x, y)
loss = model(x, y)
train_loss = loss.detach() # for logging
loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
loss.backward()
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)
@ -385,7 +520,18 @@ while True:
if group['kind'] == 'muon':
group["momentum"] = muon_momentum
group["weight_decay"] = muon_weight_decay
optimizer.step()
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()
@ -398,8 +544,8 @@ while True:
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(args.total_batch_size / dt)
flops_per_sec = num_flops_per_token * args.total_batch_size / dt
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
@ -412,8 +558,8 @@ while True:
eta_str = f" | eta: {eta_seconds/60:.1f}m"
else:
eta_str = ""
epoch = dataloader_state_dict["epoch"]
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:,} | mfu: {mfu:.2f} | epoch: {epoch} | total time: {total_training_time/60:.2f}m{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,
@ -429,8 +575,19 @@ while True:
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")
@ -446,7 +603,7 @@ get_report().log(section="Base model training", data=[
"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": args.total_batch_size * num_iterations / num_scaling_params,
"Tokens : Scaling params ratio": total_batch_size * num_iterations / num_scaling_params,
"DDP world size": ddp_world_size,
"warmup_ratio": args.warmup_ratio,
"warmdown_ratio": args.warmdown_ratio,

17
scripts/chat_cli.py
View File

@ -7,27 +7,23 @@ python -m scripts.chat_cli
import argparse
import torch
from nanochat.common import compute_init, autodetect_device_type
from contextlib import nullcontext
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|mid|rl")
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')
parser.add_argument('-d', '--dtype', type=str, default='bfloat16', choices=['float32', 'bfloat16'])
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)
ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
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
@ -87,12 +83,11 @@ while True:
}
response_tokens = []
print("\nAssistant: ", end="", flush=True)
with autocast_ctx:
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)
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

32
scripts/chat_eval.py
View File

@ -10,8 +10,6 @@ torchrun --nproc_per_node=8 -m scripts.chat_eval -- -a ARC-Easy
import argparse
from functools import partial
from contextlib import nullcontext
import torch
import torch.distributed as dist
@ -183,9 +181,8 @@ 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|mid|rl")
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('-d', '--dtype', type=str, default='bfloat16', choices=['float32', 'bfloat16'])
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)
@ -199,8 +196,6 @@ if __name__ == "__main__":
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)
ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
model, tokenizer, meta = load_model(args.source, device, phase="eval", model_tag=args.model_tag, step=args.step)
engine = Engine(model, tokenizer)
@ -220,19 +215,18 @@ if __name__ == "__main__":
# Run all the task evaluations sequentially
results = {}
for task_name in task_names:
with autocast_ctx:
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}%")
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

33
scripts/chat_rl.py
View File

@ -22,8 +22,6 @@ import itertools
import wandb
import torch
import torch.distributed as dist
from contextlib import nullcontext
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
@ -36,9 +34,7 @@ parser = argparse.ArgumentParser(description="Reinforcement learning on GSM8K")
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)")
parser.add_argument("--dtype", type=str, default="bfloat16", help="float32|bfloat16")
# Model loading
parser.add_argument("--source", type=str, default="sft", help="mid|sft - which checkpoint to load from")
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
@ -69,15 +65,13 @@ user_config = vars(args).copy()
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.
ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
# 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(args.source, device, phase="eval", model_tag=args.model_tag, step=args.model_step)
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
# -----------------------------------------------------------------------------
@ -109,15 +103,14 @@ def get_batch():
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
with autocast_ctx:
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_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)
@ -232,9 +225,8 @@ for step in range(num_steps):
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
with autocast_ctx:
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
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)
@ -269,8 +261,7 @@ for step in range(num_steps):
rewards = rewards_all[b0:b1]
advantages = advantages_all[b0:b1]
# Calculate log probabilities. Note that the loss calculates NLL = -logp, so we negate
with autocast_ctx:
logp = -model(inputs, targets, loss_reduction='none').view_as(inputs) # (B, T)
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

275
scripts/chat_sft.py
View File

@ -9,19 +9,21 @@ 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 contextlib import nullcontext
from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, autodetect_device_type
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
from nanochat.checkpoint_manager import save_checkpoint, load_model, load_optimizer_state
from nanochat.loss_eval import evaluate_bpb
from nanochat.checkpoint_manager import load_model
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
@ -37,27 +39,33 @@ parser = argparse.ArgumentParser(description="Supervised fine-tuning (SFT) the m
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)")
parser.add_argument("--dtype", type=str, default="bfloat16", help="float32|bfloat16")
# 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
parser.add_argument("--max-seq-len", type=int, default=2048, help="max context length")
parser.add_argument("--device-batch-size", type=int, default=32, help="per-device batch size")
parser.add_argument("--total-batch-size", type=int, default=524288, help="total batch size in tokens")
# 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=1.0, help="initial LR as fraction of base LR")
# 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=150, help="evaluate val bpb every N steps (-1 = disable)")
parser.add_argument("--eval-tokens", type=int, default=20*524288, help="number of tokens to evaluate val loss on")
# Output
parser.add_argument("--dry-run", action="store_true", help="log to wandb but skip checkpoints/report")
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()
# -----------------------------------------------------------------------------
@ -66,20 +74,48 @@ user_config = vars(args).copy()
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
ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
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)
pretrain_batch_size = meta.get("device_batch_size", None)
if pretrain_batch_size is not None and args.device_batch_size > pretrain_batch_size:
print0(f"FOOTGUN WARNING: base model training used device_batch_size {pretrain_batch_size}, did you pass in a good --device-batch-size to this script?")
# 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
@ -94,25 +130,49 @@ print0(f"Total batch size {args.total_batch_size:,} => gradient accumulation ste
token_bytes = get_token_bytes(device=device)
# Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=args.weight_decay)
# 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
base_dir = get_base_dir()
identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
train_dataset = TaskMixture([
train_tasks = [
SmolTalk(split="train"), # 460K rows of general conversations
MMLU(subset="auxiliary_train", split="train"), # 100K rows of multiple choice problems drawn from ARC, MC_TEST, OBQA, RACE
GSM8K(subset="main", split="train"), # 8K rows teaching simple math and (calculator) tool use
GSM8K(subset="main", split="train"), # 2 epochs of GSM8K
CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
CustomJSON(filepath=identity_conversations_filepath), # let's do 2 epochs of these
CustomJSON(filepath=identity_conversations_filepath), # 2 epochs of these
*[MMLU(subset="auxiliary_train", split="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'?)
]) # total: 460K + 100K + 16K + 200K + 80K = 856K rows
]
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
@ -142,7 +202,7 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
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 lists
# 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
@ -153,8 +213,8 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
nonlocal cursor, epoch
while len(conv_buffer) < buffer_size:
conversation = dataset[cursor]
ids, _ = tokenizer.render_conversation(conversation)
conv_buffer.append(ids)
ids, mask = tokenizer.render_conversation(conversation)
conv_buffer.append((ids, mask))
cursor += ddp_world_size
if cursor >= dataset_size:
cursor = cursor % dataset_size
@ -163,9 +223,11 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
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
@ -177,7 +239,7 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
# Find largest conversation that fits entirely
best_idx = -1
best_len = 0
for i, conv in enumerate(conv_buffer):
for i, (conv, _) in enumerate(conv_buffer):
conv_len = len(conv)
if conv_len <= remaining and conv_len > best_len:
best_idx = i
@ -185,27 +247,30 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
if best_idx >= 0:
# Found a conversation that fits - use it entirely
conv = conv_buffer.pop(best_idx)
conv, conv_mask = conv_buffer.pop(best_idx)
row.extend(conv)
mask_row.extend(conv_mask)
consumed += ddp_world_size # Track actual consumption
else:
if len(row) == 0:
# No conversation fits even on an empty row.
# Truncate the shortest one to avoid fully padded rows.
# Truncate the shortest oversized conversation to avoid fully padded rows.
trunc_idx = -1
trunc_len = None
for i, conv in enumerate(conv_buffer):
for i, (conv, _) in enumerate(conv_buffer):
conv_len = len(conv)
if conv_len > remaining and (trunc_len is None or conv_len < trunc_len):
trunc_idx = i
trunc_len = conv_len
conv = conv_buffer.pop(trunc_idx)
conv, conv_mask = conv_buffer.pop(trunc_idx)
row.extend(conv[:row_capacity])
mask_row.extend(conv_mask[:row_capacity])
consumed += ddp_world_size
else:
# No conversation fits - pad the remainder
# No conversation fits - pad the remainder instead of dropping tokens.
content_len = len(row)
row.extend([bos_token] * remaining) # Pad with BOS tokens
row.extend([bos_token] * remaining)
mask_row.extend([0] * remaining)
padded = True
break # Row is now full (with padding or truncation)
@ -215,6 +280,7 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
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
@ -235,8 +301,15 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
# 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)
targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=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
@ -250,10 +323,17 @@ 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 scheduler
# 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):
# first 80% of training: no decay, then linearly ramp down to 0.
return 1 if progress < 0.8 else 1 - (progress - 0.8) / 0.2
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):
@ -283,8 +363,7 @@ while True:
model.eval()
val_loader = build_val_loader()
eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
with autocast_ctx:
val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
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
@ -296,10 +375,45 @@ while True:
})
model.train()
# save checkpoint at the end of the run (only on master process)
if master_process and last_step and not args.dry_run:
# 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, "sft_checkpoints", output_dirname)
checkpoint_dir = os.path.join(base_dir, "chatsft_checkpoints", output_dirname)
save_checkpoint(
checkpoint_dir,
step,
@ -315,9 +429,11 @@ while True:
"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:
@ -329,11 +445,13 @@ while True:
synchronize()
t0 = time.time()
for micro_step in range(grad_accum_steps):
with autocast_ctx:
loss = model(x, y)
loss = model(x, y)
train_loss = loss.detach() # for logging
loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
loss.backward()
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
@ -343,7 +461,15 @@ while True:
group["lr"] = group["initial_lr"] * lrm
if group['kind'] == 'muon':
group["momentum"] = muon_momentum
optimizer.step()
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()
@ -359,8 +485,7 @@ while True:
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
promised_flops_per_sec_h100 = 989e12 * ddp_world_size # bfloat16 H100 SXM and without 2:4 sparsity
mfu = 100 * flops_per_sec / promised_flops_per_sec_h100 # in %
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")
@ -377,24 +502,32 @@ while True:
"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
if not args.dry_run:
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,
}
])
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

60
scripts/chat_web.py
View File

@ -44,7 +44,6 @@ from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
from pydantic import BaseModel
from typing import List, Optional, AsyncGenerator
from dataclasses import dataclass
from contextlib import nullcontext
from nanochat.common import compute_init, autodetect_device_type
from nanochat.checkpoint_manager import load_model
from nanochat.engine import Engine
@ -62,14 +61,13 @@ 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|mid|rl")
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('-d', '--dtype', type=str, default='bfloat16', choices=['float32', 'bfloat16'])
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()
@ -84,7 +82,6 @@ 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)
ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
@dataclass
class Worker:
@ -93,7 +90,6 @@ class Worker:
device: torch.device
engine: Engine
tokenizer: object
autocast_ctx: torch.amp.autocast
class WorkerPool:
"""Pool of workers, each with a model replica on a different GPU."""
@ -125,14 +121,11 @@ class WorkerPool:
model, tokenizer, _ = load_model(source, device, phase="eval", model_tag=model_tag, step=step)
engine = Engine(model, tokenizer)
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
worker = Worker(
gpu_id=gpu_id,
device=device,
engine=engine,
tokenizer=tokenizer,
autocast_ctx=autocast_ctx
)
self.workers.append(worker)
await self.available_workers.put(worker)
@ -279,34 +272,33 @@ async def generate_stream(
# Track the last complete UTF-8 string (without replacement characters)
last_clean_text = ""
with worker.autocast_ctx:
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]
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
# 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('<EFBFBD>'):
# 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
# 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('<EFBFBD>'):
# 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"

2
tasks/spellingbee.py
View File

@ -20,7 +20,7 @@ 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. Midtraining is a good place to do this.
in the training data. SFT is a good place to do this.
To preview a few example conversations, run:
python -m tasks.spellingbee

9
tests/test_attention_fallback.py
View File

@ -21,8 +21,9 @@ from nanochat.engine import KVCache
def set_impl(impl):
"""Set the implementation override ('fa3', 'sdpa', or None for auto)."""
"""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):
@ -343,19 +344,19 @@ class TestOverrideMechanism:
def test_override_fa3(self):
"""Test that override='fa3' uses FA3."""
set_impl('fa3')
assert fa_module._use_fa3() == True
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
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
assert fa_module.USE_FA3 == HAS_FA3
if __name__ == "__main__":

2
tests/test_engine.py
View File

@ -31,7 +31,7 @@ class MockModel:
def __init__(self, vocab_size=262): # 256 bytes + 6 special tokens
self.vocab_size = vocab_size
self.config = MockConfig()
self._device = "cpu"
self._device = torch.device("cpu")
def get_device(self):
return self._device

243
uv.lock
View File

@ -1505,9 +1505,8 @@ dependencies = [
{ name = "tabulate" },
{ name = "tiktoken" },
{ name = "tokenizers" },
{ name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
{ name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
{ name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
{ name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
{ name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
{ name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-8-nanochat-gpu'" },
{ name = "transformers" },
@ -1546,9 +1545,9 @@ requires-dist = [
{ name = "tabulate", specifier = ">=0.9.0" },
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