diff --git a/sandboxes/autoresearch-spark/Dockerfile b/sandboxes/autoresearch-spark/Dockerfile new file mode 100644 index 0000000..3298343 --- /dev/null +++ b/sandboxes/autoresearch-spark/Dockerfile @@ -0,0 +1,135 @@ +# syntax=docker/dockerfile:1 + +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +# autoresearch-spark sandbox image for OpenShell +# +# Autonomous LLM pretraining research on DGX Spark (GB10 / Blackwell). +# Uses CUDA 13.0 devel base for sm_121a ptxas support (Triton kernel +# compilation) and PyTorch cu128 wheels. +# +# Build: docker build -t autoresearch-spark . + +# --------------------------------------------------------------------------- +# Stage 1: System base +# --------------------------------------------------------------------------- + +# This image uses its own CUDA base (not the community base) because the +# GB10 / Blackwell GPU requires CUDA 13.0 devel for sm_121a ptxas support. +# The ARG is declared so the CI sandbox-build workflow can parse it, but +# it is intentionally unused — the FROM below pins the CUDA image directly. +ARG BASE_IMAGE=nvidia/cuda:13.0.0-cudnn-devel-ubuntu24.04 +FROM nvidia/cuda:13.0.0-cudnn-devel-ubuntu24.04 AS system + +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 + +WORKDIR /sandbox + +# Core system dependencies (mirrors sandboxes/base) +RUN apt-get update && apt-get install -y --no-install-recommends \ + ca-certificates \ + curl \ + dnsutils \ + iproute2 \ + iptables \ + iputils-ping \ + net-tools \ + netcat-openbsd \ + openssh-sftp-server \ + procps \ + traceroute \ + && rm -rf /var/lib/apt/lists/* + +# Create supervisor and sandbox users/groups (same as base) +RUN groupadd -r supervisor && useradd -r -g supervisor -s /usr/sbin/nologin supervisor && \ + groupadd -r sandbox && useradd -r -g sandbox -d /sandbox -s /bin/bash sandbox + +# --------------------------------------------------------------------------- +# Stage 2: Developer tools +# --------------------------------------------------------------------------- +FROM system AS devtools + +# Node.js 22 + build toolchain +RUN curl -fsSL https://deb.nodesource.com/setup_22.x | bash - && \ + apt-get install -y --no-install-recommends \ + build-essential \ + git \ + nodejs=22.22.1-1nodesource1 \ + vim-tiny \ + nano \ + && rm -rf /var/lib/apt/lists/* \ + && npm install -g npm@11.11.0 + +# Global npm packages (same versions as base) +RUN npm install -g \ + tar@7.5.11 \ + @hono/node-server@1.19.11 \ + opencode-ai@1.2.18 \ + @openai/codex@0.111.0 + +# GitHub CLI +RUN curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg \ + -o /usr/share/keyrings/githubcli-archive-keyring.gpg && \ + echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" \ + > /etc/apt/sources.list.d/github-cli.list && \ + apt-get update && apt-get install -y --no-install-recommends gh && \ + rm -rf /var/lib/apt/lists/* + +# Claude CLI (via native installer) +RUN curl -fsSL https://claude.ai/install.sh | bash \ + && cp /root/.local/bin/claude /usr/local/bin/claude \ + && chmod 755 /usr/local/bin/claude + +# uv (Python package/project manager) +COPY --from=ghcr.io/astral-sh/uv:0.10.8 /uv /usr/local/bin/uv +ENV UV_PYTHON_INSTALL_DIR="/sandbox/.uv/python" +RUN uv python install 3.13 && \ + ln -s $(uv python find 3.13) /usr/local/bin/python3 && \ + ln -s $(uv python find 3.13) /usr/local/bin/python && \ + uv cache clean + +# --------------------------------------------------------------------------- +# Stage 3: Final image with autoresearch +# --------------------------------------------------------------------------- +FROM devtools AS final + +# GB10 / Blackwell (sm_121a) requires CUDA 13.0's ptxas for Triton kernel +# compilation. PyTorch cu128 wheels work fine on the CUDA 13.0 runtime +# (backward compatible), but Triton needs the newer ptxas to emit sm_121a +# instructions. Without this, Triton falls back to slow generic kernels. +ENV TRITON_PTXAS_PATH="/usr/local/cuda-13.0/bin/ptxas" + +# Add venvs to PATH +ENV PATH="/sandbox/.venv/bin:/usr/local/bin:/usr/bin:/bin" \ + VIRTUAL_ENV="/sandbox/.venv" + +# Sandbox network / filesystem policy +COPY policy.yaml /etc/openshell/policy.yaml + +# Create writable venv +RUN mkdir -p /sandbox/.claude && \ + uv venv --python 3.13 --seed /sandbox/.venv && \ + uv cache clean && \ + chown -R sandbox:sandbox /sandbox/.venv + +# Copy autoresearch code +COPY autoresearch/ /sandbox/autoresearch/ + +# Install autoresearch Python dependencies (PyTorch cu128, kernels, etc.) +RUN uv sync --project /sandbox/autoresearch && \ + uv cache clean + +# Shell init (same pattern as base) +RUN printf 'export PATH="/sandbox/.venv/bin:/usr/local/bin:/usr/bin:/bin"\nexport VIRTUAL_ENV="/sandbox/.venv"\nexport UV_PYTHON_INSTALL_DIR="/sandbox/.uv/python"\nexport TRITON_PTXAS_PATH="/usr/local/cuda-13.0/bin/ptxas"\nexport PS1="\\u@\\h:\\w\\$ "\n' \ + > /sandbox/.bashrc && \ + printf '[ -f ~/.bashrc ] && . ~/.bashrc\n' > /sandbox/.profile && \ + chown sandbox:sandbox /sandbox/.bashrc /sandbox/.profile && \ + chown -R sandbox:sandbox /sandbox + +USER sandbox +WORKDIR /sandbox/autoresearch + +ENTRYPOINT ["/bin/bash"] diff --git a/sandboxes/autoresearch-spark/README.md b/sandboxes/autoresearch-spark/README.md new file mode 100644 index 0000000..f352bd4 --- /dev/null +++ b/sandboxes/autoresearch-spark/README.md @@ -0,0 +1,47 @@ +# autoresearch-spark + +Autonomous LLM pretraining research on DGX Spark (GB10 / Blackwell). + +Based on [karpathy/autoresearch](https://github.com/karpathy/autoresearch), +adapted for the NVIDIA GB10 single-GPU platform. + +## Quick start + +``` +openshell sandbox create \ + --remote my-spark \ + --gpu \ + --provider claude \ + --provider github \ + --from autoresearch-spark \ + -- claude +``` + +This launches an autoresearch sandbox on your DGX Spark with Claude as the +autonomous researcher. The agent will set up, run experiments, and iterate +on the model -- all while you sleep. + +## Why CUDA 13.0 + cu128? + +The DGX Spark has an NVIDIA GB10 GPU (Blackwell, compute capability 12.1 / +sm_121a). This creates a unique toolchain situation: + +- **CUDA 13.0 devel base image**: provides `ptxas` with sm_121a support, + which Triton needs to compile optimized GPU kernels for the GB10. +- **PyTorch cu128 wheels**: the cu130 wheels are not yet functional on this + platform, but cu128 works correctly on the CUDA 13.0 runtime (backward + compatible). +- **`TRITON_PTXAS_PATH`** is set globally in the container so Triton finds + the CUDA 13.0 ptxas automatically. + +## Platform notes + +See [autoresearch/LEARNINGS.md](autoresearch/LEARNINGS.md) for GB10-specific +findings including hyperparameter tuning, MFU expectations, and best configs +found across ~135 experiments. + +## Build + +``` +docker build -t autoresearch-spark . +``` diff --git a/sandboxes/autoresearch-spark/autoresearch/.gitignore b/sandboxes/autoresearch-spark/autoresearch/.gitignore new file mode 100644 index 0000000..99c30f5 --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/.gitignore @@ -0,0 +1,23 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv +worktrees/ +results/ +queue/ + +# Agent prompt files (generated per-session by launchers) +CLAUDE.md +AGENTS.md + +# Experimental code/artifacts +dev/ + +# Results file +results.tsv diff --git a/sandboxes/autoresearch-spark/autoresearch/.python-version b/sandboxes/autoresearch-spark/autoresearch/.python-version new file mode 100644 index 0000000..c8cfe39 --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/sandboxes/autoresearch-spark/autoresearch/LEARNINGS.md b/sandboxes/autoresearch-spark/autoresearch/LEARNINGS.md new file mode 100644 index 0000000..f2e6b24 --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/LEARNINGS.md @@ -0,0 +1,75 @@ +# Learnings + +Platform-specific notes and experiment findings accumulated across autoresearch runs. + +--- + +## Platform: NVIDIA GB10 (Blackwell, CUDA 12.1) — March 2026 + +### Environment quirks + +- **`uv` is not in `$PATH`** — must invoke as `/sandbox/.local/bin/uv run train.py` +- `train.py` already sets `TRITON_PTXAS_PATH` to the system CUDA 13.0 `ptxas`, which helps Triton-compiled kernels target the GB10. PyTorch's own CUDA kernels are still limited to sm_120 and below. + +### GPU compatibility issue + +PyTorch (as of this run) supports CUDA capability up to 12.0. The GB10 is 12.1, so PyTorch cannot use its optimized kernels (fast matmul, FlashAttention, etc.) and falls back to slow generic paths. + +**Symptoms:** +- MFU ~0.86% (vs 35–45% on a well-supported GPU like H100) +- ~15 seconds per training step (vs ~0.3s on H100) +- ~31 optimizer steps in the 5-minute budget (vs ~950 on H100) + +### Key hyperparameter adaptation + +The default `TOTAL_BATCH_SIZE = 2**19` (~524K tokens/step) was tuned for ~950 steps/run. With only 31 steps available, the model barely trains. The fix is to shrink the batch so you get far more gradient updates in the time budget: + +| TOTAL_BATCH_SIZE | steps/run | val_bpb | +|-----------------|-----------|---------| +| 2^19 (default) | ~31 | 1.8794 | +| 2^17 | ~89 | 1.6715 | +| 2^16 | ~164 | 1.4010 | +| 2^15 | ~360 | 1.3107 | +| **2^14** | **~689** | **1.3073** ← sweet spot | +| 2^13 | ~1242 | 1.3301 ↑ too noisy | + +**Also reduce `DEVICE_BATCH_SIZE` proportionally** (e.g. 8 with `TOTAL_BATCH_SIZE=2^14`) to keep `grad_accum_steps=1` — gradient accumulation hurts quality on this platform. + +### Best config found (mar15 run, val_bpb 1.2265 from baseline 1.8794) + +```python +ASPECT_RATIO = 96 # model_dim = depth * ASPECT_RATIO +HEAD_DIM = 96 # 4 attention heads +WINDOW_PATTERN = "L" # all full-context attention (no sliding window) +TOTAL_BATCH_SIZE = 2**14 # ~16K tokens/step +DEVICE_BATCH_SIZE = 8 # grad_accum_steps = 1 +EMBEDDING_LR = 0.35 +MATRIX_LR = 0.05 +WEIGHT_DECAY = 0.0 +ADAM_BETAS = (0.9, 0.99) +WARMDOWN_RATIO = 0.6 +FINAL_LR_FRAC = 0.05 +DEPTH = 4 +# Muon: beta2=0.90, momentum warmup 0.85->0.95 over 5000 steps +``` + +### Other findings (mar15, ~135 experiments) + +**Helped:** +- Full-context attention (`WINDOW_PATTERN="L"`) over sliding window (`"SSSL"`) +- Larger head dim (96–128) over smaller (32–64) at this model size +- Slow Muon momentum warmup (0.85→0.95 over 5000 steps) +- No weight decay (`WEIGHT_DECAY=0.0`) +- `ADAM_BETAS=(0.9, 0.99)` — higher beta2 for small batches +- `FINAL_LR_FRAC=0.05` — don't decay LR fully to zero +- Muon `beta2=0.90` (less smoothing of second moment) + +**Did not help / hurt:** +- GQA (grouped-query attention) — speed gain not worth quality loss +- SwiGLU activation — worse than ReLU² +- Gradient clipping — overhead reduces step count +- Wider MLP (4x→8x expansion) — fewer steps, worse result +- Removing value embeddings — significantly worse +- Post-norm — much worse than pre-norm +- Parallel attention+MLP (GPT-J style) — much worse +- Label smoothing — breaks train/val metric alignment diff --git a/sandboxes/autoresearch-spark/autoresearch/generate.py b/sandboxes/autoresearch-spark/autoresearch/generate.py new file mode 100644 index 0000000..153dcfd --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/generate.py @@ -0,0 +1,273 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Interactive text generation from a trained model checkpoint. +Usage: python generate.py [--checkpoint model.pt] [--prompt "Once upon a time"] +""" + +import argparse +import os + +os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1" +os.environ.setdefault("TRITON_PTXAS_PATH", "/usr/local/cuda-13.0/bin/ptxas") + +import torch +import torch.nn as nn +import torch.nn.functional as F +from dataclasses import dataclass + +from prepare import Tokenizer + +# --------------------------------------------------------------------------- +# Model (copied from train.py to avoid importing training code) +# --------------------------------------------------------------------------- + + +@dataclass +class GPTConfig: + sequence_len: int = 2048 + vocab_size: int = 32768 + n_layer: int = 12 + n_head: int = 6 + n_kv_head: int = 6 + n_embd: int = 768 + window_pattern: str = "SSSL" + + +def norm(x): + return F.rms_norm(x, (x.size(-1),)) + + +def has_ve(layer_idx, n_layer): + return layer_idx % 2 == (n_layer - 1) % 2 + + +def apply_rotary_emb(x, cos, sin): + assert x.ndim == 4 + d = x.shape[3] // 2 + x1, x2 = x[..., :d], x[..., d:] + return torch.cat([x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos], 3) + + +class CausalSelfAttention(nn.Module): + def __init__(self, config, layer_idx): + super().__init__() + self.n_head = config.n_head + self.n_kv_head = config.n_kv_head + self.head_dim = config.n_embd // config.n_head + self.c_q = nn.Linear(config.n_embd, self.n_head * self.head_dim, bias=False) + self.c_k = nn.Linear(config.n_embd, self.n_kv_head * self.head_dim, bias=False) + self.c_v = nn.Linear(config.n_embd, self.n_kv_head * self.head_dim, bias=False) + self.c_proj = nn.Linear(config.n_embd, config.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 + ) + + def forward(self, x, ve, cos_sin, window_size): + B, T, C = x.size() + q = self.c_q(x).view(B, T, self.n_head, self.head_dim) + k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim) + v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim) + if ve is not None: + ve = ve.view(B, T, self.n_kv_head, self.head_dim) + gate = 2 * torch.sigmoid(self.ve_gate(x[..., : self.ve_gate_channels])) + v = v + gate.unsqueeze(-1) * ve + cos, sin = cos_sin + q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin) + q, k = norm(q), norm(k) + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + if self.n_head != self.n_kv_head: + groups = self.n_head // self.n_kv_head + k = k.repeat_interleave(groups, dim=1) + v = v.repeat_interleave(groups, dim=1) + w = window_size[0] + if w < T: + rows = torch.arange(T, device=x.device).unsqueeze(1) + cols = torch.arange(T, device=x.device).unsqueeze(0) + mask = (cols <= rows) & (cols > rows - w) + attn_mask = torch.zeros(T, T, device=x.device, dtype=q.dtype).masked_fill( + ~mask, float("-inf") + ) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + else: + y = F.scaled_dot_product_attention(q, k, v, is_causal=True) + return self.c_proj(y.transpose(1, 2).contiguous().view(B, T, -1)) + + +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) + + def forward(self, x): + return self.c_proj(F.relu(self.c_fc(x)).square()) + + +class Block(nn.Module): + def __init__(self, config, layer_idx): + super().__init__() + self.attn = CausalSelfAttention(config, layer_idx) + self.mlp = MLP(config) + + def forward(self, x, ve, cos_sin, window_size): + x = x + self.attn(norm(x), ve, cos_sin, window_size) + x = x + self.mlp(norm(x)) + return x + + +class GPT(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.window_sizes = self._compute_window_sizes(config) + self.transformer = nn.ModuleDict( + { + "wte": nn.Embedding(config.vocab_size, config.n_embd), + "h": nn.ModuleList([Block(config, i) for i in range(config.n_layer)]), + } + ) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.resid_lambdas = nn.Parameter(torch.ones(config.n_layer)) + self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer)) + head_dim = config.n_embd // config.n_head + kv_dim = config.n_kv_head * head_dim + self.value_embeds = nn.ModuleDict( + { + str(i): nn.Embedding(config.vocab_size, kv_dim) + for i in range(config.n_layer) + if has_ve(i, config.n_layer) + } + ) + self.rotary_seq_len = config.sequence_len * 10 + cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim) + self.register_buffer("cos", cos, persistent=False) + self.register_buffer("sin", sin, persistent=False) + + def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None): + if device is None: + device = self.transformer.wte.weight.device + channel_range = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device) + inv_freq = 1.0 / (base ** (channel_range / head_dim)) + freqs = torch.outer( + torch.arange(seq_len, dtype=torch.float32, device=device), inv_freq + ) + cos, sin = freqs.cos().bfloat16(), freqs.sin().bfloat16() + return cos[None, :, None, :], sin[None, :, None, :] + + def _compute_window_sizes(self, config): + pattern = config.window_pattern.upper() + long_window = config.sequence_len + char_to_window = {"L": (long_window, 0), "S": (long_window // 2, 0)} + sizes = [ + char_to_window[pattern[i % len(pattern)]] for i in range(config.n_layer) + ] + sizes[-1] = (long_window, 0) + return sizes + + def forward(self, idx): + B, T = idx.size() + cos_sin = self.cos[:, :T], self.sin[:, :T] + x = norm(self.transformer.wte(idx)) + x0 = x + 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 + x = block(x, ve, cos_sin, self.window_sizes[i]) + x = norm(x) + logits = self.lm_head(x).float() + return 15 * torch.tanh(logits / 15) + + +# --------------------------------------------------------------------------- +# Generation +# --------------------------------------------------------------------------- + + +def generate( + model, + tokenizer, + prompt, + max_new_tokens=200, + temperature=1.0, + top_k=50, + device="cuda", +): + model.eval() + ids = tokenizer.encode(prompt, prepend=tokenizer.get_bos_token_id()) + x = torch.tensor(ids, dtype=torch.long, device=device).unsqueeze(0) + max_ctx = model.config.sequence_len + + with torch.no_grad(): + for _ in range(max_new_tokens): + logits = model(x[:, -max_ctx:])[:, -1, :] / temperature + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + logits[logits < v[:, -1:]] = float("-inf") + next_id = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) + x = torch.cat([x, next_id], dim=1) + + return tokenizer.decode(x[0, len(ids) :].tolist()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint", default="model.pt") + parser.add_argument("--prompt", default=None) + parser.add_argument("--max_new_tokens", type=int, default=200) + parser.add_argument("--temperature", type=float, default=0.8) + parser.add_argument("--top_k", type=int, default=50) + args = parser.parse_args() + + device = "cuda" if torch.cuda.is_available() else "cpu" + + print(f"Loading checkpoint: {args.checkpoint}") + ckpt = torch.load(args.checkpoint, map_location=device, weights_only=False) + config = GPTConfig(**ckpt["config"]) + model = GPT(config).to(device) + state_dict = {k.replace("_orig_mod.", ""): v for k, v in ckpt["model"].items()} + model.load_state_dict(state_dict) + print( + f"Loaded ({config.n_layer}L {config.n_embd}d, val_bpb={ckpt.get('val_bpb', '?'):.4f})" + ) + + tokenizer = Tokenizer.from_directory() + + if args.prompt is not None: + output = generate( + model, + tokenizer, + args.prompt, + args.max_new_tokens, + args.temperature, + args.top_k, + device, + ) + print(f"\n{args.prompt}{output}") + else: + print("Interactive mode. Ctrl+C to exit.\n") + while True: + try: + prompt = input(">>> ") + except (KeyboardInterrupt, EOFError): + break + if not prompt: + continue + output = generate( + model, + tokenizer, + prompt, + args.max_new_tokens, + args.temperature, + args.top_k, + device, + ) + print(f"{output}\n") + + +if __name__ == "__main__": + main() diff --git a/sandboxes/autoresearch-spark/autoresearch/prepare.py b/sandboxes/autoresearch-spark/autoresearch/prepare.py new file mode 100644 index 0000000..1c65b6d --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/prepare.py @@ -0,0 +1,434 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +One-time data preparation for autoresearch experiments. +Downloads data shards and trains a BPE tokenizer. + +Usage: + python prepare.py # full prep (download + tokenizer) + python prepare.py --num-shards 8 # download only 8 shards (for testing) + +Data and tokenizer are stored in ~/.cache/autoresearch/. +""" + +import os +import sys +import time +import math +import argparse +import pickle +from multiprocessing import Pool + +import requests +import pyarrow.parquet as pq +import rustbpe +import tiktoken +import torch + +# --------------------------------------------------------------------------- +# Constants (fixed, do not modify) +# --------------------------------------------------------------------------- + +MAX_SEQ_LEN = 2048 # context length +TIME_BUDGET = 300 # training time budget in seconds (5 minutes) +EVAL_TOKENS = 40 * 524288 # number of tokens for val eval + +# --------------------------------------------------------------------------- +# Configuration +# --------------------------------------------------------------------------- + +CACHE_DIR = os.path.join(os.path.expanduser("~"), ".cache", "autoresearch") +DATA_DIR = os.path.join(CACHE_DIR, "data") +TOKENIZER_DIR = os.path.join(CACHE_DIR, "tokenizer") +BASE_URL = "https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle/resolve/main" +MAX_SHARD = 6542 # the last datashard is shard_06542.parquet +VAL_SHARD = MAX_SHARD # pinned validation shard (shard_06542) +VAL_FILENAME = f"shard_{VAL_SHARD:05d}.parquet" +VOCAB_SIZE = 8192 + +# BPE split pattern (GPT-4 style, with \p{N}{1,2} instead of {1,3}) +SPLIT_PATTERN = r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,2}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""" + +SPECIAL_TOKENS = [f"<|reserved_{i}|>" for i in range(4)] +BOS_TOKEN = "<|reserved_0|>" + +# --------------------------------------------------------------------------- +# Data download +# --------------------------------------------------------------------------- + + +def download_single_shard(index): + """Download one parquet shard with retries. Returns True on success.""" + filename = f"shard_{index:05d}.parquet" + filepath = os.path.join(DATA_DIR, filename) + if os.path.exists(filepath): + return True + + url = f"{BASE_URL}/{filename}" + max_attempts = 5 + for attempt in range(1, max_attempts + 1): + try: + response = requests.get(url, stream=True, timeout=30) + response.raise_for_status() + temp_path = filepath + ".tmp" + with open(temp_path, "wb") as f: + for chunk in response.iter_content(chunk_size=1024 * 1024): + if chunk: + f.write(chunk) + os.rename(temp_path, filepath) + print(f" Downloaded {filename}") + return True + except (requests.RequestException, IOError) as e: + print(f" Attempt {attempt}/{max_attempts} failed for {filename}: {e}") + for path in [filepath + ".tmp", filepath]: + if os.path.exists(path): + try: + os.remove(path) + except OSError: + pass + if attempt < max_attempts: + time.sleep(2**attempt) + return False + + +def download_data(num_shards, download_workers=8): + """Download training shards + pinned validation shard.""" + os.makedirs(DATA_DIR, exist_ok=True) + num_train = min(num_shards, MAX_SHARD) + ids = list(range(num_train)) + if VAL_SHARD not in ids: + ids.append(VAL_SHARD) + + # Count what's already downloaded + existing = sum( + 1 + for i in ids + if os.path.exists(os.path.join(DATA_DIR, f"shard_{i:05d}.parquet")) + ) + if existing == len(ids): + print(f"Data: all {len(ids)} shards already downloaded at {DATA_DIR}") + return + + needed = len(ids) - existing + print(f"Data: downloading {needed} shards ({existing} already exist)...") + + workers = max(1, min(download_workers, needed)) + with Pool(processes=workers) as pool: + results = pool.map(download_single_shard, ids) + + ok = sum(1 for r in results if r) + print(f"Data: {ok}/{len(ids)} shards ready at {DATA_DIR}") + + +# --------------------------------------------------------------------------- +# Tokenizer training +# --------------------------------------------------------------------------- + + +def list_parquet_files(): + """Return sorted list of parquet file paths in the data directory.""" + files = sorted( + f + for f in os.listdir(DATA_DIR) + if f.endswith(".parquet") and not f.endswith(".tmp") + ) + return [os.path.join(DATA_DIR, f) for f in files] + + +def text_iterator(max_chars=1_000_000_000, doc_cap=10_000): + """Yield documents from training split (all shards except pinned val shard).""" + parquet_paths = [p for p in list_parquet_files() if not p.endswith(VAL_FILENAME)] + nchars = 0 + for filepath in parquet_paths: + pf = pq.ParquetFile(filepath) + for rg_idx in range(pf.num_row_groups): + rg = pf.read_row_group(rg_idx) + for text in rg.column("text").to_pylist(): + doc = text[:doc_cap] if len(text) > doc_cap else text + nchars += len(doc) + yield doc + if nchars >= max_chars: + return + + +def train_tokenizer(): + """Train BPE tokenizer using rustbpe, save as tiktoken pickle.""" + tokenizer_pkl = os.path.join(TOKENIZER_DIR, "tokenizer.pkl") + token_bytes_path = os.path.join(TOKENIZER_DIR, "token_bytes.pt") + + if os.path.exists(tokenizer_pkl) and os.path.exists(token_bytes_path): + print(f"Tokenizer: already trained at {TOKENIZER_DIR}") + return + + os.makedirs(TOKENIZER_DIR, exist_ok=True) + + parquet_files = list_parquet_files() + if len(parquet_files) < 2: + print( + "Tokenizer: need at least 2 data shards (1 train + 1 val). Download more data first." + ) + sys.exit(1) + + # --- Train with rustbpe --- + print("Tokenizer: training BPE tokenizer...") + t0 = time.time() + + tokenizer = rustbpe.Tokenizer() + vocab_size_no_special = VOCAB_SIZE - len(SPECIAL_TOKENS) + tokenizer.train_from_iterator( + text_iterator(), vocab_size_no_special, pattern=SPLIT_PATTERN + ) + + # Build tiktoken encoding from trained merges + pattern = tokenizer.get_pattern() + mergeable_ranks = {bytes(k): v for k, v in tokenizer.get_mergeable_ranks()} + tokens_offset = len(mergeable_ranks) + special_tokens = {name: tokens_offset + i for i, name in enumerate(SPECIAL_TOKENS)} + enc = tiktoken.Encoding( + name="rustbpe", + pat_str=pattern, + mergeable_ranks=mergeable_ranks, + special_tokens=special_tokens, + ) + + # Save tokenizer + with open(tokenizer_pkl, "wb") as f: + pickle.dump(enc, f) + + t1 = time.time() + print(f"Tokenizer: trained in {t1 - t0:.1f}s, saved to {tokenizer_pkl}") + + # --- Build token_bytes lookup for BPB evaluation --- + print("Tokenizer: building token_bytes lookup...") + special_set = set(SPECIAL_TOKENS) + token_bytes_list = [] + for token_id in range(enc.n_vocab): + token_str = enc.decode([token_id]) + if token_str in special_set: + token_bytes_list.append(0) + else: + token_bytes_list.append(len(token_str.encode("utf-8"))) + token_bytes_tensor = torch.tensor(token_bytes_list, dtype=torch.int32) + torch.save(token_bytes_tensor, token_bytes_path) + print(f"Tokenizer: saved token_bytes to {token_bytes_path}") + + # Sanity check + test = "Hello world! Numbers: 123. Unicode: 你好" + encoded = enc.encode_ordinary(test) + decoded = enc.decode(encoded) + assert decoded == test, f"Tokenizer roundtrip failed: {test!r} -> {decoded!r}" + print(f"Tokenizer: sanity check passed (vocab_size={enc.n_vocab})") + + +# --------------------------------------------------------------------------- +# Runtime utilities (imported by train.py) +# --------------------------------------------------------------------------- + + +class Tokenizer: + """Minimal tokenizer wrapper. Training is handled above.""" + + def __init__(self, enc): + self.enc = enc + self.bos_token_id = enc.encode_single_token(BOS_TOKEN) + + @classmethod + def from_directory(cls, tokenizer_dir=TOKENIZER_DIR): + with open(os.path.join(tokenizer_dir, "tokenizer.pkl"), "rb") as f: + enc = pickle.load(f) + return cls(enc) + + def get_vocab_size(self): + return self.enc.n_vocab + + def get_bos_token_id(self): + return self.bos_token_id + + def encode(self, text, prepend=None, num_threads=8): + if prepend is not None: + prepend_id = ( + prepend + if isinstance(prepend, int) + else self.enc.encode_single_token(prepend) + ) + if isinstance(text, str): + ids = self.enc.encode_ordinary(text) + if prepend is not None: + ids.insert(0, prepend_id) + elif isinstance(text, list): + ids = self.enc.encode_ordinary_batch(text, num_threads=num_threads) + if prepend is not None: + for row in ids: + row.insert(0, prepend_id) + else: + raise ValueError(f"Invalid input type: {type(text)}") + return ids + + def decode(self, ids): + return self.enc.decode(ids) + + +def get_token_bytes(device="cpu"): + path = os.path.join(TOKENIZER_DIR, "token_bytes.pt") + with open(path, "rb") as f: + return torch.load(f, map_location=device) + + +def _document_batches(split, tokenizer_batch_size=128): + """Infinite iterator over document batches from parquet files.""" + parquet_paths = list_parquet_files() + assert len(parquet_paths) > 0, "No parquet files found. Run prepare.py first." + val_path = os.path.join(DATA_DIR, VAL_FILENAME) + if split == "train": + parquet_paths = [p for p in parquet_paths if p != val_path] + assert len(parquet_paths) > 0, "No training shards found." + else: + parquet_paths = [val_path] + epoch = 1 + while True: + for filepath in parquet_paths: + pf = pq.ParquetFile(filepath) + for rg_idx in range(pf.num_row_groups): + rg = pf.read_row_group(rg_idx) + batch = rg.column("text").to_pylist() + for i in range(0, len(batch), tokenizer_batch_size): + yield batch[i : i + tokenizer_batch_size], epoch + epoch += 1 + + +def make_dataloader(tokenizer, B, T, split, buffer_size=1000): + """ + BOS-aligned dataloader with best-fit packing. + Every row starts with BOS. Documents packed using best-fit to minimize cropping. + When no document fits remaining space, crops shortest doc to fill exactly. + 100% utilization (no padding). + """ + assert split in ["train", "val"] + row_capacity = T + 1 + batches = _document_batches(split) + bos_token = tokenizer.get_bos_token_id() + doc_buffer = [] + epoch = 1 + + def refill_buffer(): + nonlocal epoch + doc_batch, epoch = next(batches) + token_lists = tokenizer.encode(doc_batch, prepend=bos_token) + doc_buffer.extend(token_lists) + + # Pre-allocate buffers: [inputs (B*T) | targets (B*T)] + row_buffer = torch.empty((B, row_capacity), dtype=torch.long) + cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=True) + gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device="cuda") + cpu_inputs = cpu_buffer[: B * T].view(B, T) + cpu_targets = cpu_buffer[B * T :].view(B, T) + inputs = gpu_buffer[: B * T].view(B, T) + targets = gpu_buffer[B * T :].view(B, T) + + while True: + for row_idx in range(B): + pos = 0 + while pos < row_capacity: + while len(doc_buffer) < buffer_size: + refill_buffer() + + remaining = row_capacity - pos + + # Find largest doc that fits entirely + best_idx = -1 + best_len = 0 + for i, doc in enumerate(doc_buffer): + doc_len = len(doc) + if doc_len <= remaining and doc_len > best_len: + best_idx = i + best_len = doc_len + + if best_idx >= 0: + doc = doc_buffer.pop(best_idx) + row_buffer[row_idx, pos : pos + len(doc)] = torch.tensor( + doc, dtype=torch.long + ) + pos += len(doc) + else: + # No doc fits — crop shortest to fill remaining + shortest_idx = min( + range(len(doc_buffer)), key=lambda i: len(doc_buffer[i]) + ) + doc = doc_buffer.pop(shortest_idx) + row_buffer[row_idx, pos : pos + remaining] = torch.tensor( + doc[:remaining], dtype=torch.long + ) + pos += remaining + + cpu_inputs.copy_(row_buffer[:, :-1]) + cpu_targets.copy_(row_buffer[:, 1:]) + gpu_buffer.copy_(cpu_buffer, non_blocking=True) + yield inputs, targets, epoch + + +# --------------------------------------------------------------------------- +# Evaluation (DO NOT CHANGE — this is the fixed metric) +# --------------------------------------------------------------------------- + + +@torch.no_grad() +def evaluate_bpb(model, tokenizer, batch_size): + """ + Bits per byte (BPB): vocab size-independent evaluation metric. + Sums per-token cross-entropy (in nats), sums target byte lengths, + then converts nats/byte to bits/byte. Special tokens (byte length 0) + are excluded from both sums. + Uses fixed MAX_SEQ_LEN so results are comparable across configs. + """ + token_bytes = get_token_bytes(device="cuda") + val_loader = make_dataloader(tokenizer, batch_size, MAX_SEQ_LEN, "val") + steps = EVAL_TOKENS // (batch_size * MAX_SEQ_LEN) + total_nats = 0.0 + total_bytes = 0 + for _ in range(steps): + x, y, _ = next(val_loader) + loss_flat = model(x, y, reduction="none").view(-1) + y_flat = y.view(-1) + nbytes = token_bytes[y_flat] + mask = nbytes > 0 + total_nats += (loss_flat * mask).sum().item() + total_bytes += nbytes.sum().item() + return total_nats / (math.log(2) * total_bytes) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Prepare data and tokenizer for autoresearch" + ) + parser.add_argument( + "--num-shards", + type=int, + default=10, + help="Number of training shards to download (-1 = all). Val shard is always pinned.", + ) + parser.add_argument( + "--download-workers", + type=int, + default=8, + help="Number of parallel download workers", + ) + args = parser.parse_args() + + num_shards = MAX_SHARD if args.num_shards == -1 else args.num_shards + + print(f"Cache directory: {CACHE_DIR}") + print() + + # Step 1: Download data + download_data(num_shards, download_workers=args.download_workers) + print() + + # Step 2: Train tokenizer + train_tokenizer() + print() + print("Done! Ready to train.") diff --git a/sandboxes/autoresearch-spark/autoresearch/program.md b/sandboxes/autoresearch-spark/autoresearch/program.md new file mode 100644 index 0000000..dea9bcc --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/program.md @@ -0,0 +1,114 @@ +# autoresearch + +This is an experiment to have the LLM do its own research. + +## Setup + +To set up a new experiment, work with the user to: + +1. **Agree on a run tag**: propose a tag based on today's date (e.g. `mar5`). The branch `autoresearch/` must not already exist — this is a fresh run. +2. **Create the branch**: `git checkout -b autoresearch/` from current master. +3. **Read the in-scope files**: The repo is small. Read these files for full context: + - `README.md` — repository context. + - `prepare.py` — fixed constants, data prep, tokenizer, dataloader, evaluation. Do not modify. + - `train.py` — the file you modify. Model architecture, optimizer, training loop. +4. **Verify data exists**: Check that `~/.cache/autoresearch/` contains data shards and a tokenizer. If not, tell the human to run `uv run prepare.py`. +5. **Initialize results.tsv**: Create `results.tsv` with just the header row. The baseline will be recorded after the first run. +6. **Confirm and go**: Confirm setup looks good. + +Once you get confirmation, kick off the experimentation. + +## Experimentation + +Each experiment runs on a single GPU. The training script runs for a **fixed time budget of 5 minutes** (wall clock training time, excluding startup/compilation). You launch it simply as: `uv run train.py`. + +**What you CAN do:** +- Modify `train.py` — this is the only file you edit. Everything is fair game: model architecture, optimizer, hyperparameters, training loop, batch size, model size, etc. + +**What you CANNOT do:** +- Modify `prepare.py`. It is read-only. It contains the fixed evaluation, data loading, tokenizer, and training constants (time budget, sequence length, etc). +- Install new packages or add dependencies. You can only use what's already in `pyproject.toml`. +- Modify the evaluation harness. The `evaluate_bpb` function in `prepare.py` is the ground truth metric. + +**The goal is simple: get the lowest val_bpb.** Since the time budget is fixed, you don't need to worry about training time — it's always 5 minutes. Everything is fair game: change the architecture, the optimizer, the hyperparameters, the batch size, the model size. The only constraint is that the code runs without crashing and finishes within the time budget. + +**VRAM** is a soft constraint. Some increase is acceptable for meaningful val_bpb gains, but it should not blow up dramatically. + +**Simplicity criterion**: All else being equal, simpler is better. A small improvement that adds ugly complexity is not worth it. Conversely, removing something and getting equal or better results is a great outcome — that's a simplification win. When evaluating whether to keep a change, weigh the complexity cost against the improvement magnitude. A 0.001 val_bpb improvement that adds 20 lines of hacky code? Probably not worth it. A 0.001 val_bpb improvement from deleting code? Definitely keep. An improvement of ~0 but much simpler code? Keep. + +**The first run**: Your very first run should always be to establish the baseline, so you will run the training script as is. + +## Output format + +Once the script finishes it prints a summary like this: + +``` +--- +val_bpb: 0.997900 +training_seconds: 300.1 +total_seconds: 325.9 +peak_vram_mb: 45060.2 +mfu_percent: 39.80 +total_tokens_M: 499.6 +num_steps: 953 +num_params_M: 50.3 +depth: 8 +``` + +Note that the script is configured to always stop after 5 minutes, so depending on the computing platform of this computer the numbers might look different. You can extract the key metric from the log file: + +``` +grep "^val_bpb:" run.log +``` + +## Logging results + +When an experiment is done, log it to `results.tsv` (tab-separated, NOT comma-separated — commas break in descriptions). + +The TSV has a header row and 5 columns: + +``` +commit val_bpb memory_gb status description +``` + +1. git commit hash (short, 7 chars) +2. val_bpb achieved (e.g. 1.234567) — use 0.000000 for crashes +3. peak memory in GB, round to .1f (e.g. 12.3 — divide peak_vram_mb by 1024) — use 0.0 for crashes +4. status: `keep`, `discard`, or `crash` +5. short text description of what this experiment tried + +Example: + +``` +commit val_bpb memory_gb status description +a1b2c3d 0.997900 44.0 keep baseline +b2c3d4e 0.993200 44.2 keep increase LR to 0.04 +c3d4e5f 1.005000 44.0 discard switch to GeLU activation +d4e5f6g 0.000000 0.0 crash double model width (OOM) +``` + +## The experiment loop + +The experiment runs on a dedicated branch (e.g. `autoresearch/mar5` or `autoresearch/mar5-gpu0`). + +LOOP FOREVER: + +1. Look at the git state: the current branch/commit we're on +2. Tune `train.py` with an experimental idea by directly hacking the code. +3. git commit +4. Run the experiment: `uv run train.py > run.log 2>&1` (redirect everything — do NOT use tee or let output flood your context) +5. Read out the results: `grep "^val_bpb:\|^peak_vram_mb:" run.log` +6. If the grep output is empty, the run crashed. Run `tail -n 50 run.log` to read the Python stack trace and attempt a fix. If you can't get things to work after more than a few attempts, give up. +7. Record the results in the tsv (NOTE: do not commit the results.tsv file, leave it untracked by git) +8. If val_bpb improved (lower), you "advance" the branch, keeping the git commit +9. If val_bpb is equal or worse, you git reset back to where you started + +The idea is that you are a completely autonomous researcher trying things out. If they work, keep. If they don't, discard. And you're advancing the branch so that you can iterate. If you feel like you're getting stuck in some way, you can rewind but you should probably do this very very sparingly (if ever). + +**Timeout**: Each experiment should take ~5 minutes total (+ a few seconds for startup and eval overhead). If a run exceeds 10 minutes, kill it and treat it as a failure (discard and revert). + +**Crashes**: If a run crashes (OOM, or a bug, or etc.), use your judgment: If it's something dumb and easy to fix (e.g. a typo, a missing import), fix it and re-run. If the idea itself is fundamentally broken, just skip it, log "crash" as the status in the tsv, and move on. + +**NEVER STOP**: Once the experiment loop has begun (after the initial setup), do NOT pause to ask the human if you should continue. Do NOT ask "should I keep going?" or "is this a good stopping point?". The human might be asleep, or gone from a computer and expects you to continue working *indefinitely* until you are manually stopped. You are autonomous. If you run out of ideas, think harder — read papers referenced in the code, re-read the in-scope files for new angles, try combining previous near-misses, try more radical architectural changes. The loop runs until the human interrupts you, period. + +As an example use case, a user might leave you running while they sleep. If each experiment takes you ~5 minutes then you can run approx 12/hour, for a total of about 100 over the duration of the average human sleep. The user then wakes up to experimental results, all completed by you while they slept! diff --git a/sandboxes/autoresearch-spark/autoresearch/pyproject.toml b/sandboxes/autoresearch-spark/autoresearch/pyproject.toml new file mode 100644 index 0000000..182f570 --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/pyproject.toml @@ -0,0 +1,30 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +[project] +name = "autoresearch" +version = "0.1.0" +description = "Autonomous pretraining research swarm" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "kernels>=0.11.7", + "matplotlib>=3.10.8", + "numpy>=2.2.6", + "pandas>=2.3.3", + "pyarrow>=21.0.0", + "requests>=2.32.0", + "rustbpe>=0.1.0", + "tiktoken>=0.11.0", + "torch==2.9.1", +] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu128" }, +] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true diff --git a/sandboxes/autoresearch-spark/autoresearch/train.py b/sandboxes/autoresearch-spark/autoresearch/train.py new file mode 100644 index 0000000..9224f72 --- /dev/null +++ b/sandboxes/autoresearch-spark/autoresearch/train.py @@ -0,0 +1,803 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Autoresearch pretraining script. Single-GPU, single-file. +Cherry-picked and simplified from nanochat. +Usage: uv run train.py +""" + +import os + +os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True" +os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1" +# Use system CUDA 13.0 ptxas which supports sm_121a (NVIDIA GB10 / Blackwell) +os.environ.setdefault("TRITON_PTXAS_PATH", "/usr/local/cuda-13.0/bin/ptxas") + +import gc +import math +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb + +# --------------------------------------------------------------------------- +# GPT Model +# --------------------------------------------------------------------------- + + +@dataclass +class GPTConfig: + sequence_len: int = 2048 + vocab_size: int = 32768 + n_layer: int = 12 + n_head: int = 6 + n_kv_head: int = 6 + n_embd: int = 768 + window_pattern: str = "SSSL" + + +def norm(x): + return F.rms_norm(x, (x.size(-1),)) + + +def has_ve(layer_idx, n_layer): + """Returns True if layer should have Value Embedding (alternating, last always included).""" + return layer_idx % 2 == (n_layer - 1) % 2 + + +def apply_rotary_emb(x, cos, sin): + assert x.ndim == 4 + d = x.shape[3] // 2 + x1, x2 = x[..., :d], x[..., d:] + y1 = x1 * cos + x2 * sin + y2 = x1 * (-sin) + x2 * cos + return torch.cat([y1, y2], 3) + + +class CausalSelfAttention(nn.Module): + def __init__(self, config, layer_idx): + super().__init__() + self.n_head = config.n_head + self.n_kv_head = config.n_kv_head + self.n_embd = config.n_embd + self.head_dim = self.n_embd // self.n_head + assert self.n_embd % self.n_head == 0 + assert self.n_kv_head <= self.n_head and self.n_head % self.n_kv_head == 0 + self.c_q = 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.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 + ) + + def forward(self, x, ve, cos_sin, window_size): + B, T, C = x.size() + q = self.c_q(x).view(B, T, self.n_head, self.head_dim) + k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim) + v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim) + + # Value residual (ResFormer): mix in value embedding with input-dependent gate per head + if ve is not None: + ve = ve.view(B, T, self.n_kv_head, self.head_dim) + gate = 2 * torch.sigmoid(self.ve_gate(x[..., : self.ve_gate_channels])) + v = v + gate.unsqueeze(-1) * ve + + cos, sin = cos_sin + q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin) + q, k = norm(q), norm(k) + + # SDPA expects (B, heads, T, head_dim) + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + # GQA: expand k, v to match q heads + if self.n_head != self.n_kv_head: + groups = self.n_head // self.n_kv_head + k = k.repeat_interleave(groups, dim=1) + v = v.repeat_interleave(groups, dim=1) + # Sliding window causal mask + w = window_size[0] + if w < T: + rows = torch.arange(T, device=x.device).unsqueeze(1) + cols = torch.arange(T, device=x.device).unsqueeze(0) + mask = (cols <= rows) & (cols > rows - w) + attn_mask = torch.zeros(T, T, device=x.device, dtype=q.dtype).masked_fill( + ~mask, float("-inf") + ) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + else: + y = F.scaled_dot_product_attention(q, k, v, is_causal=True) + y = y.transpose(1, 2).contiguous().view(B, T, -1) + y = self.c_proj(y) + return y + + +class MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.c_fc = 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) + + def forward(self, x): + x = self.c_fc(x) + x = F.relu(x).square() + x = self.c_proj(x) + return x + + +class Block(nn.Module): + def __init__(self, config, layer_idx): + super().__init__() + self.attn = CausalSelfAttention(config, layer_idx) + self.mlp = MLP(config) + + def forward(self, x, ve, cos_sin, window_size): + x = x + self.attn(norm(x), ve, cos_sin, window_size) + x = x + self.mlp(norm(x)) + return x + + +class GPT(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.window_sizes = self._compute_window_sizes(config) + self.transformer = nn.ModuleDict( + { + "wte": nn.Embedding(config.vocab_size, config.n_embd), + "h": nn.ModuleList([Block(config, i) for i in range(config.n_layer)]), + } + ) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.resid_lambdas = nn.Parameter(torch.ones(config.n_layer)) + self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer)) + # Value embeddings + head_dim = config.n_embd // config.n_head + kv_dim = config.n_kv_head * head_dim + self.value_embeds = nn.ModuleDict( + { + str(i): nn.Embedding(config.vocab_size, kv_dim) + for i in range(config.n_layer) + if has_ve(i, config.n_layer) + } + ) + # Rotary embeddings + self.rotary_seq_len = config.sequence_len * 10 + cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim) + self.register_buffer("cos", cos, persistent=False) + self.register_buffer("sin", sin, persistent=False) + + @torch.no_grad() + def init_weights(self): + # Embedding and unembedding + torch.nn.init.normal_(self.transformer.wte.weight, mean=0.0, std=1.0) + torch.nn.init.normal_(self.lm_head.weight, mean=0.0, std=0.001) + # Transformer blocks + n_embd = self.config.n_embd + s = 3**0.5 * n_embd**-0.5 + for block in self.transformer.h: + torch.nn.init.uniform_(block.attn.c_q.weight, -s, s) + torch.nn.init.uniform_(block.attn.c_k.weight, -s, s) + torch.nn.init.uniform_(block.attn.c_v.weight, -s, s) + torch.nn.init.zeros_(block.attn.c_proj.weight) + torch.nn.init.uniform_(block.mlp.c_fc.weight, -s, s) + torch.nn.init.zeros_(block.mlp.c_proj.weight) + # Per-layer scalars + self.resid_lambdas.fill_(1.0) + self.x0_lambdas.fill_(0.1) + # Value embeddings + for ve in self.value_embeds.values(): + torch.nn.init.uniform_(ve.weight, -s, s) + # Gate weights init to zero (sigmoid(0)=0.5, scaled by 2 -> 1.0 = neutral) + for block in self.transformer.h: + if block.attn.ve_gate is not None: + torch.nn.init.zeros_(block.attn.ve_gate.weight) + # Rotary embeddings + head_dim = self.config.n_embd // self.config.n_head + cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim) + self.cos, self.sin = cos, sin + # Cast embeddings to bf16 + self.transformer.wte.to(dtype=torch.bfloat16) + for ve in self.value_embeds.values(): + ve.to(dtype=torch.bfloat16) + + def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None): + if device is None: + device = self.transformer.wte.weight.device + channel_range = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device) + inv_freq = 1.0 / (base ** (channel_range / head_dim)) + t = torch.arange(seq_len, dtype=torch.float32, device=device) + freqs = torch.outer(t, inv_freq) + cos, sin = freqs.cos(), freqs.sin() + cos, sin = cos.bfloat16(), sin.bfloat16() + cos, sin = cos[None, :, None, :], sin[None, :, None, :] + return cos, sin + + def _compute_window_sizes(self, config): + pattern = config.window_pattern.upper() + assert all(c in "SL" for c in pattern) + long_window = config.sequence_len + short_window = long_window // 2 + char_to_window = {"L": (long_window, 0), "S": (short_window, 0)} + window_sizes = [] + for layer_idx in range(config.n_layer): + char = pattern[layer_idx % len(pattern)] + window_sizes.append(char_to_window[char]) + window_sizes[-1] = (long_window, 0) + return window_sizes + + def estimate_flops(self): + """Estimated FLOPs per token (forward + backward).""" + nparams = sum(p.numel() for p in self.parameters()) + value_embeds_numel = sum(ve.weight.numel() for ve in self.value_embeds.values()) + nparams_exclude = ( + self.transformer.wte.weight.numel() + + value_embeds_numel + + self.resid_lambdas.numel() + + self.x0_lambdas.numel() + ) + h = self.config.n_head + q = self.config.n_embd // self.config.n_head + t = self.config.sequence_len + attn_flops = 0 + for window_size in self.window_sizes: + window = window_size[0] + effective_seq = t if window < 0 else min(window, t) + attn_flops += 12 * h * q * effective_seq + return 6 * (nparams - nparams_exclude) + attn_flops + + def num_scaling_params(self): + wte = sum(p.numel() for p in self.transformer.wte.parameters()) + value_embeds = sum(p.numel() for p in self.value_embeds.parameters()) + lm_head = sum(p.numel() for p in self.lm_head.parameters()) + transformer_matrices = sum(p.numel() for p in self.transformer.h.parameters()) + scalars = self.resid_lambdas.numel() + self.x0_lambdas.numel() + total = wte + value_embeds + lm_head + transformer_matrices + scalars + return { + "wte": wte, + "value_embeds": value_embeds, + "lm_head": lm_head, + "transformer_matrices": transformer_matrices, + "scalars": scalars, + "total": total, + } + + def setup_optimizer( + self, + unembedding_lr=0.004, + embedding_lr=0.2, + matrix_lr=0.02, + weight_decay=0.0, + adam_betas=(0.8, 0.95), + scalar_lr=0.5, + ): + model_dim = self.config.n_embd + matrix_params = list(self.transformer.h.parameters()) + value_embeds_params = list(self.value_embeds.parameters()) + embedding_params = list(self.transformer.wte.parameters()) + lm_head_params = list(self.lm_head.parameters()) + resid_params = [self.resid_lambdas] + x0_params = [self.x0_lambdas] + assert len(list(self.parameters())) == ( + len(matrix_params) + + len(embedding_params) + + len(lm_head_params) + + len(value_embeds_params) + + len(resid_params) + + len(x0_params) + ) + # Scale LR ∝ 1/√dmodel (tuned at 768 dim) + dmodel_lr_scale = (model_dim / 768) ** -0.5 + print(f"Scaling AdamW LRs by 1/sqrt({model_dim}/768) = {dmodel_lr_scale:.6f}") + param_groups = [ + dict( + kind="adamw", + params=lm_head_params, + lr=unembedding_lr * dmodel_lr_scale, + betas=adam_betas, + eps=1e-10, + weight_decay=0.0, + ), + dict( + kind="adamw", + params=embedding_params, + lr=embedding_lr * dmodel_lr_scale, + betas=adam_betas, + eps=1e-10, + weight_decay=0.0, + ), + dict( + kind="adamw", + params=value_embeds_params, + lr=embedding_lr * dmodel_lr_scale, + betas=adam_betas, + eps=1e-10, + weight_decay=0.0, + ), + dict( + kind="adamw", + params=resid_params, + lr=scalar_lr * 0.01, + betas=adam_betas, + eps=1e-10, + weight_decay=0.0, + ), + dict( + kind="adamw", + params=x0_params, + lr=scalar_lr, + betas=(0.96, 0.95), + eps=1e-10, + weight_decay=0.0, + ), + ] + for shape in sorted({p.shape for p in matrix_params}): + group_params = [p for p in matrix_params if p.shape == shape] + param_groups.append( + dict( + kind="muon", + params=group_params, + lr=matrix_lr, + momentum=0.95, + ns_steps=5, + beta2=0.95, + weight_decay=weight_decay, + ) + ) + optimizer = MuonAdamW(param_groups) + for group in optimizer.param_groups: + group["initial_lr"] = group["lr"] + return optimizer + + def forward(self, idx, targets=None, reduction="mean"): + B, T = idx.size() + assert T <= self.cos.size(1) + cos_sin = self.cos[:, :T], self.sin[:, :T] + + x = self.transformer.wte(idx) + x = norm(x) + x0 = x + 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 + x = block(x, ve, cos_sin, self.window_sizes[i]) + x = norm(x) + + softcap = 15 + logits = self.lm_head(x) + logits = logits.float() + logits = softcap * torch.tanh(logits / softcap) + + if targets is not None: + loss = F.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + ignore_index=-1, + reduction=reduction, + ) + return loss + return logits + + +# --------------------------------------------------------------------------- +# Optimizer (MuonAdamW, single GPU only) +# --------------------------------------------------------------------------- + +polar_express_coeffs = [ + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +] + + +@torch.compile(dynamic=False, fullgraph=True) +def adamw_step_fused( + p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t +): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t**step_t + bias2 = 1 - beta2_t**step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + +@torch.compile(dynamic=False, fullgraph=True) +def muon_step_fused( + stacked_grads, + stacked_params, + momentum_buffer, + second_momentum_buffer, + momentum_t, + lr_t, + wd_t, + beta2_t, + ns_steps, + red_dim, +): + # Nesterov momentum + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + # Polar express orthogonalization + X = g.bfloat16() + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + # NorMuon variance reduction + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_( + v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2 + ) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + # Cautious weight decay + parameter update + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +class MuonAdamW(torch.optim.Optimizer): + """Combined optimizer: Muon for 2D matrix params, AdamW for others.""" + + def __init__(self, param_groups): + super().__init__(param_groups, defaults={}) + # 0-D CPU tensors to avoid torch.compile recompilation when values change + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + + def _step_adamw(self, group): + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["step"] += 1 + self._adamw_step_t.fill_(state["step"]) + self._adamw_lr_t.fill_(group["lr"]) + self._adamw_beta1_t.fill_(group["betas"][0]) + self._adamw_beta2_t.fill_(group["betas"][1]) + self._adamw_eps_t.fill_(group["eps"]) + self._adamw_wd_t.fill_(group["weight_decay"]) + adamw_step_fused( + p, + grad, + state["exp_avg"], + state["exp_avg_sq"], + self._adamw_step_t, + self._adamw_lr_t, + self._adamw_beta1_t, + self._adamw_beta2_t, + self._adamw_eps_t, + self._adamw_wd_t, + ) + + def _step_muon(self, group): + params = group["params"] + if not params: + return + p = params[0] + state = self.state[p] + num_params = len(params) + shape, device, dtype = p.shape, p.device, p.dtype + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros( + num_params, *shape, dtype=dtype, device=device + ) + if "second_momentum_buffer" not in state: + state_shape = ( + (num_params, shape[-2], 1) + if shape[-2] >= shape[-1] + else (num_params, 1, shape[-1]) + ) + state["second_momentum_buffer"] = torch.zeros( + state_shape, dtype=dtype, device=device + ) + red_dim = -1 if shape[-2] >= shape[-1] else -2 + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1]) ** 0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_fused( + stacked_grads, + stacked_params, + state["momentum_buffer"], + state["second_momentum_buffer"], + self._muon_momentum_t, + self._muon_lr_t, + self._muon_wd_t, + self._muon_beta2_t, + group["ns_steps"], + red_dim, + ) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + + @torch.no_grad() + def step(self): + for group in self.param_groups: + if group["kind"] == "adamw": + self._step_adamw(group) + elif group["kind"] == "muon": + self._step_muon(group) + + +# --------------------------------------------------------------------------- +# Hyperparameters (edit these directly, no CLI flags needed) +# --------------------------------------------------------------------------- + +# Model architecture +ASPECT_RATIO = 64 # model_dim = depth * ASPECT_RATIO +HEAD_DIM = 128 # target head dimension for attention +WINDOW_PATTERN = "SSSL" # sliding window pattern: L=full, S=half context + +# Optimization +TOTAL_BATCH_SIZE = 2**19 # ~524K tokens per optimizer step +EMBEDDING_LR = 0.6 # learning rate for token embeddings (Adam) +UNEMBEDDING_LR = 0.004 # learning rate for lm_head (Adam) +MATRIX_LR = 0.04 # learning rate for matrix parameters (Muon) +SCALAR_LR = 0.5 # learning rate for per-layer scalars (Adam) +WEIGHT_DECAY = 0.2 # cautious weight decay for Muon +ADAM_BETAS = (0.8, 0.95) # Adam beta1, beta2 +WARMUP_RATIO = 0.0 # fraction of time budget for LR warmup +WARMDOWN_RATIO = 0.5 # fraction of time budget for LR warmdown +FINAL_LR_FRAC = 0.0 # final LR as fraction of initial + +# Model size +DEPTH = 8 # number of transformer layers +DEVICE_BATCH_SIZE = 128 # per-device batch size (reduce if OOM) + +# --------------------------------------------------------------------------- +# Setup: tokenizer, model, optimizer, dataloader +# --------------------------------------------------------------------------- + +t_start = time.time() +torch.manual_seed(42) +torch.cuda.manual_seed(42) +torch.set_float32_matmul_precision("high") +device = torch.device("cuda") +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) +H100_BF16_PEAK_FLOPS = 989.5e12 + +tokenizer = Tokenizer.from_directory() +vocab_size = tokenizer.get_vocab_size() +print(f"Vocab size: {vocab_size:,}") + + +def build_model_config(depth): + base_dim = depth * ASPECT_RATIO + model_dim = ((base_dim + HEAD_DIM - 1) // HEAD_DIM) * HEAD_DIM + num_heads = model_dim // HEAD_DIM + return GPTConfig( + sequence_len=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=WINDOW_PATTERN, + ) + + +config = build_model_config(DEPTH) +print(f"Model config: {asdict(config)}") + +with torch.device("meta"): + model = GPT(config) +model.to_empty(device=device) +model.init_weights() + +param_counts = model.num_scaling_params() +print("Parameter counts:") +for key, value in param_counts.items(): + print(f" {key:24s}: {value:,}") +num_params = param_counts["total"] +num_flops_per_token = model.estimate_flops() +print(f"Estimated FLOPs per token: {num_flops_per_token:e}") + +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 +grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd + +optimizer = model.setup_optimizer( + unembedding_lr=UNEMBEDDING_LR, + embedding_lr=EMBEDDING_LR, + scalar_lr=SCALAR_LR, + adam_betas=ADAM_BETAS, + matrix_lr=MATRIX_LR, + weight_decay=WEIGHT_DECAY, +) + +model = torch.compile(model, dynamic=False) + +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +x, y, epoch = next(train_loader) # prefetch first batch + +print(f"Time budget: {TIME_BUDGET}s") +print(f"Gradient accumulation steps: {grad_accum_steps}") + +# Schedules (all based on progress = training_time / TIME_BUDGET) + + +def get_lr_multiplier(progress): + if progress < WARMUP_RATIO: + return progress / WARMUP_RATIO if WARMUP_RATIO > 0 else 1.0 + elif progress < 1.0 - WARMDOWN_RATIO: + return 1.0 + else: + cooldown = (1.0 - progress) / WARMDOWN_RATIO + return cooldown * 1.0 + (1 - cooldown) * FINAL_LR_FRAC + + +def get_muon_momentum(step): + frac = min(step / 300, 1) + return (1 - frac) * 0.85 + frac * 0.95 + + +def get_weight_decay(progress): + return WEIGHT_DECAY * (1 - progress) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + +t_start_training = time.time() +smooth_train_loss = 0 +total_training_time = 0 +step = 0 + +while True: + torch.cuda.synchronize() + t0 = time.time() + for micro_step in range(grad_accum_steps): + with autocast_ctx: + loss = model(x, y) + train_loss = loss.detach() + loss = loss / grad_accum_steps + loss.backward() + x, y, epoch = next(train_loader) + + # Progress and schedules + progress = min(total_training_time / TIME_BUDGET, 1.0) + lrm = get_lr_multiplier(progress) + muon_momentum = get_muon_momentum(step) + muon_weight_decay = get_weight_decay(progress) + for group in optimizer.param_groups: + group["lr"] = group["initial_lr"] * lrm + if group["kind"] == "muon": + group["momentum"] = muon_momentum + group["weight_decay"] = muon_weight_decay + optimizer.step() + model.zero_grad(set_to_none=True) + + train_loss_f = train_loss.item() + + # Fast fail: abort if loss is exploding or NaN + if math.isnan(train_loss_f) or train_loss_f > 100: + print("FAIL") + exit(1) + + torch.cuda.synchronize() + t1 = time.time() + dt = t1 - t0 + + if step > 10: + total_training_time += dt + + # Logging + ema_beta = 0.9 + smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f + debiased_smooth_loss = smooth_train_loss / (1 - ema_beta ** (step + 1)) + pct_done = 100 * progress + tok_per_sec = int(TOTAL_BATCH_SIZE / dt) + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / H100_BF16_PEAK_FLOPS + remaining = max(0, TIME_BUDGET - total_training_time) + + print( + f"\rstep {step:05d} ({pct_done:.1f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.0f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.1f}% | epoch: {epoch} | remaining: {remaining:.0f}s ", + end="", + flush=True, + ) + + # GC management (Python's GC causes ~500ms stalls) + if step == 0: + gc.collect() + gc.freeze() + gc.disable() + elif (step + 1) % 5000 == 0: + gc.collect() + + step += 1 + + # Time's up — but only stop after warmup steps so we don't count compilation + if step > 10 and total_training_time >= TIME_BUDGET: + break + +print() # newline after \r training log + +total_tokens = step * TOTAL_BATCH_SIZE + +# Final eval +model.eval() +with autocast_ctx: + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + +# Final summary +t_end = time.time() +startup_time = t_start_training - t_start +steady_state_mfu = ( + 100 + * num_flops_per_token + * TOTAL_BATCH_SIZE + * (step - 10) + / total_training_time + / H100_BF16_PEAK_FLOPS + if total_training_time > 0 + else 0 +) +peak_vram_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 + +print("---") +print(f"val_bpb: {val_bpb:.6f}") +print(f"training_seconds: {total_training_time:.1f}") +print(f"total_seconds: {t_end - t_start:.1f}") +print(f"peak_vram_mb: {peak_vram_mb:.1f}") +print(f"mfu_percent: {steady_state_mfu:.2f}") +print(f"total_tokens_M: {total_tokens / 1e6:.1f}") +print(f"num_steps: {step}") +print(f"num_params_M: {num_params / 1e6:.1f}") +print(f"depth: {DEPTH}") + +# Save checkpoint +ckpt = { + "model": model.state_dict(), + "config": asdict(config), + "tokenizer_vocab_size": 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All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +# Development policy for autoresearch-spark sandbox. +# +# Extends the runtime policy with PyPI and PyTorch wheel access so you can +# install or update Python packages at runtime (e.g. `uv pip install ...`). +# +# Use this when iterating on dependencies or testing new packages. +# For production runs, use the default policy.yaml which locks down to +# runtime-only network access. + +version: 1 + +# --- Sandbox setup configuration (queried once at startup) --- + +filesystem_policy: + include_workdir: true + read_only: + - /usr + - /lib + - /proc + - /dev/urandom + - /app + - /etc + - /var/log + read_write: + - /sandbox + - /tmp + - /dev/null + +landlock: + compatibility: best_effort + +process: + run_as_user: sandbox + run_as_group: sandbox + +# --- Network policies (queried per-CONNECT request) --- + +network_policies: + claude_code: + name: claude-code + endpoints: + - { host: api.anthropic.com, port: 443, protocol: rest, enforcement: enforce, access: full, tls: terminate } + - { host: statsig.anthropic.com, port: 443 } + - { host: sentry.io, port: 443 } + - { host: raw.githubusercontent.com, port: 443 } + - { host: platform.claude.com, port: 443 } + - { host: storage.googleapis.com, port: 443 } + binaries: + - { path: /usr/local/bin/claude } + - { path: /usr/bin/node } + + github_ssh_over_https: + name: github-ssh-over-https + endpoints: + - host: github.com + port: 443 + protocol: rest + tls: terminate + enforcement: enforce + rules: + - allow: + method: GET + path: "/**/info/refs*" + - allow: + method: POST + path: "/**/git-upload-pack" + binaries: + - { path: /usr/bin/git } + + github_rest_api: + name: github-rest-api + endpoints: + - host: api.github.com + port: 443 + protocol: rest + tls: terminate + enforcement: enforce + access: read-only + binaries: + - { path: /usr/local/bin/claude } + - { path: /usr/bin/gh } + + nvidia_inference: + name: nvidia-inference + endpoints: + - { host: integrate.api.nvidia.com, port: 443 } + binaries: + - { path: /usr/bin/curl } + - { path: /bin/bash } + - { path: /usr/local/bin/opencode } + + huggingface: + name: huggingface + endpoints: + - { host: huggingface.co, port: 443 } + - { host: cas-bridge.xethub.hf.co, port: 443 } + - { host: cas-server.xethub.hf.co, port: 443 } + - { host: transfer.xethub.hf.co, port: 443 } + binaries: + - { path: "/sandbox/.uv/python/**" } + - { path: /sandbox/.venv/bin/python } + - { path: /sandbox/.venv/bin/python3 } + + # --- Dev-only: package installation at runtime --- + pypi: + name: pypi + endpoints: + - { host: pypi.org, port: 443 } + - { host: files.pythonhosted.org, port: 443 } + - { host: download.pytorch.org, port: 443 } + - { host: github.com, port: 443 } + - { host: objects.githubusercontent.com, port: 443 } + - { host: api.github.com, port: 443 } + - { host: downloads.python.org, port: 443 } + - { host: releases.astral.sh, port: 443 } + binaries: + - { path: /sandbox/.venv/bin/python } + - { path: /sandbox/.venv/bin/python3 } + - { path: /sandbox/.venv/bin/pip } + - { path: /usr/local/bin/uv } + - { path: "/sandbox/.uv/python/**" } + + opencode: + name: opencode + endpoints: + - { host: registry.npmjs.org, port: 443 } + - { host: opencode.ai, port: 443 } + - { host: integrate.api.nvidia.com, port: 443 } + binaries: + - { path: /usr/lib/node_modules/opencode-ai/bin/.opencode } + - { path: /usr/bin/node } + - { path: /usr/local/bin/opencode } diff --git a/sandboxes/autoresearch-spark/policy.yaml b/sandboxes/autoresearch-spark/policy.yaml new file mode 100644 index 0000000..44e2e1b --- /dev/null +++ b/sandboxes/autoresearch-spark/policy.yaml @@ -0,0 +1,121 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +# Runtime policy for autoresearch-spark sandbox. +# +# Python dependencies (PyTorch cu128, etc.) are installed at container build +# time via `uv sync`, so PyPI / PyTorch wheel access is NOT needed at runtime. +# +# For development (installing new packages at runtime), use the dev policy +# which adds pypi + pytorch endpoints. See policy-dev.yaml. + +version: 1 + +# --- Sandbox setup configuration (queried once at startup) --- + +filesystem_policy: + include_workdir: true + read_only: + - /usr + - /lib + - /proc + - /dev/urandom + - /app + - /etc + - /var/log + read_write: + - /sandbox + - /tmp + - /dev/null + +landlock: + compatibility: best_effort + +process: + run_as_user: sandbox + run_as_group: sandbox + +# --- Network policies (queried per-CONNECT request) --- +# +# Runtime needs: agent tools (Claude, Codex, OpenCode), GitHub (git ops), +# and HuggingFace (prepare.py downloads training data shards at runtime). + +network_policies: + claude_code: + name: claude-code + endpoints: + - { host: api.anthropic.com, port: 443, protocol: rest, enforcement: enforce, access: full, tls: terminate } + - { host: statsig.anthropic.com, port: 443 } + - { host: sentry.io, port: 443 } + - { host: raw.githubusercontent.com, port: 443 } + - { host: platform.claude.com, port: 443 } + - { host: storage.googleapis.com, port: 443 } + binaries: + - { path: /usr/local/bin/claude } + - { path: /usr/bin/node } + + github_ssh_over_https: + name: github-ssh-over-https + endpoints: + - host: github.com + port: 443 + protocol: rest + tls: terminate + enforcement: enforce + rules: + - allow: + method: GET + path: "/**/info/refs*" + - allow: + method: POST + path: "/**/git-upload-pack" + binaries: + - { path: /usr/bin/git } + + github_rest_api: + name: github-rest-api + endpoints: + - host: api.github.com + port: 443 + protocol: rest + tls: terminate + enforcement: enforce + access: read-only + binaries: + - { path: /usr/local/bin/claude } + - { path: /usr/bin/gh } + + nvidia_inference: + name: nvidia-inference + endpoints: + - { host: integrate.api.nvidia.com, port: 443 } + binaries: + - { path: /usr/bin/curl } + - { path: /bin/bash } + - { path: /usr/local/bin/opencode } + + # HuggingFace — prepare.py downloads training data shards + tokenizer data + # at runtime. The xethub subdomains handle large file transfers via HF's + # content-addressable storage. + huggingface: + name: huggingface + endpoints: + - { host: huggingface.co, port: 443 } + - { host: cas-bridge.xethub.hf.co, port: 443 } + - { host: cas-server.xethub.hf.co, port: 443 } + - { host: transfer.xethub.hf.co, port: 443 } + binaries: + - { path: "/sandbox/.uv/python/**" } + - { path: /sandbox/.venv/bin/python } + - { path: /sandbox/.venv/bin/python3 } + + opencode: + name: opencode + endpoints: + - { host: registry.npmjs.org, port: 443 } + - { host: opencode.ai, port: 443 } + - { host: integrate.api.nvidia.com, port: 443 } + binaries: + - { path: /usr/lib/node_modules/opencode-ai/bin/.opencode } + - { path: /usr/bin/node } + - { path: /usr/local/bin/opencode }